Proteome-wide Mendelian randomization and colocalization analysis identify therapeutic targets for cutaneous melanoma

Proteome-wide Mendelian randomization and colocalization analysis identify therapeutic targets for cutaneous melanoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proteome-wide Mendelian randomization and colocalization analysis identify therapeutic targets for cutaneous melanoma Wenrong Luo, Di Zhou, He Fang, Lie Zhu, Zheyuan Hu, Xiang Jie, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6171084/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cutaneous melanoma (CM) is the deadliest form of skin cancer. Mendelian randomization (MR) and local analysis have been widely used in the search for therapeutic targets for diseases. Methods: Plasma proteins data were obtained from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) database. The GWAS data for CM were extracted from the Finnish (R10) database. Proteome-wide MR analysis to assess the causal relationship between plasma proteins and CM. Colocalization analysis was used to identify causal variants shared between plasma proteins and CM. A phenotype-wide association study (PheWAS) was used to assess the potential adverse effects of proteins that could treat CM on 2480 phenotypes in the Finnish (R10) database. Results: MR analysis revealed that 5,6-hydroxyindole-2-carboxylate oxidase (TYRP1) (OR: 0.23, 95% CI: 0.12-0.44) and dipeptidase 1(DPEP1) (OR: 0.63, 95% CI: 0.12-0.44) were associated with CM. The evidence from the colocalization analysis supported an inverse association between DPEP1 levels and the risk of CM, but the evidence from the colocalization analysis of TYRP1 was low grade. PheWAS suggested that DPEP1 as a therapeutic target for CM may cause dementia. Conclusions: Our investigation examined the causal relationships between two plasma proteins and CM, providing a comprehensive understanding of potential therapeutic targets. Cutaneous melanoma Plasma proteins Therapeutic targets Mendelian randomization Colocalization analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cutaneous melanoma (CM), the deadliest form of skin cancer, arises from the malignant transformation of melanocytes. 1 The incidence of CM is steadily increasing worldwide and has become an urgent public health problem. It is predicted that there will be 100,640 new CM patients in the United States in 2024, and the number of new patients by state in the United States is shown in Fig. 1 and Supplementary Table 1. 2 Despite significant advances in understanding the genetic underpinnings of melanoma, with the identification of key oncogenes such as CDKN2A, MC1R and N-Ras, 3 – 5 the prognosis for patients with advanced-stage disease remains grim. The development of targeted therapies and immune checkpoint inhibitors has improved outcomes for some patients, yet there is a persistent need for novel therapeutic options, particularly for those with resistant or refractory CM. In light of this, there has been growing interest in employing proteome-wide association studies (PWAS) to delineate the landscape of protein alterations and their genetic determinants in melanoma. Proteins, unlike genes and RNA, execute the vast majority of cellular functions and are the primary targets of therapeutic drugs. Existing evidence has shown that in addition to the gene mutations mentioned above, the occurrence, development and metastasis of melanoma are related to a variety of circulating proteins. For example, premelanosome protein, CD38, monoacylglycerol lipase and tumor necrosis factor-related apoptosis-inducing ligand. 6 – 9 However, the known associations between plasma proteins and CM are derived from observational studies or gene microarrays, which may be prone to confusion and reverse causality. Moreover, ethical constraints make it impossible to conduct randomized controlled trials to explore the causal relationship between these thousands of plasma proteins and CM. Proteome-wide Mendelian randomization (MR) analysis is a novel analytical strategy, that leverages the natural random assignment of genetic variants to infer causal relationships between proteins and disease outcomes, thus bypassing the confounding and reverse causality encountered in traditional observational studies. In this study, we applied genome-wide MR analysis to perform an extensive collection of genetic and proteomic data from a CM cohort to systematically screen for proteins that could serve as potential therapeutic targets for CM. Subsequently, we excluded the confounding bias of linkage disequilibrium (LD) between the target protein and CM by colocalization analysis. Finally, a phenome-wide association study (PheWAS) was used to explore whether there were other adverse reactions when PheWAS was used as therapeutic targets for CM. With the above analysis, we aimed to identify new potential therapeutic targets for CM and provide new therapeutic options for the treatment of CM. Methods Study design and ethics Our workflow used in this study is depicted in Fig. 2 . Our data were sourced from a multitude of aggregated Genome Wide Association Study (GWAS) datasets originating from original studies published in public databases. Informed consent was obtained prior to the publication of these original studies. It is noteworthy that our analysis solely relied upon aggregated statistics, thus obviating the necessity for additional ethical clearance. Proteomic data source Protein quantitative trait loci (pQTLs) were extracted from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) database as the focal exposures for this investigation. The dataset comprises a plasma proteomic profile encompassing 54,219 European participants, featuring a total of 2940 proteins ( https://www.synapse.org/#! Synapse: syn51365303). 10 In this study, pQTLs served as instrumental variables. To ensure the precision and stability of our findings, the following stringent criteria were used: 1. single nucleotide polymorphisms (SNPs) within ± 1 Mb of the gene locus were identified. 2. A genome-wide significance threshold of P < 5×10 − 8 was utilized for SNPs significantly associated with plasma proteins. 3. To guarantee the inclusion of independent SNPs and mitigate the impact of LD, a threshold LD parameter (r 2 ) of 0.001 was set, with a genetic distance of 10,000 kb. 4. An F-value exceeding 10 was applied to exclude weak instrumental variable biases. 11 CM GWAS statistics CM genetic association study data were obtained from the Finnish database, version R10 ( https://r10.finngen.fi/ ). The dataset included data from 3194 patients and 314,193 controls. MR analysis MR analysis serves as a robust methodology for elucidating causal relationships between exposures and outcomes, leveraging genetic variants as instrumental variables. In this study, we employed MR analysis in conjunction with the R software package "Two-Sample-MR" (version 0.5.6) to explore the causal association between plasma proteins (exposure) and CM (outcome). Our analytical approach involved employing the Wald ratio method for proteins detected by a single SNP and the inverse variance weighting (IVW) method for proteins associated with multiple SNPs. 12 To assess the heterogeneity of individual causal effects, we utilized Cochrane's Q statistic, while MR-Egger's intercept term was used to evaluate horizontal pleiotropy. 13 Results exhibiting a significance level below 0.05 indicated the presence of heterogeneity or pleiotropy and were thus discarded. Furthermore, to enhance the precision of our findings, the false discovery rate (FDR) was used to control for multiple comparisons, ensuring an FDR < 0.05 threshold. 14 Colocalization analysis Colocalization analysis is as a pivotal methodology aimed at discerning the presence of shared causal genetic variations between exposure and outcome. This analytical approach effectively mitigates the impact of LD or other confounding factors on the observed outcome. For proteins exhibiting positive MR analysis results, colocalization analysis was conducted utilizing the coloc R package to ascertain whether the observed associations between identified proteins and CM stemmed from LD effects. Prior to initiating the colocalization analysis, we formulated five hypotheses: H0 indicates that the SNPs within the selected loci were not related to either protein A or disease B; H1 indicates that the SNPs within the selected loci were associated with protein A, but not disease B; H2 indicates that the SNPs within the selected loci were associated with disease B but not with protein A; H3 indicates that the SNPs in the selected loci were related to either protein A or disease B but that protein A and disease B had different causal variants. H4 indicates the SNPs within the selected loci were associated with protein A and disease B at the same time, and with the same causal mutation. 14 Subsequently, the posterior probability (PP) for each hypothesis was calculated, and the evidence of colocalization was categorized as low (PPH4 ≤ 0.5), medium (0.5 < PPH4 < 0.8), or high (PPH4 ≥ 0.8) based on the PPH4 value. PheWAS PheWAS typically investigate potential adverse effects linked to drug targets through an examination of the interplay between SNPs or phenotypes and various other phenotypic traits, often termed reverse GWAS. In the current investigation, the aforementioned MR-positive plasma proteins were designated as the exposure variable, while the outcomes comprised phenotypic data sourced from the Finnish database, version R10, encompassing a breadth of 2408 phenotypes. This comprehensive dataset was used for comprehensive spectrum MR analysis. Statistical significance was defined as an FDR less than 0.05. Results Plasma proteins related to CM A total of 1927 plasma proteins were ultimately included in the MR analysis after screening using the most stringent threshold criteria. Details of the associated plasma proteins are provided in Supplementary Table 2. Two-sample MR analysis was performed by IVW or Wald ratio methods using SNPs corresponding to these 1927 plasma proteins as exposure variables and CM as outcomes. The FDR was used to correct the MR analysis results. The results showed that 128 plasma proteins were strongly associated with CM (Supplementary Table 3). After FDR integration, only two plasma proteins were negatively correlated with CM (Fig. 3 ), and no protein showed a positive correlation. Among them, 5, 6-dihydroxyindole-2-carboxylate oxidase (TRYP1) (OR: 0.23, 95% CI: 0.12–0.44, FDR: 0.02) and dipeptidase 1 (DPEP1) (OR: 0.63, 95% CI: 0.50–0.78, FDR: 0.02) were negatively correlated with CM (Fig. 4 ). Candidate therapeutic targets for CM identified by Colocalization To further substantiate the potential association with CM, we conducted a colocalization analysis focusing on the two proteins identified as positively associated in the MR analysis (Supplementary Table 4 and Supplementary Table 5). The results demonstrated a significant colocalization effect of rs12447902 in DPEP1 and CM (PPH4 = 1), confirming the presence of substantial colocalization. As a consequence, we categorized DPEP1 as a tier 1 target. The most significant colocalization effect observed between TRYP1 and CM was from rs10809826, but that of PPH4 was only 0.14, suggesting a relatively low-level of evidence for colocalization. We therefore listed TRYP1 as a Tier 3 target. The results of the colocalization analysis are shown in Fig. 5 . Hence, considering the strength of the colocalization evidence, DPEP1 may be a more efficacious therapeutic target for treating CM than TRYP1 PheWAS for two plasma proteins linked to CM To assess whether DPEP1 and TYRP1 have potential roles in other phenotypes, we performed PheWAS with DPEP1 and TYRP1 as exposures and 2408 phenotypes from the Finnish database (version R10) as outcomes. (Fig. 6 ) DPEP1 was found to be associated with dementia and other non-melanoma skin cancers, while TYRP1 was associated with actinic keratosis. (Supplementary Table 6 and Supplementary Table 7) Discussion CM is characterized by the aberrant transformation and unbridled proliferation of melanocytes within the basal layer of the epidermis, culminating in the formation of a malignant skin tumor. 15 CM susceptibility is influenced by various risk factors, including but not limited to sun exposure, indoor tanning practices, familial history of the disease, and the presence of numerous nevi. While CM can afflict individuals across all age cohorts, its incidence notably escalates within the elderly people. 16 CM poses a greater threat than to other types of skin malignancies, including squamous cell carcinoma and basal cell carcinoma, primarily due to its propensity for metastasis. 17 CM has a propensity for metastasis, initially through lymphatic dissemination in its early stages and subsequently through hematogenous spread as the disease progresses. Among the organs targeted by metastatic melanoma, the lungs are most frequently affected. Notably, early-stage CM (stages I-II) has a favorable prognosis and is often amenable to complete surgical resection with a commendable five-year survival rate of approximately 99.4%. However, the outlook has dramatically shifted for advanced-stage disease (stages III-IV), with markedly diminished survival rates; stage III CM has a five-year survival rate of only 68%, while stage IV CM has a five-year survival rate less than 30%. 18 The advent of numerous targeted and immune-based therapeutics has revolutionized the treatment landscape for unresectable cutaneous melanoma (CM), leading to substantial enhancements in patient survival rates. Among these advancements, traditional targeted therapies predominantly focus on inhibiting the BRAF and MEK pathways, with notable drugs such as vemurafenib, dabrafenib, and trametinib playing pivotal roles in clinical management. 19 – 21 Nevertheless, while targeted therapies often yield favorable therapeutic outcomes initially, prolonged treatment durations commonly precipitate the emergence of drug resistance, presenting a formidable clinical obstacle. Hence, there is an urgent need to identify a stable, safe, and efficacious novel target for the management of CM. 1 MR analysis serves as a methodological approach utilizing genetic variants correlated with exposure to evaluate potential causal links between the exposure and subsequent outcomes. The fundamental objective is to mitigate biases arising from confounding factors, particularly reverse causation, inherent within epidemiological inquiries. In this study, two-sample MR analysis was performed to identify potential therapeutic targets for CM. DPEP1 and TRYP1 were found to be negatively correlated with CM in this study, and are expected to be potential therapeutic targets for CM. The gene encoding dipeptidase 1 is localized to the q24 band of human chromosome 16. 22 DPEP1 is a zinc-dependent metalloproteinase responsible for the degradation of excess peptides and antibiotics, such as thienamycin, penem, and carbapenem derivatives. Additionally, it plays a crucial role in the metabolism of glutathione and leukotrienes. 23 DPEP1is associated with a variety of tumors. DPEP1, which has been identified as a reliable marker for high-grade intraepithelial neoplasia and colorectal cancer, holds promise for early tumor detection and prognostic categorization. 24 , 25 DPEP1 has the potential to enhance the detection efficacy for hepatocellular carcinoma (HCC) when it is combined with alongside alpha-fetoprotein (AFP). The diagnostic accuracy of DPEP1 alone in diagnosing HCC was determined to be 0.75 (95% CI 0.68–0.83), with corresponding sensitivity and specificity values of 78.79% and 62.5%, respectively. However, when DPEP1 was combined with AFP, its diagnostic accuracy increased to 0.82 (95% CI 0.75–0.89). For AFP-negative HCC patients, the diagnostic accuracy of DPEP1 was 0.79 (95%CI 0.71–0.88), with sensitivity and specificity rates of 73.68% and 68.75%, respectively. Moreover, DPEP1 exhibited notable efficacy in discriminating small HCC lesions (with a tumor diameter < 3 cm), achieving an accuracy of 0.76 (95% CI 0.64–0.88). 26 Additionally, DPEP1 has been identified as the primary adhesion receptor for neutrophils and plays a pivotal role in orchestrating the recruitment of white blood cells during pulmonary inflammation. Consequently, its involvement exacerbates the tissue damage incurred during endotoxemia. 27 5,6-dihydroxyindole-2-carboxylate oxidase is specifically expressed in melanocytes and melanoma cells. 28 5,6-dihydroxyindole-2-carboxylate oxidase, also recognized as tyrosinase-related protein 1 (TYRP1) or gp75 glycoprotein in certain studies, serves as a pivotal enzyme within the melanin biosynthesis pathway. TYRP1 facilitates the oxidation of the melanin precursor tyrosine, thereby promoting melanin synthesis. 29 In this enzymatic process, TYRP1 catalyzes the oxidation of tyrosine to yield 5,6-dihydroxyindole-2-carboxylic acid, a critical intermediate within the melanin biosynthesis pathway. 30 Melanin, a natural pigment, contributes significantly to the pigmentation of skin, hair, and eyes. The enzymatic activity of TYRP1 critically modulates melanin synthesis, consequently influencing the hue of these tissues. Genetic and environmental determinants can modulate TYRP1 activity levels, underscoring its pivotal regulatory role in the genesis of skin and hair pigmentation. In an open-label, dose-escalation, phase I study comprising 27 patients with advanced melanoma who experienced tumor progression following or during at least one prior therapy, IMC-20D7S, a recombinant human IgG1 monoclonal antibody targeting TYRP1, was intravenously administered every 2 or 3 weeks. The findings demonstrated a complete response in one patient, with a disease control rate (stable disease or better) of 41%. 31 The aforementioned investigations underscore TYRP1 as a promising therapeutic avenue for CM; however, the need persists for expanded sample sizes and prolonged follow-up to comprehensively assess its safety and efficacy. This result aligns with the encouraging outcomes of our MR analysis, confirming the reliability of our study findings. Nevertheless, upon subsequent colocalization analysis, we observed limited evidence supporting the association between TYRP1 and CM, indicating that while TYRP1 is a therapeutic target for CM, its relationship with the condition is not governed by colocalization. Ultimately, we conducted a PheWAS of DPEP1 and TYRP1 to explore potential additional side effects of these plasma proteins when they air considered as prospective therapeutic targets for CM (Supplementary Table 6 and Supplementary Table 7). We have included FDR adjustments to improve the precision of our results. In addition to being a potential therapeutic target for CM, DPEP1 was also found to be a candidate target for the treatment of other non-melanoma tumors (OR = 0.767, 95% CI: 0.693–0.849, FDR < 0.01), such as basal cell carcinoma. Nevertheless, it is imperative to acknowledge the potential association of DPEP1 with dementia (OR = 1.161, 95% CI: 1.087–1.240, FDR < 0.05), including Alzheimer’s disease. Multiple investigators have conducted proteomic analyses of cerebrospinal fluid samples obtained from individuals with Parkinson's disease exhibiting cognitive decline, revealing notable variations in the expression pattern of DPEP1 in comparison to that observed in healthy control subjects. 32 However, significant differences exist in protein composition and proportion between cerebrospinal fluid and blood, and the proteins we identified were from blood. Consequently, large-scale clinical trials are necessary to validate our findings. Nonetheless, this should not deter us from being vigilant about potential side effects in elderly people, particularly those with dementia, during the drug development process. The outcomes of PheWAS pertaining to TYRP1 indicate a strong association between TYRP1 and actinic keratosis (AK). AK represents a chronic cutaneous condition characterized by the presence of clinical and subclinical lesions predominantly observed on sun-exposed areas of the head, neck, and limbs. 33 AK is considered to be a precancerous lesion of keratinocyte carcinoma. 34 AK and TYRP1 could be interconnected through ultraviolet-induced alterations in melanin within the skin. The strengths of our study lie in its comprehensive approach, including the use of a wide array of plasma proteins as exposures, the use of a substantial number of CM cases as outcomes, the validation of findings through two independent datasets, and the supplementary analysis of colocalization. Employing an MR analysis mitigates biases arising from confounding and reverse causality, thus enhancing the robustness of causal inference. Furthermore, we conducted a PheWAS to elucidate the potential roles of the target proteins. Nevertheless, several limitations should be acknowledged. First, our analysis predominantly drew from European populations, constraining the generalizability of our conclusions. Second, while our focus was on the impact of plasma proteins on CM, the influence of environmental factors, notably ultraviolet radiation, cannot be overlooked. Thus, there is a need to develop a risk prediction model that incorporates environmental exposure factors alongside plasma proteins to better understand CM etiology. Finally, the absence of cell and in vivo experiments conducted under authentic conditions impedes our ability to undertake randomized controlled trials for result validation. Conclusion This proteome-wide MR and colocalization analysis revealed two plasma proteins, DEPE1 and TYRP1, were highly expressed and causally associated with CM, especially DEPE1. These findings provide guidance and new directions for targeted therapy. We believe that these findings will be useful in the treatment of CM and may also help to reduce the individual and societal burden caused by CM. Declarations Competing interests: The authors declare no potential conflicts of interest. Funding information: National Natural Science Foundation of China. Grant/Award Number 32071186. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and materials: All data generated or analysed during this study are included in this published article and its supplementary information files. Author Contribution W.L. was responsible for the data collection and writing of the first draft of the manuscript, D.Z. and H.F. for the data analysis and manuscript proofreading, and L.Z., Z.H. X.J. participated in the interpretation of the data. X.Z. provided methodological guidance for the study. M.W. developed the concept of the manuscript, guided the analysis process throughout, and revised the first draft several times. All the authors have read and approved the final version of the manuscript. Acknowledgements: Not applicable. References Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402:485–502. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12–49. Helgadottir H, Olsson H, Tucker MA, Yang XR, Höiom V, Goldstein AM. Phenocopies in melanoma-prone families with germ-line CDKN2A mutations. Genet Med. 2018;20:1087–90. Landi MT, Bauer J, Pfeiffer RM, Elder DE, Hulley B, Minghetti P, Calista D, Kanetsky PA, Pinkel D, Bastian BC. MC1R germline variants confer risk for BRAF-mutant melanoma. Science. 2006;313:521–2. Delmas V, Beermann F, Martinozzi S, Carreira S, Ackermann J, Kumasaka M, Denat L, Goodall J, Luciani F, Viros A, Demirkan N, Bastian BC, et al. Beta-catenin induces immortalization of melanocytes by suppressing p16INK4a expression and cooperates with N-Ras in melanoma development. Genes Dev. 2007;21:2923–35. Zhang S, Chen K, Liu H, Jing C, Zhang X, Qu C, Yu S. PMEL as a Prognostic Biomarker and Negatively Associated With Immune Infiltration in Skin Cutaneous Melanoma (SKCM). J Immunother. 2021;44:214–23. Ben Baruch B, Blacher E, Mantsur E, Schwartz H, Vaknine H, Erez N, Stein R. Stromal CD38 regulates outgrowth of primary melanoma and generation of spontaneous metastasis. Oncotarget. 2018;9:31797–811. Baba Y, Funakoshi T, Mori M, Emoto K, Masugi Y, Ekmekcioglu S, Amagai M, Tanese K. Expression of monoacylglycerol lipase as a marker of tumour invasion and progression in malignant melanoma. J Eur Acad Dermatol Venereol. 2017;31:2038–45. Xue L, Zhang W, Ju Y, Xu X, Bo H, Zhong X, Hu Z, Zheng C, Fang B, Tang S. TNFSF10, an autophagy related gene, was a prognostic and immune infiltration marker in skin cutaneous melanoma. J Cancer. 2023;14:2417–30. Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622:329–38. Wang Q, Song YX, Wu XD, Luo YG, Miao R, Yu XM, Guo X, Wu DZ, Bao R, Mi WD, Cao JB. Gut microbiota and cognitive performance: A bidirectional two-sample Mendelian randomization. J Affect Disord. 2024;353:38–47. Zhang W, Ma L, Zhou Q, Gu T, Zhang X, Xing H. Therapeutic Targets for Diabetic Kidney Disease: Proteome-Wide Mendelian Randomization and Colocalization Analyses. Diabetes. 2024;73:618 – 27. Chen J, Xu F, Ruan X, Sun J, Zhang Y, Zhang H, Zhao J, Zheng J, Larsson SC, Wang X, Li X, Yuan S. Therapeutic targets for inflammatory bowel disease: proteome-wide Mendelian randomization and colocalization analyses. EBioMedicine. 2023;89:104494. Yuan S, Jiang F, Chen J, Lebwohl B, Green PHR, Leffler D, Larsson SC, Li X, Ludvigsson JF. Phenome-wide Mendelian randomization analysis reveals multiple health comorbidities of coeliac disease. EBioMedicine. 2024;101:105033. Miller AJ, Mihm MC Jr.. Melanoma N Engl J Med. 2006;355:51–65. Di Raimondo C, Lozzi F, Di Domenico PP, Campione E, Bianchi L. The Diagnosis and Management of Cutaneous Metastases from Melanoma. Int J Mol Sci 2023;24. Wagstaff W, Mwamba RN, Grullon K, Armstrong M, Zhao P, Hendren-Santiago B, Qin KH, Li AJ, Hu DA, Youssef A, Reid RR, Luu HH, et al. Melanoma: Molecular genetics, metastasis, targeted therapies, immunotherapies, and therapeutic resistance. Genes Dis. 2022;9:1608–23. Lazaroff J, Bolotin D. Targeted Therapy and Immunotherapy in Melanoma. Dermatol Clin. 2023;41:65–77. Ascierto PA, Stroyakovskiy D, Gogas H, Robert C, Lewis K, Protsenko S, Pereira RP, Eigentler T, Rutkowski P, Demidov L, Zhukova N, Schachter J, et al. Overall survival with first-line atezolizumab in combination with vemurafenib and cobimetinib in BRAF(V600) mutation-positive advanced melanoma (IMspire150): second interim analysis of a multicentre, randomised, phase 3 study. Lancet Oncol. 2023;24:33–44. Dayimu A, Gupta A, Matin RN, Nobes J, Board R, Payne M, Rao A, Fusi A, Danson S, Eccles B, Carser J, Brown CO, et al. A randomised phase 2 study of intermittent versus continuous dosing of dabrafenib plus trametinib in patients with BRAF(V600) mutant advanced melanoma (INTERIM). Eur J Cancer. 2024;196:113455. Bai X, Shaheen A, Grieco C, d'Arienzo PD, Mina F, Czapla JA, Lawless AR, Bongiovanni E, Santaniello U, Zappi H, Dulak D, Williamson A, et al. Dabrafenib plus trametinib versus anti-PD-1 monotherapy as adjuvant therapy in BRAF V600-mutant stage III melanoma after definitive surgery: a multicenter, retrospective cohort study. EClinicalMedicine. 2023;65:102290. Austruy E, Jeanpierre C, Antignac C, Whitmore SA, Van Cong N, Bernheim A, Callen DF, Junien C. Physical and genetic mapping of the dipeptidase gene DPEP1 to 16q24.3. Genomics. 1993;15:684–7. Nakagawa H, Inazawa J, Inoue K, Misawa S, Kashima K, Adachi H, Nakazato H, Abe T. Assignment of the human renal dipeptidase gene (DPEP1) to band q24 of chromosome 16. Cytogenet Cell Genet. 1992;59:258–60. McIver CM, Lloyd JM, Hewett PJ, Hardingham JE. Dipeptidase 1: a candidate tumor-specific molecular marker in colorectal carcinoma. Cancer Lett. 2004;209:67–74. Eisenach PA, Soeth E, Röder C, Klöppel G, Tepel J, Kalthoff H, Sipos B. Dipeptidase 1 (DPEP1) is a marker for the transition from low-grade to high-grade intraepithelial neoplasia and an adverse prognostic factor in colorectal cancer. Br J Cancer. 2013;109:694–703. Li X, Li Y, Li X, Jiang LN, Zhu L, Lu FM, Zhao JM. [A preliminary discussion on carnosine dipeptidase 1 as a potential novel biomarker for the diagnostic and prognostic evaluation of hepatocellular carcinoma]. Zhonghua Gan Zang Bing Za Zhi. 2023;31:627–33. Choudhury SR, Babes L, Rahn JJ, Ahn BY, Goring KR, King JC, Lau A, Petri B, Hao X, Chojnacki AK, Thanabalasuriar A, McAvoy EF et al. Dipeptidase-1 Is an Adhesion Receptor for Neutrophil Recruitment in Lungs and Liver. Cell. 2019;178:1205-21.e17. Ghanem G, Fabrice J. Tyrosinase related protein 1 (TYRP1/gp75) in human cutaneous melanoma. Mol Oncol. 2011;5:150–5. Gautron A, Migault M, Bachelot L, Corre S, Galibert MD, Gilot D. Human TYRP1: Two functions for a single gene? Pigment Cell Melanoma Res. 2021;34:836–52. Olivares C, Jiménez-Cervantes C, Lozano JA, Solano F, García-Borrón JC. The 5,6-dihydroxyindole-2-carboxylic acid (DHICA) oxidase activity of human tyrosinase. Biochem J. 2001;354:131–9. Khalil DN, Postow MA, Ibrahim N, Ludwig DL, Cosaert J, Kambhampati SR, Tang S, Grebennik D, Kauh JS, Lenz HJ, Flaherty KT, Hodi FS, et al. An Open-Label, Dose-Escalation Phase I Study of Anti-TYRP1 Monoclonal Antibody IMC-20D7S for Patients with Relapsed or Refractory Melanoma. Clin Cancer Res. 2016;22:5204–10. Yin GN, Lee HW, Cho JY, Suk K. Neuronal pentraxin receptor in cerebrospinal fluid as a potential biomarker for neurodegenerative diseases. Brain Res. 2009;1265:158–70. Del Regno L, Catapano S, Di Stefani A, Cappilli S, Peris K. A Review of Existing Therapies for Actinic Keratosis: Current Status and Future Directions. Am J Clin Dermatol. 2022;23:339–52. Eisen DB, Asgari MM, Bennett DD, Connolly SM, Dellavalle RP, Freeman EE, Goldenberg G, Leffell DJ, Peschin S, Sligh JE, Wu PA, Frazer-Green L, et al. Guidelines of care for the management of actinic keratosis: Executive summary. J Am Acad Dermatol. 2021;85:945–55. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6171084","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425903971,"identity":"186bf29a-525a-4c59-9182-01a293685911","order_by":0,"name":"Wenrong Luo","email":"","orcid":"","institution":"Changzheng Hospital, the Second Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenrong","middleName":"","lastName":"Luo","suffix":""},{"id":425903972,"identity":"a2999e1e-29f3-4304-b123-750c53aa46d7","order_by":1,"name":"Di Zhou","email":"","orcid":"","institution":"Changzheng Hospital, the Second Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Zhou","suffix":""},{"id":425903973,"identity":"32d9dd5f-b94b-45b8-a991-116a8f72da80","order_by":2,"name":"He Fang","email":"","orcid":"","institution":"Changhai Hospital, the First Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Fang","suffix":""},{"id":425903974,"identity":"dfa687be-385b-4bef-a463-8473b3833509","order_by":3,"name":"Lie Zhu","email":"","orcid":"","institution":"Changzheng Hospital, the Second Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lie","middleName":"","lastName":"Zhu","suffix":""},{"id":425903975,"identity":"cb443b08-90b1-42c1-a311-0359d3e0475b","order_by":4,"name":"Zheyuan Hu","email":"","orcid":"","institution":"Changzheng Hospital, the Second Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheyuan","middleName":"","lastName":"Hu","suffix":""},{"id":425903976,"identity":"22f509c8-2e86-4416-b265-49348d0d8c6c","order_by":5,"name":"Xiang Jie","email":"","orcid":"","institution":"Changzheng Hospital, the Second Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Jie","suffix":""},{"id":425903977,"identity":"553fdbfc-80cd-497c-ad03-315b5b840913","order_by":6,"name":"Xiaohai Zhu","email":"","orcid":"","institution":"Changzheng Hospital, the Second Affiliated Hospital of Naval Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohai","middleName":"","lastName":"Zhu","suffix":""},{"id":425903978,"identity":"30e9794b-b99f-437f-969e-54c31a74076e","order_by":7,"name":"Minjuan Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYNACNgY5BmZStRiTriWxgWjF8tNOp0l8KDucPr+d9+AHhhqbaIJaDG7nbpOcce5w7obDfMkSDMfScglaZyCdu02atw2ohZnHQIKx4TBhLfKzgVr+th1Ol2/mMf5BlBYGoMOkGdsOJzAc5jEjzhagXzZb9pxLN9wA1GKRQIxfgA7beONHmbW8fP8Z4xsfamyIcBgKSCBN+SgYBaNgFIwCXAAAeII7tBxWnJ4AAAAASUVORK5CYII=","orcid":"","institution":"Naval Military Medical University","correspondingAuthor":true,"prefix":"","firstName":"Minjuan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-06 13:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6171084/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6171084/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78258947,"identity":"fdb80d86-e666-48ea-b562-6c8c86a39ecb","added_by":"auto","created_at":"2025-03-11 11:13:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":222882,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of new cutaneous melanoma patients in U.S. states in 2024.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6171084/v1/2850c20a2534b747fd621d71.png"},{"id":78257490,"identity":"dd6379b2-a449-452d-ac5d-5aa10197d728","added_by":"auto","created_at":"2025-03-11 10:57:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283492,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of Proteome-wide Mendelian randomization and colocalization analysis.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6171084/v1/ad7fe5e774c76546d490b9ec.png"},{"id":78257498,"identity":"6d7cef64-ded6-4179-a5e3-d4a2a601179f","added_by":"auto","created_at":"2025-03-11 10:57:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":323338,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of the results of Mendelian randomization analysis.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6171084/v1/ee1f2b3f282d9937072020dd.png"},{"id":78257488,"identity":"55190a86-cdb2-470d-a137-5b8e3d64c13f","added_by":"auto","created_at":"2025-03-11 10:57:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83355,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of Mendelian randomization analysis.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6171084/v1/5a8496b46c921f2a51bca27b.png"},{"id":78257494,"identity":"11a3caf6-c6c1-4209-892e-f2c024f5d2fe","added_by":"auto","created_at":"2025-03-11 10:57:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1138445,"visible":true,"origin":"","legend":"\u003cp\u003eResults of colocalization analysis of proteins with positive results of Mendelian randomization analysis\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6171084/v1/8f23f97ba99b7cdd90cf6155.png"},{"id":78257502,"identity":"15c32a0e-dafd-4036-a8ae-78a44d3a1d36","added_by":"auto","created_at":"2025-03-11 10:57:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1009707,"visible":true,"origin":"","legend":"\u003cp\u003eResults of phenome-wide association study of proteins with positive results of Mendelian randomization analysis\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6171084/v1/0872e00931bab1cd0a705c95.png"},{"id":84473726,"identity":"f28515e8-70d6-471b-8b98-a5ebe5e519b4","added_by":"auto","created_at":"2025-06-12 10:54:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3467958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6171084/v1/ab971655-178d-4df6-a5ac-fc7cbfb62dca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proteome-wide Mendelian randomization and colocalization analysis identify therapeutic targets for cutaneous melanoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCutaneous melanoma (CM), the deadliest form of skin cancer, arises from the malignant transformation of melanocytes.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The incidence of CM is steadily increasing worldwide and has become an urgent public health problem. It is predicted that there will be 100,640 new CM patients in the United States in 2024, and the number of new patients by state in the United States is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Despite significant advances in understanding the genetic underpinnings of melanoma, with the identification of key oncogenes such as CDKN2A, MC1R and N-Ras, \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e the prognosis for patients with advanced-stage disease remains grim. The development of targeted therapies and immune checkpoint inhibitors has improved outcomes for some patients, yet there is a persistent need for novel therapeutic options, particularly for those with resistant or refractory CM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn light of this, there has been growing interest in employing proteome-wide association studies (PWAS) to delineate the landscape of protein alterations and their genetic determinants in melanoma. Proteins, unlike genes and RNA, execute the vast majority of cellular functions and are the primary targets of therapeutic drugs. Existing evidence has shown that in addition to the gene mutations mentioned above, the occurrence, development and metastasis of melanoma are related to a variety of circulating proteins. For example, premelanosome protein, CD38, monoacylglycerol lipase and tumor necrosis factor-related apoptosis-inducing ligand.\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, the known associations between plasma proteins and CM are derived from observational studies or gene microarrays, which may be prone to confusion and reverse causality. Moreover, ethical constraints make it impossible to conduct randomized controlled trials to explore the causal relationship between these thousands of plasma proteins and CM. Proteome-wide Mendelian randomization (MR) analysis is a novel analytical strategy, that leverages the natural random assignment of genetic variants to infer causal relationships between proteins and disease outcomes, thus bypassing the confounding and reverse causality encountered in traditional observational studies.\u003c/p\u003e \u003cp\u003eIn this study, we applied genome-wide MR analysis to perform an extensive collection of genetic and proteomic data from a CM cohort to systematically screen for proteins that could serve as potential therapeutic targets for CM. Subsequently, we excluded the confounding bias of linkage disequilibrium (LD) between the target protein and CM by colocalization analysis. Finally, a phenome-wide association study (PheWAS) was used to explore whether there were other adverse reactions when PheWAS was used as therapeutic targets for CM. With the above analysis, we aimed to identify new potential therapeutic targets for CM and provide new therapeutic options for the treatment of CM.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy design and ethics\u003c/p\u003e\u003cp\u003eOur workflow used in this study is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Our data were sourced from a multitude of aggregated Genome Wide Association Study (GWAS) datasets originating from original studies published in public databases. Informed consent was obtained prior to the publication of these original studies. It is noteworthy that our analysis solely relied upon aggregated statistics, thus obviating the necessity for additional ethical clearance.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eProteomic data source\u003c/p\u003e\u003cp\u003eProtein quantitative trait loci (pQTLs) were extracted from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) database as the focal exposures for this investigation. The dataset comprises a plasma proteomic profile encompassing 54,219 European participants, featuring a total of 2940 proteins (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.synapse.org/#!\u003c/span\u003e\u003cspan address=\"https://www.synapse.org/#!\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Synapse: syn51365303).\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In this study, pQTLs served as instrumental variables. To ensure the precision and stability of our findings, the following stringent criteria were used: 1. single nucleotide polymorphisms (SNPs) within ± 1 Mb of the gene locus were identified. 2. A genome-wide significance threshold of P \u0026lt; 5×10\u003csup\u003e− 8\u003c/sup\u003e was utilized for SNPs significantly associated with plasma proteins. 3. To guarantee the inclusion of independent SNPs and mitigate the impact of LD, a threshold LD parameter (r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) of 0.001 was set, with a genetic distance of 10,000 kb. 4. An F-value exceeding 10 was applied to exclude weak instrumental variable biases.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCM GWAS statistics\u003c/p\u003e\u003cp\u003eCM genetic association study data were obtained from the Finnish database, version R10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r10.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://r10.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The dataset included data from 3194 patients and 314,193 controls.\u003c/p\u003e\u003cp\u003eMR analysis\u003c/p\u003e\u003cp\u003eMR analysis serves as a robust methodology for elucidating causal relationships between exposures and outcomes, leveraging genetic variants as instrumental variables. In this study, we employed MR analysis in conjunction with the R software package \"Two-Sample-MR\" (version 0.5.6) to explore the causal association between plasma proteins (exposure) and CM (outcome). Our analytical approach involved employing the Wald ratio method for proteins detected by a single SNP and the inverse variance weighting (IVW) method for proteins associated with multiple SNPs.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e To assess the heterogeneity of individual causal effects, we utilized Cochrane's Q statistic, while MR-Egger's intercept term was used to evaluate horizontal pleiotropy.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Results exhibiting a significance level below 0.05 indicated the presence of heterogeneity or pleiotropy and were thus discarded. Furthermore, to enhance the precision of our findings, the false discovery rate (FDR) was used to control for multiple comparisons, ensuring an FDR \u0026lt; 0.05 threshold.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eColocalization analysis\u003c/p\u003e\u003cp\u003eColocalization analysis is as a pivotal methodology aimed at discerning the presence of shared causal genetic variations between exposure and outcome. This analytical approach effectively mitigates the impact of LD or other confounding factors on the observed outcome. For proteins exhibiting positive MR analysis results, colocalization analysis was conducted utilizing the coloc R package to ascertain whether the observed associations between identified proteins and CM stemmed from LD effects. Prior to initiating the colocalization analysis, we formulated five hypotheses: H0 indicates that the SNPs within the selected loci were not related to either protein A or disease B; H1 indicates that the SNPs within the selected loci were associated with protein A, but not disease B; H2 indicates that the SNPs within the selected loci were associated with disease B but not with protein A; H3 indicates that the SNPs in the selected loci were related to either protein A or disease B but that protein A and disease B had different causal variants. H4 indicates the SNPs within the selected loci were associated with protein A and disease B at the same time, and with the same causal mutation.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Subsequently, the posterior probability (PP) for each hypothesis was calculated, and the evidence of colocalization was categorized as low (PPH4 ≤ 0.5), medium (0.5 \u0026lt; PPH4 \u0026lt; 0.8), or high (PPH4 ≥ 0.8) based on the PPH4 value.\u003c/p\u003e\u003cp\u003ePheWAS\u003c/p\u003e\u003cp\u003ePheWAS typically investigate potential adverse effects linked to drug targets through an examination of the interplay between SNPs or phenotypes and various other phenotypic traits, often termed reverse GWAS. In the current investigation, the aforementioned MR-positive plasma proteins were designated as the exposure variable, while the outcomes comprised phenotypic data sourced from the Finnish database, version R10, encompassing a breadth of 2408 phenotypes. This comprehensive dataset was used for comprehensive spectrum MR analysis. Statistical significance was defined as an FDR less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePlasma proteins related to CM\u003c/p\u003e \u003cp\u003eA total of 1927 plasma proteins were ultimately included in the MR analysis after screening using the most stringent threshold criteria. Details of the associated plasma proteins are provided in Supplementary Table\u0026nbsp;2. Two-sample MR analysis was performed by IVW or Wald ratio methods using SNPs corresponding to these 1927 plasma proteins as exposure variables and CM as outcomes. The FDR was used to correct the MR analysis results. The results showed that 128 plasma proteins were strongly associated with CM (Supplementary Table\u0026nbsp;3). After FDR integration, only two plasma proteins were negatively correlated with CM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and no protein showed a positive correlation. Among them, 5, 6-dihydroxyindole-2-carboxylate oxidase (TRYP1) (OR: 0.23, 95% CI: 0.12\u0026ndash;0.44, FDR: 0.02) and dipeptidase 1 (DPEP1) (OR: 0.63, 95% CI: 0.50\u0026ndash;0.78, FDR: 0.02) were negatively correlated with CM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCandidate therapeutic targets for CM identified by Colocalization\u003c/p\u003e \u003cp\u003eTo further substantiate the potential association with CM, we conducted a colocalization analysis focusing on the two proteins identified as positively associated in the MR analysis (Supplementary Table\u0026nbsp;4 and Supplementary Table\u0026nbsp;5). The results demonstrated a significant colocalization effect of rs12447902 in DPEP1 and CM (PPH4\u0026thinsp;=\u0026thinsp;1), confirming the presence of substantial colocalization. As a consequence, we categorized DPEP1 as a tier 1 target. The most significant colocalization effect observed between TRYP1 and CM was from rs10809826, but that of PPH4 was only 0.14, suggesting a relatively low-level of evidence for colocalization. We therefore listed TRYP1 as a Tier 3 target. The results of the colocalization analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Hence, considering the strength of the colocalization evidence, DPEP1 may be a more efficacious therapeutic target for treating CM than TRYP1\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePheWAS for two plasma proteins linked to CM\u003c/p\u003e \u003cp\u003eTo assess whether DPEP1 and TYRP1 have potential roles in other phenotypes, we performed PheWAS with DPEP1 and TYRP1 as exposures and 2408 phenotypes from the Finnish database (version R10) as outcomes. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) DPEP1 was found to be associated with dementia and other non-melanoma skin cancers, while TYRP1 was associated with actinic keratosis. (Supplementary Table\u0026nbsp;6 and Supplementary Table\u0026nbsp;7)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCM is characterized by the aberrant transformation and unbridled proliferation of melanocytes within the basal layer of the epidermis, culminating in the formation of a malignant skin tumor.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e CM susceptibility is influenced by various risk factors, including but not limited to sun exposure, indoor tanning practices, familial history of the disease, and the presence of numerous nevi. While CM can afflict individuals across all age cohorts, its incidence notably escalates within the elderly people.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e CM poses a greater threat than to other types of skin malignancies, including squamous cell carcinoma and basal cell carcinoma, primarily due to its propensity for metastasis.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e CM has a propensity for metastasis, initially through lymphatic dissemination in its early stages and subsequently through hematogenous spread as the disease progresses. Among the organs targeted by metastatic melanoma, the lungs are most frequently affected. Notably, early-stage CM (stages I-II) has a favorable prognosis and is often amenable to complete surgical resection with a commendable five-year survival rate of approximately 99.4%. However, the outlook has dramatically shifted for advanced-stage disease (stages III-IV), with markedly diminished survival rates; stage III CM has a five-year survival rate of only 68%, while stage IV CM has a five-year survival rate less than 30%.\u003csup\u003e18\u003c/sup\u003e The advent of numerous targeted and immune-based therapeutics has revolutionized the treatment landscape for unresectable cutaneous melanoma (CM), leading to substantial enhancements in patient survival rates. Among these advancements, traditional targeted therapies predominantly focus on inhibiting the BRAF and MEK pathways, with notable drugs such as vemurafenib, dabrafenib, and trametinib playing pivotal roles in clinical management.\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Nevertheless, while targeted therapies often yield favorable therapeutic outcomes initially, prolonged treatment durations commonly precipitate the emergence of drug resistance, presenting a formidable clinical obstacle. Hence, there is an urgent need to identify a stable, safe, and efficacious novel target for the management of CM.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMR analysis serves as a methodological approach utilizing genetic variants correlated with exposure to evaluate potential causal links between the exposure and subsequent outcomes. The fundamental objective is to mitigate biases arising from confounding factors, particularly reverse causation, inherent within epidemiological inquiries. In this study, two-sample MR analysis was performed to identify potential therapeutic targets for CM. DPEP1 and TRYP1 were found to be negatively correlated with CM in this study, and are expected to be potential therapeutic targets for CM.\u003c/p\u003e \u003cp\u003eThe gene encoding dipeptidase 1 is localized to the q24 band of human chromosome 16.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e DPEP1 is a zinc-dependent metalloproteinase responsible for the degradation of excess peptides and antibiotics, such as thienamycin, penem, and carbapenem derivatives. Additionally, it plays a crucial role in the metabolism of glutathione and leukotrienes.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e DPEP1is associated with a variety of tumors. DPEP1, which has been identified as a reliable marker for high-grade intraepithelial neoplasia and colorectal cancer, holds promise for early tumor detection and prognostic categorization.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e DPEP1 has the potential to enhance the detection efficacy for hepatocellular carcinoma (HCC) when it is combined with alongside alpha-fetoprotein (AFP). The diagnostic accuracy of DPEP1 alone in diagnosing HCC was determined to be 0.75 (95% CI 0.68\u0026ndash;0.83), with corresponding sensitivity and specificity values of 78.79% and 62.5%, respectively. However, when DPEP1 was combined with AFP, its diagnostic accuracy increased to 0.82 (95% CI 0.75\u0026ndash;0.89). For AFP-negative HCC patients, the diagnostic accuracy of DPEP1 was 0.79 (95%CI 0.71\u0026ndash;0.88), with sensitivity and specificity rates of 73.68% and 68.75%, respectively. Moreover, DPEP1 exhibited notable efficacy in discriminating small HCC lesions (with a tumor diameter\u0026thinsp;\u0026lt;\u0026thinsp;3 cm), achieving an accuracy of 0.76 (95% CI 0.64\u0026ndash;0.88).\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Additionally, DPEP1 has been identified as the primary adhesion receptor for neutrophils and plays a pivotal role in orchestrating the recruitment of white blood cells during pulmonary inflammation. Consequently, its involvement exacerbates the tissue damage incurred during endotoxemia.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5,6-dihydroxyindole-2-carboxylate oxidase is specifically expressed in melanocytes and melanoma cells.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e 5,6-dihydroxyindole-2-carboxylate oxidase, also recognized as tyrosinase-related protein 1 (TYRP1) or gp75 glycoprotein in certain studies, serves as a pivotal enzyme within the melanin biosynthesis pathway. TYRP1 facilitates the oxidation of the melanin precursor tyrosine, thereby promoting melanin synthesis.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e In this enzymatic process, TYRP1 catalyzes the oxidation of tyrosine to yield 5,6-dihydroxyindole-2-carboxylic acid, a critical intermediate within the melanin biosynthesis pathway.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Melanin, a natural pigment, contributes significantly to the pigmentation of skin, hair, and eyes. The enzymatic activity of TYRP1 critically modulates melanin synthesis, consequently influencing the hue of these tissues. Genetic and environmental determinants can modulate TYRP1 activity levels, underscoring its pivotal regulatory role in the genesis of skin and hair pigmentation. In an open-label, dose-escalation, phase I study comprising 27 patients with advanced melanoma who experienced tumor progression following or during at least one prior therapy, IMC-20D7S, a recombinant human IgG1 monoclonal antibody targeting TYRP1, was intravenously administered every 2 or 3 weeks. The findings demonstrated a complete response in one patient, with a disease control rate (stable disease or better) of 41%.\u003csup\u003e31\u003c/sup\u003e The aforementioned investigations underscore TYRP1 as a promising therapeutic avenue for CM; however, the need persists for expanded sample sizes and prolonged follow-up to comprehensively assess its safety and efficacy. This result aligns with the encouraging outcomes of our MR analysis, confirming the reliability of our study findings. Nevertheless, upon subsequent colocalization analysis, we observed limited evidence supporting the association between TYRP1 and CM, indicating that while TYRP1 is a therapeutic target for CM, its relationship with the condition is not governed by colocalization.\u003c/p\u003e \u003cp\u003eUltimately, we conducted a PheWAS of DPEP1 and TYRP1 to explore potential additional side effects of these plasma proteins when they air considered as prospective therapeutic targets for CM (Supplementary Table\u0026nbsp;6 and Supplementary Table\u0026nbsp;7). We have included FDR adjustments to improve the precision of our results. In addition to being a potential therapeutic target for CM, DPEP1 was also found to be a candidate target for the treatment of other non-melanoma tumors (OR\u0026thinsp;=\u0026thinsp;0.767, 95% CI: 0.693\u0026ndash;0.849, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01), such as basal cell carcinoma. Nevertheless, it is imperative to acknowledge the potential association of DPEP1 with dementia (OR\u0026thinsp;=\u0026thinsp;1.161, 95% CI: 1.087\u0026ndash;1.240, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including Alzheimer\u0026rsquo;s disease. Multiple investigators have conducted proteomic analyses of cerebrospinal fluid samples obtained from individuals with Parkinson's disease exhibiting cognitive decline, revealing notable variations in the expression pattern of DPEP1 in comparison to that observed in healthy control subjects.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e However, significant differences exist in protein composition and proportion between cerebrospinal fluid and blood, and the proteins we identified were from blood. Consequently, large-scale clinical trials are necessary to validate our findings. Nonetheless, this should not deter us from being vigilant about potential side effects in elderly people, particularly those with dementia, during the drug development process. The outcomes of PheWAS pertaining to TYRP1 indicate a strong association between TYRP1 and actinic keratosis (AK). AK represents a chronic cutaneous condition characterized by the presence of clinical and subclinical lesions predominantly observed on sun-exposed areas of the head, neck, and limbs.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e AK is considered to be a precancerous lesion of keratinocyte carcinoma.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e AK and TYRP1 could be interconnected through ultraviolet-induced alterations in melanin within the skin.\u003c/p\u003e \u003cp\u003eThe strengths of our study lie in its comprehensive approach, including the use of a wide array of plasma proteins as exposures, the use of a substantial number of CM cases as outcomes, the validation of findings through two independent datasets, and the supplementary analysis of colocalization. Employing an MR analysis mitigates biases arising from confounding and reverse causality, thus enhancing the robustness of causal inference. Furthermore, we conducted a PheWAS to elucidate the potential roles of the target proteins.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations should be acknowledged. First, our analysis predominantly drew from European populations, constraining the generalizability of our conclusions. Second, while our focus was on the impact of plasma proteins on CM, the influence of environmental factors, notably ultraviolet radiation, cannot be overlooked. Thus, there is a need to develop a risk prediction model that incorporates environmental exposure factors alongside plasma proteins to better understand CM etiology. Finally, the absence of cell and in vivo experiments conducted under authentic conditions impedes our ability to undertake randomized controlled trials for result validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis proteome-wide MR and colocalization analysis revealed two plasma proteins, DEPE1 and TYRP1, were highly expressed and causally associated with CM, especially DEPE1. These findings provide guidance and new directions for targeted therapy. We believe that these findings will be useful in the treatment of CM and may also help to reduce the individual and societal burden caused by CM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding information:\u003c/h2\u003e\n\u003cp\u003eNational Natural Science Foundation of China. Grant/Award Number 32071186.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: Not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: All data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eW.L. was responsible for the data collection and writing of the first draft of the manuscript, D.Z. and H.F. for the data analysis and manuscript proofreading, and L.Z., Z.H. X.J. participated in the interpretation of the data. X.Z. provided methodological guidance for the study. M.W. developed the concept of the manuscript, guided the analysis process throughout, and revised the first draft several times. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLong GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402:485\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelgadottir H, Olsson H, Tucker MA, Yang XR, H\u0026ouml;iom V, Goldstein AM. Phenocopies in melanoma-prone families with germ-line CDKN2A mutations. Genet Med. 2018;20:1087\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandi MT, Bauer J, Pfeiffer RM, Elder DE, Hulley B, Minghetti P, Calista D, Kanetsky PA, Pinkel D, Bastian BC. MC1R germline variants confer risk for BRAF-mutant melanoma. Science. 2006;313:521\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelmas V, Beermann F, Martinozzi S, Carreira S, Ackermann J, Kumasaka M, Denat L, Goodall J, Luciani F, Viros A, Demirkan N, Bastian BC, et al. Beta-catenin induces immortalization of melanocytes by suppressing p16INK4a expression and cooperates with N-Ras in melanoma development. Genes Dev. 2007;21:2923\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Chen K, Liu H, Jing C, Zhang X, Qu C, Yu S. PMEL as a Prognostic Biomarker and Negatively Associated With Immune Infiltration in Skin Cutaneous Melanoma (SKCM). J Immunother. 2021;44:214\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen Baruch B, Blacher E, Mantsur E, Schwartz H, Vaknine H, Erez N, Stein R. Stromal CD38 regulates outgrowth of primary melanoma and generation of spontaneous metastasis. Oncotarget. 2018;9:31797\u0026ndash;811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaba Y, Funakoshi T, Mori M, Emoto K, Masugi Y, Ekmekcioglu S, Amagai M, Tanese K. Expression of monoacylglycerol lipase as a marker of tumour invasion and progression in malignant melanoma. J Eur Acad Dermatol Venereol. 2017;31:2038\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue L, Zhang W, Ju Y, Xu X, Bo H, Zhong X, Hu Z, Zheng C, Fang B, Tang S. TNFSF10, an autophagy related gene, was a prognostic and immune infiltration marker in skin cutaneous melanoma. J Cancer. 2023;14:2417\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622:329\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Song YX, Wu XD, Luo YG, Miao R, Yu XM, Guo X, Wu DZ, Bao R, Mi WD, Cao JB. Gut microbiota and cognitive performance: A bidirectional two-sample Mendelian randomization. J Affect Disord. 2024;353:38\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Ma L, Zhou Q, Gu T, Zhang X, Xing H. Therapeutic Targets for Diabetic Kidney Disease: Proteome-Wide Mendelian Randomization and Colocalization Analyses. Diabetes. 2024;73:618\u0026thinsp;\u0026ndash;\u0026thinsp;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Xu F, Ruan X, Sun J, Zhang Y, Zhang H, Zhao J, Zheng J, Larsson SC, Wang X, Li X, Yuan S. Therapeutic targets for inflammatory bowel disease: proteome-wide Mendelian randomization and colocalization analyses. EBioMedicine. 2023;89:104494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Jiang F, Chen J, Lebwohl B, Green PHR, Leffler D, Larsson SC, Li X, Ludvigsson JF. Phenome-wide Mendelian randomization analysis reveals multiple health comorbidities of coeliac disease. EBioMedicine. 2024;101:105033.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller AJ, Mihm MC Jr.. Melanoma N Engl J Med. 2006;355:51\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Raimondo C, Lozzi F, Di Domenico PP, Campione E, Bianchi L. The Diagnosis and Management of Cutaneous Metastases from Melanoma. Int J Mol Sci 2023;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagstaff W, Mwamba RN, Grullon K, Armstrong M, Zhao P, Hendren-Santiago B, Qin KH, Li AJ, Hu DA, Youssef A, Reid RR, Luu HH, et al. Melanoma: Molecular genetics, metastasis, targeted therapies, immunotherapies, and therapeutic resistance. Genes Dis. 2022;9:1608\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazaroff J, Bolotin D. Targeted Therapy and Immunotherapy in Melanoma. Dermatol Clin. 2023;41:65\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAscierto PA, Stroyakovskiy D, Gogas H, Robert C, Lewis K, Protsenko S, Pereira RP, Eigentler T, Rutkowski P, Demidov L, Zhukova N, Schachter J, et al. Overall survival with first-line atezolizumab in combination with vemurafenib and cobimetinib in BRAF(V600) mutation-positive advanced melanoma (IMspire150): second interim analysis of a multicentre, randomised, phase 3 study. Lancet Oncol. 2023;24:33\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDayimu A, Gupta A, Matin RN, Nobes J, Board R, Payne M, Rao A, Fusi A, Danson S, Eccles B, Carser J, Brown CO, et al. A randomised phase 2 study of intermittent versus continuous dosing of dabrafenib plus trametinib in patients with BRAF(V600) mutant advanced melanoma (INTERIM). Eur J Cancer. 2024;196:113455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai X, Shaheen A, Grieco C, d'Arienzo PD, Mina F, Czapla JA, Lawless AR, Bongiovanni E, Santaniello U, Zappi H, Dulak D, Williamson A, et al. Dabrafenib plus trametinib versus anti-PD-1 monotherapy as adjuvant therapy in BRAF V600-mutant stage III melanoma after definitive surgery: a multicenter, retrospective cohort study. EClinicalMedicine. 2023;65:102290.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustruy E, Jeanpierre C, Antignac C, Whitmore SA, Van Cong N, Bernheim A, Callen DF, Junien C. Physical and genetic mapping of the dipeptidase gene DPEP1 to 16q24.3. Genomics. 1993;15:684\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakagawa H, Inazawa J, Inoue K, Misawa S, Kashima K, Adachi H, Nakazato H, Abe T. Assignment of the human renal dipeptidase gene (DPEP1) to band q24 of chromosome 16. Cytogenet Cell Genet. 1992;59:258\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcIver CM, Lloyd JM, Hewett PJ, Hardingham JE. Dipeptidase 1: a candidate tumor-specific molecular marker in colorectal carcinoma. Cancer Lett. 2004;209:67\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisenach PA, Soeth E, R\u0026ouml;der C, Kl\u0026ouml;ppel G, Tepel J, Kalthoff H, Sipos B. Dipeptidase 1 (DPEP1) is a marker for the transition from low-grade to high-grade intraepithelial neoplasia and an adverse prognostic factor in colorectal cancer. Br J Cancer. 2013;109:694\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Li Y, Li X, Jiang LN, Zhu L, Lu FM, Zhao JM. [A preliminary discussion on carnosine dipeptidase 1 as a potential novel biomarker for the diagnostic and prognostic evaluation of hepatocellular carcinoma]. Zhonghua Gan Zang Bing Za Zhi. 2023;31:627\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhury SR, Babes L, Rahn JJ, Ahn BY, Goring KR, King JC, Lau A, Petri B, Hao X, Chojnacki AK, Thanabalasuriar A, McAvoy EF et al. Dipeptidase-1 Is an Adhesion Receptor for Neutrophil Recruitment in Lungs and Liver. Cell. 2019;178:1205-21.e17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhanem G, Fabrice J. Tyrosinase related protein 1 (TYRP1/gp75) in human cutaneous melanoma. Mol Oncol. 2011;5:150\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGautron A, Migault M, Bachelot L, Corre S, Galibert MD, Gilot D. Human TYRP1: Two functions for a single gene? Pigment Cell Melanoma Res. 2021;34:836\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlivares C, Jim\u0026eacute;nez-Cervantes C, Lozano JA, Solano F, Garc\u0026iacute;a-Borr\u0026oacute;n JC. The 5,6-dihydroxyindole-2-carboxylic acid (DHICA) oxidase activity of human tyrosinase. Biochem J. 2001;354:131\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalil DN, Postow MA, Ibrahim N, Ludwig DL, Cosaert J, Kambhampati SR, Tang S, Grebennik D, Kauh JS, Lenz HJ, Flaherty KT, Hodi FS, et al. An Open-Label, Dose-Escalation Phase I Study of Anti-TYRP1 Monoclonal Antibody IMC-20D7S for Patients with Relapsed or Refractory Melanoma. Clin Cancer Res. 2016;22:5204\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin GN, Lee HW, Cho JY, Suk K. Neuronal pentraxin receptor in cerebrospinal fluid as a potential biomarker for neurodegenerative diseases. Brain Res. 2009;1265:158\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Regno L, Catapano S, Di Stefani A, Cappilli S, Peris K. A Review of Existing Therapies for Actinic Keratosis: Current Status and Future Directions. Am J Clin Dermatol. 2022;23:339\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEisen DB, Asgari MM, Bennett DD, Connolly SM, Dellavalle RP, Freeman EE, Goldenberg G, Leffell DJ, Peschin S, Sligh JE, Wu PA, Frazer-Green L, et al. Guidelines of care for the management of actinic keratosis: Executive summary. J Am Acad Dermatol. 2021;85:945\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cutaneous melanoma, Plasma proteins, Therapeutic targets, Mendelian randomization, Colocalization analysis","lastPublishedDoi":"10.21203/rs.3.rs-6171084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6171084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cutaneous melanoma (CM) is the deadliest form of skin cancer. Mendelian randomization (MR) and local analysis have been widely used in the search for therapeutic targets for diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Plasma proteins data were obtained from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) database. The GWAS data for CM were extracted from the Finnish (R10) database. Proteome-wide MR analysis to assess the causal relationship between plasma proteins and CM. Colocalization analysis was used to identify causal variants shared between plasma proteins and CM. A phenotype-wide association study (PheWAS) was used to assess the potential adverse effects of proteins that could treat CM on 2480 phenotypes in the Finnish (R10) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e MR analysis revealed that 5,6-hydroxyindole-2-carboxylate oxidase (TYRP1) (OR: 0.23, 95% CI: 0.12-0.44) and dipeptidase 1(DPEP1) (OR: 0.63, 95% CI: 0.12-0.44) were associated with CM. The evidence from the colocalization analysis supported an inverse association between DPEP1 levels and the risk of CM, but the evidence from the colocalization analysis of TYRP1 was low grade. PheWAS suggested that DPEP1 as a therapeutic target for CM may cause dementia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our investigation examined the causal relationships between two plasma proteins and CM, providing a comprehensive understanding of potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Proteome-wide Mendelian randomization and colocalization analysis identify therapeutic targets for cutaneous melanoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 10:57:43","doi":"10.21203/rs.3.rs-6171084/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c61c5f5d-2284-4618-aaaa-a0c14f46aec7","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T10:53:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 10:57:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6171084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6171084","identity":"rs-6171084","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}