Hub Genes and Key Pathway Identification in Colorectal Cancer Based on Bioinformatic Analysis.
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ABSTRACT:
SUBMITTER: Lv J
PROVIDER: S-EPMC6874977 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Colorectal cancer (CRC) is the most common cancer of the digestive system. The aim of the present study was to identify the potential biomarkers and uncover the underlying mechanisms. The gene and miRNA expression profiles were obtained from GEO database. The differentially expressed genes (DEGs) and miRNAs (DE miRNAs) were identified by GEO2R. The gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by KOBAS 3.0. The protein-protein interaction (PPI) network and miRNA-gene network were constructed by Cytoscape software. Then, the identified genes were verified by quantitative real-time PCR in both CRC tissue samples and cell lines. A total of 600 upregulated DEGs, 283 downregulated DEGs, 13 upregulated DE miRNAs and 7 downregulated DE miRNAs were identified. GO analysis results showed that upregulated DEGs were significantly enriched in binding, organelle and cellular process. Downregulated DEGs were enriched in binding, extracellular region and chemical homeostasis. KEGG analysis showed that the DEGs were mostly enriched in cell cycle and pathways in cancer. A total of eight genes were identified as biomarkers, including CAD, ITGA2, E2F3, BCL2, PRKACB, IGF1, SGK1 and NR3C1. Experimental validation showed that seven of the eight identified genes had the same expression trend as predicted, except for ITGA2. Besides, hsa-miR-552 and hsa‑miR-30a were identified as key miRNAs. the present study provides a series of biomarkers and mechanisms for the diagnosis and therapy of CRC. We also prove that although bioinformatics analysis is a wonderful approach, experiment validation is necessary.
| S-EPMC5652954 | biostudies-literature
Project description:Scarring diseases, such as Peyronie’s disease (PD), usually lead to disorders in the immune system. Previous studies suggested that the PD process was regulated by immune signaling. However, the pathogenetic mechanism remains incompletely characterized. This article used bioinformatic approaches to identify hub genes, key pathways and key immune-related genes that play essential roles in PD pathogenesis. Two Gene Expression Omnibus (GEO) datasets, GSE126005 and GSE146500, were used to analyse the transcriptional profiling in both PD and normal samples. R software was applied to examine the difference in the expression of hub genes and key immune-related genes. The candidates for hub genes were further validated through protein–protein interactions (PPIs), gene correlation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. In addition, candidate miRNA‒mRNA pairs were functionally assessed. A total of 39 candidate genes were identified, the expression levels of which in PD fibroblast cells were different from those in normal cells (16 showed reduced expression in PD and 21 candidates overexpressed in PD). We found that these genes could interact with each other through PPI analysis. According to the functional enrichment analysis, the candidates may regulate some major biological processes, including cytokine‒cytokine receptor interactions and the JAK-STAT signaling pathway. IL6, IL21R, IFNE, CXCL2, EGF, and ANGPTL5 were identified as key immune-related genes. The findings may help understand the role of immunologic contributors in PD, thus shedding light on the development of more effective strategies to prevent and treat this kind of disease.
| S-EPMC9767094 | biostudies-literature
Project description:This study aimed to explore key genes as potential biomarkers for colorectal cancer (CRC) diagnosis and prognosis in order to improve their clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded the microarray datasets GSE143939, GSE196006, and GSE200427 from the GEO database and applied the GEO2R tool to obtain differentially expressed genes (DEGs) between colorectal cancer tissue samples and normal tissue samples. Differentially expressed genes were analyzed using the DAVID online database for gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses. Protein-protein interaction network was constructed and related module analysis was performed using STRING and Cytoscape. In total, 241 DEGs were identified, including 127 downregulated and 114 upregulated genes. DEGs enriched functions and pathways included cellular response to chemical stimulus, extracellular region, carbonate dehydratase activity, cell division, spindle, and cell division. The abundant functions and pathways of DEGs included cellular response to chemical stimulus, extracellular region, carbonate dehydratase activity, cell division, spindle, cell adhesion molecule binding, Aldosterone-regulated sodium reabsorption, and Cell cycle-related processes. Fifteen key genes were identified, and bioprocess analyses showed that these genes were mainly enriched in cell cycle, cell division, mitotic spindle, and tubulin binding processes. It was found that CDK1, CEP55, MKI67, and TOP2A may be involved in CRC cancer invasion and recurrence. The pivotal genes identified in this study contribute to our understanding of the molecular and pathogenic mechanisms of CRC carcinogenesis and progression, and provide possible biomarkers for the diagnosis and treatment of CRC.
| S-EPMC10735105 | biostudies-literature
Project description:BackgroundBladder cancer (BC) is the second most frequent malignancy of the urinary system. The aim of this study was to identify key microRNAs (miRNAs) and hub genes associated with BC as well as analyse their targeted relationships.MethodsAccording to the microRNA dataset GSE112264 and gene microarray dataset GSE52519, differentially expressed microRNAs (DEMs) and differentially expressed genes (DEGs) were obtained using the R limma software package. The FunRich software database was used to predict the miRNA-targeted genes. The overlapping common genes (OCGs) between miRNA-targeted genes and DEGs were screened to construct the PPI network. Then, gene ontology (GO) analysis was performed through the "cluster Profiler" and "org.Hs.eg.db" R packages. The differential expression analysis and hierarchical clustering of these hub genes were analysed through the GEPIA and UCSC Cancer Genomics Browser databases, respectively. KEGG pathway enrichment analyses of hub genes were performed through gene set enrichment analysis (GSEA).ResultsA total of 12 DEMs and 10 hub genes were identified. Differential expression analysis of the hub genes using the GEPIA database was consistent with the results for the UCSC Cancer Genomics Browser database. The results indicated that these hub genes were oncogenes, but VCL, TPM2, and TPM1 were tumour suppressor genes. The GSEA also showed that hub genes were most enriched in those pathways that were closely associated with tumour proliferation and apoptosis.ConclusionsIn this study, we built a miRNA-mRNA regulatory targeted network, which explores an understanding of the pathogenesis of cancer development and provides key evidence for novel targeted treatments for BC.
| S-EPMC8194198 | biostudies-literature
Project description:BackgroundBladder cancer (BLCA) is the ninth most common cancer worldwide, with high mortality and recurrence rates. Studies have increasingly reported that molecular diagnosis contributes to the early diagnosis and prognostic assessment of diseases. Thus, this study aims to find new biomarkers for the diagnosis and prognosis of BLCA.MethodsThe microarray datasets GSE147983 and The Cancer Genome Atlas (TCGA)-BLCA mRNA were obtained from the Gene Expression Omnibus (GEO) and TCGA. Differentially expressed genes (DEGs) were screened using the R "Limma" package. The "ClusterProfiler" package was used to conduct Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs. A DEG protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. The functional module was reanalyzed using Cytoscape's Molecular Complex Detection ("MCODE") plugin, and key genes related to BLCA were identified via the "cytoHubba" plugin. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and the Tumor Immune Estimation Resource (TIMER) were used to verify the correlation between hub gene expression and immunity. A survival analysis of hub genes was performed using the Kaplan-Meier Plotter online tool.ResultsA total of 355 DEGs were screened out, including 236 upregulated and 119 downregulated DEGs. Some of the GO terms and pathways, such as chromosome separation, cell cycle, and cell senescence, were found to be significantly enriched in the DEGs. The key genes were kinesin family member 11 (KIF11), DLG associated protein 5 (DLGAP5), non-SMC condensin I complex subunit G (NCAPG), cell division cycle 20 (CDC20), cyclin B2 (CCNB2), BUB1 mitotic checkpoint serine (BUB1B), TPX2 microtubule nucleation factor (TPX2), NUF2 component of NDC80 kinetochore complex (NUF2), kinesin family member 2C (KIF2C), and cyclin B1 (CCNB1). Nine of them were immune-related, including KIF11, DLGAP5, NCAPG, CDC20, CCNB2, BUB1B, NUF2, KIF2C, and CCNB1. Survival analysis showed that the overexpression of BUB1B, CCNB1, CDC20, and DLGAP5 significantly reduced overall survival (OS) in patients with BLCA.ConclusionsThis study provided a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of BLCA by screening potential biomarkers of BLCA.
| S-EPMC9189183 | biostudies-literature
Project description:The molecular mechanisms underlying hepatocellular carcinoma (HCC) progression remain largely undefined. Here, we identified 176 commonly upregulated genes in HCC tissues based on three Gene Expression Omnibus datasets and The Cancer Genome Atlas (TCGA) cohort. We integrated survival and methylation analyses to further obtain 12 upregulated genes for validation. These genes were overexpressed in HCC tissues at the transcription and protein levels, and increased mRNA levels were related to higher tumor grades and cancer stages. The expression of all markers was negatively associated with overall and disease-free survival in HCC patients. Most of these hub genes can promote HCC proliferation and/or metastasis. These 12 hub genes were also overexpressed and had strong prognostic value in many other cancer types. Methylation and gene copy number analyses indicated that the upregulation of these hub genes was probably due to hypomethylation or increased gene copy numbers. Further, the methylation levels of three genes, KPNA2, MCM3, and LRRC1, were associated with HCC clinical features. Moreover, the levels of most hub genes were related to immune cell infiltration in HCC microenvironments. Finally, we identified three upregulated genes (KPNA2, TARBP1, and RNASEH2A) that could comprehensively and accurately provide diagnostic and prognostic value for HCC patients.
| S-EPMC7138582 | biostudies-literature
Project description:BackgroundColitis-associated colon cancer (CAC) patients have a younger age of onset, more multiple lesions and invasive tumors than sporadic colon cancer patients. Early detection of CAC using endoscopy is challenging, and the incidence of septal colon cancer remains high. Therefore, identifying biomarkers that can predict the tumorigenesis of CAC is in urgent need.ResultsA total of 275 DEGs were identified in CAC. IGF1, BMP4, SPP1, APOB, CCND1, CD44, PTGS2, CFTR, BMP2, KLF4, and TLR2 were identified as hub DEGs, which were significantly enriched in the PI3K-Akt pathway, stem cell pluripotency regulation, focal adhesion, Hippo signaling, and AMPK signaling pathways. Sankey diagram showed that the genes of both the PI3K-AKT signaling and focal adhesion pathways were upregulated (e.g., SPP1, CD44, TLR2, CCND1, and IGF1), and upregulated genes were predicted to be regulated by the crucial miRNAs: hsa-mir-16-5p, hsa-mir-1-3p, et al. Hub gene-TFs network revealed FOXC1 as a core transcription factor. In ulcerative colitis (UC) patients, KLF4, CFTR, BMP2, TLR2 showed significantly lower expression in UC-associated cancer. BMP4 and IGF1 showed higher expression in UC-Ca compared to nonneoplastic mucosa. Survival analysis showed that the differential expression of SPP1, CFRT, and KLF4 were associated with poor prognosis in colon cancer.ConclusionOur study provides novel insights into the mechanism underlying the development of CAC. The hub genes and signaling pathways may contribute to the prevention, diagnosis and treatment of CAC.
| S-EPMC9219145 | biostudies-literature
Project description:Background Lung cancer is one of the most common cancers and the primary cause of cancer-related deaths in the world. The 5-year survival of lung cancer patients is lower than 15%. As a common subtype of lung cancer, lung adenocarcinoma still has a high morbidity and mortality, although many strategies have been made, such as surgical operation, chemotherapy, targeted therapy. The use of gene expression microarray has provided a feasible and effective approach for the study on lung cancer. However, the biomarkers and potential therapeutic targets of lung adenocarcinomas are still not completely identified. Our study is aimed to find biomarkers and therapeutic targets of lung adenocarcinomas by identifying the key protein-coding gene in lung adenocarcinomas by bioinformatical approaches. Methods We selected and obtained messenger RNA microarray datasets from Gene Expression Omnibus database to identify differentially expressed genes between lung adenocarcinomas and normal lung tissue. The differentially expressed genes were clarified by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the protein-protein interaction (PPI) network and statistical analyses. Subsequently, quantitative real-time PCR was used to verify the results of bioinformatic analysis. Results We obtained 1,264, 896 and 408 differentially expressed genes from GSE32863, GSE43458 and GSE63459, respectively. The 242 common differentially expressed genes in three datasets were related to cell adhesion molecules, ECM-receptor interaction, leukocyte transendothelial migration according to KEGG analysis. GO analysis showed that these common differentially expressed genes were enriched in tumor-related functions. ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T and KIAA0101 have the strongest protein-protein interaction relationships based on protein-protein interaction networks. Survival analysis showed that these nine genes were closely related to the survival of lung adenocarcinomas. The further qRT-PCR assays indicated that seven key genes (ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T) display differential profile between clinical lung adenocarcinoma specimens and their matched normal tissues. Conclusions ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T may be key protein coding genes in lung adenocarcinoma, and deserve further study to verify their feasibility and effectiveness as biomarkers and therapeutic targets for lung adenocarcinomas.
| S-EPMC8799172 | biostudies-literature
Project description:BackgroundOvarian cancer is one of the most lethal malignant gynecologic tumors worldwide. We aimed to identify critical hallmarks of the surface epithelium between normal ovaries and serous ovarian carcinomas and then obtain the hub genes associated with these critical hallmarks.MethodsWe chose GSE36668, GSE54388 and GSE69428 as data sources and then determined the common gene sets through gene set enrichment analysis (GSEA) to explore essential hallmarks and hub genes driving normal ovarian cells to evolve progressively into a neoplastic state. The differentially expressed genes (DEGs) extracted separately in each gene set were analyzed again through the Metascape website. Kaplan-Meier plotter was used to obtain significant prognostic information. The hub genes were obtained through protein-protein interaction (PPI) network by Cytoscape. Hub genes were confirmed to have higher mRNA and protein expression levels in ovarian cancer tissues than in normal tissues through public databases [Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas]. Correlation analysis of six hub genes showed a strong correlation via R.ResultsWe obtained 11 common hallmarks from GSEA of the three mentioned datasets and 22 hub genes that showed a significant association with poor survival.ConclusionsNot only the gene sets enriched by GSEA but also the hub genes extracted by the PPI network indicate that the most fundamental trait of ovarian cancer cells involves their ability to sustain chronic proliferation.
| S-EPMC8797326 | biostudies-literature
Project description:Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis. Methods: CRC's data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients' overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan-Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes' expression was examined in the GSE41258 dataset. Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan-Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset. Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.
| S-EPMC9421078 | biostudies-literature