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

doi:10.21203/rs.3.rs-4438111/v1

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

https://doi.org/10.21203/rs.3.rs-4438111/v1

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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

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.

1.1 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 < 0.001, p2 = 10000 KB, (3) F > 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.

2.1 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). 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 > 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).

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.

CTS

cathepsin

SNP

Single nucleotide polymorphism

MLC

metastatic liver cancer

Mendelian randomization

instrumental variance

IVW

inverse-variance weighted

Bayesian weighted

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

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.

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Table 1 is available in the Supplementary Files section.

Supplementary Materials are not available with this version.

No competing interests reported.

Table1.MediatingeffectsforplasmaproteinsincasualrelationshipbetweenCTSandMLC.jpg

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

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Abstract

Figures

0 .Introduction

1. Materials and Methods

1.1 Study Design

1.2 Data Sources and Instrumental Variables Selection

1.3 MR Analysis

1.4 Sensitivity analysis

1.5 Statistical software

2. Results

2.1 Investigating the Causal Relationship Between 11 Types of CTSs and MLC

2.2 Reverse MR Analysis of CTS and MLC

2.3 Identifying the Mediating Factors: Plasma Proteins

2.4 Establishing the Causal Network of CTS, Plasma Proteins, and MLC

3. Discussion

Abbreviations

Declarations

References

Tables

Supplementary Materials

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Supplementary Files

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