BACKGROUND

Lung adenocarcinoma (LUAD) shows high recurrence rate and poor prognosis. Genes associated with ubiquitin play a role in the onset and advancement of cancers; however, they have yet to be employed for the diagnosis and prognosis of LUAD.

METHODS

First, gene modules correlated with ubiquitin were identified by WGCNA. The expression profiles obtained were intersected with differential genes taken between the LUAD and control samples. The genes were then further compressed using univariate and multifactorial Cox regression analyses and risk models. In addition, the model was validated by constructing a nomogram using clinical characteristics and Riskscore. Next, the differences in immune infiltration between different subgroups were explored, and immunotherapy and drug sensitivity evaluations were performed. The biological role of in LUAD was also explored using CCK-8, wound healing assay and transwell.

RESULTS

The intersection between the module genes between LUAD samples and control samples and differentially expressed genes (DEGs) yielded 197 intersected genes after we screened three particular modules with the strongest ubiquitin association by WGCNA. 32 genes associated with LUAD prognosis were screened, and , , , , , , , and were selected as independent prognosis genes for risk modeling. Patients were classified into low- and high-risk groups by the Riskscore. Low-risk patients had markedly better overall survival (OS) than those in the high-risk group. The quantity of immune cell infiltration between the two patient groups varied notably, and the expression of model genes was negatively connected with the infiltration of the great majority of immune cells. The medications TAE684, Cisplatin, and Midostaurin exhibited the largest negative correlation with Riskscore, according to drug sensitivity study. Lastly, we demonstrated through tests that knockdown markedly reduced LUAD cell survival, migration, and invasion.

CONCLUSION

This study is the first to systematically integrate the ubiquitin pathway with multi-omics data, constructing a robust risk model for LUAD prognosis and immune characteristics, providing a theoretical reference for future exploration of potential biomarkers for LUAD patients' diagnosis.