Multi-Omics and Machine Learning-Uncovered FLT1-Mediated Epithelial-Endothelial Crosstalk in Cellular Senescence Driving Clear Cell Renal Cell Carcinoma Malignancy.

Abstract

Clear cell renal cell carcinoma (ccRCC) is distinguished by the absence of definitive diagnostic markers and efficacious treatment modalities, factors that collectively contribute to its unfavorable clinical prognosis. The targeting of senescent cells has recently emerged as a promising therapeutic strategy. Nevertheless, the precise role of cellular senescence in the pathophysiology of ccRCC has yet to be comprehensively elucidated. This study sought to investigate the role of cellular senescence levels in ccRCC through comprehensive transcriptomic, proteomic, spatial transcriptomic, and single-cell analyses. The study determined that elevated levels of cellular senescence contribute to a suppressed immune microenvironment, thereby exacerbating the prognosis for ccRCC patients. We utilized an extensive array of machine learning algorithms, in conjunction with multi-omics technologies, validated through immunofluorescence, RT-qPCR, and additional techniques, to collectively identify FLT1 as a pivotal single gene driving ccRCC progression. Our work reveals a FLT1-centered network of related factors, where FLT1 acts as the core single gene, closely associated with key factors VEGFA and AKT1. This network mediates crosstalk between endothelial and epithelial cells: endothelial cells expressing FLT1 alone, AKT1 alone, or co-expressing FLT1/AKT1 exhibited enhanced malignancy; among epithelial cells, proximal tubular epithelial cells with high VEGFA expression (a factor closely related to FLT1) represented the most aggressive subtype and acted as "pioneer cells" driving tumor progression. This FLT1-centric mechanism is evolutionarily conserved, as validated in mouse single-cell datasets. Clinically, ccRCC patients with low expression of the FLT1-centered network (particularly low FLT1) showed better responses to immunotherapy. For patients with high FLT1 expression, a combination therapy targeting this network-screened via molecular docking and dynamics simulations-may improve prognosis. This includes FLT1 inhibitors (Sorafenib, Regorafenib, Lenvatinib), supplemented by AKT1 inhibitors (Capivasertib) and VEGFA inhibitors (Bevacizumab) to suppress FLT1-associated malignant cell populations.