Microbial Signatures in Head and Neck versus Gastrointestinal Tumors: Identification and Prognostic Modeling

Area of Science:

• Oncology
• Microbiome Research
• Bioinformatics

Background:

• Intra-tumor microbial communities play a role in cancer development and progression.
• Distinguishing between different cancer types, such as head and neck and gastrointestinal cancers, is crucial for effective treatment.
• Identifying reliable biomarkers for cancer diagnosis and prognosis remains a significant challenge.

Purpose of the Study:

• To identify key intra-tumor microbial signatures that differentiate head and neck cancers from gastrointestinal cancers.
• To explore the diagnostic and prognostic potential of these identified microbial markers.
• To develop a robust model for cancer classification and survival prediction based on microbial signatures.

Main Methods:

• Utilized data from the Cancer Microbiome Atlas and Cancer Genome Atlas for 463 tumor samples and 47 controls.
• Employed Wilcoxon test for microbial population differences, followed by logistic regression, LASSO, and RFE for marker selection.
• Constructed a Support Vector Machine (SVM) classification model and a nomogram for validation, alongside Kaplan-Meier curves for survival analysis.

Main Results:

• Identified 23 microbes with significant distribution differences; selected 8 overlapping microbes as optimal markers.
• The SVM model achieved high accuracy (AUC 0.937 training, 0.856 validation) in distinguishing cancer types.
• Nomogram showed strong predictive accuracy (C-index 0.8944 training, 0.8023 validation); specific microbes correlated with improved survival.

Conclusions:

• Eight intra-tumor microbes form a robust signature for differentiating head and neck from gastrointestinal tumors.
• Capnocytophaga, Lachnospiraceae, and Bacteroidales show promise as prognostic biomarkers for enhancing cancer survival prediction.
• This study highlights the potential of intra-tumor microbiome analysis in cancer diagnostics and prognostics.