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Published on: January 12, 2020
Potential drug targets for ovarian cancer identified through Mendelian randomization and colocalization analysis
Sicong Liu1, Hao Lin2, Ke Zhang1
1Department of Obstetrics and Gynecology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210003, China.
Background:
The existing drugs for ovarian cancer (OC) are unsatisfactory and thus new drug targets are urgently required. We conducted proteome-wide Mendelian randomization (MR) and colocalization analysis to pinpoint potential targets for OC.
Methods:
Data on protein quantitative trait loci (pQTL) for 734 plasma proteins were obtained from large genome-proteome-wide association studies. Genetic associations with OC were derived from the Ovarian Cancer Association Consortium, which included 25,509 cases and 40,941 controls. MR analysis was performed to evaluate the association between the proteins and the OC risk. Colocalization analysis was conducted to check whether the identified proteins and OC shared causal variants. In addition, the phenome-wide MR analysis was performed to clarify protein associations across the phenotype, and drug target databases were examined for target validation.
Results:
Genetically predicted circulating levels of 44 proteins were associated with OC risk at Benjamini-Hochberg correction. Genetically predicted 17 proteins had evidence of the increased risk of OC (CLEC11A, MFAP2, TYMP, PDIA3, IL1R1, SPINK1, PLAU, DKK2, IL6ST, DLK1, LRRC15, CDON, ANGPTL1, SEMA4D, AKR1A1, TNFAIP6, and FCGR2B); 27 proteins decreased the risk of OC(SIGLEC9, RARRES1, SPINT3, TMEM132A, HAVCR2, CNTN2, TGFBI, GSTA1, HGFAC, TREML2, GRAMD1C, ASAH2, CPNE1, CCL25, MAPKAPK2, POFUT1, PREP, NTNG1, CA10, CACNA2D3, CA8, MAN1C1, MRC2, IL10RB, RBP4, GP5 and CALCOCO2). Bayesian colocalization demonstrated that GRAMD1C, RBP4, PLAU, PDIA3, MFAP2, POFUT1, MAN1C1 and DKK2 shared the same variant with OC. The phe-MR analyses assessed the side effects of these 44 identified proteins, and the drug target database offered information on both approved and investigational indications.
Conclusion:
This study provides proof of a causal relationship between genetically predicted 44 proteins associated with OC risk, which could serve as promising drug targets for OC.
Insights
This study identified 44 proteins linked to ovarian cancer (OC) risk using proteome-wide Mendelian randomization. These proteins, including GRAMD1C and RBP4, show causal relationships and potential as novel therapeutic targets for ovarian cancer.
Area of Science:
- Genetics and Genomics
- Oncology
- Proteomics
Background:
- Existing ovarian cancer (OC) treatments are insufficient, necessitating the discovery of new therapeutic targets.
- Proteome-wide Mendelian randomization (MR) and colocalization analyses are employed to identify potential OC drug targets.
Purpose of the Study:
- To pinpoint novel protein targets for ovarian cancer (OC) by investigating genetic associations with plasma protein levels.
- To establish causal relationships between specific proteins and OC risk.
Main Methods:
- Utilized protein quantitative trait loci (pQTL) data for 734 plasma proteins from large genome-proteome-wide association studies.
- Performed Mendelian randomization (MR) and Bayesian colocalization analyses with a large ovarian cancer cohort (25,509 cases, 40,941 controls).
- Conducted phenome-wide MR and examined drug target databases for validation.
Main Results:
- Genetically predicted levels of 44 proteins were significantly associated with OC risk (Benjamini-Hochberg correction).
- Identified 17 proteins associated with increased OC risk and 27 with decreased risk.
- Bayesian colocalization confirmed shared causal variants for 8 proteins (GRAMD1C, RBP4, PLAU, PDIA3, MFAP2, POFUT1, MAN1C1, DKK2) with OC.
Conclusions:
- Established a causal relationship between genetically predicted levels of 44 proteins and ovarian cancer (OC) risk.
- These 44 proteins represent promising candidates for novel drug targets in ovarian cancer therapy.
