Associations of plasma protein levels with risk of colorectal cancer: a proteome-wide Mendelian randomization study

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Mendelian Randomization (MR) has been primarily applied for repurposing licensed drugs and uncovering new therapeutic targets. Objective This study aims to systematically identify potential plasma protein targets for CRC using proteome-wide Mendelian randomization and evaluate their safety through phenome-wide association studies. Methods We conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of CRC. The plasma proteins were sourced from the Finland and Iceland decode database, encompassing GWAS data for plasma proteins (Olink-619 samples across 2925 proteins, SomaScan − 828 samples across 7596 proteins and Iceland decode database across 4907 proteins). Additionally, GWAS data for CRC were extracted from the UK Biobank-SAIGE database, including 3051 cases and 382,756 controls. Subsequently, colocalization analysis was performed to identify shared causal variants between plasma proteins and CRC. Finally, a phenome-wide association study (Phe-WAS) was conducted to examine the potential adverse effects of druggable proteins for CRC, utilizing the extensive UK Biobank-SAIGE database, encompassing 783 phenotypes. Results The MR analysis identified GREM1, DKKL1, and CHRDL2 as plasma proteins whose genetically predicted levels were positively associated with CRC risk, whereas TMEM132A exhibited an inverse association with CRC risk ( P_ fdr 0.7), suggesting that these proteins represent potential direct targets for CRC intervention. Further phenotype-wide association studies showed no significant potential side effects of these targets ( P _fdr > 0.05). Conclusion This proteome-wide Mendelian randomization study offers a comprehensive molecular landscape of CRC, identifying GREM1, DKKL1, CHRDL2, and TMEM132A as potential therapeutic targets. Our research provides a critical foundation for future experimental validation and therapeutic development in colorectal cancer management. Colorectal cancer Mendelian randomization plasma proteins drug target phenome-wide association study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colorectal cancer (CRC), including advanced or metastatic forms, remains a significant global health challenge. According to recent epidemiological data, CRC is the third most common malignancy worldwide, with colon cancer constituting a large proportion of these cases 1 . Approximately 20% of colon cancer patients present with metastatic disease at diagnosis, and nearly 50% of all CRC patients will eventually develop metastases, contributing to poor prognosis and high mortality rates in advanced stages 2 . Molecular research has revealed that genetic and epigenetic alterations in critical signaling pathways, including WNT, MAPK, and PI3K, drive CRC tumorigenesis and progression 3 , 4 . However, despite advances in understanding the molecular biology of CRC, the heterogeneity of the disease complicates treatment, particularly in metastatic settings 5 . Treatment of advanced or metastatic CRC typically involves a combination of surgery, chemotherapy, and radiation therapy. Standard chemotherapy regimens often include fluoropyrimidines combined with oxaliplatin or irinotecan. However, despite these therapies, the five-year survival rate for metastatic CRC remains below 15% 6 . Given the limitations of current treatments, recent research has turned to precision medicine, focusing on targeted therapies and immunotherapy 7 . In the context of CRC treatment, PD-1/PD-L1 checkpoint inhibitors have clinical benefits, particularly in patients with microsatellite instability-high (MSI-H) or mismatch repair-deficient (dMMR) tumors. However, the immunosuppressive nature of the tumor microenvironment and the emergence of drug resistance limit the broader applicability of these therapies 8 , 9 . Poly (ADP-ribose) polymerase (PARP) inhibitors have shown efficacy in CRC patients harboring homologous recombination repair (HRR) deficiencies. Despite this, the use of current PARP inhibitors is restricted by off-target toxicities and the development of resistance. Furthermore, antibody-drug conjugates (ADCs) have shown promising efficacy in certain CRC phenotypes, such as tumors with HER2 overexpression. However, the clinical application of ADCs remains in its early stages, with antigen heterogeneity and the complexity of ADC internalization mechanisms being significant challenges to optimizing therapeutic outcomes 10 . Targeted approaches—aimed at molecules such as epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF)—have shown promise but are often hampered by the development of resistance 11 12 . While these emerging therapies have demonstrated promising results in specific patient subgroups, their overall efficacy remains limited. As a result, the identification of new molecular targets and a deeper understanding of the mechanisms underlying these therapies are essential for improving outcomes in CRC patients. Among these, proteomics has emerged as a key tool in understanding the complex molecular mechanisms underlying CRC. Proteomics—the large-scale study of proteins—offers a unique perspective on how proteins interact, post-translationally modify, and contribute to cancer progression 13 . Proteins serve as the functional workhorses of cellular processes, and alterations in protein expression or function can lead to tumorigenesis. By applying proteomic technologies, researchers can identify novel proteins involved in CRC progression that may serve as potential therapeutic targets 14 .Recent advancements in proteomics have highlighted several proteins that may play crucial roles in the development and progression of CRC 15 . Through large-scale proteomic analyses and proteome-wide Mendelian randomization (MR) studies, these proteins are being investigated for their potential roles in tumor growth, angiogenesis, and immune evasion. This study aims to explore the potential therapeutic value of these proteins in CRC, laying the foundation for future precision medicine strategies. Methods 2.1 Study Design This study employed a comprehensive two-sample Mendelian Randomization (MR) framework to investigate the causal relationship between plasma proteins and CRC. Genome-Wide Association Studies (GWAS) summary statistics were utilized, ensuring all participants provided informed consent. Given that only publicly available summary data were used, additional ethical approval was not required. The overall study design is illustrated in Figure 1. This study was reported in accordance with the STROBE-MR (Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization) guidelines to ensure transparent and comprehensive reporting of the Mendelian randomization analysis. 2.2 Plasma Protein Quantitative Trait Loci (pQTLs) Selection Plasma protein quantitative trait loci (pQTLs) were obtained from multiple sources: Finngen Database: DF10 v1 proteomics QTL results SomaScan: 828 samples across 7,596 proteins; Olink: 619 samples across 2,925 proteins) [(https://r10.finngen.fi/)]. Iceland Decode Database: 4,907 aptamers in 35,559 Icelandic individuals (PMID: 34857953). pQTLs were classified into cis-pQTLs (proximal to the corresponding gene) and trans-pQTLs (distant or on a different chromosome). Only cis-pQTLs were considered as instrumental variables (IVs) to reduce pleiotropy risks. The IV selection criteria were: a. The single nucleotide polymorphisms (SNPs) located within a vicinity of ±1 Mb around the gene region (cis-acting pQTLs); b. A genome-wide significant threshold of P < 5×10 -8 to identify highly correlated SNPs with plasma proteins; c. A threshold of 0.001 for the linkage disequilibrium parameter (LD) (r 2 ) and a genetic distance of 10,000 kb are set to guarantee the inclusion of independent SNPs and mitigate the influence of linkage disequilibrium on the outcomes; d. F-value > 10, to decrease weak instrumental variable bias. 2.3 GWAS Summary Statistics for CRC CRC genetic association data were extracted from UK Biobank-SAIGE (3,051 cases, 382,756 controls) [(https://www.leelabsg.org/resources)] (PMID: 33568819). 2.4 Mendelian Randomization Analysis We conducted a two-sample MR analysis, treating plasma protein levels as exposures and CRC as the outcome. MR estimates were calculated using: Inverse Variance Weighted (IVW) method: Primary analysis assuming all IVs are valid instruments. MR-Egger regression: To detect and adjust for directional pleiotropy. Weighted Median method: Providing a robust causal estimate even if up to 50% of instruments are invalid. Weighted Mode method: Identifies causal effects based on the most frequent estimate. For proteins with only one SNP available, the Wald ratio method was applied. To account for multiple testing, the False Discovery Rate (FDR) correction was used, with statistical significance set at P _fdr < 0.05. Analyses were performed using R packages TwoSampleMR (v0.6.0), MendelianRandomization (v0.8.0), and MRPRESSO (v1.0) [(https://www.R-project.org)]. 2.5 Colocalization Analysis Colocalization analysis was conducted to determine whether the same causal genetic variant influences both the plasma protein and CRC risk. SNPs within ±1 Mb of the respective genes were analyzed using five hypotheses: H0: No association with either protein or disease. H1: Associated with the protein but not with disease. H2: Associated with disease but not with the protein. H3: Associated with either protein or disease, but independently. H4: A shared causal SNP affecting both protein and disease. Significant colocalization was defined as PPH3 + PPH4 ≥ 0.7. 2.6 Phenome-Wide Association Study (Phe-WAS) To assess potential pleiotropic effects, we conducted a phenome-wide association study (Phe-WAS) using 783 phenotypes from the UK Biobank-SAIGE database. Plasma proteins identified as significant in MR analysis were used as exposures. A P_ FDR < 0.05 was considered significant. Results 3.1 MR Analysis Identifies Causal Proteins Associated with CRC A total of 3,339 plasma proteins were included in the MR analysis. Relevant SNP information is shown in Supplementary Table 1. After adjusting for multiple testing, four proteins exhibited significant associations with CRC risk: GREM1: OR = 1.184 (95% CI: 1.12–1.25, P_ FDR = 2.25 × 10⁻⁶) DKKL1: OR = 1.07 (95% CI: 1.04–1.12, P_ FDR = 0.049) CHRDL2: OR = 1.389 (95% CI: 1.21–1.60, P _FDR = 0.0038) TMEM132A (Protective): OR = 0.857 (95% CI: 0.79–0.93, P_ FDR = 0.0489) These results suggest that higher genetically predicted levels of GREM1, DKKL1, and CHRDL2 were associated with an increased risk of CRC, Genetically predicted TMEM132A levels were inversely associated with CRC risk, suggesting a potential protective role. (Supplementary Table 2, Figures 2 and 3). 3.2 Colocalization Analysis Confirms Shared Genetic Variants To validate MR findings, colocalization analysis was performed. All four proteins (GREM1, DKKL1, CHRDL2, and TMEM132A) exhibited strong colocalization signals (PPH3 + PPH4 ≥ 0.7), indicating that shared causal variants influence both plasma protein levels and CRC risk (Supplementary Table 3). 3.3 Phenome-Wide Association Study (Phe-WAS) Identifies Potential Effects on Other Traits To evaluate off-target effects, we performed Phe-WAS across 783 phenotypes. The analysis identified the following significant associations (P_FDR < 0.05): GREM1: Associated with 8 phenotypes (P_FDR < 0.05). DKKL1: Associated with 3 phenotypes (P_FDR < 0.05). CHRDL2: Associated with 6 phenotypes (P_FDR < 0.05). TMEM132A: Associated with 6 phenotypes (P_FDR < 0.05). While some associations were identified, no significant adverse effects were found for any of these proteins (Supplementary Table 4, Figure 4). Discussion The treatment of CRC, particularly in its advanced stages, faces significant limitations despite the use of multimodal therapies, including surgery, chemotherapy, and targeted therapy. In our study, we explored the potential roles of GREM1, DKKL1, CHRDL2, and TMEM132A as key proteins implicated in CRC pathogenesis, using Phe-WAS Mendelian randomization (PWMR). Our results suggest that these proteins, through their involvement in critical pathways such as Wnt signaling and BMP antagonism, may serve as novel therapeutic targets for CRC. This section will explore the significance of these findings in the context of recent research and discuss the therapeutic potential and challenges associated with targeting these proteins. GREM1 plays a dual role in cancer biology by antagonizing BMP signaling, which is known to suppress tumor growth and promote cell differentiation in the gastrointestinal tract 16 , 17 . In CRC, GREM1 overexpression has been shown to disrupt BMP-mediated tumor suppression, thereby allowing Wnt signaling to dominate and drive tumor progression. Studies have demonstrated that BMP antagonists like GREM1 are associated with increased cellular proliferation, resistance to differentiation, and enhanced invasive potential in CRC 18 . Our study reinforces these findings, showing that elevated GREM1 expression correlates with more aggressive CRC phenotypes. When compared with other studies, such as Gao, Z.et al. 16 , who explored BMP antagonists in CRC progression, the results align with the concept that restoring BMP signaling through GREM1 inhibition could reverse tumor growth and enhance the differentiation of cancer cells. Additionally, this protein’s involvement in Wnt signaling suggests a strategic point for therapeutic intervention, as targeting GREM1 may simultaneously modulate multiple tumor-promoting pathways. However, as highlighted in recent literature, targeting GREM1 could result in unintended effects on normal tissue homeostasis, particularly in organs where BMP signaling plays a critical role, such as the intestine 19 . DKKL1, a member of the Dickkopf glycoprotein family, functions primarily as an antagonist of the Wnt/β-catenin signaling pathway, which plays a key role in cell growth and differentiation. Dysregulation of this pathway is a hallmark of CRC, where DKKL1 not only promotes tumor proliferation and invasion but also modulates the tumor microenvironment by influencing immune cell activity 20 21 . Our Mendelian randomization analysis identified genetically predicted DKKL1 levels as positively associated with CRC risk, consistent with previous findings suggesting a potential role of DKKL1 in oncogenic Wnt signaling. Given its dual role in Wnt regulation and immune modulation, DKKL1 has emerged as a promising therapeutic target, particularly for combination therapies involving immunotherapy 22 . Studies have shown that anti-DKKL1 therapies can synergize with immune checkpoint inhibitors to enhance CD8 + T-cell activity while reducing myeloid-derived suppressor cells (MDSCs), thereby overcoming tumor immune evasion 20 , 23 . Our findings support the potential of DKKL1 as a direct therapeutic target, with colocalization analysis revealing shared genetic variation between DKKL1 and CRC, and phenotype-wide association studies showing no significant side effects linked to DKKL1 inhibition. Future research should focus on the long-term effects of DKKL1 inhibition and its integration with immunotherapies, particularly for patients with microsatellite-stable CRC, who typically exhibit poor responses to current treatments. CHRDL2 (Chordin-like protein 2), a BMP antagonist, plays a key role in modulating BMP signaling, which is essential for maintaining cellular differentiation and tissue homeostasis. Dysregulation of BMP signaling, particularly through CHRDL2-mediated inhibition of BMP-4, can disrupt its tumor-suppressive effects, thereby promoting CRC progression 24 . Our MR analysis identified CHRDL2 as genetically associated with increased CRC risk ( P _fdr 0.7) further supporting its role as a direct therapeutic target. Given its ability to inhibit BMP signaling, targeting CHRDL2 could restore normal cellular differentiation, reducing tumor proliferation and enhancing therapeutic outcomes 24 . Prior research has linked BMP pathway disruption to increased tumor aggressiveness, although clinical studies specifically addressing CHRDL2 inhibition are limited to preclinical models. Our study strengthens the genetic basis for CHRDL2's involvement in CRC, showing no significant side effects ( P _fdr > 0.05) and positioning it as a viable target for therapeutic intervention. Future research should focus on developing CHRDL2-specific inhibitors and evaluating their efficacy in vivo models, potentially combining these with immunotherapy or Wnt pathway inhibitors to maximize therapeutic benefit and overcome resistance mechanisms. In contrast to the positive associations of genetically predicted GREM1, DKKL1, and CHRDL2 levels with CRC risk, our study suggests that TMEM132A may have a potential protective role. The TMEM (Transmembrane Protein) family comprises various proteins with largely unexplored functions, particularly in cancer biology. Depending on the specific protein, TMEM members can either promote or inhibit tumorigenesis 25 . While TMEM132A has been primarily studied in neurobiology, its role in CRC has not been extensively examined until now 26 , 27 . Our study provides exploratory evidence that TMEM132A acts as a protective factor in colorectal cancer (CRC), marking a novel association not previously identified in the literature. The findings suggest that higher TMEM132A expression correlates with reduced tumor growth and invasiveness, indicating a potential tumor-suppressive role through its regulation of cell adhesion and migration. This aligns with prior studies showing high TMEM132A expression correlates with better prognostic outcomes in other cancers, suggesting that its protective effects may extend across malignancies 26 . While promising, these results are preliminary and warrant further investigation to validate TMEM132A’s role and elucidate its mechanisms, particularly its interactions with critical pathways such as Wnt signaling. Additional studies are necessary to confirm the therapeutic potential of targeting TMEM132A in CRC. The integration of PWMR with proteomic analysis provides a powerful approach for uncovering causal relationships between protein expression and CRC risk. By leveraging genetic data, PWMR enables the identification of protein biomarkers that may serve as therapeutic targets, while proteomics offers detailed insights into their functional roles within the tumor microenvironment. Our study demonstrates that combining these methodologies can reveal critical proteins, such as GREM1, DKKL1, CHRDL2, and TMEM132A, that are causally linked to CRC progression. Recent advances in multi-omics integration have further underscored the importance of combining proteomics with genomics and transcriptomics to obtain a comprehensive view of cancer biology. For example, recent studies utilizing CRISPR-Cas9 screens combined with proteomics have identified key proteins that drive CRC resistance to therapy, providing new opportunities for targeted treatments. The Phe-WAS analysis reveals associations with various phenotypes; however, these potential adverse effects are primarily observed in phenotypes related to tumors. These potential drug targets could offer new directions for drug design, and it is worth noting that the potential for fewer side effects from these drugs may be a significant advantage. Future research should focus on applying high-throughput proteomic platforms to validate the therapeutic potential of these proteins, particularly in the context of combination therapies. While our study provides valuable insights into the roles of GREM1, DKKL1, CHRDL2, and TMEM132A in CRC, several limitations must be acknowledged. First, our findings are based on genetic associations and in silico analyses, which cannot definitively establish causality. The protein-trait relationships require further validation through functional experiments. Potential constraints include population stratification, database limitations, and the complex nature of protein interactions, which may influence the generalizability of our results. Additionally, the reliance on summary-level genetic data and proteomics databases means that individual variability and potential technical biases cannot be fully excluded. These limitations underscore the need for future prospective studies and experimental validation to confirm our computational findings. Further studies should employ comprehensive proteomic screens and functional assays to validate these findings and explore the broader proteomic landscape of CRC and also investigate the potential for combination therapies that target these proteins in conjunction with established treatments like chemotherapy, immunotherapy, and radiation. Such approaches may enhance treatment efficacy and overcome resistance mechanisms, ultimately improving outcomes for patients with advanced CRC. Conclusion In this study, we utilized proteome-wide Mendelian randomization (PWMR) to investigate the roles of GREM1, DKKL1, CHRDL2, and TMEM132A in colorectal cancer (CRC) progression. Our findings suggest that these proteins are intricately involved in key pathways, such as Wnt signaling and BMP antagonism, that drive tumorigenesis, immune evasion, and metastasis in CRC. Specifically, GREM1 and CHRDL2 act as BMP antagonists, promoting tumor growth by disrupting the BMP signaling pathway, while DKKL1 modulates the Wnt/β-catenin pathway, contributing to both tumor progression and immune suppression. TMEM132A, in contrast, appears to function as a tumor suppressor, with its downregulation linked to enhanced tumor invasiveness. These proteins, particularly GREM1 and DKKL1, demonstrate significant potential as therapeutic targets in CRC, offering new avenues for targeted therapies that could overcome the limitations of current treatments. Future research should focus on validating these findings in clinical settings and exploring their integration into combination therapy strategies to enhance treatment efficacy for advanced CRC patients. Declarations Author contributions ZK Pan: Formal analysis, Investigation, Methodology, Software, Visualization, Writing- original draft; MH Wu: Formal analysis, Investigation, Methodology, Software, Visualization, Writing-review & editing; HS: Formal analysis, Investigation, Software, Supervision; NJ Ni: Methodology, Project administration; QL Geng: Conceptualization, Data curation; JS Ye: Formal analysis, Funding acquisition, Investigation, Methodology, Software, Supervision, Visualization, Writing-review & editing. All authors contributed equally to the manuscript and read and approved the final version of the manuscript. Ethics approval and consent to participate Not applicable. Human and animal rights No animals/humans were used for studies that are the basis of this research. Consent for publication Not applicable Availability of data and materials The authors confirm that the data supporting the findings of this research are available within the article. The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Funding None. The authors report no involvement in the research by the sponsor that could have influenced the outcome of this work. Conflict of interest The authors declare that the research was no conflict of interest. Acknowledgements We are grateful to the UK Biobank Pharma Proteomics Project and the Finnish database for supplying data on summary statistics for MR analyses. We also want to thank all the researchers who shared these data and the study participants. Data availability statement The original data used in this study are publicly available from the following databases: UK Biobank-SAIGE (Colorectal Cancer GWAS data, https://www.leelabsg.org/resources) FinnGen Database (Proteomics QTL results, https://r10.finngen.fi/) SomaScan Proteomics Database (828 samples, 7,596 proteins) Olink Proteomics Database (619 samples, 2,925 proteins) Iceland Decode Database (4,907 aptamers in 35,559 individuals) All summary-level genetic data are anonymized and publicly accessible. Researchers can request additional details by contacting the corresponding author. Ethics statement This study exclusively utilized publicly available, anonymized genetic summary data from large-scale genomic databases. No individual-level patient data were accessed or processed. Key Ethical Considerations: All source databases obtained appropriate ethical approvals Data were aggregated and anonymized prior to analysis No direct human or animal experimentation was conducted No additional institutional review board (IRB) approval was required Informed consent for data collection was obtained by the original data repositories. This study adheres to the principles of the Declaration of Helsinki and follows international guidelines for genetic data research. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Morgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. 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Bioinformatics. 2018;34:721–4. Additional Declarations No competing interests reported. Supplementary Files TableS1.SNP.xlsx Supplementary Table 1. SNPs for MR analysis. TableS2MR.xlsx Supplementary Table 2. MR results. TableS3colocalizationanalysis.xlsx Supplementary Table 3. Results of gene co-localization analysis between 4 plasma proteins. TableS4PheWAS.csv Supplementary Table 4. Phe-WAS results between 4 CRC-associated plasma proteins and other disease outcome. TableS5.xlsx Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Clinical Proteomics → Version 1 posted Editorial decision: Revision requested 22 Apr, 2025 Reviews received at journal 14 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 26 Mar, 2025 Submission checks completed at journal 26 Mar, 2025 First submitted to journal 25 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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CI: confidence interval; OR: odds ratio.\u003c/p\u003e","description":"","filename":"Figure2.Forest.png","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/850a694c545d06d5a231c5d7.png"},{"id":81940935,"identity":"e2e91c7c-eb4f-4dfd-8a95-e30a580cbf6f","added_by":"auto","created_at":"2025-05-05 06:59:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27141,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of MR results: Causal relationship between plasma proteins and CRC.\u003c/p\u003e","description":"","filename":"Figure3.VolcanoPlot.png","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/fdbba150e45ad69f5b4a0ace.png"},{"id":81940937,"identity":"e096c325-01e9-48d9-aaa9-e2206f6efa8c","added_by":"auto","created_at":"2025-05-05 06:59:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50922,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot of result of Phe-WAS analysis of associations between 4 CRC-associated plasma proteins and other disease outcomes.\u003c/p\u003e","description":"","filename":"Figure4.MRPhewasmanhattan.png","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/07433c8144af8eb76159a733.png"},{"id":84242591,"identity":"a1453aac-3e5a-4c31-b7f7-706661e87861","added_by":"auto","created_at":"2025-06-09 16:10:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978933,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/c65d9da8-729e-4e5d-9385-a2be6a9804d2.pdf"},{"id":81940936,"identity":"db1dd7f6-7b40-4765-ac69-93c0d48f6b2b","added_by":"auto","created_at":"2025-05-05 06:59:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2121070,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. SNPs for MR analysis.\u003c/p\u003e","description":"","filename":"TableS1.SNP.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/0bd7b63152d4a6a2a9dce839.xlsx"},{"id":81940938,"identity":"905c00c4-629a-4969-ae94-59940ab5674b","added_by":"auto","created_at":"2025-05-05 06:59:47","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":822137,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 2. MR results.\u003c/p\u003e","description":"","filename":"TableS2MR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/478e422101068b6b4a7c45d3.xlsx"},{"id":81941340,"identity":"16ca3029-002e-4ba6-8d7f-4cc9a2ced763","added_by":"auto","created_at":"2025-05-05 07:07:47","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10669,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 3. Results of gene co-localization analysis between 4 plasma proteins.\u003c/p\u003e","description":"","filename":"TableS3colocalizationanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/b5d38c712efbf8c2917ba835.xlsx"},{"id":81941931,"identity":"b704b3ce-23aa-4130-808b-0e8ebb175f33","added_by":"auto","created_at":"2025-05-05 07:15:47","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1498811,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 4. Phe-WAS results between 4 CRC-associated plasma proteins and other disease outcome.\u003c/p\u003e","description":"","filename":"TableS4PheWAS.csv","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/1da796cc11643ce51be97df5.csv"},{"id":81940939,"identity":"d9a775af-bbf3-4105-b946-3d7ecc1550e6","added_by":"auto","created_at":"2025-05-05 06:59:47","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13993,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6304313/v1/a2dce8fb29cb73200062da7f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of plasma protein levels with risk of colorectal cancer: a proteome-wide Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC), including advanced or metastatic forms, remains a significant global health challenge. According to recent epidemiological data, CRC is the third most common malignancy worldwide, with colon cancer constituting a large proportion of these cases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Approximately 20% of colon cancer patients present with metastatic disease at diagnosis, and nearly 50% of all CRC patients will eventually develop metastases, contributing to poor prognosis and high mortality rates in advanced stages \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Molecular research has revealed that genetic and epigenetic alterations in critical signaling pathways, including WNT, MAPK, and PI3K, drive CRC tumorigenesis and progression \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, despite advances in understanding the molecular biology of CRC, the heterogeneity of the disease complicates treatment, particularly in metastatic settings\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Treatment of advanced or metastatic CRC typically involves a combination of surgery, chemotherapy, and radiation therapy. Standard chemotherapy regimens often include fluoropyrimidines combined with oxaliplatin or irinotecan. However, despite these therapies, the five-year survival rate for metastatic CRC remains below 15%\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the limitations of current treatments, recent research has turned to precision medicine, focusing on targeted therapies and immunotherapy \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In the context of CRC treatment, PD-1/PD-L1 checkpoint inhibitors have clinical benefits, particularly in patients with microsatellite instability-high (MSI-H) or mismatch repair-deficient (dMMR) tumors. However, the immunosuppressive nature of the tumor microenvironment and the emergence of drug resistance limit the broader applicability of these therapies \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Poly (ADP-ribose) polymerase (PARP) inhibitors have shown efficacy in CRC patients harboring homologous recombination repair (HRR) deficiencies. Despite this, the use of current PARP inhibitors is restricted by off-target toxicities and the development of resistance. Furthermore, antibody-drug conjugates (ADCs) have shown promising efficacy in certain CRC phenotypes, such as tumors with HER2 overexpression. However, the clinical application of ADCs remains in its early stages, with antigen heterogeneity and the complexity of ADC internalization mechanisms being significant challenges to optimizing therapeutic outcomes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Targeted approaches\u0026mdash;aimed at molecules such as epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF)\u0026mdash;have shown promise but are often hampered by the development of resistance \u003csup\u003e11 12\u003c/sup\u003e. While these emerging therapies have demonstrated promising results in specific patient subgroups, their overall efficacy remains limited. As a result, the identification of new molecular targets and a deeper understanding of the mechanisms underlying these therapies are essential for improving outcomes in CRC patients.\u003c/p\u003e \u003cp\u003eAmong these, proteomics has emerged as a key tool in understanding the complex molecular mechanisms underlying CRC. Proteomics\u0026mdash;the large-scale study of proteins\u0026mdash;offers a unique perspective on how proteins interact, post-translationally modify, and contribute to cancer progression\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Proteins serve as the functional workhorses of cellular processes, and alterations in protein expression or function can lead to tumorigenesis. By applying proteomic technologies, researchers can identify novel proteins involved in CRC progression that may serve as potential therapeutic targets \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.Recent advancements in proteomics have highlighted several proteins that may play crucial roles in the development and progression of CRC \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThrough large-scale proteomic analyses and proteome-wide Mendelian randomization (MR) studies, these proteins are being investigated for their potential roles in tumor growth, angiogenesis, and immune evasion. This study aims to explore the potential therapeutic value of these proteins in CRC, laying the foundation for future precision medicine strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a comprehensive two-sample Mendelian Randomization (MR) framework to investigate the causal relationship between plasma proteins and CRC. Genome-Wide Association Studies (GWAS) summary statistics were utilized, ensuring all participants provided informed consent. Given that only publicly available summary data were used, additional ethical approval was not required. The overall study design is illustrated in Figure 1. This study was reported in accordance with the STROBE-MR (Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization) guidelines to ensure transparent and comprehensive reporting of the Mendelian randomization analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Plasma Protein Quantitative Trait Loci (pQTLs) Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma protein quantitative trait loci (pQTLs) were obtained from multiple sources:\u003c/p\u003e\n\u003cp\u003eFinngen Database: DF10 v1 proteomics QTL results\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003eSomaScan: 828 samples across 7,596 proteins; Olink: 619 samples across 2,925 proteins) [(https://r10.finngen.fi/)].\u003c/li\u003e\n \u003cli\u003eIceland Decode Database: 4,907 aptamers in 35,559 Icelandic individuals (PMID: 34857953).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003epQTLs were classified into cis-pQTLs (proximal to the corresponding gene) and trans-pQTLs (distant or on a different chromosome). Only cis-pQTLs were considered as instrumental variables (IVs) to reduce pleiotropy risks. The IV selection criteria were:\u003c/p\u003e\n\u003cp\u003ea. The single nucleotide polymorphisms (SNPs) located within a vicinity of \u0026plusmn;1 Mb around the gene region (cis-acting pQTLs);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eb. A genome-wide significant threshold of \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e to identify highly correlated SNPs with plasma proteins;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ec. A threshold of 0.001 for the linkage disequilibrium parameter (LD) (r\u003csup\u003e2\u003c/sup\u003e) and a genetic distance of 10,000 kb are set to guarantee the inclusion of independent SNPs and mitigate the influence of linkage disequilibrium on the outcomes;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ed. F-value \u0026gt; 10, to decrease weak instrumental variable bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 GWAS Summary Statistics for CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCRC genetic association data were extracted from UK Biobank-SAIGE (3,051 cases, 382,756 controls) [(https://www.leelabsg.org/resources)] (PMID: 33568819).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Mendelian Randomization Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a two-sample MR analysis, treating plasma protein levels as exposures and CRC as the outcome. MR estimates were calculated using:\u003c/p\u003e\n\u003cp\u003eInverse Variance Weighted (IVW) method: Primary analysis assuming all IVs are valid instruments.\u003c/p\u003e\n\u003cp\u003eMR-Egger regression: To detect and adjust for directional pleiotropy.\u003c/p\u003e\n\u003cp\u003eWeighted Median method: Providing a robust causal estimate even if up to 50% of instruments are invalid.\u003c/p\u003e\n\u003cp\u003eWeighted Mode method: Identifies causal effects based on the most frequent estimate.\u003c/p\u003e\n\u003cp\u003eFor proteins with only one SNP available, the Wald ratio method was applied. To account for multiple testing, the False Discovery Rate (FDR) correction was used, with statistical significance set at \u003cem\u003eP\u003c/em\u003e_fdr \u0026lt; 0.05. Analyses were performed using R packages TwoSampleMR (v0.6.0), MendelianRandomization (v0.8.0), and MRPRESSO (v1.0) [(https://www.R-project.org)].\u003c/p\u003e\n\u003cp\u003e2.5 Colocalization Analysis\u003c/p\u003e\n\u003cp\u003eColocalization analysis was conducted to determine whether the same causal genetic variant influences both the plasma protein and CRC risk. SNPs within \u0026plusmn;1 Mb of the respective genes were analyzed using five hypotheses:\u003c/p\u003e\n\u003cp\u003eH0: No association with either protein or disease.\u003c/p\u003e\n\u003cp\u003eH1: Associated with the protein but not with disease.\u003c/p\u003e\n\u003cp\u003eH2: Associated with disease but not with the protein.\u003c/p\u003e\n\u003cp\u003eH3: Associated with either protein or disease, but independently.\u003c/p\u003e\n\u003cp\u003eH4: A shared causal SNP affecting both protein and disease.\u003c/p\u003e\n\u003cp\u003eSignificant colocalization was defined as PPH3 + PPH4 \u0026ge; 0.7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Phenome-Wide Association Study (Phe-WAS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess potential pleiotropic effects, we conducted a phenome-wide association study (Phe-WAS) using 783 phenotypes from the UK Biobank-SAIGE database. Plasma proteins identified as significant in MR analysis were used as exposures. A \u003cem\u003eP_\u003c/em\u003eFDR \u0026lt; 0.05 was considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 MR Analysis Identifies Causal Proteins Associated with CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 3,339 plasma proteins were included in the MR analysis.\u0026nbsp;Relevant SNP information is shown in Supplementary Table 1.\u0026nbsp;After adjusting for multiple testing, four proteins exhibited significant associations with CRC risk:\u003c/p\u003e\n\u003cul class=\"decimal_type\" start=\"50\"\u003e\n \u003cli\u003eGREM1: OR = 1.184 (95% CI: 1.12\u0026ndash;1.25, \u003cem\u003eP_\u003c/em\u003eFDR = 2.25 \u0026times; 10⁻⁶)\u003c/li\u003e\n \u003cli\u003eDKKL1: OR = 1.07 (95% CI: 1.04\u0026ndash;1.12, \u003cem\u003eP_\u003c/em\u003eFDR = 0.049)\u003c/li\u003e\n \u003cli\u003eCHRDL2: OR = 1.389 (95% CI: 1.21\u0026ndash;1.60, \u003cem\u003eP\u003c/em\u003e_FDR = 0.0038)\u003c/li\u003e\n \u003cli\u003eTMEM132A (Protective): OR = 0.857 (95% CI: 0.79\u0026ndash;0.93, \u003cem\u003eP_\u003c/em\u003eFDR = 0.0489)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese results suggest that higher genetically predicted levels of GREM1, DKKL1, and CHRDL2 were associated with an increased risk of CRC, Genetically predicted TMEM132A levels were inversely associated with CRC risk, suggesting a potential protective role. (Supplementary Table 2, Figures 2 and 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Colocalization Analysis Confirms Shared Genetic Variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate MR findings, colocalization analysis was performed. All four proteins (GREM1, DKKL1, CHRDL2, and TMEM132A) exhibited strong colocalization signals (PPH3 + PPH4 \u0026ge; 0.7), indicating that shared causal variants influence both plasma protein levels and CRC risk (Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Phenome-Wide Association Study (Phe-WAS) Identifies Potential Effects on Other Traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate off-target effects, we performed Phe-WAS across 783 phenotypes.\u0026nbsp;The analysis identified the following significant associations (P_FDR \u0026lt; 0.05):\u003c/p\u003e\n\u003cul class=\"decimal_type\" start=\"50\"\u003e\n \u003cli\u003eGREM1: Associated with 8 phenotypes (P_FDR \u0026lt; 0.05).\u003c/li\u003e\n \u003cli\u003eDKKL1: Associated with 3 phenotypes (P_FDR \u0026lt; 0.05).\u003c/li\u003e\n \u003cli\u003eCHRDL2: Associated with 6 phenotypes (P_FDR \u0026lt; 0.05).\u003c/li\u003e\n \u003cli\u003eTMEM132A: Associated with 6 phenotypes (P_FDR \u0026lt; 0.05).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhile some associations were identified, \u003cstrong\u003eno significant adverse effects\u003c/strong\u003e were found for any of these proteins (Supplementary Table 4, Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe treatment of CRC, particularly in its advanced stages, faces significant limitations despite the use of multimodal therapies, including surgery, chemotherapy, and targeted therapy. In our study, we explored the potential roles of GREM1, DKKL1, CHRDL2, and TMEM132A as key proteins implicated in CRC pathogenesis, using Phe-WAS Mendelian randomization (PWMR). Our results suggest that these proteins, through their involvement in critical pathways such as Wnt signaling and BMP antagonism, may serve as novel therapeutic targets for CRC. This section will explore the significance of these findings in the context of recent research and discuss the therapeutic potential and challenges associated with targeting these proteins.\u003c/p\u003e \u003cp\u003eGREM1 plays a dual role in cancer biology by antagonizing BMP signaling, which is known to suppress tumor growth and promote cell differentiation in the gastrointestinal tract \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In CRC, GREM1 overexpression has been shown to disrupt BMP-mediated tumor suppression, thereby allowing Wnt signaling to dominate and drive tumor progression. Studies have demonstrated that BMP antagonists like GREM1 are associated with increased cellular proliferation, resistance to differentiation, and enhanced invasive potential in CRC \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Our study reinforces these findings, showing that elevated GREM1 expression correlates with more aggressive CRC phenotypes. When compared with other studies, such as Gao, Z.et al. \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, who explored BMP antagonists in CRC progression, the results align with the concept that restoring BMP signaling through GREM1 inhibition could reverse tumor growth and enhance the differentiation of cancer cells. Additionally, this protein\u0026rsquo;s involvement in Wnt signaling suggests a strategic point for therapeutic intervention, as targeting GREM1 may simultaneously modulate multiple tumor-promoting pathways. However, as highlighted in recent literature, targeting GREM1 could result in unintended effects on normal tissue homeostasis, particularly in organs where BMP signaling plays a critical role, such as the intestine\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDKKL1, a member of the Dickkopf glycoprotein family, functions primarily as an antagonist of the Wnt/β-catenin signaling pathway, which plays a key role in cell growth and differentiation. Dysregulation of this pathway is a hallmark of CRC, where DKKL1 not only promotes tumor proliferation and invasion but also modulates the tumor microenvironment by influencing immune cell activity \u003csup\u003e20 21\u003c/sup\u003e. Our Mendelian randomization analysis identified genetically predicted DKKL1 levels as positively associated with CRC risk, consistent with previous findings suggesting a potential role of DKKL1 in oncogenic Wnt signaling. Given its dual role in Wnt regulation and immune modulation, DKKL1 has emerged as a promising therapeutic target, particularly for combination therapies involving immunotherapy \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Studies have shown that anti-DKKL1 therapies can synergize with immune checkpoint inhibitors to enhance CD8\u003csup\u003e+\u003c/sup\u003e T-cell activity while reducing myeloid-derived suppressor cells (MDSCs), thereby overcoming tumor immune evasion \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our findings support the potential of DKKL1 as a direct therapeutic target, with colocalization analysis revealing shared genetic variation between DKKL1 and CRC, and phenotype-wide association studies showing no significant side effects linked to DKKL1 inhibition. Future research should focus on the long-term effects of DKKL1 inhibition and its integration with immunotherapies, particularly for patients with microsatellite-stable CRC, who typically exhibit poor responses to current treatments.\u003c/p\u003e \u003cp\u003eCHRDL2 (Chordin-like protein 2), a BMP antagonist, plays a key role in modulating BMP signaling, which is essential for maintaining cellular differentiation and tissue homeostasis. Dysregulation of BMP signaling, particularly through CHRDL2-mediated inhibition of BMP-4, can disrupt its tumor-suppressive effects, thereby promoting CRC progression \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Our MR analysis identified CHRDL2 as genetically associated with increased CRC risk (\u003cem\u003eP\u003c/em\u003e_fdr\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with colocalization analysis (PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.7) further supporting its role as a direct therapeutic target. Given its ability to inhibit BMP signaling, targeting CHRDL2 could restore normal cellular differentiation, reducing tumor proliferation and enhancing therapeutic outcomes \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Prior research has linked BMP pathway disruption to increased tumor aggressiveness, although clinical studies specifically addressing CHRDL2 inhibition are limited to preclinical models. Our study strengthens the genetic basis for CHRDL2's involvement in CRC, showing no significant side effects (\u003cem\u003eP\u003c/em\u003e_fdr\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and positioning it as a viable target for therapeutic intervention. Future research should focus on developing CHRDL2-specific inhibitors and evaluating their efficacy in vivo models, potentially combining these with immunotherapy or Wnt pathway inhibitors to maximize therapeutic benefit and overcome resistance mechanisms.\u003c/p\u003e \u003cp\u003eIn contrast to the positive associations of genetically predicted GREM1, DKKL1, and CHRDL2 levels with CRC risk, our study suggests that TMEM132A may have a potential protective role. The TMEM (Transmembrane Protein) family comprises various proteins with largely unexplored functions, particularly in cancer biology. Depending on the specific protein, TMEM members can either promote or inhibit tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. While TMEM132A has been primarily studied in neurobiology, its role in CRC has not been extensively examined until now\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Our study provides exploratory evidence that TMEM132A acts as a protective factor in colorectal cancer (CRC), marking a novel association not previously identified in the literature. The findings suggest that higher TMEM132A expression correlates with reduced tumor growth and invasiveness, indicating a potential tumor-suppressive role through its regulation of cell adhesion and migration. This aligns with prior studies showing high TMEM132A expression correlates with better prognostic outcomes in other cancers, suggesting that its protective effects may extend across malignancies\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. While promising, these results are preliminary and warrant further investigation to validate TMEM132A\u0026rsquo;s role and elucidate its mechanisms, particularly its interactions with critical pathways such as Wnt signaling. Additional studies are necessary to confirm the therapeutic potential of targeting TMEM132A in CRC.\u003c/p\u003e \u003cp\u003eThe integration of PWMR with proteomic analysis provides a powerful approach for uncovering causal relationships between protein expression and CRC risk. By leveraging genetic data, PWMR enables the identification of protein biomarkers that may serve as therapeutic targets, while proteomics offers detailed insights into their functional roles within the tumor microenvironment. Our study demonstrates that combining these methodologies can reveal critical proteins, such as GREM1, DKKL1, CHRDL2, and TMEM132A, that are causally linked to CRC progression.\u003c/p\u003e \u003cp\u003eRecent advances in multi-omics integration have further underscored the importance of combining proteomics with genomics and transcriptomics to obtain a comprehensive view of cancer biology. For example, recent studies utilizing CRISPR-Cas9 screens combined with proteomics have identified key proteins that drive CRC resistance to therapy, providing new opportunities for targeted treatments. The Phe-WAS analysis reveals associations with various phenotypes; however, these potential adverse effects are primarily observed in phenotypes related to tumors. These potential drug targets could offer new directions for drug design, and it is worth noting that the potential for fewer side effects from these drugs may be a significant advantage. Future research should focus on applying high-throughput proteomic platforms to validate the therapeutic potential of these proteins, particularly in the context of combination therapies.\u003c/p\u003e \u003cp\u003eWhile our study provides valuable insights into the roles of GREM1, DKKL1, CHRDL2, and TMEM132A in CRC, several limitations must be acknowledged. First, our findings are based on genetic associations and in silico analyses, which cannot definitively establish causality. The protein-trait relationships require further validation through functional experiments. Potential constraints include population stratification, database limitations, and the complex nature of protein interactions, which may influence the generalizability of our results. Additionally, the reliance on summary-level genetic data and proteomics databases means that individual variability and potential technical biases cannot be fully excluded. These limitations underscore the need for future prospective studies and experimental validation to confirm our computational findings. Further studies should employ comprehensive proteomic screens and functional assays to validate these findings and explore the broader proteomic landscape of CRC and also investigate the potential for combination therapies that target these proteins in conjunction with established treatments like chemotherapy, immunotherapy, and radiation. Such approaches may enhance treatment efficacy and overcome resistance mechanisms, ultimately improving outcomes for patients with advanced CRC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we utilized proteome-wide Mendelian randomization (PWMR) to investigate the roles of GREM1, DKKL1, CHRDL2, and TMEM132A in colorectal cancer (CRC) progression. Our findings suggest that these proteins are intricately involved in key pathways, such as Wnt signaling and BMP antagonism, that drive tumorigenesis, immune evasion, and metastasis in CRC. Specifically, GREM1 and CHRDL2 act as BMP antagonists, promoting tumor growth by disrupting the BMP signaling pathway, while DKKL1 modulates the Wnt/β-catenin pathway, contributing to both tumor progression and immune suppression. TMEM132A, in contrast, appears to function as a tumor suppressor, with its downregulation linked to enhanced tumor invasiveness. These proteins, particularly GREM1 and DKKL1, demonstrate significant potential as therapeutic targets in CRC, offering new avenues for targeted therapies that could overcome the limitations of current treatments. Future research should focus on validating these findings in clinical settings and exploring their integration into combination therapy strategies to enhance treatment efficacy for advanced CRC patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZK Pan: Formal analysis, Investigation, Methodology, Software, Visualization, Writing- original draft; MH Wu: Formal analysis, Investigation, Methodology, Software, Visualization, Writing-review \u0026amp; editing; HS: Formal analysis, Investigation, Software, Supervision; NJ Ni: Methodology, Project administration; QL Geng: Conceptualization, Data curation; JS Ye: Formal analysis, Funding acquisition, Investigation, Methodology, Software, Supervision, Visualization, Writing-review \u0026amp; editing. All authors contributed equally to the manuscript and read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman and animal rights\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo animals/humans were used for studies that are the basis of this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this research are available within the article. The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone. The authors report no involvement in the research by the sponsor that could have influenced the outcome of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the UK Biobank Pharma Proteomics Project and the Finnish database for supplying data on summary statistics for MR analyses. We also want to thank all the researchers who shared these data and the study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original data used in this study are publicly available from the following databases:\u003c/p\u003e\n\u003cp\u003eUK Biobank-SAIGE (Colorectal Cancer GWAS data, https://www.leelabsg.org/resources)\u003c/p\u003e\n\u003cp\u003eFinnGen Database (Proteomics QTL results, https://r10.finngen.fi/)\u003c/p\u003e\n\u003cp\u003eSomaScan Proteomics Database (828 samples, 7,596 proteins)\u003c/p\u003e\n\u003cp\u003eOlink Proteomics Database (619 samples, 2,925 proteins)\u003c/p\u003e\n\u003cp\u003eIceland Decode Database (4,907 aptamers in 35,559 individuals)\u003c/p\u003e\n\u003cp\u003eAll summary-level genetic data are anonymized and publicly accessible. Researchers can request additional details by contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study exclusively utilized publicly available, anonymized genetic summary data from large-scale genomic databases. No individual-level patient data were accessed or processed.\u003c/p\u003e\n\u003cp\u003eKey Ethical Considerations:\u003c/p\u003e\n\u003cp\u003eAll source databases obtained appropriate ethical approvals\u003c/p\u003e\n\u003cp\u003eData were aggregated and anonymized prior to analysis\u003c/p\u003e\n\u003cp\u003eNo direct human or animal experimentation was conducted\u003c/p\u003e\n\u003cp\u003eNo additional institutional review board (IRB) approval was required\u003c/p\u003e\n\u003cp\u003eInformed consent for data collection was obtained by the original data repositories. This study adheres to the principles of the Declaration of Helsinki and follows international guidelines for genetic data research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMorgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut. 2023;72:338\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66:683\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaharati A, Moghbeli M. PI3K/AKT signaling pathway as a critical regulator of epithelial-mesenchymal transition in colorectal tumor cells. Cell Commun Signal. 2023;21:201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoveitypour Z, Panahi F, Vakilian M, et al. Signaling pathways involved in colorectal cancer progression. Cell Biosci. 2019;9:97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFearon ER. Molecular genetics of colorectal cancer. Annu Rev Pathol. 2011;6:479\u0026ndash;507.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiawah S, Venook AP. Targeted therapy for colorectal cancer metastases: A review of current methods of molecularly targeted therapy and the use of tumor biomarkers in the treatment of metastatic colorectal cancer. Cancer. 2019;125:4139\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi M, Zheng X, Niu M, Zhu S, Ge H, Wu K. Combination strategies with PD-1/PD-L1 blockade: current advances and future directions. Mol Cancer. 2022;21:28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Du Y, Xue C, et al. Efficacy and safety of anti-PD-1/PD-L1 therapy in the treatment of advanced colorectal cancer: a meta-analysis. BMC Gastroenterol. 2022;22:431.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriscitiello C, Morganti S, Curigliano G. Antibody-drug conjugates in solid tumors: a look into novel targets. J Hematol Oncol. 2021;14:20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParseghian C, Eluri M, Kopetz S, Raghav K. Mechanisms of resistance to EGFR-targeted therapies in colorectal cancer: more than just genetics. Front Cell Dev Biol. 2023;11:1176657.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou J, Ji Q, Li Q. Resistance to anti-EGFR therapies in metastatic colorectal cancer: underlying mechanisms and reversal strategies. J Exp Clin Cancer Res. 2021;40:328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng N, Liu J, Hai S, Liu Y, Zhao H, Liu W. Role of Post-Translational Modifications in Colorectal Cancer Metastasis. Cancers (Basel). 2024;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe C, Xia L, Gong R et al. Integrating plasma proteome with genome reveals novel protein biomarkers in colorectal cancer. Clin Transl Oncol. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenk D, Zuo C, Kislinger T, Sepiashvili L. Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers. Clin Proteom. 2024;21:6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Z, Houthuijzen JM, Ten Dijke P, Brazil DP. GREM1 signaling in cancer: tumor promotor and suppressor? J Cell Commun Signal. 2023;17:1517\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu D, Zhao D, Wang N, Cai F, Jiang M, Zheng Z. Current status and prospects of GREM1 research in cancer (Review). Mol Clin Oncol. 2023;19:69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Z, Cao Y. Gremlin1: a BMP antagonist with therapeutic potential in Oncology. Invest New Drugs. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi H, Gieniec KA, Wright JA, et al. The Balance of Stromal BMP Signaling Mediated by GREM1 and ISLR Drives Colorectal Carcinogenesis. Gastroenterology. 2021;160:1224\u0026ndash;39. e1230.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu HY, Chen Z, Wang L, et al. Dickkopf-1: A Promising Target for Cancer Immunotherapy. Front Immunol. 2021;12:658097.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMortezaee K. WNT/beta-catenin regulatory roles on PD-(L)1 and immunotherapy responses. Clin Exp Med. 2024;24:15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuto S, Enta A, Maruya Y et al. Wnt/beta-Catenin Signaling and Resistance to Immune Checkpoint Inhibitors: From Non-Small-Cell Lung Cancer to Other Cancers. Biomedicines. 2023;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Yi M, Niu M, Mei Q, Wu K. Myeloid-derived suppressor cells: an emerging target for anticancer immunotherapy. Mol Cancer. 2022;21:184.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, Liu X, Gao H, et al. Overexpression of colorectal cancer oncogene CHRDL2 predicts a poor prognosis. Oncotarget. 2017;8:11489\u0026ndash;506.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmit K, Michiels C. TMEM Proteins in Cancer: A Review. Front Pharmacol. 2018;9:1345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, Wang S, He M, et al. Multidimensional analysis of TMEM132A in pan-cancer: unveiling its potential as a biomarker for treatment response prediction. J Cancer. 2024;15:4386\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez-Pulido L, Ponting CP. TMEM132: an ancient architecture of cohesin and immunoglobulin domains define a new family of neural adhesion molecules. Bioinformatics. 2018;34:721\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Mendelian randomization, plasma proteins, drug target, phenome-wide association study","lastPublishedDoi":"10.21203/rs.3.rs-6304313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6304313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe treatment of advanced or metastatic colorectal cancer (CRC) poses a global challenge. Mendelian Randomization (MR) has been primarily applied for repurposing licensed drugs and uncovering new therapeutic targets.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to systematically identify potential plasma protein targets for CRC using proteome-wide Mendelian randomization and evaluate their safety through phenome-wide association studies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of CRC. The plasma proteins were sourced from the Finland and Iceland decode database, encompassing GWAS data for plasma proteins (Olink-619 samples across 2925 proteins, SomaScan \u0026minus;\u0026thinsp;828 samples across 7596 proteins and Iceland decode database across 4907 proteins). Additionally, GWAS data for CRC were extracted from the UK Biobank-SAIGE database, including 3051 cases and 382,756 controls. Subsequently, colocalization analysis was performed to identify shared causal variants between plasma proteins and CRC. Finally, a phenome-wide association study (Phe-WAS) was conducted to examine the potential adverse effects of druggable proteins for CRC, utilizing the extensive UK Biobank-SAIGE database, encompassing 783 phenotypes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe MR analysis identified GREM1, DKKL1, and CHRDL2 as plasma proteins whose genetically predicted levels were positively associated with CRC risk, whereas TMEM132A exhibited an inverse association with CRC risk (\u003cem\u003eP_\u003c/em\u003efdr\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The colocalization analysis identified these four proteins as shared variation with CRC (PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.7), suggesting that these proteins represent potential direct targets for CRC intervention. Further phenotype-wide association studies showed no significant potential side effects of these targets (\u003cem\u003eP\u003c/em\u003e_fdr\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis proteome-wide Mendelian randomization study offers a comprehensive molecular landscape of CRC, identifying GREM1, DKKL1, CHRDL2, and TMEM132A as potential therapeutic targets. Our research provides a critical foundation for future experimental validation and therapeutic development in colorectal cancer management.\u003c/p\u003e","manuscriptTitle":"Associations of plasma protein levels with risk of colorectal cancer: a proteome-wide Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 06:59:42","doi":"10.21203/rs.3.rs-6304313/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-22T11:40:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-14T10:12:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T12:54:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T05:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77280707228104997489369716255201418773","date":"2025-04-08T00:14:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328389349058526539491436209605483277374","date":"2025-04-07T23:38:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13929868081715683034157955477450431769","date":"2025-04-07T16:53:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T16:18:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333717316010075863947232298754081920309","date":"2025-04-01T14:36:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T14:02:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-26T12:21:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T12:19:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Proteomics","date":"2025-03-25T13:26:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5dd7f528-c89f-49db-a2b6-0f0ca96d8326","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:02:28+00:00","versionOfRecord":{"articleIdentity":"rs-6304313","link":"https://doi.org/10.1186/s12014-025-09545-5","journal":{"identity":"clinical-proteomics","isVorOnly":false,"title":"Clinical Proteomics"},"publishedOn":"2025-06-04 15:57:30","publishedOnDateReadable":"June 4th, 2025"},"versionCreatedAt":"2025-05-05 06:59:42","video":"","vorDoi":"10.1186/s12014-025-09545-5","vorDoiUrl":"https://doi.org/10.1186/s12014-025-09545-5","workflowStages":[]},"version":"v1","identity":"rs-6304313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6304313","identity":"rs-6304313","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}