Deciphering anoikis resistance and identifying prognostic biomarkers in clear cell renal cell carcinoma epithelial...

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Clear cell renal cell carcinoma (ccRCC) presents ongoing prognostic and management difficulties.
• Anoikis, a form of programmed cell death crucial for tumor suppression, is often resistant in advanced cancers.
• Understanding anoikis resistance mechanisms is vital for improving ccRCC patient outcomes.

Purpose of the Study:

• To investigate anoikis resistance in ccRCC evolution and progression.
• To identify novel prognostic biomarkers and develop a risk stratification model for ccRCC.
• To explore the relationship between anoikis resistance, the tumor microenvironment, and patient prognosis.

Main Methods:

• Single-cell RNA sequencing (scRNA-seq) of ccRCC tumors to analyze anoikis-related genes (ARGs) and identify Anoikis-related Epithelial Subclusters (ARESs).
• Differential gene expression analysis using GEO microarray datasets.
• Development and validation of a prognostic risk model using LASSO, multivariate Cox regression, and external datasets (BEST, ULCAN, RT-PCR).
• Functional enrichment analysis (KEGG pathways), immune infiltration assessment in the tumor microenvironment (TME), and drug sensitivity analysis.
• Spatial transcriptomics to identify ARESs in situ and explore their locational context.

Main Results:

• scRNA-seq revealed dynamic ARG expression and enhanced intercellular communication in ARESs, particularly in MYC-expressing epithelial subclusters, indicating anoikis resistance.
• A robust prognostic model identified TIMP1, PECAM1, and CDKN1A as key predictive genes for ccRCC.
• The high-risk group, characterized by greater anoikis resistance and immune infiltration, demonstrated a significantly poorer prognosis.
• Spatial analysis confirmed the presence and location of ARESs within the tumor context.
• Nomogram integrating clinical parameters and prognostic genes was developed for improved prediction.

Conclusions:

• The study establishes a novel ccRCC risk signature based on three prognostic genes (TIMP1, PECAM1, CDKN1A) linked to anoikis resistance and immune infiltration.
• Findings provide insights into the spatial distribution and intercellular interactions within ccRCC, contributing to understanding tumor heterogeneity.
• The developed risk model and nomogram offer valuable tools for enhancing patient prognosis and guiding personalized therapeutic strategies in ccRCC management.