Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with limited targeted treatment options and poor clinical outcomes. We developed an AI-driven multi-omics pipeline that links prognostic modeling to multitarget drug repurposing for ESCC. Summary-data-based Mendelian randomization was integrated with bulk transcriptomic datasets to identify esophageal cancer-related druggable genes that are differentially expressed. Cox regression and non-negative matrix factorization were then used to define prognostic genes and molecular subgroups, and a Lasso Cox model with SHapley Additive explanation provided an interpretable prognostic signature. Single-cell RNA sequencing analysis mapped the hub genes interleukin 22 receptor subunit alpha 1 (IL22RA1) and family with sequence similarity 221 member A (FAM221A) to epithelial cell populations and associated them with proliferative and DNA repair programs, supporting their role in tumor progression, supporting their role in ESCC progression. To translate these targets into a therapeutic strategy, we applied machine learning-based drug sensitivity prediction, ADMET-AI toxicity, pharmacokinetic profiling, and molecular docking, which converged on the checkpoint kinase inhibitor AZD7762 (3-(carbamoylamino)-5-(3-fluorophenyl)-N-(3S)-piperidin-3-yl thiophene-2-carboxamide) as a promising multitarget inhibitor of IL22RA1 and FAM221A. In vitro assays confirmed that IL22RA1 and FAM221A promote ESCC cell proliferation, migration, and invasion. Taken together, this AI-driven multi-omics framework delivers a prognostic model, defines biologically distinct ESCC subgroups, and nominates AZD7762 as a rational multitarget drug repurposing candidate, providing a precision oncology strategy.
Zhuang et al. (Tue,) studied this question.