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Predictive Immune Modeling of Solid Tumors

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Published on: February 25, 2020

Integrating Single-Cell Transcriptome-Wide Mendelian Randomization and Differentially Expressed Gene Analyses to

Jie Zheng^1,2,3, Qian Yang^1,2,3, Haoyu Liu^1,2

¹Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|November 11, 2025

Summary

This study introduces MR-DEG, a novel framework for causal inference in cancer drug development. It identifies 1000 gene-cancer pairs, prioritizing 33 drug targets for immune-related cancer prevention.

Keywords:

Mendelian randomization cancer prevention differential expression gene drug targets immune‐cell eQTLs

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

Genetics and Genomics
Cancer Research
Immunology
Pharmacology

Background:

Single-cell expression quantitative trait loci (sc-eQTL) data are valuable for immune-related cancer drug development.
Pleiotropy poses a significant challenge for causal inference in genetic studies.
Accurate causal inference is crucial for identifying reliable drug targets.

Purpose of the Study:

To develop and validate a framework, MR-DEG, that integrates Mendelian randomization (MR) and differential gene expression (DEG) analysis.
To strengthen causal inference for gene-cancer associations using sc-eQTL data.
To identify and prioritize immune-related drug targets for cancer prevention.

Main Methods:

Integration of Mendelian randomization (MR) and differential gene expression (DEG) analysis into the MR-DEG framework.
Application of eight conventional MR and colocalization methods to analyze 11,021 dynamic gene expression profiles during CD4+ T cell activation.
Evaluation of gene-cancer pair causality, pleiotropy, time-dependent effects, and integration with clinical trial evidence.

Main Results:

Identified 1000 putative gene-cancer pairs, with 517 involving genes differentially expressed in cancer tissues.
Classified 265 pairs as likely causal using conventional MR, with MR-DEG identifying an additional 89 likely causal pairs from potentially pleiotropic associations.
Prioritized 200 gene-cancer pairs, representing 33 unique genes, as potential drug targets for clinical investigation in cancer prevention.

Conclusions:

The MR-DEG framework effectively reduces pleiotropic bias and enhances causal inference in genetic association studies.
Combining genetic, transcriptomic, and clinical trial data is a powerful strategy for prioritizing drug targets.
This approach offers a robust method for identifying and validating immune-related drug targets for cancer prevention.