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

• Published in: NPJ systems biology and applications Vol. 11; no. 1; pp. 11 - 13
• Main Authors: Kashyap, Pragya, Raj, Kalbhavi Vadhi, Sharma, Jyoti, Dutt, Naveen, Yadav, Pankaj
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1751; 631/67; 692/53; Adenocarcinoma; Air pollution; Algorithms; Apoptosis; Artificial intelligence; Bioinformatics; Biology; Biomedical and Life Sciences; Biopsy; Carcinogens; Carcinoma, Non-Small-Cell Lung - classification; Carcinoma, Non-Small-Cell Lung - microbiology; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Squamous Cell - classification; Carcinoma, Squamous Cell - microbiology; Cell cycle; Classification; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Discriminant Analysis; Humans; Immunotherapy; Learning algorithms; Life Sciences; Lung - microbiology; Lung - pathology; Lung cancer; Lung carcinoma; Lung Neoplasms - classification; Lung Neoplasms - microbiology; Machine Learning; Medical prognosis; Microbiomes; Microbiota; Microbiota - genetics; Mutation; Neural networks; Outdoor air quality; Patients; Physiology; Radon; Squamous cell carcinoma; Systems Biology
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-025-00491-4

Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinical tests and biopsies that delay treatment initiation. To address this, we developed a machine learning-based approach utilizing resected lung-tissue microbiome of AC and SCC patients for subtype classification. Differentially enriched taxa were identified using LEfSe, revealing ten potential microbial markers. Linear discriminant analysis (LDA) was subsequently applied to enhance inter-class separability. Next, benchmarking was performed across six different supervised-classification algorithms viz . logistic-regression, naïve-bayes, random-forest, extreme-gradient-boost (XGBoost), k-nearest neighbor, and deep neural network. Noteworthy, XGBoost, with an accuracy of 76.25%, and AUROC (area-under-receiver-operating-characteristic) of 0.81 with 69% specificity and 76% sensitivity, outperform the other five classification algorithms using LDA-transformed features. Validation on an independent dataset confirmed its robustness with an AUROC of 0.71, with minimal false positives and negatives. This study is the first to classify AC and SCC subtypes using lung-tissue microbiome.