Extracellular Matrix-Related Hubs Genes Have Adverse Effects on Gastric Adenocarcinoma Prognosis Based on Bioinformatics Analysis.
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ABSTRACT:
SUBMITTER: Alatan H
PROVIDER: S-EPMC8303807 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGastric cancer (GC) is a malignant tumor that originates from the epithelium of the gastric mucosa and has a poor prognosis. Stomach adenocarcinoma (STAD) covers 95% of total gastric cancer. This study aimed to identify the prognostic value of RNA methylation-related genes in gastric cancer.MethodsIn this study, The Cancer Genome Atlas (TCGA)-STAD and GSE84426 cohorts were downloaded from public databases. Patients were classified by consistent cluster analysis based on prognosis-related differentially expressed RNA methylation genes Prognostic genes were obtained by differential expression, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. The prognostic model was established and validated in the training set, test set and validation set respectively. Independent prognostic analysis was implemented. Finally, the expression of prognostic genes was affirmed by reverse transcription quantitative PCR (RT-qPCR).ResultsIn total, four prognostic genes (ACTA2, SAPCD2, PDK4 and APOD) related to RNA methylation were identified and enrolled into the risk signature. The STAD patients were divided into high- and low-risk groups based on the medium value of the risk score, and patients in the high-risk group had a poor prognosis. In addition, the RNA methylation-relevant risk signature was validated in the test and validation sets, and was authenticated as a reliable independent prognostic predictor. The nomogram was constructed based on the independent predictors to predict the 1/3/5-year survival probability of STAD patients. The gene set enrichment analysis (GSEA) result suggested that the poor prognosis in the high-risk subgroup may be related to immune-related pathways. Finally, the experimental results indicated that the expression trends of RNA methylation-relevant prognostic genes in gastric cancer cells were in agreement with the result of bioinformatics.ConclusionOur study established a novel RNA methylation-related risk signature for STAD, which was of considerable significance for improving prognosis of STAD patients and offering theoretical support for clinical therapy.
| S-EPMC10909369 | biostudies-literature
Project description:The aim of the present study was to investigate the prognostic value of genes that participate in the development of gastric adenocarcinoma, via exploring gene cross talk in disease?related pathways. Differentially expressed genes (DEGs) in the gastric samples were identified by analyzing the expression data downloaded from the GEO database. The DEGs were subjected to the human protein?protein interaction (PPI) network to construct the PPI network of DEGs, which was then used for the identification of key genes in cancer samples via the expression deviation score and degree in the network. A total of 635 DEGs, including 432 downregulated and 203 upregulated ones were screened in the gastric adenocarcinomas samples. The PPI network of DEGs comprised 590 DEGs and 4,299 interaction pairs. A total of 200 key genes were obtained, which were significantly enriched in six downregulated and six upregulated pathways. Cross talk genes in the connected pathways were analyzed, and the Kyoto Encyclopedia of Genes and Genomes pathways hsa00980 (Metabolism of xenobiotics by cytochrome P450) and hsa00982 (Drug metabolism) were reported to share 8 cross talk genes: ADH7, ALDH3A1, GSTA1, GSTA2, UGT2B17, UGT2B10, ADH1B and CYP2C18. Among all cross talk genes, ADH7, ALDH3A1 and CLDN3 were the most specific genes. The high? and low?risk samples identified by the prognosis model presented a remarkable difference in total survival time, indicating its robustness and sensitivity as the prognosis genes for gastric adenocarcinoma. ADH7, ALDH3A1, GSTA1, GSTA2, UGT2B17, UGT2B10, ADH1B, CYP2C18ADH7, ALDH3A1 and CLDN3 may be used as the prognosis markers and target biomarkers for chemotherapies in gastric adenocarcinoma.
| S-EPMC5562048 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. There are great geographical differences in the incidence of GC, and somatic mutation rates of driver genes are also different. The present study is aimed at screening core prognosis-related candidate genes in Chinese gastric cancer population based on integrated bioinformatics for the early diagnosis and prognosis of GC.MethodsIn the present study, the differentially expressed genes (DEGs) in GC were identified using four microarray datasets from the Gene Expression Omnibus (GEO) database. The samples of these datasets were all from China. Functional enrichment analysis of DEGs was conducted to evaluate the underlying molecular mechanisms involved in GC. Protein-protein interaction (PPI) network and cytoHubba were performed to determine hub genes associated with GC. Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) were performed to validate the hub genes.ResultsA total of 240 DEGs were obtained through the RRA method, including 80 upregulated genes and 160 downregulated genes. Upregulated genes were mainly enriched in extracellular matrix organization, extracellular matrix, and extracellular matrix structural constituent. The downregulated genes were mainly enriched in digestion, extracellular space, and oxidoreductase activity. The KEGG pathway enrichment analysis showed that the upregulated genes were mainly associated with ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway. And downregulated genes were mainly associated with the metabolism of xenobiotics by cytochrome P450, metabolic pathways, and gastric acid secretion. The transcriptional and translational expression levels of the genes including COL1A1, COL5A2, COL12A1, and VCAN were higher in GC tissues than normal tissues.ConclusionA total of four genes including COL1A1, COL5A2, COL12A1, and VCAN were considered potential GC biomarkers in the Chinese population. And ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway were revealed to be important mechanisms of GC. Our findings provide novel insights into the occurrence and progression of GC in the Chinese population.
| S-EPMC7748906 | biostudies-literature
Project description:To screen the key genes in the development of gastric cancer and their influence on prognosis. The GEO database was used to screen gastric cancer-related gene chips as a training set, and the R packages limma tool was used to analyze the differential genes expressed in gastric cancer tissues compared to normal tissues, and then the selected genes were verified in the validation set. The String database was used to calculate their Protein-protein interaction (PPI) network, using Cytoscape software's Centiscape and other plug-ins to analyze key genes in the PPI network. The DAVID database was used to enrich and annotate gene functions of differential genes and PPI key module genes, and further explore correlation between expression level and clinical stage and prognosis. Based on clinical data and patient samples, differential expression of key node genes was verified by immunohistochemistry. The 63 characteristic differential genes screened had good discrimination between gastric cancer and normal tissues, and are mainly involved in regulating extracellular matrix receptor interactions and the PI3k-AKT signaling pathway. Key nodes in the PPI network regulate tumor proliferation and metastasis. Analysis of the expression levels of key node genes found that relative to normal tissues, the expression of ITGB1 and COL1A2 was significantly increased in gastric cancer tissues, and patients with late clinical stages of tumors had higher expression of ITGB1 and COL1A2 in tumor tissues, and their survival time was longer (P < 0.05). This study found that ITGB1 and COL1A2 are key genes in the development of gastric cancer and can be used as prognostic markers and potential new targets for gastric cancer.
| S-EPMC8322082 | biostudies-literature
Project description:AbstractTo screen the prognosis-related autophagy genes of female lung adenocarcinoma by the transcriptome data and clinical data from The Cancer Genome Atlas (TCGA) database.In this study, screen meaningful female lung adenocarcinoma differential genes in TCGA, use univariate Cox proportional regression model to select genes related to prognosis, and establish the best risk model. In this study, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were applied for carrying out bioinformatics analysis of gene function.The gene expression and clinical data of 264 female lung adenocarcinoma patient samples were downloaded from TCGA. Twelve down-regulated genes: NRG3, DLC1, NLRC4, DAPK2, HSPB8, PPP1R15A, FOS, NRG1, PRKCQ, GRID1, MAP1LC3C, GABARAPL1. Up-regulated 15 genes: PARP1, BNIP3, P4HB, ATIC, IKBKE, ITGB4, VMP1, PTK6, EIF4EBP1, GAPDH, ATG9B, ERO1A, TMEM74, CDKN2A, BIRC5. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that these genes were significantly associated with autophagy and mitochondria (animals). Multifactor Cox analysis of autophagy-related genes showed that ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as independent prognostic indicators. According to the multivariate Cox proportional hazard regression model, there was a significant difference in the survival rate observed between the high-risk group (n = 124) and the low-risk group (n = 126) during the 10-year follow-up (P < .05). Univariate Cox analysis showed that tumor stage, T, M, and N stages, and risk score were all related to the survival rate of female lung adenocarcinoma patients. Multivariate Cox analysis found that autophagy-related risk scores were independent predictors, with an area under curve (AUC) value of 0.842. At last, there is autophagy genes differentially expressed among various clinicopathological parameters: ATG4A, BAK1, CCR2, DLC1, ERO1A, FKBP1A, ITGA6.The risk score can be used as an independent prognostic indicator for female patients with lung adenocarcinoma. The autophagy genes ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as prognostic genes in female lung adenocarcinoma, which may be the targets of treatment in the future.
| S-EPMC8735786 | biostudies-literature
Project description:Gastric cancer has remained in the top five cancers for over ten years, both in terms of incidence and mortality due to the shortage of biomarkers for disease follow-up and effective therapies. Aiming to fill this gap, we performed a bioinformatics assessment on our data and two additional GEO microarray profiles, followed by a deep analysis of the 40 differentially expressed genes identified. PPI network analysis and MCODE plug-in pointed out nine upregulated hub genes coding for proteins from the collagen family (COL12A1, COL5A2, and COL10A1) or involved in the assembly (BGN) or degradation of collagens (CTHRC1), and also associated with cell adhesion (THBS2 and SPP1) and extracellular matrix degradation (FAP, SULF1). Those genes were highly upregulated at the mRNA and protein level, the increase being correlated with pathological T stages. The high expression of BGN (p = 8 × 10-12), THBS2 (p = 1.2 × 10-6), CTHRC1 (p = 1.1 × 10-4), SULF1 (p = 3.8 × 10-4), COL5A1 (p = 1.3 × 10-4), COL10A1 (p = 5.7 × 10-4), COL12A1 (p = 2 × 10-3) correlated with poor overall survival and an immune infiltrate based especially on immunosuppressive M2 macrophages (p-value range 4.82 × 10-7-1.63 × 10-13). Our results emphasize that these genes could be candidate biomarkers for GC progression and prognosis and new therapeutic targets.
| S-EPMC8950589 | biostudies-literature
Project description:The extracellular matrix (ECM) plays a central role in the formation of the tumor microenvironment. The deposition of the ECM is associated with poor prognosis in a variety of tumors. Aberrant ECM deposition could undermine the effect of chemotherapy and immunotherapy. However, there is no systematic analysis on the relationship between the ECM and prognosis or chemotherapy effect. In the present study, we applied the gene set variation analysis (GSVA) algorithm to score 2199 canonical pathways in 2125 cases of probe or sequencing data and identified the core matrisome as the driving factor in gastric cancer progression. We classified gastric cancer samples into three clusters according to the composition of the ECM and evaluated clinical and multi-omics characterization of ECM phenotypes. The ECM score was evaluated by GSVA score of core matrisome and a higher ECM score predicted poor prognosis of gastric cancer [Hazard Ratio (HR), 2.084; p-value < 2 × 10-16]. In The Cancer Genome Atlas (TCGA) cohort and KUGH, YUSH, and KUCM cohorts, we verified that patients with a low ECM score could benefit from chemotherapy. By contrast, patients with a high ECM score did not achieve satisfactory response from chemotherapy. Determining the characteristics of the ECM microenvironment might help to predict the prognosis and chemotherapy response of patients with gastric cancer, and help to resolve the enigma of chemoresistance acquisition, as well as providing inspiration to develop combination therapy.
| S-EPMC8503650 | biostudies-literature
Project description:Bladder cancer is the 11th most common cancer in the world. Bladder cancer can be roughly divided into muscle invasive bladder cancer (MIBC) and non-muscle invasive bladder cancer (NMIBC). The aim of the present study was to identify the key genes and pathways associated with the progression of NMIBC to MIBC and to further analyze its molecular mechanism and prognostic significance. We analyzed microarray data of NMIBC and MIBC gene expression datasets (GSE31684) listed in the Gene Expression Omnibus (GEO) database. After the dataset was analyzed using R software, differentially expressed genes (DEGs) of NMIBC and MIBC were identified. These DEGs were analyzed using Gene Ontology (GO) enrichment, KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis. The effect of these hub genes on the survival of bladder cancer patients was analyzed in The Cancer Genome Atlas (TCGA) database. A total of 389 DEGs were obtained, of which 270 were up-regulated and 119 down-regulated. GO and KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the pathway of protein digestion and absorption, extracellular matrix (ECM) receiver interaction, phantom, toll-like receptor (TLR) signaling pathway, focal adhesion, NF-κB signaling pathway, PI3K/Akt signaling pathway, and other signaling pathways. Top five hub genes COL1A2, COL3A1, COL5A1, POSTN, and COL12A1 may be involved in the development of MIBC. These results may provide us with a further understanding of the occurrence and development of MIBC, as well as new targets for the diagnosis and treatment of MIBC in the future.
| S-EPMC7251326 | biostudies-literature
Project description:Gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD), is one of the most lethal malignancies in the world. It is vital to classify and detect the hub genes and key pathways participated in the initiation and progression of GAC. In this study, we collected and sequenced 15 pairs of GAC tumor tissues and the adjacent normal tissues. Differentially expressed genes (DEGs) were analyzed and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis were used to annotate the unique biological significance and important pathways of enriched DEGs. Moreover, we constructed the protein-protein interaction (PPI) network by Cytoscape and conducted KEGG enrichment analysis of the prime module. We further applied the TCGA database to start the survival analysis of these hub genes by Kaplan-Meier estimates. Finally, we obtained total 233 DEGs consisted of 64 up-regulated genes and 169 down-regulated genes. GO enrichment analysis found that DEGs most significantly enriched in single organism process, extracellular region, and extracellular region part. KEGG pathway enrichment analysis suggested that DEGs most significantly enriched in Protein digestion and absorption, Gastric acid secretion, and ECM-receptor interaction. Furthermore, the PPI network showed that the top 10 hub genes in GAC were IL8, COL1A1, MMP9, SST, COL1A2, TIMP1, FN1, SPARC, ALDH1A1, and SERPINE1 respectively. The prime gene interaction module in PPI network was enriched in protein digestion and absorption, ECM receptor interaction, the PI3K-Akt signaling pathway, and pathway in cancer. Survival analysis based on the TCGA database found that the expression of the FN1, SERPINE1, and SPARC significantly predicted poor prognosis of GAC. Collectively, we identified several hub genes and key pathways associated with GAC initiation and progression by analyzing the microarray data on DEGs, which provided a detailed molecular mechanism underlying GAC occurrence and progression.
| S-EPMC6534579 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is disease with a high morbidity. The purpose of this study was to identify genes essential to GC development in patients and to reveal the underlying mechanisms of progression.MethodsBioinformatics analysis is an effective tool for discovering essential genes of different disease states. We used the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs), the DAVID online tool to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs, the STRING database to construct the protein-protein interaction (PPI) network of DEGs, the Oncomine and the Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) databases to analyze the gene expression differences, the Human pan-Cancer Methylation database (MethHC) to compare the DNA methylation of genes, and the Kaplan-Meier plotter to show the survival analysis of DEGs. We performed Real-Time quantitative PCR (RT-qPCR) experiment to confirm our analysis results.ResultsAfter the integration of four Gene Expression Series (GSEs), we identified 407 DEGs. GO and KEGG pathway analysis indicated that the upregulated DEGs were significantly enriched in Extracellular Matrix (ECM) related functions and pathways. The main DEGs were collagens (COLs). Moreover, the downregulated DEGs were enriched in ethanol oxidation. Several groups of DEGs, such as insulin-like growth factor binding protein (IGFBP), collagen (COL) and serpin peptidase inhibitors (SERPIN) gene families, constituted several PPI networks. In the Oncomine database, all of the collagen genes were highly expressed in breast cancer, esophageal cancer, GC, head and neck cancer and pancreatic cancer, compared with normal tissues. Consistently, from the TCGA-STAD database, most of the collagens (COLs) were highly expressed and exhibited methylated variation in GC patients. In GC patients, some of these collagen (COL) genes related to worse prognosis, as evidenced by the results from the Kaplan-Meier plotter database analysis. Our RT-qPCR results showed that collagen type III α1 chain (COL3A1) was highly expressed in GC cells. Collagen type V α1 chain (COL5A1) was highly expressed, except in AGS cells, which was consistent with our analysis.ConclusionsCollagen (COL) family genes might serve as progression and prognosis markers of GC.
| S-EPMC8797647 | biostudies-literature