Integrated Multi-Omics Analysis for Molecular Subtyping in NSCLC: A Cohort Study
Yan, B.; Wang, Z.; Peng, Y.; Liu, K.; Zhou, J.-G.; Wang, S.
Show abstract
Lung cancer remains the leading cause of cancer-related morbidity and mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of cases. Current histopathological classification and driver gene testing provide limited prognostic and therapeutic guidance due to intra-tumoral heterogeneity and incomplete characterization of the tumor microenvironment (TME). Here, we constructed single- and multi-omics molecular classification systems for NSCLC by integrating transcriptomic, genomic, epigenomic, proteomic, and TME data from TCGA LUAD and LUSC cohorts. Single-omics analyses revealed distinct molecular patterns, including DNA methylation subtypes associated with sex and histology. Multi-omics integration identified five consensus subtypes closely corresponding to histology, with three immune-activated (CS2-CS4) and two immunosuppressive (CS1, CS5) subtypes. While overall survival did not differ significantly, progression-free survival analysis highlighted CS4 as a high-risk subtype with KRAS-driven genomic alterations. These findings provide a framework for NSCLC molecular stratification and highlight molecular and immune features that could guide future research on targeted therapies and immunotherapy.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Development Of A Consensus Molecular Classifier For Pancreatic Ductal Adenocarcinoma 94%
- Profiling diverse sequence tandem repeats in colorectal cancer reveals co-occurrence of microsatellite and chromosomal instability involving Chromosome 8 93%
- Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence 93%
Similar papers in this journal
- Genomic Insights Guiding Personalized First-Line Immunotherapy Response in Lung and Bladder Tumors 95%
- Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse 92%
- Panomics reveals patient-individuality as the major driver for colorectal cancer progression 92%
Similar papers in this journal
- The Epithelial and Stromal Immune Microenvironment in Gastric Cancer: A Comprehensive Analysis Reveals Prognostic Factors with Digital Cytometry 95%
- Prognosis-related molecular subtypes and immune features associated with hepatocellular carcinoma 94%
- Dysregulation of lncRNA MALAT1 Contributes to Lung Cancer in African Americans by modulating the tumor immune microenvironment 94%
Similar papers in this journal
- Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection 94%
- Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning：esophageal cancer as an example 92%
- Estimating the effect of tissue- and blood-derived cell reference matrices on deconvolving bulk transcriptomic datasets 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.