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Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression
Published on: March 17, 2020
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A novel prognostic model for lung squamous cell carcinoma based on multi-omics analysis and machine learning
Jian Li1, Zengqiang Shen1, Dabei Liu1
1Department of Thoracic Surgery, The Shanxi Provincial People's Hospital, Shanxi, China.
Plos One
|December 19, 2025
Summary
This study identifies four molecular subtypes of lung squamous-cell carcinoma (LUSC) based on tertiary lymphoid structures (TLS). A new prognostic index (LUSCSPI) predicts survival and guides personalized chemotherapy for LUSC patients.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Lung squamous-cell carcinoma (LUSC) is aggressive with poor prognosis.
- Tertiary lymphoid structures (TLS) impact immunotherapy efficacy.
- Prognostic and immunological roles of TLS-associated subtypes in LUSC are unclear.
Purpose of the Study:
- To identify molecular subtypes of LUSC based on TLS-related genes.
- To develop a prognostic signature for LUSC.
- To explore potential therapeutic strategies based on identified subtypes.
Main Methods:
- Multi-omics analysis (mRNA, DNA methylation, mutation, lncRNA) of 39 TLS-related genes.
- Integrated consensus clustering to define LUSC subtypes.
- LASSO regression to build a prognostic signature (LUSCSPI).
Main Results:
- Four molecular subtypes (CS1-CS4) of LUSC were identified.
- Significant survival differences observed between CS1 and CS3.
- LUSCSPI identified high-risk (poor prognosis) and low-risk (favorable prognosis) groups.
- Specific chemotherapeutic agents linked to each risk group.
Conclusions:
- LUSCSPI serves as an independent prognostic factor for LUSC.
- Multi-omics approach enables prognostic stratification for personalized LUSC treatment.
- Identified subtypes and prognostic index can guide LUSC disease management.
