A Novel Prognostic Signature Associated With the Tumor Microenvironment in Kidney Renal Clear Cell Carcinoma

• Published in: Frontiers in oncology Vol. 12; p. 912155
• Main Authors: Pei, Dongchen, Xu, Chaojie, Wang, Dong, Shi, Xiaoxue, Zhang, Yurui, Liu, Yi, Guo, Jianhua, Liu, Nan, Zhu, Haipeng
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 04.07.2022
• Subjects: bioinformatics; kidney renal clear cell carcinoma; NMF (nonnegative matrix factorization); Oncology; prognosis signature; tumor microenvironment
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2022.912155

Background The tumor microenvironment (TME) is a complex and evolving environment, and the tumor immune microenvironment in kidney renal clear cell carcinoma (KIRC) has a strong suppressive profile. This study investigates the potential prognostic role and value of genes of the tumor microenvironment in KIRC. Methods The transcriptome sequencing data of 530 cases and 39 cases of KIRC and the corresponding clinical prognosis information were downloaded from TCGA data and GEO data, respectively, and TME-related gene expression profiles were extracted. A prognostic signature was constructed and evaluated using univariate Cox regression analysis and LASSO regression analysis. Gene set enrichment analysis (GSEA) was used to obtain the biological process of gene enrichment in patients with high and low-risk groups. Results A prognostic signature consisting of eight TME-related genes (LRFN1, CSF1, UCN, TUBB2B, SERPINF1, ADAM8, ABCB4, CCL22) was constructed. Kaplan-Meier survival analysis yielded significantly lower survival times for patients in the high-risk group than in the low-risk group, and the AUC values for the ROC curves of this prognostic signature were essentially greater than 0.7, and univariate and multifactorial Cox regression analyses indicated that the risk score was independent risk factors for KIRC prognosis. GSEA analysis showed that immune-related biological processes were enriched in the high-risk group and that risk values were strongly associated with multiple immune cell scores and immune checkpoint-related genes (PDCD1, CTLA4). Conclusions The prognostic signature can accurately predict the prognosis of KIRC patients, which may provide new ideas for future precision immunotherapy of KIRC.