Integrating multi-omics analysis and machine learning to identify molecular subtypes and construct prognostic models for lung squamous cell carcinoma

Abstract LUSC had a high morbidity and mortality rate in China, resulting in high social burdens. Most LUSC patients are already in the highly advanced cancer stage at diagnosis, and the clinical treatment is relatively difficult and the prognosis is relatively poor. The progress and application of sequencing technologies and machine learning algorithms offer new therapeutic perspectives and survival opportunities for LUSC patients. First, we gained multi-omics data on LUSC from the TCGA and GEO databases and performed batch effect. A total of ten different clustering methods were adopted to conduct multiomics consensus ensemble analysis. Then, we combined the integration analysis with ten machine learning algorithms to develop a CMLS. Besides, we explored the immune landscape and immunotherapeutic response of LUSC. Lastly, we identified potential therapeutic agents in LUSC. We independently identified two subtypes (CS1 and CS2) from ten multi-omics integrated clustering algorithms and CS2 showed the most favourable survival outcome among all subtypes. Subsequently, we identified 24 PRGs based on markers between subtypes and constructed CMLS using ten machine learning algorithms. In the TCGA-LUSC and META-LUSC sets, patients with high-CMLS group had a poorer clinical prognosis. Moreover, we evaluated the immunological landscape of LUSC using "IOBR" package. Low-CMLS group exhibited significantly higher levels of immune cell infiltration, including NK cells, T cells and B cells, suggesting that they may have better survival outcomes. CMLS might predict the prognosis and immune response of LUSC patients in some degree. In conclusion, our study provided novel ways to optimise the clinical diagnosis and therapeutic approaches of LUSC.