孟德尔随机化分析在结直肠癌血液标志物遗传背景研究中的应用

doi:10.16288/j.yczz.24-179

遗传 ›› 2024, Vol. 46 ›› Issue (10): 833-848.doi: 10.16288/j.yczz.24-179

孟德尔随机化分析在结直肠癌血液标志物遗传背景研究中的应用

万欣坤¹^,²(), 虞诗诚², 梅松青²^,³, 钟雯²^,³()

1.华中科技大学生命科学与技术学院，武汉 430074
2.广州国家实验室，广州 510005
3.广州医科大学附属肿瘤医院，广州 511436

收稿日期:2024-06-18 修回日期:2024-08-21 出版日期:2024-08-30 发布日期:2024-08-30
通讯作者: 钟雯，博士，研究员，博士生导师，研究方向：精准医学。E-mail: zhong_wen@gzlab.ac.cn
作者简介:万欣坤，博士研究生，专业方向：生物与医药。E-mail: xinkunw@hust.edu.cn
基金资助:
国家自然科学基金项目(8230102287)

Application of Mendelian randomization analysis in investigating the genetic background of blood biomarkers for colorectal cancer

Xinkun Wan¹^,²(), Shicheng Yu², Songqing Mei²^,³, Wen Zhong²^,³()

1. College of Life Science and Technology,Huazhong University of Science and Technology, Wuhan 430074, China
2. Guangzhou National Laboratory, Guangzhou 510005, China
3. Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 511436, China

Received:2024-06-18 Revised:2024-08-21 Published:2024-08-30 Online:2024-08-30
Supported by:
National Natural Science Foundation of China(8230102287)

摘要/Abstract

摘要：

结直肠癌(colorectal cancer，CRC)是一种发生在结肠和直肠的消化道癌症，是全球第三常见癌症，也是癌症相关死亡的第二大原因。CRC的早期发现对于预防转移、降低死亡率、改善预后和提高患者生活质量至关重要。遗传因素在CRC的发生中扮演重要角色，可解释高达35%的疾病风险。全基因组关联研究已发现多个与CRC风险相关的遗传位点，但缺乏直接因果关系的证据。虽然传统的血液标志物如癌胚抗原(carcinoembryonic antigen，CEA)和糖类抗原19-9(CA19-9)已被广泛用于CRC诊断和监测，但在早期诊断应用中的灵敏度和准确性有限，仍需要开发新的能够反映遗传背景的生物标志物或其组合，以促进早期诊断和提高诊断准确性。同时，理解这些生物标志物的遗传机制对于揭示CRC的发病机制至关重要，并有助于开发更加精准的个体化治疗策略。孟德尔随机化(Mendelian randomization，MR)分析作为一种新兴的流行病学工具，通过减少观察性研究中存在的偏差问题，能够更精确地评估遗传变异与疾病间的因果关系。目前，MR分析已被用于评估多种血液标志物对CRC风险的因果影响，可更准确地表明血液标志物与CRC发病机制之间的潜在因果关系。本文综述了MR分析在CRC血液标志物研究中的应用，旨在为CRC的早期诊断和个体化治疗提供理论基础。

关键词: 结直肠癌, 孟德尔随机化, 工具变量, 遗传因素, 生物标志物, 早期诊断

Abstract:

Colorectal cancer (CRC), a malignancy affecting the colon and rectum, ranks as the third most common cancer worldwide and the second leading cause of cancer-related deaths. Early detection of CRC is crucial for preventing metastasis, reducing mortality, improving prognosis, and enhancing patients’ quality of life. Genetic factors play a significant role in CRC development, accounting for up to 35% of the disease risk. Genome-wide association studies have identified several genetic loci associated with CRC risk. However, these studies often lack direct evidence of causality. While traditional blood biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are widely used for CRC diagnosis and monitoring, their sensitivity and accuracy in early diagnosis are limited. Thus, there is a pressing need to develop new biomarkers that reflect the genetic background of CRC to improve early detection and diagnostic accuracy. In addition, understanding the genetic mechanisms underlying these biomarkers is essential for elucidating CRC pathogenesis and developing precise personalized treatment strategies. Mendelian randomization (MR) analysis, as an emerging epidemiological tool, can accurately assess the causal relationship between genetic variations and diseases by reducing confounding biases in observational studies. MR analysis has been applied in evaluating the causal impact of various blood biomarkers on CRC risk, shedding lights on the potential causal relationships between these biomarkers and CRC pathogenesis in the context of genetic background. In this review, we summarize the applications of MR analysis in studies of blood biomarkers for CRC, aiming to enhance the early diagnosis and personalized treatment of CRC.

Key words: colorectal cancer, Mendelian randomization, instrumental variables, genetics, biomarker, early diagnosis

万欣坤, 虞诗诚, 梅松青, 钟雯. 孟德尔随机化分析在结直肠癌血液标志物遗传背景研究中的应用[J]. 遗传, 2024, 46(10): 833-848.

Xinkun Wan, Shicheng Yu, Songqing Mei, Wen Zhong. Application of Mendelian randomization analysis in investigating the genetic background of blood biomarkers for colorectal cancer[J]. Hereditas(Beijing), 2024, 46(10): 833-848.

图/表 5

图1

图2

表1

炎症和免疫标志物与CRC因果关系的MR分析结果"

暴露	相关研究	人群	结局样本量	结果	OR(95% CI)	P-value
VEGF	Kong等^[46]	欧洲	218,792	正相关	1.352(1.019~1.315)	0.024
	Meng等l^[47]	欧洲	218,792	正相关	1.17(1.02~1.35)	0.026
	Yang等^[48]	欧洲	218,792	正相关	1.173(1.019~1.351)	0.026
IL-12p70	Kong等^[46]	欧洲	218,792	正相关	1.273(1.133~1.430)	0.0000468
	Meng等^[47]	欧洲	218,792	正相关	1.27(1.13~1.43)	0.00122
	Yang等^[48]	欧洲	218,792	正相关	1.273(1.133~1.430)	0.0000468
IL-13	Kong等^[46]	欧洲	218,792	正相关	1.149(1.012~1.299)	0.028
	Meng等^[47]	欧洲	218,792	正相关	1.15(1.02~1.30)	0.028
	Yang等^[48]	欧洲	218,792	正相关	1.149(1.015~1.299)	0.028
	Dong等^[58]	欧洲	218,792	负相关	0.78(0.65~0.95)	0.013
IL-10	Kong等^[46]	欧洲	218,792	正相关	1.230(1.013~1.493)	0.037
	Meng等^[47]	欧洲	218,792	正相关	1.23(1.01~1.49)	0.037
	Yang等^[48]	欧洲	218,792	正相关	1.230(1.013~1.4913)	0.037
	Ma等^[49]	欧洲	NA	正相关	1.49(1.20~1.87)	0.000431
	Dong等^[58]	欧洲	218,792	正相关	1.40(1.18~1.65)	0.000094
IL-7	Kong等^[46]	欧洲	218,792	正相关	1.191(1.023~1.386)	0.024
	Meng等^[47]	欧洲	218,792	正相关	1.19(1.02~1.39)	0.024
	Yang等^[48]	欧洲	218,792	正相关	1.191(1.023~1.386)	0.024
M-CSF	Kong等^[46]	欧洲	218,792	正相关	0.854(0.764~0.955)	0.005
M-CSF	Meng等^[47]	欧洲	218,792	正相关	0.85(0.76~0.96)	0.005
IL-12B	Meng等^[47]	欧洲	218,792	正相关	1.19(1.00~1.42)	0.046
CRP	Nimptsch等^[14]	欧洲	NA	正相关	1.74(1.06~2.85)	NA
	Hua等^[51]	欧洲	16,918	不相关	NA	NA
	Wang等^[52]	欧洲	53,324	不相关	1.04(0.97~1.12)	0.256
	Choi 等^[53]	韩国	59,605	不相关	NA	NA
IL-8	Jiang等^[57]	欧洲	177,028	负相关	0.65(0.43~0.98)	0.041
IL-2	Dong等^[58]	欧洲	218,792	负相关	0.76(0.63~0.92)	0.0043
IL-17F	Dong等^[58]	欧洲	218,792	负相关	0.78(0.62~1.00)	0.015
IL-31	Dong等^[58]	欧洲	218,792	负相关	0.88(0.79~0.98)	0.023
IL-36α	Dong等^[58]	欧洲	218,792	正相关	1.23(1.01~1.49)	0.040
IL-17RD	Dong等^[58]	欧洲	218,792	正相关	1.22(1.00~1.48)	0.048
Resistin	Pham等^[62]	欧洲	309,154	不相关	1.01(0.96~1.06)	0.067
GDF-15	Yeung等^[63]	欧洲	387314	不相关	0.91(0.80~1.04)	NA

表1

表2

表3

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暴露	相关研究	人群	结局样本量	结果	OR(95% CI)	P-value
Fasting insulin	Murphy等^[15]	欧洲	112,373	正相关	1.65(1.15~2.36)	0.035
Fasting glucose	Murphy等^[15]	欧洲	112,373	不相关	1.04(0.88~1.23)	0.81
HbA1c	Murphy等^[15]	欧洲	112,373	正相关	1.09(1.00~1.19)	0.08
TC	Iwagami等^[16]	日本	45,978	正相关	1.15(1.01~1.32)	0.042
HDL-C,	Iwagami等^[16]	日本	45,978	不相关	1.11(0.98~1.27)	0.111
Triglycerides	Iwagami等^[16]	日本	45,978	不相关	1.06(0.90~1.26)	0.484
LDL-C	Iwagami等^[16]	日本	45,978	不相关	1.17(0.91~1.50)	0.232
Apolipoprotein B	Liu等^[73]	欧洲	218,792	正相关	1.188(1.001~1.411)	0.0491
IGF1	Murphy等^[17]	欧洲	99,152	正相关	1.08(1.03~1.12)	0.00033
	Larsson等^[81]	欧洲	-	正相关	1.11(1.01~1.22)	0.03
	Larsson等^[81]	日本	-	正相关	1.22(1.09~1.36)	0.00039
IGFBP3	Murphy等^[17]	欧洲	99,152	正相关	1.12(1.06~1.18)	0.000042
SHBG	Dimou等^[82]	欧洲	85,638	不相关	NA	NA
DHEAS	Jayarathna等^[83]	欧洲	387,318	负相关	0.70(0.51~0.96)	0.03
Adiponectin	Nimptsch等^[86]	欧洲	2880	不相关	0.99(0.93~1.06)	NA

暴露	相关研究	人群	结局样本量	结果	OR(95% CI)	P-value
Serum Albumin	Lv等^[93]	日本	202,807	负相关	0.75(0.59~0.95)	NA
GREM1	Sun等^[95]	欧洲	43199	正相关	1.14(1.10~1.18)	1.55E-14
CLSTN3	Sun等^[95]	欧洲	43199	负相关	0.21(0.13~0.34)	2.41E-10
CSF2RA	Sun等^[95]	欧洲	43199	负相关	0.16(0.09~0.28)	3.28E-10
CD86	Sun等^[95]	欧洲	43199	负相关	0.23(0.14~0.39)	3.30E-8
VIMP	Cai等^[96]	欧洲	377,673	正相关	1.00101(1.00008~1.00195)	0.03327
TNFRSF11B	Cai等^[96]	欧洲	377,673	正相关	1.00055(1.00007~1.00103)	0.02346
C5orf38	Cai等^[96]	欧洲	377,673	正相关	1.00083(1.00014~1.00152)	0.01877
SLC5A8	Cai等^[96]	欧洲	377,673	正相关	1.00252(1.00076~1.00428)	0.00502
MICB	Cai等^[96]	欧洲	377,673	负相关	0.99882(0.99785~0.99979)	0.01764
Oleic	May-Wilson等^[97]	欧洲	27640	负相关	0.77(0.65~0.922)	0.0039
Palmitoleic	May-Wilson等^[97]	欧洲	27640	负相关	0.36(0.15~0.84)	0.018
Linoleic	May-Wilson等^[97]	欧洲	27640	负相关	0.95(0.93~0.98)	0.00037
Arachidonic acid	May-Wilson等^[97]	欧洲	27640	正相关	1.05(1.02~1.07)	0.00017
Stearic acid	May-Wilson等^[97]	欧洲	27640	正相关	1.17(1.01~1.35)	0.041

孟德尔随机化分析在结直肠癌血液标志物遗传背景研究中的应用

Application of Mendelian randomization analysis in investigating the genetic background of blood biomarkers for colorectal cancer

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