Prognostic and predictive value of immune/stromal-related gene biomarkers in renal cell carcinoma

Abstract

Immune/stromal-associated genes may be promising biomarkers for cancer diagnosis and the determination of clinical cancer treatment options. The aim of the present study was to identify prognostic stromal/immune-associated genes in renal cell carcinoma (RCC). RCC gene expression data (885 cases) were obtained from The Cancer Genome Atlas database. Immune/stromal scores were calculated by using the ESTIMATE package in R. Immune/stromal scores were significantly associated with Tumor-Node-Metastasis stage, clinical stage and overall survival rate (P<0.05). There were 419 differentially expressed genes (DEGs) based on immune scores and 738 DEGs based on stromal scores. Among these DEGs, 406 DEGs based on stromal scores and 252 DEGs based on immune scores were significantly associated with overall survival rate (P<0.05). The biological functions of these DEGs were primarily enriched in the 'immune response' and 'regulation of cell migration and proliferation'. These DEGs were observed in a protein-protein interaction network. A LASSO Cox regression model was used to build a prognostic 6 gene-based classifier, including the IL21R, ATP6V1C2, GBP1, P2RY10, GBP4 and TNNC2 genes [area under the curve (AUC) =0.776]. The predictive model which combined this classifier with clinical prognostic factors had a high accuracy in predicting patient survival in RCC (combined AUC =0.899). Taken together, these results demonstrated that there are significant associations between immune/stromal scores and clinicopathological staging. A set of tumor microenvironmentassociated genes that have powerful prognostic value in patients with RCC were identified in the present study.

Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma

“…The expression patterns of these genes expressed in the tumor microenvironment provide a powerful indicator of prognosis of patients with RCC. The differences in predictive IRGs identified by Wang et al (42) Our present study may be explained by the former's use of the ESTIMATE package of R to score the immune/stromal of TCGA samples and then to screen differentially expressed genes using the Lasso Cox regression model to build a prognostic six gene-based clinical model to predict the survival of patients with ccRCC. In contrast, here we screened for differentially expressed IRGs acquired from the ImmPort database, and we then identified IRGs related to survival among the differentially expressed genes and used the Lasso Cox regression model to select IRGs with the highest ability to predict prognosis to construct the prognostic model.…”

“…The difference is that our study is based on ccRCC patients and established a clinical immune gene model to predict the clinical prognosis of ccRCC patients. Another analysis of TGCA RCC data identified a prognostic 6-DEG classifier, including genes encoding IL21R, ATP6V1C2, GBP1, P2RY10, GBP4, and TNNC2 ( 42 ). Further analysis using this model revealed significant associations between immune/stromal scores and clinicopathological staging.…”

Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma

“…MS4A1 is associated with apoptosis of B-cell lymphoma Ramos cells ( Kawabata et al, 2013 ). P2RY10 has been reported to be a tumor microenvironment-associated gene and a potential diagnostic biomarker of metastatic melanoma ( Wang et al, 2018 , 2020 ). PLA2G2D has been reported to moderate inflammation and could be a potential biomarker for treating inflammatory disorders ( Miki et al, 2013 ).…”

Co-expression Network Identification and Clinical Prognostic Evaluation of Hub Genes in Head and Neck Squamous Cell Carcinoma

“…In contrast to the limitations of traditional experimentation, the development of microarray and sequencing technology provides an excellent tool and platform for cancer research, with the application of big data bioinformatics rapidly developing [11][12][13][14]. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provide a large amount of relevant data for cancer research [15,16].…”