Integrative Bioinformatics Analysis Identified Hub Genes in Association with Development of Lung Adenocarcinoma

Abstract Background: Cancer-related death is mainly caused by lung adenocarcinoma (LUAD), an aggressive malignant tumor. Therefore, the identification of important LUAD-related genes and the further analysis of its prognostic significance are critical for the survival of LUAD patients.Methods: Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods were adopted to screen out TCGA-LUAD database and the gene expression profiles of GSE32863 from GEO. Functional annotation analysis and protein-protein interaction (PPI) network were conducted on differential co-expression genes. Furthermore, survival analysis was carried out on twelve hub genes that were identified by applying the CytoHubba plugin of Cytoscape. Finally, we validated the protein level of survival-related gene in HPA database.Results: A total of 358 differential co-expression genes were extracted from the database of TCGA and GEO. These genes were mainly enriched in extracellular structure organization, cell−substrate adhesion and response to acid chemical (biological process), cell−cell junction and collagen−containing extracellular matrix (cellular component), DNA−binding transcription activator activity, glycosaminoglycan binding, and growth factor binding (molecular function). In the KEGG analysis, the main pathways were Drug metabolism−cytochrome P450. Moreover, in a PPI network, the 12 hub genes (GNG11, ADRB2, ADCY4, TGFBR2, IL6, GPC3, VIPR1, GRK5, CAV1, RAMP3, RAMP2, and CALCRL) were identified. The expression level of ADCY4, VIPR1, and TGFBR2 was corresponded with clinical stages and overall survival (OS) in LUAD patients. Finally, the protein level of ADCY4 and VIPR1 was down-regulated consistently with mRNA levels in LUAD samples.Conclusion: Perhaps, ADCY4, VIPR1 and TGFBR2 play important role in tumorigenesis, so they will serve as prognostic biomarkers and therapeutic targets of LUAD in the future.

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Prognostic Value of BIRC5 in Lung Adenocarcinoma Lacking EGFR, KRAS, and ALK Mutations by Integrated Bioinformatics Analysis

Disease Markers ◽

10.1155/2019/5451290 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Yajuan Cao ◽

Weikang Zhu ◽

Wanqing Chen ◽

Jianchun Wu ◽

Guozhen Hou ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Lung Adenocarcinoma ◽

Prognostic Value ◽

Expression Profiles ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes

Objective. This study was aimed at investigating the prognostic significance of Baculoviral IAP repeat containing 5 (BIRC5) in lung adenocarcinoma (LAD) lacking EGFR, KRAS, and ALK mutations (triple-negative (TN) adenocarcinomas). Methods. The gene expression profiles were obtained from Gene Expression Omnibus (GEO). The identification of the differentially expressed genes (DEGs) was performed by GeneSpring GX. Gene set enrichment analysis (GSEA) was used to execute gene ontology function and pathway enrichment analysis. The protein interaction network was constructed by Cytoscape. The hub genes were extracted by MCODE and cytoHubba plugin from the network. Then, using BIRC5 as a candidate, the prognostic value in LAD and TN adenocarcinomas was verified by the Kaplan-Meier plotter and The Cancer Genome Atlas (TCGA) database, respectively. Finally, the mechanism of BIRC5 was predicted by a coexpressed network and enrichment analysis. Results. A total of 38 upregulated genes and 121 downregulated genes were identified. 9 hub genes were extracted. Among them, the mRNA expression of 5 genes, namely, BIRC5, MCM4, CDC20, KIAA0101, and TRIP13, were significantly upregulated among TN adenocarcinomas (all P<0.05). Notably, only the overexpression of BIRC5 was associated with unfavorable overall survival (OS) in TN adenocarcinomas (log rank P=0.0037). TN adenocarcinoma patients in the BIRC5 high-expression group suffered from a significantly high risk of distant metastasis (P=0.046), advanced N stage (P=0.033), and tumor-bearing (P=0.031) and deceased status (P=0.003). The mechanism of BIRC5 and coexpressed genes may be linked closely with the cell cycle. Conclusion. Overexpressed in tumors, BIRC5 is associated with unfavorable overall survival in TN adenocarcinomas. BIRC5 is a potential predictor and therapeutic target in TN adenocarcinomas.

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17031053 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1053

Author(s):

Chengzhang Li ◽

Jiucheng Xu

Keyword(s):

Gene Expression ◽

Target Genes ◽

Expression Profiles ◽

Differential Expression Analysis ◽

Gene Expression Profiles ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Gene Differential Expression ◽

Oncomine Database

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

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Overexpression of SKA Complex Is Associated With Poor Prognosis in Gliomas

Frontiers in Neurology ◽

10.3389/fneur.2021.755681 ◽

2022 ◽

Vol 12 ◽

Author(s):

Shoukai Yu

Keyword(s):

Chromosome Segregation ◽

Poor Prognosis ◽

Nuclear Division ◽

Prognostic Significance ◽

Mrna Levels ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Spindle Microtubules

The spindle and kinetochore-associated complex is composed of three members: SKA1, SKA2, and SKA3. It is necessary for stabilizing spindle microtubules attaching to kinetochore (KT) in the middle stage of mitosis. The SKA complex is associated with poor prognosis in several human cancers. However, the role of SKA complex in rare malignant diseases, such as gliomas, has not been fully investigated. We investigated several databases, including Oncomine, UALCAN, and cBioPortal to explore the expression profile and prognostic significance of SKA complex in patients with gliomas. Gene ontology and Kyoto Encyclopedia of Genes and Genome pathways were used to analyze the potential enriched pathways. The genes co-expressed with SKA complex were identified and used for developing a protein-protein interaction (PPI) network using the STRING database. We found a significant overexpression of the mRNA levels of SKA1, SKA2, and SKA3 in patients with glioma patients. Higher expression of SKA1 and SKA3, but not SKA2, was significantly correlated with shorter overall survival of patients with glioma. In glioma, SKA complex was found to be involved in nuclear division, chromosome segregation, and DNA replication. The results of PPI network identified 10 hub genes (CCNB2, UBE2C, BUB1B, TPX2, CCNA2, CCNB1, MELK, TOP2A, PBK, and KIF11), all of which were overexpressed and negatively associated with prognosis of patients with glioma. In conclusion, our study sheds new insights into the biological role and prognostic significance of SKA complex in glioma.

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Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

10.21203/rs.3.rs-580657/v1 ◽

2021 ◽

Author(s):

Zimeng Wei ◽

Min Zhao ◽

Linnan Zang

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Analysis Tool ◽

Hub Genes ◽

Kegg Analysis ◽

Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

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Identification of Molecular Markers Associated with Diagnosis and Prognosis in Lung Adenocarcinoma by Bioinformatics Analysis

10.21203/rs.3.rs-79373/v1 ◽

2020 ◽

Author(s):

Zimeng Wei ◽

Linnan Zang ◽

Min Zhao

Keyword(s):

Gene Expression ◽

Molecular Markers ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Diagnosis And Treatment ◽

Analysis Tool ◽

Hub Genes ◽

Kegg Analysis ◽

Diagnosis And Prognosis ◽

Ppi Networks

Abstract Background：Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Our aim is to identify the candidate key genes and pathways associated with diagnosis and prognosis in LUAD.Methods：In this study, three gene expression profiles GSE118370, GSE32863 and GSE43458 were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, the miRNAs and the hub genes network was constructed via miRWalk. Finally, receiver operating characteristic (ROC) curve and Kaplan-Meier plotter were performed to analyze the diagnosis and prognosis efficacy of hub genes. Results: A total of 311 DEGs were identified, including 74 up-regulated and 238 down-regulated genes. GO analysis results showed that DEGs were mainly enriched in biological processes including composition of extracellular matrix, regulation of angiogenesis and so on. KEGG analysis results revealed DEGs were mainly enrolled in cell adhesion signaling pathway. Subsequently, 10 hub genes, CDC20, CENPF, TPX2, TOP2A, KIAA0101, CDCA7, ASPM, ECT2, UBE2T and COL1A1, were identified. And TOP2A, CDCA7, TPX2 and COL1A1 showed strong relationships with each other and the miRNAs nearby in miRNAs-mRNA network obtained by miRWalk website. Finally, all these 10 hub genes were found significantly related to the diagnosis and prognosis of LUAD (p<0.05). Conclusions: The identification of hub genes in this study will help us to understand the pathogenesis of LUAD, especially the molecular mechanisms of its development. Our results suggested that TOP2A, CDCA7, TPX2 and COL1A1 might present predictive value for the development and prognosis in LUAD, and might be used as potential molecular markers for the diagnosis and treatment of LUAD.

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Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

10.21203/rs.3.rs-700226/v1 ◽

2021 ◽

Author(s):

Li Tao ◽

ChaoLiang Xiong ◽

Li Xue

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Pathway Analysis ◽

Expression Profiles ◽

Diagnostic Value ◽

Functional Enrichment ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Gene Markers

Abstract Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitis and subsequent destruction of cartilage and bone. This study aimed to explore RA-related gene markers and the underlying molecular mechanism.Material and Methods: The expression profiles of GSE77298, GSE55235 and GSE12021 were obtained from the Gene Expression Omnibus database. Then, the differential gene expression analysis was conducted between GSE77298 and GSE55235 datasets. Limma package and a Venn diagram were utilized to screen the overlapping differentially expressed genes (DEGs), and Functional enrichment and pathway analysis were performed by using DAVID database. Subsequently, a protein-protein interaction (PPI) network was established, and candidate hub genes were recognized by using STRING and Cytoscape software. Finally, another dataset (GSE12021) was used for the validation of diagnostic value of the candidate hub genes and to identify real hub genes by using receiver operating characteristic (ROC) curves.Results: A total of 385 DEGs were detected, which include 19 downregulated genes and 366 upregulated genes. GO and KEGG pathway analysis showed that DEGs was mainly enriched in various immune and inflammatory response-related functions and pathways. The PPI network was composed of 374 nodes and 767 edges. A total of 8 real hub genes (HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D) which displayed an excellent diagnostic value for RA were identified.Conclusion: these findings may provide novel and reliable biomarkers for RA, which have some interesting implications for early diagnosis, prognosis and targeted therapy.

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Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

10.21203/rs.2.15313/v1 ◽

2019 ◽

Author(s):

Yanyan Tang ◽

Ping Zhang

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Signaling Pathway ◽

Pathway Analysis ◽

Kegg Pathway ◽

Expression Profiles ◽

Ductal Adenocarcinoma ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Identification of Novel Biomarkers Related to Lung Squamous Cell Carcinoma Using Integrated Bioinformatics Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/9059116 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Haiyan Wang ◽

Lizhi Huang ◽

Li Chen ◽

Jing Ji ◽

Yuanyuan Zheng ◽

...

Keyword(s):

Gene Expression ◽

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Early Stage ◽

Prognostic Significance ◽

Lung Squamous Cell Carcinoma ◽

Ppi Network ◽

Hub Genes ◽

Clinicopathologic Characteristics

Background. Lung squamous cell carcinoma (LUSC) is one of the most common types of lung carcinoma and has specific clinicopathologic characteristics. In this study, we screened novel molecular biomarkers relevant to the prognosis of LUSC to explore new diagnostic and treatment approaches for this disease. Methods. We downloaded GSE73402 from the Gene Expression Omnibus (GEO) database. GSE73402 contains 62 samples, which could be classified as four subtypes according to their pathology and stages. Via weighted gene coexpression network analysis (WGCNA), the main module was identified and was further analyzed using differentially expressed genes (DEGs) analysis. Then, by protein-protein interaction (PPI) network and Gene Expression Profiling Interactive Analysis (GEPIA), hub genes were screened for potential biomarkers of LUSC. Results. Via WGCNA, the yellow module containing 349 genes was identified, and it is strongly related to the subtype of CIS (carcinoma in situ). DEGs analysis detected 180 genes that expressed differentially between the subtype of CIS and subtype of early-stage carcinoma (Stage I and Stage II). A PPI network of DEGs was constructed, and the top 20 genes with the highest correlations were selected for GEPIA database to explore their effect on LUSC survival prognosis. Finally, ITGA5, TUBB3, SCNN1B, and SERPINE1 were screened as hub genes in LUSC. Conclusions. ITGA5, TUBB3, SCNN1B, and SERPINE1 may have great diagnostic and prognostic significance for LUSC and have great potential to be new treatment targets for LUSC.

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