OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2019.10796 OL-0-0-10796 Articles Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis LiZhenhua 1 SangMeixiang 2 TianZiqiang 1 LiuZhao 3 LvJian 4 ZhangFan 1 ShanBaoen 2 Department of Thoracic Surgery, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China Hebei Cancer Research Center, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China Department of Gastrointestinal Surgery, Peking University Cancer Hospital, Beijing 100142, P.R. China Second Department of Surgery, The Fourth Affiliated Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China Correspondence to: Dr Baoen Shan, Hebei Cancer Research Center, The Fourth Affiliated Hospital of Hebei Medical University, 14 Jiankang Road, Shijiazhuang, Hebei 050000, P.R. China, E-mail: baoenshan@163.com 11 2019 04 09 2019 18 5 4429 4440 22092018 06062019 Copyright: © Li et al. 2019 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Lung cancer is one of the most widespread neoplasms worldwide. To identify the key biomarkers in its carcinogenesis and development, the mRNA microarray datasets GSE102287, GSE89047, GSE67061 and GSE74706 were obtained from the Gene Expression Omnibus database. GEO2R was used to identify the differentially expressed genes (DEGs) in lung cancer. The Database for Annotation, Visualization and Integrated Discovery was used to analyze the functions and pathways of the DEGs, while the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape were used to obtain the protein-protein interaction (PPI) network. Kaplan Meier curves were used to analyze the effect of the hub genes on overall survival (OS). Module analysis was completed using Molecular Complex Detection in Cytoscape, and one co-expression network of these significant genes was obtained with cBioPortal. A total of 552 DEGs were identified among the four microarray datasets, which were mainly enriched in ‘cell proliferation’, ‘cell growth’, ‘cell division’, ‘angiogenesis’ and ‘mitotic nuclear division’. A PPI network, composed of 44 nodes and 886 edges, was constructed, and its significant module had 16 hub genes in the whole network: Opa interacting protein 5, exonuclease 1, PCNA clamp-associated factor, checkpoint kinase 1, hyaluronan-mediated motility receptor, maternal embryonic leucine zipper kinase, non-SMC condensin I complex subunit G, centromere protein F, BUB1 mitotic checkpoint serine/threonine kinase, cyclin A2, thyroid hormone receptor interactor 13, TPX2 microtubule nucleation factor, nucleolar and spindle associated protein 1, kinesin family member 20A, aurora kinase A and centrosomal protein 55. Survival analysis of these hub genes revealed that they were markedly associated with poor OS in patients with lung cancer. In summary, the hub genes and DEGs delineated in the research may aid the identification of potential targets for diagnostic and therapeutic strategies in lung cancer.

 lung cancer bioinformatics analysis differentially expressed genes Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis protein-protein interaction survival analysis Introduction

Lung cancer, one of the most common malignant tumors, is the leading cause of cancer-associated morbidity in the population worldwide; it is the most common cancer among males and the fourth most common tumor in women (1). Lung cancer is divided into different pathological subtypes, including adenocarcinoma, squamous cell carcinoma and small cell lung cancer (SCLC) (1). The occurrence, development and metastasis of lung cancer include a number of orchestrated steps, including DNA mutations and injury (2). Despite an increased understanding of the underlying molecular mechanisms of the disease and the implementation of novel therapeutic strategies, the 5-year survival rate remains low. The study of the molecular mechanism of cancer guides the classification and treatment of lung cancer, and promotes the rapid progress of targeted therapy and immunotherapy. The large-scale research and clinical trials of these new therapies provide prospects for the individualized treatment of lung cancer.

Much progress has been made with lung cancer biomarkers over the last decade, and biomarkers have been widely applied in the diagnosis, treatment and prognosis evaluation of lung cancer, with further biomarkers now being studied. For example, anaplastic lymphoma kinase (ALK) was initially identified to be abnormally downregulated in lung cancer and a fusion of echinoderm microtubule-associated protein-like 4 (EML4) and ALK genes was found in 3.7–7% of non-SCLC (NSCLC) (3). Due to ALK fusion, 57–74% of patients with lung adenocarcinoma respond well to ALK inhibitors such as crizotinib (3). The study revealed that the median progression-free survival (PFS) and response rates of patients who received crizotinib were significantly improved compared with those of patients treated with chemotherapy (4). The epidermal growth factor receptor (EGFR), a tyrosine kinase receptor, was overexpressed in 62% of patients with NSCLC (5). Tyrosine kinase inhibitors have been the standard treatment of patients with EGFR mutations due to their high response rate (55–78%) and PFS rate (1). Therefore, the discovery of new diagnostic and therapeutic targets is of great significance for the early diagnosis, drug development and targeted therapy of lung cancer.

Bioinformatics analysis has been commonly applied in cancer research to identify genetic changes associated with cancer. Previous studies have performed bioinformatics analysis to identify differentially expressed genes (DEGs) in various types of cancer, as well as to determine their roles in biological processes, molecular functions and different pathways (6,7). Accordingly, the present study analyzed data generated by microarray technology to explore the potential pathogenesis of lung cancer. Specifically, given the high number false-positives associated with the analysis of a single microarray, four public mRNA datasets were screened in the present study to identify DEGs between lung cancer and adjacent non-cancerous tissue samples. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, and a protein-protein interaction (PPI) network analysis was used to assist in demonstrating the molecular pathogenesis underlying the carcinogenesis and development of lung cancer. A total of 552 DEGs and 16 hub genes were identified and they may serve as candidate biomarkers in lung cancer.

 Materials and methods <sec> <title>Public mRNA datasets

Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo) is an open platform to store genetic data (8). Four gene expression profiles (GSE102287, GSE89047, GSE67061 and GSE74706) were acquired from the GEO The GSE102287 dataset contained 32 cancer samples and 34 normal samples (9). The GSE89047 dataset consisted of 8 cancer samples and 8 normal samples. The GSE67061 contained 56 cancer samples and 17 normal samples. The GSE74706 contained 18 cancer samples and 18 normal samples (10). The datasets consisted of a number of pathological subtypes of lung cancer, including NSCLC and lung squamous cell carcinoma. In the current study, in order to be more representative, a specific pathological type was not specified when selecting datasets.

 Identification of DEGs

GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r) is an interactive online tool to identify DEGs from GEO series (11). GEO2R was applied to distinguish DEGs between normal and lung cancer tissue samples. Duplicate and absent probe sets were removed. The cut-off criteria for the identification of DEGs were |log2 fold-change|>1 and adjusted P<0.05.

 Functional annotation for DEGs with KEGG and GO analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID; www.david.abcc.ncifcrf.gov) provides typical batch annotation and GO (www.geneontology.org) analysis to highlight the most relevant GO terms associated with a given gene list (12). GO covers three aspects of biology, including biological process, molecular function and cellular component. KEGG (version 90.0; www.kegg.jp), is one of the most commonly used biological information databases in the world (13). Following KEGG and GO analysis in DAVID, functional annotation for DEGs was performed. P<0.05 was considered to indicate a statistically significant difference.

 Construction of the PPI network and identification of a significant module

The Search Tool for the Retrieval of Interacting Genes (version 11.0; string.embl.de) (14), an online open tool, was applied to construct a PPI network, and Cytoscape (version 3.7.1) (15) was used to present the network. Using a confidence cutoff of >0.4, a node score cutoff of 0.2, a degree cutoff of 10, a maximum depth of 100 and a k-core of 2, the significant modules in the aforementioned PPI network were identified using the Molecular Complex Detection tool (version 1.5.1) (16). Subsequently, functional annotation for the genes in this module were performed using KEGG and GO analysis in DAVID.

 Analysis and identification of hub genes

Hub genes with ≥43 degrees were selected. cBioPortal (www.cbioportal.org) integrates The Cancer Genome Atlas (TCGA; portal.gdc.cancer.gov), the International Cancer Genome Consortium (icgc.org) and other cancer genome database data to provide online visualization tools. Based on the hub genes screened, a gene co-expression network was constructed and cBioportal was used to search for genes with a similar expression pattern to the hub genes in lung cancer and to investigate the interaction between genes (17). Furthermore, hub genes were analyzed with the biological process analysis, and were visualized using the BiNGO tool in Cytoscape (version 3.7.1) (18). The Kaplan-Meier plotter (kmplot.com/analysis) and the log rank test were used to plot and compared survival curves, respectively. The Kaplan-Meier plotter is an online tool that integrates gene expression data and clinical data from TCGA, GEO and the European Genome-Phenome Archive databases (www.ebi.ac.uk/ega/home). According to the different quantile expression levels of the proposed biomarkers, patients were divided into two groups to analyze the prognostic value of specific genes (19).

 Results <sec> <title>Screening of DEGs in lung cancer

The analysis of the GSE67061, GSE74706, GSE89047 and GSE102287 datasets revealed 5,553, 5,562, 4,028 and 4,703 DEGs, respectively (Fig. 1A). Venn diagram analysis revealed that 552 DEGs (389 downregulated and 163 upregulated genes) were present in the four datasets (Fig. 1B; Table SI).

 Functional annotation for DEGs using KEGG and GO analysis

The results of GO analysis revealed that the biological processes were primarily enriched in ‘cell proliferation’, ‘cell growth’, ‘cell division’, ‘cell adhesion’, ‘angiogenesis’, ‘mitotic nuclear division’, ‘mitotic cytokinesis’, ‘leukocyte migration’, ‘GTPase activity’ and ‘epithelial cell proliferation’. Variations in molecular function were enriched in ‘calcium ion binding’, ‘heparin binding’, ‘PDZ domain binding’, ‘integrin binding’, ‘GTPase activator activity’, ‘growth factor binding’, ‘collagen binding’, ‘carbohydrate binding’, ‘scavenger receptor activity’ and ‘protein kinase binding’. Changes in cellular component were mainly enriched in ‘extracellular matrix’, ‘extracellular region’, ‘extracellular space’, ‘sarcolemma’, ‘cell cortex’, ‘spindle pole’, ‘midbody’, ‘microtubule cytoskeleton’, ‘spindle’ and ‘collagen trimer’. KEGG pathway analysis revealed that DEGs were mainly enriched in ‘cell cycle’, ‘oocyte meiosis’, ‘hypertrophic cardiomyopathy’, ‘vascular smooth muscle contraction’, ‘dilated cardiomyopathy’, ‘pathways in cancer’, ‘cell adhesion molecules’, ‘fanconi anemia pathway’, ‘renin-angiotensin system’ and ‘leukocyte transendothelial migration’ (Fig. 2).

 Construction of the PPI network and identification of a significant module

A PPI network was constructed and a significant module with 44 nodes and 886 edges was identified (Fig. 3; Table SII). KEGG pathway and GO analysis of the DEGs involved in this module were analyzed using DAVID. Results revealed that genes in this module were significantly enriched in ‘cell division’, ‘cell cycle’ and ‘mitotic nuclear division’ (Table I).

 Hub gene selection and analysis

Hub genes with ≥43 degrees were selected and a total of 16 genes were identified as previously described (20): Opa interacting protein 5 (OIP5), exonuclease 1 (EXO1), PCNA clamp-associated factor (KIAA0101), checkpoint kinase 1 (CHEK1), hyaluronan-mediated motility receptor (HMMR), maternal embryonic leucine zipper kinase (MELK), non-SMC condensin I complex subunit G (NCAPG), centromere protein F (CENPF), BUB1 mitotic checkpoint serine/threonine kinase (BUB1), cyclin A2 (CCNA2), thyroid hormone receptor interactor 13 (TRIP13), TPX2 microtubule nucleation factor (TPX2), nucleolar and spindle associated protein 1 (NUSAP1), kinesin family member 20A (KIF20A), aurora kinase A (AURKA) and centrosomal protein 55 (CEP55; Table II). A co-expression network of these genes was obtained using cBioPortal (Fig. 4). The biological process analysis for these genes is presented in Fig. 5. Kaplan-Meier survival curves were used to perform the overall survival analysis. Patients with lung cancer with a high expression level of OIP5, EXO1, KIAA0101, CHEK1, HMMR, MELK, NCAPG, CENPF, BUB1, CCNA2, TRIP13, TPX2, NUSAP1, KIF20A, AURKA and CEP55 exhibited a worse 5-year overall survival time compared with patients with low expression (Figs. 6 and 7).

 Discussion

Lung cancer is one of the most common malignancies worldwide, both in terms of incidence and mortality (21,22). Despite significant advances in diagnostic and treatment strategies, the prognosis of patients with lung cancer remains unsatisfactory. Therefore, there is a requirement for the identification of lung cancer biomarkers to serve as novel diagnostic and therapeutic targets. Bioinformatics analysis has been widely applied to investigate genetic alterations in the progression of diseases, and may enable the identification of novel therapeutic targets.

Previous studies have screened biomarkers associated with the different pathological subtypes of lung cancer (23–26). Similarly, the present study screened potential biomarkers of lung cancer. However, the present study differs from the previous literature in a number of ways. In the current study, research data was derived from different datasets, which allows diversification of data results. Four datasets were selected to reduce the errors associated with a single dataset and differences of sequencing platforms, so as to improve the credibility of the results. The aim of the present study was to screen common biomarkers and drug targets in various pathological types of lung cancer using bioinformatics analysis. Finally, different results were achieved due to the different data sources and statistical methods used. However, certain biomarkers identified in the current study are consistent with previously published studies (27–31).

In the present study, 552 common DEGs were identified in the four microarray datasets. GO enrichment analysis revealed that changes in the most significant module were mainly enriched in ‘cell division’, ‘mitotic nuclear division’ and ‘G2/M transition of mitotic cell cycle’, while changes in KEGG analysis were mainly enriched in the ‘cell cycle’ and ‘p53 signaling pathway’. Previous studies demonstrated that dysregulation of the cell cycle is associated with carcinogenesis and the progression of tumors (32,33). In the current study, a PPI network consisting of 44 nodes and 886 edges was constructed. The 16 genes with the highest degrees in the PPI network included OIP5, EXO1, KIAA0101, CHEK1, HMMR, MELK, NCAPG, CENPF, BUB1, CCNA2, TRIP13, TPX2, NUSAP1, KIF20A, AURKA and CEP55. Subsequently, survival analysis of these genes revealed that they were significantly associated with a worse 5-year overall survival time of patients with lung cancer.

The mechanism of lung cancer is driven by specific genetic and epigenetic changes (34). In certain types of cancer, such as gastric colorectal cancer, the expression of OIP5 is upregulated and may be associated with the occurrence of cancer (35,36). However, its function in lung cancer remains unknown. EXO1 is a nuclease that modulates DNA recombination, maintains genomic stability and mediates cell cycle arrest. Several reports have indicated that functional polymorphisms of EXO1 may be associated with the occurrence of lung cancer, and it may serve as a novel biomarker for the diagnosis and treatment of lung cancer (37,38). KIAA0101 is involved in cell cycle regulation and DNA repair and is expressed at high levels in several types of cancer, including gastric and lung cancer (27,39,40). Previous studies reported that high expression levels of KIAA0101 and CHEK1 in lung cancer are associated with a poor prognosis (27,41).

Man et al (28) revealed that HMMR, the receptor for hyaluronic acid, was upregulated in lung adenocarcinoma samples compared with healthy adjacent non-cancerous tissues. MELK is expressed in several types of human cancer (42,43), including SCLC. Inoue et al (42) reported that inhibition of MELK may be a therapeutic strategy for SCLC. Zhang et al (44) reported that NCAPG may be implicated in hepatocellular carcinoma cell proliferation and migration, and may provide a promising novel therapeutic target for the treatment of advanced hepatocellular carcinoma. However, the clinical significance of NCAPG in lung cancer remains unknown.

Previous studies reported that CENPF serves a role in the tumorigenesis of hepatocellular carcinoma and prostate cancer (45,46); however, its role in lung cancer requires further investigation. A number of studies demonstrated that BUB1 serves important roles in breast and endometrial cancer (47–49). However, Haruki et al (47–49) reported that the BUB gene family members, including BUB1, are not commonly associated with mitotic checkpoint defects in lung cancer. The potential association between BUB1 and lung cancer requires further investigation. Kim et al (29) reported that a functional single nucleotide polymorphism in the promoter region of CCNA2 was associated with an increased risk of lung cancer. TRIP13 is an ATPase that serves a key role in mitotic checkpoint complex inactivation and is associated with the progression of lung adenocarcinoma (30). Li et al (30) demonstrated that increased TRIP13 expression promoted lung adenocarcinoma progression and may serve as a potential therapeutic target or biomarker for the disease.

Yang et al (50,51) revealed that TPX2 was associated with lung squamous carcinoma cell radioresistance and may serve as a therapeutic target to enhance cell radiosensitivity in lung squamous carcinoma. Furthermore, Schneider et al (50,51) demonstrated that the expression of the TPX2, mitosis-associated gene, was associated with the prognosis of patients with NSCLC. Previous studies reported that overexpression of NUSAP1 was associated with a poor prognosis in prostate cancer, hepatocellular and oral squamous cell carcinoma (52,53); however; little is known about the association of NUSAP1 with lung cancer. Zhao et al (54) demonstrated that KIF20A may confer a malignant phenotype in lung adenocarcinoma by regulating cell proliferation and apoptosis. AURKA, an oncogene, encodes a serine-threonine kinase that regulates mitotic processes in mammalian cells and serves as a potential therapeutic target of NSCLC (55,56). Lo et al (55,56) reported that AURKA upregulation is restricted to specific subtypes and poorly differentiated tumors in NSCLC. Ma et al (31) revealed that CEP55 was upregulated in lung cancer cells and was associated with poor clinical outcomes in patients with lung cancer, and that it may serve as a prognostic biomarker for the disease.

The current study is only a preliminary report, and heterogeneous results due to the limitations of the source and quantity of samples may have occurred. Furthermore, statistical differences may not translate to the expected clinical significance. In order to be more representative, a specific pathological type of lung cancer was not selected in the current study. However, this may lead to poor specificity in lung cancer subtypes. The 16 hub genes identified revealed clinical significance in the validation of survival analysis. However, further validation in the subsequent basic and clinical trial studies is required. In addition to DEGs, further studies investigating differentially expressed microRNAs and their association with genes, particularly DEGs, are required.

In summary, the current study identified DEGs that may be involved in the carcinogenesis or progression of lung cancer. A total of 552 DEGs and 16 hub genes were identified, and these may serve as potential diagnostic biomarkers or therapeutic targets for lung cancer. The results suggested that data mining and integration may be a promising tool for the identification of biomarkers in malignant tumors. As tumor biomarkers only have meaning if they are integrated with clinical data, further experiments should be conducted to verify the results obtained in the current study.

 Supplementary Material Supporting Data Acknowledgements

Not applicable.

 Funding

No funding was received.

 Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the GEO repository (www.ncbi.nlm.nih.gov/geo).

 Authors' contributions

ZHL and BES designed the overall research. ZL, JL and FZ collected the data. ZHL, MXS, ZQT, ZL and BES contributed to data analysis and visualization. JL and FZ drafted and revised the manuscript. All authors approved the final version of the manuscript.

 Ethics approval and consent to participate

Not applicable.

 Patient consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

 Abbreviations DEGs

differentially expressed genes

 PPI

protein-protein interaction

 GO

Gene Ontology

 KEGG

Kyoto Encyclopedia of Genes and Genomes

 GEO

Gene Expression Omnibus

 DAVID

Database for Annotation, Visualization and Integrated Discovery

 References BrayFFerlayJSoerjomataramISiegelRLTorreLAJemalAGlobal cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countriesCA Cancer J Clin68394424201810.3322/caac.2149230207593 Nana-SinkamSPPowellCAMolecular biology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelinesChest143e30Se39S201310.1378/chest.12-234623649444 SolomonBJMokTKimDWWuYLNakagawaKMekhailTFelipECappuzzoFPaoliniJUsariTFirst-line crizotinib versus chemotherapy in ALK-positive lung cancerN Engl J Med37121672177201410.1056/NEJMoa140844025470694 MokTSPersonalized medicine in lung cancer: What we need to knowNat Rev Clin Oncol8661668201110.1038/nrclinonc.2011.12621862980 SharmaSVBellDWSettlemanJHaberDAEpidermal growth factor receptor mutations in lung cancerNat Rev Cancer7169181200710.1038/nrc208817318210 ShaoYLiangBLongFJiangSJDiagnostic microRNA biomarker discovery for non-small-cell lung cancer adenocarcinoma by integrative bioinformatics analysisBiomed Res Int20172563085201710.1155/2017/256308528698868 WangLQZhaoLHQiaoYZIdentification of potential therapeutic targets for lung cancer by bioinformatics analysisMol Med Rep1319751982201610.3892/mmr.2015.475226739332 EdgarRDomrachevMLashAEGene Expression Omnibus: NCBI gene expression and hybridization array data repositoryNucleic Acids Res30207210200210.1093/nar/30.1.20711752295 MitchellKAZingoneAToulabiLBoeckelmanJRyanBMComparative transcriptome profiling reveals coding and noncoding RNA differences in NSCLC from African Americans and European AmericansClin Cancer Res2374127425201710.1158/1078-0432.CCR-17-052729196495 MarwitzSDepnerSDvornikovDMerkleRSzczygiełMMüller-DeckerKLucarelliPWäschMMairbäurlHRabeKFDownregulation of the TGFβ pseudoreceptor BAMBI in non-small cell lung cancer enhances TGFβ signaling and invasionCancer Res7637853801201610.1158/0008-5472.CAN-15-132627197161 BarrettTWilhiteSELedouxPEvangelistaCKimIFTomashevskyMMarshallKAPhillippyKHShermanPMHolkoMNCBI GEO: Archive for functional genomics data sets-updateNucleic Acids Res41D991D995201310.1093/nar/gks119323193258 HuangDWShermanBTTanQCollinsJRAlvordWGRoayaeiJStephensRBaselerMWLaneHCLempickiRAThe DAVID Gene Functional Classification Tool: A novel biological module-centric algorithm to functionally analyze large gene listsGenome Biol8R183200710.1186/gb-2007-8-9-r18317784955 KanehisaMThe KEGG databaseNovartis Found Symp24791103200210.1002/0470857897.ch812539951 SzklarczykDFranceschiniAWyderSForslundKHellerDHuerta-CepasJSimonovicMRothASantosATsafouKPSTRING v10: Protein-protein interaction networks, integrated over the tree of lifeNucleic Acids Res43D447D452201510.1093/nar/gku100325352553 ShannonPMarkielAOzierOBaligaNSWangJTRamageDAminNSchwikowskiBIdekerTCytoscape: A software environment for integrated models of biomolecular interaction networksGenome Res1324982504200310.1101/gr.123930314597658 BaderGDHogueCWAn automated method for finding molecular complexes in large protein interaction networksBMC Bioinformatics42200310.1186/1471-2105-4-212525261 CeramiEGaoJDogrusozUGrossBESumerSOAksoyBAJacobsenAByrneCJHeuerMLLarssonEThe cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics dataCancer Discov2401404201210.1158/2159-8290.CD-12-009522588877 MaereSHeymansKKuiperMBiNGO: A Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networksBioinformatics2134483449200510.1093/bioinformatics/bti55115972284 NagyÁLánczkyAMenyhártOGyőrffyBValidation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasetsSci Rep89227201810.1038/s41598-018-29514-329907753 LiTGaoXHanLYuJLiHIdentification of hub genes with prognostic values in gastric cancer by bioinformatics analysisWorld J Surg Oncol16114201810.1186/s12957-018-1519-y29921304 BunnPAJrKarnofsky award 2016: A lung cancer journey, 1973 to 2016J Clin Oncol35243252201710.1200/JCO.2016.70.406428056194 HirschFRScagliottiGVMulshineJLKwonRCurranWJJrWuYLPaz-AresLLung cancer: Current therapies and new targeted treatmentsLancet389299311201710.1016/S0140-6736(16)30958-827574741 MendellJTMicroRNAs: Critical regulators of development, cellular physiology and malignancyCell Cycle411791184200510.4161/cc.4.9.203216096373 XiaoYFengMRanHHanXLiXIdentification of key differentially expressed genes associated with non-small cell lung cancer by bioinformatics analysesMol Med Rep1763796386201829532892 WenPChidanguroTShiZGuHWangNWangTLiYGaoJIdentification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysisMol Med Rep1815381550201829845250 TangQZhangHKongMMaoXCaoXHub genes and key pathways of non-small lung cancer identified using bioinformaticsOncol Lett1623442354201830008938 KatoTDaigoYAragakiMIshikawaKSatoMKajiMOverexpression of KIAA0101 predicts poor prognosis in primary lung cancer patientsLung Cancer75110118201210.1016/j.lungcan.2011.05.02421689861 ManYCaoJJinSXuGPanBShangLCheDYuQYuYNewly identified biomarkers for detecting circulating tumor cells in lung adenocarcinomaTohoku J Exp Med2342940201410.1620/tjem.234.2925175030 KimDHParkSEKimMJiYIKangMYJungEHKoEKimYKimSShimYMParkJA functional single nucleotide polymorphism at the promoter region of cyclin A2 is associated with increased risk of colon, liver, and lung cancersCancer11740804091201110.1002/cncr.2593021858804 LiWZhangGLiXWangXLiQHongLShenYZhaoCGongXChenYZhouJThyroid hormone receptor interactor 13 (TRIP13) overexpression associated with tumor progression and poor prognosis in lung adenocarcinomaBiochem Biophys Res Commun499416424201810.1016/j.bbrc.2018.03.12929567476 MaXPZhangWWuBQQinJCorrelations between mRNA levels of centrosomal protein 55 (CEP55) and clinical features of patients with lung cancerMed Sci Monit2430933097201810.12659/MSM.90726629750778 ChiangITWangWSLiuHCYangSTTangNYChungJGCurcumin alters gene expression-associated DNA damage, cell cycle, cell survival and cell migration and invasion in NCI-H460 human lung cancer cells in vitroOncol Rep3418531874201510.3892/or.2015.415926238775 WanJZouSHuMZhuRXuJJiaoYFanSThoc1 inhibits cell growth via induction of cell cycle arrest and apoptosis in lung cancer cellsMol Med Rep923212327201410.3892/mmr.2014.208824682263 WenJFuJZhangWGuoMGenetic and epigenetic changes in lung carcinoma and their clinical implicationsMod Pathol24932943201110.1038/modpathol.2011.4621423157 ChunHKChungKSKimHCKangJEKangMAKimJTChoiEHJungKEKimMHSongEYOIP5 is a highly expressed potential therapeutic target for colorectal and gastric cancersBMB Rep43349354201010.5483/BMBRep.2010.43.5.34920510019 GongMXuYDongWGuoGNiWWangYWangYAnRExpression of Opa interacting protein 5 (OIP5) is associated with tumor stage and prognosis of clear cell renal cell carcinomaActa Histochem115810815201310.1016/j.acthis.2013.03.00823664661 TangJTangSLiuJWuQWanLXuQGenetic risk of lung cancer associated with a single nucleotide polymorphism from EXO1: A meta analysisInt J Clin Exp Med81113211138201526379914 HsuNYWangHCWangCHChiuCFTsengHCLiangSYTsaiCWLinCCBauDTLung cancer susceptibility and genetic polymorphisms of Exo1 gene in TaiwanAnticancer Res29725730200919331228 ZhuKDiaoDDangCShiLWangJYanRYuanDLiKElevated KIAA0101 expression is a marker of recurrence in human gastric cancerCancer Sci104353359201310.1111/cas.1208323240630 FanSLiXTieLPanYLiXKIAA0101 is associated with human renal cell carcinoma proliferation and migration induced by erythropoietinOncotarget71352013537201626575329 LiuBQuJXuFGuoYWangYYuHQianBMiR-195 suppresses non-small cell lung cancer by targeting CHEK1Oncotarget694459456201525840419 InoueHKatoTOlugbileSTamuraKChungSMiyamotoTMatsuoYSalgiaRNakamuraYParkJHEffective growth-suppressive activity of maternal embryonic leucine-zipper kinase (MELK) inhibitor against small cell lung cancerOncotarget71362113633201610.18632/oncotarget.729726871945 PitnerMKTaliaferroJMDalbyKNBartholomeuszCMELK: A potential novel therapeutic target for TNBC and other aggressive malignanciesExpert Opin Ther Targets21849859201710.1080/14728222.2017.136318328764577 ZhangQSuRShanCGaoCWuPNon-SMC condensin I complex, subunit G (NCAPG) is a novel mitotic gene required for hepatocellular cancer cell proliferation and migrationOncol Res26269276201810.3727/096504017X1507596756098029046167 KimHEKimDGLeeKJSonJGSongMYParkYMKimJJChoSWChiSGCheongHSFrequent amplification of CENPF, GMNN and CDK13 genes in hepatocellular carcinomasPLoS One7e43223201210.1371/journal.pone.004322322912832 AytesAMitrofanovaALefebvreCAlvarezMJCastillo-MartinMZhengTEasthamJAGopalanAPientaKJShenMMCross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancyCancer Cell25638651201410.1016/j.ccr.2014.03.01724823640 TakagiKMikiYShibaharaYNakamuraYEbataAWatanabeMIshidaTSasanoHSuzukiTBUB1 immunolocalization in breast carcinoma: Its nuclear localization as a potent prognostic factor of the patientsHorm Cancer492102201310.1007/s12672-012-0130-x23288590 LiLXuDBZhaoXLHaoTYCombination analysis of Bub1 and Mad2 expression in endometrial cancer: Act as a prognostic factor in endometrial cancerArch Gynecol Obstet288155165201310.1007/s00404-012-2706-723334889 HarukiNSaitoHHaranoTNomotoSTakahashiTOsadaHFujiiYTakahashiTMolecular analysis of the mitotic checkpoint genes BUB1, BUBR1 and BUB3 in human lung cancersCancer Lett162201205200110.1016/S0304-3835(00)00675-311146226 YangJGaoFXuXWangYZhuSTargeting protein for Xenopus kinesin-like protein 2 knockdown enhances radiation sensitivity of human lung squamous carcinoma cellClin Exp Pharmacol Physiol4410601068201710.1111/1440-1681.1280028636807 SchneiderMAChristopoulosPMuleyTWarthAKlingmuellerUThomasMHerthFJDienemannHMuellerNSTheisFMeisterMAURKA, DLGAP5, TPX2, KIF11 and CKAP5: Five specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patientsInt J Oncol50365372201710.3892/ijo.2017.383428101582 ZhangMYangDLiuXLiuYLiangJHeHZhongKLinLTaoGZhangCZhouJExpression of Nusap1 in the surgical margins of hepatocellular carcinoma and its association with early recurrenceNan Fang Yi Ke Da Xue Xue Bao33937938inside back cover2013(In Chinese)23803217 GordonCAGulzarZGBrooksJDNUSAP1 expression is upregulated by loss of RB1 in prostate cancer cellsProstate75517526201510.1002/pros.2293825585568 ZhaoXZhouLLLiXNiJChenPMaRWuJFengJOverexpression of KIF20A confers malignant phenotype of lung adenocarcinoma by promoting cell proliferation and inhibiting apoptosisCancer Med746784689201810.1002/cam4.171030105795 MaZLZhangBJWangDTLiXWeiJLZhaoBTJinYLiYLJinYXTanshinones suppress AURKA through up-regulation of miR-32 expression in non-small cell lung cancerOncotarget62011120120201526036635 Lo IaconoMMonicaVSaviozziSCeppiPBraccoEPapottiMScagliottiGVAurora Kinase A expression is associated with lung cancer histological-subtypes and with tumor de-differentiationJ Transl Med9100201110.1186/1479-5876-9-10021718475

Volcano plots and Venn diagram. (A) Differentially expressed genes were selected with |log2 fold change|>1 and adjusted P<0.05 among the mRNA expression profiling datasets GSE102287, GSE67061, GSE89047 and GSE74706. The red triangles represent downregulated genes and the green circles represent upregulated genes. (B) A total of 552 intersecting genes were identified in the four datasets. FC, fold-change.

Functional and pathway enrichment analysis of differentially expressed genes in lung cancer.

Construction of the PPI network and identification of a significant module. (A) The PPI network was constructed using Cytoscape. (B) The most significant module was obtained from the PPI network using Molecular Complex Detection, and included 44 nodes and 886 edges. PPI, protein-protein interaction.

Hub genes and their co-expression genes were analyzed using cBioPortal. Nodes with a bold black outline represent hub genes. Red nodes with a thin black outline represent the co-expression genes. Blue arrows point to potential downstream targets of genes.

Biological process analysis of hub genes was constructed using the BiNGO (version 3.0.3) plugin in Cytoscape. The color depth of nodes refers to the corrected P-value of ontologies. The size of nodes refers to the numbers of genes that are involved in the ontologies.

Overall survival analysis of 8 hub genes (OIP5, EXO1, HMMR, MELK, BUB1, CCNA2, NUSAP1 and KIF20A) was performed using the Kaplan-Meier plotter online platform. P<0.05 was considered to indicate a statistically significant difference. HR, hazard ratio.

Overall survival analysis of eight hub genes (KIAA0101, CHEK1, NCAPG, CENPF, TRIP13, TPX2, AURKA and CEP55) were performed using the Kaplan-Meier plotter online platform. P<0.05 was considered to indicate a statistically significant difference. HR, hazard ratio.

GO and KEGG pathway enrichment analysis of the differentially expressed genes in the most significant module.

Category Term Count in gene set P-value
GOTERM_BP Mitotic nuclear division 19 <0.001
GOTERM_BP Cell division 18 <0.001
GOTERM_BP G2/M transition of mitotic cell cycle 11 <0.001
GOTERM_BP Mitotic cytokinesis 5 <0.001
GOTERM_MF Protein binding 39 <0.001
GOTERM_MF Protein serine/threonine kinase activity 8 <0.001
GOTERM_MF Protein kinase binding 8 <0.001
GOTERM_CC Nucleoplasm 29 <0.001
GOTERM_CC Spindle 9 <0.001
GOTERM_CC Midbody 9 <0.001
KEGG_PATHWAY Cell cycle 10 <0.001
KEGG_PATHWAY p53 signaling pathway 4 <0.001
KEGG_PATHWAY FoxO signaling pathway 4 0.006

GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; MF, molecular function; CC, cellular component.

Functional roles of 16 hub genes with ≥43 degrees of interaction.

Gene symbol Gene name Function
OIP5 Opa interacting protein 5 Required for recruitment of centromere protein A to centromeres and normal chromosome segregation during mitosis. Expression of this gene is upregulated in several types of cancer, making it a putative therapeutic target.
EXO1 Exonuclease 1 Encodes a protein with 5′ to 3′ exonuclease activity, as well as an RNase H activity.
KIAA0101 PCNA clamp-associated factor PCNA-binding protein that acts as a regulator of DNA repair during DNA replication. Also acts as a regulator of centrosome number.
CHEK1 Checkpoint kinase 1 Required for checkpoint-mediated cell cycle arrest in response to DNA damage or the presence of unreplicated DNA.
HMMR Hyaluronan-mediated motility receptor Encodes a protein involved in cell motility.
MELK Maternal embryonic leucine zipper kinase Serine/threonine-protein kinase involved in various processes, such as cell cycle regulation, self-renewal of stem cells, apoptosis and splicing regulation.
NCAPG Non-SMC condensin I complex subunit G Encodes a subunit of the condensin complex, which is responsible for the condensation and stabilization of chromosomes during mitosis and meiosis.
CENPF Centromere protein F Encodes a protein that associates with the centromere-kinetochore complex.
BUB1 BUB1 mitotic checkpoint Encodes a serine/threonine-protein kinase that serves a central role in
serine/threonine kinase mitosis. Mutations in this gene have been associated with aneuploidy and several forms of cancer.
CCNA2 Cyclin A2 Encodes a protein that binds and activates cyclin-dependent kinase 2 and promotes transition through G1/S and G2/M.
TRIP13 Thyroid hormone receptor interactor 13 Encodes a protein that interacts with thyroid hormone receptors, which may serve a role in early-stage non-small cell lung cancer.
TPX2 Targeting protein for Xklp2 Spindle assembly factor required for normal assembly of mitotic spindles.
NUSAP1 Nucleolar and spindle-associated protein 1 Nucleolar-spindle-associated protein that serves a role in spindle microtubule organization.
KIF20A Kinesin family member 20A Mitotic kinesin required for chromosome passenger complex-mediated cytokinesis.
AURKA Aurora kinase A Encodes a cell cycle-regulated kinase involved in microtubule formation and/or stabilization at the spindle pole during chromosome segregation.
CEP55 Centrosomal protein 55 Serves a role in mitotic exit and cytokinesis.