Construction and validation of a ferroptosis-related long noncoding RNA signature in clear cell renal cell carcinoma

Abstract

Background Clear cell renal cell carcinoma (ccRCC) is characterized by the accumulation of lipid-reactive oxygen species. Ferroptosis, due to the lipid peroxidation, has been reported to be strongly correlated with tumorigenesis and progression. However, the functions of the ferroptosis process in ccRCC remain unclear. Methods After sample cleaning, data integration, and batch effect removal, we used the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases to screen out the expression and prognostic value of ferroptosis-related lncRNAs and then performed the molecular subtyping using the K-means method. Then, the functional pathway enrichment and immune microenvironment infiltration between the different clusters were carried out. The results showed a significant difference in immune cell infiltration between the two clusters and the associated marker responded to individualized differences in treatment. Then, least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a prognostic signature based on 5 lncRNAs. This signature could accurately predicted patient prognosis and served as an independent clinical risk factor. We then combined significant clinical parameters in multivariate Cox regression and the prognostic signature to construct a clinical predictive nomogram, which provides appropriate guidance for predicting the overall survival of ccRCC patients. Results The prognostic differentially expressed ferroptosis-related LncRNAs (DEFRlncRNAs) were found, and 5 lncRNAs were finally used to establish the prognostic signature in the TCGA cohort, with subsequently validation in the internal and external cohorts. Moreover, we conducted the molecular subtyping and divided the patients in the TCGA cohort into two clusters showing differences in Hallmark pathways, immune infiltration, immune target expression, and drug therapies. Differences between clusters contributed to individualizing treatment. Furthermore, a nomogram was established to better predict the clinical outcomes of the ccRCC patients. Conclusions Our study conducted molecular subtyping and established a novel predictive signature based on the ferroptosis-related lncRNAs, which contributed to the prognostic prediction and individualizing treatment of ccRCC patients.

Targeting ferroptosis in renal cell carcinoma: Potential mechanisms and novel therapeutics

“…Lai et al prognostic model based on the ferroptosis-associated lncRNA signature may improve the survival prediction of RCC through making a classification in tumors [ 70 ]. These ferroptosis-related lncRNAs play an important role in the immune environment, immunotherapy response, and drug sensitivity of RCC, which helps to determine the individualized prognosis and treatment for RCC patients [ 71 , 72 ]. Dong et al also established a ferroptosis-related lncRNA model that could accurately predict the prognosis of RCC, which is associated with oxygen metabolic processes and immune microenvironment [ 73 ].…”

The Roles of Ferroptosis-related Long Non-coding RNAs in Urologic Cancers

“…Based on the ferroptosisrelated genes and lncRNAs obtained from the FerrDb and GENCODE databases, a risk assessment model was constructed including 3 frlncRNAs (DUXAP8, LINC02609, and LUCAT1) which significantly correlate with the overall survival of KIRC as an independent factor [13]. This was followed by employing 5 frlncRNAs LINC00460, LINC00894, VPS9D1-AS1, CYTOR, FOXD2-AS1 for construction and validation for the prognostic signature of KIRC [14].…”

“…Recent studies identified a series of frlncRNAs associated immune infiltration and immune microenvironment in ccRCC [50][51][52]. The frlncRNAs constructed a high level of CD8 + T cells, T cell regulatory, follicular helper T cells, memory B cells and activated CD4 memory T cells infiltrations which correlate with high-risk and poor prognosis of RCC patients [14,53]. However, underlying mechanism(s) of frlncRNAs-regulated immune cells infiltration in urological cancers are still unclear.…”

Multi-omics comprehensive analysis of renal clear cell carcinoma to distinguish subtypes with different molecular characterizations and therapeutic strategies

“…Gu et al classi ed KIRC into two categories with signi cant survival differences based on an unsupervised deep learning approach (SdA) (Gu and Zhao, 2019). In addition, there have been several studies of KIRC typing based on signature genes to explore subtypes with speci c molecular biomarkers, such as immune-and in ammation-associated genes and ferroptosis-related lncRNAs (Zhao et al, 2019;Zheng et al, 2021;Zhu et al, 2022). However, no KIRC typing study has thus far explored the combination of multi-omics data with multiple clustering algorithms.…”