Mendelian Randomization Analysis of Plasma Proteins Reveals Potential Novel Tumor Markers for Gastric Cancer

doi:10.21203/rs.3.rs-4951820/v1

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Mendelian Randomization Analysis of Plasma Proteins Reveals Potential Novel Tumor Markers for Gastric Cancer

https://doi.org/10.21203/rs.3.rs-4951820/v1

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This study aimed to elucidate the potential causal relationship between 4,907 plasma proteins and the risk of gastric cancer using a two-sample Mendelian randomization approach. We utilized genome-wide association study (GWAS) data to perform two-sample Mendelian randomization analyses, treating the 4,907 plasma proteins as exposure factors and gastric cancer as the outcome. Instrumental variables for plasma proteins were selected based on strongly correlated SNPs identified through data processing and screening of the GWAS data provided by the deCode database. We employed a set of statistical methods centered on inverse variance weighting (IVW) for Mendelian randomization analysis to estimate the odds ratios (ORs) for the effects of these plasma proteins on gastric cancer susceptibility. According to the IVW method, 14 plasma proteins were associated with gastric cancer (p < 0.005). Specifically, CHST15 (OR = 0.7553, 95% CI = 0.6346 − 0.8988), L1CAM (OR = 0.7230, 95% CI = 0.5876 − 0.8896), FTMT (OR = 0.8246, 95% CI = 0.7241 − 0.9391), and PMM2 (OR = 0.5767, 95% CI = 0.3943 − 0.8433) were negatively correlated with gastric cancer, whereas ABO (OR = 1.1868, 95% CI = 1.0638 − 1.3240), FAM3D (OR = 1.2109, 95% CI = 1.0850 − 1.3515), FAM3B (OR = 1.2988, 95% CI = 1.0953 − 1.5402), ADH7 (OR = 1.3568, 95% CI = 1.1044 − 1.6670), MAP1LC3A (OR = 1.3704, 95% CI = 1.1194 − 1.6778), PGLYRP1 (OR = 1.4071, 95% CI = 1.1235 − 1.7623), PDE5A (OR = 1.7446, 95% CI = 1.2693 − 2.3978), GLUL (OR = 3.1203, 95% CI = 1.5017 − 6.4839), NFE2L1 (OR = 3.1759, 95% CI = 1.6163 − 6.2402), and MAFG (OR = 3.1945, 95% CI = 1.5329 − 6.6575) were positively correlated. Convergent results from Weighted Median and MR-Egger analyses confirmed these associations. Reverse Mendelian randomization analysis indicated that gastric cancer does not significantly alter the levels of these 14 plasma proteins (p > 0.05). Sensitivity analyses, including assessments of heterogeneity and horizontal pleiotropy, confirmed the robustness and reliability of our findings without significant bias. Pathway enrichment analysis of gene expression associated with these 14 plasma proteins, using GO and KEGG pathways, revealed that CHST15, L1CAM, FTMT, and PMM2 may serve as protective factors against gastric cancer, while ABO, FAM3D, FAM3B, ADH7, MAP1LC3A, PGLYRP1, PDE5A, GLUL, NFE2L1, and MAFG may contribute to gastric cancer pathogenesis. These results highlight the complex biological interactions between plasma proteins and tumorigenesis, providing valuable insights for preventive and therapeutic strategies in gastric malignancy management.

Biological sciences/Cancer

Biological sciences/Cancer/Cancer screening

Biological sciences/Cancer/Gastrointestinal cancer

Plasma protein

Gastric cancer

Mendelian randomization

Tumor marker

Gastric cancer(GC) is the second most prevalent malignancy in China[1], with gastric adenocarcinoma accounting for 95% of cases. Globally, it ranks among the highest in incidence and mortality, with the World Health Organization reporting it as the fifth most commonly diagnosed cancer and the fourth leading cause of cancer-related death [2.3]. Major risk factors include Helicobacter pylori infection, smoking, alcohol consumption, and an unhealthy diet[4]. Early-stage GC often presents with no obvious symptoms, leading to diagnosis at more advanced stages. Recent advancements in bioinformatics have identified several tumor markers for GC, such as carcinoembryonic antigen (CEA) and CA199; however, their sensitivity remains suboptimal [5]. Consequently, there is a pressing need to discover new circulating tumor markers to improve early detection and screening of GC.

With the rapid advancement of proteomics technologies, a plethora of aberrantly expressed plasma proteins have been identified, providing a substantial and reliable data foundation for exploring new tumor markers for GC. In recent years, genome-wide association studies (GWAS) have identified numerous genetic polymorphisms associated with GC. Protein quantitative trait loci (pQTLs) refer to genetic variants that influence the abundance of specific proteins, and GWAS of circulating protein levels can pinpoint these pQTL clusters. By co-localizing with disease variants, pQTLs can guide the identification of pathogenic genes and disease pathways, thereby elucidating the association between circulating proteins and disease[6.7]. Mendelian randomization(MR), increasingly utilized in medical research, employs genetic variants as instrumental variables (IVs) to mitigate confounding effects and analyze exposure-outcome relationships, thereby clarifying the inherent causal relationships [8]. Compared to traditional observational studies and randomized controlled trials, MR offers several advantages: (a). By leveraging existing large-scale GWAS data, MR studies are more cost- and time-efficient. (b).MR can reveal potential causal relationships between exposure factors and diseases. (c). MR analysis, analogous to the random allocation of treatments in randomized controlled trials, benefits from the random segregation of genetic alleles during gamete formation, thereby minimizing the biases inherent in conventional randomized controlled trials[9]This approach has been shown to enhance our understanding of the complex mechanisms by which aberrant expression of plasma circulating proteins may promote gastric cancer. Not only does it deepen our knowledge of gastric cancer, but it also provides potential targets and strategic pathways for prevention and therapeutic intervention. This study aims to use MR analysis, in conjunction with data from GWAS, to explore the causal relationship between plasma proteins and gastric cancer risk, thereby offering guidance for early screening and efficacy assessment of gastric cancer.

1. Material: GWAS data on GC were sourced from the Finngen database (https://www.finngen.fi/en/access_results), specifically from the cohort labeled "Malignant neoplasm of stomach (controls excluding all cancers)" (Ncase = 1,741; Ncontrol = 345,118; R11)[10]. Data on 4,907 plasma proteins were obtained from the study by Ferkingstad et al. (6) and downloaded from the deCODE database (https://www.decode.com)(Table1).

2. Selection of Instrumental Variables (IVs): MR (MR) analysis examines the effect of exposure on an outcome by substituting instrumental variables (IVs) for the exposure and outcome in the analysis. To ensure the validity of IVs, three assumptions must be met: (a) the relevance assumption, where IVs must be strongly associated with the exposure; (b) the independence assumption, where IVs should not be associated with the outcome except through the exposure; and (c) the exclusivity assumption, where IVs affect the outcome solely through the exposure and are not influenced by other potential confounders(Fig. 1.) [11]. IVs from GWAS data are selected following these steps: (a) SNPs are retained if p < 5e-8, indicating strong association with the exposure; (b) SNPs within 10,000 kb of each other are clustered for linkage disequilibrium, with r2 < 0.001 SNPs selected; (c) SNPs are aligned for exposure and outcome allele directions, excluding palindromic or ambiguous SNPs; (d) SNPs are assigned an F-statistic value (F = R^2*(n-2)/(1-R^2); R^2 = 2*(1-MAF)*MAF*beta^2/(2*(1-MAF)*MAF*beta^2 + 2*(1-MAF)*MAF*se^2*n)) and those with F < 10 are excluded[12]. To ensure a sufficient number of IVs for reverse MR analysis, SNPs associated with GC are selected with a more stringent threshold of p < 5e-6.

Table 1

Data sources for the Mendelian randomization analysis in the study.
`	database	Sample size	Population	Sources
Plasma protein	deCODE	4091	Icelander	Ferkingstad, et al(6)
Gastric cancer	FinnGen	1741/345118	European	Kurki, M.I., et al.(9)

3. MR analysis: In this study, MR analysis was conducted using the TwoSampleMR package (v0.6.6) in R software (v4.41). The primary analysis employed the inverse-variance weighted (IVW) method to assess the causal effects of genetically predicted protein levels on GC risk. To provide supplementary evidence to the IVW results, we also utilized MR-Egger regression, weighted median, simple mode, and weighted mode analyses [13]. To minimize the risk of false positives, we set a stringent threshold for the IVW analysis results with a p-value cut-off of p < 0.005. The research design is illustrated in Fig. 2.

4. Sensitivity analyses: In this study, sensitivity analyses were conducted using the TwoSampleMR package (v0.6.6) in R software (v4.41). Genetic variation heterogeneity was assessed with Cochran’s Q test, where p > 0.05 indicated the absence of significant heterogeneity. Horizontal pleiotropy was evaluated using the Egger intercept, with p > 0.05 suggesting no evidence of pleiotropy[14]. Additionally, we employed the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to detect outliers indicative of horizontal pleiotropy among the multiple instrumental variables. A p-value > 0.05 from MR-PRESSO indicated no significant horizontal pleiotropy[15].

5. Reverse MR analysis: To better elucidate the causal relationship between exposure and outcome, we conducted reverse MR analysis and sensitivity analysis by treating GC as the exposure and the plasma proteins identified as associated with GC in forward MR as the outcomes. This approach helps identify potential reverse causality, mitigate confounding factors, and ensure the robustness of our study[16].

6. GO/KEGG pathway enrichment and GeneMANIA analysis: To deeply understand the relationship between the screened proteins and GC, we also performed pathway enrichment analysis of the screened biomarker genes of GC through GO (Gene Ontology) database and KEGG (Kyoto Encyclopedia of Genes and Genomes) database by using the clusterProfiler software package (V4.12.2). Encyclopedia of Genes and Genomes) database for pathway enrichment analysis. GeneMANIA database (http://genemania.org/) is a website for analyzing protein-protein interactions (PPIs), through which we analyzed the screened protein genes and determined the PPIs of the screened protein network.

After performing association analysis and removing linkage disequilibrium from GWAS data involving 4,907 plasma proteins, 1,733 GWASs were excluded. Subsequently, a forward MR analysis of the remaining 3,174 proteins identified 14 that are associated with the incidence of gastric cancer(Fig. 3.4.). These proteins include Carbohydrate Sulfotransferase 15 (CHST15), L1 Cell Adhesion Molecule (L1CAM), Ferritin Mitochondrial (FTMT), Phosphomannomutase 2 (PMM2), Alpha 1-3-N-Acetylgalactosaminyltransferase and Alpha 1-3-Galactosyltransferase (ABO), FAM3 Metabolism Regulating Signaling Molecule D (FAM3D), FAM3 Metabolism Regulating Signaling Molecule B (FAM3B), Alcohol Dehydrogenase 7 (Class IV) (ADH7), Microtubule Associated Protein 1 Light Chain 3 Alpha (MAP1LC3A), Peptidoglycan Recognition Protein 1 (PGLYRP1), Phosphodiesterase 5A (PDE5A), Glutamate-Ammonia Ligase (GLUL), NFE2 Like BZIP Transcription Factor 1 (NFE2L1), and MAF BZIP Transcription Factor G (MAFG). Of these, CHST15 (OR = 0.7553, 95% CI = 0.6346–0.8988, p = 0.0016), L1CAM (OR = 0.7230, 95% CI = 0.5876–0.8896, p = 0.0022), FTMT (OR = 0.8246, 95% CI = 0.7241–0.9391, p = 0.0036), and PMM2 (OR = 0.5767, 95% CI = 0.3943–0.8433, p = 0.0045) were negatively associated with GC, suggesting they may act as protective factors. Conversely, ABO (OR = 1.1868, 95% CI = 1.0638–1.3240, p = 0.0021), FAM3D (OR = 1.2109, 95% CI = 1.0850–1.3515, p = 0.0006), FAM3B (OR = 1.2988, 95% CI = 1.0953–1.5402, p = 0.0026), ADH7 (OR = 1.3568, 95% CI = 1.1044–1.6670, p = 0.0036), MAP1LC3A (OR = 1.3704, 95% CI = 1.1194–1.6778, p = 0.0023), PGLYRP1 (OR = 1.4071, 95% CI = 1.1235–1.7623, p = 0.0029), PDE5A (OR = 1.7446, 95% CI = 1.2693–2.3978, p = 0.0006), GLUL (OR = 3.1203, 95% CI = 1.5017–6.4839, p = 0.0023), NFE2L1 (OR = 3.1759, 95% CI = 1.6163–6.2402, p = 0.0008), and MAFG (OR = 3.1945, 95% CI = 1.5329–6.6575, p = 0.0019) were positively associated with GC, indicating they may contribute to disease pathogenesis(Fig. 5).

Sensitivity analysis: Sensitivity analysis confirmed the robustness of our findings, including heterogeneity analysis using Cochran’s Q test and horizontal pleiotropy assessment via Egger intercept and MR-PRESSO. Results indicated that the Cochran’s Q test p-values for the 14 plasma proteins in relation to GC were all greater than 0.05, suggesting no significant heterogeneity. Similarly, Egger intercept p-values were also above 0.05 for these analyses, indicating no impact from horizontal pleiotropy. Furthermore, the MR-PRESSO global test p-value greater than 0.05 further corroborated the absence of horizontal pleiotropy(Table 2.).

Table 2

Results of a sensitivity analysis of a positive Mendelian randomization analysis for gastric cancer as an outcome.
exposure	Heterogeneity analysis						pleiotropy analysis
	Inverse variance weighted			MR Egger			MR Egger			MR PRESSO Global Test
	Q	Q_df	Q_pval	Q	Q_df	Q_pval	egger_intercept	se	pval	pval
NFE2L1	1.0371	2	0.5954	0.8887	1	0.3458	-0.0711	0.1845	0.7659	NA
ADH7	4.8742	7	0.6753	4.4976	6	0.6097	-0.0125	0.0203	0.5619	0.7440
FAM3D	12.4284	16	0.7140	12.3972	15	0.6488	-0.0038	0.0216	0.8621	0.7260
PDE5A	7.9004	5	0.1618	7.3846	4	0.1169	-0.0375	0.0709	0.6251	0.3200
MAP1LC3A	9.8619	20	0.9706	9.7928	19	0.9579	0.0046	0.0173	0.7955	0.9510
PMM2	2.4588	4	0.6520	1.9495	3	0.5830	0.0487	0.0683	0.5270	0.6710
GLUL	2.0417	2	0.3603	0.0584	1	0.8091	0.1338	0.0950	0.3931	NA
MAFG	1.0849	2	0.5813	0.2127	1	0.6446	0.4546	0.4867	0.5218	NA
PGLYRP1	8.9380	11	0.6276	8.0488	10	0.6241	0.0260	0.0276	0.3679	0.7260
L1CAM	9.0492	16	0.9114	7.3080	15	0.9485	0.0236	0.0179	0.2068	0.9030
CHST15	5.3654	8	0.7179	5.3221	7	0.6207	0.0052	0.0248	0.8411	0.7050
FTMT	6.1914	10	0.7989	6.1913	9	0.7206	0.0004	0.0272	0.9895	0.7250
FAM3B	6.8700	13	0.9087	6.3215	12	0.8990	-0.0176	0.0237	0.4732	0.8590
ABO	2.7005	9	0.9750	1.8211	8	0.9860	-0.0269	0.0287	0.3758	0.9610

Inverse MR analysis: To further clarify the causal relationship between these 14 proteins and GC, we conducted a reverse MR analysis. For selecting instrumental variables (IVs), we adjusted the criteria to include SNPs with p < 5e-6, resulting in eight IVs. After performing association analysis and linkage disequilibrium processing of gastric cancer GWAS data, 8 IVs were selected. The IVW analysis revealed p-values greater than 0.05, indicating that GC incidence does not affect the abnormal expression of these proteins(Fig. 6.). This finding was corroborated by MR-Egger regression, weighted median, simple mode, and weighted mode analyses. Sensitivity analysis of the reverse MR results showed Cochran's Q test p-values greater than 0.05, confirming no significant heterogeneity. Additionally, the Egger intercept and MR-PRESSO global test p-values were also above 0.05, indicating no impact from horizontal pleiotropy(Table 3).

Table 3

Results of the sensitivity analysis of the reverse Mendelian randomization analysis with gastric cancer as exposure.
outcome	Heterogeneity analysis						pleiotropy analysis
	Inverse variance weighted			MR Egger			MR Egger			MR PRESSO Global Test
	Q	Q_df	Q_pval	Q	Q_df	Q_pval	egger_intercept	se	pval	pval
NFE2L1	12.1007	7	0.0973	8.0219	6	0.2365	-0.0188	0.0108	0.1313	0.0830
ADH7	11.6433	7	0.1129	6.1575	6	0.4058	-0.0222	0.0096	0.0601	0.1220
FAM3D	2.6798	7	0.9130	1.8055	6	0.9367	-0.0087	0.0093	0.3859	0.8980
PDE5A	6.2716	7	0.5084	4.1989	6	0.6498	-0.0138	0.0096	0.2000	0.4390
MAP1LC3A	12.6129	7	0.0821	7.9496	6	0.2418	-0.0216	0.0115	0.1098	0.0820
PMM2	9.1661	7	0.2409	6.8849	6	0.3316	-0.0147	0.0104	0.2082	0.2110
GLUL	10.7245	7	0.1511	6.9253	6	0.3278	-0.0182	0.0101	0.1196	0.1280
MAFG	5.2824	7	0.6255	3.3473	6	0.7642	-0.0132	0.0095	0.2136	0.5990
PGLYRP1	6.1423	7	0.5232	6.0812	6	0.4142	-0.0024	0.0098	0.8142	0.6010
L1CAM	3.9475	7	0.7858	3.2654	6	0.7749	-0.0082	0.0100	0.4405	0.8110
CHST15	6.3373	7	0.5010	4.5598	6	0.6014	-0.0128	0.0096	0.2308	0.5190
FTMT	3.7138	7	0.8121	3.4073	6	0.7563	0.0052	0.0093	0.5999	0.8160
FAM3B	6.1797	7	0.5189	6.1785	6	0.4035	-0.0003	0.0102	0.9742	0.4890
ABO	4.0811	7	0.7704	3.9829	6	0.6790	-0.0023	0.0073	0.7647	0.7160

GO/KEGG pathway enrichment analysis and GeneMANIA analysis: Pathway enrichment analysis using the GO database revealed the following: In the Biological Process (BP), significant enrichment was observed in the glycoprotein biosynthetic process (p = 0.0016) and glycoprotein metabolic process (p = 0.0026). In the Cellular Component (CC), these genes were significantly enriched in the neuronal cell body (p = 0.0450). In the Molecular Function (MF), these genes were significantly enriched in cytokine activity (p = 0.0135)(Fig. 7a.b.). KEGG database analysis indicated significant enrichment in Ferroptosis (p = 0.0008)(Fig. 7c.d.). Lastly, GeneMANIA network analysis of these genes revealed that Physical Interactions accounted for 69.92%, Shared Protein Domains for 14.63%, Co-expression for 11.72%, Co-localization for 2.16%, Genetic Interactions for 0.98%, and Predicted interactions for 0.59%(Fig. 7e.).

We conducted MR analysis using GWAS data for 4907 plasma proteins to identify those potentially associated with GC. This analysis highlighted 14 plasma proteins of interest. Sensitivity analyses were performed to minimize the impact of heterogeneity and horizontal pleiotropy on our findings. Additionally, reverse MR demonstrated that the incidence of GC does not lead to abnormal expression of these proteins, further validating the causal relationship between these proteins and GC. Among the identified proteins, ABO, FAM3D, FAM3B, ADH7, MAP1LC3A, PGLYRP1, PDE5A, GLUL, NFE2L1, and MAFG showed a positive association with GC, indicating their potential as biomarkers for early detection of this malignancy. Enrichment analyses using GO and KEGG databases revealed significant involvement of MAP1LC3A and FTMT in Ferroptosis, a process that is critically implicated in tumor development and progression according to current research [17].

The ABO protein, a circulating protein associated with human ABO blood types, is foundational for the formation of these blood types due to genetic variations. Research dating back to 1950 has indicated an association between ABO blood groups and gastric cancer[18]. A large-scale meta-analysis conducted by Cui et al. elucidated the relationship between aberrant expression of ABO genes and cancer, revealing that individuals with blood type A (OR = 1.19, p = 3.90 × 10^–15) and blood type AB (OR = 1.10, p = 0.007) are associated with an increased risk of gastric cancer[19]. A/B antigens have pro-coagulant and angiogenic properties and act as receptors and ligands for key immune proteins. They can also alter cell motility and resistance to apoptosis, potentially linking blood type to tumor development and metastasis [20.21]. FAM3D and FAM3B are metabolic-regulating signaling molecules of the FAM3 family. Zhu et al. found that upregulation of FAM3B induces cisplatin resistance in GC cells by modulating epithelial-mesenchymal transition (EMT), suggesting an association with GC [22]. Additionally, Park et al. reported that FAM3D expression is highly correlated with Helicobacter pylori infection in gastritis, a significant risk factor for GC [23]. ADH7, a member of the alcohol dehydrogenase family, is highly expressed in gastric epithelial tissues and converts ethanol to acetaldehyde [24.25]. Acetaldehyde is a known toxin and carcinogen [26]. While acetaldehyde is rapidly metabolized to acetate by aldehyde dehydrogenase, further research is needed to determine whether its excessive expression increases the risk of gastric cancer. Autophagy, crucial for maintaining homeostasis, limiting inflammation, and preventing tissue damage and genomic instability, involves microtubule-associated protein 1 light chain 3 (MAP1LC3), a key regulator of autophagosome formation[27.28]. A retrospective study by Liao et al. found that abnormal MAP1LC3A expression is linked to GC and poor prognosis [29]. Peptidoglycan recognition protein 1 (PGLYRP1) is an innate immune protein with critical roles in antimicrobial and antitumor defense, exhibiting direct bactericidal activity against both Gram-positive and Gram-negative bacteria [30]. Recent research indicates that the gene encoding PGLYRP1 is highly co-expressed with genes encoding co-suppressor molecules, suggesting its potential as a promising target for cancer immunotherapy and possibly a new target for GC immunotherapy [31]. Glutamate-ammonia ligase (GLUL), involved in glutamine synthesis, has recently been found to have significant positive correlations with various cancers and may serve as a novel cancer treatment target [32.33]. However, a recent study by Jiang et al. reported that GLUL expression is significantly reduced in GC tissues compared to adjacent normal tissues. This study also found that GLUL stabilizes E-cadherin by antagonizing β-catenin, thus inhibiting GC progression[34]. Our findings indicate that high GLUL expression increases GC risk, which may seem contradictory. This discrepancy may arise because the highly invasive nature of GC might lead to substantial glutamine uptake from the environment, reducing the need for endogenous glutamine synthesis. Further research is needed to elucidate the underlying mechanisms [35]. NFE2 Like BZIP Transcription Factor 1 (NFE2L1) is a key regulator of antioxidant, detoxification, and cell protection genes in response to cellular stress [36]. Increasing evidence highlights its critical role in tumor development, including growth, invasion, migration, and metastasis [37]. MAF BZIP transcription factor G (MAFG) is a basic leucine zipper (bZIP) protein belonging to the v-maf oncogene family. It is implicated in regulating hematopoietic gene expression [38], although no studies have yet linked it to cancer development. However, its antisense gene, MAFG-AS1, has been associated with the onset and progression of various cancers, including gastric cancer. This long non-coding RNA (lncRNA) promotes tumorigenesis by inhibiting apoptosis and enhancing proliferation, migration, invasion, aerobic glycolysis, ferroptosis, and angiogenesis [39.40].

In our study, we employed bidirectional two-sample MR to identify 14 plasma proteins with causal relationships to GC (GC). Gene enrichment analyses using GO and KEGG databases, along with co-expression network analysis via the GeneMANIA online tool, provided deeper insights into the relationships between these proteins and GC. Our findings may enhance gene-based screening methods for early GC and offer valuable evidence for the development of screening and prevention strategies. Future research will focus on further validating the functional roles of the identified proteins through additional experiments to explore their impact on GC cells.

This study has several limitations. First, the GWAS data for GC lack stratification by specific subtypes, precluding subtype analyses. Second, inherent limitations of MR (MR) studies must be addressed, including ensuring that instrumental variables (IVs) satisfy the assumptions of relevance, independence, and exclusivity to mitigate horizontal pleiotropy and confounding biases. Despite our efforts to meet these conditions, completely eliminating these biases remains challenging. Third, potential weak instrument biases in MR analysis may lead to inaccuracies and reduced statistical power; we employed various statistical methods and sensitivity analyses to address this issue, yet some inaccuracies may persist. Lastly, the GWAS data utilized primarily come from European populations, and further validation is needed to confirm whether these findings apply to other ethnic groups.

In summary, our study identifies a causal relationship between 14 genetically determined plasma proteins and GC. Notably, proteins such as ABO, FAM3D, FAM3B, ADH7, MAP1LC3A, PGLYRP1, PDE5A, GLUL, NFE2L1, and MAFG demonstrate potential as serological biomarkers for early GC detection and as novel therapeutic targets. However, further research is required to elucidate the specific mechanisms through which these candidate proteins contribute to GC.

The authors declare no competing interests.

Author Contribution

Fan WH contributed to the design of the work, the acquisition, analysis, interpretation of data, and the drafting and revision of articles. Wu ZJ,Xu SH, Liu ZH, and Huang YM contributed to the design, drafting, and revision of this article. WangP contributed to the design of the work, the acquisition of funding, the management of the project, and the drafting and revision of the terms. All authors agree to take personal responsibility for their contributions. All authors have read and approved the final manuscript

Acknowledgement

We thank the deCODE database for providing the GWAS summary statistics for our analysis. We want to acknowledge the participants and investigators of the FinnGen study. The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organizations and biobanks within Finland and international industry partners.

Data Availability

The data used to perform all the analyses is publicly accessible through the following sources: GWAS data for gastric cancer: https://storage.googleapis.com/finngen-public-data-r11/summary_stats/finngen_R11_C3_STOMACH_EXALLC.gz. GWAS data for plasma protein: https://download.decode.is/form/folder/proteomics.

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No competing interests reported.

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Mendelian Randomization Analysis of Plasma Proteins Reveals Potential Novel Tumor Markers for Gastric Cancer

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