Literature DB >> 34266354

Dynamic changes of plasma extracellular vesicle long RNAs during perioperative period of colorectal cancer.

Qing Hua1,2,3, Wenhao Xu2,4, Xuefang Shen1,2, Xi Tian2,4, Hailiang Zhang2,4, Yan Li5, Pingbo Xu1,2.   

Abstract

Extracellular vesicles (EVs) long RNAs (exLRs) have been shown to be indicators for the diagnosis and prognosis of colorectal cancer (CRC); however, the dynamic changes of exLRs during perioperative period and their cellular sources in CRC remains largely unknown. In this study, exLR sequencing (exLR-seq) was performed on plasma samples from three CRC patients at four time points (before surgery [T0], after extubation [T1], 1 day after surgery [T2], and 3 days after surgery [T3]). Bioinformatics approaches were used to investigate the profile and biofunctions of exLRs and their cellular sources. Greater than 12,000 mRNAs and 2,000 lncRNAs were reliably detected in each exLR-seq sample. Compared with T0, there were 110 differentially expressed genes (DEGs) in T1, 60 DEGs in T2, and 50 DEGs in T3. A total of 11 DEGs were found at all three time points and were related to membrane potential. In addition, compared to T0, 22 differentially expressed lncRNAs (DELRs) were found in T1, 19 DELRs in T2, and 38 DELRs in T3. Moreover, only three DELRs were detected at all three time points. Interestingly, EVs from CD8 + T cells, CD4+ memory T cells and NK cells decreased after surgery and the absolute quantity of EVs from immune cells were reduced as well. In summary, this study was the first to characterize the dynamic changes of exLRs during perioperative period and the cellular sources. These findings established the foundation for further studies involving the effects of these dynamically changed exLRs on CRC.

Entities:  

Keywords:  Extracellular vesicles; colorectal cancer; long RNA sequencing; perioperative period

Mesh：

Substances：

Year:  2021        PMID： 34266354      PMCID： PMC8806447          DOI： 10.1080/21655979.2021.1943281

Source DB:  PubMed          Journal:  Bioengineered        ISSN： 2165-5979            Impact factor:   3.269


Introduction

Colorectal cancer (CRC) is one of the most common malignancies worldwide [1,2]. According to the 2020 global cancer statistics, there are approximately 1.9 million newly diagnosed CRC patients and 935,000 CRC-related mortalities, accounting for 10% of global cancer cases and 9.4% of cancer-related deaths [3]. Despite the rapid development of advanced chemotherapy, targeted therapy and immunotherapy for tumors during the past decades, surgical resection remains the major treatment for CRC [4-7]. However, previous studies have demonstrated that surgical trauma causes long-term oncologic outcome by facilitating metastasis and recurrence of tumors [8,9]. During the perioperative period, a variety of factors participate in the metastasis and recurrence of primary tumors, such as dissemination of tumor cells, drugs used in anesthetic and analgesic procedures, destruction of the extracellular matrix, release of vascular endothelial growth factor (VEGF), post-operative immunosuppression [10]. Therefore, there is an urgent need to identify biomarkers involved in the postoperative metastasis and recurrence of tumors and evaluate the dynamic changes and biofunctions of them. Extracellular vesicles (EVs) are lipid bilayer-enclosed, nanosized endocytic vesicles which could be secreted by most cell types [11,12]. EVs can modify the function of recipient cells by various bioactive contents, such as proteins (enzymes, extracellular matrix proteins, transcription factors, and receptors), DNAs, RNAs, and lipids [13]. It has been shown that EV long RNAs (exLRs), including circular RNA (circRNA), long non-coding RNA (lncRNA), and messenger RNA (mRNA), are abundant in human plasma [14-18]. ExLRs are considered to be valuable and functional [18,19] and play an important role in the progression of tumor development [20-22]. For example, Nabet et al. reported that an unshielded exosome RNA (RN7SL1) could act as a damage-associated molecular pattern (DAMP) to activate the pattern recognition receptor (PRR) RIG-I, driving anti-viral signaling when transferred to recipient breast cancer cells via an exosome, and ultimately leads to tumor growth and therapy resistance [16]. The CD274 mRNA in plasma-derived EVs is related to the response to anti-PD-1 antibodies in melanoma and non-small cell lung cancer [17]. In CRC, circulating EV microRNAs and lncRNAs are considered to be potential diagnostic biomarkers and related to mitomycin resistance [23-27]. These results showed that ExLRs could act as the cell-to-cell mediators of human cancers and promoted the progression of cancers. However, the dynamic changes of ExLRs during perioperative period and their biofunctions in the progression of CRC remains largely unknown. In the current study, we first evaluated the expression profile of exLRs during the perioperative period. ExLRs sequencing (exLR-seq) was performed on plasma samples collected from three CRC patients at four specific timepoints (before surgery [T0], after extubation [T1], 1 day after surgery [T2], and 3 days after surgery [T3]) to detect the effects of surgical stress on the exLR expression profile. In addition, the biofunctions of the changed exLRs were also investigated to assess the effects of exLRs on CRC progression. Moreover, tracking the cellular source of circulating EVs provides biological information about the origin and the functional states [28]. However, the cellular origin of dynamic changes in circulating EVs during the post-operative period has not been thoroughly investigated. In this study, we also explored and compared the distinct cellular origins from plasma EV samples during the post-operative period. Our study first evaluated the dynamic changes of exLRs during the perioperative period and their biological functions to find out appropriate biomarkers involved in the postoperative metastasis and recurrence of CRC. We also track the cellular sources of those dynamically changed exLRs to figure out the origin and functional states of these exLRs.

Material and methods

Patient specimens and clinical assessments

The present study recruited three CRC patients, all of whom underwent right hemicolectomy at Fudan University Shanghai Cancer Center by the same surgeon. All the participants were histologically confirmed to have colorectal adenocarcinoma (stage II) by two pathologists. Tumor staging was determined according to the AJCC Cancer Staging Manual. None of the patients received any other forms of therapy on the time of enrollment. This study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center and informed written consent was obtained from all patients.

Isolation of plasma from blood

Peripheral blood samples were collected from three CRC patients at four times (before surgery, after extubation, 1 day after surgery, and 3 days after surgery) in 10-mL EDTA-coated vacutainer tubes. Plasma was then separated by centrifugation at 3000 rpm (~800 × g) for 10 min at 25°C within 2 h after blood collection. Then, samples were centrifuged at 13,000 rpm (~16,000 × g) for 10 min at 4°C to remove debris. The plasma samples were then stored at – 80°C until use, according to a previous publication [18].

Isolation of EVs and EV RNA

For every patient, 1 mL of plasma was used. EVs were isolated by affinity-based binding to spin columns via an exoRNeasy Serum/Plasma Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Briefly, melted plasma was mixed with binding buffer and added to the exoEasy membrane affinity spin column. Samples were subjected to ultrafiltration using an Amicon Ultra-0.5 Centrifugal Filter 10 kDa (Merck Millipore, Germany) to reduce the eluate volume to 50 µL and exchange the buffer with phosphate buffer saline (PBS). For transmission electron microscopy (TEM), the size distribution measurement, and western blotting, the EVs were eluted with 400 μL of XE elution buffer, according to previous publications [29]. For TEM, ultrathin sections (100 nm) were cut using a LeicaUC6 ultra-microtome and post-stained with uranyl acetate for 10 min and with lead citrate for 5 min at room temperature before observation in a FEI Tecnai T20 TEM, operated at 120 kV. For EV RNA isolation, EVs were lysed on the column using QIAzol (Qiagen), and total RNA was then eluted and purified, as per other publications [30].

ExLR-seq analysis

The strategy for exLR-seq analysis includes plasma preparation, isolation of EV and EV RNAs, RNA-seq library construction, sequencing, and data analysis. Briefly, to remove DNA, total EV RNA isolated from 1 mL of plasma was treated with DNase I (NEB; Ipswich, Massachusetts, USA). RNA-seq libraries were generated using SMART technology (Clontech). ExLR-seq was performed on an Illumina sequencing platform (San Diego, California, USA) with 150 bp paired-end run metrics. Gene expression levels were calculated in transcripts per kilobase million (TPM). Annotations of mRNAs and lncRNAs were retrieved from the GENCODE database, according to previous publications [20,31].

Identification of differentially expressed mRNAs and lncRNAs

Transcriptional profiles of EVs from the plasma of three CRC patients were evaluated during the perioperative period (before surgery [T0], after extubation [T1], 1 day after surgery [T2], and 3 days after surgery [T3]). Significantly differentially expressed genes (DEGs) and differentially-expressed lncRNAs (DELRs) were identified using the Limma R package (version 3.6.3) with a |logFC| > 1 and p < 0.1.

Functional enrichment analysis of DEGs and DELRs

The intersective hub genes during the perioperative period were selected for further analyses using a Venn diagram. A protein–protein interaction (PPI) network of hub genes was constructed using GeneMANIA (http://genemania.org/). Biological processes, cellular components, and molecular function of gene ontology (GO) functional analysis and Kyoto encyclopedia genes and genomes (KEGG) pathway were predicted using the Web-based Gene seT AnaLysis Toolkit (WebGestalt [http://www.webgestalt.org/]) and visualized using R software. In the functional analyses, the input parameters including gene names of all the DEGs, gene ontology (GO) and Kyoto encyclopedia genes and genomes (KEGG) pathways and the results can change depending on the query/input information.

Western blot analysis

Fifty milligrams of exosomes were extracted using 2X SDS lysis buffer, separated by 4%–12% SDS-PAGE, transferred to a PVDF membrane, blocked with 5% BSA in TBST, and probed with specific primary antibodies against Calnexin (1:1000 dilution; Abcam, Cambridge, UK), CD63 (1:1000 dilution; Abcam), and TSG101 (1:1500 dilution; Abcam). β-actin (1:5000 dilution; Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA) was used as a loading control. The chemiluminescent signals were detected with a chemiluminescence imaging system and quantified by Image J software (v1.37).

Data and statistical analyses

All statistical analyses were two-sided. A |logFC| > 1 and p < 0.05 were considered statistically significant. The following R software packages were used in this study: e1071, glmnet, varSelRF, pROC, and caret. Nonparametric T test was used in Limma Package and the comparison of immune cells producing EVs before and after surgery. Hypergeometric test was used in the functional enrichment analysis. Spearman’s rank correlation coefficient was utilized in the correlation analysis of different types of immune cells.

Results

Firstly, we evaluated the dynamic changes of mRNAs and lncRNAs during the perioperative period via ExLR-seq. Then, we detected the biological functions of these DEGs to find out appropriate biomarkers involved in the postoperative metastasis and recurrence of CRC. Finally, we track the cellular sources of those dynamically changed exLRs to figure out the origin and functional states of these exLRs.

Isolation and identification of EVs

To evaluate the integrity of isolated EVs from plasma, EV morphology was inspected by electron microscopy. As shown in Figure 1, the isolated vesicles in plasma were cup-shaped, rounded, and double membrane-bound vesicle-like (Figure 1a). Furthermore, flow cytometry exhibited a heterogeneous population of spherical nanoparticles, with abundant peaks ranging from 50 to 200 nm (Figure 1b). In addition, western blot analysis revealed characteristic exosomal marker (CD63 and TSG101) expression in isolated vesicles, but not in peripheral blood mononuclear cells (PBMCs). Calnexin, which is an intracellularly enriched protein in PBMCs and often used as a negative-control protein marker for EV identification, was detected in PBMCs, but not in isolated vesicles (Figure 1c). These data indicated that the isolated vesicles were composed mostly of exosomes.
Figure 1.

Human blood EVs confirmation. EVs were isolated and purified from plasma using membrane affinity spin columns. (a) Electron microscopy image of isolated vesicles. (b) Size distribution measurements of isolated vesicles. (c) Western blots of calnexin, which can be detected in PBMCs, but not in isolated vesicles, was used as a control. EV markers TSG101 and CD63 in isolated vesicles were detected in EVs, but not in PBMCs

Human blood EVs confirmation. EVs were isolated and purified from plasma using membrane affinity spin columns. (a) Electron microscopy image of isolated vesicles. (b) Size distribution measurements of isolated vesicles. (c) Western blots of calnexin, which can be detected in PBMCs, but not in isolated vesicles, was used as a control. EV markers TSG101 and CD63 in isolated vesicles were detected in EVs, but not in PBMCs

Dynamic changes of mRNAs in EVs before and after surgery

ExLR-seq was conducted using plasma samples from three CRC patients at four timepoints. Approximately 12,924 mRNAs were reliably detected in each sample. Dynamic changes were observed in the expression profiles of mRNAs in EVs during the postoperative period. Briefly, as shown in Figures 2A, 110 DEGs in T1 were compared with those in T0. In addition, compared with T0, there were 60 DEGs in T2 (Figure 2b) and 50 DEGs in T3 (Figure 2c). Taking the intersection of DEGs in the three groups revealed that a total of 11 DEGs (hub DEGs), including DGKI, GRB14, KIAA1549, WT1, ACKR4, PLXNB3, KCNH8, TCTEX1D1, ILDR2, DYTN and CHRNA2, changed at all three timepoints compared to T0 (Figure 2d). Although it did not reach significance owing to the sample size, there existed an obvious trend (logFC| > 1 and p < 0.1) and details are shown in table 1 (Table 1, Fig S1).
Figure 2.

Comprehensive mRNAs in extracellular vesicles and functional annotations before and after surgery. (a-c) Significant DEGs in extracellular vesicles were screened and identified using the ‘Limma’ R package between samples before surgery and after extubation (Fig. A), or 1 day after surgery (Fig. B), or 3 days after surgery (Fig. C). (d) A total of 11 common DEGs were obtained in extracellular vesicles before and different time points after surgery using a Venn diagram

Comprehensive mRNAs in extracellular vesicles and functional annotations before and after surgery. (a-c) Significant DEGs in extracellular vesicles were screened and identified using the ‘Limma’ R package between samples before surgery and after extubation (Fig. A), or 1 day after surgery (Fig. B), or 3 days after surgery (Fig. C). (d) A total of 11 common DEGs were obtained in extracellular vesicles before and different time points after surgery using a Venn diagram

Functional annotations of mRNAs in EVs

Next, functional annotations of 11 altered mRNAs in EVs were determined. With respect to the biological process, nine of the 11 DEGs were involved in the biological regulation process and seven DEGs participated in the localization process and stimulus response. For the cellular component, nine DEGs were membrane components. In addition, molecular function analysis showed that six DEGs were protein-binding mRNAs (Figure 3a). To further determine the interactions among the 11 DEGs, the protein–protein interaction network was used (Figure 3b). Moreover, GO function analysis showed that the 11 hub DEGs most significantly involved in the regulation of membrane potential, cell chemotaxis, chemical synaptic transmission, and mesenchymal-epithelial transition (Figure 3c). KEGG pathway analysis revealed that the hub DEGs were enriched for some pathways, such as nicotinic acetylcholine receptors activities, Tie2 signaling, other semaphoring interactions, and choline and glycerolipid metabolism (Figure 3d).
Figure 3.

The functional annotations of mRNAs in EVs. (a) Biological processes, cellular components, and molecular function analysis from GO items of 11 hub genes were evaluated. (b) The protein–protein interaction network was used, showing direct interactions and potential associations between proteins. (c) The 11 hub genes most significantly involved in changed GO functions. (d) Significantly altered KEGG and Reactome pathways were predicted

The functional annotations of mRNAs in EVs. (a) Biological processes, cellular components, and molecular function analysis from GO items of 11 hub genes were evaluated. (b) The protein–protein interaction network was used, showing direct interactions and potential associations between proteins. (c) The 11 hub genes most significantly involved in changed GO functions. (d) Significantly altered KEGG and Reactome pathways were predicted

Dynamic changes of lncRNAs in EVs before and after surgery

Except for mRNAs, approximately 2200 lncRNAs were detected in each sample. Dynamic changes were also observed in the expression profiles of lncRNAs in EVs during the post-operative period. As shown in Figure 4a, we identified 22 DELRs between T0 and T1. In addition, 19 DELRs were detected between T0 and T2 (Figure 4b) and 38 DELRs between T0 and T3 (Figure 4c). Furthermore, we found that only three DELRs (C15orf54, RP11-446N19.1, and RP11-87H9.4) changed at all three timepoints (Figure 4d). Although it did not reach significance owing to the sample size, there existed an obvious trend (logFC| > 1 and p < 0.1) and details are shown in table 2 (Table 2, Fig S2).
Figure 4.

Comprehensive lncRNAs in EVs before and after surgery. (a-c) Significant lncRNAs in EVs were also screened and identified using the ‘Limma’ R package between samples before surgery and after extubation (Fig. A), or 1 day after surgery (Fig. B), or 3 days after surgery (Fig. C). (d) A total of 3 common DEGs were obtained in EVs before and different time points after surgery using a Venn diagram

Comprehensive lncRNAs in EVs before and after surgery. (a-c) Significant lncRNAs in EVs were also screened and identified using the ‘Limma’ R package between samples before surgery and after extubation (Fig. A), or 1 day after surgery (Fig. B), or 3 days after surgery (Fig. C). (d) A total of 3 common DEGs were obtained in EVs before and different time points after surgery using a Venn diagram

Cell source analysis of EVs

Because blood EVs are derived from a variety of tissues, the xCell tool (http://xcell.ucsf.edu) was used to characterize the proportions of cell types derived from EVs. xCell is a webtool that performs cell-type enrichment analysis from gene expression data for 64 immune and stroma cell types. xCell is a gene signature-based method learned from thousands of pure cell types from various sources. xCell applies a novel technique for reducing associations between closely related cell types. We identified 67 immune and stroma cell types and evaluated the correlation between immune and stroma cells (Figure 5a). Dynamic changes of the origination of EVs during the postoperative period were also investigated (Figure 5b). Consensus clustering were utilized to explore potential clusters and Consensus clustering (or aggregated clustering) is a robust approach that relies on multiple iterations of the chosen clustering method on sub-samples of the dataset. Specifically, EVs derived from platelets were gradually reduced after surgery. Clinically, the immunosuppressive microenvironment caused by surgery can lead to tumor metastases and recurrence and the role of EVs in immune regulation has been intensively studied. Following infection, the release of EVs carrying immunomodulatory molecules by various immune cells can influence primary and secondary immune responses [32,33]. Therefore, we further investigated the changes in immune cells derived from EVs before and after surgery. As shown in Figure 5c, the total number of EVs derived from immune cells decreased after surgery. EVs derived from CD8 + T, CD4+ memory T, and natural killer (NK) cells decreased as well (Figure 5c).
Figure 5.

Cell source analysis of EVs. (a) The correlation of immune and stroma cell expression derived from EVs. (b) Dynamic changes of cells derived from EVs before and after surgery. (c) Comparison of immune cells producing EVs before and after surgery. * p < 0.05, ** p < 0.01, *** p < 0.01 vs before surgery

Cell source analysis of EVs. (a) The correlation of immune and stroma cell expression derived from EVs. (b) Dynamic changes of cells derived from EVs before and after surgery. (c) Comparison of immune cells producing EVs before and after surgery. * p < 0.05, ** p < 0.01, *** p < 0.01 vs before surgery

Discussion

Recently, EVs, as a molecular approach to analyze tumor diagnosis and progression, has attracted more and more attention [31,34]. While studies of EVs on cancer development have expanded rapidly, few studies have investigated the influence of surgical stress on exLRs and the cellular origins of exLRs. Our research showed that there are a large variety of exLRs in human blood. Given the high abundance and heterogeneity of blood exLRs, we intended to find the differences of expression profile and function in exLRs before and after surgery and their influence on CRC progression and prognosis. As the size similarity between exosomes and other EVs, including ectosomes and MVs, has deeply hindered the development of isolation processes. It is critical to pay attention to the isolation of plasma, EV purification and preparation of EV RNA in order to obtain reliable exLR-seq data. In this study EVs were isolated via density gradient centrifugation (DG), which was intensively used in plasma and cell culture supernatants. Although the process is time-consuming and highly instrument-dependent, DG is easy to perform and yields exosomes with higher purity and size uniformity. The proportion of exosomes in the isolated EVs was confirmed from the aspect of morphology, particle size, and characteristic exosomal markers (CD63 and TSG101). Rapid development of EV isolation technology makes it possible to use EVs and exLRs for further cancer-related studies. In this study, surgical stress caused significant changes to the expression profile of exLRs. 11 mRNAs, including DGKI, GRB14, KIAA1549, WT1, ACKR4, PLXNB3, KCNH8, TCTEX1D1, ILDR2, DYTN and CHRNA2, have dynamically changed at all three timepoints compared with T0. Several of these exLRs were associated with CRC progression. For example, WT1 expression in CRC primary tumors could be a novel independent marker for prognosis and tumor progression [35,36]. GRB14, which belongs to a small family of adapter proteins, could encode a growth factor receptor-binding protein that interacts with insulin receptors and insulin-like growth factor receptors (IGF-Rs) [37]. IGF-1 R expression is associated with tumor progression and poor prognosis in several cancer types, including gastrointestinal malignancies [38]. KIAA1549 belongs to the UPF0606 family and is related to oncogenic MAPK signaling [39]. ACKR4, which is a receptor for C–C type chemokines, has been shown to bind T cells and dendritic cell-activated chemokines and plays a significant role in controlling the migration of immune and cancer cells [40]. These findings indicated that mRNAs dynamically changed during the peri-operative period may play a role in the development of CRC. However, the exact effect of these mRNAs and the underlying mechanisms require further investigation. Our data also showed that lncRNAs are enriched in EVs; however, only three common lncRNAs were detected at three time points compared with T0. This finding may due to our small sample size. In a corollary study, we will expand our samples to further explore the exact effect of surgery on EVs and the underlying mechanism. Innate immune cells, such as NK cells, neutrophils, mast cells (MCs), macrophages, eosinophils and adaptive immune cells, including DCs, T cells, and B cells, derived from exosomes can directly interact with cancer cells and uptake by tumor cells inducing different types of immune responses [41,42]. Accumulating evidence has shown that EVs derived from immune cells can promote pro-tumor and anti-tumor immunity, which suggests a complex relationship between the immune cell-derived EVs and the immune system [43-46]. NK cells that were previously exposed to neuroblastoma (NB) can secrete exosomes containing NK cell receptors, such as CD56, KIR2DL2, and NKG2D receptors, which can subsequently stimulate normal NK cells, generating greater and more efficient cytotoxicity against NB tumor cells [45]. CD8 + T cell-derived exosomes with membrane expression of Fas ligand (FasL) can promote invasion and metastasis of Fas+ tumor cells through matrix metalloproteinase-9 (MMP-9)-mediated degradation of extracellular matrix proteins [43]. In our study, we found that total EVs derived from immune cells, especially from CD8 + T cells, CD4+ memory T cells and NK cells, decreased after surgery. Further studies are warranted to fully characterize EVs derived from immune cells, and to learn how to precisely engineer exosomes for therapeutic antitumor treatment. Significant DELRs between T0 and T1 Significant DELRs between T0 and T2 Significant DEGs between T0 and T1 Significant DEGs between T0 and T2 Significant DEGs between T0 and T3 In short, this report presented abundant exLRs in human plasma and the exLR dynamic changes. ExLRs originating from CD8 + T and CD4+ memory T cells were reduced during the perioperative period. Future studies will focus on the specific exLRs dynamically changed during the perioperative period and their origins to explore the impact on the occurrence and progression of CRC and the potential underlying mechanism.

Conclusions

To the best of our knowledge, we investigated the effects of surgical stress on the expression profile and cellular sources of blood exLR by exLR sequencing of CRC patients at four time points before and after surgery. In addition, we also investigated the function of these changed exLRs during the perioperative period. These findings open an avenue for the investigation of EVs at different time points and lay foundation to find out exLRs involved in the postoperative metastasis and recurrence of tumors. Click here for additional data file.

Significant DELRs between T0 and T1

LncRNAMean(T0)Mean(T1)logFCpValueFDR
AC002451.32.217133880.114178067−4.279340.07652250.683824
AC005523.21.527293310.0228,35613−6.063550.07652250.683824
AC006369.23.4133285130.091342467−5.223750.07652250.683824
AC093627.80.3254089936.5531665674.3318660.07652250.683824
C15orf540.08135224711.778791277.1777940.07652250.683824
CADM3-AS16.0569169930.548054667−3.466190.07652250.683824
CTC-471J1.80.743284010.091342467−3.024560.07652250.683824
LINC006498.3143093430.548054667−3.92320.07652250.683824
LINC006625.1406111870.411041−3.644590.07652250.683824
LINC009201.4535659630.1370137−3.40720.07652250.683824
LINC011335.0825206770.479548−3.40580.07652250.683824
LINC011375.4269044830.662232667−3.034720.07652250.683824
MIATNB4.272276250.433876667−3.299650.07652250.683824
POT1-AS10.101690311.4464937333.8303060.07652250.683824
RNASEH1-AS15.5468310970.502383667−3.46480.07652250.683824
RP11-115D19.14.294104451.141780667−1.911070.07652250.683824
RP11-164H13.12.225932480.0228,35613−6.606980.07652250.683824
RP11-216 L13.193.4910729270.416797333−3.066250.07652250.683824
RP11-234K19.10.75052733311.016330473.8755950.07652250.683824
RP11-291B21.27.877612970.1370137−5.845370.07652250.683824
RP11-299G20.50.0813522476.9114034676.4086530.07652250.683824
RP11-303E16.22.2260796770.1826849−3.607080.07652250.683824
RP11-333E1.10.0416959671.09174434.7105830.07652250.683824
RP11-379H18.12.295738540.342534333−2.744640.07652250.683824
RP11-446N19.10.0625439335.90127726.5600130.07652250.683824
RP11-539L10.24.573414010.1370137−5.060880.07652250.683824
RP11-631N16.20.2454820336.7500551674.781210.07652250.683824
RP11-77K12.92.7996392970.525219−2.414250.07652250.683824
RP11-87H9.42.3330009470.0228,35613−6.674760.07652250.683824
RP11-95D17.14.8966578870.269692367−4.182410.07652250.683824
RP11-982M15.80.5084515532.9962668672.5589840.07652250.683824
RP5-1028K7.23.8826085930.274027367−3.824630.07652250.683824
TNRC6C-AS12.948292490.411041−2.842530.07652250.683824

Significant DELRs between T0 and T2

LncRNAMean(T0)Mean(T2)logFCpValueFDR
AC007563.52.765860970.155973413−4.148360.07652250.621384
AC098617.10.1667838334.9944141834.9042640.07652250.621384
AC112721.10.020847980.9137313335.4537910.07652250.621384
ADAMTS9-AS24.1953055970.038,993,353−6.74940.07652250.621384
AZIN1-AS13.075850920.17547009−4.131690.07652250.621384
C15orf540.08135224723.330725098.1638330.07652250.621384
CH507-145 C22.0.386423182.6957856872.8024520.07652250.621384
CTA-292E10.63.032952830.584900297−2.374460.07652250.621384
CTC-523E23.30.5420473333.5867822832.7261990.07652250.621384
CTD-2547L24.35.053907190.038993353−7.018030.07652250.621384
KANSL1-AS13.4315061670.05849003−5.874510.07652250.621384
LINC004670.4067612433.1449399172.9507780.07652250.621384
LINC014800.10228422.3008093474.4914860.07652250.621384
LINC015340.2710237331.0890821632.0066220.07652250.621384
MEG30.0204568372.2390885176.7741850.07652250.621384
NEXN-AS10.8338605479.4829220533.5074540.07652250.621384
POT1-AS10.101690312.6673811734.713170.07652250.621384
PWAR53.0062758070.136476737−4.461250.07652250.621384
RP11-242D8.14.3634041970.409430207−3.413760.07652250.621384
RP11-303E16.22.2260796770.38993353−2.513210.07652250.621384
RP11-345P4.90.06137054.8580664336.3066930.07652250.621384
RP11-366 L5.10.0203380633.0318058237.2198510.07652250.621384
RP11-379F4.40.1250878671.325420173.4054360.07652250.621384
RP11-427M20.13.0523979030.102365933−4.898140.07652250.621384
RP11-446N19.10.0625439333.9297527935.9734250.07652250.621384
RP11-479O9.40.7773596674.552957432.550150.07652250.621384
RP11-495P10.11.3982939770.077986707−4.16430.07652250.621384
RP11-705 C15.30.1627044972.890376664.1509310.07652250.621384
RP11-746M1.10.32730941.688782912.3672570.07652250.621384
RP11-809N8.41.439807430.253456793−2.506060.07652250.621384
RP11-87H9.42.3330009470.020473183−6.832310.07652250.621384
RP1-47M23.30.0416959671.7490100035.3904870.07652250.621384
RP5-1028K7.23.8826085930.368517333−3.397220.07652250.621384
THAP7-AS14.0525577170.136476737−4.892110.07652250.621384
ZNF674-AS13.014490140.214463443−3.813110.07652250.621384
LncRNAMean(T0)Mean(T3)logFCpValueFDR
AC007563.52.765860970.215818967−3.679840.07652250.355949
AC091814.20.2237186831.4850522832.7307560.07652250.355949
AC093627.80.3254089934.2325741733.7012090.07652250.355949
AC098617.10.1667838335.8204276875.1250740.07652250.355949
AC112721.10.020847981.7098722176.3578370.07652250.355949
ARF4-AS10.0409136670.8935914174.4489610.07652250.355949
BAALC-AS12.8324590730.172655167−4.036090.07652250.355949
C15orf540.08135224712.676109397.2837140.07652250.355949
CDKN2B-AS14.442316140.36374714−3.61030.07652250.355949
CITF22-92A6.10.0818273330.851461363.3792860.07652250.355949
CTB-111H14.10.284732872.8475588033.3220450.07652250.355949
CTBP1-AS0.0416959670.949288334.5088670.07652250.355949
CTD-2574D22.20.8338605473.8666815632.2132180.07652250.355949
CTD-2587H24.10.10423990.6099931832.5488860.07652250.355949
CTD-3014M21.13.5690172270.161665397−4.464440.07652250.355949
CYB561D20.0406761231.1309322074.7971860.07652250.355949
EIF1B-AS10.5420473334.0392565772.8975990.07652250.355949
KIF9-AS10.183042560.994623562.4419710.07652250.355949
LINC002610.0409136672.716800646.0531820.07652250.355949
LINC004670.4067612434.4649628433.4563940.07652250.355949
LINC009201.4535659630.080832697−4.168510.07652250.355949
LINC010020.1667838331.5087210533.1772750.07652250.355949
LINC015340.2710237331.0668346971.9768460.07652250.355949
MAP3K14-AS10.020847980.6507349074.964090.07652250.355949
NEXN-AS10.8338605478.2474966733.3060780.07652250.355949
NR2F1-AS13.881814850.172655167−4.490770.07652250.355949
PROSER2-AS10.22502521.8955354133.0744470.07652250.355949
RP11-1094M14.0.32730941.9357238372.5641460.07652250.355949
RP11-156E8.10.0818273330.9199155873.4908470.07652250.355949
RP11-182 L21.60.2237186833.0907076433.788180.07652250.355949
RP11-234K19.10.7505273334.7995303272.6769170.07652250.355949
RP11-244H3.10.0204568370.803832615.296240.07652250.355949
RP11-282O18.30.1627044974.7221756774.8591260.07652250.355949
RP11-288C18.10.10423990.5534628372.4085790.07652250.355949
RP11-299G20.50.0813522479.7728926276.908460.07652250.355949
RP11-319G9.50.0203380630.972492775.5794330.07652250.355949
RP11-320 M2.10.10228420.715465832.80630.07652250.355949
RP11-333E1.10.0416959670.6425047773.9457270.07652250.355949
RP11-366 L5.10.0203380631.6974606636.3830520.07652250.355949
RP11-379F4.40.1250878672.128023384.08850.07652250.355949
RP11-427M20.13.0523979030.028775863−6.728940.07652250.355949
RP11-428J1.50.101690310.8038448332.9827350.07652250.355949
RP11-446N19.10.0625439332.9918086775.5800040.07652250.355949
RP11-467L13.70.6508179873.835074362.5589290.07652250.355949
RP11-553A21.30.183042566.5354667875.1580390.07652250.355949
RP11-596 C23.60.10228420.545199632.4142010.07652250.355949
RP11-624L4.10.0203380631.4896092536.1946080.07652250.355949
RP11-705 C15.30.1627044971.1890719872.869510.07652250.355949
RP11-732A19.20.0818273330.5966474932.8662240.07652250.355949
RP11-7F17.81.40332628718.644600993.7318360.07652250.355949
RP11-867G23.30.0416959670.7161913634.1023650.07652250.355949
RP11-867G23.80.0203380631.5659236.2666870.07652250.355949
RP11-87H9.42.3330009470.028775863−6.341180.07652250.355949
RP3-329E20.20.2045683672.2827843173.480140.07652250.355949
SPAG5-AS10.1459358672.1913794373.9084330.07652250.355949
TIPARP-AS10.0833919330.7059307173.0815470.07652250.355949

Significant DEGs between T0 and T1

GENEMean(T0)Mean(T1)logFCpValueFDR
TSPYL58.2976983331−3.052710.06360260.381797
NAGPA8.1643931−3.029350.06360260.381797
ZNF1127.0316116671−2.813860.06360260.381797
GSTZ17.7362563331−2.951640.06360260.381797
SLC25A334.3600613331−2.124350.06360260.381797
C1orf1865.7602081−2.526120.06360260.381797
CC2D2A5.3404961−2.416970.06360260.381797
DCLK17.2652786671−2.861020.06360260.381797
CXorf574.5854936671−2.197080.06360260.381797
IL2RA3.6273033331−1.85890.06360260.381797
C2orf816.7069353331−2.745650.06360260.381797
RPUSD28.423551−3.074430.06360260.381797
RABL2A5.8302741−2.543560.06360260.381797
AC092835.6.3812386671−2.673840.06360260.381797
WT15.0041916671−2.323140.06360260.381797
GCNT44.2039051−2.071730.06360260.381797
TIGD25.4048091−2.434240.06360260.381797
MYT12.7375596671−1.452890.06360260.381797
C14orf804.0389673331−2.013990.06360260.381797
GOLGA7B5.0572433331−2.338350.06360260.381797
UNC13 C3.1317913331−1.646990.06360260.381797
ZNF705A5.3144691−2.409930.06360260.381797
POC1A3.8266126671−1.936070.06360260.381797
TIFAB7.8488993331−2.972490.06360260.381797
NLRP75.4826753331−2.454880.06360260.381797
CFAP535.9400121−2.570470.06360260.381797
TCTEX1D114.6207292.208120.06360260.381797
VNN3110.840451673.4383530.06360260.381797
HTR2A13.3661836671.7511140.06360260.381797
ILDR215.0656313332.3407420.06360260.381797
EP400NL15.0922322.712671−2.476020.07652250.381797
DCPS14.969145332.027602667−2.884140.07652250.381797
ZNF354 C11.6242221.205520667−3.269410.07652250.381797
FLT48.5310116671.616561667−2.399790.07652250.381797
PTPRO13.120341672.027602667−2.693960.07652250.381797
RITA17.7934303331.159849333−2.748320.07652250.381797
KCNH86.5724593331.159849333−2.50250.07652250.381797
CCDC16715.4630132.210287667−2.806520.07652250.381797
TRPV210.679989331.959095667−2.446650.07652250.381797
A4GALT7.1123753331.753575333−2.020030.07652250.381797
GALK110.698927332.895356−1.885650.07652250.381797
LTBP29.8373983331.662232667−2.565150.07652250.381797
TMEM2346.1524971.274027333−2.271780.07652250.381797
DNALI17.8756221.114178−2.821410.07652250.381797
NEK310.274526331.760042−2.545390.07652250.381797
MTFP19.7923293331.433876667−2.771730.07652250.381797
SELO8.1857791.114178−2.877140.07652250.381797
PCED1A9.7525303331.959095667−2.315590.07652250.381797
GSTCD10.692551671.159849333−3.20460.07652250.381797
MMP23B5.1195693331.479548−1.790870.07652250.381797
FOXRED17.6483891.639397333−2.221990.07652250.381797
C9orf1727.5594226672.210287667−1.774040.07652250.381797
FAM206A12.3604961.913424667−2.691510.07652250.381797
MIPEP12.835862.118945−2.598760.07652250.381797
NUDT16L110.973190672.210287667−2.311680.07652250.381797
PEMT8.4857096671.890589−2.16620.07652250.381797
GPSM14.3775773331.122587333−1.96330.07652250.381797
CDCA59.4815286671.114178−3.089140.07652250.381797
TRIM669.3925593331.799246333−2.384130.07652250.381797
EVC7.8473893331.479548−2.407060.07652250.381797
OSBPL77.091371.228356−2.529340.07652250.381797
IL23A6.111041.388205333−2.13820.07652250.381797
GYPE12.219362.552821667−2.2590.07652250.381797
ATG9B6.7181921.269692333−2.403590.07652250.381797
CHTF184.9288353331.205520667−2.031590.07652250.381797
NDOR15.7565753331.593726−1.852810.07652250.381797
TMEM150A6.2620621.182685−2.404570.07652250.381797
LRFN34.1991651.525219−1.461090.07652250.381797
WDR839.8176853332.804013667−1.807890.07652250.381797
HMCN15.9504316671.433876667−2.053070.07652250.381797
ASB139.4395161.411041−2.741950.07652250.381797
BCDIN3D5.3998091.220657333−2.145250.07652250.381797
S1PR25.8670146671.045671333−2.48820.07652250.381797
AMDHD15.0820836671.525219−1.73640.07652250.381797
SLC25A177.0057071.593726−2.136130.07652250.381797
ALG14.5790641.045671333−2.130620.07652250.381797
SSPN6.0933176672.152322−1.501330.07652250.381797
POGLUT16.7638436671.662232667−2.024720.07652250.381797
PARS24.5676063331.0228,35667−2.158860.07652250.381797
STK32B5.8249383331.570890333−1.890660.07652250.381797
FAM207A8.4989703331.616561667−2.394360.07652250.381797
LRWD19.2511223331.365369667−2.760340.07652250.381797
ERP274.5384576671.319698667−1.781990.07652250.381797
UBE3D6.7062911.685068333−1.992710.07652250.381797
RAG16.4268111.342534333−2.259140.07652250.381797
THNSL16.2410413331.411041−2.145030.07652250.381797
CHRNA22.41152166716.412141672.7667480.07652250.381797
KLHDC8A3.7101566671.416797333−1.388850.07652250.381797
SDHAF46.6976333332.096109333−1.675940.07652250.381797
DDX285.2409416671.867753333−1.488520.07652250.381797
FLG5.705931.022835667−2.479890.07652250.381797
GLYCTK3.642941.456712333−1.322390.07652250.381797
MTERF28.7446111.822082−2.262810.07652250.381797
DDIAS7.5813006671.137013667−2.73720.07652250.381797
NIPAL27.5839666671.822082−2.057360.07652250.381797
QTRT16.3797641.548054667−2.043050.07652250.381797
SMYD59.2825326671.822082−2.348930.07652250.381797
AN-P2RY4.5271503331.844917667−1.295050.07652250.381797
SEC61A24.1828146671.388205333−1.591250.07652250.381797
ARL67.9305651.039818−2.931090.07652250.381797
FAM179A5.0371666671.274027333−1.983220.07652250.381797
SLC46A310.1859891.091342333−3.222410.07652250.381797
CAPN35.2987726671.09807−2.270690.07652250.381797
ITIH54.4533393331.137013667−1.969640.07652250.381797
FCN25.7700941.525219−1.919580.07652250.381797
ASB92.9648536671.159849333−1.354020.07652250.381797
ZNF904.6344956671.068507−2.116820.07652250.381797
MPP33.7751976671.024517333−1.881610.07652250.381797
JMJD73.8198343331.068507−1.837910.07652250.381797
PPIC7.1037711.182685−2.586520.07652250.381797
PAK34.3716051.639397333−1.4150.07652250.381797
MAP96.3296096671.416797333−2.159480.07652250.381797
NR2C2AP3.038,4131.388205333−1.13010.07652250.381797
PIGV4.7879951.479548−1.694270.07652250.381797
TNFAIP8L34.4637353331.525219−1.549240.07652250.381797
HSD11B1L4.3418611.159849333−1.904380.07652250.381797
ACPP2.2915596671.137013667−1.011080.07652250.381797
RYR15.8635861.296863−2.176760.07652250.381797
ACKR46.2683661.182685−2.406020.07652250.381797
ZNF6745.2655646671.119454333−2.233790.07652250.381797
SLC16A142.6387181.228356−1.103110.07652250.381797
NR6A11.2863956675.9171752.2015740.07652250.381797
ABCB91.2250253332.8647611.2256030.07652250.381797
GALNT181.2045683333.5846991.5733360.07652250.381797
PRRT21.1227417.4461282.7294650.07652250.381797
KHK1.7713753335.5772083331.6546730.07652250.381797
VSIG21.42709933311.465909673.0061930.07652250.381797
DYTN2.38298833313.682377672.5214750.07652250.381797
KIAA15491.0416965.5770243332.4205610.07652250.381797
NT5DC31.5837433336.6956422.0798840.07652250.381797
GRB141.04169619.047471674.1925930.07652250.381797
SPHAR1.8338606678.9117866672.2808310.07652250.381797
CLDN121.4378076674.5904906671.6747780.07652250.381797
DGKI1.4169596676.6464232.2297790.07652250.381797
TAF6L1.1423663334.5170173331.9833450.07652250.381797
LIPC2.4440023339.2153491.9147930.07652250.381797
PPP1R13L1.16678411.218008673.2652070.07652250.381797
PLXNB33.98969515.271079671.9364520.07652250.381797

Significant DEGs between T0 and T2

GENEMean(T0)Mean(T2)logFCpValueFDR
CCHCR17.5953361−2.925110.06360260.412752
CBR311.614660331−3.537880.06360260.412752
CCDC405.2989823331−2.405720.06360260.412752
CLEC4C4.4536373331−2.154980.06360260.412752
IL23A6.111041−2.611420.06360260.412752
WT15.0041916671−2.323140.06360260.412752
OC4-APO3.8177541−1.932720.06360260.412752
APOC23.8177541−1.932720.06360260.412752
SMOC25.3911216671−2.430590.06360260.412752
APOC36.5555011−2.712710.06360260.412752
DTX34.3021506671−2.105060.06360260.412752
DHRS114.4715521−2.160780.06360260.412752
CCL203.2477796671−1.699450.06360260.412752
NLRP75.4826753331−2.454880.06360260.412752
GPA332.5773333331−1.365880.06360260.412752
ZBED6CL12.7318126671.4498590.06360260.412752
PRRT312.7313136671.4495950.06360260.412752
SAA115.3303686672.4142350.06360260.412752
TCTEX1D115.4564962.4479750.06360260.412752
CEBPE18.1481083.0264650.06360260.412752
DIRAS114.6489916672.2169180.06360260.412752
DEPDC415.405252.4343610.06360260.412752
VNN314.8040296672.2642450.06360260.412752
WFDC1111.9386683.577570.06360260.412752
ADGRG315.0345942.3318750.06360260.412752
ILDR219.5762493333.2594610.06360260.412752
IFI27L115.0303906672.330670.06360260.412752
OPLAH14.9751586672.3147430.06360260.412752
TLR514.7452443332.2464820.06360260.412752
RETN17.1455872.8370530.06360260.412752
MAK15.0001732.3219780.06360260.412752
GCKR16.5855392.7193020.06360260.412752
FBXO4014.3392072.1174310.06360260.412752
MMP214.330273671.45041−3.304530.07652250.412752
KCNH86.5724593331.097483333−2.582230.07652250.412752
PGAP27.8338703331.311946667−2.578020.07652250.412752
ZSWIM36.3541593331.406925667−2.175160.07652250.412752
KATNAL27.1948961.194966667−2.590.07652250.412752
GSTZ17.7362563331.17547−2.71840.07652250.412752
ARHGAP225.8227473331.11698−2.38210.07652250.412752
NEK310.274526331.857853667−2.467360.07652250.412752
CYP2E19.4107106671.214463333−2.953980.07652250.412752
MMP23B5.1195693331.429937−1.840070.07652250.412752
ABCA99.5737651.757507667−2.445560.07652250.412752
SCARB19.1622493332.033324−2.171860.07652250.412752
KIF5C9.2732563331.038993333−3.157890.07652250.412752
SFT2D37.4457476671.584900333−2.232020.07652250.412752
C2orf816.7069353331.389933667−2.270640.07652250.412752
GZMK7.5168271.11698−2.750520.07652250.412752
FGFR34.5884196671.019377333−2.170310.07652250.412752
ADTRP4.905161.122839−2.127150.07652250.412752
LRP5L4.6508813331.020473333−2.188270.07652250.412752
CCR75.3655341.245678333−2.106790.07652250.412752
CSRP27.6360793331.194966667−2.675860.07652250.412752
ERP274.5384576671.11698−2.02260.07652250.412752
PLA2R13.7669036671.143312333−1.720160.07652250.412752
CLEC1A5.8427953331.506913667−1.955060.07652250.412752
RSPO311.8249111.389933667−3.088740.07652250.412752
C1orf563.5359616671.019496667−1.794250.07652250.412752
IDUA3.5003851.17547−1.574280.07652250.412752
FOLR24.3401856671.370437−1.663120.07652250.412752
CHRNA22.41152166725.9948943.4302130.07652250.412752
HSD17B32.2887806676.3935806671.4820450.07652250.412752
C14orf804.0389673331.584900333−1.349590.07652250.412752
APOBEC3H3.5864656671.327571−1.433770.07652250.412752
ADAMTSL33.5721881.019496667−1.808950.07652250.412752
C19orf445.2229616671.682383667−1.634360.07652250.412752
CERCAM4.2308671.573249−1.427210.07652250.412752
FBLN18.0635411.467920333−2.457640.07652250.412752
FAM184A7.2538603331.584900333−2.194360.07652250.412752
ZNF6304.9708393331.467920333−1.759720.07652250.412752
C15orf652.8899421.370437−1.07640.07652250.412752
VPS9D11.8387303335.3107716671.5302120.07652250.412752
ALG144.9712416671.194966667−2.056640.07652250.412752
ID47.3152166671.081892667−2.757340.07652250.412752
SPINT13.6213153331.019377333−1.828830.07652250.412752
MAP104.5404291.584900333−1.518440.07652250.412752
ABI3BP7.1484411.214463333−2.557310.07652250.412752
RBKS3.8325683331.184258667−1.694330.07652250.412752
DCAF41.4909644.6814616671.6507140.07652250.412752
CIB22.7681226671.05849−1.38690.07652250.412752
GREB1L4.8935383331.467920333−1.73710.07652250.412752
HSD11B1L4.3418611.467920333−1.564540.07652250.412752
RYR15.8635861.29245−2.181670.07652250.412752
SNTG13.0705251.389933667−1.143470.07652250.412752
ACKR46.2683661.429937−2.132140.07652250.412752
ZNF8354.5834973331.23396−1.893150.07652250.412752
TIFAB7.8488993331.102366−2.831890.07652250.412752
NCBP2L1.0613706675.6364112.4088480.07652250.412752
FKBP101.0409136672.4517513331.2359620.07652250.412752
DYTN2.3829883339.1309061.9379860.07652250.412752
MMP11.0208484.2387903332.0538850.07652250.412752
SEC14L21.5694656676.7967822.1145780.07652250.412752
MCEMP16.16586766727.794934332.1724460.07652250.412752
FICD1.104245.1525032.222220.07652250.412752
KIAA15491.0416963.8030681.8682290.07652250.412752
ADORA2B2.3134226677.6657953331.7284070.07652250.412752
ARMC21.5491276673.9282626671.3424350.07652250.412752
GRB141.04169618.487554334.1495480.07652250.412752
CEL1.02084810.009395333.2935150.07652250.412752
SRPK31.3254094.0963003331.6278840.07652250.412752
KCNMB11.71183233312.599577672.8797620.07652250.412752
DNAH21.7505273338.4834006672.2768530.07652250.412752
DGKI1.4169596679.6806916672.7723110.07652250.412752
SDCBP21.0625444.3382082.0295770.07652250.412752
WDSUB12.1466398.5311416671.9906590.07652250.412752
WASF13.33887715.1181322.1788450.07652250.412752
CRP1.4881133334.6493156671.6435340.07652250.412752
ZDHHC151.0833923.3728406671.6384090.07652250.412752
ACCSL1.6101424.9075806671.6078240.07652250.412752
LRRC691.3127196672.7346346671.0587890.07652250.412752
PLXNB33.98969517.176984332.1061260.07652250.412752
DGAT21.1016903334.8064476672.1252520.07652250.412752
PIGV4.7879951.479548−1.694270.07652250.381797
TNFAIP8L34.4637353331.525219−1.549240.07652250.381797
HSD11B1L4.3418611.159849333−1.904380.07652250.381797
ACPP2.2915596671.137013667−1.011080.07652250.381797
RYR15.8635861.296863−2.176760.07652250.381797
ACKR46.2683661.182685−2.406020.07652250.381797
ZNF6745.2655646671.119454333−2.233790.07652250.381797
SLC16A142.6387181.228356−1.103110.07652250.381797
NR6A11.2863956675.9171752.2015740.07652250.381797
ABCB91.2250253332.8647611.2256030.07652250.381797
GALNT181.2045683333.5846991.5733360.07652250.381797
PRRT21.1227417.4461282.7294650.07652250.381797
KHK1.7713753335.5772083331.6546730.07652250.381797
VSIG21.42709933311.465909673.0061930.07652250.381797
DYTN2.38298833313.682377672.5214750.07652250.381797
KIAA15491.0416965.5770243332.4205610.07652250.381797
NT5DC31.5837433336.6956422.0798840.07652250.381797
GRB141.04169619.047471674.1925930.07652250.381797
SPHAR1.8338606678.9117866672.2808310.07652250.381797
CLDN121.4378076674.5904906671.6747780.07652250.381797
DGKI1.4169596676.6464232.2297790.07652250.381797
TAF6L1.1423663334.5170173331.9833450.07652250.381797
LIPC2.4440023339.2153491.9147930.07652250.381797
PPP1R13L1.16678411.218008673.2652070.07652250.381797
PLXNB33.98969515.271079671.9364520.07652250.381797
NR6A11.2863956673.1916753331.3109790.07652250.209152
DKK11.2250253334.0890773331.7389640.07652250.209152
SCGB1C21.1431985.9877953332.388950.07652250.209152
GLIS31.1227414.2737331.9284720.07652250.209152
NCBP2L1.0613706672.9747843331.4868560.07652250.209152
FKBP101.0409136672.9019156671.4791550.07652250.209152
PPP1R14 C1.0625443.1902171.5861320.07652250.209152
GRTP12.9727928.0868716671.4437630.07652250.209152
VSIG21.4270993337.2699093332.348,8520.07652250.209152
DYTN2.3829883339.4019011.9801810.07652250.209152
MMP11.0208487.4866893332.874560.07652250.209152
SEC14L21.56946566713.379223333.0916490.07652250.209152
JSRP11.2847333.1927763331.3133430.07652250.209152
TRIM16L1.8948746678.5697712.1771540.07652250.209152
FKBP141.1667842.7457363331.2346560.07652250.209152
KIAA15491.0416965.5964692.4255830.07652250.209152
TMEM171.0813523332.7653063331.3546030.07652250.209152
SH3D211.0813523332.3184553331.1003270.07652250.209152
ADORA2B2.3134226676.5589393331.5034340.07652250.209152
GRB141.04169622.808972334.4525950.07652250.209152
KIAA1211 L1.104242.3014696671.0595020.07652250.209152
DYNC1I12.34231221.4850783.197330.07652250.209152
CEL1.0208484.8925376672.2608150.07652250.209152
SUCNR13.72530033339.453026673.4047070.07652250.209152
CRYM1.1627043336.5644603332.4971920.07652250.209152
SRPK31.3254097.2908922.4596580.07652250.209152
KCNMB11.71183233315.1673893.1473590.07652250.209152
SPHAR1.8338606675.4967611.5836980.07652250.209152
DNAH21.7505273339.2551946672.4024740.07652250.209152
DGKI1.4169596674.9663863331.8093980.07652250.209152
WASF13.33887733.653913.3333390.07652250.209152
PRSS501.04067611.528692673.4696360.07652250.209152
HBE12.09825533345.8056284.4482630.07652250.209152
PPP1R13L1.1667842.6094033331.1611820.07652250.209152
ACCSL1.61014212.291112.9323550.07652250.209152
PLXNB33.98969546.172920673.5326970.07652250.209152
HOOK21.4169596673.5730921.3343740.07652250.209152
DGAT21.1016903332.6422943331.2620720.07652250.209152
FAM229A1.0610143333.2192103331.6012630.07652250.209152
ADORA2B2.3134226677.6657953331.7284070.07652250.412752
ARMC21.5491276673.9282626671.3424350.07652250.412752
GRB141.04169618.487554334.1495480.07652250.412752
CEL1.02084810.009395333.2935150.07652250.412752
SRPK31.3254094.0963003331.6278840.07652250.412752
KCNMB11.71183233312.599577672.8797620.07652250.412752
DNAH21.7505273338.4834006672.2768530.07652250.412752
DGKI1.4169596679.6806916672.7723110.07652250.412752
SDCBP21.0625444.3382082.0295770.07652250.412752
WDSUB12.1466398.5311416671.9906590.07652250.412752
WASF13.33887715.1181322.1788450.07652250.412752
CRP1.4881133334.6493156671.6435340.07652250.412752
ZDHHC151.0833923.3728406671.6384090.07652250.412752
ACCSL1.6101424.9075806671.6078240.07652250.412752
LRRC691.3127196672.7346346671.0587890.07652250.412752
PLXNB33.98969517.176984332.1061260.07652250.412752
DGAT21.1016903334.8064476672.1252520.07652250.412752
PIGV4.7879951.479548−1.694270.07652250.381797
TNFAIP8L34.4637353331.525219−1.549240.07652250.381797

Significant DEGs between T0 and T3

GENEMean(T0)Mean(T3)logFCpValueFDR
WT15.0041916671−2.323140.06360260.209152
CLEC1A5.8427953331−2.546660.06360260.209152
APOC36.5555011−2.712710.06360260.209152
XG2.1062196671−1.074660.06360260.209152
EYA19.1779061−3.198170.06360260.209152
PRRT313.7992916671.925730.06360260.209152
P4HTM12.0074821.0053870.06360260.209152
OR2B6110.215558333.3526960.06360260.209152
CAPN813.6701591.8758430.06360260.209152
GLDC13.7488041.906430.06360260.209152
TCTEX1D113.9473381.980880.06360260.209152
RNF21512.5827033331.3688820.06360260.209152
DIRAS114.195382.0688010.06360260.209152
KCNQ412.1615213331.1120470.06360260.209152
DEPDC413.9007503331.9637520.06360260.209152
GRIK513.5911861.844460.06360260.209152
GFAP12.8117271.4914570.06360260.209152
SLC29A212.1963676671.135120.06360260.209152
NKAIN214.2926676672.1018740.06360260.209152
HTR2A14.0169813332.0061120.06360260.209152
WFDC113.8853096671.958030.06360260.209152
ZMYND1213.4712366671.795450.06360260.209152
PGA312.3354151.2236790.06360260.209152
KBTBD1212.2964446671.1994020.06360260.209152
ILDR2110.930743.4503190.06360260.209152
IFI27L112.5274661.3376920.06360260.209152
OPLAH13.0642736671.6155450.06360260.209152
IGSF2112.1888091.1301460.06360260.209152
KCNA213.1541143331.6572350.06360260.209152
TLR513.0608036671.6139110.06360260.209152
VIPR216.1126613332.6118010.06360260.209152
HIST1H1T14.3337626672.115620.06360260.209152
SLC4A1113.4059523331.7680580.06360260.209152
MAK12.6806923331.4226060.06360260.209152
CDHR1125.6779044.6824560.06360260.209152
PPP1R3214.5057286672.171760.06360260.209152
GCKR13.9548446671.9836210.06360260.209152
FBXO4012.4913683331.3169380.06360260.209152
KCNH86.5724593331.080832667−2.604290.07652250.209152
DZIP17.6294051.424371667−2.421240.07652250.209152
ARHGAP225.8227473331.086327667−2.422240.07652250.209152
NKX2-34.3420621.383955333−1.649580.07652250.209152
PLA2R13.7669036671.060624667−1.828460.07652250.209152
SLC2A49.0141203331.014388−3.151580.07652250.209152
OC4-APO3.8177541.071939667−1.83250.07652250.209152
APOC23.8177541.071939667−1.83250.07652250.209152
CRTAC16.8970756671.201323−2.521360.07652250.209152
CHRNA22.41152166713.602720672.495880.07652250.209152
LAYN6.2379661.028776−2.600150.07652250.209152
SMOC25.3911216671.083884667−2.314370.07652250.209152
FAM155A4.7723791.101041−2.115840.07652250.209152
IFNG3.5564833331.028776−1.789520.07652250.209152
VPS9D11.83873033311.014386332.5826070.07652250.209152
SLC35D31.5727913334.8198603331.6156640.07652250.209152
LRFN43.2093806671.115103333−1.525120.07652250.209152
ACKR46.2683661.101041−2.509220.07652250.209152
CYS11.388685.6067963332.0134620.07652250.209152
HSD11B1L4.3418611.159849333−1.904380.07652250.381797
ACPP2.2915596671.137013667−1.011080.07652250.381797
RYR15.8635861.296863−2.176760.07652250.381797
ACKR46.2683661.182685−2.406020.07652250.381797
ZNF6745.2655646671.119454333−2.233790.07652250.381797
SLC16A142.6387181.228356−1.103110.07652250.381797
NR6A11.2863956675.9171752.2015740.07652250.381797
ABCB91.2250253332.8647611.2256030.07652250.381797
GALNT181.2045683333.5846991.5733360.07652250.381797
PRRT21.1227417.4461282.7294650.07652250.381797
KHK1.7713753335.5772083331.6546730.07652250.381797
VSIG21.42709933311.465909673.0061930.07652250.381797
DYTN2.38298833313.682377672.5214750.07652250.381797
KIAA15491.0416965.5770243332.4205610.07652250.381797
NT5DC31.5837433336.6956422.0798840.07652250.381797
GRB141.04169619.047471674.1925930.07652250.381797
SPHAR1.8338606678.9117866672.2808310.07652250.381797
CLDN121.4378076674.5904906671.6747780.07652250.381797
DGKI1.4169596676.6464232.2297790.07652250.381797
TAF6L1.1423663334.5170173331.9833450.07652250.381797
LIPC2.4440023339.2153491.9147930.07652250.381797
PPP1R13L1.16678411.218008673.2652070.07652250.381797
PLXNB33.98969515.271079671.9364520.07652250.381797
  46 in total

1.  Exosomes released from macrophages infected with intracellular pathogens stimulate a proinflammatory response in vitro and in vivo.

Authors:  Sanchita Bhatnagar; Kazuhiko Shinagawa; Francis J Castellino; Jeffrey S Schorey
Journal:  Blood       Date:  2007-07-31       Impact factor: 22.113

2.  Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis.

Authors:  Yan Li; Qiupeng Zheng; Chunyang Bao; Shuyi Li; Weijie Guo; Jiang Zhao; Di Chen; Jianren Gu; Xianghuo He; Shenglin Huang
Journal:  Cell Res       Date:  2015-07-03       Impact factor: 25.617

3.  NK Cell-derived Exosomes From NK Cells Previously Exposed to Neuroblastoma Cells Augment the Antitumor Activity of Cytokine-activated NK Cells.

Authors:  Alireza Shoae-Hassani; Amir Ali Hamidieh; Maryam Behfar; Rashin Mohseni; Seyed A Mortazavi-Tabatabaei; Shahab Asgharzadeh
Journal:  J Immunother       Date:  2017-09       Impact factor: 4.456

Review 4.  Exosomes in cancer therapy: a novel experimental strategy.

Authors:  Dong Gao; Lingling Jiang
Journal:  Am J Cancer Res       Date:  2018-11-01       Impact factor: 6.166

5.  Immune surveillance properties of human NK cell-derived exosomes.

Authors:  Luana Lugini; Serena Cecchetti; Veronica Huber; Francesca Luciani; Gianfranco Macchia; Francesca Spadaro; Luisa Paris; Laura Abalsamo; Marisa Colone; Agnese Molinari; Franca Podo; Licia Rivoltini; Carlo Ramoni; Stefano Fais
Journal:  J Immunol       Date:  2012-08-17       Impact factor: 5.422

6.  B cell-derived exosomes can present allergen peptides and activate allergen-specific T cells to proliferate and produce TH2-like cytokines.

Authors:  Charlotte Admyre; Barbara Bohle; Sara M Johansson; Margarete Focke-Tejkl; Rudolf Valenta; Annika Scheynius; Susanne Gabrielsson
Journal:  J Allergy Clin Immunol       Date:  2007-09-14       Impact factor: 10.793

7.  Activated T cell exosomes promote tumor invasion via Fas signaling pathway.

Authors:  Zhijian Cai; Fei Yang; Lei Yu; Zhou Yu; Lingling Jiang; Qingqing Wang; Yunshan Yang; Lie Wang; Xuetao Cao; Jianli Wang
Journal:  J Immunol       Date:  2012-05-09       Impact factor: 5.422

8.  Transcriptome and long noncoding RNA sequencing of three extracellular vesicle subtypes released from the human colon cancer LIM1863 cell line.

Authors:  Maoshan Chen; Rong Xu; Hong Ji; David W Greening; Alin Rai; Keiichi Izumikawa; Hideaki Ishikawa; Nobuhiro Takahashi; Richard J Simpson
Journal:  Sci Rep       Date:  2016-12-05       Impact factor: 4.379

9.  Investigation of Insulin-Like Growth Factor-1 (IGF-1), P53, and Wilms' Tumor 1 (WT1) Expression Levels in the Colon Polyp Subtypes in Colon Cancer.

Authors:  Ali Aslan; Havva Erdem; Muruvvet Akcay Celik; Arzu Sahin; Soner Cankaya
Journal:  Med Sci Monit       Date:  2019-07-25

Review 10.  Circulating Extracellular Vesicle MicroRNA as Diagnostic Biomarkers in Early Colorectal Cancer-A Review.

Authors:  Brendan J Desmond; Elizabeth R Dennett; Kirsty M Danielson
Journal:  Cancers (Basel)       Date:  2019-12-23       Impact factor: 6.639
View more
  3 in total

1.  MicroRNA-301b-3p facilitates cell proliferation and migration in colorectal cancer by targeting HOXB1.

Authors:  Jianyong Xiong; Lijuan Zhang; Ren Tang; Zhengming Zhu
Journal:  Bioengineered       Date:  2021-12       Impact factor: 3.269

2.  LncRNA SNHG12 in extracellular vesicles derived from carcinoma-associated fibroblasts promotes cisplatin resistance in non-small cell lung cancer cells.

Authors:  Deli Tan; Gang Li; Peng Zhang; Chao Peng; Bo He
Journal:  Bioengineered       Date:  2022-01       Impact factor: 3.269

3.  Long non-coding RNA COL4A2-AS1 facilitates cell proliferation and glycolysis of colorectal cancer cells via miR-20b-5p/hypoxia inducible factor 1 alpha subunit axis.

Authors:  Zijun Yu; Yeming Wang; Jianwu Deng; Dong Liu; Lingling Zhang; Hua Shao; Zilu Wang; Wenjun Zhu; Cheng Zhao; Qungang Ke
Journal:  Bioengineered       Date:  2021-12       Impact factor: 3.269
  3 in total

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