The identification of hub biomarkers and pathways in lung cancer and prognostic evaluation.

Background: Lung cancer is the most frequently diagnosed malignant tumor and the highest mortality worldwide, and can be divided into two differential histologic subtypes, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). However, there are significant differences in diagnosis and prognosis between NSCLC and SCLC. We aimed to identify hub differentially expressed genes (DEGs) and pathways for diagnostic and prognostic prediction in NSCLC and SCLC.
Methods: Three expression profiles (GSE43346, GSE40275 and GSE18842) were obtained through GEO2R tools from Gene Expression Omnibus (GEO) database. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to investigate functional enrichment of the DEGs. The protein-protein interaction network was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Kaplan-Meier analysis was performed using Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA).
Results: We have identified 84 overlap DEGs that may play an important role in SCLC & NSCLC. However, we also found some genes were only significantly differential expressed in SCLC or NSCLC. There were 87 DEGs unique to SCLC tissues and 28 DEGs unique to NSCLC ones. Functional analysis results indicated that these DEGs had different biological functions and were significantly enriched in different pathways. Hub DEGs were identified via protein-protein interaction network and cross-validated using Kaplan-Meier plotter and GEPIA. The 14 hub DEGs were highly correlated with the overall survival of NSCLC. Kyoto Encyclopedia of Genes and Genome (KEGG) re-analysis of 14 hub DEGs showed that RRM2, CHEK1 and SERPINB5 enriched in the p53 signaling pathway, RRM2 and TYMS enriched in pyrimidine metabolism pathway maybe play a key role in SCLC&NSCLC and were significantly related to overall survival in patients with NSCLC. Conclusions: RRM2, CHEK1, TYMS and SERPINB5, which are mainly enriched in the p53 signaling pathway and pyrimidine metabolism pathway, were significantly associated with the overall survival of NSCLC patients. These genes could serve as potential prognostic markers in NSCLC and therapeutic target in lung cancer for personalized oncology. 2022 Translational Cancer Research. All rights reserved.

Introduction

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer death in 2020, which account for approximately one-tenth cancers diagnosed and one in 5 deaths. There are estimated 2.2 million new cancer cases and 1.8 million deaths in the world (1). Lung cancer can be divided into two histologic subtypes, including non-small cell lung cancer (NSCLC, about 85% of all lung cancer) and small cell lung cancer (SCLC, about 15%). NSCLC is made up of three major histologic subtypes: large-cell lung, two major pathological types adenocarcinoma (LUAD) and lung squamous-cell carcinoma (LUSC) (2). The treatments of lung cancer mainly included surgery, chemotherapy, radiotherapy and immunotherapy. As we know, there are different treatment options and prognosis for these subtypes. Therapeutic advances contributed to survival gains. With the progress on targeted therapies and immunotherapies, the 2-years survival rate for NSCLC increased from 34% during 2009–2010 to 42% during 2015–2016 in the United States, but SCLC survival remained low and steady at 14% to 15% (3-5). There are significant differences in survival between NSCLC and SCLC. Therefore, it is important to assess the difference of lung cancer to detect prognostic markers which are likely to affect future treatments and prognosis.

Gene expression profile array and bioinformatics analysis have been applied to study potential clinical biomarkers and molecular mechanisms. Some key differentially expressed genes (DEGs) have been identified by integrated bioinformatics analysis and are significantly associated with the treatments and prognosis in some cancers (6-11). Relevant biomarkers have been used as valuable tools in the prognosis and prediction of therapy response, significantly influencing the clinical course and outcome of the disease (12). In our study, we selected gene expression profile from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) and analyzed the differential expression genes between differential lung cancer tissues and normal tissues to explore the hub pathways and key genes. We applied integrated bioinformatics methods to further investigate potential gene biomarkers and molecular mechanisms in lung cancer. Novel prognostic biomarkers will further inform clinical therapeutic decision-making. We present the following article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-245/rc).

Methods

Microarray data and identification of DEGs

The gene expression profiles of lung cancer (GSE43346, GSE40275 and GSE18842) were downloaded from Gene Expression Omnibus public database (13-16). The above three gene expression profiles were performed by the Affymetrix Human Gene Expression Array. GSE43346 contains 42 normal tissue samples and 23 SCLC samples. GSE40275 includes 43 normal tissue samples, 19 SCLC samples and 16 NSCLC samples. GSE18842 includes 45 normal tissue samples and 46 NSCLC samples.

GEO2R online tool and Venn diagram software were applied to screen overlap DEGs from above three gene expression profiles. |logFC| >2 and adjusted P value <0.05 were used as cutoff criteria by GEO2R online tools. The logFC <−2 or logFC >2 were considered down-regulated or up-regulated genes, respectively.

Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) functional enrichment analysis

The GO and KEGG were performed using Database for Annotation, Visualization and Integrated Discovery v6.8 (DAVID, https://david.ncifcrf.gov/) (17,18). The DAVID v6.8, an online set of functional annotation tools, was used to analyze biological process, cellular component, molecular function and pathways for DEGs. GO terms and KEGG pathways with P value <0.05 were considered statistical significant.

Protein-protein interaction network construction and module analysis

Protein-protein interaction network was obtained through the Search Tool for the Retrieval of Interacting Genes database (STRING, http://string-db.org) (19). The plugin MCODE of Cytoscape was applied to detect significant modules in the protein-protein interaction network (https://cytoscape.org/). The cutoff criteria were set with degree cutoff =2, node score cutoff =0.2, maximum depth =100, and k-core =2 (20). The interactions of module DEGs in the PPI networks were analysed using plugin cytoHubba (21). The specific connectivity genes that overlapped in the NSCLC, SCLC or NSCLC & SCLC PPI networks, are defined as hub DEGs.

Validation of hub DEGs and survival analysis

The online Kaplan-Meier plotter database (https://kmplot.com/analysis/) and The GEPIA server (http://gepia.cancer-pku.cn/) were applied to assess the survival rate of patients with LUAD or LUSC (22,23). The Kaplan-Meier plotter database, which the tool of a meta-analysis-based discovery and validation of survival biomarkers, includes 54,000 genes on survival in 21 cancer types including 3,452 lung cancer patients. The criteria we selected were HR with 95% CI and log-rank P<0.05 as a threshold.

The GEPIA is an interactive web serve of analyzing tumor/normal differential expression analysis, correlation analysis and patient survival analysis. The GEPIA was used to get stage plots and further validate the expression of hub DEGs between LUAD, LUSC and normal lung tissues (P<0.05).

Ethical consideration

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Results

Differentially expressed genes in SCLC and NSCLC

We extracted 1,945 and 1,137 DEGs using the online GEO2R tool between SCLC and normal lung tissues from GSE43346 and GSE40275. The 1,014 and 1,367 DEGs were extracted from GSE18842 and GSE40275 in NSCLC tissues compared with normal tissues. We identified 84 overlap DEGs in SCLC & NSCLC tissues by online Venn diagram software, including 54 up-regulated and 30 down-regulated DEGs. The 70 up-regulated and 17 down-regulated DEGs were identified unique to SCLC. The 20 up-regulated and 8 down-regulated DEGs were identified unique to NSCLC ( and ).

Table 1

All overlap differentially expressed genes in SCLC & NSCLC, unique to SCLC or NSCLC tissues compared with normal tissues

Cancer	DEGs	No.	Genes name
SCLC & NSCLC	Up-regulated	54	TPX2, CCNB1, HMGB3, DSP, GINS1, ANLN, UCHL1, EZH2, CHEK1, KIF11, CDC6, AURKA, KIF14, KIF4A, TYMS, CDCA7, MELK, NDC80, RFC4, CCNA2, BUB1, PBK, NUF2, PRR11, PTTG1, MMP12, UBE2T, ECT2, KIF23, DEPDC1, GGH, ASPM, ATAD2, BRIP1, UBE2C, CCNB2, PRC1, CEP55, RRM2, TOP2A, HELLS, CCNE2, BUB1B, RAD51AP1, MKI67, DTL, EXO1, KIF20A, KIAA0101, TTK, CDKN3, NCAPG, CENPF, NUSAP1
	Down-regulated	30	CHRDL1, EGR1, FHL1, AOC3, ZFP36, ZBTB16, EDNRB, ARRB1, SH2D3C, TNS1, C7, GPM6A, GADD45B, MFAP4, PTGDS, SDPR, NR4A1, FOS, TPSB2, AQP1, ADH1B, FABP4, FAM107A, PGM5, GPX3, FXYD1, FOSB, VIPR1, CFD, FBLN5
Only SCLC	Up-regulated	70	DONSON, DEPDC1B, RAB3IP, PCSK1, RFC3, BRCA1, ACTL6A, ISL1, PLK1, NUP62CL, MYEF2, ASF1B, CDH2, KIFC1, CBFA2T2, ZNF711, TMPO, DCX, RNF182, FANCA, GDAP1, PCNA, TPH1, SLC36A4, LOC643201, GNG4, H2AFY2, CCDC14, MIAT, STMN1, BEST3, TPD52, INTS7, NOL4, STXBP5L, TUBB2B, CEP78, GPR137C, PGAP1, HOXD10, NELL1, RAB3B, PMAIP1, DDC, MSH2, RRM1, SCG3, ESCO2, KIF1A, CBX3, MEST, MPHOSPH9, NRCAM, CDKN2C, GRP, RIMS2, MCM6, AGPAT5, CENPI, SCN8A, FZD3, GMNN, SSX2IP, SLCO5A1, ASCL1, CDKN2A, RFC5, USP1, LOC81691, ST18
	Down-regulated	17	LAMA2, HLA-DRB4, DPP4, MAOA, C3, RBPMS, ADAMTS1, AQP3, CD74, SYNE1, ANPEP, CCL21, RNASE1, MYL9, SNTB1, KIAA1462, DCN
Only NSCLC	Up-regulated	20	SERPINB5, AKR1B10, GJB6, ARNTL2, SLC7A11, SPRR1A, PLOD2, KRT6A, GCLC, FAP, FAM83B, NQO1, PSAT1, S100A2, SULF1, CLDN1, GPR87, CP, STEAP1, RPL39L
	Down-regulated	8	GRK5, KLF2, ADARB1, CLDN5, DENND3, CCBE1, MFNG, SELENBP1

SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; DEGs, differentially expressed genes.

Figure 1

Venn diagrams of all screened differentially expressed genes identified from three gene expression profiles in SCLC & NSCLC, unique to SCLC or NSCLC. There are 54 up-regulated and 30 down-regulated DEGs in SCLC & NSCLC, 70 up-regulated and 17 down-regulated DEGs unique to SCLC, 20 up-regulated and 8 down-regulated DEGs unique to NSCLC were identified via online Venn diagram software, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; DEGs, differentially expressed genes.

Functional and pathway enrichment analysis

The online DAVID was utilized for GO and KEGG enrichment analysis of overlap DEGs in lung cancer. The GO analysis includes biological processes, cellular components, and molecular functions. For biological process analysis, 54 up-regulated and 30 down-regulated DEGs in SCLC & NSCLC were mainly related to mitotic nuclear division, cell division, mitotic cytokinesis, regulation of cell cycle, DNA replication and mitotic spindle organization, while the DEGs unique to SCLC were mainly involved in DNA damage response, detection of DNA damage, DNA replication, neuron migration, error-prone translesion synthesis, error-free translesion synthesis and negative regulation of cyclin-dependent protein serine/threonine kinase activity. The biological process analysis showed DEGs unique to NSCLC were associated with oxidation-reduction process, morphogenesis of an epithelium, aging, calcium-independent cell-cell adhesion via plasma membrane cell-adhesion molecules, positive regulation of vascular endothelial growth factor production and endothelial cell migration. Furthermore, cellular component analysis indicated that overlap DEGs in SCLC & NSCLC were located in the nucleus, nucleoplasm, midbody, spindle, cytoplasm and cytosol, while DEGs unique to SCLC in DNA replication factor C complex, chromatin, nuclear envelope, nuclear chromosome, telomeric region, transport vesicle membrane and nucleoplasm, and DEGs unique to NSCLC in extracellular space and extracellular exosome. Additionally, the results of molecular function analysis indicated that overlap DEGs in lung cancer were particularly enriched in protein binding, ATP binding, protein kinase binding, microtubule binding, chromatin binding and microtubule motor activity, while DEGs unique to SCLC in dinucleotide insertion or deletion binding, enzyme binding, mutLalpha complex binding, damaged DNA binding, DNA clamp loader activity and single-stranded DNA-dependent ATPase activity, and DEGs unique to NSCLC in structural molecule activity ().

Table 2

Gene ontology analysis of differentially expressed genes in SCLC & NSCLC, unique to SCLC or NSCLC

Cancer	Category	Term	Count	%	P value	FDR
SCLC & NSCLC	GOTERM_BP_DIRECT	GO:0007067~mitotic nuclear division	17	20.24	5.53E-14	2.09E-11
	GOTERM_BP_DIRECT	GO:0051301~cell division	19	22.62	6.21E-14	2.09E-11
	GOTERM_BP_DIRECT	GO:0000281~mitotic cytokinesis	6	7.14	2.66E-07	5.97E-05
	GOTERM_BP_DIRECT	GO:0051726~regulation of cell cycle	8	9.52	2.43E-06	4.09E-04
	GOTERM_BP_DIRECT	GO:0006260~DNA replication	8	9.52	1.06E-05	0.001430782
	GOTERM_BP_DIRECT	GO:0007052~mitotic spindle organization	5	5.95	1.31E-05	0.001476073
	GOTERM_CC_DIRECT	GO0005634~nucleus	54	64.29	1.21E-10	1.78E-08
	GOTERM_CC_DIRECT	GO0005654~nucleoplasm	36	42.86	3.51E-09	2.58E-07
	GOTERM_CC_DIRECT	GO0030496~midbody	10	11.90	7.12E-09	3.49E-07
	GOTERM_CC_DIRECT	GO0005819~spindle	9	10.71	7.78E-08	2.86E-06
	GOTERM_CC_DIRECT	GO0005737~cytoplasm	45	53.57	2.61E-06	7.67E-05
	GOTERM_CC_DIRECT	GO0005829~cytosol	34	40.48	3.40E-06	8.33E-05
	GOTERM_MF_DIRECT	GO:0005515~protein binding	64	76.19	1.20E-07	2.35E-05
	GOTERM_MF_DIRECT	GO:0005524~ATP binding	22	26.19	2.67E-06	2.61E-04
	GOTERM_MF_DIRECT	GO:0019901~protein kinase binding	10	11.90	5.77E-05	0.003748425
	GOTERM_MF_DIRECT	GO:0008017~microtubule binding	7	8.33	4.00E-04	0.016613263
	GOTERM_MF_DIRECT	GO:0003682~chromatin binding	9	10.71	4.39E-04	0.016613263
	GOTERM_MF_DIRECT	GO:0003777~microtubule motor activity	5	5.95	5.11E-04	0.016613263
Only SCLC	GOTERM_BP_DIRECT	GO:0042769~DNA damage response, detection of DNA damage	4	4.60	6.88E-04	0.34328918
	GOTERM_BP_DIRECT	GO:0006260~DNA replication	6	6.90	9.15E-04	0.34328918
	GOTERM_BP_DIRECT	GO:0001764~neuron migration	5	5.75	0.001660073	0.415018297
	GOTERM_BP_DIRECT	GO:0042276~error-prone translesion synthesis	3	3.45	0.003726164	0.558924545
	GOTERM_BP_DIRECT	GO:0070987~error-free translesion synthesis	3	3.45	0.003726164	0.558924545
	GOTERM_BP_DIRECT	GO:0045736~negative regulation of cyclin-dependent protein serine/threonine kinase activity	3	3.45	0.004986668	0.621663134
	GOTERM_CC_DIRECT	GO:0005663~DNA replication factor C complex	3	3.45	3.11E-04	0.046840782
	GOTERM_CC_DIRECT	GO:0000785~chromatin	5	5.75	7.61E-04	0.046840782
	GOTERM_CC_DIRECT	GO:0005635~nuclear envelope	6	6.90	8.41E-04	0.046840782
	GOTERM_CC_DIRECT	GO:0000784~nuclear chromosome, telomeric region	5	5.75	0.003071132	0.12821977
	GOTERM_CC_DIRECT	GO:0030658~transport vesicle membrane	3	3.45	0.013254179	0.376099937
	GOTERM_CC_DIRECT	GO:0005654~nucleoplasm	22	25.29	0.013512573	0.376099937
	GOTERM_MF_DIRECT	GO:0019899~enzyme binding	6	6.90	0.019926207	1
	GOTERM_MF_DIRECT	GO:0032405~MutLalpha complex binding	2	2.30	0.0277565	1
	GOTERM_MF_DIRECT	GO:0003684~damaged DNA binding	3	3.45	0.035139311	1
	GOTERM_MF_DIRECT	GO:0003689~DNA clamp loader activity	2	2.30	0.036838541	1
	GOTERM_MF_DIRECT	GO:0043142~single-stranded DNA-dependent ATPase activity	2	2.30	0.045836807	1
Only NSCLC	GOTERM_BP_DIRECT	GO:0055114~oxidation-reduction process	5	17.86	0.01408762	1
	GOTERM_BP_DIRECT	GO:0002009~morphogenesis of an epithelium	2	7.14	0.022285495	1
	GOTERM_BP_DIRECT	GO:0007568~aging	3	10.71	0.028682612	1
	GOTERM_BP_DIRECT	GO:0016338~calcium-independent cell-cell adhesion via plasma membrane cell-adhesion molecules	2	7.14	0.033248128	1
	GOTERM_BP_DIRECT	GO:0010575~positive regulation of vascular endothelial growth factor production	2	7.14	0.042550374	1
	GOTERM_BP_DIRECT	GO:0043542~endothelial cell migration	2	7.14	0.045631924	1
	GOTERM_CC_DIRECT	GO:0005615~extracellular space	7	25.00	0.010282349	0.575811533
	GOTERM_CC_DIRECT	GO:0070062~extracellular exosome	9	32.14	0.037100315	1
	GOTERM_MF_DIRECT	GO:0005198~structural molecule activity	4	14.29	0.006274785	0.489433259

SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; FDR, false discovery rate.

The top 6 KEGG analysis results using DAVID software are shown in . The overlap DEGs in SCLC&NSCLC are mainly associated with cell cycle, the p53 signaling pathway, oocyte meiosis, progesterone-mediated oocyte maturation and HTLV-I infection pathways, and DEGs unique to SCLC enriched in mismatch repair, DNA replication, cell cycle, tryptophan metabolism, serotonergic synapse and nucleotide excision repair pathways. The DEGs unique to NSCLC were not enriched in any signaling pathway (all pathways P value >0.05).

Table 3

KEGG analysis of differentially expressed genes in SCLC & NSCLC, unique to SCLC or NSCLC

Cancer	Category	Term	Count	%	P value	FDR
SCLC & NSCLC	KEGG_PATHWAY	hsa04110: cell cycle	11	13.10	1.76E-09	1.46E-07
	KEGG_PATHWAY	hsa04115: p53 signaling pathway	6	7.14	4.31E-05	0.001786741
	KEGG_PATHWAY	hsa04114: oocyte meiosis	6	7.14	4.71E-04	0.013041937
	KEGG_PATHWAY	hsa04914: progesterone-mediated oocyte maturation	4	4.76	0.014735684	0.277141512
	KEGG_PATHWAY	hsa05166: HTLV-I infection	6	7.14	0.016695272	0.277141512
Only SCLC	KEGG_PATHWAY	hsa03430: mismatch repair	4	4.60	2.76E-04	0.029521615
	KEGG_PATHWAY	hsa03030: DNA replication	4	4.60	0.001057086	0.056554103
	KEGG_PATHWAY	hsa04110: cell cycle	5	5.75	0.00508098	0.181221637
	KEGG_PATHWAY	hsa00380: tryptophan metabolism	3	3.45	0.021332245	0.514320928
	KEGG_PATHWAY	hsa04726: serotonergic synapse	4	4.60	0.024529657	0.514320928
	KEGG_PATHWAY	hsa03420: nucleotide excision repair	3	3.45	0.028840426	0.514320928
Only NSCLC		None

KEGG, Kyoto Encyclopedia of Genes and Genome; SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; FDR, false discovery rate.

Protein-protein interaction network and module analysis

The overlap DEGs in SCLC&NSCLC, unique to SCLC or NSCLC were used to construct the protein-protein interaction network using STRING and Cytoscape, respectively. A total of 84 DEGs in SCLC&NSCLC were imported into online STRING, which contained 84 nodes and 1,122 edges. The 2 important modules were identified using Cytoscape MCODE, which contains 47 hub genes and 5 hub genes, respectively (). The 7 of 12 topological analysis methods using cytoHubba have identified 100% (47/47) hub DEGs that we have screened using plugin MCODE in the SCLC & NSCLC PPI networks. The remaining 5 methods have identified at least 25 of the 47 hub DEGs (Table S1).

Figure 2

DEGs Protein-protein interaction network analysis. (A) DEGs protein-protein interaction network complex in SCLC & NSCLC, which contained 84 nodes and 1,122 edges. (B) Module 1 and (C) Module 2 identified by Cytoscape MCODE plugin in SCLC & NSCLC. (D) DEGs protein-protein interaction network complex unique to SCLC, which contained 84 nodes and 123 edges. (E) Module 1 and (F) Module 2 unique to SCLC. (G) DEGs protein-protein interaction network complex including 28 nodes and 13 edges unique to NSCLC. Two modules unique to NSCLC (H,I). The nodes represent proteins, and the edges represent protein interactions. DEGs, differentially expressed genes; SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

The 87 DEGs unique to SCLC were analyzed using STRING. The results of protein-protein interaction network showed that a total of 84 nodes and 123 edges were acquired. We applied Cytotype MCODE for further analysis to get two hub modules including 11 hub nodes and 3 hub nodes. The 7 of 12 methods using cytoHubba have identified at least 8 of the 11 hub DEGs we have screened using plugin MCODE unique to SCLC PPI networks (Table S2). We also imported 28 DEGs unique to NSCLC into online STRING for protein-protein interaction network. The 28 nodes and 13 edges were included in protein-protein interaction network. Two modules were obtained using Cytotype MCODE. Every module contained 4 hub nodes (). The 11 of 12 methods using cytoHubba also have identified at least 7 of the 8 hub DEGs unique to NSCLC PPI networks (Table S3).

Survival analysis and cross-validation of hub DEGs

We used the Kaplan-Meier plotter database and GEPIA to further analyze prognosis value of hub DEGs in lung cancer. As we know, 85% of lung cancer patients were NSCLC, which mainly contains LUAD and LUSC. There was survival information of unique to NSLSC patients in above two databases. There are 1,925 NSCLC patients for survival analysis using Kaplan-Meier plotter database. We conducted cross-validation survival analysis of 52 overlap hub DEGs in SCLC & NSCLC and 8 hub DEGs unique to NSCLC associated with NSCLC patients. The results demonstrated that 12 overlap hub DEGs in SCLC & NSCLC and 2 hub DEGs unique to NSCLC were significantly associated with the overall survival of NSCLC patients (P<0.05, , ).

Table 4

Survival analysis of hub DEGs using Kaplan-Meier plotter database and GEPIA

Cancer	Genes
SCLC & NSCLC	ANLN, CHEK1, DTL, ECT2, KIF11, MKI67, NCAPG, PRC1, PTTG1, RRM2, TYMS, KIF14
Only NSCLC	KRT6A, SERPINB5

The overlap 12 hub DEGs in SCLC & NSCLC and 2 hub DEGs unique to in NSCLC were significantly associated with overall survival of NSCLC patients (P<0.05). DEGs, differentially expressed genes; GEPIA, Gene Expression Profiling Interactive Analysis; SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Figure 3

Overall survival analysis of 14 hub DEGs in NSCLC using Kaplan-Meier plotter database. K6C meant KRT6A; CAP-G meant NCAPG; ASE1 meant PRC1. DEGs, differentially expressed genes; NSCLC, non-small cell lung cancer.

Figure 4

Overall survival analysis of 14 hub DEGs in NSCLC via GEPIA website. DEGs, differentially expressed genes; NSCLC, non-small cell lung cancer; GEPIA, Gene Expression Profiling Interactive Analysis.

The online GEPIA software was used to validate the expression of 14 hub DEGs in NSCLC tissues compared with normal lung tissues. A total of 11 of 14 hub DEGs were also overexpressed in LAUD and LUSC (P<0.05), but the other 3 hub DEGs KIF14, KRT6A and SERPINB5 were only significantly different in LUSC not in LAUD (P>0.05) ().

Figure 5

Validation of 14 hub DEGs expression by GEPIA website in LAUD and LUSC tissues compared with normal tissues. The red box indicates tumor samples, and the gray box indicates normal samples. *P<0.05. DEGs, differentially expressed genes; GEPIA, Gene Expression Profiling Interactive Analysis; LUAD, lung adenocarcinoma; LUSC, lung squamous-cell carcinoma.

Pathway enrichment re-analysis and stage analysis of hub DEGs

The 14 hub DEGs were re-analyzed to identify the more important pathways using DAVID software. The results showed that the p53 signaling pathway and pyrimidine metabolism pathway were significantly associated with the survival of NSCLC patients (P<0.05). The RRM2, CHEK1 and SERPINB5 enriched in the p53 signaling pathway, RRM2 and TYMS enriched in pyrimidine metabolism pathway maybe play a key role in lung cancer. We used the GEPIA to validate the expression of these 4 hub DEGs in different stages of NSCLC. Statistical analysis identified the expression of RRM2, CHEK1, TYMS and SERPINB5 were significant differential across different stages ( and ).

Table 5

Re-analysis of 14 hub DEGs via KEGG pathway enrichment

Pathway ID	Name	Count	%	P value	Genes
hsa04115	p53 signaling pathway	3	21.43	5.54E-04	RRM2, CHEK1, SERPINB5
hsa00240	Pyrimidine metabolism pathway	2	14.29	0.057460904	RRM2, TYMS
hsa04110	Cell cycle pathway	2	14.29	0.070192126	PTTG1, CHEK1

DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genome.

Figure 6

Pathological stage plot of hub DEGs in NSCLC. RRM2, CHEK1, TYMS and SERPINB5 showed significant differential across different stages. DEGs, differentially expressed genes; NSCLC, non-small cell lung cancer.

Discussion

According to WHO criteria for lung tumors classification and diagnosis, lung cancer is generally divided into two histologic subtypes SCLC and NSCLC, and NSCLC is the main histological subtype of lung cancer. In order to identify the genetic differences between SCLC and NSCLC, we separately extracted DEGs in SCLC or NSCLC through the GEO database. Our study suggested hub DEGs play an important role not only in SCLC but also in NSCLC. We also found that some genes were only significantly differential expressed in SCLC or NSCLC. These results revealed that there are consistent differences and similarities between SCLC and NSCLC.

We further analyzed gene functional enrichment and interaction of DEGs using online DAVID, STRING database and Cytoscape MCODE. The overlap 84 DEGs found in both SCLC & NSCLC were mainly related to cell cycle, the p53 signaling pathway, oocyte meiosis, progesterone-mediated oocyte maturation and HTLV-I infection pathways. The DEGs unique to SCLC were enriched in mismatch repair, DNA replication, cell cycle, tryptophan metabolism, serotonergic synapse and nucleotide excision repair pathways. The DEGs unique to NSCLC were not associated with anyone pathway. Due to the lack of prognosis information of SCLC patients, we only carried out survival analysis of hub DEGs related to NSCLC patients. The results showed 14 hub DEGs were significantly associated with the overall survival of LAUD and LUSC patients. KEGG pathway enrichment re-analysis and stage analysis revealed that RRM2, CHEK1, TYMS and SERPINB5 maybe new effective biomarkers in NSCLC prognosis, which were enriched in the p53 signaling pathway and pyrimidine metabolism pathway.

The ribonucleotide reductase regulatory subunit M2 (RRM2), one of two non-identical subunits for ribonucleotide reductase, catalyzes ribonucleotides to form deoxyribonucleotides. Transcription of RRM2 results in two isoforms that differ in the lengths of their N-termini. RRM2 maintains to support DNA synthesis and repair and overexpressed in colorectal cancer, breast cancer, and cervical cancer (24-26). The expression of RRM2 in primary oral squamous cell carcinoma (OSCC) was significantly increased compared with normal tissues, and its overexpression was significantly associated with pathological grade, proliferation and migration, and recurrence in OSCC (27). High expression of RRM2 was associated with an immunosuppressive tumor-immune microenvironment and contributed to immune escape in prostate cancer (28). Studies have demonstrated that overexpressed miR-20a dramatically suppresses NSCLC cell proliferation and migration by inhibiting RRM2-mediated PI3K/Akt signaling, while the expression of RRM2 was upregulated in NSCLC (29). The RRM2 overexpression, as an independent predictive factor of poor prognosis in patients with lung adenocarcinoma, was significantly associated with tumor stage and TNM classification and reduced the activation of p53 signaling pathway (30).

The protein encoded by checkpoint kinase 1 (CHEK1), belongs to the Ser/Thr protein kinase family, and mediates cell cycle arrest in response to DNA damage or the presence of unreplicated DNA. This protein also integrates signals from ATM and ATR that are associated with chromatin in meiotic prophase I. CHEK1 promotes the phosphorylation of CDC25A protein phosphatase to delay cell cycle progression in response to double-stranded DNA breaks. High expression of CHEK1 was associated with poor clinical characteristics of multiple myeloma patients (31). CHEK1 inhibitors have been shown to potentiate in combination with chemotherapy and their single-agent antitumor, in particular gemcitabine (32-35). Studies indicated the therapeutic effects of CHEK1 inhibition are related to p53-deficiency (36).

Thymidylate synthase (TYMS) catalyzes the methylation of deoxyuridylate to deoxythymidylate. The function of TYMS is to maintain the dTMP (thymidine-5-prime monophosphate) pool critical for DNA replication and repair. TYMS has been a target for cancer chemotherapeutic agents, like 5-fluoro-2-prime-deoxyuridine, 5-fluorouracil, and some folate analogs. Some studies indicated that TYMS variants were associated with high-dose methotrexate in childhood acute lymphoblastic leukemia, severe hand-foot-syndrome, the risk of persistence of pre-neoplastic cervical lesions and the risk of head and neck cancer (37-40). TYMS levels associated with prognosis and chemotherapy response drives the phenotypes of epithelial-tomesenchymal transition in NSCLC. The results established TYMS as a theranostic NSCLC marker related with survival, chemo-resistance and epithelial-to-mesenchymal transition (41,42).

The serpin family B member 5 (SERPINB5), located on chromosome 18q21.33 as a tumor suppressor gene, plays a critical in cancer cell invasion and metastasis. SERPINB5 variants are significantly associated with gallbladder cancer risk (43). Upregulated Maspin inhibited the expression of IKKα to promote cell apoptosis and delayed the development of the precancerous lesions in precancerous rats (44). The high expression of SERPINB5 significantly increased the recurrence rate and shortened disease-free survival in patients with oral squamous cell carcinoma (45). Cytoplasmic immunoreactive scores results showed that the expression of SERPINB5 was significantly higher in cervical cancer patients compared to cervical intraepithelial neoplasia. These studies indicated SERPINB5 was related to the survival in cervical cancer, pancreatic ductal adenocarcinoma and oral squamous cell carcinoma (45-47). Wang et al. reported that SERPINB5 had a statistically negative correlation with NSCLC prognosis and might be a promising prognostic signature in NSCLC (48).

The above studies have reported that the 4 hub DEGs (RRM2, CHEK1, TYMS and SERPINB5) were closely related to progression and prognosis of different cancers. A small number of studies have demonstrated that above 4 hub DEGs enriched in the p53 signaling or pyrimidine metabolism pathways play a vital role in lung cancer and overall survival of NSCLC patients. The current study also has several limitations. Firstly, there is not survival information for SCLC patients currently available in the online Kaplan-Meier plotter and GEPIA database. So, survival analysis has been not carried out for patients with SCLC. Secondly, we only assess the prognostic value and may miss some valuable information, which lacked of more clinical characteristics information from public databases, such as age, sex and treatment. Thirdly, our findings lacked molecular biological experimental validation of hub DEGs in NSCLC or SCLC.

Conclusions

Our study identified 4 hub DEGs (RRM2, CHEK1, TYMS and SERPINB5) in SCLC&NSCLC tissues compared with normal tissues. Functional analysis results indicated that these DEGs had different biological functions and were significantly enriched in different pathways. RRM2, CHEK1, TYMS and SERPINB5, which are mainly enriched in the p53 signaling and pyrimidine metabolism pathway, were significantly associated with the overall survival of NSCLC patients. These genes and pathways could serve as potential prognostic markers for personalized oncology in NSCLC or SCLC. However, more basic experiments and molecular mechanisms are needed to be confirmed for clinical applications.

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