# Investigating the Genetic Links Between Immune Cell Profiles and Bladder Cancer: A Multidisciplinary Bioinformatics Approach

## TL;DR

This study explores how immune cells in bladder cancer tumors relate to cancer risk and identifies potential biomarkers for diagnosis and treatment.

## Contribution

The novel contribution is the identification of immune-related genes and biomarkers using Mendelian randomization and machine learning in bladder cancer.

## Key findings

- Eight immune cell subtypes were found significantly associated with bladder cancer risk.
- Five potential diagnostic biomarkers (COLEC12, TMCC1, CEP55, KLK3, COL4A1) were identified with high diagnostic accuracy (AUC of 0.903).
- Immune-related differentially expressed genes were linked to cancer progression and immune modulation.

## Abstract

Background: Bladder cancer (BC) is a common malignancy in the urinary system, with an increasing incidence rate. Immune cell infiltration within the tumor microenvironment (TME) plays a crucial role in BC progression and treatment response. However, the immune cell composition of the TME presents a significant challenge to the effectiveness of current therapeutic strategies. Methods: We performed bidirectional Mendelian randomization (MR) analysis to investigate the impact of immune cells on BC risk. Single nucleotide polymorphisms (SNPs) related to immune cells were annotated, and candidate genes associated with BC risk were identified. Differential expression analysis identified immune-related differentially expressed genes (iDEGs), and a protein–protein interaction (PPI) network along with functional enrichment analysis were conducted to explore their roles in tumor development. Machine learning-based feature selection was applied to identify potential biomarkers and therapeutic targets. Results: MR analysis revealed eight immune cell subtypes significantly associated with BC. Using SNPs linked to these immune cells, 129 candidate genes were identified through the SNPense tool and cross-referenced with differentially expressed genes in BC, resulting in identification of 28 iDEGs. Machine learning identified five potential diagnostic biomarkers (COLEC12, TMCC1, CEP55, KLK3, COL4A1) with an AUC of 0.903, which are implicated in immune modulation and cancer progression. Conclusions: This study provides new insights into immune mechanisms in BC and identifies promising biomarkers for early diagnosis and therapeutic intervention.