Dataset Information

Gene expression signature-based prognostic risk score in gastric cancer.

ABSTRACT: Despite continual efforts to develop a prognostic model of gastric cancer by using clinical and pathologic parameters, a clinical test that can discriminate patients with good outcomes from those with poor outcomes after gastric cancer surgery has not been established. We aim to develop practical biomarker-based risk score that can predict relapse of gastric cancer after surgical treatment.Microarray technologies were used to generate and analyze gene expression profiling data from 65 gastric cancer patients to identify biomarker genes associated with relapse. The association of expression patterns of identified genes with relapse and overall survival was validated in independent gastric cancer patients.We uncovered two subgroups of gastric cancer that were strongly associated with the prognosis. For the easy translation of our findings into practice, we developed a scoring system based on the expression of six genes that predicted the likelihood of relapse after curative resection. In multivariate analysis, the risk score was an independent predictor of relapse in a cohort of 96 patients. We were able to validate the robustness of the six-gene signature in an additional independent cohort.The risk score derived from the six-gene set successfully prognosticated the relapse of gastric cancer patients after gastrectomy.

SUBMITTER: Cho JY

PROVIDER: S-EPMC3078023 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Gene expression signature-based prognostic risk score in gastric cancer.

Cho Jae Yong JY Lim Jae Yun JY Cheong Jae Ho JH Park Yun-Yong YY Yoon Se-Lyun SL Kim Soo Mi SM Kim Sang-Bae SB Kim Hoguen H Hong Soon Won SW Park Young Nyun YN Noh Sung Hoon SH Park Eun Sung ES Chu In-Sun IS Hong Waun Ki WK Ajani Jaffer A JA Lee Ju-Seog JS

Clinical cancer research : an official journal of the American Association for Cancer Research 20110329 7

<h4>Purpose</h4>Despite continual efforts to develop a prognostic model of gastric cancer by using clinical and pathologic parameters, a clinical test that can discriminate patients with good outcomes from those with poor outcomes after gastric cancer surgery has not been established. We aim to develop practical biomarker-based risk score that can predict relapse of gastric cancer after surgical treatment.<h4>Experimental design</h4>Microarray technologies were used to generate and analyze gene ...[more]

PMID: 21447720

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Project description:BackgroundGastric adenocarcinoma is an important contributor to cancer mortality and morbidity. This study aimed to explore the prognostic value of mutation patterns in gastric adenocarcinoma.Materials and methodsWe extracted somatic mutation data for 437 gastric adenocarcinoma samples from The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma (STAD) cohort. Kaplan-Meier survival in the R package maftools was used to analyze associations between mutations and survival. Multivariate Cox proportional model was used to establish risk formula. A four-gene-based risk score was developed to predict the overall survival of patients with gastric adenocarcinoma. We used the Tianjin cohort dataset with survival information to further evaluate the clinical value of this mutation signature.ResultsForty-five survival-related mutated genes were identified and verified, most of which were co-occurring in their mutation pattern and co-occurring with MLH3 and polymerase ϵ (POLE) mutations. Gastric adenocarcinoma samples with the 45 mutated genes had a significantly higher mutation count. Four-gene [UTRN, MUC16, coiled-coil domain-containing protein 178 (CCDC178), and HYDIN] mutation status was used to build a prognostic risk score that could be translated into the clinical setting. The association between the four-gene-based signature and overall survival remained statistically significant after controlling for age, sex, TNM stage, and POLE mutation status in the multivariate model [hazard ratio (HR), 1.88; 95% CI, 1.33-2.7; p < 0.001]. The prognostic significance of the four-gene-based risk score identified in TCGA cohort was validated in the Tianjin cohort.ConclusionA four-mutated gene risk formula was developed that correlated with the overall survival of patients with gastric adenocarcinoma using a multivariable Cox regression model. In two independent genomic datasets from TCGA and Tianjin cohorts, low risk scores were associated with higher tumor mutation loads and improved outcome in patients with gastric adenocarcinoma. This finding may have implications for prognostic prediction and therapeutic guidance for gastric adenocarcinoma.

Project description:Background: Autophagy plays a vital role in cancer initiation, malignant progression, and resistance to treatment. However, autophagy-related genes (ARGs) have rarely been analyzed in gastric cancer (GC). The purpose of this study was to analyze ARGs in GC using bioinformatic analysis and to identify new biomarkers for predicting the overall survival (OS) of patients with GC. Methods: The gene expression profiles and clinical data of patients with GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and ARGs were obtained from two other datasets (the Human Autophagy Database and Molecular Signatures Database). Lasso, univariate, and multivariate Cox regression analyses were performed to identify the OS-related ARGs. Finally, a six-ARG model was identified as a prognostic indicator using the risk-score model, and survival and prognostic performance were analyzed based on the Kaplan-Meier test and ROC curve. Estimate calculations were used to assess the immune status of this model, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed for investigating the functions and terms associated with the model-related genes in GC. Results: The six ARGs, DYNLL1, PGK2, HPR, PLOD2, PHYHIP, and CXCR4, were identified using Lasso and Cox regression analyses. Survival analysis revealed that the OS of GC patients in the high-risk group was significantly lower than that of the low-risk group (p < 0.05). The ROC curves revealed that the risk score model exhibited better prognostic performance with respect to OS. Multivariate Cox regression analysis indicated that the model was an independent predictor of OS and was not affected by most of the clinical traits (p < 0.05). The model-related genes were associated with immune suppression and several biological process terms, such as extracellular structure organization and matrix organization. Moreover, the genes were associated with the P13K-Akt signaling pathway, focal adhesion, and MAPK signaling pathway. Conclusions: This study presents potential prognostic biomarkers for GC patients that would aid in determining the best patient-specific course of treatment.

Project description:BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodsStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.ResultsLow stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

Dataset Information

Gene expression signature-based prognostic risk score in gastric cancer.

Publications

Gene expression signature-based prognostic risk score in gastric cancer.

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