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A gene expression-based risk model reveals prognosis of gastric cancer.

ABSTRACT: Background:The prognosis of gastric cancer is difficult to determine, although clinical indicators provide valuable evidence. Methods:In this study, using screened biomarkers of gastric cancer in combination with random forest variable hunting and multivariable Cox regression, a risk score model was developed to predict the survival of gastric cancer. Survival difference between high/low-risk groups were compared. The relationship between risk score and other clinicopathological indicators was evaluated. Gene set enrichment analysis (GSEA) was used to identify pathways associated with risk scores. Results:The patients with high risk scores (median overall survival: 20.2 months, 95% CI [16.9-26.0] months) tend to exhibit early events compared with those with low risk scores (median survival: 70.0 months, 95% CI [46.9-101] months, p = 1.80e-5). Further validation was implemented in another three independent datasets (GSE15459, GSE26253, GSE62254). Correlation analyses between clinical observations and risk scores were performed, and the results indicated that the risk score was not significantly associated with gender, age and primary tumor size but was significantly associated with grade and tumor stage. In addition, the risk score was also not influenced by radiation therapy. Cox multivariate regression and three-year survival nomogram suggest that the risk score is an important indicator of gastric cancer prognosis. GSEA was used to identified KEGG pathways significantly associated with risk score, and signaling pathways involved in focal adhesion and the TGF-beta signaling pathway were identified. Conclusion:The risk score model successfully predicted the survival of 1,294 gastric cancer samples from four independent datasets and is among the most important indicators in clinical clinicopathological information for the prognosis of gastric cancer. To our knowledge, it is the first report to predict the survival of gastric cancer using optimized expression panel.

SUBMITTER: Deng X

PROVIDER: S-EPMC5807894 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A gene expression-based risk model reveals prognosis of gastric cancer.

Deng Xiaorong X Xiao Qun Q Liu Feng F Zheng Cihua C

PeerJ 20180102

<h4>Background</h4>The prognosis of gastric cancer is difficult to determine, although clinical indicators provide valuable evidence.<h4>Methods</h4>In this study, using screened biomarkers of gastric cancer in combination with random forest variable hunting and multivariable Cox regression, a risk score model was developed to predict the survival of gastric cancer. Survival difference between high/low-risk groups were compared. The relationship between risk score and other clinicopathological i ...[more]

PMID: 29441228

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Project description:Gastric cancer (GC) has a high incidence and mortality rate. If discovered late, GC tends to have a poor prognosis. Improvements in the prognostic accuracy of GC through combined analysis of multiple relevant genes and clinical factors may solve this problem. In the present study, GSE62254 (including 300 GC tissues), obtained from the Gene Expression Omnibus database, was used as a training set, and the mRNA?sequencing data of GC (including 384 GC tissues) downloaded from the Cancer Genome Atlas database served as a validation set. Based on the t?test and Wilcoxon test, the significantly differentially expressed genes (DEGs) were obtained by screening the intersecting DEGs. The prognosis-associated genes and clinical factors were identified using Cox regression analysis in the R survival package. The optimal prognosis?associated pathways were examined using the Cox?proportional hazards (Cox?PH) model in the R penalized package. Finally, risk prediction models were constructed and validated using the Cox?PH model and the Kaplan?Meier method, respectively. There were a total of 382 significant DEGs, including 268 upregulated genes and 114 downregulated genes. A total of 50 prognosis?associated genes were identified, 16 optimal prognosis?associated pathways (including mitochondrial pathway and the tyrosine?protein kinase JAK?signal transducer and activator of transcription signaling pathway, which involve caspase 7, phosphoinositide?3?kinase regulatory subunit 3, peroxisome proliferator?activated receptor ? and collagen triple helix repeat containing 1) and four prognosis?associated clinical factors [including Pathologic_N, Pathologic_stage, mutL homolog 1 (MLH1) mutation and recurrence]. The pathway? and clinical?factor?based risk prediction model exhibited marked prognostic accuracy. The clinical?factor?based risk prediction model with improved P?values for prognosis prediction may be superior to the pathway?based risk prediction model in predicting the prognosis of GC patients.

Project description:Background:Gastric cancer (GC) is one of the high-risk cancers that lacks effective methods for prognosis prediction. Therefore, we searched for immune cells related to the prognosis of GC and studied the role of related genes in GC prognosis. Methods:In this study, we collected the mRNA data of GC from The Cancer Genome Atlas (TCGA) database and studied the immune cells that were closely related to the prognosis of GC. Spearman correlation analysis was performed to show the association between immune cell-related genes and the differentially expressed genes (DEGs) of GC. Univariate and multivariate Cox regression analyses were conducted on the immune cell-related genes with a high correlation with GC. A prognostic risk score model was constructed and the most significant feature genes were identified. Kaplan-Meier method was then used to compare the overall survival (OS) of patients with high-risk and low-risk, and receiver operating characteristic (ROC) analysis was used to assess the accuracy of the risk model. In addition, GC patients were grouped according to the median expression of the features genes, and survival analysis was further carried out. Results:It was noted that regulatory T cells (Tregs) were significantly correlated with the prognosis of GC, and 172 genes related to Tregs were found to be closely associated with GC. An optimal prognostic risk model was constructed, and a 5-gene (including LRFN4, ADAMTS12, MCEMP1, HP and MUC15) signature-based risk score was established. Survival analysis showed significant difference in OS between low-risk and high-risk samples. ROC analysis results indicated that the risk model had a high accuracy for the prognosis prediction of samples (AUC?=?0.717). The results of survival analysis on each feature gene based on expression levels were consistent with the results of multivariate Cox analysis for predicting the risk rate of the 5 genes. Conclusion:These results proved that the 5-gene signature-based risk score could be used to predict the survival of GC patients, and these 5 genes were closely related to Tregs. These findings are of great significance for studying the role of immune cells and related immune factors in regulating the prognosis of GC.

Project description:The Cadherin gene family holds immense significance in maintaining the integrity and functionality of stomach tissues, playing crucial roles in cell-cell adhesion, cell migration and differentiation. Dysregulation of cadherin expression and function has been closely associated with various gastric diseases, particularly gastric cancer (GC). Understanding the regulation and clinical implications of cadherin genes in GC is essential to improve our knowledge and to identify new potential prognostic markers and therapeutic targets. In this study, we provide an overview on the role of cadherin family genes in GC using bioinformatics analysis. We analyzed the expression, mutational status, and prognostic value of these genes based on available public datasets. Our methodology involved data mining, differential expression analysis, functional enrichment analysis, and survival analysis to explore the association between cadherin gene expression and clinical outcomes in GC patients. Additionally, we investigated the relationship between cadherin expression and immune cell infiltration to gain insights into the tumor microenvironment's role in GC progression. Our bioinformatics analysis revealed significant differential expression of 16 cadherin genes in GC samples compared to normal tissues. Approximately up to 52% of the analyzed cancer samples exhibited genomic alterations in these cadherins, indicating their potential relevance in GC development. Functional enrichment analysis demonstrated that these differentially expressed cadherins were closely associated with critical cellular processes, including cell adhesion and immune-modulation. Remarkably, lower expression levels of most cadherin genes were linked to improved prognosis in GC patients, suggesting their potential importance as valuable prognostic biomarkers. The findings deriving from our comprehensive study provide important insights into the dysregulation of cadherin genes in GC and their impact on gene expression, molecular pathways, and prognosis. The associations with clinical outcomes and immune cell infiltration highlight the potential role of cadherin genes as prognostic biomarkers and therapeutic targets in GC.

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A gene expression-based risk model reveals prognosis of gastric cancer.

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A gene expression-based risk model reveals prognosis of gastric cancer.

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