Integrating Bulk and Single-Cell RNA-Seq Reveals Glycolysis-Associated Macrophages and Its Related Tumor Subgroup Signatures to Predict Prognosis and Therapy in Clear Cell Renal Cell Carcinoma.

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of RCC and is marked by aggressive nature and poor survival. However, therapeutic options remain limited and yield suboptimal outcomes. Macrophages exhibit marked heterogeneity within ccRCC, exerting a substantial impact on the malignant progression of tumors and resistance to therapeutics.

METHODS

This study utilized single-cell sequencing and transcriptomics to identify a subset of macrophages associated with glycolysis and the most interactive tumor subpopulation, in order to explore the link between macrophages and ccRCC risk. Moreover, employing machine learning techniques, we crafted a precise gene signature to predict patient prognosis, with clinical implications.

RESULTS

We identified a macrophage subpopulation primarily characterized by glycolytic metabolism and a closely associated tumor cell subpopulation, both significantly correlated with poor prognoses in ccRCC patients. Then, we use hdWGCNA to identify key genes and functional modules of cell subgroups, and use 101 types of machine learning methods to establish a prognosis model of six genes: CENPA, ITM2B, TUBA1B, TNFSF13B, SNX3, and TNNT1 in the RNAseq cohorts of ccRCC patients. Patients in the high-risk group exhibited poorer prognoses, with functional enrichment indicating the presence of modules associated with malignant progression. Additionally, immune infiltration analysis revealed higher levels of immune cell infiltration in this group, suggesting their potential responsiveness to immunotherapeutic interventions.

CONCLUSION

Our study introduces a novel, robust ccRCC prognostic model, providing new insights and potential therapeutic strategies for precision treatment of ccRCC patients.