The FAcilitates Chromatin Transcription (FACT) complex is a chromatin remodeler with emerging roles in cancer progression. This study utilizes a multi-omics strategy combining single-cell RNA sequencing (scRNA-seq), differential expression analysis, prognostic evaluation, and immune infiltration profiling to define the oncogenic functions of its subunits Structure-Specific Recognition Protein 1 (SSRP1) and SUPpressor of Ty 16 Homolog (SUPT16H). Experimental validation in liver and breast cancer cell lines confirms significant overexpression of both subunits, particularly in hepatocellular carcinoma. Single-cell analysis reveals FACT enrichment in malignant cell types and progressive upregulation along pseudotime. Elevated FACT expression correlates with an immunosuppressive tumor microenvironment marked by reduced cytotoxic immune cell infiltration. Drug sensitivity screening identifies FK866 as a potent inhibitor, and functional assays verify that its efficacy depends on FACT expression, as siRNA knockdown of SSRP1 and SUPT16H induced resistance. These results establish FACT as a critical oncogenic driver and a promising therapeutic target, providing insights for overcoming immune evasion and advancing precision oncology.

This study systematically investigates the roles of messenger RNAs (mRNAs) and microRNAs (miRNAs) in Renal Cell Carcinoma (RCC) and their potential as diagnostic and prognostic biomarkers via a stratification approach. By utilizing The Cancer Genome Atlas (TCGA) database, differentially expressed mRNAs (namely DEGs) and miRNAs (namely DEMs) are identified, and survival prognosis-related biomarkers are determined through Kaplan-Meier analysis and lasso regression. Prognostic models are established for RCC ethnicity, pathologic stages, and metastatic status, with validation through plotting risk heatmaps, risk curves, survival curves, and Receiver Operating Characteristic (ROC) curves. A total of 45 mRNA and 33 miRNA biomarkers are identified across different prognostic models, resulting in enhanced prediction accuracy with increased stratification. The literature review confirms abnormal expressions of 28 prognostic RNAs reported in experiments and 15 prognostic RNAs reported in bioinformatics studies. The study also introduces 35 novel prognostic RNAs as potential treatment targets for RCC. The mRNA+miRNA prognostic models exhibit the most robust predictive capability, indicating their potential clinical relevance. Overall, the study contributes to a precise prognosis of RCC by exploring novel biomarkers and potential therapeutic targets.