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

2021 ◽  
Author(s):  
Prashant V. Thakkar ◽  
Olga Kudryashova ◽  
Daria Melikhova ◽  
Naira Samarina ◽  
Sandrine Degryse ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Functional Clustering ◽  
Tumor Immune Microenvironment

scholarly journals Differentiated tumor immune microenvironment of Epstein-Barr virus-associated and negative gastric cancer: implication in prognosis and immunotherapy

Oncotarget ◽  
2017 ◽  
Vol 8 (40) ◽  
pp. 67094-67103 ◽  
Author(s):  
Jing Ma ◽  
Jianhui Li ◽  
Yiming Hao ◽  
Yongzhan Nie ◽  
Zengshan Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Epstein Barr Virus ◽  
Immune Microenvironment ◽  
Barr Virus ◽  
Tumor Immune Microenvironment ◽  
Epstein Barr

scholarly journals New insights into the inflamed tumor immune microenvironment of gastric cancer with lymphoid stroma: from morphology and digital analysis to gene expression

2018 ◽  
Vol 22 (1) ◽  
pp. 77-90 ◽  
Author(s):  
Irene Gullo ◽  
Patrícia Oliveira ◽  
Maria Athelogou ◽  
Gilza Gonçalves ◽  
Marta L. Pinto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Digital Analysis ◽  
Immune Microenvironment ◽  
Lymphoid Stroma ◽  
Tumor Immune Microenvironment

scholarly journals Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer

2020 ◽  
Vol 31 (6) ◽  
pp. 760-768 ◽  
Author(s):  
Y. Jiang ◽  
H. Wang ◽  
J. Wu ◽  
C. Chen ◽  
Q. Yuan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Noninvasive Imaging ◽  
Immune Microenvironment ◽  
Imaging Evaluation ◽  
Tumor Immune Microenvironment

scholarly journals Role of LATS1/2 in Prognosis of Advanced Gastric Cancer and Its Relationship With the Tumor Immune Microenvironment

2020 ◽  
Vol 10 ◽  
Author(s):  
Yixian Guo ◽  
Xu Liu ◽  
Danhua Xu ◽  
Chen Huang ◽  
Zeyu Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

scholarly journals Prognostic significance of tumor immune microenvironment and immunotherapy: Novel insights and future perspectives in gastric cancer

2018 ◽  
Vol 24 (32) ◽  
pp. 3583-3616 ◽  
Author(s):  
Daniela Cornelia Lazăr ◽  
Mihaela Flavia Avram ◽  
Ioan Romoșan ◽  
Mărioara Cornianu ◽  
Sorina Tăban ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Significance ◽  
Future Perspectives ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

scholarly journals Construction and Validation of a Universal Applicable Prognostic Signature for Gastric Cancer Based on Seven Immune-Related Gene Correlated With Tumor Associated Macrophages

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Critical Role ◽  
External Validation ◽  
Low Risk ◽  
Prognostic Signature ◽  
Tumor Associated Macrophages ◽  
Cox Regression Analysis ◽  
Significant Difference

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.

scholarly journals Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Shan Yu ◽  
Yan Wang ◽  
Ke Peng ◽  
Minzhi Lyu ◽  
Fenglin Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Patients ◽  
Roc Analysis ◽  
Checkpoint Inhibitors ◽  
Cox Proportional Hazards Model ◽  
Prognostic Signature ◽  
Risk Patients ◽  
Gastric Cancer Patients

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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