Cathepsins Influence Metastatic Liver Cancer via Plasma Proteins: a Mendelian randomization Study

Cathepsins Influence Metastatic Liver Cancer via Plasma Proteins: a Mendelian randomization Study | 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 Cathepsins Influence Metastatic Liver Cancer via Plasma Proteins: a Mendelian randomization Study Taijun Yi, Zejin Lin, Chengrui Zhong, Ziyan Mai, Yongling Liang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4438111/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: Cathepsins (CTSs), lysosomal cysteine proteases, have been reported to play roles in the initiation, infiltration, and dissemination of tumors in previous researches. However, the precise causal relationship between CTSs and metastatic liver cancer (MLC) remains undetermined. This study aimed to investigate the potential causal relationship between CTSs and MLC, as well as to examine the mediating effect of plasma proteins in this relationship, ultimately establishing a causal network among them. Methods: Data were obtained from genome-wide association analysis (GWAS). Inverse variance weighting (IVW), Bayesian weighting (BW), MR-Egger regression, Weighted median (WM) and MR-conmix methods were employed for Mendelian randomization (MR) Analysis. Sensitivity analysis included Cochran's Q test, Mr-Egger intercept, MR-PRESSO test and leave-one-out validation. Results: Univariable MR revealed that an increase in CTSF (cathepsin F), CTSD (cathepsin D), and CSTV (cathepsin V) was associated with a reduced risk of MLC among 11 CTSs. While reverse MR did not yield significant findings. And total of 42 plasma proteins were identified to have a causal relationship with MLC, among which 13 types were found to mediate the association between the 3 CTSs and MLC. Conclusions: Our study suggests a potential causal relationship involving 3 CTSs, 13 plasma proteins, and MLC. These results provide valuable references for disease prediction, targeted therapy and mechanistic research of MLC. Cathepsin Metastatic liver cancer Plasma protein Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 0 .Introduction Metastatic liver cancer (MLC), a malignancy originating from various primary sites and spreading to the liver( 1 ), is a significant entity within the spectrum of malignant tumors. MLC arises from diverse sources such as the colon, rectum, liver, pancreas, stomach, breast, lung, etc( 2 ). It is noteworthy that MLC plays a pivotal role in cancer progression and patient mortality rates and is closely linked to reduced 5-year survival rates and diminished quality of life( 3 ). Despite advancements in comprehensive treatments, including surgery, chemotherapy, ablation, targeted and immuno-therapies, contributing to substantial progress in MLC diagnosis and management etc, have made great progress of MLC diagnosis and treatment, it remains a critical determinant of poor prognosis for patients with malignant neoplasms. For instance, colorectal cancer (CRC), the most prevalent primary tumor associated with MLC, affeCTS approximately 25% of newly diagnosed patients and 40%-50% of advanced CRC patients develop liver metastases( 1 ), Without prompt treatment, the median survival time can be reduced to 6–12 months, with a 5-year survival rate of merely 9%( 1 ). Thus, understanding the pathogenesis of MLC and finding breakthroughs in its diagnosis and treatment is of paramount importance. CTSs are important components of lysosomal hydrolysis system, encompassing cysteine (e.g.,CTSB, C, F, H, K, L, O and V), aspartic acid (CTSD and E) and serine families (CTSA and G) ( 4 ). CTSs participate in almost all lysosomal related processes, including protein metabolic degradation, lipid metabolism, autophagy, stress response, antigen presentation, growth factor receptor cycling and lysosomal-mediated cell death( 5 ). Consequently, the dysregulation of CTSs activity is closely linked to a range of diseases, such as cardiovascular diseases( 6 ), osteoporosis and arthritis( 7 ) and neurodegenerative diseases( 8 ). Recent studies have highlighted the association between CTS and tumorigenesis. CTSB, in particular, is implicated in the progression of various malignant diseases, including head and neck, breast, lung, melanoma, colon, and osteosarcoma( 5 ). In highly metastatic LM5 and LM7 cells, the expression of CTSD, CTSK and CTSL are increased, and the expression of CTSF, CTSH and CTSV are decreased( 9 ). While other studies have found no significant difference in CTSB activity between hepatocellular carcinoma and normal tissues or between pre- and post- operative peripheral blood( 10 ). CTSD has been shown to induce or inhibit cell apoptosis depending on microenvironmental changes( 11 ), and CTSF plays an crucial role in the suppression of gastric cancer by LINC00982 (Long noncoding RNA LINC00982) inhibiting gastric cancer( 12 ). These findings suggest that the relationship between different CTSs and tumors may vary( 5 ). In terms of tumor metastasis, CTS is thought to facilitate the process; for example, the combined depletion of CTSS by macrophages and tumor cells significantly reduces brain metastasis in breast cancer( 13 ), and CTSL and CTSS promote liver and lung metastasis of pancreatic cance( 14 ). However, few studies have clearly clarified the causal relationship between CTSs and MLC. Plasma proteins play key roles in numerous biological processes, including signal transduction, material transport, growth, repair and defense( 15 ). Imbalances in plasma protein expression are frequently linked to pathological processes, particularly tumorigenesis. Numerous studies have reported positive( 16 – 23 ) or negatively( 24 – 33 ) correlations between plasma protein levels and tumor development, indicating that plasma protein levels may be influenced by tumors or play a role in promoting or inhibiting tumor growth. Consequently, plasma proteins are widely utilized as biomarkers or therapeutic targets in clinical settings( 15 , 34 ). Nonetheless, our understanding of the complex relationship between plasma proteins and tumors, especially MLC, remains limited. The application of genome-wide association studies (GWAS) has led to the widespread adoption of MR for disease etiology and causal inference. This method employs genetic variation as Instrumental variable (IV) to infer the relationship between exposure and outcome( 35 ), based on three core assumptions: ( 1 ) the IV is closely related to exposure (correlation hypothesis); ( 2 ) the IV is unaffected by confounders influencing exposure and outcome (independent assumption); ( 3 ) the IV influences the outcome solely through the exposure under investigation, without additional pathways (exclusion limiting hypothesis). Compared to traditional observational studies, this approach effectively mitigates the impact of common confounders, providing a more reliable depiction of causality and suggesting directional relationships( 36 ). In this study, we employed two-sample MR analysis to explore the potential causal relationship between CTSs, plasma proteins and MLC through univariable and mediating MR method. This work aims to offer insights into the diagnosis, treatment, and mechanistic research of MLC. 1. Materials and Methods 1.1 Study Design The study design is outlined in Fig. 1. We conducted a two-sample MR analysis utilizing data from GWAS. Initially, we investigated the potential causal relationships between 11 CTSs (B, E, F, G, H, O, S, L, A, D, V) and MLC and screened 3 positive CTSs. Subsequently, we examined the causal associations between plasma proteins and MLC. Finally, we identified plasma proteins that may serve as mediators in the relationship between the 3 CTSs and MLC, thereby calculating mediated effect and establishing a comprehensive causal network among these factors. 1.2 Data Sources and Instrumental Variables Selection Summary data for CTSs were sourced from two large-scale GWAS studies ( https://gwas.mrcieu.ac.uk/ ). Data for CTSB, E, F, G, H, O, S, L were from a study of Sun BB et al.( 15 ), in which 3301 samples and 10 534 735 SNPs (Single nucleotide polymorphism) were included. Data for CTSA, D, V and plasma proteins were obtained from the study by Suhre K et al.( 37 ), involving 1,000 samples and 501,428 SNPs. MLC data were extracted from the study by Jiang L et al.( 38 ), which comprised 456,348 individuals of European descent from the UK Biobank (UKB) ( https://www.ebi.ac.uk/gwas/ ). All samples were of European ancestry and randomized for sex and age was employed to minimize potential biases. IVs were selected throng the following criteria: ( 1 ) an association threshold of p < 5e-6, ( 2 ) r2 10. The F-statistic formula is as follows( 39 – 41 ). Summary data for the selected IVs are provided in Supplementary Material 1. 1.3 MR Analysis Causality was primarily ascertained using the IVW method. For exposures with a single IV, the Wald ratio method was applied. Additionally, BW, MR-Egger regression, WM and MR-conmix methods were employed for corroborative analysis. The IVW( 42 ) method assumes that all SNPs are valid and independent, with the regression intercept constrained to zero, and the inverse of the outcome variance used as the weight for fitting. This method provides the most accurate results when each genetic variant fulfills the assumptions of instrumental variables, making IVW the gold standard for causal effect assessment. The BW( 43 ) method accounts for the uncertainty of weak effects due to polygenicity and addresses potential violations of IV assumptions caused by polytropy by detecting outliers through Bayesian weighting. It is also corrected for the posterior covariance, which is often underestimated in variative inference, using closed-form formulas, thereby enhancing the reliability of the test method. MR-conmix( 44 ) provides causal estimates even in the presence of invalid instrumental variables and was primarily used for supplementary results in this study. Summary data of MR analysis are provided in Supplementary Material 2, and scatter plots for positive exposures are provided in Supplementary Material 3. 1.4 Sensitivity analysis A range of methods were employed for sensitivity analysis( 45 , 46 ). Heterogeneity was assessed using Cochran's Q test, with a Q_pval > 0.05 indicating the absence of heterogeneity, in which case the fixed effect model was utilized. Otherwise, the random effects model (IVW-mre) was selected for further analysis( 47 ). Pleiotropy was evaluated using the MR-PRESSO test and MR-Egger intercept, with a p-value > 0.05 suggesting the absence of horizontal pleiotropy. Sensitivity analysis and visualization were conducted using the leave-one-out method( 48 ). Summary data of sensitivity analysis are provided in Supplementary Material 2. 1.5 Statistical software The research primarily utilized TwosampleMR, MR-PRESSO and other R packages (version 4.2.1) for analysis, with graphical representations created using GraphPad Prism 9.5 and Adobe Illustrator tools. 2. Results 2.1 Investigating the Causal Relationship Between 11 Types of CTSs and MLC MR analysis (as depicted in Figs. 2 and 3) indicated that among the 11 CTSs, increased levels of CTSF (OR = 0.500, 95%CI = 0.593 ~ 0.864, p-values for IVW, BW, WM, and MR-ConMic < 0.05), CTSD (OR = 0.017, 95%CI = 0.548 ~ 0.942, p-values for IVW, BW < 0.05) and CTSV (OR = 0.023, 95%CI = 0.455 ~ 0.943, p-values for IVW, BW < 0.05) were associated with a reduced risk of MLC (b < 0). Additionally, MR-Egger regression suggested an association between CTSB and MLC (p = 0.035, OR = 1.645, 95%CI = 1.077–2.514, P < 0.05, b = 0.498). Bayesian analysis indicated a causal relationship between CTSZ and MLC (p = 0.002, OR = 0.718, 95%CI = 0.585–0.881, b = -0.331), while IVW showed no statistical significance for the two associations. The remaining analyses indicated negative results. Special considerations for sensitivity analysis revealed heterogeneity in the associations of CTSZ and CTSA with MLC, as indicated by the Q test (p 0.05). The MR PRESSO results for CTSZ were p = 0.029 and p-distortion Test = 0.338, indicating no evidence for horizontal pleiotropy. However, due to SNP limitations, horizontal pleiotropy test results for CTSV could not be obtained. Combined with the Q test, MR-Egger intercept and PRESSO test results, no heterogeneity or horizontal pleiotropy was detected in other exposures. Furthermore, leave-one-out analysis demonstrated no marked difference in the causal estimations of CTSs on MLC, suggesting that the identified causal associations were not influenced by any single IV (as shown in Fig. 4). 2.2 Reverse MR Analysis of CTS and MLC To explore the potential reverse causality between CTSs and MLC, a reverse MR analysis was performed. The results showed no indication of reverse causality between the 11 CTSs and MLC, as illustrated in Fig. 5. Furthermore, various sensitivity analyses demonstrated no heterogeneity or horizontal pleiotropy. The leave-one-out method further confirmed that no individual SNPs significantly impacted the overall results, as shown in Fig. 4. 2.3 Identifying the Mediating Factors: Plasma Proteins To elucidate the potential mechanism of CTSs on MLC, MR Analysis was performed with 1124 plasma proteins as the exposure and MLC as the outcome. Initially, 42 plasma proteins were identified as significantly correlated with MLC through IVW screening (Fig. 6). Subsequently, MR Analysis between the 3 CTSs and the 42 plasma proteins was performed, suggesting that 13 plasma proteins may mediate the relationship between CTS and MLC. Specifically, CTSF was found to potentially mediate the effects of Endostatin, CDK8/cyclin C (Cyclin-dependent kinase 8:Cyclin-C complex), RBP (Retinol-binding protein 4), Ficolin-3, Prekallikrein (Plasma kallikrein), GIB (Phospholipase A2), MAPK14 (Mitogen-activated protein kinase 14), Caspase-10 and CPNE1 (Copine-1) to affect the risk of MLC. CTSD was shown to mediate the effects of ARGI1 (Arginase-1), RBP (Retinol-binding protein 4), and Tropomyosin 2 (Tropomyosin beta chain), while CTSV was associated with the mediation of CTSD, CNTN2 (Contactin-2), sRAGE (Advanced glycosylation end product-specific receptor, soluble), Tropomyosin 2, MAPK14(Mitogen-activated protein kinase 14), and CPNE1. Some proteins could not undergo sensitivity analysis due to a lack of enough IVs, but the majority of the conducted sensitivity analyses demonstrated robust results (Fig. 3). And the leave-one-out method showed no individual SNPs significantly impacted the overall results (Fig. 4). Among these 13 plasma proteins, elevated levels of Endostatin, ARGI1, CDK8/cyclin C, Ficolin-3, sRAGE, Prekallikrein, GIB, MAPK14, and CPNE1 were found to potentially reduce MLC risk, whereas increased levels of CNTN2, RBP, Tropomyosin 2, and Caspase-10 may elevate MLC risk (Fig. 6). Reverse MR of the 13 plasma proteins in relation to MLC, using IVW, did not yield significant results, implying that MLC may not influence the levels of these plasma proteins (Fig. 7). The total effects of CTSs on MLC (b0) and the effects of CTSs on 13 plasma proteins (b1) were established. After the SNPs used in the calculation of b1 were excluded, the effects of 13 plasma proteins on MLC were further calculated (b2). Based on these data above, the mediating effects of plasma proteins were calculated using the formula: Mediating effect = b0 - b1*b2 (Table 1). 2.4 Establishing the Causal Network of CTS, Plasma Proteins, and MLC Further MR analysis was conducted on the 13 plasma proteins in relation to the 3 CTSs, revealing that ARGI1, RBP, Tropomyosin 2, and GIB were causally associated with CTSD, and MAPK14 was associated with CTSV. Mutual MR analysis of the 13 plasma proteins was performed to explore their interrelationships, with a causal association identified between sRAGE and Ficolin-3 (Fig. 5) Based on these results above, a comprehensive causal network of 3 CTSs, 14 plasma proteins and MLC was established (Fig. 8). 3. Discussion In this study, we utilized genetic variants to infer causal relationships, revealing potential causal associations among 11 CTSs, plasma proteins, and MLC through reciprocal MR analysis. Based on the European population, we draw the following conclusions: ( 1 ) CTSF, D, and V may reduce the risk of MLC; ( 2 )42 plasma proteins may have a causal relationship with MLC; ( 3 )13 plasma proteins potentially mediating the relationship between the 3 CTSs and MLC. The mechanisms underlying the development and progression of MLC have garnered significant interest. The classic "seed and soil" hypothesis posits that the interplay of complex biological systems facilitates the metastatic process to the liver( 49 ). Indeed, MLC is contingent upon microenvironment changes in the the primary tumor, metastasis pathways and the liver, involving diverse processes such as immunity and metabolism( 50 ). Various proteins, including CTSs and plasma proteins, have been confirmed to play crucial role in these processes. CTS, a key hydrolase in lysosomes, is the principal effector of protein catabolism and autophagy( 51 ), and it plays a vital role in numerous physiological processes in the human body( 5 ). The relationship between CTSs and tumors has attracted considerable attention. Studies have demonstrated that CTSs alters the tumor microenvironment through the transport and degradation of extracellular matrix (ECM)( 52 ), as well as the activation, processing or degradation of various chemokines, growth factors and cytokines. CTSs can also release cell adhesion molecules involved in tissue invasion and metastasis( 53 ), highlighting the close connection between CTSs and tumors and their significant potential in the field of oncology. On one hand, CTSs have been considered predictive and prognostic indicators for various tumors, with their levels changing in glioblastoma ( 54 ), breast cancer( 55 ) lung cancer( 56 ), colorectal cancer( 57 ), liver cancer( 58 ) and other malignancies. The increase of CTSs in most tumors indicates a state of progression, poor prognosis and low survival probability. On the other hand, the role of CTSs as tumor therapeutic targets has been continuously explored. For instance, ASPER-29, a novel inhibitor of CTSL and CTSS, has shown a marked ability to inhibit pancreatic cancer cell metastasis( 14 ). Additionally, the pan-CTS inhibitor JPM-OEt significantly reduces tumor burden, angiogenesis, and invasion in RIP1-Tag2 mice( 59 ), and its derivative E-64 has also been confirmed to prevent liver colonization of lung cancer cells in experiments( 60 ). Recent studies have even suggested that CTS may affect the efficacy of tumor treatment( 61 , 62 ). The possible inhibitory effect of three CTSs on MLC found in this study aligns with the conclusions of some previous studies. In brain tumors, CTSF levels were lower in ependymomas, glioblastomas and medulloblastomas than in normal brain( 63 ). Ji C et al. ( 64 )found that down-regulation of CTSF expression can effectively inhibit GC cell apoptosis and promote its proliferation, suggesting that the CTSF gene plays a tumor suppressor role in GC and may become a target for GC treatment. Zheng L et al. also discovered that LINC00982 promotes the expression of CTSF, thereby inhibiting gastric cancer progression( 12 ).Further more, knockdown of CTSF in the absence of PUMA and p21 can induce leukemia development( 65 ), and another study suggested that CTSF may play an anti-tumor role by regulating the immune response in NSCLC( 66 ). Regarding the effect of CTSD on tumors, it is believed that CTSD can induce or inhibit cell apoptosis according to microenvironmental changes( 11 ). Previous studies have shown that CTSD can promote tumor proliferation, invasion and prevent apoptosis( 67 , 68 ), as well as inhibit tumor growth( 11 , 69 , 70 ). CTSV, a relatively newly identified CTS, has been suggested to play a pro-tumor role( 71 ), but it has also been reported to be down-regulated in highly metastatic LM5 and LM7 cells( 9 ), and is associated with good prognosis in thymoma( 72 , 73 ) and estrogen ER-negative breast cancer( 74 ). Conversely, CTSB, CTSL, CTSS, CTSZ, etc, which have been more extensively studied, have been indicated to promote tumor progression in previous researches( 5 , 9 , 54 – 57 , 75 ), while IVW in this study suggested no causal relationship between them and MLC. Only MR-Egger results suggested that increased CTSB levels promotes MLC progress and BW suggested a negative relationship between CTSZ and MLC. Similarly, plasma exert significant influence on tumor growth, migration, invasion and the construction of microenvironment. Numerous prior investigations have endeavored to harness plasma proteins as pivotal biomarkers and therapeutic targets for cancer, achieving measurable success and practical clinical application( 15 , 34 ). In an effort to delve into the interplay between plasma proteins and metastatic liver cancer (MLC), and to uncover potential mediators, this study employed a series of Mendelian randomization (MR) analyses. The findings revealed 43 plasma proteins with a causal link to MLC, among which 13 were identified as intermediary proteins, including Endostatin. The relationship between the majority of these proteins and MLC aligns with prior research: Endostatin( 24 – 26 ), Ficolin-3( 27 – 29 ), sRAGE( 30 ), Prekallikrein( 31 ), GIB( 32 ) and ARGI1( 33 ) were negatively correlated with MLC, implying that elevated levels of these proteins may confer a reduced risk of MLC. Conversely, increased levels of CNTN2( 16 ), RBP( 17 ) and Tropomyosin 2( 18 ) may heighten MLC risk. However, divergences from previous findings was observed with CPNE1( 19 , 20 ), MAPK14( 21 ) and CDK8NAcyclin C( 23 ), that are, this study suggested an inverse causal relationship between the three proteins and MLC, which contrary to their previously proposed roles in promoting tumorigenesis. In conclusion, plasma proteins may play different roles in different tumor microenvironments and have different or even opposite effects on tumors. While this study demonstrates some important findings, there are several limitations. Firstly and foremost, data constraints hindered access to a comprehensive pool of MLC-related GWAS data from diverse sources, precluding a more nuanced and detailed investigation. Secondly, the number of SNPs limited the application of a comprehensive suite of MR methods for sensitivity analyses, necessitating further validation of the robustness of certain results. Nonetheless, by combining multiple sensitivity analyses, our study provides strong evidence for most of the positive results. Additionally, the study's focus on the European population means that the findings may not be generalizable to other ethnicities, while it do hold referential value for analogous research in diverse populations. Lastly, the discordance between the effects of certain proteins observed in this study and previous research warrants further investigation to uncover potential unknown mechanisms. In summary, this study identified increased levels of CTS variants CTSF, CTSD, and CSTV as potentially reducing the risk of MLC. It also identified plasma proteins that may influence MLC risk, explored mediating factors, and established a causal network encompassing three CTS variants, 14 plasma proteins, and MLC. These proteins could offer valuable insights into the mechanisms underlying MLC, serve as markers for its occurrence, progression, and prognosis, and guide early diagnosis and evaluation of MLC outcomes. They may also represent potential therapeutic targets for MLC. Abbreviations CTS cathepsin SNP Single nucleotide polymorphism MLC metastatic liver cancer MR Mendelian randomization IV instrumental variance IVW inverse-variance weighted BW Bayesian weighted WM weighted median CDK8/cyclin C Cyclin-dependent kinase 8:Cyclin-C complex RBP Retinol-binding protein 4 Prekallikrein Plasma kallikrein GIB Phospholipase A2 MAPK14 Mitogen-activated protein kinase 14 CPNE1 Copine-1 ARGI1 Arginase-1 CNTN2 Contactin-2 Tropomyosin 2 Tropomyosin beta chain NSCLC Non-small cell lung cancer Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All studies used publicly available GWAS abstract data. Competing interests The authors declare no conflicts of interest. Funding Not applicable Author contributions Taijun Yi: research designing, data acquiring and analyzing,manuscripts writing. Zejin Lin: Data processing, paper revising and picture production. Chengrui Zhong: research designing and paper revising. Yunle Wan and Guolin Li: paper revising and approving. Ziyan Mai, Yongling Liang, Zhiping Chen, Jiayan Wu, Zeyu Lin, Jiandong Yu, Zhu Lin, Huilin Jin ：Supervise and provide comments for article writing and revision. All authors contributed to the article and approved the submitted version. Acknowledgments The authors thank Sun BB et al, Suhre K et al and Jiang L et al for providing public GWAS datas. We thank all authors and institutions cited and included in this article. And we thank Si-cheng Xu of Sun Yat-sen Memorial Hospital of Sun Yat-sen University for his assistance. References Tsilimigras DI, Brodt P, Clavien PA, Muschel RJ, D'Angelica MI, Endo I, et al. Liver metastases. Nat reviews Disease primers. 2021;7(1):27. de Ridder J, de Wilt JH, Simmer F, Overbeek L, Lemmens V, Nagtegaal I. Incidence and origin of histologically confirmed liver metastases: an explorative case-study of 23,154 patients. Oncotarget. 2016;7(34):55368–76. Siegel RL, Miller KD, Jemal A, Cancer statistics. 2020. CA: a cancer journal for clinicians. 2020;70(1):7–30. Xu T, Nicolson S, Sandow JJ, Dayan S, Jiang X, Manning JA, et al. Cp1/cathepsin L is required for autolysosomal clearance in Drosophila. Autophagy. 2021;17(10):2734–49. Olson OC, Joyce JA. Cysteine cathepsin proteases: regulators of cancer progression and therapeutic response. Nat Rev Cancer. 2015;15(12):712–29. Lutgens SP, Cleutjens KB, Daemen MJ, Heeneman S. Cathepsin cysteine proteases in cardiovascular disease. FASEB journal: official publication Federation Am Soc Experimental Biology. 2007;21(12):3029–41. Yasuda Y, Kaleta J, Brömme D. The role of cathepsins in osteoporosis and arthritis: rationale for the design of new therapeutics. Adv Drug Deliv Rev. 2005;57(7):973–93. Nixon RA, Cataldo AM, Mathews PM. The endosomal-lysosomal system of neurons in Alzheimer's disease pathogenesis: a review. Neurochem Res. 2000;25(9–10):1161–72. Husmann K, Muff R, Bolander ME, Sarkar G, Born W, Fuchs B. Cathepsins and osteosarcoma: Expression analysis identifies cathepsin K as an indicator of metastasis. Mol Carcinog. 2008;47(1):66–73. Niewczas M, Paczek L, Krawczyk M, Pawlak J, Bartłomiejczyk I, Górnicka B. [Enzymatic activity of cathepsin B, cathepsin B and L, plasmin, trypsin and collagenase in hepatocellular carcinoma]. Pol Arch Med Wewn. 2002;108(1):653–62. Minarowska A, Minarowski L, Karwowska A, Gacko M. Regulatory role of cathepsin D in apoptosis. Folia Histochem Cytobiol. 2007;45(3):159–63. Zheng L, Cao J, Liu L, Xu H, Chen L, Kang L, et al. Long noncoding RNA LINC00982 upregulates CTSF expression to inhibit gastric cancer progression via the transcription factor HEY1. Am J Physiol Gastrointest liver Physiol. 2021;320(5):G816–28. Sevenich L, Bowman RL, Mason SD, Quail DF, Rapaport F, Elie BT, et al. Analysis of tumour- and stroma-supplied proteolytic networks reveals a brain-metastasis-promoting role for cathepsin S. Nat Cell Biol. 2014;16(9):876–88. Yuan L, Zhao J, Zhao S, Dong T, Dong R, Liu D, et al. ASPER-29 suppresses the metastasis of pancreatic cancer cells by dual inhibition of cathepsin-L and cathepsin-S. Chemico-Biol Interact. 2022;353:109811. Zeng Z, Li Y, Pan Y, Lan X, Song F, Sun J, et al. Cancer-derived exosomal miR-25-3p promotes pre-metastatic niche formation by inducing vascular permeability and angiogenesis. Nat Commun. 2018;9(1):5395. Yan Y, Jiang Y. RACK1 affects glioma cell growth and differentiation through the CNTN2-mediated RTK/Ras/MAPK pathway. Int J Mol Med. 2016;37(1):251–7. Papiernik D, Urbaniak A, Kłopotowska D, Nasulewicz-Goldeman A, Ekiert M, Nowak M et al. Retinol-Binding Protein 4 Accelerates Metastatic Spread and Increases Impairment of Blood Flow in Mouse Mammary Gland Tumors. Cancers. 2020;12(3). Mele V, Basso C, Governa V, Glaus Garzon JF, Muraro MG, Däster S et al. Identification of TPM2 and CNN1 as Novel Prognostic Markers in Functionally Characterized Human Colon Cancer-Associated Stromal Cells. Cancers. 2022;14(8). Tang H, Zhu J, Du W, Liu S, Zeng Y, Ding Z, et al. CPNE1 is a target of miR-335-5p and plays an important role in the pathogenesis of non-small cell lung cancer. J experimental Clin cancer research: CR. 2018;37(1):131. Gao C, Zhu R, Shen J, Xu T, She Y, Chen Z. RBM12 regulates the progression of hepatocellular cancer via miR-497-5p/CPNE1 Axis. Environ Res. 2023;239(Pt 1):117203. Fang JY, Richardson BC. The MAPK signalling pathways and colorectal cancer. Lancet Oncol. 2005;6(5):322–7. Kim JG, Kim SI, Song SH, Gu JY, Lee M, Kim HK. Diagnostic and prognostic role of circulating neutrophil extracellular trap markers and prekallikrein in patients with high-grade serous ovarian cancer. Front Oncol. 2022;12:992056. Al-Sanea MM. Synthesis and biological evaluation of small molecule modulators of CDK8/Cyclin C complex with phenylaminoquinoline scaffold. PeerJ. 2020;8:e8649. Yoon SS, Eto H, Lin CM, Nakamura H, Pawlik TM, Song SU, et al. Mouse endostatin inhibits the formation of lung and liver metastases. Cancer Res. 1999;59(24):6251–6. Noro T, Miyake K, Suzuki-Miyake N, Igarashi T, Uchida E, Misawa T, et al. Adeno-associated viral vector-mediated expression of endostatin inhibits tumor growth and metastasis in an orthotropic pancreatic cancer model in hamsters. Cancer Res. 2004;64(20):7486–90. te Velde EA, Vogten JM, Gebbink MF, van Gorp JM, Voest EE, Borel Rinkes IH. Enhanced antitumour efficacy by combining conventional chemotherapy with angiostatin or endostatin in a liver metastasis model. Br J Surg. 2002;89(10):1302–9. Luo JH, Ren B, Keryanov S, Tseng GC, Rao UN, Monga SP, et al. Transcriptomic and genomic analysis of human hepatocellular carcinomas and hepatoblastomas. Hepatology (Baltimore MD). 2006;44(4):1012–24. Jang H, Jun Y, Kim S, Kim E, Jung Y, Park BJ, et al. FCN3 functions as a tumor suppressor of lung adenocarcinoma through induction of endoplasmic reticulum stress. Cell Death Dis. 2021;12(4):407. Ma D, Liu P, Wen J, Gu Y, Yang Z, Lan J, et al. FCN3 inhibits the progression of hepatocellular carcinoma by suppressing SBDS-mediated blockade of the p53 pathway. Int J Biol Sci. 2023;19(2):362–76. Chen RC, Yi PP, Zhou RR, Xiao MF, Huang ZB, Tang DL, et al. The role of HMGB1-RAGE axis in migration and invasion of hepatocellular carcinoma cell lines. Mol Cell Biochem. 2014;390(1–2):271–80. Roeise O, Sivertsen S, Ruud TE, Bouma BN, Stadaas JO, Aasen AO. Studies on components of the contact phase system in patients with advanced gastrointestinal cancer. Cancer. 1990;65(6):1355–9. Peng Z, Chang Y, Fan J, Ji W, Su C. Phospholipase A2 superfamily in cancer. Cancer Lett. 2021;497:165–77. Atta IS. Efficacy of expressions of Arg-1, Hep Par-1, and CK19 in the diagnosis of the primary hepatocellular carcinoma subtypes and exclusion of the metastases. Histol Histopathol. 2021;36(9):981–93. Geyer PE, Holdt LM, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol. 2017;13(9):942. Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization Jama. 2017;318(19):1925–6. Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrology: JASN. 2016;27(11):3253–65. Suhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun. 2017;8:14357. Jiang L, Zheng Z, Fang H, Yang J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet. 2021;53(11):1616–21. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21(3):223–42. Levin MG, Judy R, Gill D, Vujkovic M, Verma SS, Bradford Y, et al. Genetics of height and risk of atrial fibrillation: A Mendelian randomization study. PLoS Med. 2020;17(10):e1003288. Gill D, Efstathiadou A, Cawood K, Tzoulaki I, Dehghan A. Education protects against coronary heart disease and stroke independently of cognitive function: evidence from Mendelian randomization. Int J Epidemiol. 2019;48(5):1468–77. Pagoni P, Korologou-Linden RS, Howe LD, Davey Smith G, Ben-Shlomo Y, Stergiakouli E, et al. Causal effects of circulating cytokine concentrations on risk of Alzheimer's disease and cognitive function. Brain Behav Immun. 2022;104:54–64. Zhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinf (Oxford England). 2020;36(5):1501–8. Burgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11(1):376. Niu PP, Song B, Wang X, Xu YM. Serum Uric Acid Level and Multiple Sclerosis: A Mendelian Randomization Study. Front Genet. 2020;11:254. Su D, Ai Y, Zhu G, Yang Y, Ma P. Genetically predicted circulating levels of cytokines and the risk of osteoarthritis: A mendelian randomization study. Front Genet. 2023;14:1131198. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7. Paget S. The distribution of secondary growths in cancer of the breast. 1889. Cancer Metastasis Rev. 1989;8(2):98–101. Li X, Ramadori P, Pfister D, Seehawer M, Zender L, Heikenwalder M. The immunological and metabolic landscape in primary and metastatic liver cancer. Nat Rev Cancer. 2021;21(9):541–57. Dennemärker J, Lohmüller T, Müller S, Aguilar SV, Tobin DJ, Peters C, et al. Impaired turnover of autophagolysosomes in cathepsin L deficiency. Biol Chem. 2010;391(8):913–22. Guinec N, Dalet-Fumeron V, Pagano M. In vitro study of basement membrane degradation by the cysteine proteinases, cathepsins B, B-like and L. Digestion of collagen IV, laminin, fibronectin, and release of gelatinase activities from basement membrane fibronectin. Biological chemistry Hoppe-Seyler. 1993;374(12):1135-46. Yin M, Soikkeli J, Jahkola T, Virolainen S, Saksela O, Hölttä E. TGF-β signaling, activated stromal fibroblasts, and cysteine cathepsins B and L drive the invasive growth of human melanoma cells. Am J Pathol. 2012;181(6):2202–16. Colin C, Voutsinos-Porche B, Nanni I, Fina F, Metellus P, Intagliata D, et al. High expression of cathepsin B and plasminogen activator inhibitor type-1 are strong predictors of survival in glioblastomas. Acta Neuropathol. 2009;118(6):745–54. Xiao Y, Cong M, Li J, He D, Wu Q, Tian P, et al. Cathepsin C promotes breast cancer lung metastasis by modulating neutrophil infiltration and neutrophil extracellular trap formation. Cancer Cell. 2021;39(3):423–e377. Schweiger A, Staib A, Werle B, Krasovec M, Lah TT, Ebert W, et al. Cysteine proteinase cathepsin H in tumours and sera of lung cancer patients: relation to prognosis and cigarette smoking. Br J Cancer. 2000;82(4):782–8. Campo E, Muñoz J, Miquel R, Palacín A, Cardesa A, Sloane BF, et al. Cathepsin B expression in colorectal carcinomas correlates with tumor progression and shortened patient survival. Am J Pathol. 1994;145(2):301–9. Wang J, Chen L, Li Y, Guan XY. Overexpression of cathepsin Z contributes to tumor metastasis by inducing epithelial-mesenchymal transition in hepatocellular carcinoma. PLoS ONE. 2011;6(9):e24967. Joyce JA, Baruch A, Chehade K, Meyer-Morse N, Giraudo E, Tsai FY, et al. Cathepsin cysteine proteases are effectors of invasive growth and angiogenesis during multistage tumorigenesis. Cancer Cell. 2004;5(5):443–53. Navab R, Mort JS, Brodt P. Inhibition of carcinoma cell invasion and liver metastases formation by the cysteine proteinase inhibitor E-64. Clin Exp Metastasis. 1997;15(2):121–9. Bruchard M, Mignot G, Derangère V, Chalmin F, Chevriaux A, Végran F, et al. Chemotherapy-triggered cathepsin B release in myeloid-derived suppressor cells activates the Nlrp3 inflammasome and promotes tumor growth. Nat Med. 2013;19(1):57–64. Shree T, Olson OC, Elie BT, Kester JC, Garfall AL, Simpson K, et al. Macrophages and cathepsin proteases blunt chemotherapeutic response in breast cancer. Genes Dev. 2011;25(23):2465–79. Di Rosa M, Sanfilippo C, Libra M, Musumeci G, Malaguarnera L. Different pediatric brain tumors are associated with different gene expression profiling. Acta Histochem. 2015;117(4–5):477–85. Ji C, Zhao Y, Kou YW, Shao H, Guo L, Bao CH, et al. Cathepsin F Knockdown Induces Proliferation and Inhibits Apoptosis in Gastric Cancer Cells. Oncol Res. 2018;26(1):83–93. Janic A, Valente LJ, Wakefield MJ, Di Stefano L, Milla L, Wilcox S, et al. DNA repair processes are critical mediators of p53-dependent tumor suppression. Nat Med. 2018;24(7):947–53. Song L, Wang X, Cheng W, Wu Y, Liu M, Liu R, et al. Expression signature, prognosis value and immune characteristics of cathepsin F in non-small cell lung cancer identified by bioinformatics assessment. BMC Pulm Med. 2021;21(1):420. Berchem G, Glondu M, Gleizes M, Brouillet JP, Vignon F, Garcia M, et al. Cathepsin-D affects multiple tumor progression steps in vivo: proliferation, angiogenesis and apoptosis. Oncogene. 2002;21(38):5951–5. Liaudet-Coopman E, Beaujouin M, Derocq D, Garcia M, Glondu-Lassis M, Laurent-Matha V, et al. Cathepsin D: newly discovered functions of a long-standing aspartic protease in cancer and apoptosis. Cancer Lett. 2006;237(2):167–79. Wang Z, Chen K, Zhang K, He K, Zhang D, Guo X, et al. Agrocybe cylindracea fucoglucogalactan induced lysosome-mediated apoptosis of colorectal cancer cell through H3K27ac-regulated cathepsin D. Carbohydr Polym. 2023;319:121208. Zhang J, Lin Y, Hu X, Wu Z, Guo W. VPS52 induces apoptosis via cathepsin D in gastric cancer. J Mol Med. 2017;95(10):1107–16. Lecaille F, Chazeirat T, Saidi A, Lalmanach G, Cathepsin V. Molecular characteristics and significance in health and disease. Mol Aspects Med. 2022;88:101086. Li H, Ren B, Yu S, Gao H, Sun PL. The clinicopathological significance of thymic epithelial markers expression in thymoma and thymic carcinoma. BMC Cancer. 2023;23(1):161. Kiuchi S, Tomaru U, Ishizu A, Imagawa M, Kiuchi T, Iwasaki S, et al. Expression of cathepsins V and S in thymic epithelial tumors. Hum Pathol. 2017;60:66–74. Sereesongsaeng N, McDowell SH, Burrows JF, Scott CJ, Burden RE. Cathepsin V suppresses GATA3 protein expression in luminal A breast cancer. Breast cancer research: BCR. 2020;22(1):139. Sevenich L, Schurigt U, Sachse K, Gajda M, Werner F, Müller S, et al. Synergistic antitumor effects of combined cathepsin B and cathepsin Z deficiencies on breast cancer progression and metastasis in mice. Proc Natl Acad Sci USA. 2010;107(6):2497–502. Tables Table 1 is available in the Supplementary Files section. Supplementary Materials Supplementary Materials are not available with this version. Additional Declarations No competing interests reported. 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above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2.ForestplotCTStoMLC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4438111/v1/cef1627ac8c7252165a2b17f.jpg"},{"id":57705256,"identity":"3b628998-cd00-4751-b971-ea66b958c724","added_by":"auto","created_at":"2024-06-04 14:49:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1462405,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure3.HotplotforPositiveMRanalysesandSensitivityAnalyses.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4438111/v1/a99047cacf6f5fde5e90856c.jpg"},{"id":57705262,"identity":"00a629bc-3a73-4873-9908-3fd618ddd64e","added_by":"auto","created_at":"2024-06-04 14:49:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":578267,"visible":true,"origin":"","legend":"\u003cp\u003eSee 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legend.\u003c/p\u003e","description":"","filename":"Figure8.Causalnetworkof3CTSs13plasmaproteinsandMLC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4438111/v1/1ceaf14a8c3c5a7d99eb5852.jpg"},{"id":58468573,"identity":"d331b4fc-7df1-4f3c-8ea6-7480e9f3185c","added_by":"auto","created_at":"2024-06-17 04:54:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7988260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4438111/v1/39f2e03a-b91c-4aeb-a03c-7447de37eecd.pdf"},{"id":57706457,"identity":"f9551f95-a526-4e9e-aa14-1cc4fc327ab7","added_by":"auto","created_at":"2024-06-04 14:57:10","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":96651,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.MediatingeffectsforplasmaproteinsincasualrelationshipbetweenCTSandMLC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4438111/v1/23e61d8d89926800ad1e68e3.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cathepsins Influence Metastatic Liver Cancer via Plasma Proteins: a Mendelian randomization Study","fulltext":[{"header":"0 .Introduction","content":"\u003cp\u003eMetastatic liver cancer (MLC), a malignancy originating from various primary sites and spreading to the liver(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), is a significant entity within the spectrum of malignant tumors. MLC arises from diverse sources such as the colon, rectum, liver, pancreas, stomach, breast, lung, etc(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It is noteworthy that MLC plays a pivotal role in cancer progression and patient mortality rates and is closely linked to reduced 5-year survival rates and diminished quality of life(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Despite advancements in comprehensive treatments, including surgery, chemotherapy, ablation, targeted and immuno-therapies, contributing to substantial progress in MLC diagnosis and management etc, have made great progress of MLC diagnosis and treatment, it remains a critical determinant of poor prognosis for patients with malignant neoplasms. For instance, colorectal cancer (CRC), the most prevalent primary tumor associated with MLC, affeCTS approximately 25% of newly diagnosed patients and 40%-50% of advanced CRC patients develop liver metastases(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), Without prompt treatment, the median survival time can be reduced to 6\u0026ndash;12 months, with a 5-year survival rate of merely 9%(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Thus, understanding the pathogenesis of MLC and finding breakthroughs in its diagnosis and treatment is of paramount importance.\u003c/p\u003e \u003cp\u003eCTSs are important components of lysosomal hydrolysis system, encompassing cysteine (e.g.,CTSB, C, F, H, K, L, O and V), aspartic acid (CTSD and E) and serine families (CTSA and G) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). CTSs participate in almost all lysosomal related processes, including protein metabolic degradation, lipid metabolism, autophagy, stress response, antigen presentation, growth factor receptor cycling and lysosomal-mediated cell death(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Consequently, the dysregulation of CTSs activity is closely linked to a range of diseases, such as cardiovascular diseases(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), osteoporosis and arthritis(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and neurodegenerative diseases(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have highlighted the association between CTS and tumorigenesis. CTSB, in particular, is implicated in the progression of various malignant diseases, including head and neck, breast, lung, melanoma, colon, and osteosarcoma(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In highly metastatic LM5 and LM7 cells, the expression of CTSD, CTSK and CTSL are increased, and the expression of CTSF, CTSH and CTSV are decreased(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). While other studies have found no significant difference in CTSB activity between hepatocellular carcinoma and normal tissues or between pre- and post- operative peripheral blood(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). CTSD has been shown to induce or inhibit cell apoptosis depending on microenvironmental changes(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and CTSF plays an crucial role in the suppression of gastric cancer by LINC00982 (Long noncoding RNA LINC00982) inhibiting gastric cancer(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These findings suggest that the relationship between different CTSs and tumors may vary(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In terms of tumor metastasis, CTS is thought to facilitate the process; for example, the combined depletion of CTSS by macrophages and tumor cells significantly reduces brain metastasis in breast cancer(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and CTSL and CTSS promote liver and lung metastasis of pancreatic cance(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, few studies have clearly clarified the causal relationship between CTSs and MLC.\u003c/p\u003e \u003cp\u003ePlasma proteins play key roles in numerous biological processes, including signal transduction, material transport, growth, repair and defense(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Imbalances in plasma protein expression are frequently linked to pathological processes, particularly tumorigenesis. Numerous studies have reported positive(\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) or negatively(\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) correlations between plasma protein levels and tumor development, indicating that plasma protein levels may be influenced by tumors or play a role in promoting or inhibiting tumor growth. Consequently, plasma proteins are widely utilized as biomarkers or therapeutic targets in clinical settings(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Nonetheless, our understanding of the complex relationship between plasma proteins and tumors, especially MLC, remains limited.\u003c/p\u003e \u003cp\u003eThe application of genome-wide association studies (GWAS) has led to the widespread adoption of MR for disease etiology and causal inference. This method employs genetic variation as Instrumental variable (IV) to infer the relationship between exposure and outcome(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), based on three core assumptions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the IV is closely related to exposure (correlation hypothesis); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the IV is unaffected by confounders influencing exposure and outcome (independent assumption); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the IV influences the outcome solely through the exposure under investigation, without additional pathways (exclusion limiting hypothesis). Compared to traditional observational studies, this approach effectively mitigates the impact of common confounders, providing a more reliable depiction of causality and suggesting directional relationships(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we employed two-sample MR analysis to explore the potential causal relationship between CTSs, plasma proteins and MLC through univariable and mediating MR method. This work aims to offer insights into the diagnosis, treatment, and mechanistic research of MLC.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1 Study Design\u003c/h2\u003e\n \u003cp\u003eThe study design is outlined in Fig.\u0026nbsp;1. We conducted a two-sample MR analysis utilizing data from GWAS. Initially, we investigated the potential causal relationships between 11 CTSs (B, E, F, G, H, O, S, L, A, D, V) and MLC and screened 3 positive CTSs. Subsequently, we examined the causal associations between plasma proteins and MLC. Finally, we identified plasma proteins that may serve as mediators in the relationship between the 3 CTSs and MLC, thereby calculating mediated effect and establishing a comprehensive causal network among these factors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2 Data Sources and Instrumental Variables Selection\u003c/h2\u003e\n \u003cp\u003eSummary data for CTSs were sourced from two large-scale GWAS studies (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003c/span\u003e). Data for CTSB, E, F, G, H, O, S, L were from a study of Sun BB et al.(\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e), in which 3301 samples and 10 534 735 SNPs (Single nucleotide polymorphism) were included. Data for CTSA, D, V and plasma proteins were obtained from the study by Suhre K et al.(\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e), involving 1,000 samples and 501,428 SNPs. MLC data were extracted from the study by Jiang L et al.(\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e), which comprised 456,348 individuals of European descent from the UK Biobank (UKB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003c/span\u003e). All samples were of European ancestry and randomized for sex and age was employed to minimize potential biases.\u003c/p\u003e\n \u003cp\u003eIVs were selected throng the following criteria: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) an association threshold of p \u0026lt; 5e-6, (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) r2 \u0026lt; 0.001, p2 = 10000 KB, (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) F \u0026gt; 10. The F-statistic formula is as follows(\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e). Summary data for the selected IVs are provided in Supplementary Material 1.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e1.3 MR Analysis\u003c/h2\u003e\n \u003cp\u003eCausality was primarily ascertained using the IVW method. For exposures with a single IV, the Wald ratio method was applied. Additionally, BW, MR-Egger regression, WM and MR-conmix methods were employed for corroborative analysis.\u003c/p\u003e\n \u003cp\u003eThe IVW(\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e) method assumes that all SNPs are valid and independent, with the regression intercept constrained to zero, and the inverse of the outcome variance used as the weight for fitting. This method provides the most accurate results when each genetic variant fulfills the assumptions of instrumental variables, making IVW the gold standard for causal effect assessment. The BW(\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e) method accounts for the uncertainty of weak effects due to polygenicity and addresses potential violations of IV assumptions caused by polytropy by detecting outliers through Bayesian weighting. It is also corrected for the posterior covariance, which is often underestimated in variative inference, using closed-form formulas, thereby enhancing the reliability of the test method. MR-conmix(\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e) provides causal estimates even in the presence of invalid instrumental variables and was primarily used for supplementary results in this study.\u003c/p\u003e\n \u003cp\u003eSummary data of MR analysis are provided in Supplementary Material 2, and scatter plots for positive exposures are provided in Supplementary Material 3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e1.4 Sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eA range of methods were employed for sensitivity analysis(\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). Heterogeneity was assessed using Cochran's Q test, with a Q_pval \u0026gt; 0.05 indicating the absence of heterogeneity, in which case the fixed effect model was utilized. Otherwise, the random effects model (IVW-mre) was selected for further analysis(\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e). Pleiotropy was evaluated using the MR-PRESSO test and MR-Egger intercept, with a p-value \u0026gt; 0.05 suggesting the absence of horizontal pleiotropy. Sensitivity analysis and visualization were conducted using the leave-one-out method(\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSummary data of sensitivity analysis are provided in Supplementary Material 2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e1.5 Statistical software\u003c/h2\u003e\n \u003cp\u003eThe research primarily utilized TwosampleMR, MR-PRESSO and other R packages (version 4.2.1) for analysis, with graphical representations created using GraphPad Prism 9.5 and Adobe Illustrator tools.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Investigating the Causal Relationship Between 11 Types of CTSs and MLC\u003c/h2\u003e \u003cp\u003eMR analysis (as depicted in Figs.\u0026nbsp;2 and 3) indicated that among the 11 CTSs, increased levels of CTSF (OR\u0026thinsp;=\u0026thinsp;0.500, 95%CI\u0026thinsp;=\u0026thinsp;0.593\u0026thinsp;~\u0026thinsp;0.864, p-values for IVW, BW, WM, and MR-ConMic\u0026thinsp;\u0026lt;\u0026thinsp;0.05), CTSD (OR\u0026thinsp;=\u0026thinsp;0.017, 95%CI\u0026thinsp;=\u0026thinsp;0.548\u0026thinsp;~\u0026thinsp;0.942, p-values for IVW, BW\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and CTSV (OR\u0026thinsp;=\u0026thinsp;0.023, 95%CI\u0026thinsp;=\u0026thinsp;0.455\u0026thinsp;~\u0026thinsp;0.943, p-values for IVW, BW\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were associated with a reduced risk of MLC (b\u0026thinsp;\u0026lt;\u0026thinsp;0). Additionally, MR-Egger regression suggested an association between CTSB and MLC (p\u0026thinsp;=\u0026thinsp;0.035, OR\u0026thinsp;=\u0026thinsp;1.645, 95%CI\u0026thinsp;=\u0026thinsp;1.077\u0026ndash;2.514, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, b\u0026thinsp;=\u0026thinsp;0.498). Bayesian analysis indicated a causal relationship between CTSZ and MLC (p\u0026thinsp;=\u0026thinsp;0.002, OR\u0026thinsp;=\u0026thinsp;0.718, 95%CI\u0026thinsp;=\u0026thinsp;0.585\u0026ndash;0.881, b = -0.331), while IVW showed no statistical significance for the two associations. The remaining analyses indicated negative results.\u003c/p\u003e \u003cp\u003eSpecial considerations for sensitivity analysis revealed heterogeneity in the associations of CTSZ and CTSA with MLC, as indicated by the Q test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further analysis using the random effects model (IVW-mre) confirmed the absence of a causal relationship between CTSZ and CTSA with MLC (p-IVW-mre\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The MR PRESSO results for CTSZ were p\u0026thinsp;=\u0026thinsp;0.029 and p-distortion Test\u0026thinsp;=\u0026thinsp;0.338, indicating no evidence for horizontal pleiotropy. However, due to SNP limitations, horizontal pleiotropy test results for CTSV could not be obtained. Combined with the Q test, MR-Egger intercept and PRESSO test results, no heterogeneity or horizontal pleiotropy was detected in other exposures. Furthermore, leave-one-out analysis demonstrated no marked difference in the causal estimations of CTSs on MLC, suggesting that the identified causal associations were not influenced by any single IV (as shown in Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Reverse MR Analysis of CTS and MLC\u003c/h2\u003e \u003cp\u003eTo explore the potential reverse causality between CTSs and MLC, a reverse MR analysis was performed. The results showed no indication of reverse causality between the 11 CTSs and MLC, as illustrated in Fig.\u0026nbsp;5. Furthermore, various sensitivity analyses demonstrated no heterogeneity or horizontal pleiotropy. The leave-one-out method further confirmed that no individual SNPs significantly impacted the overall results, as shown in Fig.\u0026nbsp;4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identifying the Mediating Factors: Plasma Proteins\u003c/h2\u003e \u003cp\u003eTo elucidate the potential mechanism of CTSs on MLC, MR Analysis was performed with 1124 plasma proteins as the exposure and MLC as the outcome. Initially, 42 plasma proteins were identified as significantly correlated with MLC through IVW screening (Fig.\u0026nbsp;6).\u003c/p\u003e \u003cp\u003eSubsequently, MR Analysis between the 3 CTSs and the 42 plasma proteins was performed, suggesting that 13 plasma proteins may mediate the relationship between CTS and MLC. Specifically, CTSF was found to potentially mediate the effects of Endostatin, CDK8/cyclin C (Cyclin-dependent kinase 8:Cyclin-C complex), RBP (Retinol-binding protein 4), Ficolin-3, Prekallikrein (Plasma kallikrein), GIB (Phospholipase A2), MAPK14 (Mitogen-activated protein kinase 14), Caspase-10 and CPNE1 (Copine-1) to affect the risk of MLC. CTSD was shown to mediate the effects of ARGI1 (Arginase-1), RBP (Retinol-binding protein 4), and Tropomyosin 2 (Tropomyosin beta chain), while CTSV was associated with the mediation of CTSD, CNTN2 (Contactin-2), sRAGE (Advanced glycosylation end product-specific receptor, soluble), Tropomyosin 2, MAPK14(Mitogen-activated protein kinase 14), and CPNE1. Some proteins could not undergo sensitivity analysis due to a lack of enough IVs, but the majority of the conducted sensitivity analyses demonstrated robust results (Fig.\u0026nbsp;3). And the leave-one-out method showed no individual SNPs significantly impacted the overall results (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eAmong these 13 plasma proteins, elevated levels of Endostatin, ARGI1, CDK8/cyclin C, Ficolin-3, sRAGE, Prekallikrein, GIB, MAPK14, and CPNE1 were found to potentially reduce MLC risk, whereas increased levels of CNTN2, RBP, Tropomyosin 2, and Caspase-10 may elevate MLC risk (Fig.\u0026nbsp;6). Reverse MR of the 13 plasma proteins in relation to MLC, using IVW, did not yield significant results, implying that MLC may not influence the levels of these plasma proteins (Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eThe total effects of CTSs on MLC (b0) and the effects of CTSs on 13 plasma proteins (b1) were established. After the SNPs used in the calculation of b1 were excluded, the effects of 13 plasma proteins on MLC were further calculated (b2). Based on these data above, the mediating effects of plasma proteins were calculated using the formula: Mediating effect\u0026thinsp;=\u0026thinsp;b0 - b1*b2 (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Establishing the Causal Network of CTS, Plasma Proteins, and MLC\u003c/h2\u003e \u003cp\u003eFurther MR analysis was conducted on the 13 plasma proteins in relation to the 3 CTSs, revealing that ARGI1, RBP, Tropomyosin 2, and GIB were causally associated with CTSD, and MAPK14 was associated with CTSV. Mutual MR analysis of the 13 plasma proteins was performed to explore their interrelationships, with a causal association identified between sRAGE and Ficolin-3 (Fig.\u0026nbsp;5)\u003c/p\u003e \u003cp\u003eBased on these results above, a comprehensive causal network of 3 CTSs, 14 plasma proteins and MLC was established (Fig.\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cp\u003eIn this study, we utilized genetic variants to infer causal relationships, revealing potential causal associations among 11 CTSs, plasma proteins, and MLC through reciprocal MR analysis. Based on the European population, we draw the following conclusions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) CTSF, D, and V may reduce the risk of MLC; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)42 plasma proteins may have a causal relationship with MLC; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)13 plasma proteins potentially mediating the relationship between the 3 CTSs and MLC.\u003c/p\u003e \u003cp\u003eThe mechanisms underlying the development and progression of MLC have garnered significant interest. The classic \"seed and soil\" hypothesis posits that the interplay of complex biological systems facilitates the metastatic process to the liver(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Indeed, MLC is contingent upon microenvironment changes in the the primary tumor, metastasis pathways and the liver, involving diverse processes such as immunity and metabolism(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Various proteins, including CTSs and plasma proteins, have been confirmed to play crucial role in these processes.\u003c/p\u003e \u003cp\u003eCTS, a key hydrolase in lysosomes, is the principal effector of protein catabolism and autophagy(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), and it plays a vital role in numerous physiological processes in the human body(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The relationship between CTSs and tumors has attracted considerable attention. Studies have demonstrated that CTSs alters the tumor microenvironment through the transport and degradation of extracellular matrix (ECM)(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), as well as the activation, processing or degradation of various chemokines, growth factors and cytokines. CTSs can also release cell adhesion molecules involved in tissue invasion and metastasis(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), highlighting the close connection between CTSs and tumors and their significant potential in the field of oncology. On one hand, CTSs have been considered predictive and prognostic indicators for various tumors, with their levels changing in glioblastoma (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), breast cancer(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) lung cancer(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), colorectal cancer(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), liver cancer(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) and other malignancies. The increase of CTSs in most tumors indicates a state of progression, poor prognosis and low survival probability. On the other hand, the role of CTSs as tumor therapeutic targets has been continuously explored. For instance, ASPER-29, a novel inhibitor of CTSL and CTSS, has shown a marked ability to inhibit pancreatic cancer cell metastasis(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, the pan-CTS inhibitor JPM-OEt significantly reduces tumor burden, angiogenesis, and invasion in RIP1-Tag2 mice(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), and its derivative E-64 has also been confirmed to prevent liver colonization of lung cancer cells in experiments(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Recent studies have even suggested that CTS may affect the efficacy of tumor treatment(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe possible inhibitory effect of three CTSs on MLC found in this study aligns with the conclusions of some previous studies. In brain tumors, CTSF levels were lower in ependymomas, glioblastomas and medulloblastomas than in normal brain(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Ji C et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)found that down-regulation of CTSF expression can effectively inhibit GC cell apoptosis and promote its proliferation, suggesting that the CTSF gene plays a tumor suppressor role in GC and may become a target for GC treatment. Zheng L et al. also discovered that LINC00982 promotes the expression of CTSF, thereby inhibiting gastric cancer progression(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).Further more, knockdown of CTSF in the absence of PUMA and p21 can induce leukemia development(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), and another study suggested that CTSF may play an anti-tumor role by regulating the immune response in NSCLC(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Regarding the effect of CTSD on tumors, it is believed that CTSD can induce or inhibit cell apoptosis according to microenvironmental changes(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Previous studies have shown that CTSD can promote tumor proliferation, invasion and prevent apoptosis(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), as well as inhibit tumor growth(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). CTSV, a relatively newly identified CTS, has been suggested to play a pro-tumor role(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e), but it has also been reported to be down-regulated in highly metastatic LM5 and LM7 cells(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and is associated with good prognosis in thymoma(\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e) and estrogen ER-negative breast cancer(\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Conversely, CTSB, CTSL, CTSS, CTSZ, etc, which have been more extensively studied, have been indicated to promote tumor progression in previous researches(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e), while IVW in this study suggested no causal relationship between them and MLC. Only MR-Egger results suggested that increased CTSB levels promotes MLC progress and BW suggested a negative relationship between CTSZ and MLC.\u003c/p\u003e \u003cp\u003eSimilarly, plasma exert significant influence on tumor growth, migration, invasion and the construction of microenvironment. Numerous prior investigations have endeavored to harness plasma proteins as pivotal biomarkers and therapeutic targets for cancer, achieving measurable success and practical clinical application(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In an effort to delve into the interplay between plasma proteins and metastatic liver cancer (MLC), and to uncover potential mediators, this study employed a series of Mendelian randomization (MR) analyses. The findings revealed 43 plasma proteins with a causal link to MLC, among which 13 were identified as intermediary proteins, including Endostatin. The relationship between the majority of these proteins and MLC aligns with prior research: Endostatin(\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), Ficolin-3(\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), sRAGE(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), Prekallikrein(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), GIB(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and ARGI1(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) were negatively correlated with MLC, implying that elevated levels of these proteins may confer a reduced risk of MLC. Conversely, increased levels of CNTN2(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), RBP(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and Tropomyosin 2(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) may heighten MLC risk. However, divergences from previous findings was observed with CPNE1(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), MAPK14(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and CDK8NAcyclin C(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), that are, this study suggested an inverse causal relationship between the three proteins and MLC, which contrary to their previously proposed roles in promoting tumorigenesis. In conclusion, plasma proteins may play different roles in different tumor microenvironments and have different or even opposite effects on tumors.\u003c/p\u003e \u003cp\u003eWhile this study demonstrates some important findings, there are several limitations. Firstly and foremost, data constraints hindered access to a comprehensive pool of MLC-related GWAS data from diverse sources, precluding a more nuanced and detailed investigation. Secondly, the number of SNPs limited the application of a comprehensive suite of MR methods for sensitivity analyses, necessitating further validation of the robustness of certain results. Nonetheless, by combining multiple sensitivity analyses, our study provides strong evidence for most of the positive results. Additionally, the study's focus on the European population means that the findings may not be generalizable to other ethnicities, while it do hold referential value for analogous research in diverse populations. Lastly, the discordance between the effects of certain proteins observed in this study and previous research warrants further investigation to uncover potential unknown mechanisms.\u003c/p\u003e \u003cp\u003eIn summary, this study identified increased levels of CTS variants CTSF, CTSD, and CSTV as potentially reducing the risk of MLC. It also identified plasma proteins that may influence MLC risk, explored mediating factors, and established a causal network encompassing three CTS variants, 14 plasma proteins, and MLC. These proteins could offer valuable insights into the mechanisms underlying MLC, serve as markers for its occurrence, progression, and prognosis, and guide early diagnosis and evaluation of MLC outcomes. They may also represent potential therapeutic targets for MLC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecathepsin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetastatic liver cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstrumental variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einverse-variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eweighted median\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDK8/cyclin C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclin-dependent kinase 8:Cyclin-C complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRetinol-binding protein 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePrekallikrein\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlasma kallikrein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGIB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhospholipase A2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAPK14\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitogen-activated protein kinase 14\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPNE1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopine-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARGI1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArginase-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNTN2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContactin-2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTropomyosin 2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTropomyosin beta chain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll studies used publicly available GWAS abstract data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTaijun Yi:\u0026nbsp;\u003c/strong\u003eresearch designing, data acquiring and analyzing,manuscripts writing.\u003cstrong\u003e\u0026nbsp;Zejin Lin:\u0026nbsp;\u003c/strong\u003eData processing, paper revising and picture production. \u003cstrong\u003eChengrui Zhong:\u0026nbsp;\u003c/strong\u003eresearch designing and paper revising. \u003cstrong\u003eYunle Wan\u003c/strong\u003e \u003cstrong\u003eand\u003c/strong\u003e \u003cstrong\u003eGuolin Li:\u003c/strong\u003e paper revising and approving. \u003cstrong\u003eZiyan Mai, Yongling Liang, Zhiping Chen, Jiayan Wu, Zeyu Lin, Jiandong Yu, Zhu Lin, Huilin Jin\u003c/strong\u003e：Supervise and provide comments for article writing and revision. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Sun BB et al, Suhre K et al and Jiang L et al for providing public GWAS datas. We thank all authors and institutions cited and included in this article. And we thank Si-cheng Xu of Sun Yat-sen Memorial Hospital of Sun Yat-sen University for his assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsilimigras DI, Brodt P, Clavien PA, Muschel RJ, D'Angelica MI, Endo I, et al. Liver metastases. Nat reviews Disease primers. 2021;7(1):27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Ridder J, de Wilt JH, Simmer F, Overbeek L, Lemmens V, Nagtegaal I. Incidence and origin of histologically confirmed liver metastases: an explorative case-study of 23,154 patients. Oncotarget. 2016;7(34):55368\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A, Cancer statistics. 2020. CA: a cancer journal for clinicians. 2020;70(1):7\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu T, Nicolson S, Sandow JJ, Dayan S, Jiang X, Manning JA, et al. Cp1/cathepsin L is required for autolysosomal clearance in Drosophila. Autophagy. 2021;17(10):2734\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlson OC, Joyce JA. Cysteine cathepsin proteases: regulators of cancer progression and therapeutic response. Nat Rev Cancer. 2015;15(12):712\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLutgens SP, Cleutjens KB, Daemen MJ, Heeneman S. Cathepsin cysteine proteases in cardiovascular disease. FASEB journal: official publication Federation Am Soc Experimental Biology. 2007;21(12):3029\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasuda Y, Kaleta J, Br\u0026ouml;mme D. The role of cathepsins in osteoporosis and arthritis: rationale for the design of new therapeutics. Adv Drug Deliv Rev. 2005;57(7):973\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNixon RA, Cataldo AM, Mathews PM. The endosomal-lysosomal system of neurons in Alzheimer's disease pathogenesis: a review. Neurochem Res. 2000;25(9\u0026ndash;10):1161\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHusmann K, Muff R, Bolander ME, Sarkar G, Born W, Fuchs B. Cathepsins and osteosarcoma: Expression analysis identifies cathepsin K as an indicator of metastasis. Mol Carcinog. 2008;47(1):66\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiewczas M, Paczek L, Krawczyk M, Pawlak J, Bartłomiejczyk I, G\u0026oacute;rnicka B. [Enzymatic activity of cathepsin B, cathepsin B and L, plasmin, trypsin and collagenase in hepatocellular carcinoma]. Pol Arch Med Wewn. 2002;108(1):653\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinarowska A, Minarowski L, Karwowska A, Gacko M. Regulatory role of cathepsin D in apoptosis. Folia Histochem Cytobiol. 2007;45(3):159\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng L, Cao J, Liu L, Xu H, Chen L, Kang L, et al. Long noncoding RNA LINC00982 upregulates CTSF expression to inhibit gastric cancer progression via the transcription factor HEY1. Am J Physiol Gastrointest liver Physiol. 2021;320(5):G816\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSevenich L, Bowman RL, Mason SD, Quail DF, Rapaport F, Elie BT, et al. Analysis of tumour- and stroma-supplied proteolytic networks reveals a brain-metastasis-promoting role for cathepsin S. Nat Cell Biol. 2014;16(9):876\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan L, Zhao J, Zhao S, Dong T, Dong R, Liu D, et al. ASPER-29 suppresses the metastasis of pancreatic cancer cells by dual inhibition of cathepsin-L and cathepsin-S. Chemico-Biol Interact. 2022;353:109811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng Z, Li Y, Pan Y, Lan X, Song F, Sun J, et al. Cancer-derived exosomal miR-25-3p promotes pre-metastatic niche formation by inducing vascular permeability and angiogenesis. Nat Commun. 2018;9(1):5395.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Jiang Y. RACK1 affects glioma cell growth and differentiation through the CNTN2-mediated RTK/Ras/MAPK pathway. Int J Mol Med. 2016;37(1):251\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapiernik D, Urbaniak A, Kłopotowska D, Nasulewicz-Goldeman A, Ekiert M, Nowak M et al. Retinol-Binding Protein 4 Accelerates Metastatic Spread and Increases Impairment of Blood Flow in Mouse Mammary Gland Tumors. Cancers. 2020;12(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMele V, Basso C, Governa V, Glaus Garzon JF, Muraro MG, D\u0026auml;ster S et al. Identification of TPM2 and CNN1 as Novel Prognostic Markers in Functionally Characterized Human Colon Cancer-Associated Stromal Cells. Cancers. 2022;14(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang H, Zhu J, Du W, Liu S, Zeng Y, Ding Z, et al. CPNE1 is a target of miR-335-5p and plays an important role in the pathogenesis of non-small cell lung cancer. J experimental Clin cancer research: CR. 2018;37(1):131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao C, Zhu R, Shen J, Xu T, She Y, Chen Z. RBM12 regulates the progression of hepatocellular cancer via miR-497-5p/CPNE1 Axis. Environ Res. 2023;239(Pt 1):117203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang JY, Richardson BC. The MAPK signalling pathways and colorectal cancer. Lancet Oncol. 2005;6(5):322\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JG, Kim SI, Song SH, Gu JY, Lee M, Kim HK. Diagnostic and prognostic role of circulating neutrophil extracellular trap markers and prekallikrein in patients with high-grade serous ovarian cancer. Front Oncol. 2022;12:992056.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Sanea MM. Synthesis and biological evaluation of small molecule modulators of CDK8/Cyclin C complex with phenylaminoquinoline scaffold. PeerJ. 2020;8:e8649.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon SS, Eto H, Lin CM, Nakamura H, Pawlik TM, Song SU, et al. Mouse endostatin inhibits the formation of lung and liver metastases. Cancer Res. 1999;59(24):6251\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoro T, Miyake K, Suzuki-Miyake N, Igarashi T, Uchida E, Misawa T, et al. Adeno-associated viral vector-mediated expression of endostatin inhibits tumor growth and metastasis in an orthotropic pancreatic cancer model in hamsters. Cancer Res. 2004;64(20):7486\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ete Velde EA, Vogten JM, Gebbink MF, van Gorp JM, Voest EE, Borel Rinkes IH. Enhanced antitumour efficacy by combining conventional chemotherapy with angiostatin or endostatin in a liver metastasis model. Br J Surg. 2002;89(10):1302\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo JH, Ren B, Keryanov S, Tseng GC, Rao UN, Monga SP, et al. Transcriptomic and genomic analysis of human hepatocellular carcinomas and hepatoblastomas. Hepatology (Baltimore MD). 2006;44(4):1012\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang H, Jun Y, Kim S, Kim E, Jung Y, Park BJ, et al. FCN3 functions as a tumor suppressor of lung adenocarcinoma through induction of endoplasmic reticulum stress. Cell Death Dis. 2021;12(4):407.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa D, Liu P, Wen J, Gu Y, Yang Z, Lan J, et al. FCN3 inhibits the progression of hepatocellular carcinoma by suppressing SBDS-mediated blockade of the p53 pathway. Int J Biol Sci. 2023;19(2):362\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen RC, Yi PP, Zhou RR, Xiao MF, Huang ZB, Tang DL, et al. The role of HMGB1-RAGE axis in migration and invasion of hepatocellular carcinoma cell lines. Mol Cell Biochem. 2014;390(1\u0026ndash;2):271\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoeise O, Sivertsen S, Ruud TE, Bouma BN, Stadaas JO, Aasen AO. Studies on components of the contact phase system in patients with advanced gastrointestinal cancer. Cancer. 1990;65(6):1355\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng Z, Chang Y, Fan J, Ji W, Su C. Phospholipase A2 superfamily in cancer. Cancer Lett. 2021;497:165\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtta IS. Efficacy of expressions of Arg-1, Hep Par-1, and CK19 in the diagnosis of the primary hepatocellular carcinoma subtypes and exclusion of the metastases. Histol Histopathol. 2021;36(9):981\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeyer PE, Holdt LM, Teupser D, Mann M. Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol. 2017;13(9):942.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmdin CA, Khera AV, Kathiresan S. Mendelian Randomization Jama. 2017;318(19):1925\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekula P, Del Greco MF, Pattaro C, K\u0026ouml;ttgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrology: JASN. 2016;27(11):3253\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuhre K, Arnold M, Bhagwat AM, Cotton RJ, Engelke R, Raffler J, et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun. 2017;8:14357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang L, Zheng Z, Fang H, Yang J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet. 2021;53(11):1616\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21(3):223\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin MG, Judy R, Gill D, Vujkovic M, Verma SS, Bradford Y, et al. Genetics of height and risk of atrial fibrillation: A Mendelian randomization study. PLoS Med. 2020;17(10):e1003288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill D, Efstathiadou A, Cawood K, Tzoulaki I, Dehghan A. Education protects against coronary heart disease and stroke independently of cognitive function: evidence from Mendelian randomization. Int J Epidemiol. 2019;48(5):1468\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePagoni P, Korologou-Linden RS, Howe LD, Davey Smith G, Ben-Shlomo Y, Stergiakouli E, et al. Causal effects of circulating cytokine concentrations on risk of Alzheimer's disease and cognitive function. Brain Behav Immun. 2022;104:54\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinf (Oxford England). 2020;36(5):1501\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11(1):376.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu PP, Song B, Wang X, Xu YM. Serum Uric Acid Level and Multiple Sclerosis: A Mendelian Randomization Study. Front Genet. 2020;11:254.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu D, Ai Y, Zhu G, Yang Y, Ma P. Genetically predicted circulating levels of cytokines and the risk of osteoarthritis: A mendelian randomization study. Front Genet. 2023;14:1131198.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaget S. The distribution of secondary growths in cancer of the breast. 1889. Cancer Metastasis Rev. 1989;8(2):98\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Ramadori P, Pfister D, Seehawer M, Zender L, Heikenwalder M. The immunological and metabolic landscape in primary and metastatic liver cancer. Nat Rev Cancer. 2021;21(9):541\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDennem\u0026auml;rker J, Lohm\u0026uuml;ller T, M\u0026uuml;ller S, Aguilar SV, Tobin DJ, Peters C, et al. Impaired turnover of autophagolysosomes in cathepsin L deficiency. Biol Chem. 2010;391(8):913\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuinec N, Dalet-Fumeron V, Pagano M. In vitro study of basement membrane degradation by the cysteine proteinases, cathepsins B, B-like and L. Digestion of collagen IV, laminin, fibronectin, and release of gelatinase activities from basement membrane fibronectin. Biological chemistry Hoppe-Seyler. 1993;374(12):1135-46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin M, Soikkeli J, Jahkola T, Virolainen S, Saksela O, H\u0026ouml;ltt\u0026auml; E. TGF-β signaling, activated stromal fibroblasts, and cysteine cathepsins B and L drive the invasive growth of human melanoma cells. Am J Pathol. 2012;181(6):2202\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColin C, Voutsinos-Porche B, Nanni I, Fina F, Metellus P, Intagliata D, et al. High expression of cathepsin B and plasminogen activator inhibitor type-1 are strong predictors of survival in glioblastomas. Acta Neuropathol. 2009;118(6):745\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao Y, Cong M, Li J, He D, Wu Q, Tian P, et al. Cathepsin C promotes breast cancer lung metastasis by modulating neutrophil infiltration and neutrophil extracellular trap formation. Cancer Cell. 2021;39(3):423\u0026ndash;e377.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchweiger A, Staib A, Werle B, Krasovec M, Lah TT, Ebert W, et al. Cysteine proteinase cathepsin H in tumours and sera of lung cancer patients: relation to prognosis and cigarette smoking. Br J Cancer. 2000;82(4):782\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampo E, Mu\u0026ntilde;oz J, Miquel R, Palac\u0026iacute;n A, Cardesa A, Sloane BF, et al. Cathepsin B expression in colorectal carcinomas correlates with tumor progression and shortened patient survival. Am J Pathol. 1994;145(2):301\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Chen L, Li Y, Guan XY. Overexpression of cathepsin Z contributes to tumor metastasis by inducing epithelial-mesenchymal transition in hepatocellular carcinoma. PLoS ONE. 2011;6(9):e24967.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoyce JA, Baruch A, Chehade K, Meyer-Morse N, Giraudo E, Tsai FY, et al. Cathepsin cysteine proteases are effectors of invasive growth and angiogenesis during multistage tumorigenesis. Cancer Cell. 2004;5(5):443\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavab R, Mort JS, Brodt P. Inhibition of carcinoma cell invasion and liver metastases formation by the cysteine proteinase inhibitor E-64. Clin Exp Metastasis. 1997;15(2):121\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruchard M, Mignot G, Derang\u0026egrave;re V, Chalmin F, Chevriaux A, V\u0026eacute;gran F, et al. Chemotherapy-triggered cathepsin B release in myeloid-derived suppressor cells activates the Nlrp3 inflammasome and promotes tumor growth. Nat Med. 2013;19(1):57\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShree T, Olson OC, Elie BT, Kester JC, Garfall AL, Simpson K, et al. Macrophages and cathepsin proteases blunt chemotherapeutic response in breast cancer. Genes Dev. 2011;25(23):2465\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Rosa M, Sanfilippo C, Libra M, Musumeci G, Malaguarnera L. Different pediatric brain tumors are associated with different gene expression profiling. Acta Histochem. 2015;117(4\u0026ndash;5):477\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi C, Zhao Y, Kou YW, Shao H, Guo L, Bao CH, et al. Cathepsin F Knockdown Induces Proliferation and Inhibits Apoptosis in Gastric Cancer Cells. Oncol Res. 2018;26(1):83\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanic A, Valente LJ, Wakefield MJ, Di Stefano L, Milla L, Wilcox S, et al. DNA repair processes are critical mediators of p53-dependent tumor suppression. Nat Med. 2018;24(7):947\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong L, Wang X, Cheng W, Wu Y, Liu M, Liu R, et al. Expression signature, prognosis value and immune characteristics of cathepsin F in non-small cell lung cancer identified by bioinformatics assessment. BMC Pulm Med. 2021;21(1):420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerchem G, Glondu M, Gleizes M, Brouillet JP, Vignon F, Garcia M, et al. Cathepsin-D affects multiple tumor progression steps in vivo: proliferation, angiogenesis and apoptosis. Oncogene. 2002;21(38):5951\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiaudet-Coopman E, Beaujouin M, Derocq D, Garcia M, Glondu-Lassis M, Laurent-Matha V, et al. Cathepsin D: newly discovered functions of a long-standing aspartic protease in cancer and apoptosis. Cancer Lett. 2006;237(2):167\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Chen K, Zhang K, He K, Zhang D, Guo X, et al. Agrocybe cylindracea fucoglucogalactan induced lysosome-mediated apoptosis of colorectal cancer cell through H3K27ac-regulated cathepsin D. Carbohydr Polym. 2023;319:121208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Lin Y, Hu X, Wu Z, Guo W. VPS52 induces apoptosis via cathepsin D in gastric cancer. J Mol Med. 2017;95(10):1107\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLecaille F, Chazeirat T, Saidi A, Lalmanach G, Cathepsin V. Molecular characteristics and significance in health and disease. Mol Aspects Med. 2022;88:101086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Ren B, Yu S, Gao H, Sun PL. The clinicopathological significance of thymic epithelial markers expression in thymoma and thymic carcinoma. BMC Cancer. 2023;23(1):161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiuchi S, Tomaru U, Ishizu A, Imagawa M, Kiuchi T, Iwasaki S, et al. Expression of cathepsins V and S in thymic epithelial tumors. Hum Pathol. 2017;60:66\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSereesongsaeng N, McDowell SH, Burrows JF, Scott CJ, Burden RE. Cathepsin V suppresses GATA3 protein expression in luminal A breast cancer. Breast cancer research: BCR. 2020;22(1):139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSevenich L, Schurigt U, Sachse K, Gajda M, Werner F, M\u0026uuml;ller S, et al. Synergistic antitumor effects of combined cathepsin B and cathepsin Z deficiencies on breast cancer progression and metastasis in mice. Proc Natl Acad Sci USA. 2010;107(6):2497\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"},{"header":"Supplementary Materials","content":"\u003cp\u003eSupplementary Materials are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Cathepsin, Metastatic liver cancer, Plasma protein, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4438111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4438111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cathepsins (CTSs), lysosomal cysteine proteases, have been reported to play roles in the initiation, infiltration, and dissemination of tumors in previous researches. However, the precise causal relationship between CTSs and metastatic liver cancer (MLC) remains undetermined. This study aimed to investigate the potential causal relationship between CTSs and MLC, as well as to examine the mediating effect of plasma proteins in this relationship, ultimately establishing a causal network among them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data were obtained from genome-wide association analysis (GWAS). Inverse variance weighting (IVW), Bayesian weighting (BW), MR-Egger regression, Weighted median (WM) and MR-conmix methods were employed for Mendelian randomization (MR) Analysis. Sensitivity analysis included Cochran's Q test, Mr-Egger intercept, MR-PRESSO test and leave-one-out validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Univariable MR revealed that an increase in CTSF (cathepsin F), CTSD (cathepsin D), and CSTV (cathepsin V) was associated with a reduced risk of MLC among 11 CTSs. While reverse MR did not yield significant findings. And total of 42 plasma proteins were identified to have a causal relationship with MLC, among which 13 types were found to mediate the association between the 3 CTSs and MLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our study suggests a potential causal relationship involving 3 CTSs, 13 plasma proteins, and MLC. These results provide valuable references for disease prediction, targeted therapy and mechanistic research of MLC.\u003c/p\u003e","manuscriptTitle":"Cathepsins Influence Metastatic Liver Cancer via Plasma Proteins: a Mendelian randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 14:49:05","doi":"10.21203/rs.3.rs-4438111/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":"b6caad86-569c-4be8-a4bc-3370b8fcec49","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-30T06:23:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 14:49:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4438111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4438111","identity":"rs-4438111","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}