ABSTRACT
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.
Subject(s)
Carcinoma, Renal Cell , Carcinoma , Kidney Neoplasms , Humans , Prognosis , Risk FactorsABSTRACT
BACKGROUND: Systemic lupus erythematosus (SLE) is a prototypic systemic autoimmune disease. Complement component 4 (C4) has be proved to play a role in pathogenesis of SLE. In the present study, we investigated the effect of C4 on T cells differentiation. METHODS: Thirty SLE patients were included in this study. CD4+ T cells were isolated from healthy subjects, and dendritic cells (DCs) were isolated from healthy subjects or SLE patients. C4 was supplemented to co-incubate with T cells and DCs. RESULTS: Serum C4 concentration was positively correlated with regulatory T cell (Treg) percentage (R(2) = 0.5907, p < 0.001) and TGFß concentration (R(2) = 0.5641, p < 0.001) in SLE patients. Different concentrations of C4 had no effect on T cells differentiation. Co-incubated T cells with DCs and C4 for 7 days, the Treg percentage and TGF-ß concentration were significantly elevated. In addition, pre-treated DCs (from healthy subjects or SLE patients) with C4 and then co-incubated with T cells, the increases of Treg percentage and TGF-ß concentration were also observed. CONCLUSION: C4 takes part in T cells differentiation to Treg cells via DCs.