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

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

• Review • Previous Articles Next Articles

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 Online:2024-08-30 Published:2024-08-30
Contact: Wen Zhong E-mail:xinkunw@hust.edu.cn;zhong_wen@gzlab.ac.cn
Supported by:
National Natural Science Foundation of China(8230102287)

Abstract

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

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.

Figures/Tables 5

Fig. 1

Fig. 2

Table 1

MR analysis of the causal relationship between inflammatory and immune markers and CRC"

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

Table 1

Table 2

Table 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

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

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