The cuproptosis related genes signature predicts the prognosis and correlates with the immune status of clear cell renal cell carcinoma

Author:

Sun Peng,Xu Hua,Zhu Ke,Li Min,Han Rui,Shen Jiran,Xia Xingyuan,Chen Xiaojuan,Fei Guanghe,Zhou Sijing,Wang Ran

Abstract

Background: Clear cell renal cell carcinoma (CCRCC) has a high incidence and poor prognosis. Cuproptosis, an independent pattern of cell death associated with copper, plays an important role in cancer proliferation and metastasis. The role of cuproptosis-related genes (CRGs) in CCRCC is unclear.Methods: Transcriptome and clinical information for CCRCC were downloaded from The Cancer Genome Atlas (TCGA) database. After dividing the training and testing cohort, a 4-CRGs risk signature (FDX1, DLD, DLAT, CDKN2A) was identified in the training cohort using Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. The effect of the 4-CRGs risk signature on prognosis was assessed using Kaplan-Meier (KM) curves and time-dependent receiver operating characteristic (ROC) curves and verified using the testing cohort. For different risk groups, the immune statue was assessed using the CIBERSORT algorithm, the ssGSEA method and immune checkpoint expression data. Finally, a competitive endogenous RNA (ceRNA) network was constructed using miRTarbase and starBase databases to identify molecules that may have a regulatory relationship with CRCCC.Results: There were significant changes in the overall survival (OS), immune microenvironment, immune function, and checkpoint gene expression among the different risk groups. A ceRNA network consisting of one mRNA, two miRNAs, and 12 lncRNAs was constructed.Conclusion: The 4-CRGs risk signature provides a new method to predict the prognosis of patients with CCRCC and the effect of immunotherapy. We propose a new cuproptosis-associated ceRNA network that can help to further explore the molecular mechanisms of CCRCC.

Funder

National Natural Science Foundation of China

Publisher

Frontiers Media SA

Subject

Genetics (clinical),Genetics,Molecular Medicine

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1. Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network;International Journal of Molecular Sciences;2024-04-11

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