Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P<0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

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Screening and Authentication of Key Genes and Prognostic Value Analysis in Oligodendroglial Tumors

10.21203/rs.3.rs-31293/v1 ◽

2020 ◽

Author(s):

Zhonghua Lv ◽

Bongbo Bao ◽

Peng Liang

Keyword(s):

Overall Survival ◽

Expression Profiles ◽

Ppi Network ◽

Hub Genes ◽

Tumor Patients ◽

Altered Expression ◽

Oligodendroglial Tumor ◽

Tumor Tissues ◽

Key Genes ◽

Tumor Pathogenesis

Abstract Background: Emerging evidence indicates that various functional genes with altered expression are involved in the human tumor progression. This study is aimed at identifying novel key genes that may be used for oligodendroglial tumor diagnosis, prognosis, and targeted therapy. Methods: This study included three expression proﬁles (GSE15824, GSE29796 and GSE108474) obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the diﬀerentially expressed genes (DEGs) between normal samples and oligodendroglial tumor, including oligodendroglioma and anaplastic oligodendroglioma. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identiﬁed DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identiﬁed. ONCOMINE and The Cancer Genome Atlas (TCGA) databases were used to verify the expression of the hub genes in oligodendroglial tumor tissues and the hub genes on the overall survival of oligodendroglial tumor patients. Results: A total of 128 DEGs were identiﬁed from the three expression proﬁles. These DEGs were enriched with functional processes and pathways related to oligodendroglial tumor pathogenesis. From the PPI network, five hub genes were identiﬁed. The expression of the five hub genes was all upregulated in oligodendroglial tumor tissues compared with the control tissues. Kaplan-Meier survival curves indicated that high expression of cullin 3 (CUL3), cop9 signalosome subunit 8 (COPS8), cullin associated and neddylation dissociated 1 (CAND1), F-box protein 22 (FBXO22), and leucine rich repeat containing 41 (LRRC41) predicted poor overall survival in oligodendroglial tumor patients (all log-rank P < 0.01). Conclusions: These results revealed that the DEGs may serve as candidate key genes during oligodendroglial tumor pathogenesis. The five hub genes, including CUL3, COPS8, CAND1, FBXO22, and LRRC41, may serve as promising prognostic biomarkers in oligodendroglial tumor.

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Screening and Identification of Significant Genes in Hepatocellular Carcinoma via Bioinformatical Analysis and Validation in Clinical Tissue

10.21203/rs.3.rs-99437/v1 ◽

2020 ◽

Author(s):

jun guo zhou ◽

Dongsheng Wang ◽

Xiaorong Zhong ◽

Wei Ying ◽

Yanchao Feng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Prognostic Factors ◽

Function Analysis ◽

Hub Genes ◽

P53 Signaling ◽

Immunohistochemistry Staining ◽

Bioinformatical Analysis ◽

Screening And Identification ◽

Key Genes

Abstract Background: To screen out significant genes associated with occurrence and development of hepatocellular carcinoma (HCC) via bioinformatical analysis and validation using clinical specimens. Methods: Gene expression chips were obtained from GEO database, differentially expressed genes (DEGs) between HCC and para-cancerous tissues were identified by GEO2R and Venn diagrams. What’s more, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were carried out by DAVID. The protein-protein interaction (PPI) network and module analysis of DEGs were performed by STRING and Cytoscape to get hub genes. Subsequently, the influence of hub genes on overall survival and the expression levels were determined with Ualcan and GEPIA, and found the pathway via re-analysis of DAVID. Besides, immunohistochemistry staining were performed to verify the key genes, and follow-up results including prognoses and the clinicopathological features were statistically analyzed. Results: 49 up-regulated DEGs and 122 down-regulated DEGs were selected. There were to tally 33 Hub genes were screened out, while 28 of them were related to prognosis and high expression in HCC. Furthermore, CDK1 and RRM2 were significantly enriched in p53 signaling pathway. Meanwhile, CDK1, RRM2 were highly expressed in HCC tissues by immunohistochemistry staining. Additionally, CDK1 and RRM2 were negatively correlated with overall survival. Tumor size and AFP were significant prognostic factors, while CDK1 and RRM2 were independent prognostic factors. Conclusion: This study confirmed CDK1 and RRM2 as the key genes in HCC which could be potential biological target for diagnosis and treatment.

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Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.8930 ◽

2020 ◽

Vol 8 ◽

pp. e8930 ◽

Cited By ~ 3

Author(s):

Xi Ma ◽

Lin Zhou ◽

Shusen Zheng

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Survival Analysis ◽

Poor Prognosis ◽

Bioinformatics Analysis ◽

Expression Profiles ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis ◽

Key Genes ◽

Overall Survival Analysis

Background Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis. Methods Messenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk. Results Eleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes. Conclusions This study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.

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Combining Bioinformatics and Experiments to Identify and Verify Gene Prognostic Markers in Hepatocellular Carcinoma

Clinical Oncology and Research ◽

10.31487/j.cor.2021.04.02 ◽

2021 ◽

pp. 1-12

Author(s):

Li Luo ◽

Rong Wang ◽

Liaoyun Zhang ◽

Piao Zhang ◽

Dongmei Tian ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Proportional Hazards ◽

Malignant Tumors ◽

Expression Profiles ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

Hub Genes

Background: Hepatocellular Carcinoma (HCC) is one of the highly malignant tumors threatening human health. The current research aimed to identify potential prognostic gene biomarkers for HCC. Materials and Methods: Microarray data of gene expression profiles of HCC from GEO were downloaded. After screening overlapping differentially expressed genes (DEGs) by R software. The STRING database and Cytoscape were used to identify hub genes. Cox proportional hazards regression was performed to screen the potential prognostic genes. Moreover, quantitative real-time PCR analyses were performed to detect the expression of ANLN in liver cancer cells and tissues. Finally, its possible pathways and functions were predicted using gene set enrichment analysis (GSEA). Result: A total of 566 DEGs were obtained from the overlapping analysis of three mRNA microarray dataset. Six key hub genes including RACGAP1, KIF20, DLGAP5, CDK1, BUB1B and ANLN, were associated with poor prognosis of patients with HCC. Higher expression of ANLN was associated with reduced overall survival and disease-free survival in patients with HCC. Multivariate analysis revealed that ANLN expression was an independent risk factor affecting overall survival. RT-PCR and Western blot analysis further demonstrated that ANLN expression was increased in HCC compared with patient-matched adjacent normal tissues. Notably, Gene enrichment analysis revealed that DEGs in ANLN-high patients were enriched in cell cycle, DNA duplication and p53 signaling pathway. Conclusion: The high expression of RACGAP1, KIF20, DLGAP5, CDK1, BUB1B and ANLN might be poor prognostic biomarkers in HCC patients, and may help to individualize the management of HCC.

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Hub gene identification and prognostic model establishment for patients with HBV-related hepatocellular carcinoma

10.21203/rs.3.rs-269454/v2 ◽

2021 ◽

Author(s):

Lianmei Wang ◽

Jing Meng ◽

Shasha Qin ◽

Aihua Liang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Expression Profiles ◽

Cyclin B1 ◽

Mitotic Checkpoint ◽

Cancer Genome ◽

Hub Genes ◽

Cox Regression Analysis ◽

Model Based

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~50% of HCC cases. Identification of biomarkers is pivotal for the therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. Cyclin-dependent kinase 1 (CDK1), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Aurora kinase A (AURKA), Cyclin B2 (CCNB2), Enhancer of zeste homolog 2 (EZH2), Cell division cycle 20 (CDC20), DNA topoisomerase II alpha (TOP2A), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and ZW10 interactor (ZWINT), were identified as the top-ten hub genes. The expression of hub-genes was verified in the liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP), the cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. A four-gene prognostic related model based on the expression of ZWINT, EZH2, FOXM1 and CDK1 were established through Cox regression analysis in ICGC-LIRI-JP project, and verified in TCGA-HCC cohort. Furthermore, a nomogram model based on pathology stage, gender and four-genes prognostic model was built to predict the prognosis for HBV–HCC patients. In conclusion, ZWINT, EZH2, FOXM1 and CDK1 play a pivotal role in HBV-HCC, and are potential therapeutic targets of HBV HCC.

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Hub Gene Identification and Prognostic Model Establishment for Patients with HBV-related Hepatocellular Carcinoma

10.21203/rs.3.rs-269454/v1 ◽

2021 ◽

Author(s):

Lianmei Wang ◽

Jing Liu ◽

Zhong Xian ◽

Jingzhuo Tian ◽

Chunying Li ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Expression Profiles ◽

Expression Patterns ◽

Cyclin B1 ◽

Mitotic Checkpoint ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Hub Genes

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~ 50% of HCC cases. Establishment of a prognostic model is pivotal for clinical therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. DNA topoisomerase II alpha (TOP2A), Disks large-associated protein 5 (DLGAP5), RAD51 associated protein 1 (RAD51AP1), ZW10 interactor (ZWINT), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Cyclin B2 (CCNB2), Aurora kinase A (AURKA), and Cyclin-dependent kinase 1 (CDK1) were identified as the top-ten hub genes. These hub-genes were verified by the Liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP) project, The Cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. FOXM1 and CDK1 were found to be prognostic-related molecules for HBV-HCC patients. The expression patterns of FOXM1 and CDK1were consistently in human and mouse. Furthermore, a nomogram model based on histology grade, pathology stage, sex and, expression of FOXM1 and CDK1 was built to predict the prognosis for HBV–HCC patients. The nomogram model could be used to predict the prognosis of HBV-HCC cases.

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Identification and Analysis of the Key Genes associated with the Development from Liver Cirrhosis to Hepatocellular Carcinoma Based on Bioinformatics Analysis

10.21203/rs.2.24361/v1 ◽

2020 ◽

Author(s):

Tianyu Li ◽

Fang Ding ◽

Huimin Yan

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cirrhosis ◽

Poor Prognosis ◽

Cancer Genomics ◽

Hierarchical Cluster ◽

Expression Level ◽

Ppi Network ◽

Hub Genes ◽

Monooxygenase Activity ◽

Key Genes

Abstract Background: Hepatocellular carcinoma (HCC) is the most frequent primary liver tumor, and one of the most common malignant cancer with poor prognosis. Liver cirrhosis is the major risk factor for HCC. The aim of this study was to identify potential key genes associated with the development from liver cirrhosis to HCC and explore their potential mechanisms. Methods: Four microarray datasets GSE17548, GSE63898, GSE25097 and GSE89377 were downloaded from the Gene Expression Omnibus database. A protein-protein interaction (PPI) network was constructed using the STRING database, and potential hub genes were screened using MCODE plug-in in Cytoscape software. The Oncomine database was used to verify the expression of differential genes in cirrhosis and HCC. In order to further verify those hub genes, the hierarchical cluster between normal and HCC tissues was constructed using the UCSC Cancer Genomics Browser. The UALCAN database was used to verify the difference of hub genes in normal and HCC tissues and in different tumor grades. Finally, the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC.Results: A total of 360 DEGs, including 280 downregulated and 80 upregulated genes, were identified. Gene ontology enrichment (GO) analysis showed that these DEGs were mainly enriched in monooxygenase activity, cofactor binding, and oxidoreductase activity (acting on CH-OH group of donors). The mainly enriched pathways were complement and coagulation cascades, prion diseases, and arachidonic acid metabolism. By extracting key modules from the PPI network, 16 hub genes were screened out. In the hierarchical cluster of hub genes between normal and HCC tissues, the results showed that the expression level of 16 hub genes in HCC tissues was significantly higher than that in normal tissues. In addition, expression level of the hub genes was significantly associated with the tumor grades. The survival analysis showed that six hub DEGs, including KIF20A,HMMR, RRM2, TPX2, TTK and UBE2C, were closely associated with the poor prognosis of HCC.Conclusion: Our study discovered six novel potential genes associated with the development from liver cirrhosis to HCC. These key genes may be used as prognostic biomarkers and molecular therapeutic targets for HCC.

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Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis

PeerJ ◽

10.7717/peerj.7408 ◽

2019 ◽

Vol 7 ◽

pp. e7408 ◽

Cited By ~ 9

Author(s):

Shucai Xie ◽

Xili Jiang ◽

Jianquan Zhang ◽

Shaowei Xie ◽

Yongyong Hua ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Hepatitis B ◽

Pathway Analysis ◽

Expression Profiles ◽

Financial Burden ◽

Disease Free Survival ◽

Ppi Network ◽

Hub Genes ◽

Bioinformatics Analyses ◽

Extracellular Region

Background Hepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses. Methods In the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein–protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC. Results First, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival. Conclusions The present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

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Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Cell Transplantation ◽

10.1177/0963689719893950 ◽

2019 ◽

Vol 28 (1_suppl) ◽

pp. 76S-86S ◽

Cited By ~ 5

Author(s):

Zengyuan Zhou ◽

Yuzheng Li ◽

Haiyue Hao ◽

Yuanyuan Wang ◽

Zihao Zhou ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Prognostic Biomarkers ◽

Ppi Network ◽

Hub Genes ◽

P53 Signaling

Hepatocellular carcinoma (HCC) is a widespread, common type of cancer in Asian countries, and the need for biomarker-matched molecularly targeted therapy for HCC has been increasingly recognized. However, the effective treatment for HCC is unclear. Therefore, identifying additional hub genes and pathways as novel prognostic biomarkers for HCC is necessary. In this study, the expression profiles of GSE121248, GSE45267 and GSE84402 were obtained from the Gene Expression Omnibus (GEO), including 132 HCC and 90 noncancerous liver tissues. Differentially expressed genes (DEGs) between HCC and noncancerous samples were identified by GEO2 R and Venn diagrams. In total, 109 DEGs were identified in these datasets, including 24 upregulated genes and 85 downregulated genes. Subsequently, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) preliminary analyses of the DEGs were performed using DAVID. The protein–protein interaction (PPI) network of the DEGs was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized in Cytoscape. Module analysis of the PPI network was performed using MCODE to get hub genes. Moreover, the influence of the hub genes on overall survival was determined with Kaplan–Meier plotter. All hub genes were analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and KEGG. Overall, the hub genes DTL, CDK1, CCNB1, RACGAP1, ECT2, NEK2, BUB1B, PBK, TOP2A, ASPM, HMMR, RRM2, CDKN3, PRC1, and ANLN were upregulated in HCC, and the survival rate was lower for HCC with increased expression of these hub genes. CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway, and CCNB1, CDK1, and BUB1B were enriched in the cell cycle. In brief, we screened 15 hub genes and pathways to identify potential prognostic markers for HCC treatment. However, the specific occurrence and development of HCC with expression of the hub genes should be verified in vivo and in vitro.

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Comprehensive Analysis and Identification of Key Driver Genes for Distinguishing Between Esophageal Adenocarcinoma and Squamous Cell Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.676156 ◽

2021 ◽

Vol 9 ◽

Author(s):

Feng Wang ◽

Lan Zhang ◽

Yue Xu ◽

Yilin Xie ◽

Shenglei Li

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Esophageal Adenocarcinoma ◽

Squamous Cell ◽

Differentially Expressed ◽

Control Group ◽

Ppi Network ◽

Hub Genes ◽

Kaplan Meier ◽

Key Genes

Background: Esophageal cancer (EC) is one of the deadliest cancers in the world. However, the mechanism that drives the evolution of EC is still unclear. On this basis, we identified the key genes and molecular pathways that may be related to the progression of esophageal adenocarcinoma and squamous cell carcinoma to find potential markers or therapeutic targets.Methods: GSE26886 were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) among normal samples, EA, and squamous cell carcinoma were determined using R software. Then, potential functions of DEGs were determined using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The STRING software was used to identify the most important modules in the protein–protein interaction (PPI) network. The expression levels of hub genes were confirmed using UALCAN database. Kaplan–Meier plotters were used to confirm the correlation between hub genes and outcomes in EC.Results: In this study, we identified 1,098 genes induced in esophageal adenocarcinoma (EA) and esophageal squamous cell carcinoma (ESCC), and 669 genes were reduced in EA and ESCC, suggesting that these genes may play an important role in the occurrence and development of EC tumors. Bioinformatics analysis showed that these genes were involved in cell cycle regulation and p53 and phosphoinositide 3-kinase (PI3K)/Akt signaling pathway. In addition, we identified 147 induced genes and 130 reduced genes differentially expressed in EA and ESCC. The expression of ESCC in the EA group was different from that in the control group. By PPI network analysis, we identified 10 hub genes, including GNAQ, RGS5, MAPK1, ATP1B1, HADHA, HSDL2, SLC25A20, ACOX1, SCP2, and NLN. TCGA validation showed that these genes were present in the dysfunctional samples between EC and normal samples and between EA and ESCC. Kaplan–Meier analysis showed that MAPK1, ACOX1, SCP2, and NLN were associated with overall survival in patients with ESCC and EA.Conclusions: In this study, we identified a series of DEGs between EC and normal samples and between EA and ESCC samples. We also identified 10 key genes involved in the EC process. We believe that this study may provide a new biomarker for the prognosis of EA and ESCC.

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