Pilaralisib

Research on molecular classification of lung cancer based on transcriptomic features has achieved remarkable progress. The complementary information provided by distinct molecular profiles has motivated the integration of multi‐omics datasets to refine the classification system for lung cancer. In this study, we employed a computational pipeline incorporating 10 clustering algorithms to integrate multi‐omics datasets from lung adenocarcinoma (LUAD) patients, combined with 10 machine learning methods, leading to the identification of high‐resolution molecular subtypes and the development of a consensus machine learning–driven signature (CMLS) with robust predictive performance. Our findings reveal that the CS1 subtype is associated with more favorable prognosis and enhanced immune responsiveness. Furthermore, a CMLS model constructed from 26 core genes demonstrated strong prognostic predictive power. Patients with high CMLS scores exhibited lower infiltration of CD8 + T cells, poorer survival, and diminished response to immunotherapy. In contrast, the low‐CMLS group showed improved clinical outcomes, greater responsiveness to immunotherapy, and a tendency toward an immunologically “hot” tumor phenotype. Integrated multi‐omics analysis indicated that Gremlin‐1 (GREM1) acts as a key regulator within the differential screening–selected gene aberrant in neuroblastoma (DAN) family genes–mediated transforming growth factor‐beta (TGF‐β) signaling pathway. In conclusion, our data establish a molecular classifier that stratifies patients into distinct score groups, with those in the low‐CMLS group potentially benefiting from treatment with pilaralisib.