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Abstract：
BACKGROUND: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.
METHODS: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.
RESULTS: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.
CONCLUSIONS: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.
BACKGROUND: This work was supported by Cancer Research UK (grant no. C8221/A29017).
METHODS: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.
RESULTS: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.
CONCLUSIONS: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.
BACKGROUND: This work was supported by Cancer Research UK (grant no. C8221/A29017).
摘要：
背景：了解循环蛋白在前列腺癌风险中的作用可以揭示关键的生物学途径并确定癌症预防的新靶点。
方法：我们调查了2002年基因预测的循环蛋白水平与前列腺癌总体风险的关系，以及侵袭性和早发性疾病，使用顺式pQTL孟德尔随机化(MR)和共定位。在两个MR的支持下发现蛋白质，在多次测试校正后，并使用两个独立的癌症GWAS复制共定位，欧洲血统和非洲血统之一。另外使用前列腺肿瘤组织中的空间转录组数据研究了具有前列腺特异性组织表达证据的蛋白质，以评估它们在肿瘤侵袭性中的作用。最后,我们将风险蛋白映射到药物和正在进行的临床试验目标.
结果：我们确定了20种与前列腺癌风险遗传相关的蛋白质（总体14种[8种特异性]，7代表侵略性[3个具体]，8用于早发性疾病[2具体])，其中大多数在数据可用的地方复制。其中包括与侵袭性疾病相关的蛋白质，例如PPA2[每1个SD增量的赔率（OR）=2.13，95%CI：1.54-2.93]，PYY[OR=1.87，95%CI：1.43-2.44]和PRSS3[OR=0.80，95%CI：0.73-0.89]，以及那些与早发性疾病相关的疾病，包括EHPB1[OR=2.89，95%CI：1.99-4.21]，POGLUT3[OR=0.76，95%CI：0.67-0.86]和TPM3[OR=0.47，95%CI：0.34-0.64]。我们证实了MSMB与前列腺癌总体呈负相关[OR=0.81，95％CI：0.80-0.82]，并且还发现与侵袭性[OR=0.84,95%CI:0.82-0.86]和早发性疾病[OR=0.71,95%CI:0.68-0.74]均呈负相关.利用空间转录组学数据，我们确定MSMB是全基因组最重要的预测基因,可区分良性区域和MSMB表达低5倍的高级别癌症区域.此外,与前列腺癌风险相关的10种蛋白质也映射到现有的治疗干预措施.
结论：我们的发现强调了蛋白质组学对于提高我们对前列腺癌病因学和新的治疗干预机会的理解的重要性。此外,我们证明了深入功能分析的额外益处,以三角分析风险蛋白在前列腺肿瘤临床侵袭性中的作用.使用这些综合方法，我们确定了与侵袭性和早发性疾病相关的风险蛋白亚组,作为未来前列腺癌预防和治疗研究的重点.
背景：这项工作得到了英国癌症研究中心的支持（批准号：C8221/A29017)。
方法：我们调查了2002年基因预测的循环蛋白水平与前列腺癌总体风险的关系，以及侵袭性和早发性疾病，使用顺式pQTL孟德尔随机化(MR)和共定位。在两个MR的支持下发现蛋白质，在多次测试校正后，并使用两个独立的癌症GWAS复制共定位，欧洲血统和非洲血统之一。另外使用前列腺肿瘤组织中的空间转录组数据研究了具有前列腺特异性组织表达证据的蛋白质，以评估它们在肿瘤侵袭性中的作用。最后,我们将风险蛋白映射到药物和正在进行的临床试验目标.
结果：我们确定了20种与前列腺癌风险遗传相关的蛋白质（总体14种[8种特异性]，7代表侵略性[3个具体]，8用于早发性疾病[2具体])，其中大多数在数据可用的地方复制。其中包括与侵袭性疾病相关的蛋白质，例如PPA2[每1个SD增量的赔率（OR）=2.13，95%CI：1.54-2.93]，PYY[OR=1.87，95%CI：1.43-2.44]和PRSS3[OR=0.80，95%CI：0.73-0.89]，以及那些与早发性疾病相关的疾病，包括EHPB1[OR=2.89，95%CI：1.99-4.21]，POGLUT3[OR=0.76，95%CI：0.67-0.86]和TPM3[OR=0.47，95%CI：0.34-0.64]。我们证实了MSMB与前列腺癌总体呈负相关[OR=0.81，95％CI：0.80-0.82]，并且还发现与侵袭性[OR=0.84,95%CI:0.82-0.86]和早发性疾病[OR=0.71,95%CI:0.68-0.74]均呈负相关.利用空间转录组学数据，我们确定MSMB是全基因组最重要的预测基因,可区分良性区域和MSMB表达低5倍的高级别癌症区域.此外,与前列腺癌风险相关的10种蛋白质也映射到现有的治疗干预措施.
结论：我们的发现强调了蛋白质组学对于提高我们对前列腺癌病因学和新的治疗干预机会的理解的重要性。此外,我们证明了深入功能分析的额外益处,以三角分析风险蛋白在前列腺肿瘤临床侵袭性中的作用.使用这些综合方法，我们确定了与侵袭性和早发性疾病相关的风险蛋白亚组,作为未来前列腺癌预防和治疗研究的重点.
背景：这项工作得到了英国癌症研究中心的支持（批准号：C8221/A29017)。
