Identification of Key Genes Associated with Progression and Prognosis of Bladder Cancer through Integrated Bioinformatics Analysis.
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ABSTRACT:
SUBMITTER: Verma S
PROVIDER: S-EPMC8656554 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.
| S-EPMC6924121 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer‑related deaths among cancer patients. Genes correlated with the progression and prognosis of HCC are critically needed to be identified. In the present study, 3 Gene Expression Omnibus (GEO) datasets (GSE46408, GSE65372 and GSE84402) were used to analyze the differentially expressed genes (DEGs) between HCC and non‑tumor liver tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs. A protein‑protein interaction network was established to screen the hub genes associated with HCC. The prognostic values of hub genes in HCC patients were analyzed using The Cancer Genome Atlas (TCGA) database. The expression levels of hub genes were validated based on ONCOMINE, TCGA and Human Protein Atlas (HPA) databases. Notably, 56 upregulated and 33 downregulated DEGs were markedly enriched under various GO terms and four KEGG terms. Among these DEGs, 10 hub genes with high connectivity degree were identified, including cyclin B1, cyclin A2, cyclin B2, condensin complex subunit 3, PDZ binding kinase, nucleolar and spindle‑associated protein 1, aurora kinase A, ZW10 interacting kinetochore protein, protein regulator of cytokinesis 1 and kinesin family member 4A. The upregulated expression levels of these hub genes in HCC tissues were further confirmed by ONCOMINE, TCGA, and HPA databases. Additionally, the increased mRNA expression of each hub gene was related to the unfavorable disease‑free survival and overall survival of HCC patients. The present study identified ten genes associated with HCC, which may help to provide candidate targets for the diagnosis and treatment of HCC.
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Project description:BACKGROUND:Colorectal cancer (CRC) is the third most lethal malignancy in the world, wherein colon adenocarcinoma (COAD) is the most prevalent type of CRC. Exploring biomarkers is important for the diagnosis, treatment, and prevention of COAD. METHODS:We used GEO2R and Venn online software for differential gene screening analysis. Hub genes were screened via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape, following Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, survival analysis and RNA expression validation were performed via UALCAN online software and real-time PCR. Immunohistochemistry (IHC) was performed to verify the protein expression level of hub genes from tissues of COAD patients. RESULTS:In the present study, we screened 323 common differentially expressed genes (DEGs) from four GSE datasets. Furthermore, four hub genes were selected for survival correlation analysis and expression level verification, three of which were shown to be statistically significant. CONCLUSION:Our study suggests that Serpin Family E Member 1 (SERPINE1), secreted phosphoprotein 1 (SPP1) and tissue inhibitor of metalloproteinase 1 (TIMP1) may be biomarkers closely related to the prognosis of CRC patients.
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Project description:Background and Objective: Despite striking advances in multimodality management, gastric cancer (GC) remains the third cause of cancer mortality globally and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The study aimed to identify potential key genes associated with the pathogenesis and prognosis of GC. Methods: Differentially expressed genes between GC and normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GC were identified by employing protein-protein interaction network and Cox proportional hazards model analyses. Results: We identified nine hub genes (TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1) which might be tightly correlated with the pathogenesis of GC. A prognostic gene signature consisted of CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 was constructed with a good performance in predicting overall survivals. Conclusion: The findings of this study would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GC.
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Project description:Bladder cancer is a common malignancy with mechanisms of pathogenesis and progression. This study aimed to identify the prognostic hub genes, which are the central modulators to regulate the progression and proliferation in the specific subtype of bladder cancer. The identification of the candidate hub gene was performed by weighted gene co-expression network analysis to construct a free-scale gene co-expression network. The gene expression profile of GSE97768 from the Gene Expression Omnibus database was used. The association between prognosis and hub gene was evaluated by The Cancer Genome Atlas database. Four gene-expression modules were significantly related to bladder cancer disease: the red module (human adenocarcinoma lymph node metastasis), the darkturquioise module (grade 2 carcinoma), the lightgreen module (grade 3 carcinoma), and the royalblue module (transitional cell carcinoma lymphatic metastasis). Based on betweenness centrality and survival analysis, we identified laminin subunit gamma-2 (LAMC2) in the grade 2 carcinoma, gelsolin (GSN) in the grade 3 carcinoma, and homeodomain-interacting protein kinase 2 (HIPK2) in the transitional cell carcinoma lymphatic metastasis. Subsequently, the protein levels of LAMC2 and GSN were respectively down-regulated and up-regulated in tumor tissue with the Human Protein Atlas (HPA) database. Our results suggested that LAMC2 and GSN are the central modulators to transfer information in the specific subtype of the disease.
| S-EPMC7408759 | biostudies-literature
Project description:PurposeSmoking is recognized as a risk factor for Chronic Obstructive Pulmonary Disease (COPD), yet only 20-25% of smokers eventually develop COPD. Since its molecular pathogenesis remains unclear, there is an important need to further understand genetic differences between smokers with COPD and healthy smokers, screen out high-risk and susceptible groups among smokers, and find effective therapeutic targets.MethodsBioinformatics tools were used to screen biomarkers that were significantly associated with COPD smokers and healthy smokers. qRT-PCR and Western blotting analysis were used to detect hub gene expression in CSE-treated BEAS-2B cells and lung tissue of COPD mouse models.ResultsOur study identified 132 DEGs. The GO and KEGG analyses suggested that the ECM-receptor interaction, MAPK signaling pathway, Chemokine signaling pathway, PI3K-Akt signaling pathway, extracellular matrix organization and collagen fibril organization were associated with the occurrence and development of COPD. In addition, WGCNA analysis of GSE1650 showed that the brown module was most correlated with COPD. The intersection between the brown module and DEGs was used to identify 9 HUB genes (COL14A1, SULF1, MOXD1, CXCL12, CHRNA1, COMP, POU2AF1, MMP11, THBS2) that showed consistent expression and upregulation. Both the mRNA and protein expression levels of the Hub genes (except that of MMP11) were significantly upregulated in tobacco smoke exposed mouse emphysema models and CSE treated BEAS-2B cells.ConclusionOur results suggest that COL14A1, SULF1, MOXD1, CXCL12, CHRNA1, COMP, POU2AF1, and THBS2 may be potentially useful biomarkers for identifying smokers with a risk of developing COPD. The GO and KEGG functional enrichment analyses further confirmed the significant role played by ECM in the pathogenesis of COPD. The results of this study may provide further insights into the pathogenetic mechanisms involved in COPD.
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Project description:BackgroundCoronary heart disease is the leading cause of death worldwide. Myocardial infarction (MI) is a fatal manifestation of coronary heart disease, which can present as sudden death. Although the molecular mechanisms of coronary heart disease are still unknown, global gene expression profiling is regarded as a useful approach for deciphering the pathophysiology of this disease and subsequent diseases. This study used a bioinformatics analysis approach to better understand the molecular mechanisms underlying coronary heart disease.MethodsThis experimental study was conducted in the department of cardiology, Aja University of Medical Sciences (2021-2022), Tehran, Iran. To identify the key deregulated genes and pathways in coronary heart disease, an integrative approach was used by merging three gene expression datasets, including GSE19339, GSE66360, and GSE29111, into a single matrix. The t test was used for the statistical analysis, with a significance level of P<0.05.ResultsThe limma package in R was used to identify a total of 133 DEGs, consisting of 124 upregulated and nine downregulated genes. KDM5D, EIF1AY, and CCL20 are among the top upregulated genes. Moreover, the interleukin 17 (IL-17) signaling pathway and four other signaling pathways were identified as the potent underlying pathogenesis of both coronary artery disease (CAD) and MI using a systems biology approach. Accordingly, these findings can provide expression signatures and potential biomarkers in CAD and MI pathophysiology, which can contribute to both diagnosis and therapeutic purposes.ConclusionFive signaling pathways were introduced in MI and CAD that were primarily involved in inflammation, including the IL-17 signaling pathway, TNF signaling pathway, toll-like receptor signaling pathway, C-type lectin receptor signaling pathway, and rheumatoid arthritis signaling pathway.
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Project description:The claudin multigene family is associated with various aberrant physiological and cellular signaling pathways. However, the association of claudins with survival prognosis, signaling pathways, and diagnostic efficacy in colon cancer remains poorly understood. Methods: Through the effective utilization of various bioinformatics methods, including differential gene expression analysis, gene set enrichment analysis protein-protein interaction (PPI) network analysis, survival analysis, single sample gene set enrichment analysis (ssGSEA), mutational variance analysis, and identifying receiver operating characteristic curve of claudins in The Cancer Genome Atlas colon adenocarcinoma (COAD). Results: We found that: CLDN2, CLDN1, CLDN14, CLDN16, CLDN18, CLDN9, CLDN12, and CLDN6 are elevated in COAD. In contrast, the CLDN8, CLDN23, CLDN5, CLDN11, CLDN7, and CLDN15 are downregulated in COAD. By analyzing the public datasets GSE15781 and GSE50760 from NCBI-GEO (https://www.ncbi.nlm.nih.gov/geo/), we have confirmed that CLDN1, CLDN2, and CLDN14 are significantly upregulated and CLDN8 and CLDN23 are significantly downregulated in normal colon, colon adenocarcinoma tumor, and liver metastasis of colon adenocarcinoma tissues from human samples. Various claudins are mutated and found to be associated with diagnostic efficacy in COAD. Conclusion: The claudin gene family is associated with prognosis, immune regulation, signaling pathway regulations, and diagnosis of COAD. These findings may provide new molecular insight into claudins in the treatment of colon cancer.
| S-EPMC10550083 | biostudies-literature
Project description:In recent years, the incidence and mortality of cervical cancer have increased worldwide. At the same time, increasing data have confirmed that miRNA-mRNA plays a positive or negative regulatory role in many cancers. This study attempted to screen effective miRNA-mRNA in the progression of cervical cancer, and to study the mechanism of miRNA-mRNA in the progression of cervical cancer. The expression profile data of GSE7410, GSE 63514, GSE 86100 and TCGA-CESC were downloaded, and 34 overlapping differentially expressed genes (22 up-regulated and 12 down-regulated) and 166 miRNAs (74 down-regulated and 92 up-regulated) were screened through limma package. Then, miR-197-3p/TYMS pairs were obtained by PPI, functional enrichment, Kaplan-Meier plotter analysis, Cox univariate and multivariate analysis, risk modeling, WGCNA, qPCR and dual-luciferase experiments. The results showed that TYMS was an independent prognostic factor of cervical cancer, and its expression level was negatively correlated with cervical cancer tissue grade (TMN), tumor grade, age, microsatellite stability and tumor mutation load, and positively correlated with methyl expression in DNMT1, DNMT2, DNMT3A and DNMT3B. Functional experiments showed that TYMS knockout could promote the proliferation, migration and invasion of HeLa cells and reduce apoptosis. Overexpression of TYMS showed the opposite trend, miR-197-3p was negatively correlated with the expression of TYMS. MiR-197-3p inhibitor reversed the effect of si-TYMS on the proliferation of HeLa cells. In conclusion, these results reveal that TYMS plays a very important role in the prognosis and progression of cervical cancer, and has the potential to be thought of as cervical cancer biomarkers. At the same time, miR-197-3p/TYMS axis can regulate the deterioration of cervical cancer cells, which lays a foundation for the molecular diagnosis and treatment of cervical cancer.
| S-EPMC8727538 | biostudies-literature
Project description:BackgroundAcute myocardial infarction (AMI) is a common disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in AMI through adopting integrated bioinformatics tools.MethodsThe gene expression Omnibus (GEO) database was used to obtain genes data of AMI and no-AMI whole blood. Furthermore, differentially expressed genes (DEGs) were screened using the "Limma" package in R 3.6.1 software. Functional and pathway enrichment analyses of DEGs were performed via "Bioconductor" and "GOplot" package in R 3.6.1 software. In order to screen hub DEGs, the STRING version 11.0 database, Cytoscape and molecular complex detection (MCODE) were applied. Correlation among the hub DEGs was evaluated using Pearson's correlation analysis.ResultsBy performing DEGs analysis, 289 upregulated and 62 downregulated DEGs were successfully identified from GSE66360, respectively. And they were mainly enriched in the terms of neutrophil activation, immune response, cytokine, nuclear factor kappa-B (NF-κB) signaling pathway, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Based on the data of protein-protein interaction (PPI), the top 10 hub genes were ranked, including interleukin-8 (CXCL8), TNF, N-formyl peptide receptor 2 (FPR2), growth-regulated alpha protein (CXCL1), transcription factor AP-1 (JUN), interleukin-1 beta (IL1B), platelet basic protein (PPBP), matrix metalloproteinase-9 (MMP9), toll-like receptor 2 (TLR2), and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G). What's more, the results of correlation analysis demonstrated that there was positive correlation between the 10 hub DEGs.ConclusionTen DEGs were identified as potential candidate diagnostic biomarkers for patients with AMI in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with AMI.
| S-EPMC8052032 | biostudies-literature