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Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.

Pragya Kashyap¹, Kalbhavi Vadhi Raj², Jyoti Sharma¹

¹Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, India.

NPJ Systems Biology and Applications

|January 17, 2025

Summary

This summary is machine-generated.

This study classifies lung cancer subtypes, adenocarcinoma (AC) and squamous cell carcinoma (SCC), using machine learning on lung tissue microbiome data. The XGBoost model achieved 76.25% accuracy, offering a novel diagnostic approach.

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MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

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Area of Science:

Microbiology
Oncology
Bioinformatics

Background:

Classifying lung adenocarcinoma (AC) and squamous cell carcinoma (SCC) is challenging, often requiring invasive procedures.
Delays in diagnosis can impede timely treatment initiation for lung cancer patients.

Purpose of the Study:

To develop a machine learning model for classifying AC and SCC lung cancer subtypes.
To leverage the lung tissue microbiome for accurate and non-invasive subtyping.

Main Methods:

Utilized resected lung tissue microbiome data from AC and SCC patients.
Employed LEfSe for differential taxa enrichment and Linear Discriminant Analysis (LDA) for feature enhancement.
Benchmarked six supervised machine learning algorithms, including XGBoost, logistic regression, and deep neural networks.

Main Results:

Identified ten potential microbial markers differentiating AC and SCC subtypes.
Extreme Gradient Boosting (XGBoost) demonstrated superior performance with 76.25% accuracy and an AUROC of 0.81.
Independent dataset validation confirmed model robustness with an AUROC of 0.71.

Conclusions:

The lung tissue microbiome can be effectively used for classifying lung cancer subtypes (AC vs. SCC).
Machine learning, particularly XGBoost, offers a promising avenue for improving lung cancer diagnosis and potentially reducing delays associated with traditional methods.