Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes - Research Open Access

Sep 21, 2026

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Cancer genetics remains complex, with many known somatic mutations driving tumors and GWAS identifying germline risk factors, yet the overlap between somatic driver genes and GWAS positional candidates is limited. The study tested whether genes from multiple independent cancer GWAS loci show tissue-specific co-regulation converging on cancer driver genes. Breast, prostate, colorectal, and skin GWAS were analyzed by co-expression and susceptibility-locus mapping. Prioritised genes were enriched for drivers, strongest using non-cancer tissue co-expression networks, linking risk loci to established drivers.

LanguageEnglish

CategoryResearch & Report

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Pages14

Reading Time35 min

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Abstract

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Results

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Urzúa-Traslaviña etal. BMC Medical Genomics (2024) 17:186 BMC Medical Genomics [https://doi.org/10.1186/s12920-024-01941-4](https://doi.org/10.1186/s12920-024-01941-4) RESEARCH Open Access Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes Carlos G. Urzúa-Traslaviña 1,2†, Tijs van Lieshout 1,2†, Floranne Boulogne 1,2†, Kevin Domanegg 1, Mahmoud Zidan 1, Olivier B. Bakker3,4, Annique Claringbould 1,5, Jeroen de Ridder2,6, Wilbert Zwart2,7, Harm-Jan Westra 1,2, Patrick Deelen 1,2 and Lude Franke 1,2* Abstract Background The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. Results We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples ofthe relevant tissue of origin. Conclusion We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease. Keywords Cancer susceptibility genes, Cancer drivers, Tissue-specific, Gene networks, GWAS, recount3, Breast cancer, Prostate cancer, Colon cancer, Omnigenic †Carlos G. Urzúa-Traslaviña, Tijs van Lieshout and Floranne Boulogne contributed equally to this work. *Correspondence: Lude Franke[l.h.franke@umcg.com](l.h.franke@umcg.com) 1Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands 2Oncode Institute, Utrecht, The Netherlands 3Wellcome Sanger Institute, Human Genetics, Hinxton, UK 4Open Targets, Hinxton, UK 5EMBL Heidelberg, Structural and Computational Biology Unit, Heidelberg, Germany 6University Medical Center Utrecht, Utrecht, The Netherlands 7Division of Oncogenomics, Netherlands Cancer Institute, Amsterdam, The Netherlands © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/publicdomain/zero/1.0/](http://creativecommons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unles

FAQs

What problem does the study address in cancer genetics?

It addresses the surprising small overlap between known somatic driver genes and positional candidate genes from cancer GWAS loci, which makes integration of cancer genetic backgrounds difficult.

How were candidate genes identified in this work?

The study estimated co-expression between genes and prioritised genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS.

Which cancer types and datasets were analyzed?

The analysis covered well-powered GWAS for breast, prostate, colorectal, and skin cancer, and used co-expression networks derived from relevant tissue samples.

What is the main takeaway from the results?

Genes within GWAS-identified risk loci can be linked to known somatic cancer driver genes via tissue-specific co-expression networks, explaining how different genetic sets can contribute to the same disease type.

Sep 21, 2026

Uploaded by Noah