Literature DB >> 32038049

Peripheral Blood Leukocyte RNA-Seq Identifies a Set of Genes Related to Abnormal Psychomotor Behavior Characteristics in Patients with Schizophrenia.

Yunqiao Zhang1, Xu You1, Siwu Li2, Qing Long1, Yun Zhu1, Zhaowei Teng1, Yong Zeng1.   

Abstract

BACKGROUND Schizophrenia is a multigene disease with a complex etiology and different clinical manifestations. It is of great significance to understand the etiology and pathogenesis of schizophrenia patients from different clinical dimensions and to interpret the potential molecular changes of schizophrenia patients from different clinical dimensions. MATERIAL AND METHODS RNA-Seq was performed on peripheral blood leukocytes of 50 patients with schizophrenia and 50 healthy controls. Phenotypic information of patients with schizophrenia was collected during blood sampling. Differentially expressed genes (DEGs) were screened by the edgeR package of R software. To better analyze the correlation between DEG expression values, explore the potential association between differential genes and clinical dimensions of schizophrenia, and identify hub genes, we constructed a DEG co-expression network using weighted gene co-expression network analysis (WGCNA). RESULTS We provide the transcription profiles of peripheral blood leukocytes in patients with schizophrenia and found a gene module (including 89 genes) closely related to the clinical dimension of abnormal psychomotor behavior in schizophrenia. CONCLUSIONS The findings enhance our understanding of the biological processes of schizophrenia, enabling us to identify specific clinical dimensions of genes for diagnosis and prognostic markers and possibly for targeted therapy.

Entities:  

Year:  2020        PMID： 32038049      PMCID： PMC7032534          DOI： 10.12659/MSM.922426

Source DB:  PubMed          Journal:  Med Sci Monit        ISSN： 1234-1010


Background

Schizophrenia is a common and difficult disease to treat clinically. Genetic factors play an important role in the pathogenesis of schizophrenia [1-3]. It is generally accepted that schizophrenia is a polygenic disease [4-7]. A single gene or SNP mutation site has little effect on schizophrenia [6]. Previous large-scale genome-wide association studies (GWAS) have found that multiple genetic loci are closely related to the occurrence of schizophrenia [8-10], however, identifying the risk chromosomes in schizophrenia is difficult. Great advancements have been made in genetic research on schizophrenia in recent years, with more than 100 genome-wide significant risk variations identified [10], but how reported risk variations affect the pathogenesis of schizophrenia remains elusive. Because schizophrenia is a mixed clinical syndrome, the clinical manifestations of schizophrenia vary. Some patients mainly have positive symptoms such as delusions and hallucinations [11,12]. Whereas, some patients mainly have negative symptoms such as apathy and decreased willpower [13,14]. Other patients have emotional symptoms such as mania or depression [15]. Schizophrenia is a heterogeneous aggregation of different clinical phenotypes. At present, clinical and basic research mostly focuses on schizophrenia itself. There are few studies on the relationship between different clinical manifestations and heredity. Defining the specific clinical dimensions of schizophrenia is a great challenge for psychiatrists. In the past, the traditional clinical classification system classified schizophrenia as paranoid, catatonic, simple, undifferentiated, disorganized, and residual [16]. However, due to the heterogeneity of the disease, the stability of clinical diagnosis is poor, as is the standardization of treatment. Therefore, the 5th edition of the U.S. Diagnostic and Statistical Manual on Mental Illness (DSM-5; published in 2013) excludes these traditional subtypes of schizophrenia [17,18]. It is recommended that the psychiatric symptom severity rating scale be used to evaluate symptoms in different dimensions. In recent years, some scholars have divided the clinical manifestations of schizophrenia into the following 8 symptom groups (8-dimensional symptoms): abnormal psychomotor behavior, disorganized speech, hallucination, delusion, negative symptom, impaired cognition, depression, and mania [17,19,20]. This approach can deepen psychiatrists’ further understanding of schizophrenia and guide clinical treatment and scientific research. Compared with the use of subtypes, the use of psychopathological dimensions in DSM-5 significantly improves the ability to describe the heterogeneity of the disease in a more effective and clinically useful way, and this approach can be targeted to different dimensions of symptoms for research and treatment [19-22]. To further study the relationship between different dimensions of schizophrenia and heredity, we collected clinical information from schizophrenia patients and used the dimension method in DSM-5 (Clinician-Rated Dimensions of Psychosis Symptom Severity scale) to assess the severity of the core symptoms of schizophrenia [19,20]. If the score of the core symptoms in a dimension was 2 or greater than 2, we considered the symptoms in this dimension to be positive. If the score of the core symptoms in a dimension was less than 2, we considered the symptoms in this dimension to be negative [17,19]. The total RNA of peripheral blood leukocytes from study participants was collected at the same time as the dimension score assessment. After removing ribosomal RNA (rRNA), the DNA library was constructed. Illumina HiSeqTM 4000 sequencing technology was used to obtain information on the transcription groups and to explore the differences in genes between 2 groups: schizophrenia patients and healthy controls who were roughly matched for sex and age. Weighted gene co-expression network analysis (WGCNA) was used to analyze the relationship between differential genes and 8 clinical dimensions. Interestingly, we found that Turquoise module was positively correlated with abnormal psychomotor behavior, and the difference was statistically significant. We randomly selected 5 hub genes (CXCL8, EGR1, EGR3, IL1B, and PTGS2) for quantitative analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) validation showed that the results were consistent with those of messenger RNA (mRNA) sequencing. In our study, the Turquoise module (89 genes) was identified as new risk genes for the abnormal psychomotor behavior dimension of schizophrenia, and we speculate that risk variation might affect the expression of these genes. This leads to the susceptibility of patients with schizophrenia to the abnormal psychomotor behavior dimension.

Material and Methods

Study patients

A total of 50 patients with schizophrenia were enrolled from the Honghe Second People’s Hospital and Yuxi Second People’s Hospital from May 2018 to July 2018. During the same period, 50 healthy volunteers with similar sex and age were recruited into the normal control group at the Sixth Affiliated Hospital of Kunming Medical University. After the patients with schizophrenia and healthy controls provided informed consent, we used anticoagulant extraction to obtain 5 mL of peripheral blood.

Inclusion criteria for the patient case group

Inclusion criteria for the patient case group were as follows: 1) meet the criteria for diagnosis of schizophrenia in DSM-5; 2) first onset schizophrenia or no antipsychotic drugs for 5 weeks before enrollment; 3) Han Chinese aged 18 to 60 years; 4) no significant RNA degradation in the sample; and 5) signed informed consent and voluntary participation in this study.

Exclusion criteria for the patient case group

Exclusion criteria for the patient case group were as follows: 1) combination of mental disorders caused by other mental illnesses such as depression or physical and physical diseases; 2) serious neurological diseases or severe physical diseases such as the combination of liver and kidney insufficiency; 3) a medical history of alcohol dependence, substance abuse and addiction; 4) a history of blood transfusion within 1 month before admission; 5) treatment with electroconvulsive therapy (ECT) within 3 months; and 6) severe audio-visual disorders and intellectual development disorders.

Inclusion criteria in the healthy control group

Inclusion criteria for the health control group were as follows: 1) a normal physical examination of adults without abnormalities, without any family history of mental illness; 2) no history of severe traumatic brain injury, no neurological diseases, and no major physical diseases and trauma history; and 3) of Chinese Han nationality roughly matched with the sex and age of the case group.

Ethical approval and informed consent of patients

All experimental schemes involving human participants in this study were approved by the Ethics Committee of the Sixth Affiliated Hospital of Kunming Medical University. All participants in the study provided written informed consent, and all participants agreed to use their clinical and RNA-Seq data for research and publication. The research methods adopted were based on the Helsinki Declaration and the guidelines of the Ethics Committee of the Sixth Affiliated Hospital of Kunming Medical University. Patients were told that refusing to participate in the study would not affect future treatment.

Peripheral blood leukocyte (PBL) collection and total RNA extraction

Collection of fresh whole blood samples

At approximately 7: 00 a.m., 5 mL of whole blood was extracted by venipuncture from the study participants. An EDTA anticoagulant tube was used. The samples used to extract leukocyte RNA were centrifuged within 60 minutes and transferred to a 1.5 mL EP tube. The leukocytes were mixed with 1 mL TRIzol and then transferred into a 1.5 mL freezer tube (which was tightly sealed with sealing film and stored in liquid nitrogen or −80°C freezer.

Total RNA extraction

TRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to extract leukocytes from peripheral blood. A Nanodrop ND200 (Thermo Scientific Inc., Waltham, MA, USA) was used to determine the concentration and purity of RNA. According to OD260, the concentration of the RNA sample was calculated as follows: RNA (mg/mL) = 40×OD260×dilution multiple/1000. The value of OD260/OD280 of pure RNA is generally 1.8–2.0, so the purity of RNA can be estimated according to the value of OD260/OD280. If the ratio is low, there are residual proteins. It is important to note that the value of RNA OD260/OD280 extracted by this method was between 1.8 and 2.0 (hands are the main source of RNase).

Library construction sequencing

After extracting the total RNA, the rRNA was removed using the Ribo-ZeroTM Magnetic Kit to enrich the mRNA. The enriched mRNA fragments were then converted into short fragments with fragmentation buffer, and the cDNA was reversed transcribed with a random primer. Second chain cDNA was synthesized with DNA polymerases I, RNase H, DNTP, and buffers. The samples were then amplified using QIAquick-PCR to purify the cDNA fragments, repair the ends, add polyadenine (a), and connect the Illumina sequencing adaptor. The product was sequenced with the Illumina HeSeqTM 4000, and underwent agarose gel electrophoresis and PCR amplification at Gidio Biotechnology (Guangzhou, China).

Analysis of differential expression transcription products (DEGS) of coding RNA

To identify differentially expressed transcripts between the schizophrenia patients and the healthy controls, the edgeR package () was used [23] and Benjamini-Hochberg (B-H) correction for multiple comparisons. We categorized the transcripts with multiple fold changes (>2) and false discovery rate (FDR) <0.05 as indicting significant differentially expressed genes (DEGs). Then, enrichment analysis of Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was carried out for differentially expressed coding RNA.

Comparison of our RNA sequencing of differential genes with genes identified by GWAS identification and genes identified by gene set enrichment (GSE) data set

We downloaded the GWAS identification gene on SZDB data [24] () and several data sets from the GEO public data set (GEO, ), including GSE62191 (comprising 29 patients with schizophrenia and 30 healthy controls) [25,26], GSE17612 (including 28 patients with schizophrenia and 23 cases of healthy controls) [27], and GSE12649 (including 35 patients with schizophrenia and 34 cases of healthy control) [28]. First, GEO2R was used to identify the differential genes between the schizophrenia group and the healthy control group in the GSE dataset. With P less than 0.05 indicating statistical significance (GSE dataset differential gene results are detailed in Supplementary Document 1), a Venn diagram was drawn using the Omicshare tool, a free online data analysis platform ().

WGCNA analysis

The WGCNA algorithm is a common algorithm for building gene co-expression networks. The WGCNA algorithm first assumes that the gene network obeys the scale less distribution, defines the adjoining function of the gene co-expression correlation matrix and gene network formation, calculates the difference coefficient of different nodes, and constructs a hierarchical cluster tree accordingly. Different branches of the cluster tree represent different genetic modules. The degree of gene co-expression is high, while the degree of gene co-expression is low in different modules. Finally, the relationship between the module and the specific phenotype was explored, and the target gene and gene network of the disease-specific phenotype were finally obtained. We extracted 559 upregulated and downregulated DEGs from edgeR analysis and performed WGCNA with the expressed sequencing data [29]. The R-package “WGCNA” was used to search for related modules and hub genes of clinical traits.

Analysis of trait-related characteristics module

The main goal of network co-expression analysis is to identify the modules and genes most relevant to biological significance or clinically relevant. Hence, the modules most relevant to the traits for schizophrenia were identified by analyzing the correlation between the modules and specific phenotypes or traits. By calculating the correlation between each module’s characteristic values and traits, we identified the modules and genes most relevant to the traits so that the corresponding modules could be selected for further study. In general, if a module has a significantly higher correlation with the selected trait than the other modules, it indicates that the module may have the strongest correlation with the trait. The grouping relationship is considered a trait used to determine the relationship between modules and groupings. Then, the gene function of a clinical dimension-related (and statistically significant) module was analyzed, and the GO and KEGG pathways were enriched to analyze the biological function of the module.

Protein–protein interaction (PPI) network analysis

Protein–protein interaction (PPI) was used to find the biologically or clinically meaningful modules and analyze the gene expression profiles of the study patients. Each gene in the module was calculated; the highly connected genes are often hub genes, which may have important functions. The network was visualized using String database ().

qPCR verification

Illumina offers a wide range of powerful library preparation kits that provide fast and easy library preparation workflow, and the Illumina HiSeqTM 4000 sequencing results are highly technically repeatable. However, to further verify the accuracy of our peripheral white blood cell sample sequencing results, we used qRT-PCR to validate 5 hub genes (CXCL8, EGR1, EGR3, IL1B, and PTGS2) in the Turquoise module to evaluate whether the results were consistent with mRNA sequencing results and to verify the accuracy of the Illumina HiSeqTM 4000 sequencing results. Extracted RNA was synthesized with complementary DNA (cDNA) using a reverse transcription kit (Takara). Real-time PCR was carried out with TB Green series kit (Takara) and monitored with the Bio-RAD (CFX96 real-time) system. β-actin for mRNA was applied to normalize the result. All reactions were repeated 3 times and relative gene expressions were evaluated by the 2−ΔΔCt method. Primer sequences are shown in Table 1.
Table 1

Primer sequences.

GenesForwardReverse
EGR1GGTCAGTGGCCTAGTGAGCGTGCCGCTGAGTAAATGGGA
EGR3GACATCGGTCTGACCAACGAGGGCGAACTTTCCCAAGTAGGT
IL1BATGATGGCTTATTACAGTGGCAAGTCGGAGATTCGTAGCTGGA
CXCL8AAGACATACTCCAAACCTTTCCACCCTTCAAAAACTTCTCCACAACCCTC
PTGS2CTGGCGCTCAGCCATACAGCGCACTTATACTGGTCAAATCCC
ACTBCATGTACGTTGCTATCCAGGCCTCCTTAATGTCACGCACGAT

Statistical methods

All transcript sequencing data were analyzed by R software. GEO data sets were analyzed by GEO2R tools, and qRT-PCR validation data were tested by 2 independent samples t-test. The test level a=0.05, P<0.05 was considered statistically significant.

Results

Analysis of the difference in the expression of mRNA between the 2 groups

We used edgeR software to analyze the difference in FPKM (fragments per kilobase of transcript per million) values between the healthy control group and schizophrenia patients. FDR and log2FC were used to screen differential transcripts. The screening conditions were FDR <0.05 and |log2FC| >1. A total of 559 differentially expressed transcripts were screened, including 206 downregulated transcripts and 353 upregulated transcripts (see Supplementary Table 1 for details). Also, we compared with GWAS and other brain GSE datasets and used a Venn map (Figure 1A–1E). The comparisons between GWAS identifying genes and GSE dataset identifying genes in brain regions can be found in the Supplementary Document 1; a Venn chart compared GWAS and GSE data.
Figure 1

(A–E) Comparisons between genome-wide association studies (GWAS) and gene set enrichment (GSE) dataset. Identifying genes in the brain regions can be found in Supplementary Document 1 Venn chart that compares GWAS and GSE data. (F) Gene Ontology (GO) item in differentially expressed genes (DEGs). (G) The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis chart. (H) Expression patterns of differentially expressed transcripts (69 transcripts) with an average value of FPKM (fragments per kilobase of transcript per million) greater than 1 in the healthy control group and schizophrenia group and constructed a heat map.

Differential mRNA GO/pathway enrichment analysis

After obtaining differentially expressed transcripts, GO functional analysis and KEGG pathway analysis were performed on the differentially expressed transcripts. Taking FDR <0.05 as the threshold, we calculated the P values. Meeting this condition was defined as a significant enrichment GO item in DEGs. This analysis can identify the main biological functions performed by different genes. GO analysis indicated that DEGs were mainly concentrated in response to stress, immune system process, and immune response, as shown in Figure 1F. The KEGG pathway analysis showed that DEGs are mainly involved in mineral absorption, IL-17 signaling pathways, etc. The KEGG enrichment analysis chart is shown in Figure 1G.

Cluster analysis of expression patterns

Based on the expression of transcripts, the relationship between transcripts was hierarchically clustered, and the clustering results were presented using a heat map. We analyzed the expression patterns of differentially expressed transcripts (69 transcripts) with an average value of FPKM greater than 1 in the healthy control group and schizophrenia group, and we constructed a heat map (Figure 1H). The rows were normalized (z-score), and hierarchical clustering analysis was carried out for different transcripts. Each column in the graph represents one sample: C1–C50 are the 50 samples of the healthy control group, and S1–S50 are the 50 samples of the schizophrenia group. Each row represents one transcript, and the expression of transcripts in different samples was expressed in different colors. The redder the color, the higher the expression level, and the greener the color, the lower the expression level.

WGCNA module division

The adjacency matrix was transformed into a topological overlap matrix. According to the TOM-based difference measure, genes are divided into different gene modules. According to the principle of a scale-free network, the power value was determined. The power value selected in this analysis was 5. The parameters (similarity) of the merging module were 0.75, and the number of genes needed to be included in each module was at least 30. The hierarchical clustering tree of modules is as follows: 1) Dynamic Tree Cut is a module divided according to clustering results, 2) Dynamics is a combined module division of modules that express similar patterns based on module similarity, followed by analysis according to the merged module, and 3) for Tree Diagrams, vertical distance represents the distance between 2 nodes (between genes), and the horizontal distance is meaningless. See Figure 2.
Figure 2

(A) The hierarchical clustering tree of modules. (B) The results of the trait-module correlation are shown in the Module-Trait Relationships chart.

Character module analysis

The main goal of the network co-expression analysis was to identify the most relevant modules and genes of biological of clinical significance. Therefore, the most relevant modules can be found by analyzing the correlation between modules and specific phenotypes or traits. By calculating the correlation between each module’s characteristic values and traits, we can identify the modules and genes most relevant to the traits, so that the corresponding modules can be selected for further study. In general, if one module has a significantly higher correlation with the trait than the other modules, this indicates the module may have the strongest correlation with the trait. The 8-dimensional characteristics of schizophrenia samples are detailed in Supplementary Table 2: Schizophrenia Sample Trait. The grouping relationship was used to find the relationship between the module and the grouping. The results of the trait-module correlation are shown in the Module-Trait Relationships chart (Figure 2B) which contains the correlation coefficients and P values of each trait and module characteristic values. Each column of the Profile-Related Characteristics Module (Figure 2B) represents a personality, and each row represents a genetic module. The number in each box represents the correlation between the module and the characteristic. The closer the number is to 1, the stronger the positive correlation between the module and the characteristic. The closer the module to −1, the stronger the module’s negative correlation with the trait. The numbers in parentheses represent the significance of the P value, and the smaller the number, the greater the significance. The P value was calculated using the Student’s t-test, the smaller the P value, the more significant the correlation between the representative form and the module. The results suggest that the Turquoise module is positively correlated with abnormal psychomotor behavior (P=0.002).

Turquoise module gene expression pattern and functional enrichment

We show the expression patterns of the genes contained in the Turquoise module using thermal maps and changes in the module’s eigenvalues in different samples (equivalent to the module expression pattern) by histogram, as shown in Figure 3A. The calorimetric expression of genes in different samples is shown. Red indicates upregulated genes, and green indicates downregulated genes. The expression pattern of module eigenvalues in different samples is shown. GO analysis reveals that the Turquoise module genes are mainly concentrated in biological processes, such as response, locomotion, immune system process, cell motility, and localization of cells. The GO enrichment analysis is shown in Figure 3B. The KEGG pathway analysis (Figure 3C) shows that the Turquoise module genes are mainly involved in malaria, mineral absorption, the IL-17 signaling pathway, Salmonella infection, the NF-κB signaling pathway, and the tumor necrosis factor (TNF) signaling pathway.
Figure 3

(A) Expression patterns of the genes contained in the Turquoise module. (B) The Gene Ontology (GO) enrichment analysis of the genes contained in the Turquoise module. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the genes contained in the Turquoise module.

PPI network construction and hub gene analysis

After mapping the genetic symbols of all the Turquoise modules to String, a PPI network (Figure 4) was generated. PPI statistical analysis indicates 89 nodes with 141 edges; the average node degree was 3.17, the expected number was 57, and PPI p-enrichment was 1.0e-16. At the same time, we selected the hub gene using the CytoHubba plug-in in Cytoscape to determine the top 20 hub genes with high connectivity in turn: CXCL8, FOS, PTGS2, IL1B, ATF3, EGR1, CXCL2, REL, NFE2L2, SNAI1, PLAU, HBEGF, AREG, EGR2, ETS1, PTGER3, LEF1, ADM, SERPINB2, and EGR3.
Figure 4

Turquoise modules to string, a protein–protein interaction (PPI) network was generated.

Gene validation

To further verify the accuracy of the sequencing results of our peripheral blood leukocyte samples, we randomly selected 5 hub genes (CXCL8, EGR1, EGR3, IL1B, and PTGS2) in used the Turquoise module for qRT-PCR verification. Two independent sample t-tests showed that the expression of CXCL8, EGR1, EGR3, IL1B, and PTGS2 in the schizophrenia group was higher than that of the healthy control group (P<0.05). The qRT-PCR results were consistent with those of mRNA sequencing (Figure 5).
Figure 5

(A–E) Quantitative Real-Time polymerase chain rection results of 5 hub genes in the Turquoise module.

Discussion

Information regarding mRNA expression in the brain of patients after death is very important to elucidate the molecular genetic mechanism of schizophrenia and other neuropsychiatric diseases. However, brain tissue specimens from living patients are difficult to obtain. Recent studies have found that the immune system plays a vital role in the repair, maintenance, and brain reserves of the central nervous system [30,31], that immune cells help maintain neurogenesis and spatial learning in adulthood [32], and that age-related spatial memory loss can be partially recovered through immune activation [33]. The interaction between neurons, glial cells, and the immune system contributes to the healthy functioning of the brain [34]. The occurrence of schizophrenia is closely related to immunity [35]. Peripheral blood macrophage/monocyte inflammation patterns may indicate the activation of small glial cells, which, in the brain, are macrophages, and are considered the main immune cells [36]. Neuroimmune interactions allow the nerves and immune systems to regulate each other: immune cells play an important role as peripheral neurotransmitters, peripheral T cells exhibit similar characteristics as dopamine-activated cells, and the brain can regulate immune cells [37]. The cells that compose the peripheral immune system are mainly white blood cells, which are easy to collect in a clinical setting and can be used as an alternative to brain tissue for biomarker screening studies. The results of this study can help us understand the relationship between different dimensions of schizophrenia and genetics. In the past, genetic studies of schizophrenia have paid little attention to its heterogeneity (and little attention to symptom dimensions). In this study, we describe a large transcription dataset of blood cells in a patient with schizophrenia (detailing their symptom dimensions) as well as gender and age-matching control samples of white blood cells. First, we analyzed the differential expression patterns of transcripts between the schizophrenia group and the healthy control group. A total of 559 differential transcripts (506 genes) were found. Functional enrichment analysis of differential genes indicated that 139 transcripts were involved in response to stress biological processes (GO: 0006950), and 114 transcripts were involved in immune system processes (GO: 0002376). Enrichment analysis indicated that 93 transcripts were involved in immune response processes (GO: 0006955). DEGs were mainly involved in the immune response, IL-17 signaling pathway, and NF-κB signaling pathway. Dimitrov et al. showed that the IL-17 signaling pathway may play an important role in schizophrenia [38]. Numerous studies have shown that the NF-κB signaling pathway plays an important role in nerve protrusion growth, activity-dependent plasticity, and cognitive function, and that abnormal changes in the NF-κB signaling pathways in patients with schizophrenia can occur [39,40]. A Venn diagram was used to analyze where the DEGs identified by RNA-Seq and the genes identified by GWAS overlapped. A total of 9 overlapping genes (LRP1, ABCB9, NISCH, EGR1, FES, DGKZ, FYN, CTND1, and ALDOA) were found. GSE62191 had 20 overlapping genes, GSE12649 had 55 overlapping genes, and GSE17612 had 43 overlapping genes. Our study found that FOS, IL1B, CXCL8, CASP1, CFL1, CAMP, ITPR2, ACTG1 and other inflammation and immunity related genes were differently expressed between the schizophrenia patient group and the healthy control group, These findings were consistent with the neuroinflammatory hypothesis of schizophrenia. Previous studies have shown that the loss of mTOR inhibitor TSC1 or PTEN can increase the growth of somatic cells and dendrites, and that altered TSC/mTOR or PTEN/mTOR signal can play an important role in the development of autism spectrum disorders [41-44]. In our study, we found that the ASD related genes PTEN, mTOR, TSC1, and FMR1 expression in peripheral blood of patients with schizophrenia had no significant change when compared with healthy controls. This may indicate that these 2 common psychiatric diseases have different pathogenesis. According to the differential gene thermogram, the expression of CASP1 and other genes in schizophrenia patients decreased significantly. CASP1 is a risk gene for depression [45,46]. At present, there are no studies on the relationship between CASP1 and schizophrenia. Some studies found that the depression-like symptoms of CAPS1 knockout rats were alleviated, while pleasure-like behaviors occurred, and the speed of exercise was increased in rats [45]. The decreased expression of CASP1 in patients with schizophrenia may be related to the positive symptoms of patients with schizophrenia, and the specific role of CASP1 in schizophrenia is unclear. We need to conduct more research on CASP1 in the future. To find the key module most relevant to the clinical dimension of schizophrenia, we carried out WGCNA on DEGs using edgeR analysis. Clinical sample information was collected from patients, such as abnormal psychomotor behavior, disorganized speech, hallucination, delusion, negative symptom, and 8 other dimensions. Through WGCNA of differential transcripts, we determined 3 modules (nonclustering degree of gray display). From the thermogram of the module-feature correlation, we found that the Turquoise module was the module that was most correlated with the clinical features of abnormal psychomotor behavior, and the correlation coefficient was 0.42, P=0.002. The Turquoise module contains 96 transcripts (89 genes). We used the CytoHubba plug-in in Cytoscape to select hub genes and identify the top 20 hub genes with high connectivity: CXCL8, FOS, PTGS2, IL1B, ATF3, EGR1, CXCL2, REL, NFE2L2, SNAI1, PLAU, HBEGF, AREG, EGR2, ETS1, PTGER3, LEF1, ADM, SERPINB2, and EGR3. To reveal the potential biological functions of genes in the Turquoise module, we performed GO and KEGG analysis. This analysis shows that the genes in the Turquoise module are mainly involved in the response to stress, inflammatory response, locomotion, immune system processes and other biological processes. ADORA3, IL1B, ADM, FOS, CXCL8, PER1, ETS1, LRP1, IFI16, PTGS2, PTGER3, REL, IL1RAP, HRH4, and CXCL2 were involved in inflammatory response (GO: 0006954), This indicated that inflammation related genes play an important role in the abnormal psychomotor behavior characteristics in patients with schizophrenia, These results provide a new clue for further study of the mechanism of abnormal psychomotor behavior in schizophrenia. The Turquoise module contains 5 genes (CXCL8, FOS, PTGS2, IL1B, and CXCL2) enriched in the IL-17 signaling pathway, 5 genes (PLAU, IL1B, CXCL2, CXCL8, and PTGS2) enriched in the NF-κB signaling pathway, 3 genes (IL1B, FYN, and EGR1) enriched in prion diseases, 6 genes (FOS, MAP3K8, IL1B, CXCL2, BCL3, and PTGS2) enriched in the TNF signaling pathway, and 4 genes (FOS, MAP3K8, IL1B, and CXCL8) enriched in the Toll-like receptor signaling pathway. Previous studies have found that the TNF signaling pathway and Toll-like receptor signaling pathway are closely related to the onset of schizophrenia [47-49]. WGCNA identified 89 risk genes for schizophrenia (Supplementary Table 3), and their expression changes were positively correlated with abnormal psychomotor behavior in schizophrenia. The Turquoise module contains 3 genes of the EGR family (EGR1, EGR2, and EGR3). Hub gene EGR1 plays an important role in the occurrence of schizophrenia. Kurian et al. [50] suggested that EGR1 gene expression is increased in the blood of patients with schizophrenia who have a high illusion state. Other studies have shown that EGR1 is downregulated in the prefrontal cortex of postmortem brain samples from patients with schizophrenia [51,52]. Ramaker et al. [53] performed transcriptome analysis of 24 patients with schizophrenia and 24 bipolar disorder patients and 24 control brain tissues. The results showed that the expression of EGR1 in the anterior cingulate cortex of patients with schizophrenia was significantly lower than that of the control group. Studies have found that EGR3 is considered a potential susceptibility gene for schizophrenia [54-56]. CXCL8 is a risk gene for depression [57], but recent studies have shown that chemokine CXCL8 is involved in the neurobiological process associated with schizophrenia. The increase in maternal CXCL8 levels is also related to the increased risk of mental illness in offspring [58-60]. The PTGS2 gene, also known as the COX2 gene, is closely related to schizophrenia. The PTGS2-specific inhibitor has a curative effect on schizophrenia [61,62]. Our data showed that there was a significant difference in gene expression between the schizophrenia group and the healthy control group. Further analysis of the gene and clinical phenotype by WGCNA suggested that the Turquoise module in patients with schizophrenia was significantly related to abnormal psychomotor behavior, among which key genes were involved. IL1B participates in 5 pathways, thus, we speculate that IL1B in core genes may lead to abnormal psychomotor behavior in schizophrenia through the IL-17 signaling pathway, NF-κB signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. In addition, these data provide useful resources for future research and help to test preliminary hypotheses or verify important findings.

Conclusions

In the present study, compared with the healthy control group, 206 downregulated transcripts and 353 upregulated transcripts were found in the schizophrenia group samples. Functional analysis of GO showed that DEGs were mainly enriched in response to stress, immune system process, and immune response. The KEGG pathway analysis showed that DEGs mainly participated in the TNF signaling pathway and the IL-17 signaling pathway. WGCNA divided DEGs into 3 co-expression modules, which indicated that the Turquoise module was positively correlated with abnormal psychomotor behavior (P=0.002). We randomly selected 5 hub genes (CXCL8, EGR1, EGR3, IL1B, and PTGS2) of the Turquoise module for qRT-PCR verification. The results of qRT-PCR were consistent with those of mRNA sequencing. We provided the transcription profiles of peripheral blood leukocytes in patients with schizophrenia and found a gene module (including 89 genes) closely related to the clinical dimension of abnormal psychomotor behavior in schizophrenia. We also speculate that IL1B, a hub gene, may play an important role in the abnormal psychomotor behavior of schizophrenia through multiple pathways. These findings enhance our understanding of the biological processes of schizophrenia, enabling us to identify specific clinical dimensions of genes for diagnosis and prognostic markers and possibly for targeted therapy

Supplementary Data

Supplementary Document 1

1. Genes identified by GSE62191

KRT6A, TNFSF10, GBP4, SLC14A1, SCGB1D2, DUSP1, FAM86B3P, ETV5, ARC, KIF19, DUSP6, PLIN1, TTC7A, KDF1, USP45, ZMYM5, ELK3, SPAG5-AS1, PLPP5, SCGB1D1, FKBP5, CBX3P2, HSPA8, CCT6P1, C1orf74, EGR2, HES4, DYRK1A, PGBD1, PROCR, DYNLL2, RHCE, EIF3F, SERPIND1, CRTAM, EPM2A, CLEC18A, CASP14, NHLRC2, LIF, VEGFC, DUSP5P1, ATP13A4, DUS4L, GLI2, TEX2, SPRR2C, ZNF615, LOC158435, EGR1, DCAKD, CD52, EGR4, PER2, TTC13, HLA-DRB3, TTC33, GPR149, MRVI1, HRAS, SAMHD1, MDC1, GPR20, ALDH1L1, TRPC3, GNAZ, USF2, CCND1, TRIM24, PDZD2, MOCOS, PTPN7, PAPSS1, SMCHD1, BBS2, TSACC, PRR15, CD69, CCL4, EGFR, PIGA, ATP1B2, F7, SDC4, TRPV2, TECPR2, MCM6, LOC100129397, MFGE8, ZNF442, SLC25A21-AS1, LINC00469, GALNT4, CD1B, LOC101927598, OR7G3, C2CD4B, SLC15A2, WNT7A, CALML4, RRN3, SOD3, AQP1, PGM5, IRX6, CPZ, ITPKB, DGKE, WHSC1L1, HIF3A, DNAJB6, CMYA5, SERBP1, FAM161A, TMPRSS5, NR3C1, PLAU, MLXIPL, SNX2, AOC2, TUBA3D, GGACT, LINC00671, TCP11, MXRA5, SBK1, TAF15, POU2F1, TRIB2, MTSS1L, DSCC1, FAM117B, FGD3, FKTN, NIM1K, LBH, CLC, MEGF11, NR4A1, SLC16A9, MON1B, TRIOBP, EIF4E3, ZNF223, NOP14-AS1, ECM1, SLC22A6, ZFAND2B, PLK4, THBS1, SOCS6, FOS, PRKCA, NGF, FERMT2, PIP5K1C, CISD3, ST8SIA2, NAAA, PAX4, ARHGAP9, LMNTD2, CITED4, LOC100652930, CNNM2, FAM81B, ROBO3, BORCS8-MEF2B, LOC100507663, GLTSCR1L, SLC36A1, ZBTB39, C4BPB, GPIHBP1, IRX5, LOC285957, NLRP6, LCK, POLR3H, CRABP1, YY1AP1, CTNNB1, DDIT4L, ETS1, TEX40, ABL1, USP22, PPID, ARHGEF10, SLC35F6, ENTPD5, CFAP70, DPYSL3, MYBPC1, DLGAP1-AS4, LOC100288254, TMC2, FKBP4, UTP11, DZIP1L, SOHLH1, VGF, PMFBP1, MT1X, AGRN, SMOC1, COL4A1, CHRD, CEP95, ZFHX4-AS1, NOS1, C4B, GUCA1B, RARA, ZNF443, LOC105378499, B3GNT9, BCL6B, RHOBTB3, SLC30A7, DDRGK1, ARV1, CD300LG, TP53TG5, CLASP2, PGLYRP2, CEBPB, TTC29, PLIN3, GABBR1, POU5F1P4, SPATA24, KCNQ1DN, VILL, CCL19, DUSP8, R3HDM2, NPBWR1, ITGB3, LPIN1, RHO, ADGRG6, TXNRD2, FBXW11, POLR2A, HSPBP1, GRIN2C, LOC153546, ALB, AMT, C6orf141, OAS1, DLX1, FAM189A2, PCDHB9, UCA1, ZNF256, CREM, FOXA3, KNCN, CMC2, TXNDC16, ZBTB6, KIF13A, GZMH, CT45A1, C12orf49, APOC1, DPYSL2, C3AR1, AGXT, MAOB, NPBWR2, CASP8, TUSC5, APC2, MASP2, VWF, CH25H, HLA-DRB1, CD48, NXF1, CDC5L, NFATC1, CPA5, SPON1, ETNPPL, LRAT, PHLDB2, GGA1, ANKRD11, CCIN, PGAM1, GINS2, GIMAP2, SMIM6, RAB34, GDF10, ZNF738, CCL3L3, UCKL1, NOTCH2NL, NKX2-1, EXO1, B4GALT2, DOCK3, KRTAP19-1, SCGN, TCP10, GRAMD1C, TNFRSF11B, SPRY4, KAT2B, FBXO21, PPFIBP1, VRTN, MYF6, PSIP1, ABCC3, SYT6, CSNK1G2, TTC32, CDKN2AIPNL, INTS10, CHKB, P2RY13, TDGF1, COL24A1, KAZALD1, ZC3H10, PRAP1, ARID1B, ABHD12, TGFB3, MGST1, RFX4, ECHDC2, RMND5B, METTL7A, PLEC, PAPLN, MYOD1, CTBS, SMARCA5, KNSTRN, ABL2, ARMC6, IGF2BP1, TMEM56, NBPF3, SLU7, IFI44L, CFAP157, XIRP1, CEACAM1, EPSTI1, GABPA, USP47, FOSL2, ENDOU, TTC30A, PLD6, COLCA2, UTP15, ZNF382, LINC01554, GPSM3, PTGER2, MAFA, C3orf70, SPACA6, IL10, H3F3B, SH3KBP1, CCDC122, BCL2L11, RUFY3, CALCA, ATG4D, LOC100506548, ARX, PYGB, FAM197Y2P, CD79A, ENPP1, GJA1, AVP, RRM1, TTC21A, PDE1A, HES1, PBX4, NID1, ZC4H2, NOS3, ITGAX, C6orf52, LOC439938, SLC2A4RG, SIRPB1, LPAR6, PQLC3, MUC1, KLK1, PIGQ, FRZB, IRAK1, PBXIP1, GPAT3, LOC105369230///HLA-DRB5///HLA-DRB4///HLA-DRB3///HLA-DRB1, SERPINA3, LENG8, KLHL28, SRGAP2C, PTCH1, SLC52A3, ENOX1, ZNF416, TXNIP, FN1, ST3GAL2, DHRS3, CCDC88C, ORAI2, ALDH1A2, VSIG4, FAM198A, APOL5, TMPRSS15, GLIS3-AS1, NPNT, ZCCHC7, ENPEP, LOC100133091, A2M, PSPH, GAA, SLC25A5, CELF4, SLC39A3, HFM1, DUSP2, CNGB1, AQP4, LOC101927402, ETV4, CLN3, HS6ST3, UBA6, EDN1, WFS1, PTPRS, SIK1, OSGEP, GPR45, CHRNA4, ERVH-4, ERVH-6, MCPH1, FGL1, SGSM3, TDRD9, ZNF433, MCM9, AASDH, SLC44A5, LOC101927502, PROK2, ARMC3, MATN3, PKM, RBM23, GLIS3, ARMC12, SRSF10, CD3G, GALNT3, DCN, ABCG2, MYCBPAP, IRAK4, INO80D, RCC2, RNF157, RANBP17, GMNN, KMT5B, GPCPD1, NTRK2, SLC2A4, ISYNA1, KIF1A, HLA-H, CWF19L2, AGFG2, TMCO3, HSPA12B, DAZL, ZNF563, TET1, ARPP19, MTA1, PDK4, PPAT, GIMAP8, NUDT14, ZBTB40, ZNF77, CYB561D1, PSMB7, MYOC, MPP3, TNXB, NUTF2, ZSCAN25, ADARB2, BMP8B, PON2, TPM4, PON3, SHMT1, RDH8, TPCN1, NEK9, ZNF8, TLK2, SARNP, RBBP8NL, IFT88, RB1, VCAM1, ASB4, TYMSOS, RASL11B, NETO2, ZNF595, PARD6A, ITGB1BP1, SMCR5, EMX2, MAGEA5, SLC12A7, RALGDS, AARS2, VPS13C, TYW5, ZNF182, ZNF214, GH1, GOT1L1, RPGR, SEMA3A, SYNPO, GTF2H5, HLA-DMA, ZNF175, PDZRN3, OR2H2, NIP7, TMEM254, ABCC12, GPA33, ING1, HSPB1, MRPL30, ERBB2, BCAT2, MKNK2, IL17RB, FLJ13224, ACTR5, EIF2AK3, ZNF84, LINC00602, CSDC2, COX18, MAP3K2, RHPN1, GRAMD2, ZNF250, MGAT1, APOL3, BIRC3, TWIST2, BTN1A1, MOB3B, PIM3, MAN2A2, HSD3B7, C1orf226, ZNF713, HAUS5, TRMT6, CRH, G0S2, LMX1B, GAL3ST2, ILF2, CACNA1H, SOX18, ZNF146, MRS2, KDM1B, LOC284561, MT1B, JMJD1C-AS1, PPFIA1, LEF1, KIF20B, XYLT1, OSBPL11, RAB24, FAM110B, SDHB, ALDH1L2, PITPNA, TAT, NUFIP2, LGALS3, GPC3, UTY, LOC100506544, BBOX1, SLC10A7, DBF4, ADCY1, PLK1, CD70, ZNF799, DGAT1, NUDT22, FNDC4, BBIP1, ELOVL2-AS1, HRK, CYP51A1, ZNF578, LYPD6B, RIC1, THNSL2, CD320, ALDH1A3, AKAP1, DPYD, MIAT, SERTAD4, RPS6KA3.

2. Genes identified by GSE17612

ZNF578, PMAIP1, LOC100506446, FAM204A, FRZB, S100A9, ZNF775, OGN, ALS2CR11, SERP2, FHAD1, LAT2, S100A8, PXDN, WARS2, GSDMB, PEX7, GSTM3, KDM3A, KIAA0408, PTRH1, SVOPL, SCARA5, LOC101928973, DNAJC7, MPV17, FMOD, DKK1, LOC101928238, MROH1, HK2, MLLT3, LOC101927783, TBC1D2B, LINC01361, LINC00838, AKAP12, LINC01020, DSG2, CHD2, CTTNBP2NL, TMEM218, PTPN14, GLRX3, ABCC13, LOC101928409, STK4, MAX, SLC28A1, EPB41L4A, OR10D3, SLC13A3, ATF6, USP37, CTD-2194D22.4, GPR132, GRAMD2, SEC23IP, AGBL3, LACTB, LOC101929757, TIMP4, WIPF3, ST20-AS1, TGFBR1, SATB2-AS1, LOC728613, LINC00900, FBXW2, ZNF577, IL1RN, IFT140, LOC374443, FLJ41455, SAMHD1, ACVRL1, LOC101930112///SPG7, FRY, CYP26B1, SLC13A4, LOC728613///PDCD6, STYX, SLC25A36, LOC101929607, RORA-AS1, CDH6, LOC286154, TRIB2, ATP2A1, DNAJC5G, TREML3P, FMNL1, SPAG11A///SPAG11B, RERGL, CCDC80, ARMCX4, ZDHHC3, GTSF1L, NRCAM, ITGBL1, COLEC10, SCIN, PYGL, SMYD2, TRAF3IP2, PDGFD, GPBP1L1, ZNF518B, HP11014, FUZ, ALOX5AP, LOC100996579, LOC100653005, ASXL3, RPF1, TCP11, CIRBP-AS1, PCBP1-AS1, OSR1, DOCK2, ZNF764, UGGT2, TRAT1, CDHR1, LZTS1, GHR, C19orf43, SAMSN1, MALT1, COL6A2, AGA, IPMK, CHST2, MFAP3L, CDADC1, HLA-DOA, ZNF599, JMJD6, FGF2, SPAG5, SHANK3, KREMEN2, MTHFD1L, SMIM21, VPS13B, PDPN, ATG10, SOAT1, COL3A1, FLYWCH1, LINC01118, SPRNP1, ATP23, CREBBP, DUSP9, RAPGEF1, HGD, HSBP1, C8orf34, EWSR1, TRPC3, CFAP206, COL23A1, BAG3, MLNR, HRH3, LOC440028, UPP2, GSTA3, ANAPC7, AMELY, CD59, FCGR3B///FCGR3A, SEC14L1, C10orf71, CRYBG3, TEKT5, NTS, PIGO, LOC101927021, SRSF5, SMCHD1, CHCHD5, KIF1C, GSS, RNA45S5, LOXL2, CRABP2, MOB1A, MGP, FGD2, PRADC1, TPCN2, COLEC11, LOC730098, HIST1H2APS5, PRKY///PRKX, LCOR, GIMAP1-GIMAP5///GIMAP5, ATPIF1, LOC105371967, P4HA1, NARF, ATOH7, SERP1, KANSL3, IGFN1, LTB, CCL3L3///CCL3L1///CCL3, SUPT20H, CDC42-IT1, ZNRF4, SPCS1, GPRIN2, C12orf54, TNC, GRTP1, MPPED2, SLC25A25-AS1, LOC101929036///PAH, EFHC2, DCAF12L1, NT5DC3, ATP11B, RASGRP1, PNN, HDAC4, SRSF8, UNC5C, TMEM184C, ETV1, GMPR, RHBDF2, SAMD8, F11, EIF3M, CECR5-AS1, CNOT6, SYT5, PTGER3, SYK, TMEM256, GYG2, EIF4A2, C11orf1, LOC101929248, TTTY7, SSH1, IMPAD1, LOC107985971, COX4I1, IRGM, MIR6787///SLC16A3, RBM48, LINC00507, SLCO4A1, LOC101927907, PTK7, CEP131, IKZF1, ADAMTS2, TMEM51-AS1, C1QC, HNF4A, NABP1, FAM193B, ARHGAP45, WDR86, ETV5, AHSA2, ARHGAP30, GSTCD, TLR10, SRSF10, BMP5, ZDHHC2, IQCH, SCGB1C2///SCGB1C1, BANP, MSR1, SLC22A6, IFT27, SLC35E3, CHID1, ZNF350, RNASET2, SFTPC, CREB1, POLR2F, LOC101928879///ZNF250///COMMD5, LOC101927843, DSP, LOC102724884///LOC728613///PDCD6, CACNA1I, ACTR3BP2, RABL3, ADTRP, PITPNC1, CACNA1C, PITRM1, TAF12, CYB5D2, THUMPD3, ARL4D, PIEZO1, LINC01270, COMTD1, SLC15A4, GPR161, LPCAT2, DNAJB4, IGHM, SRSF3, ZFYVE28, FGFRL1, GJB5, FCGR3B, BTG1, ZNF17, TBC1D23, AGK, AADACP1, ABCB5, TRAF6, MKS1, SRMS, PAPOLA, ALKBH1, SPG20, SLC25A24, NCAPH2, NDUFA10, ZC2HC1A, ABI3BP, NCR3, TUBB4A, MIR210HG, TYROBP, PEPD, TRPT1, C1QB, LOC338620, KISS1R, CHRDL2, PLOD2, LOC728485, JMJD4, RGS16, LOC100505585///ARHGEF1, ETV2, ZNF451, KIAA2022, IGK///IGKC, TCL6, PDLIM2, RCOR3, USP22, TM9SF3, CCDC82, SLC2A5, ZNF837, EPS8L1, BCAT1, CYAT1///IGLV1-44///IGLC1, KLHL21, PLA2G4E-AS1, GGA2, CNNM4, ZNF595, ITGB3BP, SERGEF, UBE2J1, COMMD4, ZNF395, CNTNAP3B///CNTNAP3, ADGRE2, NR4A3, LAPTM5, LOC101927787, LINC01267, CEACAM1, KATNAL2, SMAD7, LOC254896///TNFRSF10C, FUBP1, PLCXD2, JAKMIP2, PCYOX1L, SMIM8, EPB41, GATA3, TMLHE, EPHA5-AS1, TOM1, GPR137, NUP160, EPPK1, FER1L4, NR4A1, AMFR, ALDH1A1, CEP128, STXBP5-AS1, CYBA, CPXM2, TRUB1, S100A12, MTF1, HEBP2, ZNF215, ZDHHC22, LOC100505716, IGLC1, PDP2, CEACAM21, SCAMP2, CASP10, TEX9, ADSS, CHEK1, HNMT, RPS6KB1, CCDC69, WDR97, COL1A1, SNORA65, ATXN2L, DLX5, LOC101060363///PPIA, MEIG1, ABCC6P1, ITGB2, SNRPE, LOC101928314, TTLL11, ZNF493, LIN9, NDUFA7, RHBDL3, C14orf105, CCDC117, GRIN2D, CXADR, IGF2BP1, IMMP1L, MRPS6, TMF1, KANK2, FIGNL1, TMEM208, CDK5RAP1, MARCO, RPS6KA2, DNAJC22, KIZ, CR2, FUCA1, ADAMTS19, TMED3, SNX6, FCGR1CP///FCGR1B///FCGR1A, FBXO16///ZNF395, CDC14A, TFDP3, FAM181A-AS1, FBXO9, CALCA, GPR156, RPTOR, SPRTN, PRRC2B, GLP1R, SERPINB9, CWF19L1, IL1R2, LOC441178, FOXC2, NOV, NENF, LOC101928401, EMILIN2, EBF3, SLC38A5, ZNF616, EGR1, RPS6KA5, CA4, FKBP4, C11orf94, CLEC18A///CLEC18C, APOA2, UBE2G2, C11orf80, TMEM222, NPSR1-AS1, TOR1A, SIRT2, ATP5C1, HRASLS2, SDHB, RHOBTB1, RBBP9, SEMA5A, HCFC2, NSUN6, ARPP19, ACAD9, MUC6, RAP1GDS1, HBZ, TAB2, ARID5B, HSD17B1, CASZ1, MAPT-AS1, ABCC5, RPS8, ZNF271P, RNF126, HAVCR2, FAM174B, GOLM1, EFNA4, MOB2, PSTK, HCG26, SLC6A13, GPNMB, PPFIBP1, ADGRA1, SCAF4, LINC00396, PBRM1, PARVG, FBXO10, PSMD6-AS2, EXOSC10, PARL, KCTD12, PNKD, CLU, HAX1, AMIGO3///GMPPB, DNAJB1, NPTXR, GART, PRKXP1, MIA2, ST3GAL3, LINC01120, PHLDB2, CD22, C1orf162, OFCC1, DENND1B, LOC101928505, ARHGEF28, PCDHB18P, HSPB1, NAA30, DSCR8, BCL6, APOC4-APOC2///APOC4///APOC2, PCGF6, NFYC, PAPOLG, HACD4, DYX1C1, TSPAN18, GRP, ANXA3, NKX2-3, CCDC169-SOHLH2///SOHLH2, C2orf54, GH1, CCDC116, SARS2, OOEP, ADAMTS8, LOC730268///ANAPC1, LOC100130872///SPON2, LAMA3, PCAT19, C6orf120, ZNF226, MGST1, PSME1, CCR1, LINC01553, GALNT16, RABIF, MRPL16, LOC100134317///LOC284412, GPATCH2, TMEM184A, TSPY26P, ZNF91, KAZALD1, LOC100130502, SERPINA1, MIR6789///PLEKHJ1, SNRPB2, ATP5D, GUCY2F, SLC35F6, OR5AK4P, ST3GAL1, SDF2L1, C21orf59, SLITRK2, RSBN1, IDUA, GPER1, LINC00936, TAF2, CKS2, ZNF582-AS1, ATG12, PRO2958, EXOC4, PRR5L, WDR11-AS1, SLC30A7, NEDD4L, NRSN2, C10orf10, KLHL5, NRG2, ELOVL2-AS1, MFAP4, MUC16, LOC647070, LIPG, ROPN1B, SLC12A3, TDRD5, SYNPO2, LINC00545, ORAI2, ANP32A-IT1, IQCF5, MTMR1, MEIOC, RGS18, PHOSPHO2-KLHL23///KLHL23, CTTN, PARD6B, LOC102724312, FILIP1L, ALDH1A3, C2CD2, FOXE1, MLX, HHEX, ENPP3, FXR1, LOC101926921///DAB2, FNIP2, UPK3A, ALMS1, PPP5D1, XXYLT1, RSPH1, NOP14-AS1, ALPK1, ACTG2, AP1S3, MIR924HG, LOC100996246, MIR6805///RPL28, COL22A1, KLHL31, MSLN, LOC102723927, CHRNA4, NPAS3, DOCK8, C10orf25, SSH2, DCAKD, LOC105375773, LOC101927604, OGDH, UBASH3B, VWA7, RBM47, CARD6, COL6A5, COX19, ZNF654, PTGS1, C7, ZNF138, LINC00167, LOC101928747///RBMX///SNORD61, SYNE3///LINC00341, MRPL44, RXRG, LOC100421494, NIPAL3, MFSD9, HCK, AHI1, FAM53B-AS1, CCDC169-SOHLH2///CCDC169, GAS2L1, DCAF1, CSF2RA, RGS19, CEP104, ZNF217, MCM4, BID, PLSCR4, ARRDC1, PTGDR, VPS37A, RERE, CDC20, LOC100130285, ARG1, FAM27B///FAM27C, AGR3, LOC101927705///P4HA2, CRYGB, LOC101930370, EHD3, SH3GL1P2, LOC100506526, ARVCF, LOC283745, FGB, LGALS13, FER, SERPINB13, EXOSC2, CNIH3, DCLRE1C, YIPF1, CDC42EP4, KIR3DL3, HFE, LOC101060553, CD14, LOC101927450, DGCR9, CHMP6, LHFPL3, HBE1, C11orf31, ABCB9, ANKRD44, NET1, TRIM52, SGK494, ACO1, FCGR1B, ANKRD37, BSND, CYP4Z2P, C17orf62, DSEL, EIF4E, PRCAT47, AMBP, C8orf33, AP3D1, SHISA6, MMAB, LRRC1, MIR100HG, CP, PRKAR2A, PAPD5, LOC101928161, LOC105371038, N6AMT1, LOC101929050, DESI2, NHLRC2, CD37, HMCN2, PTCD2, IER2, GPCPD1, LOC105376689, FCER1G, MKLN1, CFAP97, UBN2, ENTPD5, TBC1D7, CELF4, SCARA3, HMGA2, SPATA41, GIMAP5, POLR3D, TRMT61A, PAXIP1-AS1, DTX4, TINCR, COPS6, ATIC, AVPR1A, HILPDA, WDTC1, NLRP13, DOK3, LOC340107, MTOR, SAMM50, FGFR2, FAM3D, PEX12, SFT2D2, ZBTB20, PCDHA1///PCDHA2///PCDHA3///PCDHA4///PCDHA5///PCDHA6///PCDHA7///PCDHA8///PCDHA10///PCDHA11///PCDHA12///PCDHA13///PCDHAC1///PCDHAC2///PCDHA9, ASH2L, ZC3H12C, LOC286437, MAGEA4, FAM226B///FAM226A, ZNF45, CFLAR, LRRC17, ZBTB40, PATL1, PTGIR, MAP3K3, KIAA1644, MFSD3, CFAP126, ARID1B, R3HDM4, EPB41L5, ACAP1, LST1, MORC3, RIC1, TARBP1, ZNF391, MGC4859, MCTP2, PSORS1C3, HDHD2, OAS2, PAX8-AS1, CLPTM1, CSNK1A1, PTH2, ZNF136, FGD4, DSC1, EDN3, CBX4, LRRC31, BACE2, HNRNPA1, BCL2A1, ZC3HAV1, TTC9C, MYNN, FAM63B, MAP7D3, NPR2, HECTD1, ZBTB46, PSMD10, CPNE5, LINC00521, ZBTB10, ADAMTS6, MTDH, C14orf28, DNAJC21, CACHD1, DBF4B, MAP4K4, CSN1S2AP, GPRIN3, TNRC6C, EPS8L3, LOC157273, LINC00896, ZNF670-ZNF695///ZNF670///ZNF695, GRM3, RTN4, TSPAN2, OSMR, RALGAPA1, FAM192A, LOC105369507, MRE11A, LOC101060604///NPIPA5///SMG1P3///NPIPB5///SMG1P1///LOC613037///NPIPB4///SLC7A5P1///NPIPB3, ACP6, WEE1, DNAJC10, TRPM6, HCLS1, GAS8, AHSG, FPR1, PLEKHA6, MYL9, LYN, AGTR2, TRIM4, C1orf21, EP400, TIGIT, TBX3, PRKD3, PALLD, DCAF13, RAB5C, ZNF678, IL17RA, B4GALT3, AJAP1, ACP1, MTHFSD, PXT1, FBLN5, PTGDS, SSFA2, CNPY2, METTL8, IZUMO1, DIS3L2, MFI2-AS1, MSH5-SAPCD1///SAPCD1, DNAH5, MLF1, C1QA, LINC01089, ALOX12B, MARVELD1, PGRMC1, ENDOD1, TMEM132C, PHYH, DHX9, CHP1, VGLL2, COPS9, LINC00839, SORBS1, RDH11, DTNB, SPATA6L, REM2, WIZ, LINC01085, CUL7, FAM118A, CLIP1, IL13RA1, PADI4, WHAMMP2///WHAMMP3, ELK1, TOR1AIP1, LOC100507033, LINC00629, KLF14, TMEM156, STK31, AAK1, TSSK2///DGCR14, ZC3H7A, PTAFR, CEP57L1, COLGALT1, KCTD7///RABGEF1, RSPH3, ZNF354A, LOC339803, SUSD5, FZR1, MPZL3, LOC100996542, TP53RK, RNF103, GPAA1, ORAOV1, COL14A1, KCNG1, LOXL1, CDH4, KRT12, IRF8, LINC00382, KDM5B, LGALS8, TCF3, SGSH, PPIL2, WLS, BAG6, SP100, ESR1, LOC101928631///ZNF77, HMGB2, TMEM119, MIR6132///ST7-OT3///ST7, CASP1, LMLN, MRPS2, LOC101060521///POLR3E, IL1RL2, CXCL14, SLC47A1, FARSA, ZER1, ISLR, MIR7111///RPL10A, ADAM7, CAST, RNF130, ISM1, C2orf82, HMX1, SAP30, NAA50, DYNC2LI1, INS-IGF2///IGF2, KLHL34, HAGHL, PDK1, HEXA, GTF3C3, TRIAP1, LOC728743///ZNF775, VWCE, ZNF541, VGLL3, LINC01587, MRPS18A, SLC6A5, DNMT1, ANGPT2, LOC106146153, KLC1, NT5E, PPP1R16B, NEU3, LOC101927272, SYCE2, LOC101929709, LOC100129924///C1orf50, NFIA-AS2, PLA2G4C, ZMYND10, TAF9B, ATP5F1, XRCC4, ZNF407, OK/SW-CL.58, CLDN15, DAD1, SH3TC1, SNHG16///SNORD1C///SNORD1A, VEGFA, ANKRD9, RNF185-AS1, UNC119B, AKAP13, LPAR5, LOC102723886, LOC101929373///SLC9B1, PM20D2, UNC93B1, RRP12, GPX8, SMIM10L2B///SMIM10L2A, HMGB1, ADGRG3, CHD7, PODXL, PTP4A3, SCEL, PDCD2, PTBP3, MIR7-3HG, LOC285692, DEPDC1, INTS4, KITLG, ZNRF2P1, TMPRSS3, PI15, RFXAP, SCAF11, ZNHIT2, SCOC-AS1, XBP1, TFE3, CSPG4P5, REG4, ARHGEF12, LOC101929622, TRIO, ATP2B2, AKAP14, RAP1A, STMN1, MSANTD1, ACER2, ARF5, CTSC, GATA2, OR1D2, PITX2, FOXP1, LOC441204, MTMR11, JAK3, TRIM17, APBB1IP, LOC105372881, ZNF506, GRK4, SLC25A5-AS1, ZNF408, UBE2I, P2RX2, PCGF5, INSL3, THOC7, ZNF765, ERCC6, LOC101927933///LRRC8C, TRIM16, IL1RAP, ST20, AP1G2, ALOX5, ORC5, TMEM176B, TMEM44, LPAR2, PLN, DPM2, COA5, USPL1, DIEXF, GOLPH3L, ANAPC13, DSG2-AS1, KANSL1L, ZBTB32, ARIH2OS, LINC01350, BRINP2, DYNC2H1, TAPT1, SERPINB3, GDPGP1, IRAK2, C2orf83, CANT1, SLC10A7, ADPRHL1, FLJ41170, BBS9, PRR34-AS1, DNAH17, LYPD3, MAML2, SESN2, KCNH6, LOC101927157, SNAPC5, SNRNP48, CYB5RL, AIF1, RPP30, TRIM72, ERAP1, ERI2, CTXN1, MLXIP, TMEM161A, DPYSL5, NUDT22, STUM, AQR, PKD1P1, CIRBP, RNASEK-C17orf49///C17orf49, HDGFRP3, AADAC, ALKBH6, IQGAP3, SERPINB1, CST3, ZSCAN20, PYGO1, DIO3OS, APOM, ZNF169, KLF16, TRMO, LOC105274304, LOC102800447///LOC101930566, CSF2, ZNF396, WDR11, DAPP1, MS4A4A, TTLL10, THBS1, TRA2A, ST6GAL2, SLC2A11, GPC6, IDH1, SEPSECS, TAAR3, PTPA, RABEP2, ITGA4, TMEM221, CARD10, PCMTD1, ATP4A, MGARP, ZNF823, HYKK, MCM10, ADRB1, UQCRC1, C9orf84, RPUSD3, ASTE1, PTPRC, TACR3, KCNMA1, POU2F2, COL1A2, PPARA, FCAR, RHEBL1, SLC16A1-AS1, NDC1, LOXL4, SIRPD, LINC01007, DCN, MYH11, LINC00112, B3GALNT2, OSBPL2, TSEN15, ESYT2, PPP1R11, ARHGAP27, GTF2A2, PLAA, LOC101929450, FLCN, PEBP4, PCYT2, LINC00910, SOCS2, LOC100507855///AK4, HKDC1, WBP4, RPE, PARD6G, C14orf39, CCDC177, P2RY8, PGAM5, EED, LOC100506351, CDK9, SMAD4, SPATA5, SEC23B, ZNF347, RNFT2, TTTY2B///TTTY2, PAG1, RDH12, CASP8, IKZF4, LOC100129476, GPAT2, ZNF521, LOC101930006///FRMPD2B///FRMPD2, LINC00858, LINC01366, ACTA2, PIK3IP1, RNF139-AS1, LATS1, LOC102724275, CAMK2G, TPD52L2, GNL1, STK38L, KLHL2, WDR66, SPRY4-IT1, ATP4B, ARID4B///RBM34, ECM2, TRPM1, SLC25A51, YPEL2, ZSCAN30, SCAND1, CCDC13, SPATA13, BMT2, CNOT3, CTGF, COQ9, ATP2A3, ZNF587, DKFZP586I1420, TSR2, MAPKAPK5-AS1, ASIP, IPO7, KIAA1109, FAM50B, PTPN18, LOC285889, EXOSC3, KLB, SMURF2, SBSPON, CYB5D1, C16orf86, LINC00116, SHMT2, GPRC5A, CELF2, QRSL1, HS2ST1, E2F3, MALAT1, LINC01278, PTPRCAP, PIK3CG, SLC25A30, PIAS2, DNAH1, NTPCR, RAB29, OLAH, USP42, OR51E2, LOX, RNH1, NFATC3, NFATC2IP, MYH14, STOM, LINC00565, PIK3AP1, SASH3, EFCAB1, KBTBD3, SUPT7L, TGFB2, METTL21A, EPHA8, ATP5S, SP3, AKAP10, LOC153546, IL18, AP3B1, LYSMD4, AGAP6, SEC11C, PDXDC1, ACTL8, OSTM1, XKRY2///XKRY, NUP188, RCL1, ANKIB1, SEC14L3, METTL22, TXNL4B, SZRD1, MAPK14, MND1, EMILIN1, SLC39A14, ZBTB25, LOC101929774, SERPIND1, C18orf25, WASF2, NCF1, DALRD3, FABP3P2, DANCR, MACC1, CDC42EP5, GBP5, CAPNS1, LINC01255, ZNF750, LOC101928269///LOC100506403///RUNX1, FAM214A, FBXO22, NCBP1, FLT1, NT5C, C15orf57, PDCD6, TRPM4, CMTM1, HOXB-AS3, ANKRD11, CLUL1, LDAH, GSDMD, RS1, DENND5B, ZNF821, TIMM10, LOC729732, SWAP70, LZTS2, MIR3190///MIR3191///BBC3, LOC101927164, TFR2, TEP1, VMO1, CECR2, PTPN2, ZNF789, HAND2-AS1, ZNF625-ZNF20///ZNF625, HTR3A, HGF, CD6, C3, HIVEP3, KSR1, C9orf66, LINC00313, LOC153682, LOC400768, SPATA17, CFI, PTPN21, MMP24-AS1, SIGIRR, DHX30, WWP2, ASB1, CHORDC1, SYNCRIP, IGF1R, NAGLU, RASSF7, SLC45A2, SMARCA4, LOC102724532///SP2-AS1, NUP93, GIN1, PDZD11, ATP6V0E2-AS1, ZNF26, ING5, NPTX2, FAM166A, TGFB1, LIN7C, DDI1, QKI, FGFR1, KLRD1, LLPH, STXBP6, C4B_2///C4B///C4A, C1orf87, NDUFA6, LTC4S, CABP7, LINC00895, ADAMTS7, GNRH1, TP53TG1, PTCD3, LOC101927616, GZF1, SLF1, PSMA3-AS1, ACSL6, C21orf91-OT1, ICAM1, DIAPH3, GPM6A, GOLGA1, CENPN, ARHGAP18, LHX9, WDR13, IFT22, C3orf80, PPP4R3CP, PCDHB3, HRG, GALNT5, TRA2B, SLC26A7, RASSF1-AS1, PCDH18, MSTN, LOC101927044, PEX16, LOC102724250///NBPF26///NBPF10///NBPF8///NBPF9///NBPF15///NBPF11///NBPF12///NBPF1///NBPF14, SNX18, BORCS6, LOC253805, IRAK3, ZKSCAN3, SPRED1, PTN, PDE7B, EVI5, ZNF24, IRX5, CHMP1B, TMED5, NSL1, ZNF780B, VWA1, DOK1, HPSE, LY86, LAMTOR1, GUCA1B, JUND, LOC101929084, ALOXE3, LOC101927446///GTPBP10, LOC100506548///RPL37, SEBOX///VTN, SMOX, UFSP1, RNASEH2A, ZBED5, NDUFB10, CHFR, SETDB2, PSRC1, IGHV3-23///IGHV4-31///IGHM///IGHG3///IGHG1///IGHA2///IGHA1.

3. Genes identified by GSE12649

ST8SIA4, IGFBP6, LOC101930400///AKR1C2, NOX5, TAF2, TRIM24, FUT7, HSD11B1, ABCG2, BID, CTBP2, MBIP, MGMT, CP, NXPH4, DOPEY2, TRIB2, LST1, DIAPH2, RAD52, ATP6V0E1, TAPT1, NR4A3, AAAS, RSBN1, HNRNPDL, IL6ST, AKR1C1, DST, PHACTR2, COL6A2, DZIP1, PDZD2, TMEM97, HLA-C, SLC27A3, NR3C1, PLBD1, PRRG3, ATF6B, DHX15, ASXL1, ENTPD1-AS1, GNS, TUBB1, FAM189A2, TNFSF10, PPP6C, RPS6KA5, PAPOLG, FYB, GPR137, UBR5, PSME3, SLC39A8, NOL12, SMR3A///SMR3B, UBASH3A, A2M, HLA-F, SMARCA1, ATG5, FXYD3, LAMTOR5, PPFIA1, DR1, KRT6B, ZFP2, MBNL1, RBMS1, PALB2, MPZL1, TIMM8B, TGFBR2, GNG12, DDX27, UBE2A, DNAJB6, PIAS1, ADD3-AS1, GIP, SLC25A38, SCAF11, LRRC31, MKL2, ERAP1, SF3A1, PEG10, RAB9BP1, LBH, ZNF26, LGI2, CRHBP, SDF4, TRMT61A, MGEA5, CPSF6, CEP350, RBM12, CCL3L3///CCL3L1///CCL3, GAS2L1, SVIL, CA4, CAPN9, LOC145783///ZNF280D, CPVL, EIF2S2, RRP12, CRYBA2, UCP3, DCTD, FOXD4L1///FOXD4, RRN3, PLOD2, CRB1, ZNF771, CYTL1, MAGEA6, TCF7L2, ST6GALNAC2, SUCLG2, PMS2P1, MAP2K6, PLEKHA5, ZNF611, U2SURP, MT1X, PIH1D1, ECM1, FAM13A, OLFML1, NFATC2IP, APLP2, AIP, SLC29A1, C1orf27, AQP4, GAB1, CHKB-CPT1B///CPT1B, VAMP4, FGF2, VPS13A, TMEM147, ACACB, DCX, KPNA3, ASPN, PTGS1, HAX1, ZBTB22, SSTR4, SURF1, OVCA2///DPH1, PDX1, SFPQ, CDKN2C, NKG7, TAF13, ISLR, CYP2W1, HLA-B, CSNK1A1, NT5DC2, THNSL2, GLI2, MICAL1, DNAJB5, CCL27, TTC17, SNTG1, ALDH1L1, HMGB2, INS-IGF2///IGF2, SNORD14D///SNORD14C///HSPA8, EPN3, RLF, TNFSF12-TNFSF13///TNFSF13, ETV5, SNAPC3, PLN, CDC25B, NDUFB2, SPHAR///RAB4A, MYO7A, ADAM22, ZYX, SRSF11, SLC7A8, TACC1, TRPM8, NOLC1, DNAJB12, CBR4, PRKCQ, PPT2-EGFL8///EGFL8///PPT2, CCL18, RBBP5, ROCK2, JARID2, S100A9, IRAK1, CDK19, NMB, EIF2B2, NR2C1, RAPGEF1, COL3A1, TFDP1, JTB, SENP6, RASL10A, SORBS1, IMPA1, CASP5, GREB1, CENPC, YME1L1, CFLAR, HMCES, DHX16, MTCH2, PSMB10, ITPR2, TIMM10B, WDR76, DUS2, ATG12, FERMT2, MST1R, DNAJC22, EDRF1, SLC20A2, POU2F2, DBT, MRPS7, VEGFC, SLC6A11, FOLR2, HIST1H4H, IMPACT, GATC///SRSF9, GSR, ERLIN1, IGHV4-31///IGHM///IGHG3///IGHG2///IGHG1///IGHD///IGHA2///IGHA1///IGH, RARB, MLYCD, SP100, RNF43, MYBPC1, ETNPPL, UGGT2, FLI1, OSBP2, KCNE2, LY6E, NIPAL3, CTDSPL, ROBO3, IGF1, FLII, RYR3, MED13L, RAMP3, LOC101060835///LOC100996809///HLA-DRB5///HLA-DRB4///HLA-DRB1///HLA-DQB1, UPF3A, RRM2, PPP6R2, SLC2A4RG, SDC4, CKS1B, CD52, MAP4K1, AMBRA1, GINS1, LANCL2, USP24, LOC100996809///HLA-DRB4///HLA-DRB1, ABI2, ZC3H7B, CHST1, FBXO38, PANX1, MAOB, CAMSAP1, MYBL2, CDV3, PINK1, EFNA4, CST6, DNMT3L, PTBP3, UXT, FRY, ZNF148, EIF3I, SMR3A, ELOVL2, PCLO, NTSR1, GJA9-MYCBP///MYCBP, SYT2, ANP32E, CELP, TBC1D29, SIK1, MIR6789///PLEKHJ1, HEPH, PHF20, PMPCA, BMP8A, CNOT4, SLC25A5, KATNBL1, IL23A///TRBV19, APCS, FRZB, FBXO42, LOC100507855///AK4, LAT2, MTHFD1, LOC101926921///DAB2, ZNF266, BMP1, SDF2, NAALADL1, MID1, LRIG1, GHRHR, TIMELESS, PIGA, NR4A2, PRIM2B///PRIM2, MICALL2, C12orf49, MINOS1-NBL1///NBL1, SLCO2B1, EBAG9, CRH, TARP, VRK3, CASP1, MYF6, PSAP, IPCEF1, BUD31, POT1, HCCS, TLE3, MCL1, SLC1A4, SOX9, SLC8B1, GRB10, HLA-G, TRIM38, DUSP6, RAB15, TUBB2B, DUSP1, ARG2, PLGLB1///PLGLB2, BHLHE40, MYH2, HBQ1, RAP2B, MRS2, MAGI2, FBN2, TERF1, FGL2, CENPJ, SLC14A1, BCKDK, ERLIN2, ID4, UFL1, ZNF267, RABL2A///RABL2B, HIF1A, SRSF7, BAG1, RAPSN, TRBC1, FGF5, HERPUD1, DNAJB14, SCGB1A1, APBB2, KIF13A, FBXO9, HIST1H2BJ///HIST1H2BG, EDNRB, RYR2, FKBP4, COPZ2, KCNJ8, ZBTB7A, TUSC2, SPTSSA, OSBPL3, ASGR2, IDH3B, AQP1, MIOS, PLPP3, LOC101930405, RREB1, RGL3///EPOR, PENK, SERTAD2, S100A8, SETSIP///SET, SEC16A, KPNA6, ACP1, PROC, THAP9-AS1, ZNF304, PBX2, RNF6, TAS2R10, DYX1C1-CCPG1///CCPG1, TMEM131, ARHGAP33, GK, SNRPF, RNF216, HSPB2, NOTCH2, RYBP, RCOR1, GSTA3, ZCCHC8, CENPI, NPY, C21orf2, ACOT1///ACOT2, TESC, DHX34, SRSF3, BCAT1, SLC4A4, NEUROD1, C19orf66, LOC102724229///RASA4B///RASA4, SIAH1, GPR88, GTF2H1, PAM16, KLHL35, LOC101060835///LOC100996809///HLA-DRB4///HLA-DRB3///HLA-DRB1///HLA-DQB1, ARMC9, PCF11, DDX24, RRP8, NUP54, CYLD, SIPA1L1, IFI27, MYO1E, IFNA16, NDP, SVEP1, RGS5, EXOSC2, METTL7A, LIN37, RAB20, NCOA1, INHBA, RRP1B, RAB5C, SHQ1, ABCC4, N4BP2L2, PTGIS, PEX6, CLIC5, NPAS3, TMEM100, ABCE1, NFKB2, COQ7, PEX7, CTC-338M12.4, PAF1, GCC2, BATF3, EML2, SPON1, TBC1D19, CCS, DPYSL3, CALM3///CALM2///CALM1, SH3BP5, MYRIP, DBF4, AFDN, HTR2B, RHOBTB3, PUS7, TMEM231, ABI1, HLA-DPA1, MYDGF, MT1HL1, HOXD4, C9orf16, ANK1, ARFGAP3, KRT10, ZCCHC14, PCDH7, MRPL48, ZNF443, PIGV, SRI, NOL9, MTSS1, UFM1, SREBF2, ATG16L1, RPS6KA3, TMEM109, TLN1, APEH, FAM76A, EZH1, MYOC, MAGEB3, SCAMP1, GABRA4, DUSP4, GGA3, MMP14, NPEPPS, POLQ, LOC101927792, MIR15A///DLEU2L///DLEU2, ALMS1, TAF1B, PELI1, GJA1, PMS2P9///PMS2P5, TRANK1, AP4E1, RAB40C, RNF115, WTAP, EMX2, FAM206A, EFR3B, GPX4, CEP57, SST, DND1, AATF, FAT4, CDYL, GALNS, NBN, MEIS2, FZD7, ANGEL2, CCKAR, ANK2, BOLA2B///BOLA2, CRK, ANGPTL2, TSC22D3, MFSD1, F3, OAZ2, LGALS1, TUBGCP2, CINP, UGT2A2///UGT2A1, RNASEH2B, LPAR3, GID8, ZNF729///ZNF43, TOX3, PEX26, STX6, LOC100293211, ANAPC5, SPRY4, SERPINA2, TCF3, EDN3, PPARA, ZFR, UBAC1, ASAP2, GUK1, PLG///LPA, USP7, SMC5, NTRK2, VPS54, SDC2, SATB2, AFF1, TNIK, IGF2BP2, PDE12, BRCC3, CDK5R1, WAPL, ERGIC3, SFTPB, GJB5, CDKN2B, CHAC1, CDK1, NNAT, STX4, ZNF667, SNCA, PACSIN3, ATP2B4, RMI1, ADH5, VPS37C, APOC1, LOC100420758, PAIP1, RNF128, NCOA2, ART3, NLE1, PPIG, PRPF6, MTO1, TNRC6B, BTBD7, FAM66D, GCK, AVL9, CCDC181, ZNF419, EPB41L4A-AS2, TMEM47, HSDL2, MDM2, CPD, AK2, SART3, KDM4B, HFE, MEFV, SMARCA2, APRT, EGFL7, RCAN1, PDPN, ANKRD36B, B2M, PLAU, ORAI2, PPP1R11, EIF4E2, ATP7A, ALG13, FKTN, THUMPD1, FABP7, WWC2///CLDN22, GLUL, NEDD9, IL17RB, AKAP8, HMGN5, HHEX, ANK3, HNRNPA3///HNRNPA3P1, HSBP1, SESN1, CHERP, ALDH6A1, ZNF292, GNA11, LEF1, RPP25, KDM5A, SEC24B, PTCH1, WBP4, WLS, MCM6, PSMA5, ENOX2, FAM169A, DKK3, ZC3H14, BAK1, PCDHB3, ADGRL3, CHKB, GHR, MCF2, IRF4, HLA-DRA, ACO1, ARC, LSAMP, UCP2, IL33, MMP15, TTK, GUCA2B, ZBTB20, MXD1, RABEPK, RPS9, PRKAA1, ITGB2, HPN, PLK3, LRRC42, TBL1X, ABCF1, NOS1AP, CSHL1, AMIGO3///GMPPB, ZCCHC2, HLCS, HNRNPH3, NR4A1, LOC101928635///ALDH1A2, METAP2, C18orf25, ZDHHC4, ACSS3, ALDOB, NFATC1, FCGR2C, ARHGEF2, NXF1, FUT6, MGAT2, RRAS2, GPM6A, IGHM///IGHG1///IGHD, AMMECR1, EDA, USH1C, FAM173A, PITPNM1, SPHK2, UBE2H, RGS20, ARHGAP35, TMEM259, MPRIP, HTN1, C22orf24, SOCS5, SNCG, MINA, CARTPT, DNAI2, UBE2E1, GLE1, DGKG, GRPEL1, HOXB9, FKBP2, NTRK1, SERP1, NR0B2, TM2D1, PDE3B, LEFTY1///LEFTY2, ASMT, MUS81, LPP, FBXO40, LSM1, FBXO11, SOSTDC1, LOC101927051, CA12, RAB3GAP2///AURKAPS1, ZBTB3, HNRNPU, PTPRZ1, UBQLN2, SYDE1, BTNL8, GNAL, NR2F2, ABCC9, NOTCH2NL, ASAH1, RASL11B, PLIN1, ALPL, ALAD, VPS4B, BET1, FN1, PRKAR2B, KCNJ5, NDUFB11, CTAGE5, CAV2, PALLD, KHSRP, PUS1, KCNN3, EIF1, MYO1C, SNX4, ZNF16, PHF3, PPARGC1A, NUDCD3, SLK, ACVR1B, GLRX3, WDR43, LAPTM4B, VAMP1, PPP2R2A, KCNAB1, LAIR1, ZBTB43, CES3, EPHX1, TFRC, SLC16A1, POLR3C, PPP4R3B, PLSCR4, EHD2, RGPD6///RGPD8///RGPD3///RGPD4///RGPD5, SNF8, HCG4B, LOC101929479///LOC727751///LOC642423///GOLGA2P7///GOLGA2P10, SCRT1, PRUNE2, LOC102724200///TRAPPC10, TATDN2, SEMA3G, UHRF1BP1L, WNT5A, SDHC, FOXJ3, NPY1R, INS, IGLC1, THEMIS2, GRSF1, FOXG1, MAGOHB///MAGOH, SFI1, PLEKHF2, MLH3, PPM1F, UBE2Z, TMC6, CD86, HNRNPD, CPS1-IT1, FGF18, TPD52, SLC1A2, NR1I3, FCF1, GAD1, CALU, CHST5, SLC25A30, SPAG1, ARHGEF12, TTN, OTUD4, FANCF, OCLN, NCOA3, CUL5, CPNE3, GPR182, SLC22A13, MEX3C, SKP2, DCAF11, TMPRSS6, TGS1, USP21, CSGALNACT1, DNAJC8, OPHN1, TBX3, ADGRA3, ZSCAN32, LANCL1, ZNF562, CDH6, RABGAP1, TEAD3, TP53BP2, GYG1, PANK3, THAP9, SOS1, THBD, CYP2C18, NUDT11, CREBZF, AVEN, ZNF3, ADORA2B, CXCR4, TBL2, ZNF189, ZNF696, STS, DRC3, TRPV2, ZBP1, CSNK1D, RPAP1, PXMP2, PIKFYVE, MT2A, HIST1H2AH///HIST1H2AG///HIST1H2AM///HIST1H2AL///HIST1H2AK///HIST1H2AI, MKRN2, MPO, MIR6125///USP15, KCNK12, HERC1, AHCYL1, BLOC1S1, SIGMAR1, URB1, CDK17, NCR1, KRBOX4, PRPF40A, TREM2, SMCHD1, FBXO2, SLC24A1, LOC101929500///CRIM1, PIR, SNX1, RXRB, FOXC1, DCN, STON1, TMEM63A, RBM23, TSPAN6, HMG20A, PCNP, ST8SIA5, HEMK1, KNG1, SNX3, DET1, TSC22D2, PDLIM4, NOX3, MBNL3, LSM14A, TPSAB1, LARP4, SMPD2, ALOX12, ATP8A1, SMG7-AS1, SNRNP25, DSC2, SLC39A7, GSPT1, CACNA1C, ATMIN, SPATA2, BTD, PAFAH1B1, ARPC1B, PRKY///PRKX, STC1, UBXN7, ATF4, MIR6756///MCAM, PSCA, SRPK1, CBS, H2AFV, SENP3, TM4SF1, ITIH5, TYROBP, PAK5, TRIP13, ZMPSTE24, SND1, SNORD19B///GNL3, HLA-DRB1, SGSM2, PPP1R12A, B3GALT4, SYNPO2L, ST6GAL1, ZMYM2, CLCN7, GIPR, SH3D21, GNAQ, HLA-DPB1, RAB5A, FMOD, PRKCG, IL11, PHLDA2, SAMM50, ELF2, USP34, HIST1H1A, SPTBN1, RPL17-C18orf32///SNORD58C///SNORD58A///SNORD58B///RPL17, L3MBTL1, UPK1B, MECR, CAMK1D, ECE1, RCL1, EIF3K, RAB2A, MET, ADRA1A, TENM3, DICER1, KLK2, SNU13///ANXA2, PRKCDBP, PPP1R2, CRYBB3, LAMB4, AGT, TMEM11, CORT, NDUFS7, B3GNT2, SF3B5, DIS3, TAT, CTSS, DNAJB9, DMD, LMO1, EPHB2, SF1, TRIM33, STAG2, DIAPH3, SHANK2, NUDT13, CC2D1A, CETN3, COA1, MED8, RBM10, CD22, EMC2, MPP3, DTNA, TRAPPC2, IDS, ANKRD6, CD36, ITSN2, OVOL2, FTH1, BCL11B, SLC35C1, MIR4680///PDCD4, GPC5, EGR4, MICALL1, RGS14, SNORD18C///SNORD18B///SNORD18A///SNORD16///RPL4, WDR78, PAPOLA, MSH6, LOC101929540///LOC101928670///LOC101928344///LOC100996442///LOC100288069///LOC100134822, N4BP2L1, SCP2, WASF1, ASMTL-AS1, VGLL3, RAD51, CASC1, FMNL1, TMEM223, GABRB1, RPS10-NUDT3///NUDT3, POLR1B, LOC101930363///LOC101928349///LOC100507387///FAM153C///FAM153A///FAM153B, ZNF83, SSR1, INPPL1, MRPS18B, CDC42EP3, KAT2B, MAGEB2, SRP9, DPP3, NR1H4, BBOX1, MUL1, ST3GAL6, MIS12, PTGER1, TTC28, TTC30A, SPAG9, RAPGEF3, GCN1, PLIN2, MTF2, INPP5K, KLHL21, THRA, NUP155, GYG2, CFDP1, KIF1C, SERHL2, WDR48, CHN2, FPR1, ZNF747, CASKIN2, NUP107, HTR7, REV1, PRPF4B, NEUROD4, COL8A2, PRKAR1A, TAOK2, HPD, GUCA1A, S1PR1, CD2, PRICKLE3, FAM30A, USE1, APOBEC3G, RORA, PKN2, XK, WISP3, KLHL2, GDF10, OPN1SW, ANP32D, EIF4H, MAOA, MFGE8, PNN, ARHGAP19, FANCA, ORC4, CCL21, CRLF3, ATP2C1, PHLPP2, PDZRN4, BNIP1, EXTL3, CLTA, AP5M1, BTRC, NCBP2, KSR1, TIMP3, ILF3, CSN3, NDST1, RNASEH1, OXA1L, HAUS7///TREX2, HSP90AA1, POM121C, FBXO21, IST1, PDE4B, GJA4, B4GALT1, NDUFC2-KCTD14///KCTD14, ARFIP1, MYO9B, IPO7, ANP32A, MRPS10, FUZ, EMC6, ANOS1, ELL3, PRSS3P2, SEL1L, C1orf112, GJA8, THTPA, CSRNP3, ZNF140, ITGA4, MAP3K11, CNPPD1, USP12, SPAG7, PDCD6, ARPC2, PTCH2, PPP5C, LOX, USH2A, PI4K2A, CNOT8, PKMYT1, ARHGAP5, VDR, HSF2, MIR6516///SCARNA16///SNHG20, RPRM, NNT, ZNF702P, ZBTB44, ADAM10, CD164, SSNA1, HSD17B6, HIPK1, CPT2, BMPR1A, UPF3B, KLF6, BRWD1, GON4L, DCAF16, TMCO3, EPAS1, PON2, CLCN4, LIMK2, ALDH7A1, POFUT1, LRRD1///CYP51A1, FBXL5, KLF12, LOC101927562///DUSP8, MPHOSPH6, GPAA1, ACTR1B, FGFR4, DOPEY1, PDSS2, ZNF516, ATF6, MAPK13, ACACA, PSME1, RASSF7, SLC52A1, ACTR6, MLF1, DRICH1, SYNE1, STEAP1B, CLN5, TUG1, GMCL1, TBC1D30, RBCK1, DNAJA1, TLK1, PLCB1, DTNB, OPA1, SLC1A3, PLAA, LBR, TWF1, SLITRK5, IGHV4-31///IGHM///IGHG3///IGHG1///IGHD///IGHA2///IGHA1///IGH, ANXA7, CCL24, MTMR10, SLC6A7, ZNF160, TSPYL1, IDI1, MBD4, RPS6KA2, TAL1, TAF10, FIS1, LOC101929726, SP3.

4. Genes identified by GWAS

HSPE1-MOB4, RLTPR, YJEFN3, NDST3, ADGRV1, AKAP10, AKAP6, AP3B2, BNIP3L, C6orf118, CDH11, CDIP1, CENPE, CISD2, CORO7, CPEB1, DNAJA3, EMB, EMX1, EYS, FLRT2, FYN, GPD1L, HACE1, HYAL3, JMJD1C, KCNG2, LIN28B, MAGI2, MSI2, MTMR7, NMRAL1, NOS1, NPAS3, NRBF2, PHF3, POLG, PPP1R3A, PPP2R2A, PTP4A1, RASSF1, RLBP1, SLC9B1, SPECC1, VPS37A, YWHAE, ZDHHC2, ABCB9, ACD, ACTR5, ADAMTSL3, ADRBK2, AGPHD1, AKT3, ALDOA, AMBRA1, ANKRD44, ANKRD63, ANP32E, APH1A, APOPT1, ARHGAP1, ARL3, ARL6IP4, AS3MT, ASPHD1, ATG13, ATP2A2, ATPAF2, ATXN7, BAG5, BCL11B, BCL9, BOLL, BRP44, BTBD18, C10orf32, C11orf31, C11orf87, C12orf42, C12orf65, C16orf86, C16orf92, C1orf51, C1orf54, C2orf47, C2orf69, C2orf82, C4orf27, CA14, CA8, CACNA1C, CACNA1I, CACNB2, CCDC39, CCDC68, CD14, CD46, CDC25C, CDK2AP1, CENPM, CENPT, CHADL, CHRM4, CHRNA3, CHRNA5, CHRNB4, CILP2, CKAP5, CKB, CLCN3, CLP1, CLU, CNKSR2, CNNM2, CNOT1, CNTN4, COQ10B, CR1L, CREB3L1, CSMD1, CTNNA1, CTNND1, CTRL, CUL3, CYP17A1, CYP26B1, CYP2D6, DDX28, DFNA5, DGKI, DGKZ, DNAJC19, DND1, DOC2A, DPEP2, DPEP3, DPP4, DPYD, DRD2, DRG2, DUS2L, EDC4, EFHD1, EGR1, ENKD1, EP300, EPC2, EPHX2, ERCC4, ESAM, ESRP2, ETF1, F2, FAM109B, FAM53C, FAM57B, FAM5B, FANCL, FES, FONG, FURIN, FUT9, FXR1, GALNT10, GATAD2A, GDPD3, GFOD2, GFRA3, GID4, GIGYF2, GLT8D1, GNL3, GOLGA6L4, GPM6A, GPX5, GPX6, GRAMD1B, GRIA1, GRIN2A, GRM3, HAPLN4, HARBI1, HARS2, HARS, HCN1, HIRIP3, HIST1H2BJ, HIST1H2BL, HSPA9, HSPD1, HSPE1, IFT74, IGSF9B, IK, IMMP2L, INA, INO80E, IREB2, ITIH1, ITIH3, ITIH4, KCNB1, KCNJ13, KCNV1, KCTD13, KDM3B, KDM4A, KLC1, L3MBTL2, LCAT, LRP1, LRRC48, LRRIQ3, LSM1, LUZP2, MAD1L1, MAN2A1, MAN2A2, MAPK3, MARS2, MAU2, MDK, MED19, MEF2C, MKL1, MLL5, MMP16, MPHOSPH9, MPP6, MSANTD2, MSL2, MUSTN1, MYO15A, MYO18B, MYO1A, NAB2, NAGA, NCAN, NCK1, NDUFA13, NDUFA2, NDUFA4L2, NDUFA6, NEK1, NEK4, NFATC3, NGEF, NISCH, NKAPL, NLGN4X, NOSIP, NOTCH4, NRGN, NRN1L, NT5C2, NT5DC2, NUTF2, NXPH4, OGFOD2, OSBPL3, OTUD7B. Differentially expressed mRNAs in peripheral blood Leukocyte from Schizophrenia patients versus healthy controls by RNA-seq. The score of Clinician-Rated Dimensions of Psychosis Symptom Severity scale WGCNA identified 89 risk genes related to abnormal psychomotor behavior characteristics in patients with schizophrenia.
Supplementary Table 1.

Differentially expressed mRNAs in peripheral blood Leukocyte from Schizophrenia patients versus healthy controls by RNA-seq.

IDControl_fpkmSZ_fpkmlog2(FC)P valueSymbol
ENST000000104040.00060.0676.8030550.000242MGST1
ENST000000647800.3430.97421.5060095.61E-05RELT
ENST000001642270.0010.0816.339853.93E-05BCL3
ENST000001997641.0072.72541.4364040.00132CEACAM6
ENST000002047260.0010.00722.8479970.000598GOLGA3
ENST000002161270.00280.0273.2694610.000102RASD2
ENST000002184320.10380.27881.4254240.000598PIN4
ENST000002213270.04320.34583.0008351.81E-05ZNF180
ENST000002248620.02340.001−4.548440.000707FBXL15
ENST000002252751.80123.75821.0610834.22E-05MPO
ENST000002301730.0010.00622.6322680.00066ADGRG6
ENST000002309902.39345.11261.0949960.000229HBEGF
ENST000002311730.00120.0092.9068910.000221PCDHB15
ENST000002317510.24560.6231.3429220.001308LTF
ENST000002324240.11680.43421.8943192.58E-08HES1
ENST000002337140.0010.02764.7865960.001496LANCL1
ENST000002339971.57783.36861.0942350.000295AZU1
ENST000002368261.0543.42081.6984593.56E-07MMP8
ENST000002372640.0010.04625.5298212.69E-07TBPL1
ENST000002385080.18640.52441.4922660.000341SERPINB10
ENST000002393160.00260.01722.7258250.000322INSL4
ENST0000023993835.003138.11781.9803491.06E-08EGR1
ENST000002413560.86881.88641.118549.38E-06ADORA3
ENST000002422080.0820.31541.9434872.64E-10INHBA
ENST000002424800.86942.7361.6539767.11E-08EGR2
ENST000002427372.84721.0502−1.438880.000686ITPR2
ENST000002440500.39020.90261.2098730.000287SNAI1
ENST000002443362.76127.57541.4560272.50E-06CEACAM8
ENST000002443640.01020.001−3.35052.04E-06DST
ENST000002526750.04560.14041.6224373.31E-05FUT5
ENST000002534080.00880.06362.8534510.000537GFAP
ENST000002554270.01260.09162.8619240.000663CHIT1
ENST000002572642.27065.17561.1886531.84E-10TCN1
ENST000002584000.0010.01083.4329590.000351HTR2B
ENST000002584940.0010.00762.9259997.91E-05ALDH1L2
ENST000002589633.20046.58781.0415450.000937VEZF1
ENST000002592060.03060.001−4.935460.000568IL1RN
ENST000002599510.07360.0034−4.43610.000871HLA-F
ENST000002619440.0010.00943.2326610.000368CDHR2
ENST000002623040.0130.001−3.700447.49E-06PKD1
ENST000002628090.74481.8371.3024270.000152ELL
ENST000002628653.8377.95721.0522825.32E-05BPI
ENST000002630451.61184.45961.4682423.68E-09CRISP3
ENST0000026334116.541443.68961.4012092.67E-12IL1B
ENST000002635450.0010.03585.1618888.82E-05LUC7L2
ENST000002636211.1572.71441.2302445.73E-05ELANE
ENST000002637070.0670.002−5.066094.20E-05TFCP2L1
ENST000002641560.01160.36344.9693625.28E-07MCM6
ENST000002644980.0010.00622.6322680.000744FGF2
ENST000002649560.01540.001−3.944864.17E-05EVC
ENST000002658810.11120.001−6.797013.41E-05REXO2
ENST000002659900.0960.0124−2.952691.45E-09BTAF1
ENST000002730630.01360.001−3.765537.83E-05SLC4A3
ENST000002730770.0010.01443.8479970.001435PNKD
ENST000002733470.11020.23821.1120490.000991NXPE3
ENST000002747100.00960.001−3.263030.000112PSD2
ENST000002774806.128415.0751.2985760.000139LCN2
ENST000002781754.05769.70641.258319.36E-07ADM
ENST000002785900.0050.001−2.321930.001398ZC3H12C
ENST000002787564.816810.70661.1523530.001103APLP2
ENST000002788652.0414.66921.1938991.80E-07MS4A3
ENST000002821200.04080.001−5.35053.96E-06TGOLN2
ENST000002822820.17320.35081.018215.87E-06ZNF547
ENST000002834260.02120.0044−2.268490.001026PLEKHG4B
ENST000002845510.92962.2541.2778061.59E-06TRIM11
ENST000002866270.00960.001−3.263037.95E-05KCNMA1
ENST000002921690.36180.0032−6.820986.20E-05S100A1
ENST000002936620.3310.82061.3098484.16E-06GRASP
ENST000002936770.0010.09186.5204223.14E-06RAVER1
ENST000002947020.01060.001−3.405990.001431GFI1
ENST000002959920.02860.114420.000404PCOLCE2
ENST000002964358.302617.17481.0486585.01E-06CAMP
ENST000002972390.03880.001−5.277980.000112SYTL3
ENST000002974352.39186.96661.5423585.58E-06DEFA4
ENST000002995020.32661.02361.6480551.48E-06SERPINB2
ENST000002999690.02920.008−1.86790.000283SERINC4
ENST000003000270.0010.01523.9259993.53E-05FANCI
ENST000003016240.00060.1197.6317831.71E-06TNRC6C
ENST000003016980.0190.0024−2.984890.000768PRR25
ENST000003020350.01160.10263.1448340.001254SLAMF1
ENST000003022910.0010.013.3219280.000215LUZP1
ENST000003023260.06140.16641.4383450.000946MN1
ENST000003023280.00240.02443.3457750.000313SCN11A
ENST000003027790.04140.001−5.371560.000216PXK
ENST000003033910.0010.5979.2215871.02E-13MECP2
ENST00000303562122.7578289.72581.2388731.13E-07FOS
ENST000003046391.77644.15081.2244330.000958RNASE3
ENST000003060611.09886.81822.6334624.06E-17MT1E
ENST000003060900.00320.20085.9715440.000344GNAS
ENST000003061510.00080.03465.4346284.01E-05MUC17
ENST000003065850.0010.01640.001358CHTF8
ENST00000307407260.8124576.86461.145221.53E-06CXCL8
ENST000003078850.0010.00963.2630340.000155ADCY6
ENST000003095390.21240.86382.0239149.00E-06OLR1
ENST000003099020.04220.001−5.399178.46E-06ZNF407
ENST000003129430.0010.08826.4627073.62E-06DOK3
ENST000003172166.41522.18361.7899722.73E-09EGR3
ENST000003172763.61248.68661.2658342.47E-05PER1
ENST000003177210.00480.001−2.263030.000439NCKAP5
ENST0000031939718.1328.0092−1.178815.15E-09ETS1
ENST000003227480.00320.05524.1085240.000709ZNF18
ENST000003239380.0110.16663.9208130.000919LGALS8
ENST000003250940.02580.001−4.68931.80E-05TMEM33
ENST0000032785731.7274106.711.7498949.79E-06DEFA3
ENST000003304390.15542.54184.0317928.71E-18MT1E
ENST000003308620.00460.05223.5043445.35E-06TMEM89
ENST000003312240.0080.07963.3146970.000587DLK1
ENST000003329040.2070.0994−1.058310.001385CDHR1
ENST000003375320.0840.001−6.392321.85E-05MPP7
ENST000003378430.01720.001−4.104340.000386C1QTNF6
ENST000003381830.0010.0234.5235620.000193C15orf41
ENST000003389620.32240.76341.2435876.55E-11LRP1
ENST000003398610.0010.02924.8678961.99E-06SEMA4D
ENST000003405520.54360.024−4.501440.000124LIMK2
ENST000003408000.05160.001−5.68934.78E-07ACSL4
ENST000003408550.0010.00923.2016340.000583IDS
ENST000003416710.02520.001−4.655351.97E-05LRRC37B
ENST000003418520.10280.0064−4.005620.000818TNIK
ENST000003419110.02060.0008−4.68650.001394MYB
ENST000003425790.14541.02962.8239850.000996BPTF
ENST000003431950.01220.0862.8174560.000125KCNIP2
ENST000003447220.0010.02244.4854272.76E-05SMC4
ENST000003447740.02560.0022−3.540570.000298FAM166A
ENST000003463300.05320.0018−4.885360.000158UBE2A
ENST000003469640.0010.04645.5360535.51E-06DLG1
ENST000003474010.0010.00442.1375040.001491COL6A3
ENST000003474710.00060.02725.50250.001259SMARCC2
ENST000003501990.80821.7711.131780.000665RNF38
ENST000003507730.0010.00923.2016340.000568TSC2
ENST000003525510.00080.04145.6934870.000166UBE2C
ENST000003527325.595413.95781.3187583.88E-09ABHD2
ENST000003536600.0010.0274.7548880.000636RBCK1
ENST000003556670.05760.001−5.8481.38E-05DNM2
ENST000003559460.00620.001−2.632270.000318SH3PXD2A
ENST000003562490.0010.00830.000446CEMIP
ENST000003563480.35380.74961.0831870.000111KPNA5
ENST000003566280.050.12981.376290.000832NRARP
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ENST000005815520.0010.0274.7548880.000297PIK3R5
ENST000005821170.02380.24923.3882710.000107LRRC37B
ENST000005841140.00280.15465.786970.000904WSB1
ENST000005846500.020.001−4.321930.001088ELAC2
ENST000005851940.08620.001−6.429627.64E-06ZNF286A
ENST000005876150.09820.0118−3.056940.001018UBALD1
ENST000005887350.00140.00862.618910.00112GFAP
ENST000005889820.00880.001−3.13750.000927ZBTB7C
ENST000005893770.00040.00924.5235620.000909KCNJ16
ENST000005896290.00120.10346.4290580.000392CDC37
ENST000005896400.34420.0236−3.866390.000498GPI
ENST000005898810.0010.01543.9448589.67E-05ZNF24
ENST000005904140.0010.03024.9164770.000643ZNF573
ENST000005909350.01780.001−4.153810.000365DUSP3
ENST000005917680.21420.0168−3.672430.000201USP32
ENST000005918110.4040.1552−1.380235.67E-05CANT1
ENST000005922820.00560.001−2.485430.001412ZNF260
ENST000005935910.07260.0016−5.503830.000306SH3GL1
ENST000005944420.0010.11026.783982.60E-05ZFP36
ENST000005945170.25720.1012−1.345680.000295ZNF677
ENST000005956180.0010.03825.2555017.91E-06MYO9B
ENST000005965440.21461.67542.9647840.001286CEACAM3
ENST000005968530.3192.10262.7205460.000554DNAJB1
ENST000005968940.00040.03066.2573880.000248ZNF546
ENST000005989150.01980.0002−6.629360.000546NAPSA
ENST000005993590.0010.08266.368073.77E-06TNFSF14
ENST000005998060.0010.02844.8278190.000404VAV1
ENST000006009090.3050.62741.0405761.00E-05KCNN4
ENST000006033000.00620.02061.7323040.000727DUOX2
ENST000006067310.82740.3442−1.265341.50E-05HOMEZ
ENST000006105380.01660.001−4.053110.000429TP53
ENST000006106400.0010.06726.0703890.000107BTNL8
ENST000006107240.02480.0004−5.95424.87E-05TBL2
ENST000006108310.0010.0615.9307375.27E-06CARHSP1
ENST000006116460.0160.001−41.77E-05RNF38
ENST000006117070.0320.001−50.000163GPAT3
ENST000006128950.0010.00863.1043378.80E-05ACACA
ENST000006130580.0250.001−4.643860.000301SLC4A1AP
ENST000006130710.0010.01223.6088090.000425TDRP
ENST000006131040.11440.59062.3680943.64E-05ATF3
ENST000006132730.06120.001−5.935462.13E-05TMEM185A
ENST000006133150.00420.14545.1134940.000215UBAP2L
ENST000006140350.0010.55969.1282522.46E-08TNFAIP3
ENST000006141340.0010.00682.7655350.000529JRK
ENST000006143820.13760.0166−3.051233.84E-08LFNG
ENST000006161440.22140.082−1.432960.001005BCL2L12
ENST000006169230.04240.001−5.405992.70E-05GCLC
ENST000006172490.0010.0224.4594320.00107PDIA6
ENST000006177060.0260.001−4.700440.000131CBSL
ENST000006178590.01140.001−3.510960.000702IKZF5
ENST000006180400.0010.00321.6780720.000229KCNN3
ENST000006185150.02860.001−4.837941.67E-05KIF3A
ENST000006187184.29481.8492−1.215690.00099CITED2
ENST000006187410.0010.05245.7114957.82E-06LYPLA1
ENST000006187651.6980.583−1.542270.000248RRP12
ENST000006193210.0010.04385.4528590.000148SLC16A3
ENST000006195720.00240.10145.4008790.000254FAM214A
ENST000006195800.0010.04565.5109620.001259PTMS
ENST000006195950.38640.83641.1140980.000668SERPINB10
ENST000006200470.06520.001−6.02680.000619PPIA
ENST000006201000.0010.01724.1043370.000131STAG3
ENST000006203740.0010.02244.4854270.000106ZNF195
ENST000006205710.0010.0153.9068910.000113MGAM
ENST000006212260.00020.0044.3219280.000803MUC5AC
ENST000006215370.06640.001−6.053117.61E-07CMIP
ENST000006223960.04020.24782.6239090.001249TBKBP1
ENST000006259240.0380.001−5.247930.000827RUNX2
ENST000006270590.09960.0116−3.102020.001199ALDOA
ENST000006276210.09660.81183.0710290.000682NFAT5
ENST000006292720.02520.001−4.655350.001174ATL2
ENST000006293710.0010.01944.2779858.43E-05AK2
ENST000006296880.00080.0926.845490.000992DRG1
ENST000006358330.0010.0365.1699250.000121RGS9
ENST000006363470.01820.001−4.185879.83E-05SLC9A6
ENST000006369410.0010.0133.700440.001408TCAF2C
ENST000006375810.01480.001−3.887530.000361SLC9A6
ENST000006383750.0010.06946.1168640.000359HLA-A
ENST000006384510.03640.001−5.185870.000245LIAS
ENST000006389720.01380.0002−6.108520.000663ADAMTS19
ENST000006391320.010.001−3.321930.000903BIVM-ERCC5
ENST000006391450.01580.001−3.981850.000143SCARB2
ENST000006402540.0010.01343.7441610.000711STAT6
ENST000006420440.02580.001−4.68930.000118MICU1
ENST000006422480.0010.0294.8579810.000295CTNNB1
ENST000006432420.00940.001−3.232660.001346TCAF2
ENST000006439270.0280.001−4.807359.01E-06PRKCB
ENST000006439770.0010.08666.4362952.38E-06CTNNB1
ENST000006447740.1380.001−7.108523.03E-06ACTG1
ENST000006449130.23880.065−1.877290.001064PLEKHG1
ENST000006449830.09740.39322.013270.000987ALAS2
ENST000006450950.0010.0184.1699250.000121CHD4
ENST000006456320.0010.086.3219284.39E-08VPS13A
ENST000006459880.07640.0088−3.1180.001439DSE
ENST000006461680.0010.02684.7441610.00036PRKCB
ENST000006463030.02360.001−4.560719.59E-05SPG7
ENST000006463690.0010.09226.5266951.62E-06CTNNB1
ENST000006466460.00140.0595.3972160.00048HSD17B4
ENST000006466490.0010.01343.7441611.31E-05CUX1
ENST000006466730.05463.53626.0171554.26E-07SAMHD1
ENST000006475080.0010.02264.4982510.000398SMARCE1
Supplementary Table 2.

The score of Clinician-Rated Dimensions of Psychosis Symptom Severity scale

SampleDisorganized SpeechHallucinationDelusionAbnormal psychomotor behaviorNegative symptomImpaired cognitionDepressionMania
Js 7830133430
Js 7021330302
Yx3321032102
Yx2322320302
Yx4020222102
YX120332413
Yx3422220102
Yx3922330303
Yx4720132300
Js 8620343300
YX1823340403
Js 8122220100
JS3101333300
Yx3622221120
Yx2022220102
Js 6723300403
Yx3722222100
JS4821103100
Yx2522230120
JS3330133410
JS1921300100
YX230330403
YX403122100
JS4022220122
YX1532430403
YX823332400
Yx2823230302
JS3520322400
Yx2120122102
JS5502320100
JS3222200100
JS220122010
JS420303400
Yx2633330402
JS3903102120
Yx4220322401
Yx4521122100
JS5223220300
JS5322200100
Js 6622220100
JS2522102100
Yx4631132400
JS4220222102
Yx4933340400
JS3722200120
JS2221200100
JS4122302310
JS3820222310
JS5021430403
JS2720332310
Supplementary Table 3.

WGCNA identified 89 risk genes related to abnormal psychomotor behavior characteristics in patients with schizophrenia.

IFI16, ETS1, BTAF1, LFNG, HHEX, ITPR2, MT1X, REL, RELT, NCOA4, SAMSN1, SIAH1, FBXO3, TAPBP, CTSD, GRASP, CXCL8, PTGS2, ACTG1, RNF38, HES1, ADM, PPP1R12A, FOS, SAT1, MAP3K8, FYN, NFE2L2, NRARP, DNAJB1, SNAI1, TBL1XR1, MN1, BCL3, CFL1, GABPB1, LONRF1, PLAU, ADORA3, SSBP3, F13A1, CD83, TRIM11, CITED2, IL1B, DUOX2, AREG, SERPINB2, CXCL2, VEZF1, PTGER3, FUT5, ABHD2, LEF1, TMCC3, HP1BP3, ZNF547, ATP2B1, NFE2, HBEGF, IL1RAP, ZNF331, PFKFB3, KPNA5, MT1E, LYZ, UVSSA, NXPE3, MT2A, PER1, EGR2, RBM5, HOMEZ, EGR3, BACH2, ZNF180, HRH4, EGR1, ATF3, NFAT5, PIN4, LRP1, ELL, ACSL1, EIF5, HNRNPA1, NABP1, CTDSPL2, CIRBP
  60 in total

1.  Reduced striatal dopamine synthesis capacity in patients with schizophrenia during remission of positive symptoms.

Authors:  Mihai Avram; Felix Brandl; Jorge Cabello; Claudia Leucht; Martin Scherr; Mona Mustafa; Stefan Leucht; Sibylle Ziegler; Christian Sorg
Journal:  Brain       Date:  2019-06-01       Impact factor: 13.501

2.  Psychological interventions to reduce positive symptoms in schizophrenia: systematic review and network meta-analysis.

Authors:  Irene Bighelli; Georgia Salanti; Maximilian Huhn; Johannes Schneider-Thoma; Marc Krause; Cornelia Reitmeir; Sofia Wallis; Felicitas Schwermann; Gabi Pitschel-Walz; Corrado Barbui; Toshi A Furukawa; Stefan Leucht
Journal:  World Psychiatry       Date:  2018-10       Impact factor: 49.548

3.  Immune System and Schizophrenia.

Authors:  Norbert Müller; Markus J Schwarz
Journal:  Curr Immunol Rev       Date:  2010-08

Review 4.  Effectiveness and tolerance of anti-inflammatory drugs' add-on therapy in major mental disorders: a systematic qualitative review.

Authors:  G Fond; N Hamdani; F Kapczinski; W Boukouaci; N Drancourt; A Dargel; J Oliveira; E Le Guen; E Marlinge; R Tamouza; M Leboyer
Journal:  Acta Psychiatr Scand       Date:  2013-11-11       Impact factor: 6.392

Review 5.  Anti-inflammatory treatment in schizophrenia.

Authors:  Norbert Müller; Aye-Mu Myint; Daniela Krause; Elif Weidinger; Markus J Schwarz
Journal:  Prog Neuropsychopharmacol Biol Psychiatry       Date:  2012-11-23       Impact factor: 5.067

Review 6.  Recommendations for the treatment of schizophrenia with negative symptoms. Standards of pharmacotherapy by the Polish Psychiatric Association (Polskie Towarzystwo Psychiatryczne), part 1.

Authors:  Agata Szulc; Jerzy Samochowiec; Piotr Gałecki; Marcin Wojnar; Janusz Heitzman; Dominika Dudek
Journal:  Psychiatr Pol       Date:  2019-06-30       Impact factor: 1.657

7.  Immune senescence and brain aging: can rejuvenation of immunity reverse memory loss?

Authors:  Noga Ron-Harel; Michal Schwartz
Journal:  Trends Neurosci       Date:  2009-06-10       Impact factor: 13.837

8.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Authors:  Mark D Robinson; Davis J McCarthy; Gordon K Smyth
Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

9.  Loss of mTOR repressors Tsc1 or Pten has divergent effects on excitatory and inhibitory synaptic transmission in single hippocampal neuron cultures.

Authors:  Matthew C Weston; Hongmei Chen; John W Swann
Journal:  Front Mol Neurosci       Date:  2014-02-03       Impact factor: 5.639

10.  Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome.

Authors:  Kiran Girdhar; Gabriel E Hoffman; Yan Jiang; Leanne Brown; Marija Kundakovic; Mads E Hauberg; Nancy J Francoeur; Ying-Chih Wang; Hardik Shah; David H Kavanagh; Elizabeth Zharovsky; Rivka Jacobov; Jennifer R Wiseman; Royce Park; Jessica S Johnson; Bibi S Kassim; Laura Sloofman; Eugenio Mattei; Zhiping Weng; Solveig K Sieberts; Mette A Peters; Brent T Harris; Barbara K Lipska; Pamela Sklar; Panos Roussos; Schahram Akbarian
Journal:  Nat Neurosci       Date:  2018-07-23       Impact factor: 24.884
View more
  3 in total

1.  Pleiotropic effects of telomere length loci with brain morphology and brain tissue expression.

Authors:  Gita A Pathak; Frank R Wendt; Daniel F Levey; Adam P Mecca; Christopher H van Dyck; Joel Gelernter; Renato Polimanti
Journal:  Hum Mol Genet       Date:  2021-06-26       Impact factor: 6.150

2.  The neurodevelopmental gene MSANTD2 belongs to a gene family formed by recurrent molecular domestication of Harbinger transposons at the base of vertebrates.

Authors:  Ema Etchegaray; Dominique Baas; Magali Naville; Zofia Haftek-Terreau; Jean Nicolas Volff
Journal:  Mol Biol Evol       Date:  2022-08-17       Impact factor: 8.800

3.  Dietary Habit Is Associated with Depression and Intelligence: An Observational and Genome-Wide Environmental Interaction Analysis in the UK Biobank Cohort.

Authors:  Bolun Cheng; Xiaomeng Chu; Xuena Yang; Yan Wen; Yumeng Jia; Chujun Liang; Yao Yao; Jing Ye; Shiqiang Cheng; Li Liu; Cuiyan Wu; Feng Zhang
Journal:  Nutrients       Date:  2021-03-31       Impact factor: 5.717
  3 in total

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