Exploring potential causal genetic variants and genes for endometrial cancer: Open Targets Genetics, Mendelian randomization, and multi-tissue transcriptome-wide association analysis

Guorui Zhang; Su Mao; Guangwei Yuan; Yang Wang; Jingyun Yang; Yuxin Dai

doi:10.21037/tcr-24-887

Exploring potential causal genetic variants and genes for endometrial cancer: Open Targets Genetics, Mendelian randomization, and multi-tissue transcriptome-wide association analysis

Transl Cancer Res. 2024 Nov 30;13(11):5971-5982. doi: 10.21037/tcr-24-887. Epub 2024 Nov 21.

Authors

Guorui Zhang¹, Su Mao¹, Guangwei Yuan², Yang Wang³, Jingyun Yang^{4

5}, Yuxin Dai¹

Affiliations

¹ Department of Obstetrics and Gynecology, State Key Laboratory of Complex, Severe and Rare Diseases, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² Department of Biostatistics & Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
³ Division of Biomedical Science, Analytics and Technology, Sanofi, Toronto, ON, Canada.
⁴ Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
⁵ Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA.

Abstract

Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries, with incidence rates continuing to rise globally. However, the precise mechanisms underlying EC pathogenesis remain largely unexplored. This study aims to prioritize genes associated with EC by leveraging multi-omics data through various bioinformatic methods.

Methods: We utilized the Open Targets Genetics (OTG) database to pinpoint potential causal variants and target genes for EC. To explore the pleiotropic effects of gene expression on EC, we applied the Summary-based Mendelian Randomization (SMR) using summary data from a genome-wide association study (GWAS) on EC and expression quantitative trait loci (eQTL) data from the Consortium for the Architecture of Gene Expression (CAGE). We also conducted a cross-tissue transcriptome-wide association study (TWAS) employing sparse canonical correlation analysis (sCCA). Results from the sCCA TWAS and single-tissue TWAS for 22 tissues were combined using the aggregated Cauchy association test (sCCA + ACAT) to identify genes with cis-regulated expression levels linked to EC.

Results: The OTG database recognized 15 genomic loci showing independent association with EC. Gene prioritization highlighted nine genes with relatively high locus-to-gene (L2G) scores (≥0.5), the majority of which aligned with those identified using the closest gene. Colocalization analysis identified 11 additional genes at these loci. Our SMR analysis revealed two genes, EVI2A and SRP14, exhibiting a significant pleiotropic association with EC. Cross-tissue TWAS identified 31 genes whose expression was significantly associated with EC after correction for multiple testing, with four genes (EIF2AK4, EVI2A, EVI2B, and NF1) also confirmed by gene colocalization in the OTG analysis.

Conclusions: We confirmed the involvement of EVI2A in the pathogenesis of EC and identified several other genes that may contribute to EC development. These findings offer new insights into the genetic mechanisms underlying EC and may inform future research and therapeutic strategies.

Keywords: Endometrial cancer (EC); expression quantitative trait loci (eQTL); genome-wide association study (GWAS); summary Mendelian randomization; transcriptome-wide association study (TWAS).