Integrated multiomics machine learning and mediated Mendelian randomization investigate the molecular subtypes and prognosis lung squamous cell carcinoma.

ABSTRACT:

Background

Lung squamous cell carcinoma (LUSC) lacks specific early diagnostic markers. Given the critical role of 5'-Nucleotidase Ecto (NT5E) in immune evasion and therapy resistance of cancer cells and the involvement of Dual Specificity Phosphatase 4 (DUSP4) in tumor cell proliferation through inhibition of the ERK signaling pathway, incorporating NT5E and DUSP4 into the consensus machine learning signature (CMLS) system in this study holds significant potential for investigating the early diagnosis and immune microenvironment of LUSC. The objective of this study was to explore the prognostic targets of LUSC.

Methods

Employing integrated algorithms enhances the ability to identify molecular subtypes and key features from multiple perspectives. A combination of 10 clusteri

SUBMITTER: Huang Z 

PROVIDER: S-EPMC12000960 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature