Developing and validating a SNARE-based prognostic model to forecast outcomes and immune microenvironment in lung...

SNARE-related genes (SRGs), which mediate membrane fusion events, play critical roles in the development and advancement of cancer. The aim of this research was to develop a prognostic signature based on SRGs for lung squamous cell carcinoma (LUSC) and assess the biological functions of associated SRGs. Transcriptomic and clinical data for LUSC were obtained from public databases. Least absolute shrinkage and selection operator regression generated a risk signature, which was analyzed for associations with clinicopathological features, immune microenvironment, and drug sensitivity. A nomogram was developed based on SRGs-derived signature. Biological function analysis was performed using RNA interference techniques, colony formation assays, transwell assays, and wound healing assays. Ten significantly prognostic SRGs were identified and employed to generate a risk score for LUSC. This signature categorized LUSC patients into the high- and low-risk cohorts, demonstrating notable variations in clinical-pathological features, immune infiltration patterns, gene expression profiles, and responses to drug sensitivity. The developed signature proved itself as an independent predictor of LUSC prognosis, exhibiting robust predictive power for survival outcomes. A nomogram model combining the signature with smoking history showed superior performance and clinical applicability. Knockdown of the bet1 golgi vesicular membrane trafficking protein (BET1) gene in LUSC cells significantly enhanced clonogenic capacity and promoted cancer cell migration and invasion. Our study substantiated the potential clinical significance of a risk signature derived from SRGs in LUSC' prognosis prediction. It provided novel insights into the interactions among SRGs, tumors, and immunity, potentially contributing to the advancement of precision medicine for LUSC.