Development and Validation of Anoiki-Related Lncrna Signature Prediction Model for KIRC Prognosis

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Long noncoding RNAs (lncRNAs) are implicated in various cancers.
• Limited research exists on anoikis-related lncRNAs in kidney renal clear cell carcinoma (KIRC).
• This study addresses the need for prognostic markers in KIRC by investigating anoikis-related lncRNAs.

Purpose of the Study:

• To develop a prognostic model for KIRC patients using anoikis-related lncRNAs.
• To identify potential therapeutic targets within these lncRNAs.
• To explore the relationship between anoikis-related lncRNAs and the kidney immune microenvironment.

Main Methods:

• Identified prognostic anoikis-related lncRNAs using statistical analyses (Pearson correlation, Cox regression).
• Constructed a predictive model with four lncRNAs via LASSO regression.
• Assessed model performance using Kaplan-Meier and ROC analyses.
• Evaluated immune cell infiltration and gene expression using ESTIMATE, CIBERSORT, TIMER, and QUANTISEQ.
• Validated findings through drug sensitivity assays and in vitro experiments.

Main Results:

• Identified 63 anoikis-related lncRNAs, selecting four (Z99289.2, AC084876.1, LINC00460, AC090337.2) as hub markers.
• Developed and validated a prognostic signature based on these four lncRNAs in TCGA KIRC cohort.
• The signature accurately distinguished high-risk from low-risk KIRC patients and predicted outcomes.
• In vitro experiments confirmed LINC00460 downregulation inhibits KIRC cell growth and promotes apoptosis.

Conclusions:

• Anoikis-related lncRNAs are promising prognostic markers for KIRC.
• These lncRNAs may offer insights into KIRC treatment strategies.
• The study highlights the potential of lncRNAs in understanding the KIRC immune microenvironment.