# Construction of a microbial abundance prognostic scoring model based on intratumoral microbial data for predicting the prognosis of lung squamous cell carcinoma

## TL;DR

This study creates a microbial abundance scoring model to predict the prognosis of lung squamous cell carcinoma based on tumor microbiota data.

## Contribution

The novel contribution is the construction of a MAPS model using intratumoral microbial abundance to predict LUSC prognosis.

## Key findings

- The MAPS model identified seven microbial genera significantly related to LUSC prognosis.
- Four genera showed differential abundance between LUAD tumors and normal tissues.
- The MAPS risk grouping was confirmed as a prognostic risk factor for LUSC.

## Abstract

The development of lung squamous cell carcinoma (LUSC) is associated with the intratumoral microbiota. To facilitate faster clinical decisions and predict patient prognosis, we constructed an intratumoral microbial abundance prognostic scoring (MAPS) model for LUSC and analyzed its prognostic performance. Data on the LUSC tumor microbiome, patient survival, and clinical information were downloaded from The Cancer Microbiome Atlas and The Cancer Genome Atlas databases. Differentially abundant microbial genera in LUSC tumors were analyzed, and their prognostic value was evaluated. The differential abundance of key genera in the MAPS model was validated using lung adenocarcinoma (LUAD) tumors and normal tissues. Of 52 microbial genera with increased abundance and 437 with decreased abundance in LUSC tumors, 462 were highly related to the disease. Seven of 13 genera that were significantly related to prognosis were selected to construct the MAPS model. The MAPS risk grouping was identified as a prognostic risk factor for LUSC. Among the seven genera in the MAPS model, Indibacter, Oceanospirillum, Thalassomonas, and Thermopetrobacter differed in abundance between LUAD tumors and normal tissues and may be the key intratumoral microorganisms involved in LUSC and LUAD development. In conclusion, our MAPS model may be a powerful prognostic biomarker for LUSC.