Migrasome-related lncRNAs predict prognosis and immune response of clear cell renal cell carcinoma.

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive renal malignancy. Migrasomes, newly discovered organelles involved in intercellular communication, and long non-coding RNAs (lncRNAs) are emerging regulators of cancer progression. However, the role of migrasome-associated lncRNAs in ccRCC prognosis and immune response remains unclear. This study aimed to investigate the value of migrasome-related lncRNAs and develop a risk model for ccRCC.

Methods: By employing data from The Cancer Genome Atlas, we were able to identify prognostically significant migrasome-related lncRNAs through co-expression analysis, Cox regression, and least absolute shrinkage and selection operator (LASSO) regression. Prognostic models were developed and validated using these lncRNAs, and a nomogram combining the risk score with clinical features was constructed. Furthermore, our analyses encompassed gene set enrichment, immune infiltration, mutational burden, and drug sensitivity.

Results: A prognostic model incorporating 13 lncRNAs effectively stratified patients into distinct risk categories, with the high-risk cohort demonstrating markedly inferior survival rates. The prognostic accuracy was validated through multiple analyses. Gene enrichment analysis revealed a correlation between these lncRNAs and tumor development and immune pathways. High-risk patients exhibited increased immunosuppressive cell infiltration, oncogenic mutations, and potential for immune escape. Furthermore, they demonstrated a lack of response to immunotherapy and exhibited differential responses to antineoplastic agents when compared to low-risk patients. We propose a prognostic model for ccRCC based on migrasome-related lncRNAs, providing new insights into disease progression and potential individualized treatment strategies.

Conclusions: Our study proposes a prognostic model for ccRCC based on migrasome-related lncRNAs, providing new insights into disease progression and potential individualized treatment strategies.