# Bile acid metabolism and invasion-related genes as therapeutic monitoring biomarkers in non-small cell lung cancer

## TL;DR

This study identifies key genes linked to bile acid metabolism and tumor invasion in lung cancer, which could help monitor treatment and improve patient outcomes.

## Contribution

The study introduces a novel therapeutic monitoring model based on bile acid metabolism and invasion-related genes in NSCLC.

## Key findings

- 109 genes were identified at the intersection of bile acid metabolism and tumor invasion in NSCLC.
- SLC1A5 and CD36 are critical genes linked to immunosuppression and tumor metabolism.
- A predictive model using LASSO and SVM showed strong potential for monitoring treatment response.

## Abstract

Non-small cell lung cancer (NSCLC) persists as a major contributor to global cancer morbidity and mortality. This study explores the complex interplay between bile acid metabolism (BAM), tumor invasion, and the immune microenvironment in NSCLC pathogenesis. While traditionally known for digestive functions, bile acids are crucial signaling molecules, and their metabolic pathways, coupled with invasion-related genes (IRGs), are increasingly implicated in tumorigenesis. Using the NSCLC dataset GSE225620, we identified 109 differentially expressed genes at the intersection of BAM and invasion (BAM&IRDEGs). Functional analysis revealed their significant enrichment in metabolic pathways, including fatty acid metabolism, which are critical for shaping the tumor microenvironment and fostering aggressive growth. Immune infiltration analysis indicated significant remodeling, particularly highlighting a strong correlation between activated CD8⁺ T cells and central memory CD4⁺ T cells. Key genes, including the glutamine transporter SLC1A5 and the fatty acid translocase CD36, emerged as critical nodes. Our analysis suggests these genes contribute to an immunosuppressive microenvironment by fueling tumor metabolic reprogramming and inducing CD8⁺ T cell dysfunction through altered lipid and glutamine metabolism. Leveraging these key genes, we established a therapeutic monitoring model using LASSO regression and support vector machine (SVM) algorithms. The model demonstrated robust performance in predicting treatment efficacy, underscoring its potential for monitoring therapeutic response and guiding individualized treatment decisions. Our findings highlight the clinical relevance of the metabolic-immune axis in NSCLC and propose that key BAM&IRDEGs, particularly SLC1A5 and CD36, are not only potential therapeutic targets but also promising candidates for inclusion in liquid biopsy panels. Such non-invasive tools could enable dynamic monitoring of disease progression and early detection of relapse, paving the way for more precise NSCLC management.

The online version contains supplementary material available at 10.1007/s12672-025-03925-x.