Clear cell renal cell carcinoma (ccRCC) stands as a challenging subtype of kidney cancer, frequently complicating patient prognosis due to factors like postsurgical recurrences or late-stage diagnoses. In this study, we employed bioinformatics to investigate lactylation modifications in ccRCC, focusing on the TCGA-KIRC cohort. Out of 328 lactylation-associated genes, 31 emerged as differentially expressed, with 16 showing a marked correlation with overall survival. These genes exhibited strong protein-protein interactions and significant expression correlations. Intriguingly, a notable loss of gene copy numbers suggests potential implications in tumor progression. Utilizing unsupervised clustering, KIRC samples were grouped into two distinct subcategories, each showcasing different survival outcomes. While pathway enrichment highlighted an aggressive, inflammation-driven profile for subgroup 2, subgroup 1 was characterized by metabolic prominence. Furthermore, subgroup 2 presented an intensified inflammatory response, hinting at potential immune exhaustion. Capitalizing on machine learning, we developed a risk model using the TCGA-KIRC dataset, efficiently categorizing ccRCC patients into high- and low-risk clusters. Notably, those in the low-risk group indicated a more favorable survival trajectory. Clinical evaluations further corroborated these findings, linking better outcomes with reduced risk scores. Additionally, observed mutation patterns allude to a potential association between elevated risk scores and cytokine storms. TIDE analysis illuminated possible immunotherapeutic benefits for the low-risk group, underscored by an evident rise in microsatellite instability. Finally, our drug sensitivity evaluations revealed distinct therapeutic responses between the groups. In summary, this research underscores the pivotal role of lactylation modifications in ccRCC and introduces a promising prognostic model. These revelations pave the way for enhanced prognostic precision, presenting a promising path toward personalized treatment strategies and enriching our comprehension of the multifaceted molecular landscape of the disease.
Keywords: clear cell renal cell carcinoma; lactylation-associated genes; machine learning; prognostics model; tumor immune microvironment.
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