Literature DB >> 31089709

The role of plasma microseminoprotein-beta in prostate cancer: an observational nested case-control and Mendelian randomization study in the European prospective investigation into cancer and nutrition.

K Smith Byrne1, P N Appleby1, T J Key1, M V Holmes2, G K Fensom3, A Agudo4, E Ardanaz5, H Boeing6, H B Bueno-de-Mesquita7, M D Chirlaque8, R Kaaks9, N Larrañaga10, D Palli11, A Perez-Cornago1, J R Quirós12, F Ricceri13, M J Sánchez14, G Tagliabue15, K K Tsilidis16, R Tumino17, R T Fortner9, P Ferrari18, E Riboli19, H Lilja20, R C Travis1.   

Abstract

BACKGROUND: Microseminoprotein-beta (MSP), a protein secreted by the prostate epithelium, may have a protective role in the development of prostate cancer. The only previous prospective study found a 2% reduced prostate cancer risk per unit increase in MSP. This work investigates the association of MSP with prostate cancer risk using observational and Mendelian randomization (MR) methods. PATIENTS AND METHODS: A nested case-control study was conducted with the European Prospective Investigation into Cancer and Nutrition (EPIC) with 1871 cases and 1871 matched controls. Conditional logistic regression analysis was used to investigate the association of pre-diagnostic circulating MSP with risk of incident prostate cancer overall and by tumour subtype. EPIC-derived estimates were combined with published data to calculate an MR estimate using two-sample inverse-variance method.
RESULTS: Plasma MSP concentrations were inversely associated with prostate cancer risk after adjusting for total prostate-specific antigen concentration [odds ratio (OR) highest versus lowest fourth of MSP = 0.65, 95% confidence interval (CI) 0.51-0.84, Ptrend = 0.001]. No heterogeneity in this association was observed by tumour stage or histological grade. Plasma MSP concentrations were 66% lower in rs10993994 TT compared with CC homozygotes (per allele difference in MSP: 6.09 ng/ml, 95% CI 5.56-6.61, r2=0.42). MR analyses supported a potentially causal protective association of MSP with prostate cancer risk (OR per 1 ng/ml increase in MSP for MR: 0.96, 95% CI 0.95-0.97 versus EPIC observational: 0.98, 95% CI 0.97-0.99). Limitations include lack of complete tumour subtype information and more complete information on the biological function of MSP.
CONCLUSIONS: In this large prospective European study and using MR analyses, men with high circulating MSP concentration have a lower risk of prostate cancer. MSP may play a causally protective role in prostate cancer.
© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Entities:  

Keywords:  EPIC cohort; Mendelian randomization; microseminoprotein-beta; prospective study; prostate cancer; prostate-specific antigen

Mesh:

Substances:

Year:  2019        PMID: 31089709      PMCID: PMC6594452          DOI: 10.1093/annonc/mdz121

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


Key Message

Using observational data and Mendelian randomization we found a protective association of a protein produced by the prostate, microseminoprotein-beta, with prostate cancer risk.

Introduction

Microseminoprotein-beta (MSP) is a protein secreted by the prostate epithelium into the seminal fluid [1]. In the only previous prospective study, the Multiethnic Cohort (MEC) [2], a 1 ng/ml increase in circulating MSP concentration was associated with a 2% decrease in prostate cancer risk. MSP concentrations, in both blood and semen samples from healthy males, are ∼60% higher among CC homozygotes versus TT homozygotes for rs10993994 (r2 = 0.38 and 0.23, respectively), located 57 base-pairs upstream in the 5′ promoter region of the MSMB gene [3], which encodes the protein MSP. Furthermore, a genome-wide association study (GWAS) has found carriers of the T allele to have an elevated prostate cancer risk (57% higher for TT versus CC) [2, 4]. This prospective study investigated whether circulating MSP concentrations were associated with prostate cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). We then investigated the association of rs10993994 with circulating concentrations of MSP in EPIC and used this genetic variant as an instrument for MSP to assess its potential causal role through Mendelian randomization (MR) analyses by combining EPIC-derived estimates with published data from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium [5].

Methods

Study population

Totally, 137 000 men participating in EPIC provided blood samples at recruitment between 1992 and 2000 [6]. Lifestyle questionnaires, anthropometric data, and food questionnaires were collected at recruitment. All participants provided written informed consent. Approval for the study was obtained from the ethical review boards of the participating institutions and the International Agency for Research on Cancer (IARC). The current study uses data from Germany, Greece, Italy, the Netherlands, Spain and the UK.

Follow-up

Cancer incidence was identified through record linkage to regional or national registries in most countries (see supplementary methods, available at Annals of Oncology online). Follow-up procedures continued to prostate cancer diagnosis or last follow-up completed (31 December 2007 to 14 June 2010). Cases were men who were diagnosed with incident prostate cancer (International Classification of Diseases 10th revision code C61 [7]) after blood collection and before the end of follow-up. An incidence density sampling protocol was used to select control participants at random from the cohort of men who were alive and free of cancer (excluding non-melanoma skin cancer) at the time of diagnosis of the index case and who matched on study centre, length of follow-up, age at blood collection, time of blood collection and duration of fasting at blood collection. These analyses included 1871 cases with 1871 matched controls. Information on tumour stage and grade at diagnosis was available for 1263 (67.5%) and 1554 (85.1%) of cases, respectively (see supplementary methods, available at Annals of Oncology online).

Assessment of analytes

Immunoassay measurements for prostate-specific antigen (PSA) [8] and MSP [9, 10] were conducted on the AutoDelfia® 1235 automatic immunoassay system in Dr Lilja’s laboratory at the Wallenberg Research Laboratories, Department of Translational Medicine, Lund University, Skåne University Hospital, Malmö, Sweden (see supplementary methods, available at Annals of Oncology online).

Statistical analysis

Analyte concentrations below limits of detection were set to half the lowest concentration (PSA, N = 7), and concentrations above the upper limits were set to the highest value for that analyte (MSP, N = 82; PSA, N = 65). Pearson’s χ2 tests and paired t-tests were conducted between matched case–control sets for anthropometric and lifestyle characteristics. Analysis of variance was used to assess differences in analyte concentrations in controls by strata of selected characteristics, country and study phase (matched case–control sets were identified after each of three rounds of follow-up and end point data centralisation in EPIC conducted in approximately 2004, 2008 and 2010, and samples from each phase were assayed together). Log transformations were applied to analyte concentrations and results are presented as geometric means adjusted for age at blood collection, body mass index (BMI), recruitment centre and laboratory batch. Conditional logistic regression models were used to examine the association of MSP with prostate cancer, conditioned on the matching factors and adjusted for BMI, age at blood collection and further adjusted for fourth of PSA concentration (additional adjustment was shown to not materially alter the results, see supplementary Table S1, available at Annals of Oncology online). These analyses were repeated in subgroups according to study phase, time between blood collection and diagnosis, age at blood collection, age at diagnosis, prostate tumour stage and histological grade. Additional unconditional analyses stratified by median PSA concentration and smoking status were adjusted for age, BMI, fourth of PSA concentration and matching factors. Linear trend was tested using a pseudo-continuous variable equal to medians of the fourths of MSP concentration. For subgroup analyses, likelihood ratio tests were used to test for heterogeneity.rs10993994 genotype data were available for a subset of 1068 EPIC cases and 1186 EPIC controls from the iCOGS [11], OncoArray [12] and Breast and Prostate Cancer Cohort Consortium (BPC3) [13] genotyping projects. Logistic regression models were used to investigate the association of rs10993994 with prostate cancer. We investigated the potential causal role of MSP in prostate cancer risk using MR analyses. A summary estimate of the association of rs10993994 with prostate cancer was taken from the iCOGS genotyping project in the international consortium PRACTICAL with 25 000 cases from 32 studies [5, 11], and from EPIC prostate cancer cases and controls genotyped in the OncoArray [12] and BPC3 studies [13]. Summary estimates for the association of rs10993994 with MSP were calculated using these EPIC data [12, 13]. We used the MR-Base platform to do a phenome-wide association scan for rs10993994 with 850 traits to check for pleiotropy [14], and also checked the NHGR-EBI catalogue of published GWAS [15]. Two-sample MR estimates were calculated separately using summary estimates for each of PRACTICAL (iCOGS) [5] and EPIC-derived rs10993994-prostate cancer risk estimates with the EPIC-derived rs10993994-MSP estimate, which were then combined using the inverse-variance weighted method. To address possible confounding by PSA, we conducted sensitivity analyses using the summary association of rs10993994 with residuals from a linear regression of log total PSA on MSP, also calculated within EPIC. All statistical tests are two-sided and were conducted using STATA software version 14 (StataCorp LP, College Station, TX).

Results

Data from 1871 cases and 1871 matched controls were included in the analyses. The median age at blood collection was 58 years, and, for cases, the median time between blood collection and diagnosis was 8.3 years. No significant differences were observed in selected baseline characteristics between cases and controls (Table 1).
Table 1.

Characteristics of control participants and prostate cancer patients

CharacteristicseControls (n =1871)Cases (n =1871) P a
Age at blood collection (years)b58.3 (6.9)58.3 (6.9)0.5
Weight (kg)b80.2 (11.6)80.2 (11.5)0.5
Height (cm)b172.9 (7.2)172.5 (7.1)0.9
BMI (kg/m2)b26.9 (3.5)27.1 (3.5)0.2
Smoking status, n (%)
 Never578 (31.5)621 (34.5)
 Previous826 (45.1)792 (43.9)
 Current431 (23.4)389 (21.6)0.1
Alcohol, n (%)
 <8657 (34.8)682 (36.4)
 8–15368 (19.9)363 (19.4)
 16–39542 (28.9)491 (26.2)
 >40304 (16.3)335 (17.9)0.3
Physical activity, n (%)
 Inactive277 (15.1)268 (14.9)
 Moderately inactive533 (29.1)525 (29.4)
 Active1025 (55.9)996 (55.7)0.9
Marital status, n (%)
 Married/cohabitating1377 (89.7)1333 (88.6)
 Not married/cohabitating160 (10.4)172 (11.4)0.3
Educational attainment, n (%)
 Primary/none687 (38.3)668 (38.3)
 Secondary633 (35.4)596 (34.1)
 Degree471 (26.3)482 (27.6)0.6
Geometric mean analyte concentration at blood collection
 MSP (ng/ml) (95% CI)12.8 (12.5–13.2)12.9 (12.6–13.2)0.7
 MSP adjusted for PSA (ng/ml) (95% CI)12.9 (12.6–13.3)12.8 (12.5–13.1)0.5
 PSA (ng/ml) (95% CI)0.8 (0.8–0.9)2.4 (2.3–2.5)<0.0001
Time to diagnosis, n (%)
 <2 years81 (4.4)
 2 to <4 years111 (5.9)
 4 to <6 years244 (13.1)
 6 to <8 years375 (20.2)
 8 to <10 years1049 (56.4)
Year of diagnosis, median (range)2004 (1994–2009)
Age at diagnosis (years) (SD)66.9 (6.9)
Tumour stage
 TNM-codec
 Tumour
  T1176 (19.4)
  T2529 (58.4)
  T3183 (20.2)
  T418 (1.9)
 Nodes
  N0609 (92.3)
  N146 (6.9)
  N24 (0.1)
  N31 (0.01)
 Metastases
  M0522 (94.7)
  M129 (5.3)
 EPIC stage informationc
  Localized778 (82.4)
  Metastatic205 (21.7)
 Tumour grade
  Gleason graded
   ≤6452 (55.7)
   7218 (28.6)
   ≥8120 (15.7)
 EPIC grade informationd
  Well differentiated139 (16.6)
  Moderately differentiated503 (60.1)
  Poorly differentiated191 (22.8)
  Undifferentiated4 (0.1)
 PSA (ng/ml) at diagnosis
  <320 (3.6)
  ≥3 and <10335 (59.8)
  ≥10 and <50187 (33.4)
  ≥5018 (3.2)

P-values are from analysis of variance models where characteristics are continuous and χ2 test where characteristics are categorical.

Geometric means are presented with standard deviation.

TNM-code and EPIC stage are not mutually exclusive as some individuals had information for both.

Gleason grade and EPIC grade are not mutually exclusive as some individuals had information for both.

Numbers may not sum to total due to missing values.

BMI, body mass index; MSP, microseminoprotein-beta; CI, confidence interval; PSA, prostate-specific antigen; SD, standard deviation; EPIC, European Prospective Investigation into Cancer and Nutrition.

Characteristics of control participants and prostate cancer patients P-values are from analysis of variance models where characteristics are continuous and χ2 test where characteristics are categorical. Geometric means are presented with standard deviation. TNM-code and EPIC stage are not mutually exclusive as some individuals had information for both. Gleason grade and EPIC grade are not mutually exclusive as some individuals had information for both. Numbers may not sum to total due to missing values. BMI, body mass index; MSP, microseminoprotein-beta; CI, confidence interval; PSA, prostate-specific antigen; SD, standard deviation; EPIC, European Prospective Investigation into Cancer and Nutrition. Mean MSP concentration (ng/ml) at blood collection did not differ significantly between cases and controls (Table 1). Mean PSA concentration (ng/ml) measured at blood collection was about threefold higher in cases than controls [adjusted geometric means = 2.4, 95% confidence interval (CI) 2.3–2.5 and 0.8, 0.8–0.9 respectively, P < 0.0001]. MSP concentration in controls was higher in men older at blood collection, not married, with normal/low BMI or low-alcohol intake, and who had higher educational attainment (P < 0.05 for all). Compared with never smokers, men who smoked more than 15 cigarettes per day had 30% higher MSP concentrations (Ptrend < 0.0001). PSA concentration was positively associated with age at blood collection and educational attainment, and negatively associated with greater BMI and diabetes (Table 2). MSP and PSA concentrations were positively correlated in both cases and controls (partial correlations r = 0.3 and 0.2, respectively, P < 0.0001).
Table 2.

Adjusted geometric mean MSP and PSA concentration (ng/ml) in controls by selected characteristics

Factor and subsetMSP (ng/ml)
PSA (ng/ml)
N Mean (95% CI)a P-difference/linear trendb N Mean (95% CI)a P-difference/linear trendb
Age at blood collection (years)
 <5019511.7 (10.8–12.6)1950.6 (0.5–0.6)
 50–5533912.3 (11.6–13.1)3390.7 (0.6–0.7)
 55–5950312.2 (11.7–12.9)5030.8 (0.7–0.9)
 60–6454912.7 (12.1–13.3)5490.9 (0.9–1.0)
 65–6916915.4 (14.2–16.8)1691.3 (1.1–1.4)
 >7011616.8 (15.2–18.6)<0.0001/<0.00011161.6 (1.3–1.8)<0.0001/<0.0001
Time of blood collection (h)
 00:00–09:5930513.0 (12.1–13.9)3060.9 (0.8–1.0)
 10:00–12:5926713.6 (12.6–14.6)2670.8 (0.7–0.9)
 13:00–23:59117612.4 (12.0–12.9)0.111760.8 (0.8–0.9)0.03
Marital status
 Married/cohabitating137712.9 (12.5–13.2)13770.8 (0.8–0.9)
 Not married/cohabitating16014.3 (13.1–15.6)0.011600.9 (0.8–1.0)0.9
Educational attainment
 Primary/none68712.3 (11.8–12.8)6870.8 (0.7–0.8)
 Secondary63313.2 (12.7–13.8)6330.9 (0.8–0.9)
 Degree47112.8 (12.2–13.5)0.024710.9 (0.8–0.9)0.02
Body mass index (kg/m2)
 16–2454213.7 (13.1–14.3)5420.9 (0.8–0.9)
 25–29100312.8 (12.3–13.2)10030.8 (0.8–0.9)
 >3031711.7 (10.9–12.4)0.0007/0.0013170.7 (0.7–0.8)0.02/<0.001
Smoking status
 Never57811.9 (11.4–12.4)5780.9 (0.8–0.9)
 Previous82612.1 (11.7–12.6)8260.8 (0.8–0.9)
 Current (<15 cigarettes)18115.8 (14.6–17.1)1810.9 (0.8–0.9)
 Current (≥15 cigarettes)15417.0 (15.6–18.6)<0.0001/<0.00011540.8 (0.7–0.9)0.9
Usual alcohol consumption (g/day)
 <865713.6 (13.0–14.2)6570.9 (0.8–0.9)
 8–1536813.0 (12.3–13.8)3680.9 (0.8–0.9)
 16–3954212.3 (11.8–12.9)5420.8 (0.8–0.9)
 >4030411.9 (11.2–12.7)0.002/<0.00013040.8 (0.7–0.9)0.3
Diabetic
 No176012.9 (12.5–13.2)17600.9 (0.8–0.9)
 Yes9712.1 (10.8–13.5)0.5970.7 (0.6–0.8)0.02

All means adjusted for age at blood collection, body mass index, recruitment centre and batch; adjustment not made for age at blood collection excluded for strata of age at blood collection, nor body mass index for strata of body mass index.

P-values are from analysis of variance and, where significant difference was observed and dose-dependent relationship implied, from test for linear trend.

MSP, microseminoprotein-beta; PSA, prostate-specific antigen; CI, confidence interval.

Adjusted geometric mean MSP and PSA concentration (ng/ml) in controls by selected characteristics All means adjusted for age at blood collection, body mass index, recruitment centre and batch; adjustment not made for age at blood collection excluded for strata of age at blood collection, nor body mass index for strata of body mass index. P-values are from analysis of variance and, where significant difference was observed and dose-dependent relationship implied, from test for linear trend. MSP, microseminoprotein-beta; PSA, prostate-specific antigen; CI, confidence interval. MSP concentration was not associated with prostate cancer risk after adjustment for age at blood collection and BMI [odds ratio (OR) for highest versus lowest fourth = 0.98, 95% CI 0.82–1.19, Ptrend = 0.9)]. However, after adjustment for PSA, MSP concentration was associated with prostate cancer risk (OR = 0.65, 95% CI 0.51–0.84, Ptrend = 0.001) (Table 3). There was some evidence of heterogeneity in the association by time to diagnosis (with a stronger association in men diagnosed within 8.5 years of baseline, Pheterogeneity = 0.009), age at diagnosis (Pheterogeneity = 0.03); (supplementary Table S2, available at Annals of Oncology online) and recruitment country (Pheterogeneity = 0.02; supplementary Table S3, available at Annals of Oncology online). There was no significant heterogeneity of risk by smoking status (Pheterogeneity = 0.6; supplementary Table S2, available at Annals of Oncology online).
Table 3.

Multi-variable adjusted odds ratios (95% CI) for prostate cancer by fourth of plasma MSP concentration, subdivided by selected factors

Fourth of MSP concentration (ng/ml)
1234 P for trenda P for heterogeneity of trendsb
OverallCases/controls, n508/468402/464458/468501/469
Median MSP (ng/ml) (range)7 (1–9)12 (9–13)16 (13–18)29 (18–90)
Basic OR (95% CI)c1 (reference)0.82 (0.68–0.99)0.91 (0.76–1.09)0.98 (0.82–1.19)0.9
Adjusted OR (95% CI)d1 (reference)0.84 (0.65–1.09)0.75 (0.58–0.97)0.65 (0.51–0.84)0.001
Stagee
   Localised (n=886)Cases/controls, n243/229194/221214/209235/225
Adjusted OR (95% CI)d1 (reference)0.86 (0.57–1.28)0.77 (0.52–1.15)0.64 (0.44–0.92)0.02
   Advanced (n=377)Cases/controls, n110/9587/8191/10989/92
Adjusted OR (95% CI)d1 (reference)0.79 (0.44–1.43)0.45 (0.25–0.79)0.45 (0.24–0.82)0.0020.2
Gradee (Gleason ≥8 cut-off)
   Low-intermediate (n=1357)Cases/controls, n384/349281/338346/354345/315
Adjusted OR (95% CI)d1 (reference)0.83 (0.59–1.15)0.76 (0.56–1.02)0.63 (0.46–0.86)0.004
   High (n=197)Cases/controls, n53/4944/4836/3863/62
Adjusted OR (95% CI)d1 (reference)0.86 (0.42–1.76)0.68 (0.32–1.46)0.73 (0.39–1.42)0.30.7
Death from prostate cancer (n=169)
Cases/controls, n43/4538/4246/4042/42
Basic OR (95% CI)c1 (reference)1.06 (0.59–1.94)0.89 (0.47–1.67)0.98 (0.54–1.77)0.8
Adjusted OR (95% CI)d1 (reference)0.84 (0.38–1.86)0.59 (0.27–1.30)0.40 (0.18–0.89)0.02

Test for trend was obtained by replacing the categorical variable with a continuous variable equal to the median concentration within each fourth of plasma MSP concentration.

Test for heterogeneity in the trends.

Estimates are from logistic regression conditioned on the matching variables: centre, age at blood collection, follow-up time, fasting status and time of day at blood collection, with adjustment for age and body mass index (continuous).

Additional to model ‘b’, adjustment was made for body mass index (fourths), and total PSA (fourths).

Tumour stage information was available for 1263 (67.5%) cases: 886 cases were clinically localized (defined as tumour–node–metastasis staging score of T1–T2 and N0/Nx and M0/Mx, or stage coded in the recruitment centre as localized); 377 were clinically advanced (T3–T4 and/or N1–N3 and/or M1, or stage coded in the recruitment centre as metastatic). Tumour grade information at diagnosis was available for 1554 cases (85.1%): 1357 were low-intermediate grade (defined as Gleason score <8, or grade coded as well, moderately or poorly differentiated) and 197 were high-grade (Gleason score ≥8, or grade coded as undifferentiated).

CI, confidence interval; MSP, microseminoprotein-beta; OR, odds ratio.

Multi-variable adjusted odds ratios (95% CI) for prostate cancer by fourth of plasma MSP concentration, subdivided by selected factors Test for trend was obtained by replacing the categorical variable with a continuous variable equal to the median concentration within each fourth of plasma MSP concentration. Test for heterogeneity in the trends. Estimates are from logistic regression conditioned on the matching variables: centre, age at blood collection, follow-up time, fasting status and time of day at blood collection, with adjustment for age and body mass index (continuous). Additional to model ‘b’, adjustment was made for body mass index (fourths), and total PSA (fourths). Tumour stage information was available for 1263 (67.5%) cases: 886 cases were clinically localized (defined as tumour–node–metastasis staging score of T1–T2 and N0/Nx and M0/Mx, or stage coded in the recruitment centre as localized); 377 were clinically advanced (T3–T4 and/or N1–N3 and/or M1, or stage coded in the recruitment centre as metastatic). Tumour grade information at diagnosis was available for 1554 cases (85.1%): 1357 were low-intermediate grade (defined as Gleason score <8, or grade coded as well, moderately or poorly differentiated) and 197 were high-grade (Gleason score ≥8, or grade coded as undifferentiated). CI, confidence interval; MSP, microseminoprotein-beta; OR, odds ratio. The association of MSP with prostate cancer did not differ by tumour stage or grade, or age at blood collection (all Pheterogeneity ≥ 0.05; Table 3 and supplementary Table S2, available at Annals of Oncology online). Results were not materially altered and no significant heterogeneity was observed with high grade defined as Gleason score ≥7 (supplementary Table S2, available at Annals of Oncology online). MSP was associated with risk of death from prostate cancer (OR = 0.40, 95% CI 0.18–0.89, with adjustment for age, BMI and PSA; Table 3). PSA concentration was strongly and positively associated with risk for prostate cancer, both with and without adjustment for MSP concentration (OR = 45.2, 95% CI 29.7–68.7, with adjustment for age, BMI and MSP; supplementary Table S4, available at Annals of Oncology online). In a subset of 1068 cases and 1186 controls with rs10993994 genotype data there was a 6.09 ng/ml (95% CI 5.56–6.61) per allele difference in MSP concentration, with highest concentrations observed for CC homozygotes. rs10993994 explained 42% of the variability of MSP. In controls, there was a 0.22 ng/ml (95% CI 0.09–0.35) per allele difference in PSA concentrations, with highest concentrations observed for TT homozygotes (supplementary Table S5, available at Annals of Oncology online). In this EPIC dataset, rs10993994 genotype was significantly associated with prostate cancer (OR CC versus TT = 0.73, 95% CI 0.57–0.93, Ptrend = 0.006) (supplementary Table S6, available at Annals of Oncology online). After correction for multiple testing, no significant association of rs10993994 genotype was observed with potential confounders beyond PSA concentrations in controls (supplementary Table S7, available at Annals of Oncology online). PheWAS using published data [14, 15], showed that besides prostate cancer risk, rs10993994 is associated only with the prostate cancer biomarkers PSA and prostate cancer antigen 3 (PCA3) at the genome-wide significance level. An inverse-variance weighted MR showed a one unit increase in circulating MSP concentrations (ng/ml) is associated with a 4% reduction in prostate cancer risk (OR = 0.96, 95% CI 0.95–0.97) (Table 4), and was not altered after adjustment for PSA (supplementary Table S8, available at Annals of Oncology online).
Table 4.

Odds ratio for prostate cancer risk per unit increase in MSP (ng/ml) for IV estimates and MR results using inverse-variance method

StudyOR per unit increase in MSP (ng/ml) (95% CI)
PRACTICAL for incident cancer0.96 (0.95–0.98)
EPIC (excluding PRACTICAL) for incident cancer0.97 (0.95–0.99)
All pooled0.96 (0.95–0.97)

MSP, microseminoprotein-beta; IV, instrumental variable; MR, Mendelian randomization; OR, odds ratio; CI, confidence interval; PRACTICAL, Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome; EPIC, European Prospective Investigation into Cancer and Nutrition.

Odds ratio for prostate cancer risk per unit increase in MSP (ng/ml) for IV estimates and MR results using inverse-variance method MSP, microseminoprotein-beta; IV, instrumental variable; MR, Mendelian randomization; OR, odds ratio; CI, confidence interval; PRACTICAL, Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome; EPIC, European Prospective Investigation into Cancer and Nutrition.

Discussion

In this large prospective study, we found a lower prostate cancer risk in men with higher circulating concentrations of MSP after adjustment for circulating PSA concentrations. MSP is a protein in the immunoglobulin-binding factor family primarily secreted by epithelial cells, which may have a role in tumour suppression[16] and pathogen defence [17]. These findings are in agreement with the only other published prospective investigation [2], which found an inverse association between circulating MSP concentration and prostate cancer; in the MEC study, MSP concentration was inversely associated with prostate cancer risk before and after adjustment for PSA, though the association was much stronger after adjustment, as is to be expected due to the strong positive association of PSA concentration with risk and the moderate positive association of PSA with MSP. In accordance with previous findings [2], we found no evidence that the association of MSP with risk differed by tumour stage or grade, although small numbers of cases in subgroups may have limited power to evaluate heterogeneity. We found some modest evidence for heterogeneity by country and age at diagnosis, but the results are difficult to interpret due to small numbers in subgroups and multiple statistical tests. Short follow-up time (3.8 years) and thus reverse causality was previously suggested in MEC as a possible explanation for the observed association. The present study has more than double the average follow-up (8.3 years), and while we found some observational evidence that the inverse association between MSP and prostate cancer is stronger for men diagnosed closer to blood collection, the apparent differences by time to diagnosis may be at least in part due to differences in the case mix, with cases diagnosed closer to baseline being more likely to be younger at diagnosis. Furthermore, our MR findings suggest that reverse causality is unlikely to explain the overall relationship, with genetic variation in MSP affecting lifetime levels of MSP. MSP is also secreted at lower levels by epithelial cells in the tracheobronchial tree [18, 19]. Smoking has been associated with a 2.5-fold increase in expression of MSP in the airway epithelium when compared with non-smokers [20]. Therefore, some variation in MSP concentrations may be due to smoking-induced secretory cell hyperplasia in the respiratory tract. To our knowledge, we are the only study to report higher levels of MSP among current smokers compared with non-smokers. We found no strong evidence of heterogeneity in the MSP association by smoking status but more data are needed to examine this and particularly to assess the association in non-smokers in whom any potential masking effect of smoking on circulating MSP is not present. The strength of the current MR result stems from the use of rs10993994 as an instrumental variable; rs10993994 lies in the promotor region of the MSMB region, the locus that encodes MSP, and rs10993994 is strongly associated with circulating MSP concentrations and prostate cancer [2, 5]. In general, the use of variants in the cis-acting protein-encoding locus is one of the most robust scenarios of MR [21] and a recent review of MSP function [22] suggest the rs10993994 genetic association is specific to MSP. An association of rs10993994 has been observed with concentrations of prostate cancer markers PSA and PCA3 in prostate cancer controls [15], and it remains possible that PSA may confound these results. However, given that the associations of rs10993994 with PSA and PCA3 levels are observed only in controls and that MR results were materially robust to adjustment for PSA concentration, the association of rs10993994 with PSA (and PCA3) may arise from collider bias. Such collider bias [23], which induces the association of rs10993994 with PSA and PCA3 when stratifying on prostate cancer disease status, should not invalidate the results of the MR analysis (which is not stratified on disease status). Additionally, for the biological role of PSA to confound these findings, PSA would have to be causal to prostate cancer development for which there is little evidence.

Conclusion

Using observational data from a prospective nested case–control study and MR, this study supports a possible protective role of MSP in the development of prostate cancer. Experimental studies are needed to elucidate the mechanisms through which MSP may influence prostate cancer development. If shown to be true from randomized clinical trials, therapies that raise MSP levels may provide novel opportunities for the treatment and prevention of prostate cancer. Click here for additional data file.
  20 in total

Review 1.  A candidate gene approach to searching for low-penetrance breast and prostate cancer genes.

Authors:  D J Hunter; E Riboli; C A Haiman; D Albanes; D Altshuler; S J Chanock; R B Haynes; B E Henderson; R Kaaks; D O Stram; G Thomas; M J Thun; H Blanché; J E Buring; N P Burtt; E E Calle; H Cann; F Canzian; Y C Chen; G A Colditz; D G Cox; A M Dunning; H S Feigelson; M L Freedman; J M Gaziano; E Giovannucci; S E Hankinson; J N Hirschhorn; R N Hoover; T Key; L N Kolonel; P Kraft; L Le Marchand; S Liu; J Ma; S Melnick; P Pharaoh; M C Pike; C Rodriguez; V W Setiawan; M J Stampfer; E Trapido; R Travis; J Virtamo; S Wacholder; W C Willett
Journal:  Nat Rev Cancer       Date:  2005-12       Impact factor: 60.716

2.  Polymorphisms at the Microseminoprotein-beta locus associated with physiologic variation in beta-microseminoprotein and prostate-specific antigen levels.

Authors:  Xing Xu; Camilla Valtonen-André; Charlotta Sävblom; Christer Halldén; Hans Lilja; Robert J Klein
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-08       Impact factor: 4.254

Review 3.  MSMB variation and prostate cancer risk: clues towards a possible fungal etiology.

Authors:  Siobhan Sutcliffe; Angelo M De Marzo; Karen S Sfanos; Martin Laurence
Journal:  Prostate       Date:  2014-01-24       Impact factor: 4.104

4.  Multiple novel prostate cancer predisposition loci confirmed by an international study: the PRACTICAL Consortium.

Authors:  Zsofia Kote-Jarai; Douglas F Easton; Janet L Stanford; Elaine A Ostrander; Johanna Schleutker; Sue A Ingles; Daniel Schaid; Stephen Thibodeau; Thilo Dörk; David Neal; Jenny Donovan; Freddie Hamdy; Angela Cox; Christiane Maier; Walter Vogel; Michelle Guy; Kenneth Muir; Artitaya Lophatananon; Mary-Anne Kedda; Amanda Spurdle; Suzanne Steginga; Esther M John; Graham Giles; John Hopper; Pierre O Chappuis; Pierre Hutter; William D Foulkes; Nancy Hamel; Claudia A Salinas; Joseph S Koopmeiners; Danielle M Karyadi; Bo Johanneson; Tiina Wahlfors; Teuvo L Tammela; Mariana C Stern; Roman Corral; Shannon K McDonnell; Peter Schürmann; Andreas Meyer; Rainer Kuefer; Daniel A Leongamornlert; Malgorzata Tymrakiewicz; Jo-Fen Liu; Tracy O'Mara; R A Frank Gardiner; Joanne Aitken; Amit D Joshi; Gianluca Severi; Dallas R English; Melissa Southey; Stephen M Edwards; Ali Amin Al Olama; Rosalind A Eeles
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-08       Impact factor: 4.254

5.  Levels of beta-microseminoprotein in blood and risk of prostate cancer in multiple populations.

Authors:  Christopher A Haiman; Daniel O Stram; Andrew J Vickers; Lynne R Wilkens; Katharina Braun; Camilla Valtonen-André; Mari Peltola; Kim Pettersson; Kevin M Waters; Loic Le Marchand; Laurence N Kolonel; Brian E Henderson; Hans Lilja
Journal:  J Natl Cancer Inst       Date:  2012-12-03       Impact factor: 13.506

6.  Beta-microseminoprotein in serum correlates with the levels in seminal plasma of young, healthy males.

Authors:  Camilla Valtonen-André; Charlotta Sävblom; Per Fernlund; Hans Lilja; Aleksander Giwercman; Ake Lundwall
Journal:  J Androl       Date:  2008-01-24

7.  β-Microseminoprotein endows post coital seminal plasma with potent candidacidal activity by a calcium- and pH-dependent mechanism.

Authors:  Anneli M L Edström Hägerwall; Victoria Rydengård; Per Fernlund; Matthias Mörgelin; Maria Baumgarten; Alexander M Cole; Martin Malmsten; Birthe B Kragelund; Ole E Sørensen
Journal:  PLoS Pathog       Date:  2012-04-05       Impact factor: 6.823

8.  Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array.

Authors:  Rosalind A Eeles; Ali Amin Al Olama; Sara Benlloch; Edward J Saunders; Daniel A Leongamornlert; Malgorzata Tymrakiewicz; Maya Ghoussaini; Craig Luccarini; Joe Dennis; Sarah Jugurnauth-Little; Tokhir Dadaev; David E Neal; Freddie C Hamdy; Jenny L Donovan; Ken Muir; Graham G Giles; Gianluca Severi; Fredrik Wiklund; Henrik Gronberg; Christopher A Haiman; Fredrick Schumacher; Brian E Henderson; Loic Le Marchand; Sara Lindstrom; Peter Kraft; David J Hunter; Susan Gapstur; Stephen J Chanock; Sonja I Berndt; Demetrius Albanes; Gerald Andriole; Johanna Schleutker; Maren Weischer; Federico Canzian; Elio Riboli; Tim J Key; Ruth C Travis; Daniele Campa; Sue A Ingles; Esther M John; Richard B Hayes; Paul D P Pharoah; Nora Pashayan; Kay-Tee Khaw; Janet L Stanford; Elaine A Ostrander; Lisa B Signorello; Stephen N Thibodeau; Dan Schaid; Christiane Maier; Walther Vogel; Adam S Kibel; Cezary Cybulski; Jan Lubinski; Lisa Cannon-Albright; Hermann Brenner; Jong Y Park; Radka Kaneva; Jyotsna Batra; Amanda B Spurdle; Judith A Clements; Manuel R Teixeira; Ed Dicks; Andrew Lee; Alison M Dunning; Caroline Baynes; Don Conroy; Melanie J Maranian; Shahana Ahmed; Koveela Govindasami; Michelle Guy; Rosemary A Wilkinson; Emma J Sawyer; Angela Morgan; David P Dearnaley; Alan Horwich; Robert A Huddart; Vincent S Khoo; Christopher C Parker; Nicholas J Van As; Christopher J Woodhouse; Alan Thompson; Tim Dudderidge; Chris Ogden; Colin S Cooper; Artitaya Lophatananon; Angela Cox; Melissa C Southey; John L Hopper; Dallas R English; Markus Aly; Jan Adolfsson; Jiangfeng Xu; Siqun L Zheng; Meredith Yeager; Rudolf Kaaks; W Ryan Diver; Mia M Gaudet; Mariana C Stern; Roman Corral; Amit D Joshi; Ahva Shahabi; Tiina Wahlfors; Teuvo L J Tammela; Anssi Auvinen; Jarmo Virtamo; Peter Klarskov; Børge G Nordestgaard; M Andreas Røder; Sune F Nielsen; Stig E Bojesen; Afshan Siddiq; Liesel M Fitzgerald; Suzanne Kolb; Erika M Kwon; Danielle M Karyadi; William J Blot; Wei Zheng; Qiuyin Cai; Shannon K McDonnell; Antje E Rinckleb; Bettina Drake; Graham Colditz; Dominika Wokolorczyk; Robert A Stephenson; Craig Teerlink; Heiko Muller; Dietrich Rothenbacher; Thomas A Sellers; Hui-Yi Lin; Chavdar Slavov; Vanio Mitev; Felicity Lose; Srilakshmi Srinivasan; Sofia Maia; Paula Paulo; Ethan Lange; Kathleen A Cooney; Antonis C Antoniou; Daniel Vincent; François Bacot; Daniel C Tessier; Zsofia Kote-Jarai; Douglas F Easton
Journal:  Nat Genet       Date:  2013-04       Impact factor: 38.330

9.  Microseminoprotein-Beta Expression in Different Stages of Prostate Cancer.

Authors:  Liisa Sjöblom; Outi Saramäki; Matti Annala; Katri Leinonen; Janika Nättinen; Teemu Tolonen; Tiina Wahlfors; Matti Nykter; G Steven Bova; Johanna Schleutker; Teuvo L J Tammela; Hans Lilja; Tapio Visakorpi
Journal:  PLoS One       Date:  2016-03-03       Impact factor: 3.240

Review 10.  Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.

Authors:  Daniel I Swerdlow; Karoline B Kuchenbaecker; Sonia Shah; Reecha Sofat; Michael V Holmes; Jon White; Jennifer S Mindell; Mika Kivimaki; Eric J Brunner; John C Whittaker; Juan P Casas; Aroon D Hingorani
Journal:  Int J Epidemiol       Date:  2016-06-24       Impact factor: 7.196

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  5 in total

1.  Circulating Isovalerylcarnitine and Lung Cancer Risk: Evidence from Mendelian Randomization and Prediagnostic Blood Measurements.

Authors:  Karl Smith-Byrne; Agustin Cerani; Florence Guida; Sirui Zhou; Antonio Agudo; Krasimira Aleksandrova; Aurelio Barricarte; Miguel Rodríguez Barranco; Christoph H Bochers; Inger Torhild Gram; Jun Han; Christopher I Amos; Rayjean J Hung; Kjell Grankvist; Therese Haugdhal Nøst; Liher Imaz; María Dolores Chirlaque-López; Mikael Johansson; Rudolf Kaaks; Tilman Kühn; Richard M Martin; James D McKay; Valeria Pala; Hilary A Robbins; Torkjel M Sandanger; David Schibli; Matthias B Schulze; Ruth C Travis; Paolo Vineis; Elisabete Weiderpass; Paul Brennan; Mattias Johansson; J Brent Richards
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2022-10-04       Impact factor: 4.090

2.  Extensive Mendelian randomization study identifies potential causal risk factors for severe COVID-19.

Authors:  Yitang Sun; Jingqi Zhou; Kaixiong Ye
Journal:  Commun Med (Lond)       Date:  2021-12-09

3.  The Ubiquitin-Proteasome System Does Not Regulate the Degradation of Porcine β-Microseminoprotein during Sperm Capacitation.

Authors:  Lucie Tumova; Michal Zigo; Peter Sutovsky; Marketa Sedmikova; Pavla Postlerova
Journal:  Int J Mol Sci       Date:  2020-06-10       Impact factor: 5.923

4.  Impact of glycemic traits, type 2 diabetes and metformin use on breast and prostate cancer risk: a Mendelian randomization study.

Authors:  Shiu Lun Au Yeung; Catherine Mary Schooling
Journal:  BMJ Open Diabetes Res Care       Date:  2019-12-29

5.  Systematic review of Mendelian randomization studies on risk of cancer.

Authors:  Georgios Markozannes; Afroditi Kanellopoulou; Olympia Dimopoulou; Dimitrios Kosmidis; Xiaomeng Zhang; Lijuan Wang; Evropi Theodoratou; Dipender Gill; Stephen Burgess; Konstantinos K Tsilidis
Journal:  BMC Med       Date:  2022-02-02       Impact factor: 11.150

  5 in total

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