Analysis of over 140,000 European descendants identifies genetically-predicted blood protein biomarkers associated with prostate cancer risk

Abstract

Several blood protein biomarkers have been associated with prostate cancer (PrCa) risk. However, most studies assessed only a small number of biomarkers and/or included a small sample size. To identify novel protein biomarkers of PrCa risk, we studied 79,194 cases and 61,112 controls of European ancestry, included in the PRACTICAL/ELLIPSE consortia, using genetic instruments of protein quantitative trait loci (pQTLs) for 1,478 plasma proteins. 31 proteins were associated with PrCa risk including proteins encoded by GSTP1, whose methylation level was shown previously to be associated with PrCa risk, and MSMB, SPINT2, IGF2R, and CTSS, which were previously implicated as potential target genes of PrCa risk variants identified in genome-wide association studies. 18 proteins inversely correlated and 13 positively correlated with PrCa risk. For 28 of the identified proteins, gene somatic changes of short indels, splice site, nonsense, or missense mutations were detected in PrCa patients in The Cancer Genome Atlas. Pathway enrichment analysis showed that relevant genes were significantly enriched in cancer related pathways. In conclusion, this study identifies 31 candidates of protein biomarkers for PrCa risk and provides new insights into the biology and genetics of prostate tumorigenesis.

Introduction

Prostate cancer (PrCa) is the second most frequently diagnosed malignancy and the fifth leading cause of cancer mortality among males worldwide(1). In the United States, there were 164,690 estimated new PrCa cases and 29,430 estimated deaths due to PrCa in 2018, making it a malignancy with the highest incidence and second highest mortality in males(2). The survival rate is higher when cancer is diagnosed at a localized stage while it drops substantially when PrCa is diagnosed at a metastatic stage(3). Biomarkers are needed for screening and the early detection of PrCa. Prostate-specific antigen (PSA) has been used widely for PrCa screening(4,5); however, there are controversies in using PSA screening due to the lack of a clear cutoff point for high sensitivity and specificity(6-8), unclear benefit in reducing mortality in some populations (9-11), and overdiagnosis of PrCa(12). Thus, there is a critical need to identify additional screening biomarkers aiming to reduce the mortality of PrCa.

Several other protein biomarkers measured in blood have been reported to be potentially associated with PrCa risk, such as IGF-1, IGFBP1/2, and IL-6(13-16). However, findings have been inconsistent from previous studies. Most existing studies have assessed only a small number of candidates. With the recent development of proteomics technology, there have been several studies searching the whole proteome to identify novel biomarkers for PrCa early detection and diagnosis(17-20). These studies have generated some promising findings. However, these have only included a relatively small number of subjects as it is expensive to profile the proteome in a large population-based study. More importantly, there are multiple limitations that are commonly encountered in conventional epidemiologic studies, including selection bias, potential confounding, and reverse causation. These limitations may explain some of the inconsistent results from previous studies.

To reduce these biases, we used genetic variants associated with blood protein levels as the instruments to assess the associations between genetically predicted protein levels and PrCa risk. Because of the random assortment of alleles transferred from parents to offspring during gamete formation, this approach should be less susceptible to selection bias, reverse causation, and confounding effects. Over the past few years, genome-wide association studies (GWAS) have identified hundreds of protein quantitative loci (pQTL)(21,22). With a large sample size, many of these genetic variants can serve as strong instrumental variables for evaluating the associations of genetically predicted protein levels with PrCa risk. Herein, we report results from the first large study investigating the associations between genetically predicted blood protein levels and PrCa risk using genetic instruments. We used the data from 79,194 cases and 61,112 controls of European descent included in GWAS consortia PRACTICAL, CRUK, CAPS, BPC3 and PEGASUS, as described previously(23).

Methods

A literature search was performed to identify the GWAS that uncovered genetic variants that were significantly associated with protein levels. After careful evaluation, the study conducted by Sun et al represents the largest and most comprehensive study to date(24). By using the data from two sub-cohorts of 2,731 and 831 healthy European-ancestry participants from the INTERVAL study, Sun et al identified 1,927 genetic associations with 1,478 proteins at a stringent significance level(24). The detailed information of this study has been described elsewhere(24). In brief, an aptamer-based multiplex protein assay (SOMAscan) was used to quantify 3,620 plasma proteins. The robustness of the protein measurements was verified using several methods(24). Genotypes were measured using the Affymetrix Axiom UK Biobank array, which were further imputed using a combined reference panel from 1000 Genomes and UK10K. pQTL analyses were performed within each subcohort, with adjustments for age, sex, duration between blood draw and processing, and the first three principal components. After combining the association results from the two subcohorts via fixed-effects inverse-variance meta-analysis using METAL, the genetic associations between 1,927 variants and 1,478 proteins showed a meta-analysis of P<1.5×10⁻¹¹, and a consistent direction of effect and nominal significance (P<0.05). These pQTLs were used to construct the instrumental variables for assessing associations between protein levels and the risk of developing prostate cancer. When two or more variants located at the same chromosome were identified to be associated with a particular protein, we assessed the correlations of the SNPs using the Pairwise LD function of SNiPA (http://snipa.helmholtz-muenchen.de/snipa/index.php?task=pairwise_ld). For each protein, only SNPs independent of each other, as defined by r² < 0.1 (based on 1000 Genomes Project Phase 3 version 5 data focusing on European populations), were used to construct the instruments.

We used the summary statistics data for the association of genetic variants with PrCa risk that were generated from 79,194 PrCa cases and 61,112 controls of European ancestry in the consortia PRACTICAL, CRUK, CAPS, BPC3 and PEGASUS(23,25). In brief, 46,939 PrCa cases and 27,910 controls were genotyped using OncoArray, which included 570,000 SNPs (http://epi.grants.cancer.gov/oncoarray/). Also included were data from several previous PrCa GWAS of European ancestry: UK stage 1 and stage 2; CaPS 1 and CaPS 2; BPC3; NCI PEGASUS; and iCOGS. These genotype data were imputed using the June 2014 release of the 1000 Genomes Project data as a reference. Logistic regression summary statistics were then meta-analyzed using an inverse variance fixed effect approach.

For estimating the association between genetically predicted circulating protein levels and PrCa risk, the inverse variance weighted (IVW) method, using summary statistics results, was used(26). The beta coefficient of the association between genetically predicted protein levels and PrCa risk was estimated using ${\sum }_i{\beta }_{i,GX}\ast {\beta }_{i,GY}\ast {\sigma }_{i,\mathrm{GY}}^{-2}∕\left({\sum }_i{\beta }_{i,\mathrm{GX}}^2\ast {\sigma }_{i,\mathrm{GY}}^{-2}\right)$, and the corresponding standard error was estimated using $1∕({\sum }_i{\beta }_{i,\mathrm{GX}}^2\ast {\sigma }_{i,\mathrm{GY}}^{-2}{)}^{0.5}$. Here, β_i,GX represents the beta coefficient of the association between i th SNP and the protein of interest generated from the pQTL study by Sun et al; β_i,GY and σ_i,GY represent the beta coefficient and standard error, respectively, for the association between i th SNP and PrCa risk in the PrCa GWAS. The association odds ratio (OR), confidence interval (CI), and P value were then estimated based on the calculated beta coefficient and standard error. A Benjamini-Hochberg false discovery rate (FDR) of < 0.05 was used to adjust for multiple comparisons. Furthermore, to evaluate whether the identified associations between genetically predicted circulating protein levels and PrCa risk were independent of association signals identified in GWAS, we performed conditional analyses, adjusting for the closest risk SNPs identified in previous GWAS or fine-mapping studies. For this analysis, we performed GCTA-COJO analyses(27-30) (version 1.26.0) to calculate associations of SNPs with PrCa risk, after adjusting for the risk SNP of interest. We then re-ran the IVW analyses using the association estimates generated from conditional analyses.

For each of the genes encoding the proteins that are identified in our study in association with PrCa risk, we evaluated genetic variants/mutations/indels in prostate tumor tissues from PrCa patients included in TCGA. The somatic level genetic changes were analyzed using MuTect(31) and deposited to the TCGA data portal. Data were retrieved in April, 2016, through the data portal. The proportion of assessed genes containing such somatic level genetic events tended to be enriched, when compared with the proportion of all protein-coding genes across the genome. Analysis was performed using MedCalc online software.

To further assess whether our identified PrCa associated proteins are enriched in specific pathways, molecular and cellular functions, and networks, we performed an enrichment analysis of the genes encoding identified proteins using Ingenuity Pathway Analysis (IPA) software(32). The detailed methodology of this tool has been described elsewhere(32). In brief, an ‘enrichment’ score [Fisher’s exact test (FET) P-value] that measures overlap of observed and predicted regulated gene sets was generated for each of the tested gene sets. The most significant pathways and functions with an enrichment P-value less than 0.05 were reported.

Results

Of the pQTLs for 1,478 proteins assessed in this study, association results for PrCA risk were available for pQTLs of 1,469 proteins in the PrCa GWAS. For 1,106 of these proteins, only a single pQTL was identified. Two pQTLs were identified for 302 proteins and three or more pQTLs were identified for 71 proteins. Using the inverse variance weighted (IVW) method, we identified 31 proteins for which their genetically predicted levels were associated with PrCa risk at a false discovery rate of < 0.05 (Tables 1 and 2), including 22 encoded by genes located more than 500 Kb away from any reported PrCa risk variants identified in GWAS or fine-mapping studies (Table 1). The other nine associated proteins are encoded by genes locate at previously reported PrCa risk loci (Table 2), including MSMB, SPINT2, IGF2R, and CTSS, which were previously implicated as candidate target genes of PrCa risk variants identified in GWAS(33-35). Interestingly, we also observed a significant association for glutathione S-transferase Pi, encoded by GSTP1 (Table 2), whose methylation has been identified as a potential biomarker for PrCa (36). In our study, an inverse association between protein level and PrCa risk was detected for PSP-94, DcR3, IGF-II receptor, KDEL2, Cathepsin S, ZHX3, ZN175, GPC6, RM33, PIM1, WISP-3, NCF-2, ATF6A, Laminin, Glutathione S-transferase Pi, GNMT, LRRN1, and SNAB (ORs ranging from 0.69 to 0.97). Conversely, an association between a higher protein level and increased PrCa risk was identified for TACT, GRIA4, PDE4D, TIP39, SPINT2, MICB, IL-21, ARFP2, RF1ML, TPST1, KLRF1, TM149, and NKp46 (ORs ranging from 1.11 to 1.23).

Table 1.

Twenty-two novel protein-prostate cancer associations for proteins whose encoding genes are located at genomic loci at least 500kb away from any GWAS-identified prostate cancer risk variants

Protein	Protein full name	Protein- encoding gene	Region	Index SNP(s)a	Distance of gene to the index SNP (kb)	Instrument variants	Type of pQTL	ORb	95% CIb	P value	FDR P valuec	P value after adjusting for risk SNPd
ATF6A	Cyclic AMP-dependent transcription factor ATF-6 alpha	ATF6	1q23.3	rs4845695	6,824	rs8111, rs61738953	trans, trans	0.90	0.86-0.95	1.31 × 10−4	9.18 × 10−3	1.31 × 10−4
NCF-2	Neutrophil cytosol factor 2	NCF2	1q25.3	rs199774366	20,932	rs4632248, rs28929474	trans, trans	0.95	0.92-0.97	9.93 × 10−5	7.29 × 10−3	NA*
Laminin	Laminin	LAMC1	1q25.3	rs199774366	21,377	rs62199218, rs4129858	trans, cis	0.93	0.89-0.97	4.16 × 10−4	0.03	NA*
RM33	39S ribosomal protein L33_mitochondrial	MRPL33	2p23.2	rs13385191	7,106	rs28929474	trans	0.93	0.90-0.96	9.61 × 10−6	7.43 × 10−4	9.42 × 10−6
LRRN1	Leucine-rich repeat neuronal protein 1	LRRN1	3p26.2	rs2660753	83,221	rs429358, rs6801789	trans, cis	0.97	0.95-0.99	7.21 × 10−4	0.04	7.21 × 10−4
TACT	T-cell surface protein tactile	CD96	3q13.13-3q13.2	rs7611694	1,891	rs3132451	trans	1.22	1.16-1.29	1.02 × 10−12	3.75 × 10−10	1.02 × 10−12
IL-21	Interleukin-21	IL21	4q27	rs34480284	17,469	rs12368181, rs3129897	trans, trans	1.11	1.06-1.16	7.77 × 10−6	7.43 × 10−4	NA*
PDE4D	cAMP-specific 3_5-cyclic phosphodiesterase 4D	PDE4D	5q11.2-5q12.1	rs1482679	13,879	rs3132451	trans	1.17	1.12-1.22	1.02 × 10−12	3.75 × 10−10	1.02 × 10−12
GNMT	Glycine N-methyltransferase	GNMT	6p21.1	rs4711748	763	rs57736976	cis	0.93	0.89-0.97	6.80 × 10−4	0.04	2.78 × 10−4
PIM1	Serine/threonine-protein kinase pim-1	PIM1	6p21.2	rs9469899	2,345	rs28929474	trans	0.88	0.83-0.93	9.61 × 10−6	7.43 × 10−4	9.42 × 10−6
WISP-3	WNT1-inducible-signaling pathway protein 3	WISP3	6q21	rs2273669	3,090	rs28929474	trans	0.83	0.77-0.90	9.61 × 10−6	7.43 × 10−4	9.42 × 10−6
TPST1	Protein-tyrosine sulfotransferase 1	TPST1	7q11.21	rs56232506	18,233	rs313829	cis	1.14	1.06-1.22	5.23 × 10−4	0.03	5.43 × 10−4
ARFP2	Arfaptin-2	ARFIP2	11p15.4	rs61890184	1,045	rs28929474	trans	1.23	1.12-1.35	9.61 × 10−6	7.43 × 10−4	9.42 × 10−6
GRIA4	Glutamate receptor 4	GRIA4	11q22.3	rs1800057	2,291	rs3132451	trans	1.17	1.12-1.22	1.02 × 10−12	3.75 × 10−10	1.02 × 10−12
KLRF1	Killer cell lectin-like receptor subfamily F member 1	KLRF1	12p13.31	rs2066827	2,873	rs11708955, rs62143194	trans, trans	1.13	1.05-1.20	5.74 × 10−4	0.03	5.74 × 10−4
GPC6	Glypican-6	GPC6	13q31.3-13q32.1	rs9600079	20,151	rs28929474	trans	0.81	0.73-0.89	9.61 × 10−6	7.43 × 10−4	9.42 × 10−6
TM149	IGF-like family receptor 1	IGFLR1	19q13.12	rs8102476	2,502	rs12459634	cis	1.06	1.02-1.09	7.31 × 10−4	0.04	4.68 × 10−3
TIP39	Tuberoinfundibular peptide of 39 residues	PTH2	19q13.33	rs2659124	1,428	rs375375234	trans	1.22	1.13-1.32	3.06 × 10−7	4.99 × 10−5	2.96 × 10−7
ZN175	Zinc finger protein 175	ZNF175	19q13.41	rs2735839	710	rs28929474	trans	0.91	0.87-0.95	9.61 × 10−6	7.43 × 10−4	9.42 × 10−6
NKp46	Natural cytotoxicity triggering receptor 1	NCR1	19q13.42	rs103294	620	rs2278428	cis	1.16	1.06-1.26	9.91 × 10−4	0.05	9.65 × 10−4
SNAB	Beta-soluble NSF attachment protein	NAPB	20p11.21	rs11480453	7,945	rs429358, rs7658970	trans, trans	0.91	0.86-0.96	9.77 × 10−4	0.05	9.77 × 10−4
ZHX3	Zinc fingers and homeoboxes protein 3	ZHX3	20q12	rs11480453	8,460	rs1694123	trans	0.79	0.71-0.88	9.38 × 10−6	7.43 × 10−4	9.67 × 10−6

^aClosest risk variant identified in previous GWAS or fine-mapping studies for prostate cancer risk.

^bOR (odds ratio) and CI (confidence interval) per one standard deviation increase in genetically predicted protein

^cFDR P value: false discovery rate (FDR) adjusted p value; associations with a FDR p≤0.05 considered statistically significant

^dusing COJO method(27)

NA*: the adjacent risk variant is not available in the 1000 Genomes Project data

Table 2.

Nine novel protein-prostate cancer associations for proteins whose encoding genes are located at genomic loci within 500kb of previous GWAS-identified prostate cancer risk variants

Protein	Protein name	Protein- encoding gene	Region	Index SNP(s)a	Distance of gene to the index SNP (kb)	Instrument variants	Type of pQTL	ORb	95% CIb	P value	FDR P valuec	P value after adjusting for risk SNPsd
Cathepsin S	Cathepsin S	CTSS	1q21.3	rs17599629	44	rs41271951	cis	0.91	0.88-0.95	2.73 × 10−7	4.99 × 10−5	0.16
MICB	MHC class I polypeptide-related sequence B	MICB	6p21.33	rs2596546	133	rs3134900	cis	1.09	1.05-1.12	2.07 × 10−6	2.76 × 10−4	0.03
RF1ML	Peptide chain release factor 1-like_mitochondrial	MTRF1L	6q25.2	rs3968480	109	rs503366	cis	1.18	1.08-1.29	4.67 × 10−4	0.03	0.21
IGF-II receptor	Cation-independent mannose-6-phosphate receptor	IGF2R	6q25.3	rs651164	47	rs629849	cis	0.92	0.90-0.94	3.98 × 10−10	9.73 × 10−8	9.95 × 10−11
PSP-94	Beta-microseminoprotein	MSMB	10q11.22	rs10993994	0.002	rs541781976, rs10993994	trans,cis	0.81	0.80-0.82	3.60 × 10−155	5.29 × 10−152	NA*
Glutathione S-transferase Pi	Glutathione S-transferase P	GSTP1	11q13.2	rs12785905	399	rs1695, rs62143206	cis,trans	0.94	0.91-0.97	5.91 × 10−4	0.03	3.12 × 10−3
KDEL2	KDEL motif-containing protein 2	KDELC2	11q22.3	rs1800057	199	rs74911261	cis	0.89	0.86-0.93	1.83 × 10−8	3.85 × 10−6	0.42
SPINT2	Kunitz-type protease inhibitor 2	SPINT2	19q13.2	rs8102476 rs12610267	0	rs71354995	cis	1.05	1.03-1.06	1.31 × 10−6	1.92 × 10−4	0.07
DcR3	Tumor necrosis factor receptor superfamily member 6B	TNFRSF6B	20q13.33	rs6062509	33	rs62217798	cis	0.69	0.62-0.77	1.98 × 10−11	5.81 × 10−9	0.05

^aClosest risk variant(s) identified in previous GWAS or fine-mapping studies for prostate cancer risk

^bOR (odds ratio) and CI (confidence interval) per one standard deviation increase in genetically predicted protein

^cFDR P value: false discovery rate (FDR) adjusted p value; associations with a FDR p≤0.05 considered statistically significant

^dusing COJO method(27)

NA*: the adjacent risk variant is the corresponding pQTL

To determine whether the identified significant associations between genetically predicted protein levels and PrCa risk were independent of GWAS-identified association signals, we performed conditional analyses adjusting for the GWAS-identified risk SNPs closest to the genes encoding our identified proteins (Tables 1 and 2)(27). For proteins listed in Table 1, the analysis could not be performed for three proteins due to lack of data, and for all other proteins, the associations remained essentially unchanged in the conditional analysis, suggesting these associations may be independent of GWAS-identified association signals. On the other hand, for proteins whose encoding genes locate at known PrCa risk loci, except for IGF2R, all other associations were no longer statistically significant when conditioning on GWAS-identified risk SNPs, suggesting these associations may be influenced by GWAS-identified association signals (Table 2).

By analyzing exome-sequencing data of prostate tumor-adjacent normal tissue and tumor tissue obtained from 498 PrCa patients of The Cancer Genome Atlas (TCGA), we observed somatic level changes of indels, nonsense mutations, splice site variations, or missense mutations in at least one patient for 28 of the 31 genes encoding identified associated proteins (enrichment p<0.0001 compared with the proportion of all protein-coding genes across the genome) (Supplementary Table 1). In addition to the somatic missense mutations detected in 24 genes, indels were detected in four genes (ARFIP2, LRRN1, ZNF175, and PDE4DIP), splice site variations were detected in four genes (IGF2R, IL21, MICB, and PTH2R), and a nonsense mutation was detected in KLRF1 (Supplementary Table 1). Although the majority of these somatic changes occurred in only one patient, a missense mutation in PTH2 occurred in nine patients (1.8%) (Supplementary Table 1).

Based on the IPA analysis, several cancer-related functions were enriched for the genes encoding the associated proteins identified in this study (Supplementary Table 2). The top canonical pathways identified included STAT3 Pathway (p=4.54 × 10⁻³), Glutathione Redox Reactions I (p=0.027), Glutathione-mediated Detoxification (p=0.030), Endoplasmic Reticulum Stress Pathway (p=0.031), and tRNA Splicing (p=0.044).

Discussion

This is the first large-scale study to evaluate the associations of genetically predicted protein levels with PrCa risk using GWAS-identified pQTLs as instruments. We identified 31 proteins that demonstrated a statistically significant association with PrCa risk after FDR correction, including 22 whose encoding genes were located more than 500 Kb away from any reported PrCa risk variants. Our study provides novel information to improve the understanding of genetics and etiology for PrCa, and generates a list of promising proteins as potential biomarkers for early detection of PrCa, the most common malignancy among men in most countries around the world.

In the current work, we used data from large genome-wide association studies (GWAS) involving 79,194 PrCa cases and 61,112 controls. The purpose and approach of the current analysis are different from those of the study of Schumacher et al(23). In the GWAS study, investigators evaluated each genetic variant across the genome one at a time, aiming to identify novel susceptibility variants showing an association with PrCa risk(23). The current work aimed to use genetically predicted protein expression levels as the testing unit to identify PrCa associated proteins. We used a protein-based approach that aggregates the effects of several SNPs into one testing unit whenever possible. The analysis unit for our study is proteins, while the analysis unit in GWAS by Schumacher et al(23) is genetic variants.

Previous research suggests that PSA, IGF-1, IGFBP1/2, and IL-6 measured in blood may be associated with PrCa risk. For PSA, IGFBP1/2, and IL-6, there was no corresponding pQTL identified in the study conducted by Sun et al(24), thus they were not investigated in the current study. For IGF-1, by using its pQTL rs74480769 as instrument, we did not observe a significant association with PrCa risk (OR=0.98, 95% CI: 0.90–1.07; P=0.70). The inconsistent finding of IGF-1 with previous studies could be due to either a weak instrument used in the current study or potential confounded estimates of associations in previous studies using a conventional epidemiological design. Indeed, the significant positive association of IGF-1 was observed in the Health Professionals Follow-up Study(15), but not in the Prostate Cancer Prevention Trial(14). Further research would be needed to better understand the relationship between these proteins and PrCa.

In this large study, we identified 22 associated proteins of which the encoding genes are located at genomic loci not mapped by any of the previous GWAS. The statistical power in our study is larger than GWAS because 1) the number of comparisons is smaller in our study than GWAS and thus we could use a less stringent statistical significance threshold rather than 5 × 10⁻⁸ in GWAS and 2) the predicted protein levels are continuous variables, which improves statistical power. It is worth noting that nine of the proteins identified in this study are encoded by genes locating at the GWAS-identified loci. For many of the identified proteins, the genetic instrument includes trans pQTL(s) beyond only cis pQTL(s) (Tables 1-2), thus explaining why the corresponding protein-coding genes are not always at known susceptibility loci. In vitro/in vivo studies and human studies have suggested that some of these novel genes may play an important role in prostate tumorigenesis. For example, an inter-chromosomal interaction between a known PrCa risk locus, 8q24, and CD96 was observed by the use of a chromosome conformation capture-based multi-target sequencing technology(37). GPC6 was found to be recurrently altered across tumors of advanced and lethal PrCa patients(38). PDE4D was shown to function as a proliferation-promoting factor in PrCa and was overexpressed in human prostate carcinoma(39); its inhibition had been shown to decrease PrCa cell growth(40). ATF6, which is related to the unfolded protein response, was observed to be down-regulated in high-grade prostatic intraepithelial neoplasia compared with normal prostate samples(41).

Of the nine associated proteins of which the encoding genes are located at GWAS-identified PrCa risk loci, several have also been found to potentially play functional roles in PrCa development. For example, the decreased GSTP1 expression was observed to accompany human prostatic carcinogenesis(42). It is highly expressed in benign prostate glands while tends to not express in prostate cancer glands(43). MSMB encodes MSP for prostatic secretory protein of 94 amino acids, which is secreted by the prostate and functions as a suppressor of tumor growth and metastasis(44). Besides the study of Sun et al (24), several other studies also support the potential of MSP as a serum marker for the early detection of high-grade PrCa(45,46). The decreased expression of IGF2R was thought to be partly responsible for the increased growth of LNCaP human prostate cancer cells(47). In a mouse model, the mRNA of IGF2R was significantly decreased in metastatic prostate lesions and androgen-independent PrCa(48). By analyzing patient samples, it was identified that the loss of the heterozygosity of IGF2R was an early event in the development of PrCa(49). In in vivo and human studies, it was suggested that the shedding of MICB might contribute to the impairment of NK cell antitumor immunity in PrCa formation(50,51). These previous studies provide support for a potential role of these genes in prostate carcinogenesis.

The sample size for the main association analysis of our study was large, providing high statistical power to detect the protein-PrCa associations. Also, the design of using genetic instruments reduces biases, such as selection bias and potential confounding, and eliminates potential influence due to reverse causation. On the other hand, there are several potential limitations of our study. The possibility of pleiotropy effect cannot be excluded. For example, rs28929474, which was the instrument for proteins ZN175, ARFP2, GPC6, RM33, PIM1, and WISP-3, as well as one of the two variants constituting an instrument for NCF-2, was also reported to be associated with several other traits, including glycoprotein acetyls(52-54). Similarly, rs429358, which was included in the instruments of LRRN1 and SNAB, was associated with cerebral amyloid deposition and red cell distribution width(55,56); rs62143206, which was included in the instrument of Glutathione S-transferase Pi, was also associated with the monocyte percentage of white cells and the granulocyte percentage of myeloid white cells(55). Further studies will be needed to validate our identified protein-PrCa associations. Secondly, our analysis was constrained by the pQTLs identified in previous GWAS of circulating protein levels, and thus we were unable to evaluate some important protein biomarkers for PrCa as discussed previously. We anticipated that additional protein biomarkers could be identified using newly identified pQTLs in the future. Furthermore, the current work generates a list of promising protein candidates that show an association with PrCa, which can be investigated further in future studies that directly measure levels of these proteins. Identification of circulating protein biomarkers should be useful for PrCa risk assessment.

In conclusion, in a large-scale study assessing associations between genetically predicted circulating protein levels and PrCa, we identified multiple novel proteins showing a significant association. Further investigation of these proteins will provide additional insight into the biology and genetics of PrCa and facilitate the development of appropriate biomarker panels for the early detection of PrCa.

Statement of Significance.

Integration of genomics and proteomics data identifies biomarkers associated with prostate cancer risk