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Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a

Xiaoqin Luo1,2,3,4, Chao Li5,6,7, Gang Qin8

  • 1Department of Otolaryngology, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Hereditas
|February 5, 2025
PubMed
Summary

This study identified two head and neck squamous cell carcinoma (HNSCC) subtypes and a prognostic gene signature. This aids in personalized treatment selection for improved HNSCC patient outcomes.

Keywords:
Head and neck squamous cell carcinomaImmunotherapyMachine learningMulti-omics analysesPrognostic model

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Area of Science:

  • Oncology
  • Genomics
  • Immunology

Background:

  • Immunotherapy improves head and neck squamous cell carcinoma (HNSCC) survival, but drug resistance is a major hurdle.
  • Personalized treatment strategies are crucial for enhancing HNSCC therapeutic efficacy by addressing molecular heterogeneity.

Purpose of the Study:

  • To identify molecular subtypes of HNSCC using multi-omics data.
  • To develop a machine learning-based prognostic signature for HNSCC patients.
  • To correlate molecular subtypes and prognostic signatures with immune infiltration, pathways, and treatment sensitivity.

Main Methods:

  • Integrated four HNSCC datasets (TCGA-HNSCC, GSE27020, GSE41613, GSE65858) from TCGA and GEO.
  • Utilized 10 multi-omics consensus clustering algorithms (MOVICS) to identify and validate two molecular subtypes (CS1, CS2).
  • Constructed a prognostic signature using 101 machine learning algorithms, selecting 30 prognosis-related genes (PRGs) with the Elastic Net model.

Main Results:

  • Identified two molecular subtypes (CS1 and CS2) with CS1 showing significantly better survival.
  • Developed a 30-gene prognostic signature that stratified patients into low- and high-risk groups with distinct survival outcomes.
  • Low-risk patients exhibited enhanced immune infiltration and function, while high-risk patients showed sensitivity to conventional therapies; low-risk patients responded better to immunotherapy and targeted treatments.

Conclusions:

  • Delineated two distinct molecular subtypes of HNSCC.
  • Established a robust, multi-omics-derived prognostic signature for HNSCC.
  • Provided a framework for personalized treatment selection, optimizing therapeutic strategies for HNSCC patients based on molecular profiles.