Lung adenocarcinoma (LUAD) is the most common lung cancer histological subtype. Although the unfolded protein response (UPR) has been linked to various human diseases, its role in LUAD remains unclear. To identify UPR-related genes, we applied various methods, including weighted gene co-expression network analysis, differential expression analysis, and multivariate Cox regression. Ten machine learning algorithms were used to construct a UPR-related signature (UPRRS), which was validated using multiple public LUAD datasets. The UPRRS was integrated into a nomogram used in clinical practice for prognosis prediction. We also evaluated predicted drug sensitivity patterns across different risk subgroups. We identified 33 UPR-associated hub genes. A UPRRS was developed through systematic evaluation of 101 machine-learning combinations, exhibiting stable prognostic performance across multiple cohorts. Integration of the UPRRS into a nomogram facilitated the construction of a quantitative prognostic model. Significant differences in biological processes and tumor microenvironment immune cell infiltration were observed between the high- and low-risk UPRRS groups. All five UPRRS genes (ALDH2, FKBP4, KLF4, LAIR1, SIDT2) were validated at the protein level in LUAD cell lines, and FKBP4 was further confirmed by IHC in clinical tissues. Functional experiments showed that FKBP4 knockdown inhibited proliferation, migration, and invasion of A549 and H1975 cells, supporting a potential role for FKBP4 in LUAD progression. Our UPRRS provides a promising tool for prognostic stratification and may offer additional insights into tumor immune microenvironment characterization and therapeutic response prediction in LUAD.
Rui Jiao, Chengyang Wu, Tao Zhang et al.· BMC Cancer· 0 citations
Background As the leading histological form of lung cancer, lung adenocarcinoma (LUAD) displays considerable intratumoral heterogeneity, frequent therapeutic resistance, and an unfavorable clinical outcome. Although rewiring of mitochondrial energy metabolism is known to drive tumor progression and treatment failure, a comprehensive understanding of its dual role in LUAD drug resistance and prognosis has yet to be established. Here, we built a robust predictive signature that integrates mitochondrial metabolism with drug resistance through multi-omics integration and machine learning frameworks. Methods We explored single-cell RNA sequencing profiles together with TCGA-LUAD transcriptomic data. Weighted gene co-expression network analysis (WGCNA) was applied to extract gene modules linked to mitochondrial-related genes (MRGs) and drug resistance-related genes (DRGs). From these, a five-gene (KLF4, KLF10, CAT, ALDOA, HLA-DRA) prognostic classifier, designated MDrisk, was formulated using LASSO-Cox regression and 101 combinations of 10 machine learning algorithms. Results The MDrisk model demonstrated reliable and precise prognostic capacity across training, internal test, and external GEO cohorts, serving as an independent risk factor. Elevated MDrisk scores correlated with an immunosuppressive microenvironment, higher tumor mutational burden, distinct copy-number alteration profiles, and decreased drug sensitivity in computational predictions. In vitro experiments further validated that silencing ALDOA—a central component of the signature—suppressed the proliferation, migration, and invasive capacity of LUAD cells. Conclusion The MDrisk signature derived from mitochondrial energy metabolism and drug resistance may be useful for distinguishing prognosis, immune contexture, and computationally inferred drug susceptibility in LUAD. It may offer a tool for further exploration of individualized therapy and sheds light on the interplay between metabolic dysregulation and antitumor immunity.
Chengyang Wu, Rui Jiao, Han Yan et al.· Frontiers in Medicine· 0 citations