scholarly journals Abstract 3182: Tumor immune microenvironment based molecular functional clustering reveals a prognostic signature that predicts overall survival in patients with gastric cancer

Author(s):  
Prashant V. Thakkar ◽  
Olga Kudryashova ◽  
Daria Melikhova ◽  
Naira Samarina ◽  
Sandrine Degryse ◽  
...  
Oncotarget ◽  
2017 ◽  
Vol 8 (40) ◽  
pp. 67094-67103 ◽  
Author(s):  
Jing Ma ◽  
Jianhui Li ◽  
Yiming Hao ◽  
Yongzhan Nie ◽  
Zengshan Li ◽  
...  

2018 ◽  
Vol 22 (1) ◽  
pp. 77-90 ◽  
Author(s):  
Irene Gullo ◽  
Patrícia Oliveira ◽  
Maria Athelogou ◽  
Gilza Gonçalves ◽  
Marta L. Pinto ◽  
...  

2020 ◽  
Vol 31 (6) ◽  
pp. 760-768 ◽  
Author(s):  
Y. Jiang ◽  
H. Wang ◽  
J. Wu ◽  
C. Chen ◽  
Q. Yuan ◽  
...  

2020 ◽  
Vol 10 ◽  
Author(s):  
Yixian Guo ◽  
Xu Liu ◽  
Danhua Xu ◽  
Chen Huang ◽  
Zeyu Wang ◽  
...  

2018 ◽  
Vol 24 (32) ◽  
pp. 3583-3616 ◽  
Author(s):  
Daniela Cornelia Lazăr ◽  
Mihaela Flavia Avram ◽  
Ioan Romoșan ◽  
Mărioara Cornianu ◽  
Sorina Tăban ◽  
...  

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang

BackgroundTumor-associated macrophages (TAMs) play a critical role in the progression of malignant tumors, but the detailed mechanism of TAMs in gastric cancer (GC) is still not fully explored.MethodsWe identified differentially expressed immune-related genes (DEIRGs) between GC samples with high and low macrophage infiltration in The Cancer Genome Atlas datasets. A risk score was constructed based on univariate Cox analysis and Lasso penalized Cox regression analysis in the TCGA cohort (n=341). The optimal cutoff determined by the 5-year time-dependent receiver operating characteristic (ROC) curve was considered to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE84437, n=431; GSE62254, n=300; GSE15459, n=191; and GSE26901, n=109) from the Gene Expression Omnibus (GEO) database.ResultsThe signature consisting of 7 genes (FGF1, GRP, AVPR1A, APOD, PDGFRL, CXCR4, and CSF1R) showed good performance in predicting overall survival (OS) in the 5 independent cohorts. The risk score presented an obviously positive correlation with macrophage abundance (cor=0.7, p<0.001). A significant difference was found between the high- and low-risk groups regarding the overall survival of GC patients. The high-risk group exhibited a higher infiltration level of M2 macrophages estimated by the CIBERSORT algorithm. In the five independent cohorts, the risk score was highly positively correlated with the stromal cell score, suggesting that we can also evaluate the infiltration of stromal cells in the tumor microenvironment according to the risk score.ConclusionOur study developed and validated a general applicable prognostic model for GC from the perspective of TAMs, which may help to improve the precise treatment strategy of GC.


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.


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