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

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.