# Identification of prognosis and therapy related intratumoral microbiome and immune signatures in gastric cancer

## TL;DR

This study identifies bacterial and immune signatures in gastric cancer that predict survival and treatment response, offering new insights for personalized therapy.

## Contribution

A novel immune-related RiskScore model combining intratumoral microbiome and gene data improves gastric cancer prognosis prediction.

## Key findings

- Nine bacterial genera were found to significantly affect overall survival in gastric cancer patients.
- An immune-related RiskScore model achieved high accuracy in predicting 1, 3, and 5-year survival rates.
- Tumor-associated fibroblasts were identified as key players in immune-microbial interactions in gastric cancer.

## Abstract

The impact of the intratumoral microbiome (ITM) on the treatment and prognosis of gastric cancer (GC) remains controversial. Our study analyzed the differential ITM in GC tissues and identified nine bacterial genera significantly associated with overall survival (OS), with seven as risk factors and two as protective factors. Three distinct clusters with varying survival outcomes were defined, demonstrating correlations with pathological stage and immune features. An immune-related gene-based RiskScore model incorporating genes such as Apolipoprotein D (APOD), Stanniocalcin 1 (STC1), Coagulation Factor II Thrombin Receptor (F2R), Angiotensinogen (AGT), Fatty Acid Binding Protein 4 (FABP4), Inhibin Subunit Beta A (INHBA), Caspase Recruitment Domain Family Member 11 (CARD11), and Dickkopf WNT Signaling Pathway Inhibitor 1 (DKK1) was established and validated in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. When combined with clinical factors, this RiskScore model formed a Nomogram model achieving Areas Under the Curve (AUCs) of 0.72, 0.76, and 0.79 for 1, 3, and 5-year OS predictions, respectively. This model exhibited robust predictive accuracy over time and correlated with mutation frequency, drug sensitivity, and immunotherapy response. Furthermore, single-cell analysis revealed that tumor-associated fibroblasts may play a pivotal role in immune-microbial interactions. The results were confirmed using quantitative real-time polymerase chain reaction (qPCR) and immunohistochemistry (IHC). In conclusion, the prognostic model incorporating ITM and immune-related genes aids in risk stratification and provides valuable insights and targets for GC treatment.