# Validation and identification of anoikis-related lncRNA signatures for improving prognosis in clear cell renal cell carcinoma

## TL;DR

This study identifies a new risk model using five lncRNAs related to anoikis to improve prognosis and survival prediction in clear cell renal cell carcinoma.

## Contribution

A novel risk model based on five anoikis-related lncRNAs for predicting prognosis in ccRCC is developed.

## Key findings

- The five-lncRNA risk model significantly distinguishes patients with better overall survival.
- Risk stratification reveals differences in immune infiltration and drug sensitivity.
- Potential signal pathways linked to risk stratification were preliminarily explored.

## Abstract

Background: Clear cell carcinoma (ccRCC) usually has a high metastasis rate and high mortality rate. To enable precise risk stratification, there is a need for novel biomarkers. As one form of apoptosis, anoikis results from the disruption of cell-cell connection or cell-ECM attachment. However, the impact of anoikis-related lncRNAs on ccRCC has not yet received adequate attention.

Methods: The study utilized univariate Cox regression analysis in order to identify the overall survival (OS) associated anoikis-related lncRNAs (ARLs), followed by the LASSO algorithm for selection. On this basis, a risk model was subsequently established using five anoikis-related lncRNAs. To dig the inner molecular mechanism, KEGG, GO, and GSVA analyses were conducted. Additionally, the immune infiltration landscape was estimated using the ESTIMATE, CIBERSORT, and ssGSEA algorithms.

Results: The study constructed a novel risk model based on five ARLs (AC092611.2, AC027601.2, AC103809.1, AL133215.2, and AL162586.1). Patients categorized as low-risk exhibited significantly better OS. Notably, the study observed marked different immune infiltration landscapes and drug sensitivity by risk stratification. Additionally, the study preliminarily explored potential signal pathways associated with risk stratification.

Conclusion: The study exhibited the crucial role of ARLs in the carcinogenesis of ccRCC, potentially through differential immune infiltration. Furthermore, the established risk model could serve as a valuable stratification factor for predicting OS prognosis.