AUTHOR=Zhou Zhongbao , Yang Zhenpeng , Cui Yuanshan , Lu Shuai , Huang Yongjin , Che Xuanyan , Yang Liqing , Zhang Yong 

TITLE=Identification and Validation of a Ferroptosis-Related Long Non-Coding RNA (FRlncRNA) Signature to Predict Survival Outcomes and the Immune Microenvironment in Patients With Clear Cell Renal Cell Carcinoma

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.787884

DOI=10.3389/fgene.2022.787884

ISSN=1664-8021

ABSTRACT=Background: The incidence of clear cell renal cell carcinoma (ccRCC) is increasing worldwide, contributing to 70% to 85% of kidney cancer cases. Ferroptosis is a novel type of programmed cell death and could predict prognoses in cancers. Here, we developed a ferroptosis-related long non-coding RNA (FRlncRNAs) signature to improve the prognostic prediction of ccRCC.
Methods: The transcriptome profiles of FRlncRNAs and clinical data of ccRCC were obtained from The Cancer Genome Atlas and ICGC database. Patients were randomly assigned to training cohorts, testing cohorts and overall cohorts. The FRlncRNAs signature was constructed by Lasso regression and Cox regression analysis, and Kaplan-Meier (K-M) analysis was used to access the prognosis of each group.Internal and external datasets were performed to verify the FRlncRNAs signature. 
Results: A FRlncRNAs signature comprising eight lncRNAs (AL590094.1, LINC00460, LINC00944, AC024060.1, HOXB-AS4, LINC01615, EPB41L4A-DT, LINC01550) was identified. Patients were divided into the low- and high-risk groups according to the median risk score, which the high-risk group owned a dramatical shorter survival time than that of the low-risk group. Through ROC analysis, it was found that this signature had a greater predictive capability compared with traditional evaluation methods. The risk score was an independent risk factor for overall survival suggested by multivariate Cox analysis (HR=1.065, 95%CI=1.036-1.095, P<0.001). We constructed a clinically predictive nomogram based on this signature and clinical features, which is of accurate prediction about the survival rate of patients. The GSEA analysis showed that primary pathways were P53 signaling pathway and tumor necrosis factor mediated signaling pathway. The major FRlncRNAs (LINC00460, LINC00944, LINC01550 and EPB41L4A-DT) were verified with the prognosis of ccRCC in the GEPIA and K-M Plotter databases. Their major target genes (BNIP3, RRM2 and GOT1) were closely related to the stage, grade and survival outcome of ccRCC by the validation of multiple databases. Additionally, we found two groups had a significant distinct pattern of immune function, immune checkpoint and immune infiltration, which may lead to different survival benefits.
Conclusions: The FRlncRNAs signature was accurate, reliable tools for predicting clinical outcomes and immune microenvironment of patients with ccRCC, which may be molecular biomarkers and therapeutic targets.