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Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker.

ABSTRACT: Bladder cancer (BC) is the ninth most common lethal malignancy worldwide. Great efforts have been devoted to clarify the pathogenesis of BC, but the underlying molecular mechanisms remain unclear. To screen for the genes associated with the progression and carcinogenesis of BC, three datasets were obtained from the Gene Expression Omnibus. A total of 37 tumor and 16 non-cancerous samples were analyzed to identify differentially expressed genes (DEGs). Subsequently, 141 genes were identified, including 55 upregulated and 86 downregulated genes. The protein-protein interaction network was established using the Search Tool for Retrieval of Interacting Genes database. Hub gene identification and module analysis were performed using Cytoscape software. Hierarchical clustering of hub genes was conducted using the University of California, Santa Cruz Cancer Genomics Browser. Among the hub genes, kinesin family member 11 (KIF11) was identified as one of the most significant prognostic biomarkers among all the candidates. The Kaplan Meier Plotter database was used for survival analysis of KIF11. The expression profile of KIF11 was analyzed using the ONCOMINE database. The expression levels of KIF11 in BC samples and bladder cells were measured using reverse transcription-quantitative pCR, immunohistochemistry and western blotting. In summary, KIF11 was significantly upregulated in BC and might act as a potential prognostic biomarker. The present identification of DEGs and hub genes in BC may provide novel insight for investigating the molecular mechanisms of BC.

SUBMITTER: Mo XC

PROVIDER: S-EPMC7816288 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker.

Mo Xiao-Cong XC Zhang Zi-Tong ZT Song Meng-Jia MJ Zhou Zi-Qi ZQ Zeng Jian-Xiong JX Du Yu-Fei YF Sun Feng-Ze FZ Yang Jie-Ying JY He Jun-Yi JY Huang Yue Y Xia Jian-Chuan JC Weng De-Sheng DS

Oncology letters 20210114 3

Bladder cancer (BC) is the ninth most common lethal malignancy worldwide. Great efforts have been devoted to clarify the pathogenesis of BC, but the underlying molecular mechanisms remain unclear. To screen for the genes associated with the progression and carcinogenesis of BC, three datasets were obtained from the Gene Expression Omnibus. A total of 37 tumor and 16 non-cancerous samples were analyzed to identify differentially expressed genes (DEGs). Subsequently, 141 genes were identified, inc ...[more]

PMID: 33574944

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Project description:BackgroundCervical cancer belongs to one of the most common female cancers; yet, the exact underlying mechanisms are still elusive. Recently, microarray and sequencing technologies have been widely used for screening biomarkers and molecular mechanism discovery in cancer studies. In this study, we aimed to analyse the microarray datasets using comprehensive bioinformatics tools and identified novel biomarkers associated with the prognosis of patients with cervical cancer.MethodsThe differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets including GSE138080, GSE113942 and GSE63514 were analysed using GEO2R tool. The functional enrichment analysis was performed using g:Profiler tool. The protein-protein interaction (PPI) network construction and hub genes identification were performed using the STRING database and Cytoscape software, respectively. The hub genes were subjected to expression and survival analysis in the cervical cancer. The EdU incorporation and Cell Counting Kit-8 assays were performed to evaluate the effects of hub gene knockdown on the proliferation of cervical cancer cells.ResultsA total of 89 overlapping DEGs (63 up-regulated and 26 down-regulated genes) were identified in the microarray datasets. The functional enrichment analysis indicated that the overlapping DEGs were mainly associated with "DNA replication" and "cell cycle". Furthermore, the PPI network analysis revealed that the network contains 87 nodes and 309 edges. Sub-module analysis using the Molecular Complex Detection tool identified 21 hub genes from the PPI network. The expression levels of the 21 hub genes were all up-regulated in the cervical cancer tissues when compared to normal cervical tissues as analysed by GEPIA tool. The survival analysis showed that the low expression of cell division cycle 45 (CDC45), GINS complex subunit 2 (GINS2), minichromosome maintenance complex component 2 (MCM2) and proliferating cell nuclear antigen (PCNA) was significantly correlated with the shorter overall survival of patients with cervical cancer. Moreover, the protein expression levels of GINS2, MCM2 and PCNA, but not CDC45, were significantly up-regulated in the cervical cancer tissues when compared to normal cervical tissues. Finally, knockdown of MCM2 significantly suppressed the proliferation of HeLa and SiHa cells.ConclusionIn conclusion, we screened a total of 89 overlapping DEGs from the GEO datasets, and further analysis identified four hub genes (CDC45, GINS2, MCM2 and PCNA) that were likely associated with the prognosis of patients with cervical cancer. MCM2 knockdown repressed the cervical cancer cell proliferation. The current findings may provide novel insights into understanding the pathophysiology of cervical cancer and develop therapeutic targets for patients with cervical cancer.

Project description:BACKGROUND:Gastric cancer (GC) is a prevalent malignant cancer of digestive system. To identify key genes in GC, mRNA microarray GSE27342, GSE29272, and GSE33335 were downloaded from GEO database. METHODS:Differentially expressed genes (DEGs) were obtained using GEO2R. DAVID database was used to analyze function and pathways enrichment of DEGs. Protein-protein interaction (PPI) network was established by STRING and visualized by Cytoscape software. Then, the influence of hub genes on overall survival (OS) was performed by the Kaplan-Meier plotter online tool. Module analysis of the PPI network was performed using MCODE. Additionally, potential stem loop miRNAs of hub genes were predicted by miRecords and screened by TCGA dataset. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst. RESULTS:In total, 67 DEGs were identified; upregulated DEGs were mainly enriched in biological process (BP) related to angiogenesis and extracellular matrix organization and the downregulated DEGs were mainly enriched in BP related to ion transport and response to bacterium. KEGG pathways analysis showed that the upregulated DEGs were enriched in ECM-receptor interaction and the downregulated DEGs were enriched in gastric acid secretion. A PPI network of DEGs was constructed, consisting of 43 nodes and 87 edges. Twelve genes were considered as hub genes owing to high degrees in the network. Hsa-miR-29c, hsa-miR-30c, hsa-miR-335, hsa-miR-33b, and hsa-miR-101 might play a crucial role in hub genes regulation. In addition, the transcription factors-hub genes pairs were displayed with 182 edges and 102 nodes. The high expression of 7 out of 12 hub genes was associated with worse OS, including COL4A1, VCAN, THBS2, TIMP1, COL1A2, SERPINH1, and COL6A3. CONCLUSIONS:The miRNA and TFs regulation network of hub genes in GC may promote understanding of the molecular mechanisms underlying the development of gastric cancer and provide potential targets for GC diagnosis and treatment.

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Project description:Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality, which makes the prognostic prediction challenging. Angiogenesis appears to be of critical importance in the progression and metastasis of HCC. Some of the angiogenesis-related genes promote this process, while other anti-angiogenesis genes suppress tumor growth and metastasis. Therefore, the comprehensive prognostic value of multiple angiogenesis-related genes in HCC needs to be further clarified. In this study, the mRNA expression profile of HCC patients and the corresponding clinical data were acquired from multiple public databases. Univariate Cox regression analysis was utilized to screen out differentially expressed angiogenesis-related genes with prognostic value. A multigene signature was established with the least absolute shrinkage and selection operator Cox regression in the Cancer Genome Atlas cohort, and validated through an independent cohort. The results suggested that a total of 16 differentially expressed genes (DEGs) were associated with overall survival (OS) and a 7-gene signature was constructed. The risk score of each patient was calculated using this signature, the median value of which was used to divide these patients into a high-risk group and a low-risk group. Compared with the low-risk group, the patients in the high-risk group had a poor prognosis. The risk score was an independent predictor for OS through multivariate Cox regression analysis. Then, unsupervised learning was used to verify the validity of this 7-gene signature. A nomogram by further integrating clinical information and the prognostic signature was utilized to predict prognostic risk and individual OS. Functional enrichment analyses demonstrated that these DEGs were enriched in the pathways of cell proliferation and mitosis, and the immune cell infiltration was significantly different between the two risk groups. In summary, a novel angiogenesis-related genes signature could be used to predict the prognosis of HCC and for targeted therapy.

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Dataset Information

Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker.

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Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker.

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