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A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics.

Akanksha Jarwal1, Anjali Dhall1, Akanksha Arora1

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, India.

Frontiers in Molecular Biosciences
|June 14, 2024
PubMed
Summary

This study developed machine learning models for early Head and Neck Squamous Cell Carcinoma (HNSCC) detection and HPV status classification. An Artificial Neural Network achieved high accuracy, aiding in cancer diagnosis and management.

Keywords:
HNSCCclassification modelsdeep learninggene biomarkersmachine learningsingle cell transcriptomics

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Head and Neck Squamous Cell Carcinoma (HNSCC) is a globally prevalent cancer.
  • Early detection of HNSCC is crucial but challenging due to costly and invasive methods.
  • Current diagnostic approaches for HNSCC require improvement in terms of cost-effectiveness and patient invasiveness.

Purpose of the Study:

  • To develop and evaluate machine learning and deep learning models for HNSCC detection.
  • To classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories.
  • To identify key genes associated with HNSCC and its HPV status using transcriptomics data.

Main Methods:

  • Utilized single-cell transcriptomics data (GSE181919 dataset) comprising HNSCC and normal samples.
  • Applied feature selection (mRMR) to identify significant genes and Gene Ontology (GO) enrichment analysis.
  • Developed and validated classification models, including Artificial Neural Networks, on 80% training and 20% validation data splits.

Main Results:

  • An Artificial Neural Network model achieved an AUROC of 0.91 for HNSCC classification on the validation set.
  • The same model demonstrated an AUROC of 0.83 for classifying HPV+ and HPV- HNSCC patients.
  • GO enrichment analysis indicated that selected genes are primarily involved in binding and catalytic activities.

Conclusions:

  • Developed accurate machine learning models for HNSCC detection and HPV status determination.
  • Created a user-friendly software package in Python for HNSCC prediction and HPV status identification.
  • The developed tool is accessible online, offering a potential non-invasive method for HNSCC diagnosis.