scholarly journals An individualized prognostic signature for gastric cancer patients treated with 5-Fluorouracil-based chemotherapy and distinct multi-omics characteristics of prognostic groups

Oncotarget ◽  
2016 ◽  
Vol 7 (8) ◽  
pp. 8743-8755 ◽  
Author(s):  
Xiangyu Li ◽  
Hao Cai ◽  
Weicheng Zheng ◽  
Mengsha Tong ◽  
Hongdong Li ◽  
...  
Author(s):  
Shan Yu ◽  
Yan Wang ◽  
Ke Peng ◽  
Minzhi Lyu ◽  
Fenglin Liu ◽  
...  

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.


2021 ◽  
Author(s):  
Wei Li ◽  
Changhong shi

Abstract Background: Gastric cancer is one of the most common cancer across the world. Increasing evidence suggest that p53-pathway plays a critical role in the initiation, progress and therapy of gastric cancer. However, the prognostic value of p53-pathway in gastric cancer is not fully understood. Methods: A total of 67 p53-pathway-related genes and corresponding clinical information of 415 gastric cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The consensus clustering algorithm were performed to analyze the expression level of all p53-pathway related genes. Based on the differentially expressed genes between different subgroups, Cox regression analysis were used to construct an independent prognostic signature, which was further validated in two external datasets. Results: 3 subgroups of patients with significant different survival outcome were identified according to the expression level of p53-pathway-related genes. Comparison of gene expression between those subgroups identified 12 differentially expressed genes, 3 (THBS1, SERPINE1 and GADD45B) of which were significantly associated with the overall survival outcome. We further constructed a 3-gene signature as independence prognosis signature with promising performance in survival prediction of gastric cancer. Conclusions: This study provides a potential prognostic signature for predicting prognosis of gastric cancer patients, which may benefit individual therapy of gastric cancer.


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