Potential Therapeutic Strategies Targeting CRELD1: Regulation of the Immune Microenvironment in Clear Cell Renal Cell Carcinoma

Potential Therapeutic Strategies Targeting CRELD1: Regulation of the Immune Microenvironment in Clear Cell Renal Cell Carcinoma | 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 Potential Therapeutic Strategies Targeting CRELD1: Regulation of the Immune Microenvironment in Clear Cell Renal Cell Carcinoma Xue Hu, Bosheng Luo, Yingzhuo Li, Yang Wang, Jiaping Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5881486/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 Clear cell renal cell carcinoma (ccRCC) is a highly aggressive kidney cancer subtype with poor survival rates, particularly in metastatic cases. While proteomics and immune dysregulation are implicated in ccRCC, the causal relationships between circulating proteins and ccRCC remain poorly understood. This study investigates the causal roles of circulating proteins in ccRCC pathogenesis and identifies potential therapeutic targets. Methods We conducted a two-sample Mendelian randomization (MR) analysis using cis-pQTL data from genome-wide association studies (GWAS) to identify causal relationships between circulating proteins and ccRCC. Colocalization analysis was performed to validate shared genetic loci influencing both protein levels and ccRCC susceptibility. Transcriptomic data and immune infiltration analysis explored protein expression and immune regulatory roles. Molecular docking analysis identified compounds targeting key proteins. Results Two proteins, CRELD1 and KDEL2, were identified as significantly associated with ccRCC (FDR < 0.05). CRELD1 emerged as a protective factor (OR = 0.909, 95% CI: 0.879–0.940), with consistent downregulation in ccRCC tissues. KDEL2 also demonstrated a protective association (OR = 0.747), though it was paradoxically upregulated in tumor tissues, suggesting a possible compensatory response to cellular stress. Colocalization analysis confirmed shared causal variants for CRELD1 and ccRCC susceptibility (PPH3 + PPH4 > 0.9). CRELD1 positively correlated with adaptive immune cells, including T-helper and regulatory T cells, highlighting its role in modulating the tumor immune microenvironment. Molecular docking identified Gentamicin as a promising compound targeting CRELD1, with a binding energy of -6.2 kcal/mol. Conclusions CRELD1 is a novel tumor suppressor and immune regulator in ccRCC, with potential as a diagnostic biomarker and therapeutic target. Gentamicin may offer a therapeutic strategy to upregulate CRELD1, improving immune responses and tumor suppression. These findings provide actionable insights for precision oncology in ccRCC. Clear cell renal cell carcinoma Mendelian randomization CRELD1 immune infiltration proteomics therapeutic targets Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, accounting for approximately 75% of all renal cell carcinoma cases [ 1 , 2 ] . It is characterized by an aggressive clinical course and poor prognosis, particularly when diagnosed at advanced stages. Globally, ccRCC is responsible for a significant burden of cancer-related morbidity and mortality, with a 5-year survival rate of less than 10% for patients with metastatic disease [ 3 ] . Although advances in surgical techniques and systemic therapies, including immune checkpoint inhibitors and targeted agents, have improved outcomes for some patients, the overall survival rates remain unsatisfactory, underscoring the need for novel diagnostic biomarkers and therapeutic strategies [ 2 , 4 ] . ccRCC arises from the epithelial cells of the proximal tubule in the kidney and is driven by complex molecular mechanisms, including metabolic reprogramming, dysregulated angiogenesis, and immune evasion [ 5 ] . A hallmark feature of ccRCC is the inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene, which leads to the constitutive activation of hypoxia-inducible factors (HIFs) [ 6 ] . This, in turn, promotes the overexpression of pro-angiogenic factors, such as vascular endothelial growth factor (VEGF), resulting in aberrant angiogenesis and tumor progression [ 7 ] . Despite the critical role of these pathways, the molecular heterogeneity of ccRCC presents challenges in identifying reliable biomarkers and effective therapeutic targets. Emerging evidence suggests that the tumor microenvironment (TME), particularly immune infiltration, plays a pivotal role in ccRCC pathogenesis [ 8 ] . The immune landscape of ccRCC is characterized by the presence of various immune cell populations, including T-helper cells, regulatory T cells (Tregs), and tumor-associated macrophages, which interact with tumor cells to influence disease progression and response to therapy [ 9 ] . Understanding the interplay between tumor cells and the immune microenvironment is crucial for developing novel immunotherapeutic strategies. In addition to immune dysregulation, proteomic alterations have gained attention as potential drivers of ccRCC [ 10 ] . Proteins involved in cellular signaling, immune modulation, and metabolic pathways may serve as biomarkers for early diagnosis and predictors of treatment response [ 11 ] . However, the causal relationships between circulating proteins and ccRCC remain poorly understood, limiting their clinical utility. Investigating these relationships could provide insights into the molecular mechanisms underlying ccRCC and identify novel therapeutic targets. The advent of proteomics and genome-wide association studies (GWAS) has revolutionized our understanding of the genetic and molecular landscape of complex diseases, including cancer [ 12 ] . Protein quantitative trait loci (pQTL) analysis integrates genetic and proteomic data to identify genetic variants that regulate protein expression, offering a unique opportunity to uncover causal relationships between circulating proteins and disease risk [ 13 ] . Mendelian randomization (MR) further enhances this approach by leveraging genetic variants as instrumental variables to infer causality, minimizing confounding and reverse causation biases [ 14 ] . However, many previous studies on ccRCC have primarily focused on transcriptomic or proteomic correlations without establishing causal relationships between protein expression and disease susceptibility. Additionally, while immune infiltration is known to play a key role in ccRCC progression, the molecular mechanisms linking specific circulating proteins to immune regulation remain poorly understood. Finally, although targeted therapies and immune checkpoint inhibitors have improved outcomes, therapeutic resistance remains a major challenge, highlighting the need for novel biomarkers and treatment strategies. This study employed a comprehensive MR framework to investigate the causal relationships between circulating proteins and ccRCC risk. By integrating genetic and proteomic datasets, we aimed to overcome the limitations of purely correlative studies, providing a stronger foundation for identifying clinically relevant biomarkers. Specifically, our objectives were to: (1) Identify proteins with a causal role in ccRCC pathogenesis using pQTL-MR analysis, (2) Explore their expression patterns and immune regulatory roles in the tumor microenvironment, and (3) Assess their therapeutic potential using molecular docking to identify drug candidates. These insights could provide a basis for developing novel precision oncology strategies for ccRCC. [ 15 , 16 ] . Through MR analysis, we identified two proteins, CRELD1 and KDEL2, as significantly associated with ccRCC risk. CRELD1 emerged as a protective factor, with consistent downregulation observed in ccRCC tissues and across multiple cancer types [ 17 ] . This finding was supported by colocalization analysis, which confirmed shared genetic variants influencing both CRELD1 expression and ccRCC susceptibility. Immune infiltration analysis further revealed a positive correlation between CRELD1 expression and various immune cell types, including T-helper cells and Tregs, highlighting its potential role in modulating the tumor immune microenvironment [ 18 ] . These results have several clinical implications: CRELD1's consistent downregulation and protective role suggest its potential as a diagnostic biomarker for ccRCC. Measuring circulating levels of CRELD1 could aid in early detection and risk stratification, particularly in high-risk populations; The positive correlation between CRELD1 expression and immune infiltration underscores its potential as a therapeutic target. Enhancing CRELD1 expression may improve immune-mediated tumor suppression and enhance the efficacy of existing immunotherapies; Molecular docking analysis identified Gentamicin as a promising compound with strong binding affinity to CRELD1. This finding provides a foundation for developing targeted therapies that modulate CRELD1 expression or function, offering a novel approach to ccRCC treatment [ 19 ] . This study represents a significant step forward in understanding the molecular underpinnings of ccRCC. By integrating pQTL analysis, MR, and immune profiling, we identified CRELD1 as a key protective factor and potential therapeutic target in ccRCC. These findings highlight the importance of proteomic and genomic approaches in uncovering novel biomarkers and treatment strategies for complex diseases. Future research should focus on validating these results in diverse populations and exploring the therapeutic potential of CRELD1-targeting compounds in preclinical and clinical settings. Ultimately, this study underscores the value of integrating multi-omics data to advance precision oncology and improve outcomes for ccRCC patients. Method Study design This study is to investigate the association between circulating proteins (cis-pQTL) and ccRCC through a comprehensive two-sample MR analysis. To strengthen the causal inference, colocalization analysis was performed, which assessed the overlap between genetic loci associated with circulating proteins and ccRCC susceptibility [ 20 ] . This analysis identified CRELD1 as a potential key target for further investigation. Subsequently, we explored the differential expression of CRELD1 in ccRCC using transcriptome data from the TCGA database, confirming its downregulation in ccRCC [ 21 ] . These findings were supported by a pan-cancer analysis and validated using the Human Protein Atlas database, which demonstrated consistent downregulation of CRELD1 across multiple cancer types [ 22 ] . To elucidate the functional implications of CRELD1, we analyzed its association with immune infiltration in ccRCC. Results showed that CRELD1 exhibited a positive correlation with various immune cell types, such as T-helper cells and Tregs, indicating its potential involvement in shaping the tumor immune microenvironment. Additionally, the expression of CRELD1 in different immune cell types was explored, revealing high expression levels in specific immune cells, further suggesting its regulatory role in immune processes [ 23 ] . Finally, potential therapeutic strategies targeting CRELD1 were investigated using the CTD database to identify compounds that could modulate its expression [ 24 ] . Among several candidates, Gentamicin demonstrated significant binding potential with CRELD1, as evidenced by molecular docking results, which indicated a binding energy below − 5 kcal/mol. This suggests that Gentamicin might be a promising compound for therapeutic intervention. These findings are summarized in Fig. 1 , which outlines the key steps of the study, including the MR analysis, colocalization, immune infiltration, expression analysis, and molecular docking. Data sources We used a large-scale plasma protein study GWAS involving 35559 Icelanders and 4907 aptamers, which provided a powerful dataset for identifying protein quantitative trait loci (pQTLs) [ 25 ] . This large-scale analysis revealed numerous disease-associated pQTLs, shedding light on potential causal relationships between plasma proteins and a wide range of complex diseases, including cardiovascular diseases, metabolic disorders, and cancers. Importantly, the integration of pQTLs with MR analysis allowed for the identification of several proteins as putative causal factors in disease development, providing valuable insights into potential therapeutic targets and biomarkers [ 26 ] . In addition to identifying causal proteins, integrative analyses enabled the exploration of pharmacologically modifiable proteins, paving the way for novel approaches in disease prevention and treatment. For instance, leveraging high-quality proteomics data, the study highlighted candidate proteins that may be modulated to mitigate disease risk or progression, offering a translational pathway for clinical application [ 27 ] . These findings underscore the utility of combining GWAS, MR, and integrative approaches to unravel complex disease mechanisms and pinpoint actionable targets. The ccRCC dataset comes from the GWAS directory and contains a broad sample of 752817 individuals of European ancestry (ID: GCST90320058). This dataset provides abundant resources for studying the genetic basis of susceptibility to ccRCC, and combines genetic variations with circulating protein data to identify new targets for ccRCC treatment and prevention. Acquisition of cis-pqtl High quality proteomic data including 4907 aptamers and their corresponding target genes were obtained from the original literature of 4907 protein to investigate the relationship between protein levels and genetic variation. The cis region of each protein coding gene is defined as a genomic interval extending ± 1 Mb upstream and downstream of the transcription start site, including single nucleotide polymorphisms (SNPs) within this range as potential regulatory variants [ 13 , 28 ] . For the selection of instrumental variables, we used the conditions of P < 5e-8, r2 < 0.001, and club = 10000, and limited the range of SNPs to extend ± 1 Mb upstream and downstream of the transcription start site. Finally, we determined the cis pQTL and ensured that the F-value of each included SNP was greater than 10, which ensured a deep understanding of protein regulation at the genetic level. MR analysis The MR analysis was performed using the TwoSampleMR package in R, a robust tool for causal inference. For proteins with cis-pQTLs associated with only one SNP, the Wald ratio method was employed to calculate the causal estimate, offering a straightforward approach for single IV scenarios [ 29 ] . For proteins with cis-pQTLs involving multiple SNPs, the inverse-variance weighted (IVW) method was applied, which aggregates the effects of multiple SNPs to provide a more reliable and statistically robust estimate of causality [ 30 ] . This methodological approach ensures that both single and multi-SNP associations are rigorously assessed, minimizing bias and maximizing the reliability of causal inferences between circulating protein levels and ccRCC risk [ 31 ] . By leveraging the strengths of both the Wald ratio and IVW methods, the study provides a comprehensive framework for exploring genetic regulation of proteins and their role in disease etiology. By utilizing the advantages of Wald ratio and IVW method, this study provides a comprehensive framework for exploring the genetic regulation of proteins and their role in disease etiology. We used global tests of MR egger and MR-PRESSO to examine the horizontal pleiotropy of the results. Due to the inclusion of fewer than 3 SNPs, the MR egger and MR-PRESSO tests could not be performed. Therefore, we relaxed the clump conditions on the basis of including SNPs to increase the number of included SNPs (ensuring that the SNPs of cis pQTL are still in this part) and then conducted horizontal pleiotropy testing. Colocalization analysis To assess the overlap of genetic variations within candidate gene regions associated with distinct phenotypes, such as protein expression levels and disease susceptibility, we performed colocalization analysis. This analysis evaluates whether the same genetic variation influences two phenotypes simultaneously, thereby uncovering potential causal relationships. Co localization analysis was conducted using the colon R package, and two sets of GWAS summary statistical data were collected, corresponding to the cis pQTL of protein expression and disease susceptibility data, respectively. Use the same genome reference version and perform quality control on SNPs in candidate regions (usually defined as within ± 1Mb of gene transcription start sites), including allele orientation correction, deletion rate screening, and frequency filtering, to ensure data consistency. Five posterior probabilities (PPs) were co located and calculated, corresponding to different hypotheses about genetic associations within specific genomic regions: PPH0: There is no correlation between any phenotypes in this region; PPH1༚ Only the first trait is related to the region, while the second trait is not; PPH2༚ Only the second trait is related to the region, while the first trait is unrelated to it; PPH3༚ Both of these traits are related to the region, but they are caused by different genetic variations; PPH4༚ Both of these traits are related to the region and are caused by the same genetic variation [ 32 ] . The primary focus of the analysis was on PPH3 and PPH4, as these probabilities provide insights into the relationships between the two traits. A high PPH4 value strongly suggests that the two traits share a common causal variant, supporting a potential causal relationship between protein expression and disease. Conversely, a high PPH3 value indicates that the two traits are associated with different variants within the same region, suggesting independent genetic effects [ 33 ] . Differential expression and immune infiltration The expression level of CRELD1 in ccRCC was analyzed using transcriptomic data from the TCGA database and validated using The Human Protein Atlas. Transcriptome data of ccRCC patients were first obtained from the TCGA database to evaluate the differential expression of CRELD1 between tumor tissues and adjacent normal tissues. The findings were corroborated by cross-referencing expression data from The Human Protein Atlas to ensure consistency. To further explore the role of CRELD1 in the tumor microenvironment, its association with immune infiltration in ccRCC was analyzed using TCGA data. Immune infiltration analysis focused on the relationship between CRELD1 expression and immune cell populations within the tumor immune microenvironment. From TCGA Data Portal( https://portal.gdc.cancer.gov/ )After downloading the RNA sequencing data (FPKM) and corresponding clinical information of ccRCC, the ssGSEA algorithm provided in R-package GSVA [1.46.0] was used to calculate the immune infiltration of corresponding genes in ccRCC data using the markers of 24 immune cells provided in the Immunity article (Bindea, Gabriela, et al., 2013). 24 types of immune cells include aDC [activated DC]; B cells; CD8 T cells; Cytotoxic cells; DC; Eosinophils; iDC[immature DC]; Macrophages; Mast cells; Neutrophils; NK CD56bright cells; NK CD56dim cells; NK cells; pDC[Plasmacytoid DC]; T cells; T helper cells; Tcm[T central memory]; Tem[T effector memory]; TFH[T follicular helper]; Tgd[T gamma delta]; Th1 cells; Th17 cells; Th2 cells; TReg Molecular docking Firstly, from the PubChem database( https://pubchem.ncbi.nlm.nih.gov/ )Download the structural file of the target small molecule, usually in SDF format. Subsequently, it was converted to PDB format using OpenBabel software, and then converted to PDBQT format using AutoDock Tools (ADT). In this process, ensure that all atomic types in the small molecule structure are correctly identified, add necessary hydrogen atoms, and calculate and allocate partial charges for subsequent docking use. For the receptor protein CRELD1, its three-dimensional structure is obtained from the protein database (PDB), and the corresponding crystal structure file is selected. After downloading, use ADT to preprocess the protein structure: first, remove water molecules, eutectic ligands, and other non essential impurity molecules; Secondly, check and supplement missing hydrogen atoms (especially polar hydrogen) to ensure the accuracy of protein ion states; Finally, save the processed protein structure in PDBQT format. Next, molecular docking will be conducted using AutoDock software. Firstly, based on the active site or expected binding pocket of CRELD1 protein, set reasonable grid box parameters to ensure that the entire potential binding area is covered. During the docking process, Lamarckian Genetic Algorithm (LGA) was used as the search strategy. After docking, the system obtains multiple docking conformations and records the binding energy of each conformation. In order to screen for small molecules with strong binding affinity, further systematic screening was conducted based on binding energy scores, with a focus on candidate conformations with binding energies below − 5 kcal/mol. For the selected high affinity candidate compounds, PyMOL was used for three-dimensional structural visualization. By observing key interaction sites such as hydrogen bonding, hydrophobic interactions, and charge interactions, the binding mode and possible mechanism of small molecules with CRELD1 protein were deeply analyzed. Through the rigorous preprocessing, parameter settings, and system screening mentioned above, this study aims to ensure the accuracy and repeatability of data in various stages of molecular docking, thereby providing a reliable theoretical basis for further functional validation and drug design. Result The causal relationship between proteomics and ccRCC In the cis-pQTL analysis for ccRCC, a preliminary screening identified a nominal association (P < 0.05) between 84 plasma proteins and ccRCC. However, after applying a false discovery rate (FDR) correction, only two proteins, CRELD1 and KDEL2, showed statistically significant correlations with ccRCC (FDR < 0.05, Fig. 2 ). These findings suggest that these proteins may play critical roles in the pathogenesis of ccRCC. For both CRELD1 and KDEL2, Mendelian randomization analysis using the IVW method provided robust evidence supporting their association with ccRCC. Specifically, the odds ratios (ORs) for CRELD1 and KDEL2 were 0.909 (95% CI: 0.879–0.940) and 0.747 (95% CI: 0.688–0.812), respectively, with P-values < 0.001, as shown in Fig. 2 . These results highlight the protective role of CRELD1 and KDEL2 in ccRCC development. After incorporating more SNPs, both MR egger and MR-PRESSO global tests showed no pleiotropy in the MR results(Table 1 ). Table 1 Multi efficiency analysis of MR egger and MR-PRESSO global tests MR egger(egger_intercept) MR egger(pval) MR-PRESSO global test(pval) CRELD1 0.0075 0.724 0.545 KDEL2 0.0520 0.167 0.14 To rule out reverse causality, the Steiger directionality test was applied, confirming that none of the SNPs associated with CRELD1 or KDEL2 exhibited reverse causality ( Supplementary Material 2 ). This strengthens the inference that the observed associations between these proteins and ccRCC are likely causal. Detailed association results for all cis-pQTLs and ccRCC are provided in Supplementary Material 1 , while Supplementary Material 2 further validates the directionality of these associations. Notably, the protective effects of CRELD1 and KDEL2 align with their observed expression patterns in ccRCC, providing a functional basis for these genetic findings. The two proteins were verified by colocalization evidence Among the proteins associated with ccRCC, CRELD1 and KDEL2 demonstrated strong colocalization evidence in different genomic windows, with PPH3 + PPH4 > 0.9 ( Supplementary Material 3 and Fig. 3 ). This indicates a high likelihood that the genetic variants influencing these proteins also contribute to ccRCC susceptibility. For CRELD1, colocalization analysis revealed that the shared genetic variant aligns with its role as a protective factor, as supported by the odds ratio from Mendelian randomization (OR = 0.909, 95% CI: 0.879–0.940, P < 0.001). The colocalization signal for CRELD1 emphasizes its functional relevance in ccRCC pathogenesis. Similarly, KDEL2 displayed a strong colocalization signal (OR = 0.747, 95% CI: 0.688–0.812, P < 0.001), further substantiating its potential role as a protective protein. The genomic regions exhibiting strong colocalization signals for both CRELD1 and KDEL2 are enriched for regulatory elements, as indicated by annotations in Supplementary Material 3 . For example, CRELD1's colocalized variants overlap with enhancer regions associated with immune regulation, aligning with its observed role in immune infiltration and tumor microenvironment modulation. Similarly, KDEL2 colocalized variants suggest potential involvement in endoplasmic reticulum stress response pathways, which have been implicated in tumor suppression. Colocalization plots for CRELD1 and KDEL2 (Fig. 3 A and 3 B) clearly depict the overlap of genetic signals for protein expression and ccRCC susceptibility, providing strong visual confirmation of shared causal variants. These results strengthen the evidence for their mechanistic roles in ccRCC development and highlight their potential as therapeutic targets. These colocalization findings are consistent with the differential expression results, which showed downregulation of CRELD1 and KDEL2 in ccRCC tumors. The integration of colocalization and expression data suggests that genetic regulation of these proteins contributes to ccRCC pathogenesis, further supporting their candidacy for future therapeutic exploration. Expression of Protein Target Genes in ccRCC To explore the role of the identified proteins (KDEL2 and CRELD1) in ccRCC, we analyzed their expression levels using the UALCAN database. The results revealed distinct expression patterns for the two genes: KDEL2 was found to be significantly upregulated in ccRCC tumor tissues compared to normal tissues (Fig. 4 A, P < 0.05). Specifically, the median expression of KDEL2 transcripts per million (TPM) in primary tumors (~ 50 TPM) was approximately double that in normal tissues (~ 25 TPM). This result contrasts with the protective role indicated by the Mendelian randomization and colocalization analyses, which suggested KDEL2 might act as a protective factor in ccRCC pathogenesis. One possible explanation for this paradox is that KDEL2 upregulation may reflect a compensatory response to tumor-induced cellular stress, particularly in the endoplasmic reticulum (ER) stress response pathway. KDEL2 is a member of the KDEL receptor family, which is responsible for the retrieval of ER-resident chaperone proteins, such as GRP78 and GRP94, to maintain protein homeostasis. Under physiological conditions, KDEL2 may serve a protective role by preventing proteotoxic stress. However, in a tumor environment, chronic ER stress could drive KDEL2 overexpression as an adaptive mechanism, facilitating cell survival by enhancing ER-associated degradation (ERAD) and protein folding capacity. This dual role aligns with previous findings on the context-dependent functions of ER stress response proteins, where initial protective effects may transition into pro-tumorigenic adaptations in advanced disease stages. Further functional analyses, including knockdown and overexpression studies, are needed to clarify the precise role of KDEL2 in ccRCC. CRELD1 was found to be significantly downregulated in ccRCC tumor tissues compared to normal tissues (Fig. 4 B, P < 0.05). The median CRELD1 expression in primary tumors (~ 25 TPM) was substantially lower than in normal tissues (~ 35 TPM), indicating a consistent reduction in tumor samples. These results align with the protective role of CRELD1 observed in the Mendelian randomization analysis, further supporting its involvement in ccRCC development and progression. A pan-cancer expression analysis using the UALCAN database revealed that CRELD1 was downregulated in the majority of analyzed cancers (Fig. 5 ). This downregulation was statistically significant across multiple cancer types, suggesting that CRELD1 might serve as a general tumor suppressor. Specifically, in kidney cancer, CRELD1 showed one of the most pronounced reductions in expression, consistent with its protective role in ccRCC and its potential involvement in immune regulation. To validate these findings, CRELD1 expression was further assessed using data from the Human Protein Atlas, which confirmed its low expression levels in ccRCC tissues. Additionally, immunohistochemical analyses showed weaker staining for CRELD1 in tumor tissues compared to normal kidney tissues, providing additional evidence for its downregulation in ccRCC. The integration of these analyses underscores the contrasting roles of KDEL2 and CRELD1 in ccRCC. While KDEL2's elevated expression in tumors conflicts with its suggested protective role, CRELD1's consistent downregulation across datasets highlights its potential as a tumor suppressor and therapeutic target in ccRCC. Immune Infiltration of CRELD1 in ccRCC Using data from the TCGA database, the relationship between CRELD1 expression and immune cell infiltration in ccRCC was analyzed. The results revealed significant correlations between CRELD1 expression and various immune cell types, highlighting its potential role in modulating the tumor immune microenvironment. Correlation with Immune Cell Types: CRELD1 exhibited a positive correlation with several immune cell types, including T-helper cells (R = 0.264, P < 0.001) and regulatory T cells (Tregs, R = 0.252, P < 0.001) (Fig. 6 ). These findings suggest that CRELD1 may play an active role in the recruitment or regulation of these immune cells within the tumor microenvironment. Conversely, CRELD1 showed negative correlations with certain immune cell types, such as Th2 cells (R = -0.317, P < 0.001) and neutrophils (R = -0.278, P < 0.001), Th2-driven immune responses have been linked to tumor-promoting inflammation in various cancers, including ccRCC, by facilitating immunosuppressive cytokine production (e.g., IL-4, IL-13) that supports tumor growth and immune evasion. Similarly, neutrophils contribute to tumor progression through the formation of neutrophil extracellular traps (NETs), which have been shown to enhance tumor cell proliferation, metastasis, and resistance to therapy. The negative correlation of CRELD1 with these immune populations suggests a potential role in counteracting protumorigenic inflammation, possibly through modulation of immune cell recruitment or signaling pathways in the tumor microenvironment. Expression in Immune Cells: The expression levels of CRELD1 were further analyzed across different immune cell types (Fig. 7 ). The results indicated higher expression of CRELD1 in key immune cells such as T-helper cells, Tregs, and memory T cells (Tcm and Tem) in ccRCC patients with high CRELD1 expression. In contrast, immune cells such as neutrophils, macrophages, and NK cells showed relatively lower CRELD1 enrichment, aligning with the correlation results and suggesting that CRELD1's role in ccRCC may be more pronounced in adaptive immune responses rather than innate immunity. The observed correlations suggest that CRELD1 may influence the immune infiltration profile of ccRCC tumors, particularly by enhancing the recruitment or activity of T-helper cells and Tregs, which are critical for immune regulation. This highlights its potential role in shaping the immune microenvironment and influencing tumor progression. The reduced correlation with innate immune cells such as neutrophils and macrophages may indicate a more specific regulatory function within the adaptive immune system. These findings provide further evidence for CRELD1's involvement in ccRCC pathogenesis and its potential as a therapeutic target, particularly in the context of immune modulation. Analysis of Protein Drug Targets Using the CTD database, we identified small molecules that could potentially upregulate CRELD1 expression and exhibit anti-cancer properties ( Supplementary Material 4 ). Five candidates—Diuron, Gentamicin, K-7174, Flutamide, and Nimesulide—were selected for further evaluation through molecular docking experiments based on their reported ability to regulate CRELD1 and their potential therapeutic effects. Molecular docking was performed to assess the binding affinities of these compounds to CRELD1. Among the candidates: Gentamicin demonstrated the strongest binding affinity, with a binding energy of -6.2 kcal/mol, which is below the threshold of -5 kcal/mol for significant binding (Fig. 8 A). This indicates a stable and potentially effective interaction between Gentamicin and CRELD1. The other compounds, including Diuron, K-7174, Flutamide, and Nimesulide, showed binding energies above − 5 kcal/mol, indicating weaker or less stable interactions (Fig. 8 B and 8 C). As a result, these compounds were excluded from further consideration. Detailed analysis of the molecular docking results revealed that Gentamicin interacted with key amino acid residues in the CRELD1 binding pocket, forming hydrogen bonds and hydrophobic interactions that likely stabilize the ligand-receptor complex. These findings suggest that Gentamicin may modulate CRELD1 activity and potentially influence its role in ccRCC progression. Gentamicin' ability to upregulate CRELD1 expression and their stable interaction with the protein suggest potential anti-cancer effects mediated through CRELD1. Given CRELD1's role in modulating immune infiltration and tumor suppression, Gentamicin could be explored further as a therapeutic candidate for ccRCC. The integration of molecular docking and functional data highlights Gentamicin as a promising lead compound for targeting CRELD1 in ccRCC. This is consistent with its favorable binding profile and potential regulatory effects on CRELD1 expression. Future experimental studies are warranted to validate these findings and assess the therapeutic potential of Gentamicin in vivo and in vitro. Discussion This study provides novel insights into the molecular mechanisms underlying ccRCC by integrating proteomic, genetic, and transcriptomic data to identify causal proteins and their potential therapeutic implications. Using a robust MR framework combined with colocalization and functional analyses, we identified CRELD1 and KDEL2 as key proteins associated with ccRCC susceptibility, highlighting their roles in immune regulation and tumor pathophysiology. The findings of this study not only improve our understanding of ccRCC but also lay the foundation for the development of new diagnostic biomarkers and therapeutic strategies. Our two-sample MR analysis identified CRELD1 and KDEL2 as significantly associated with ccRCC risk, with strong evidence of a causal relationship supported by genetic variants. The inverse association of CRELD1 (OR = 0.909, 95% CI: 0.879–0.940, P < 0.001) and KDEL2 (OR = 0.747, 95% CI: 0.688–0.812, P < 0.001) with ccRCC risk underscores their potential protective roles. Importantly, colocalization analysis further confirmed that shared genetic variants regulate the expression of these proteins and ccRCC susceptibility, with posterior probabilities (PPH3 + PPH4 > 0.9) suggesting a high likelihood of shared causal loci. These findings strengthen the evidence that CRELD1 and KDEL2 are not merely correlated with ccRCC but play mechanistic roles in its pathogenesis. The genomic loci associated with CRELD1 and KDEL2 are enriched for regulatory elements, such as enhancers and promoters, indicating that genetic variants may influence their expression in tumor and immune-related contexts [ 34 , 35 ] . For CRELD1, colocalized variants overlap with immune regulatory regions, aligning with its observed role in modulating the tumor immune microenvironment [ 36 ] . These findings are consistent with previous studies implicating genetic regulation of immune-related proteins in ccRCC susceptibility and progression [ 37 ] . Differential expression analysis revealed that CRELD1 is significantly downregulated in ccRCC tissues compared to normal kidney tissues, as confirmed by transcriptomic data from TCGA and validation using the Human Protein Atlas. This downregulation is consistent with its protective role identified in MR analysis and suggests that reduced CRELD1 expression may contribute to tumor progression [ 21 ] . In contrast, KDEL2 was upregulated in ccRCC tumors, which appears contradictory to its protective role inferred from MR analysis. This discrepancy may reflect complex regulatory mechanisms, such as compensatory overexpression in response to tumor-induced stress or context-specific effects of KDEL2 in the tumor microenvironment. One potential explanation is that KDEL2 is involved in ER stress response pathways, where its upregulation may be triggered as an adaptive mechanism to counteract cellular stress in tumor cells. This aligns with previous studies showing that stress-responsive proteins can exhibit paradoxical expression patterns depending on tumor stage and microenvironmental conditions. Additionally, KDEL2 may have a dual role, acting protectively in early tumorigenesis by maintaining cellular homeostasis, while later contributing to tumor survival by enhancing adaptive responses to hypoxic or metabolic stress. The KDEL receptor family, including KDEL2, plays a crucial role in ER stress adaptation by regulating the retention and retrieval of chaperone proteins. Previous studies have shown that KDEL receptors are involved in the unfolded protein response (UPR) and ERAD, both of which are critical for maintaining proteostasis under stress conditions. The paradoxical expression pattern of KDEL2 in ccRCC may thus reflect an adaptive mechanism in which tumor cells exploit ER stress pathways to enhance survival. This hypothesis is supported by research demonstrating that persistent ER stress can shift cellular responses from protective to pro-survival states, ultimately promoting tumor progression. Further mechanistic studies are required to determine whether KDEL2 functions differently depending on tumor stage and microenvironmental conditions. These hypotheses warrant further investigation to determine the precise role of KDEL2 in ccRCC progression. [ 38 ] . The observed pan-cancer downregulation of CRELD1, particularly in ccRCC, highlights its potential role as a tumor suppressor. Its consistent reduction across multiple cancers suggests that CRELD1 may function as a general regulator of tumor suppression, possibly through immune modulation or inhibition of oncogenic signaling pathways [ 39 ] . The dual role of KDEL2 in ccRCC requires further investigation to clarify its context-dependent functions, particularly its involvement in endoplasmic reticulum stress and protein homeostasis, which are known to influence tumor development [ 40 ] . Immune infiltration analysis revealed that CRELD1 expression positively correlates with adaptive immune cells, including T-helper cells (R = 0.264, P < 0.001) and regulatory T cells (Tregs, R = 0.252, P < 0.001), in ccRCC. These findings suggest that CRELD1 plays a role in shaping the tumor immune microenvironment by enhancing the recruitment or activity of these immune cell types. T-helper cells and Tregs are critical for immune regulation and maintaining an anti-tumor immune response, and their association with CRELD1 supports its role in modulating immune-mediated tumor suppression. Conversely, CRELD1 expression negatively correlates with certain innate immune cells, such as neutrophils (R = -0.278, P < 0.001) and Th2 cells (R = -0.317, P < 0.001). Th2-driven immune responses have been implicated in promoting tumorigenesis through the secretion of IL-4 and IL-13, which facilitate M2 macrophage polarization and suppress cytotoxic immune responses. In ccRCC, elevated Th2 activity is associated with poor prognosis due to its role in creating an immunosuppressive microenvironment. Additionally, neutrophils contribute to ccRCC progression by forming NETs, which not only provide a structural scaffold for tumor cells but also release proteases and cytokines that enhance tumor invasion and immune escape. The observed negative correlation between CRELD1 and these immune cell types suggests that CRELD1 may play a role in limiting these pro-tumorigenic pathways. This could occur through direct suppression of Th2-associated cytokine signaling or by modulating neutrophil recruitment and NET formation. Further functional studies are needed to elucidate whether CRELD1 actively inhibits these pathways or if its downregulation in ccRCC removes a barrier to Th2/neutrophil-mediated tumor progression. These findings align with the broader role of immune dysregulation in ccRCC, where tumor cells evade immune surveillance through modulation of the tumor microenvironment [ 41 , 42 ] . Enhancing CRELD1 expression could potentially restore immune balance, reducing tumor progression and improving responses to immunotherapy. Further analysis of immune cell-specific expression patterns revealed that CRELD1 is enriched in T-helper cells, Tregs, and memory T cells, while exhibiting lower expression in innate immune cells such as macrophages and neutrophils [ 23 ] . This suggests that CRELD1 may play a more prominent role in adaptive immune responses, making it a promising target for immunomodulatory therapies. Furthermore, our findings suggest that targeting CRELD1 could provide a novel therapeutic approach by enhancing its tumor-suppressive and immune-modulatory roles. The identification of Gentamicin as a promising compound with a strong binding affinity to CRELD1 raises the possibility of developing pharmacological agents that upregulate CRELD1 expression or activity. However, it is important to acknowledge that molecular docking provides only a computational prediction of binding affinity and does not confirm actual biochemical interactions. Therefore, further experimental validation is essential to confirm these findings. Future studies should include in vitro binding assays (e.g., surface plasmon resonance or isothermal titration calorimetry) to directly measure the interaction between Gentamicin and CRELD1. Additionally, cell-based functional assays should be conducted to determine whether Gentamicin treatment effectively upregulates CRELD1 expression and influences ccRCC cell behavior. These experimental validations will provide critical insights into the therapeutic potential of Gentamicin and inform the development of CRELD1-targeted therapies. Such an approach could be particularly relevant for patients who show resistance to current targeted therapies or immune checkpoint inhibitors. Combination therapies that incorporate CRELD1-modulating agents with existing treatment regimens may improve response rates and overall patient outcomes. Future preclinical and clinical studies will be essential to validate these findings and assess the therapeutic efficacy of CRELD1-targeting drugs in ccRCC. Molecular docking analysis further supports the therapeutic potential of CRELD1 [ 43 ] . Among the tested compounds, Gentamicin demonstrated strong binding affinity to CRELD1 (binding energy: -6.2 kcal/mol), suggesting a stable interaction that could modulate its expression or activity. Gentamicin’ ability to upregulate CRELD1 and their favorable binding profile highlight their potential as lead compounds for drug development. Future studies should focus on validating these findings in preclinical models to assess their efficacy and safety in ccRCC treatment [ 44 ] . The findings of this study have important implications for precision oncology. First, the identification of CRELD1 and KDEL2 as causal proteins provides novel biomarkers for ccRCC risk stratification and early diagnosis [ 45 ] . Measuring circulating levels of these proteins could improve the identification of high-risk individuals and facilitate early intervention. Second, the integration of genetic, proteomic, and immune data underscores the value of multi-omics approaches in uncovering actionable targets for personalized cancer therapy. By leveraging patient-specific molecular profiles, therapies targeting CRELD1 or KDEL2 could be tailored to enhance efficacy and minimize off-target effects [ 46 ] . The role of CRELD1 in immune regulation also highlights its potential as a biomarker for predicting responses to immunotherapy. Patients with high CRELD1 expression may benefit more from immune checkpoint inhibitors or combination therapies aimed at enhancing adaptive immune responses. These findings could inform the development of predictive models to guide treatment decisions and optimize outcomes for ccRCC patients [ 47 ] . This study has several strengths, including the use of robust MR analysis to establish causal relationships, the integration of colocalization and expression data to validate findings, and the exploration of immune regulatory functions to provide mechanistic insights. The large sample sizes and high-quality datasets used in the analysis further enhance the reliability and generalizability of the results [ 48 ] . Despite these strengths, our study has several limitations: (1) Population-Specific GWAS Data: The GWAS data used in our Mendelian randomization analysis are predominantly derived from European populations. This may limit the generalizability of our findings to other ethnic groups, particularly given the genetic and environmental heterogeneity of ccRCC across populations. To address this limitation, future studies should aim to validate our findings in more diverse populations, including cohorts of Asian and African ancestry. Collaborative efforts integrating multi-ethnic GWAS datasets, such as the Pan-UKB or PAGE consortium, will be essential to determine whether the identified associations are consistent across ancestries. Additionally, functional validation studies using diverse patient-derived samples will help assess the relevance of CRELD1 and KDEL2 in different genetic backgrounds, ultimately improving the clinical applicability of our findings. Future studies should incorporate diverse cohorts to validate these results. (2) Preliminary Nature of Molecular Docking Results: While molecular docking identified Gentamicin as a promising compound targeting CRELD1, these results are preliminary and require experimental validation. Further in vitro and in vivo studies are necessary to confirm the binding affinity and functional effects of Gentamicin on CRELD1 expression and activity. (3) Dual Role of KDEL2 and Its Context-Dependent Effects: Our findings suggest that KDEL2 may have a dual role in ccRCC, acting as a protective factor at the genetic level (OR = 0.747) while exhibiting upregulation in tumor tissues (TPM: 50 vs. 25). One possible explanation is that KDEL2 functions as part of an adaptive ER stress response—protecting cells under normal conditions but potentially aiding tumor survival in later stages. Further functional studies are needed to determine the precise role of KDEL2 in ccRCC progression. However, several limitations should be noted. First, the study primarily focused on European populations, which may limit the generalizability of the findings to other ethnic groups [ 49 ] . Future studies should validate these results in more diverse populations to ensure broader applicability. Second, while molecular docking identified Gentamicin as a promising compound, experimental validation in preclinical and clinical settings is necessary to confirm its therapeutic potential. Finally, the functional roles of KDEL2 in ccRCC remain unclear, requiring further investigation to resolve the discrepancies between its expression patterns and inferred protective effects. In conclusion, this study provides robust evidence for the causal roles of CRELD1 and KDEL2 in ccRCC pathogenesis, with CRELD1 emerging as a promising biomarker and therapeutic target. The protective role of CRELD1, its immune regulatory functions, and its downregulation in ccRCC underscore its potential as a tumor suppressor. Molecular docking analysis identified Gentamicin as a lead compound for targeting CRELD1, highlighting opportunities for drug development. These findings advance our understanding of ccRCC biology and offer new avenues for precision oncology, with implications for improving diagnosis, treatment, and outcomes for ccRCC patients. Future research should focus on validating these findings in diverse populations, particularly cohorts of non-European ancestry, and exploring the therapeutic potential of CRELD1-targeting compounds. Expanding the analysis to include Asian and African datasets will be crucial in assessing the broader relevance of these genetic associations and ensuring that potential therapeutic applications are applicable across populations. Such efforts will contribute to a more comprehensive understanding of ccRCC pathogenesis and facilitate the development of globally relevant precision medicine approaches. Conclusions Gentamicin have shown potential in upregulating CRELD1 expression, suggesting their possible role as therapeutic agents in ccRCC treatment. Given CRELD1’s function in modulating the tumor immune microenvironment and its association with favorable clinical outcomes, targeting this protein could lead to innovative therapeutic strategies. Our study highlights CRELD1 as both a biomarker for ccRCC prognosis and a candidate for future therapeutic intervention. The integration of CRELD1-targeting strategies with existing immunotherapies or targeted therapies could improve patient outcomes, particularly for those with limited response to current treatments. Further research, including biochemical validation, cell-based functional assays, preclinical validation, and clinical trials, is necessary to explore the full therapeutic potential of CRELD1 modulation in ccRCC. These studies will be crucial to determine whether Gentamicin’s predicted interaction with CRELD1 translates into a functional effect in a biological system. Abbreviations ccRCC Clear cell renal cell carcinoma MR Mendelian randomization GWAS Genome-wide association studies VHL Von Hippel-Lindau HIFs Hypoxia-inducible factors VEGF Vascular endothelial growth factor TME Tumor microenvironment pQTLs Protein quantitative trait loci SNPs Single nucleotide polymorphisms IVW Inverse-variance weighted PPs Posterior probabilities PDB Protein Data Bank TPM Transcripts per million Declarations Acknowledgements Not applicable. Authors’ contributions Xue Hu: Investigation, Visualization, Writing - manuscript. Bosheng Luo: Data processing, Software, Methodology. Yingzhuo Li and Yang Wang: Data processing, Formal analysis and Supervision. Jiaping Wang: Fund preparation, Writing - editing. Funding The research is financially supported by Science and technology plan project of Science and Technology Department of Yunnan Province (No:202401AY070001-005). Availability of data and materials All datasets used in this study are publicly available. The GWAS data for circulating protein levels were obtained from the published literature (PMID: 34857953) and the GWAS Catalog (GCST ID: 90320058), accessible at https://www.ebi.ac.uk/gwas/. The transcriptomic data for ccRCC were accessed from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/, cohort ID: [TCGA-KIRC]) and The Human Protein Atlas (https://www.proteinatlas.org). Molecular docking analyses utilized protein structures from the Protein Data Bank (PDB, https://www.rcsb.org) and compound structures from PubChem (https://pubchem.ncbi.nlm.nih.gov). Detailed results of the Mendelian randomization and colocalization analyses are included in the supplementary materials. Additional data supporting the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The author declares that she has no competing interests. 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Supplementary Files SupplementaryMaterial1.csv SupplementaryMaterial2.csv SupplementaryMaterial3.xlsx SupplementaryMaterial4.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5881486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432826840,"identity":"2fb65c03-3dac-4d52-973d-2984bea006ef","order_by":0,"name":"Xue Hu","email":"","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Hu","suffix":""},{"id":432826842,"identity":"48cdc0b4-5c05-487e-ab8b-2d2be51a641f","order_by":1,"name":"Bosheng Luo","email":"","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bosheng","middleName":"","lastName":"Luo","suffix":""},{"id":432826844,"identity":"b6752074-69e7-4c18-b51c-0d8c67c2ef88","order_by":2,"name":"Yingzhuo Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingzhuo","middleName":"","lastName":"Li","suffix":""},{"id":432826845,"identity":"662a755f-624d-46e0-a235-4ac3013331a9","order_by":3,"name":"Yang Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":432826846,"identity":"96f0ef7d-1b0d-4dfe-846f-d4dde67293e9","order_by":4,"name":"Jiaping Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACxmYgkQDlHPhQISEnT4oWxoMzzlgYGzaQYCPzYd62ikSGA4SUtTM/e/BwR21i/+z2Cwd450kkMDYwP3x0A6/D2MwNEs8cT5xx50zBAcltEnnsDGzGxjn4/WImkdh2LLfhRk7CAcNtEsWMDTxs0vi1sH8Da5kP0pI4RyKx4QBBLTwgW2pyN9xIP3DgYANxWsoNEtsO1G+8kcNwsOGYhLFhMwG/GPYf3/bwZ1udsdyN9Mef/9TUycmzNz98jFdLAwMbkDoMxDwGECFmPMpBAJg8QFrqgJj9AQG1o2AUjIJRMFIBACI/VaNrEja4AAAAAElFTkSuQmCC","orcid":"","institution":"Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiaping","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-22 14:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5881486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5881486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79179778,"identity":"9f07ead3-c1a0-4d4e-ae77-8d3e1e4b1ea4","added_by":"auto","created_at":"2025-03-25 10:19:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the key analytical steps in this study. \u003c/strong\u003eThis figure illustrates the main steps of the analysis: (1) Two-sample MR analysis to explore the association between cis-pQTL and ccRCC; (2) Colocalization analysis identifying CRELD1 as a key target gene; (3) Expression analysis of CRELD1 in ccRCC and pan-cancer validation confirming its downregulation; (4) Immune infiltration analysis revealing CRELD1's positive correlation with specific immune cell types and its regulatory role in immune processes; and (5) Molecular docking analysis identifying Gentamicin as a potential therapeutic compound targeting CRELD1.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/54b733ea86412fa93f21bfc4.png"},{"id":79179766,"identity":"496ddebe-2e1e-41a2-87f5-3d89cd33e8b8","added_by":"auto","created_at":"2025-03-25 10:19:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal associations between circulating proteins and ccRCC after FDR correction.\u003c/strong\u003e This figure illustrates the results of the MR analysis identifying two proteins, CRELD1 and KDEL2, as significantly associated with ccRCC (FDR \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/3b421a23df83451890314493.png"},{"id":79179061,"identity":"6303e9b0-8c65-45bf-8cc5-8c7daf278dc6","added_by":"auto","created_at":"2025-03-25 10:11:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eColocalization analysis of CRELD1 and KDEL2 with ccRCC susceptibility. \u003c/strong\u003e(A) Colocalization plot for CRELD1 shows a strong overlap of genetic signals for circulating protein levels and ccRCC susceptibility (PPH3 + PPH4 \u0026gt; 0.9), highlighting shared causal variants. (B) Colocalization plot for KDEL2 similarly demonstrates a significant overlap of genetic signals (PPH3 + PPH4 \u0026gt; 0.9).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/4c9e249eecd43268aedeca00.png"},{"id":79179768,"identity":"ccff91e0-a34b-4f76-bda2-01045e479dda","added_by":"auto","created_at":"2025-03-25 10:19:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression of KDEL2 and CRELD1 in ccRCC.\u003c/strong\u003e (A) KDEL2 is significantly upregulated in primary tumor tissues compared to normal tissues (P \u0026lt; 0.05), with a median transcript level of ~50 TPM in tumors versus ~25 TPM in normal tissues. (B) CRELD1 is significantly downregulated in primary tumor tissues (P \u0026lt; 0.05), with a median transcript level of ~25 TPM in tumors versus ~35 TPM in normal tissues. These findings suggest opposing roles for the two proteins in ccRCC pathogenesis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/eee449643e7c4299c02fe5c3.png"},{"id":79179052,"identity":"79d5ac2f-efe1-4dc8-b029-cad23ec24a98","added_by":"auto","created_at":"2025-03-25 10:11:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer expression analysis of CRELD1.\u003c/strong\u003e CRELD1 expression is significantly downregulated in multiple cancer types, including kidney cancer. This consistent reduction supports its role as a potential tumor suppressor and therapeutic target.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/208ccd7b68ebeeea299b7813.png"},{"id":79182189,"identity":"2ba08eeb-b597-42c7-8975-7e061bbf9261","added_by":"auto","created_at":"2025-03-25 10:43:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis of CRELD1 in ccRCC.\u003c/strong\u003e CRELD1 expression is positively correlated with adaptive immune cells (e.g., T-helper cells, R = 0.264; Tregs, R = 0.252; P \u0026lt; 0.001) and negatively correlated with innate immune cells (e.g., Th2 cells, R = -0.317; neutrophils, R = -0.278; P \u0026lt; 0.001), suggesting its role in shaping the tumor immune microenvironment.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/98f0f9a8ec5149f41b3f6cf0.png"},{"id":79179068,"identity":"4152f47e-18d4-4c21-896e-75d1dfce7027","added_by":"auto","created_at":"2025-03-25 10:11:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":84874,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression of CRELD1 in immune cells in ccRCC. \u003c/strong\u003eHigh CRELD1 expression is enriched in adaptive immune cells (e.g., T-helper cells, Tregs, and memory T cells) and lower in innate immune cells (e.g., neutrophils, macrophages, and NK cells), highlighting its role in adaptive immune regulation.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/a0d471c14deed2fcec2c5dcf.png"},{"id":79179774,"identity":"1c817854-7a3d-48bf-a97d-6856c97af737","added_by":"auto","created_at":"2025-03-25 10:19:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":107938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking results of CRELD1 with small molecules. \u003c/strong\u003e(A) Gentamicin showed the strongest binding to CRELD1 (binding energy: -6.2 kcal/mol), indicating a stable interaction. (B) Other compounds, including Diuron, K-7174, Flutamide, and Nimesulide, displayed weaker binding (energy \u0026gt; -5 kcal/mol). (C) Gentamicin formed hydrogen bonds and hydrophobic interactions with key CRELD1 residues, supporting its potential as a therapeutic candidate for ccRCC.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/8df41de45f0574725aeca027.png"},{"id":81707837,"identity":"26428a04-2c72-47b5-95e9-b4871ed38262","added_by":"auto","created_at":"2025-04-30 14:01:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1742538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/607eb6dd-5ba5-4fb0-a84c-36185aaae7af.pdf"},{"id":79180857,"identity":"27425e3b-4b62-4b18-bee4-1e8d556011e5","added_by":"auto","created_at":"2025-03-25 10:27:18","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":338394,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.csv","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/62bfe307997d3cdd82e52204.csv"},{"id":79179048,"identity":"03d073f7-8097-44d5-8e3d-6be100fd425a","added_by":"auto","created_at":"2025-03-25 10:11:18","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":979592,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.csv","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/30b3878191380377541e99df.csv"},{"id":79179050,"identity":"abb89fa5-0055-4f45-a847-41a1edaf0426","added_by":"auto","created_at":"2025-03-25 10:11:18","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9684,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/631ec25d4b52cbf74d1b8e95.xlsx"},{"id":79180856,"identity":"9cfb8e01-7a45-47c8-8576-e1966d1d2419","added_by":"auto","created_at":"2025-03-25 10:27:18","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":8817,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial4.xls","url":"https://assets-eu.researchsquare.com/files/rs-5881486/v1/08db714eb5e94ec5141f999a.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential Therapeutic Strategies Targeting CRELD1: Regulation of the Immune Microenvironment in Clear Cell Renal Cell Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, accounting for approximately 75% of all renal cell carcinoma cases\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It is characterized by an aggressive clinical course and poor prognosis, particularly when diagnosed at advanced stages. Globally, ccRCC is responsible for a significant burden of cancer-related morbidity and mortality, with a 5-year survival rate of less than 10% for patients with metastatic disease\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Although advances in surgical techniques and systemic therapies, including immune checkpoint inhibitors and targeted agents, have improved outcomes for some patients, the overall survival rates remain unsatisfactory, underscoring the need for novel diagnostic biomarkers and therapeutic strategies\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eccRCC arises from the epithelial cells of the proximal tubule in the kidney and is driven by complex molecular mechanisms, including metabolic reprogramming, dysregulated angiogenesis, and immune evasion\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. A hallmark feature of ccRCC is the inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene, which leads to the constitutive activation of hypoxia-inducible factors (HIFs)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. This, in turn, promotes the overexpression of pro-angiogenic factors, such as vascular endothelial growth factor (VEGF), resulting in aberrant angiogenesis and tumor progression\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Despite the critical role of these pathways, the molecular heterogeneity of ccRCC presents challenges in identifying reliable biomarkers and effective therapeutic targets. Emerging evidence suggests that the tumor microenvironment (TME), particularly immune infiltration, plays a pivotal role in ccRCC pathogenesis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The immune landscape of ccRCC is characterized by the presence of various immune cell populations, including T-helper cells, regulatory T cells (Tregs), and tumor-associated macrophages, which interact with tumor cells to influence disease progression and response to therapy\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Understanding the interplay between tumor cells and the immune microenvironment is crucial for developing novel immunotherapeutic strategies. In addition to immune dysregulation, proteomic alterations have gained attention as potential drivers of ccRCC\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Proteins involved in cellular signaling, immune modulation, and metabolic pathways may serve as biomarkers for early diagnosis and predictors of treatment response\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, the causal relationships between circulating proteins and ccRCC remain poorly understood, limiting their clinical utility. Investigating these relationships could provide insights into the molecular mechanisms underlying ccRCC and identify novel therapeutic targets.\u003c/p\u003e \u003cp\u003eThe advent of proteomics and genome-wide association studies (GWAS) has revolutionized our understanding of the genetic and molecular landscape of complex diseases, including cancer\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Protein quantitative trait loci (pQTL) analysis integrates genetic and proteomic data to identify genetic variants that regulate protein expression, offering a unique opportunity to uncover causal relationships between circulating proteins and disease risk\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Mendelian randomization (MR) further enhances this approach by leveraging genetic variants as instrumental variables to infer causality, minimizing confounding and reverse causation biases\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, many previous studies on ccRCC have primarily focused on transcriptomic or proteomic correlations without establishing causal relationships between protein expression and disease susceptibility. Additionally, while immune infiltration is known to play a key role in ccRCC progression, the molecular mechanisms linking specific circulating proteins to immune regulation remain poorly understood. Finally, although targeted therapies and immune checkpoint inhibitors have improved outcomes, therapeutic resistance remains a major challenge, highlighting the need for novel biomarkers and treatment strategies.\u003c/p\u003e \u003cp\u003eThis study employed a comprehensive MR framework to investigate the causal relationships between circulating proteins and ccRCC risk. By integrating genetic and proteomic datasets, we aimed to overcome the limitations of purely correlative studies, providing a stronger foundation for identifying clinically relevant biomarkers. Specifically, our objectives were to: (1) Identify proteins with a causal role in ccRCC pathogenesis using pQTL-MR analysis, (2) Explore their expression patterns and immune regulatory roles in the tumor microenvironment, and (3) Assess their therapeutic potential using molecular docking to identify drug candidates. These insights could provide a basis for developing novel precision oncology strategies for ccRCC.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThrough MR analysis, we identified two proteins, CRELD1 and KDEL2, as significantly associated with ccRCC risk. CRELD1 emerged as a protective factor, with consistent downregulation observed in ccRCC tissues and across multiple cancer types\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This finding was supported by colocalization analysis, which confirmed shared genetic variants influencing both CRELD1 expression and ccRCC susceptibility. Immune infiltration analysis further revealed a positive correlation between CRELD1 expression and various immune cell types, including T-helper cells and Tregs, highlighting its potential role in modulating the tumor immune microenvironment\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. These results have several clinical implications: CRELD1's consistent downregulation and protective role suggest its potential as a diagnostic biomarker for ccRCC. Measuring circulating levels of CRELD1 could aid in early detection and risk stratification, particularly in high-risk populations; The positive correlation between CRELD1 expression and immune infiltration underscores its potential as a therapeutic target. Enhancing CRELD1 expression may improve immune-mediated tumor suppression and enhance the efficacy of existing immunotherapies; Molecular docking analysis identified Gentamicin as a promising compound with strong binding affinity to CRELD1. This finding provides a foundation for developing targeted therapies that modulate CRELD1 expression or function, offering a novel approach to ccRCC treatment\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study represents a significant step forward in understanding the molecular underpinnings of ccRCC. By integrating pQTL analysis, MR, and immune profiling, we identified CRELD1 as a key protective factor and potential therapeutic target in ccRCC. These findings highlight the importance of proteomic and genomic approaches in uncovering novel biomarkers and treatment strategies for complex diseases. Future research should focus on validating these results in diverse populations and exploring the therapeutic potential of CRELD1-targeting compounds in preclinical and clinical settings. Ultimately, this study underscores the value of integrating multi-omics data to advance precision oncology and improve outcomes for ccRCC patients.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study is to investigate the association between circulating proteins (cis-pQTL) and ccRCC through a comprehensive two-sample MR analysis. To strengthen the causal inference, colocalization analysis was performed, which assessed the overlap between genetic loci associated with circulating proteins and ccRCC susceptibility\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This analysis identified CRELD1 as a potential key target for further investigation. Subsequently, we explored the differential expression of CRELD1 in ccRCC using transcriptome data from the TCGA database, confirming its downregulation in ccRCC\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These findings were supported by a pan-cancer analysis and validated using the Human Protein Atlas database, which demonstrated consistent downregulation of CRELD1 across multiple cancer types\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo elucidate the functional implications of CRELD1, we analyzed its association with immune infiltration in ccRCC. Results showed that CRELD1 exhibited a positive correlation with various immune cell types, such as T-helper cells and Tregs, indicating its potential involvement in shaping the tumor immune microenvironment. Additionally, the expression of CRELD1 in different immune cell types was explored, revealing high expression levels in specific immune cells, further suggesting its regulatory role in immune processes\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, potential therapeutic strategies targeting CRELD1 were investigated using the CTD database to identify compounds that could modulate its expression\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Among several candidates, Gentamicin demonstrated significant binding potential with CRELD1, as evidenced by molecular docking results, which indicated a binding energy below \u0026minus;\u0026thinsp;5 kcal/mol. This suggests that Gentamicin might be a promising compound for therapeutic intervention. These findings are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which outlines the key steps of the study, including the MR analysis, colocalization, immune infiltration, expression analysis, and molecular docking.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eWe used a large-scale plasma protein study GWAS involving 35559 Icelanders and 4907 aptamers, which provided a powerful dataset for identifying protein quantitative trait loci (pQTLs)\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This large-scale analysis revealed numerous disease-associated pQTLs, shedding light on potential causal relationships between plasma proteins and a wide range of complex diseases, including cardiovascular diseases, metabolic disorders, and cancers. Importantly, the integration of pQTLs with MR analysis allowed for the identification of several proteins as putative causal factors in disease development, providing valuable insights into potential therapeutic targets and biomarkers\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to identifying causal proteins, integrative analyses enabled the exploration of pharmacologically modifiable proteins, paving the way for novel approaches in disease prevention and treatment. For instance, leveraging high-quality proteomics data, the study highlighted candidate proteins that may be modulated to mitigate disease risk or progression, offering a translational pathway for clinical application\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the utility of combining GWAS, MR, and integrative approaches to unravel complex disease mechanisms and pinpoint actionable targets.\u003c/p\u003e \u003cp\u003eThe ccRCC dataset comes from the GWAS directory and contains a broad sample of 752817 individuals of European ancestry (ID: GCST90320058). This dataset provides abundant resources for studying the genetic basis of susceptibility to ccRCC, and combines genetic variations with circulating protein data to identify new targets for ccRCC treatment and prevention.\u003c/p\u003e\n\u003ch3\u003eAcquisition of cis-pqtl\u003c/h3\u003e\n\u003cp\u003eHigh quality proteomic data including 4907 aptamers and their corresponding target genes were obtained from the original literature of 4907 protein to investigate the relationship between protein levels and genetic variation. The cis region of each protein coding gene is defined as a genomic interval extending\u0026thinsp;\u0026plusmn;\u0026thinsp;1 Mb upstream and downstream of the transcription start site, including single nucleotide polymorphisms (SNPs) within this range as potential regulatory variants\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. For the selection of instrumental variables, we used the conditions of P\u0026thinsp;\u0026lt;\u0026thinsp;5e-8, r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and club\u0026thinsp;=\u0026thinsp;10000, and limited the range of SNPs to extend\u0026thinsp;\u0026plusmn;\u0026thinsp;1 Mb upstream and downstream of the transcription start site. Finally, we determined the cis pQTL and ensured that the F-value of each included SNP was greater than 10, which ensured a deep understanding of protein regulation at the genetic level.\u003c/p\u003e\n\u003ch3\u003eMR analysis\u003c/h3\u003e\n\u003cp\u003eThe MR analysis was performed using the TwoSampleMR package in R, a robust tool for causal inference. For proteins with cis-pQTLs associated with only one SNP, the Wald ratio method was employed to calculate the causal estimate, offering a straightforward approach for single IV scenarios\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. For proteins with cis-pQTLs involving multiple SNPs, the inverse-variance weighted (IVW) method was applied, which aggregates the effects of multiple SNPs to provide a more reliable and statistically robust estimate of causality\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis methodological approach ensures that both single and multi-SNP associations are rigorously assessed, minimizing bias and maximizing the reliability of causal inferences between circulating protein levels and ccRCC risk\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. By leveraging the strengths of both the Wald ratio and IVW methods, the study provides a comprehensive framework for exploring genetic regulation of proteins and their role in disease etiology. By utilizing the advantages of Wald ratio and IVW method, this study provides a comprehensive framework for exploring the genetic regulation of proteins and their role in disease etiology. We used global tests of MR egger and MR-PRESSO to examine the horizontal pleiotropy of the results. Due to the inclusion of fewer than 3 SNPs, the MR egger and MR-PRESSO tests could not be performed. Therefore, we relaxed the clump conditions on the basis of including SNPs to increase the number of included SNPs (ensuring that the SNPs of cis pQTL are still in this part) and then conducted horizontal pleiotropy testing.\u003c/p\u003e\n\u003ch3\u003eColocalization analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the overlap of genetic variations within candidate gene regions associated with distinct phenotypes, such as protein expression levels and disease susceptibility, we performed colocalization analysis. This analysis evaluates whether the same genetic variation influences two phenotypes simultaneously, thereby uncovering potential causal relationships.\u003c/p\u003e \u003cp\u003eCo localization analysis was conducted using the colon R package, and two sets of GWAS summary statistical data were collected, corresponding to the cis pQTL of protein expression and disease susceptibility data, respectively. Use the same genome reference version and perform quality control on SNPs in candidate regions (usually defined as within \u0026plusmn;\u0026thinsp;1Mb of gene transcription start sites), including allele orientation correction, deletion rate screening, and frequency filtering, to ensure data consistency. Five posterior probabilities (PPs) were co located and calculated, corresponding to different hypotheses about genetic associations within specific genomic regions: PPH0: There is no correlation between any phenotypes in this region; PPH1༚ Only the first trait is related to the region, while the second trait is not; PPH2༚ Only the second trait is related to the region, while the first trait is unrelated to it; PPH3༚ Both of these traits are related to the region, but they are caused by different genetic variations; PPH4༚ Both of these traits are related to the region and are caused by the same genetic variation\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary focus of the analysis was on PPH3 and PPH4, as these probabilities provide insights into the relationships between the two traits. A high PPH4 value strongly suggests that the two traits share a common causal variant, supporting a potential causal relationship between protein expression and disease. Conversely, a high PPH3 value indicates that the two traits are associated with different variants within the same region, suggesting independent genetic effects\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression and immune infiltration\u003c/h2\u003e \u003cp\u003eThe expression level of CRELD1 in ccRCC was analyzed using transcriptomic data from the TCGA database and validated using The Human Protein Atlas. Transcriptome data of ccRCC patients were first obtained from the TCGA database to evaluate the differential expression of CRELD1 between tumor tissues and adjacent normal tissues. The findings were corroborated by cross-referencing expression data from The Human Protein Atlas to ensure consistency.\u003c/p\u003e \u003cp\u003eTo further explore the role of CRELD1 in the tumor microenvironment, its association with immune infiltration in ccRCC was analyzed using TCGA data. Immune infiltration analysis focused on the relationship between CRELD1 expression and immune cell populations within the tumor immune microenvironment. From TCGA Data Portal( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )After downloading the RNA sequencing data (FPKM) and corresponding clinical information of ccRCC, the ssGSEA algorithm provided in R-package GSVA [1.46.0] was used to calculate the immune infiltration of corresponding genes in ccRCC data using the markers of 24 immune cells provided in the Immunity article (Bindea, Gabriela, et al., 2013). 24 types of immune cells include aDC [activated DC]; B cells; CD8 T cells; Cytotoxic cells; DC; Eosinophils; iDC[immature DC]; Macrophages; Mast cells; Neutrophils; NK CD56bright cells; NK CD56dim cells; NK cells; pDC[Plasmacytoid DC]; T cells; T helper cells; Tcm[T central memory]; Tem[T effector memory]; TFH[T follicular helper]; Tgd[T gamma delta]; Th1 cells; Th17 cells; Th2 cells; TReg\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular docking\u003c/h3\u003e\n\u003cp\u003eFirstly, from the PubChem database( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )Download the structural file of the target small molecule, usually in SDF format. Subsequently, it was converted to PDB format using OpenBabel software, and then converted to PDBQT format using AutoDock Tools (ADT). In this process, ensure that all atomic types in the small molecule structure are correctly identified, add necessary hydrogen atoms, and calculate and allocate partial charges for subsequent docking use.\u003c/p\u003e \u003cp\u003eFor the receptor protein CRELD1, its three-dimensional structure is obtained from the protein database (PDB), and the corresponding crystal structure file is selected. After downloading, use ADT to preprocess the protein structure: first, remove water molecules, eutectic ligands, and other non essential impurity molecules; Secondly, check and supplement missing hydrogen atoms (especially polar hydrogen) to ensure the accuracy of protein ion states; Finally, save the processed protein structure in PDBQT format.\u003c/p\u003e \u003cp\u003eNext, molecular docking will be conducted using AutoDock software. Firstly, based on the active site or expected binding pocket of CRELD1 protein, set reasonable grid box parameters to ensure that the entire potential binding area is covered. During the docking process, Lamarckian Genetic Algorithm (LGA) was used as the search strategy. After docking, the system obtains multiple docking conformations and records the binding energy of each conformation.\u003c/p\u003e \u003cp\u003eIn order to screen for small molecules with strong binding affinity, further systematic screening was conducted based on binding energy scores, with a focus on candidate conformations with binding energies below \u0026minus;\u0026thinsp;5 kcal/mol. For the selected high affinity candidate compounds, PyMOL was used for three-dimensional structural visualization. By observing key interaction sites such as hydrogen bonding, hydrophobic interactions, and charge interactions, the binding mode and possible mechanism of small molecules with CRELD1 protein were deeply analyzed.\u003c/p\u003e \u003cp\u003eThrough the rigorous preprocessing, parameter settings, and system screening mentioned above, this study aims to ensure the accuracy and repeatability of data in various stages of molecular docking, thereby providing a reliable theoretical basis for further functional validation and drug design.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe causal relationship between proteomics and ccRCC\u003c/h2\u003e \u003cp\u003eIn the cis-pQTL analysis for ccRCC, a preliminary screening identified a nominal association (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between 84 plasma proteins and ccRCC. However, after applying a false discovery rate (FDR) correction, only two proteins, CRELD1 and KDEL2, showed statistically significant correlations with ccRCC (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings suggest that these proteins may play critical roles in the pathogenesis of ccRCC.\u003c/p\u003e \u003cp\u003eFor both CRELD1 and KDEL2, Mendelian randomization analysis using the IVW method provided robust evidence supporting their association with ccRCC. Specifically, the odds ratios (ORs) for CRELD1 and KDEL2 were 0.909 (95% CI: 0.879\u0026ndash;0.940) and 0.747 (95% CI: 0.688\u0026ndash;0.812), respectively, with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These results highlight the protective role of CRELD1 and KDEL2 in ccRCC development. After incorporating more SNPs, both MR egger and MR-PRESSO global tests showed no pleiotropy in the MR results(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMulti efficiency analysis of MR egger and MR-PRESSO global tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR egger(egger_intercept)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR egger(pval)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR-PRESSO global test(pval)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRELD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKDEL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo rule out reverse causality, the Steiger directionality test was applied, confirming that none of the SNPs associated with CRELD1 or KDEL2 exhibited reverse causality (\u003cb\u003eSupplementary Material 2\u003c/b\u003e). This strengthens the inference that the observed associations between these proteins and ccRCC are likely causal.\u003c/p\u003e \u003cp\u003eDetailed association results for all cis-pQTLs and ccRCC are provided in \u003cb\u003eSupplementary Material 1\u003c/b\u003e, while \u003cb\u003eSupplementary Material 2\u003c/b\u003e further validates the directionality of these associations. Notably, the protective effects of CRELD1 and KDEL2 align with their observed expression patterns in ccRCC, providing a functional basis for these genetic findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe two proteins were verified by colocalization evidence\u003c/h2\u003e \u003cp\u003eAmong the proteins associated with ccRCC, CRELD1 and KDEL2 demonstrated strong colocalization evidence in different genomic windows, with PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.9 (\u003cb\u003eSupplementary Material 3\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This indicates a high likelihood that the genetic variants influencing these proteins also contribute to ccRCC susceptibility.\u003c/p\u003e \u003cp\u003eFor CRELD1, colocalization analysis revealed that the shared genetic variant aligns with its role as a protective factor, as supported by the odds ratio from Mendelian randomization (OR\u0026thinsp;=\u0026thinsp;0.909, 95% CI: 0.879\u0026ndash;0.940, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The colocalization signal for CRELD1 emphasizes its functional relevance in ccRCC pathogenesis. Similarly, KDEL2 displayed a strong colocalization signal (OR\u0026thinsp;=\u0026thinsp;0.747, 95% CI: 0.688\u0026ndash;0.812, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), further substantiating its potential role as a protective protein.\u003c/p\u003e \u003cp\u003eThe genomic regions exhibiting strong colocalization signals for both CRELD1 and KDEL2 are enriched for regulatory elements, as indicated by annotations in \u003cb\u003eSupplementary Material 3\u003c/b\u003e. For example, CRELD1's colocalized variants overlap with enhancer regions associated with immune regulation, aligning with its observed role in immune infiltration and tumor microenvironment modulation. Similarly, KDEL2 colocalized variants suggest potential involvement in endoplasmic reticulum stress response pathways, which have been implicated in tumor suppression.\u003c/p\u003e \u003cp\u003eColocalization plots for CRELD1 and KDEL2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) clearly depict the overlap of genetic signals for protein expression and ccRCC susceptibility, providing strong visual confirmation of shared causal variants. These results strengthen the evidence for their mechanistic roles in ccRCC development and highlight their potential as therapeutic targets.\u003c/p\u003e \u003cp\u003eThese colocalization findings are consistent with the differential expression results, which showed downregulation of CRELD1 and KDEL2 in ccRCC tumors. The integration of colocalization and expression data suggests that genetic regulation of these proteins contributes to ccRCC pathogenesis, further supporting their candidacy for future therapeutic exploration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExpression of Protein Target Genes in ccRCC\u003c/h2\u003e \u003cp\u003eTo explore the role of the identified proteins (KDEL2 and CRELD1) in ccRCC, we analyzed their expression levels using the UALCAN database. The results revealed distinct expression patterns for the two genes: KDEL2 was found to be significantly upregulated in ccRCC tumor tissues compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, the median expression of KDEL2 transcripts per million (TPM) in primary tumors (~\u0026thinsp;50 TPM) was approximately double that in normal tissues (~\u0026thinsp;25 TPM). This result contrasts with the protective role indicated by the Mendelian randomization and colocalization analyses, which suggested KDEL2 might act as a protective factor in ccRCC pathogenesis. One possible explanation for this paradox is that KDEL2 upregulation may reflect a compensatory response to tumor-induced cellular stress, particularly in the endoplasmic reticulum (ER) stress response pathway. KDEL2 is a member of the KDEL receptor family, which is responsible for the retrieval of ER-resident chaperone proteins, such as GRP78 and GRP94, to maintain protein homeostasis. Under physiological conditions, KDEL2 may serve a protective role by preventing proteotoxic stress. However, in a tumor environment, chronic ER stress could drive KDEL2 overexpression as an adaptive mechanism, facilitating cell survival by enhancing ER-associated degradation (ERAD) and protein folding capacity. This dual role aligns with previous findings on the context-dependent functions of ER stress response proteins, where initial protective effects may transition into pro-tumorigenic adaptations in advanced disease stages. Further functional analyses, including knockdown and overexpression studies, are needed to clarify the precise role of KDEL2 in ccRCC.\u003c/p\u003e \u003cp\u003eCRELD1 was found to be significantly downregulated in ccRCC tumor tissues compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The median CRELD1 expression in primary tumors (~\u0026thinsp;25 TPM) was substantially lower than in normal tissues (~\u0026thinsp;35 TPM), indicating a consistent reduction in tumor samples. These results align with the protective role of CRELD1 observed in the Mendelian randomization analysis, further supporting its involvement in ccRCC development and progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA pan-cancer expression analysis using the UALCAN database revealed that CRELD1 was downregulated in the majority of analyzed cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This downregulation was statistically significant across multiple cancer types, suggesting that CRELD1 might serve as a general tumor suppressor. Specifically, in kidney cancer, CRELD1 showed one of the most pronounced reductions in expression, consistent with its protective role in ccRCC and its potential involvement in immune regulation.\u003c/p\u003e \u003cp\u003eTo validate these findings, CRELD1 expression was further assessed using data from the Human Protein Atlas, which confirmed its low expression levels in ccRCC tissues. Additionally, immunohistochemical analyses showed weaker staining for CRELD1 in tumor tissues compared to normal kidney tissues, providing additional evidence for its downregulation in ccRCC.\u003c/p\u003e \u003cp\u003eThe integration of these analyses underscores the contrasting roles of KDEL2 and CRELD1 in ccRCC. While KDEL2's elevated expression in tumors conflicts with its suggested protective role, CRELD1's consistent downregulation across datasets highlights its potential as a tumor suppressor and therapeutic target in ccRCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmune Infiltration of CRELD1 in ccRCC\u003c/h2\u003e \u003cp\u003eUsing data from the TCGA database, the relationship between CRELD1 expression and immune cell infiltration in ccRCC was analyzed. The results revealed significant correlations between CRELD1 expression and various immune cell types, highlighting its potential role in modulating the tumor immune microenvironment. Correlation with Immune Cell Types: CRELD1 exhibited a positive correlation with several immune cell types, including T-helper cells (R\u0026thinsp;=\u0026thinsp;0.264, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and regulatory T cells (Tregs, R\u0026thinsp;=\u0026thinsp;0.252, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These findings suggest that CRELD1 may play an active role in the recruitment or regulation of these immune cells within the tumor microenvironment. Conversely, CRELD1 showed negative correlations with certain immune cell types, such as Th2 cells (R = -0.317, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and neutrophils (R = -0.278, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Th2-driven immune responses have been linked to tumor-promoting inflammation in various cancers, including ccRCC, by facilitating immunosuppressive cytokine production (e.g., IL-4, IL-13) that supports tumor growth and immune evasion. Similarly, neutrophils contribute to tumor progression through the formation of neutrophil extracellular traps (NETs), which have been shown to enhance tumor cell proliferation, metastasis, and resistance to therapy. The negative correlation of CRELD1 with these immune populations suggests a potential role in counteracting protumorigenic inflammation, possibly through modulation of immune cell recruitment or signaling pathways in the tumor microenvironment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExpression in Immune Cells: The expression levels of CRELD1 were further analyzed across different immune cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The results indicated higher expression of CRELD1 in key immune cells such as T-helper cells, Tregs, and memory T cells (Tcm and Tem) in ccRCC patients with high CRELD1 expression. In contrast, immune cells such as neutrophils, macrophages, and NK cells showed relatively lower CRELD1 enrichment, aligning with the correlation results and suggesting that CRELD1's role in ccRCC may be more pronounced in adaptive immune responses rather than innate immunity.\u003c/p\u003e \u003cp\u003eThe observed correlations suggest that CRELD1 may influence the immune infiltration profile of ccRCC tumors, particularly by enhancing the recruitment or activity of T-helper cells and Tregs, which are critical for immune regulation. This highlights its potential role in shaping the immune microenvironment and influencing tumor progression. The reduced correlation with innate immune cells such as neutrophils and macrophages may indicate a more specific regulatory function within the adaptive immune system.\u003c/p\u003e \u003cp\u003eThese findings provide further evidence for CRELD1's involvement in ccRCC pathogenesis and its potential as a therapeutic target, particularly in the context of immune modulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Protein Drug Targets\u003c/h2\u003e \u003cp\u003eUsing the CTD database, we identified small molecules that could potentially upregulate CRELD1 expression and exhibit anti-cancer properties (\u003cb\u003eSupplementary Material 4\u003c/b\u003e). Five candidates\u0026mdash;Diuron, Gentamicin, K-7174, Flutamide, and Nimesulide\u0026mdash;were selected for further evaluation through molecular docking experiments based on their reported ability to regulate CRELD1 and their potential therapeutic effects.\u003c/p\u003e \u003cp\u003eMolecular docking was performed to assess the binding affinities of these compounds to CRELD1. Among the candidates: Gentamicin demonstrated the strongest binding affinity, with a binding energy of -6.2 kcal/mol, which is below the threshold of -5 kcal/mol for significant binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). This indicates a stable and potentially effective interaction between Gentamicin and CRELD1. The other compounds, including Diuron, K-7174, Flutamide, and Nimesulide, showed binding energies above \u0026minus;\u0026thinsp;5 kcal/mol, indicating weaker or less stable interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). As a result, these compounds were excluded from further consideration.\u003c/p\u003e \u003cp\u003eDetailed analysis of the molecular docking results revealed that Gentamicin interacted with key amino acid residues in the CRELD1 binding pocket, forming hydrogen bonds and hydrophobic interactions that likely stabilize the ligand-receptor complex. These findings suggest that Gentamicin may modulate CRELD1 activity and potentially influence its role in ccRCC progression.\u003c/p\u003e \u003cp\u003eGentamicin' ability to upregulate CRELD1 expression and their stable interaction with the protein suggest potential anti-cancer effects mediated through CRELD1. Given CRELD1's role in modulating immune infiltration and tumor suppression, Gentamicin could be explored further as a therapeutic candidate for ccRCC.\u003c/p\u003e \u003cp\u003eThe integration of molecular docking and functional data highlights Gentamicin as a promising lead compound for targeting CRELD1 in ccRCC. This is consistent with its favorable binding profile and potential regulatory effects on CRELD1 expression. Future experimental studies are warranted to validate these findings and assess the therapeutic potential of Gentamicin in vivo and in vitro.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides novel insights into the molecular mechanisms underlying ccRCC by integrating proteomic, genetic, and transcriptomic data to identify causal proteins and their potential therapeutic implications. Using a robust MR framework combined with colocalization and functional analyses, we identified CRELD1 and KDEL2 as key proteins associated with ccRCC susceptibility, highlighting their roles in immune regulation and tumor pathophysiology. The findings of this study not only improve our understanding of ccRCC but also lay the foundation for the development of new diagnostic biomarkers and therapeutic strategies.\u003c/p\u003e \u003cp\u003eOur two-sample MR analysis identified CRELD1 and KDEL2 as significantly associated with ccRCC risk, with strong evidence of a causal relationship supported by genetic variants. The inverse association of CRELD1 (OR\u0026thinsp;=\u0026thinsp;0.909, 95% CI: 0.879\u0026ndash;0.940, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and KDEL2 (OR\u0026thinsp;=\u0026thinsp;0.747, 95% CI: 0.688\u0026ndash;0.812, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with ccRCC risk underscores their potential protective roles. Importantly, colocalization analysis further confirmed that shared genetic variants regulate the expression of these proteins and ccRCC susceptibility, with posterior probabilities (PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.9) suggesting a high likelihood of shared causal loci. These findings strengthen the evidence that CRELD1 and KDEL2 are not merely correlated with ccRCC but play mechanistic roles in its pathogenesis. The genomic loci associated with CRELD1 and KDEL2 are enriched for regulatory elements, such as enhancers and promoters, indicating that genetic variants may influence their expression in tumor and immune-related contexts\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. For CRELD1, colocalized variants overlap with immune regulatory regions, aligning with its observed role in modulating the tumor immune microenvironment\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. These findings are consistent with previous studies implicating genetic regulation of immune-related proteins in ccRCC susceptibility and progression\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDifferential expression analysis revealed that CRELD1 is significantly downregulated in ccRCC tissues compared to normal kidney tissues, as confirmed by transcriptomic data from TCGA and validation using the Human Protein Atlas. This downregulation is consistent with its protective role identified in MR analysis and suggests that reduced CRELD1 expression may contribute to tumor progression\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In contrast, KDEL2 was upregulated in ccRCC tumors, which appears contradictory to its protective role inferred from MR analysis. This discrepancy may reflect complex regulatory mechanisms, such as compensatory overexpression in response to tumor-induced stress or context-specific effects of KDEL2 in the tumor microenvironment. One potential explanation is that KDEL2 is involved in ER stress response pathways, where its upregulation may be triggered as an adaptive mechanism to counteract cellular stress in tumor cells. This aligns with previous studies showing that stress-responsive proteins can exhibit paradoxical expression patterns depending on tumor stage and microenvironmental conditions. Additionally, KDEL2 may have a dual role, acting protectively in early tumorigenesis by maintaining cellular homeostasis, while later contributing to tumor survival by enhancing adaptive responses to hypoxic or metabolic stress. The KDEL receptor family, including KDEL2, plays a crucial role in ER stress adaptation by regulating the retention and retrieval of chaperone proteins. Previous studies have shown that KDEL receptors are involved in the unfolded protein response (UPR) and ERAD, both of which are critical for maintaining proteostasis under stress conditions. The paradoxical expression pattern of KDEL2 in ccRCC may thus reflect an adaptive mechanism in which tumor cells exploit ER stress pathways to enhance survival. This hypothesis is supported by research demonstrating that persistent ER stress can shift cellular responses from protective to pro-survival states, ultimately promoting tumor progression. Further mechanistic studies are required to determine whether KDEL2 functions differently depending on tumor stage and microenvironmental conditions. These hypotheses warrant further investigation to determine the precise role of KDEL2 in ccRCC progression.\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. The observed pan-cancer downregulation of CRELD1, particularly in ccRCC, highlights its potential role as a tumor suppressor. Its consistent reduction across multiple cancers suggests that CRELD1 may function as a general regulator of tumor suppression, possibly through immune modulation or inhibition of oncogenic signaling pathways\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The dual role of KDEL2 in ccRCC requires further investigation to clarify its context-dependent functions, particularly its involvement in endoplasmic reticulum stress and protein homeostasis, which are known to influence tumor development\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImmune infiltration analysis revealed that CRELD1 expression positively correlates with adaptive immune cells, including T-helper cells (R\u0026thinsp;=\u0026thinsp;0.264, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and regulatory T cells (Tregs, R\u0026thinsp;=\u0026thinsp;0.252, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in ccRCC. These findings suggest that CRELD1 plays a role in shaping the tumor immune microenvironment by enhancing the recruitment or activity of these immune cell types. T-helper cells and Tregs are critical for immune regulation and maintaining an anti-tumor immune response, and their association with CRELD1 supports its role in modulating immune-mediated tumor suppression. Conversely, CRELD1 expression negatively correlates with certain innate immune cells, such as neutrophils (R = -0.278, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Th2 cells (R = -0.317, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Th2-driven immune responses have been implicated in promoting tumorigenesis through the secretion of IL-4 and IL-13, which facilitate M2 macrophage polarization and suppress cytotoxic immune responses. In ccRCC, elevated Th2 activity is associated with poor prognosis due to its role in creating an immunosuppressive microenvironment. Additionally, neutrophils contribute to ccRCC progression by forming NETs, which not only provide a structural scaffold for tumor cells but also release proteases and cytokines that enhance tumor invasion and immune escape. The observed negative correlation between CRELD1 and these immune cell types suggests that CRELD1 may play a role in limiting these pro-tumorigenic pathways. This could occur through direct suppression of Th2-associated cytokine signaling or by modulating neutrophil recruitment and NET formation. Further functional studies are needed to elucidate whether CRELD1 actively inhibits these pathways or if its downregulation in ccRCC removes a barrier to Th2/neutrophil-mediated tumor progression. These findings align with the broader role of immune dysregulation in ccRCC, where tumor cells evade immune surveillance through modulation of the tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Enhancing CRELD1 expression could potentially restore immune balance, reducing tumor progression and improving responses to immunotherapy. Further analysis of immune cell-specific expression patterns revealed that CRELD1 is enriched in T-helper cells, Tregs, and memory T cells, while exhibiting lower expression in innate immune cells such as macrophages and neutrophils\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This suggests that CRELD1 may play a more prominent role in adaptive immune responses, making it a promising target for immunomodulatory therapies.\u003c/p\u003e \u003cp\u003eFurthermore, our findings suggest that targeting CRELD1 could provide a novel therapeutic approach by enhancing its tumor-suppressive and immune-modulatory roles. The identification of Gentamicin as a promising compound with a strong binding affinity to CRELD1 raises the possibility of developing pharmacological agents that upregulate CRELD1 expression or activity. However, it is important to acknowledge that molecular docking provides only a computational prediction of binding affinity and does not confirm actual biochemical interactions. Therefore, further experimental validation is essential to confirm these findings. Future studies should include in vitro binding assays (e.g., surface plasmon resonance or isothermal titration calorimetry) to directly measure the interaction between Gentamicin and CRELD1. Additionally, cell-based functional assays should be conducted to determine whether Gentamicin treatment effectively upregulates CRELD1 expression and influences ccRCC cell behavior. These experimental validations will provide critical insights into the therapeutic potential of Gentamicin and inform the development of CRELD1-targeted therapies. Such an approach could be particularly relevant for patients who show resistance to current targeted therapies or immune checkpoint inhibitors. Combination therapies that incorporate CRELD1-modulating agents with existing treatment regimens may improve response rates and overall patient outcomes. Future preclinical and clinical studies will be essential to validate these findings and assess the therapeutic efficacy of CRELD1-targeting drugs in ccRCC. Molecular docking analysis further supports the therapeutic potential of CRELD1\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Among the tested compounds, Gentamicin demonstrated strong binding affinity to CRELD1 (binding energy: -6.2 kcal/mol), suggesting a stable interaction that could modulate its expression or activity. Gentamicin\u0026rsquo; ability to upregulate CRELD1 and their favorable binding profile highlight their potential as lead compounds for drug development. Future studies should focus on validating these findings in preclinical models to assess their efficacy and safety in ccRCC treatment\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe findings of this study have important implications for precision oncology. First, the identification of CRELD1 and KDEL2 as causal proteins provides novel biomarkers for ccRCC risk stratification and early diagnosis\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Measuring circulating levels of these proteins could improve the identification of high-risk individuals and facilitate early intervention. Second, the integration of genetic, proteomic, and immune data underscores the value of multi-omics approaches in uncovering actionable targets for personalized cancer therapy. By leveraging patient-specific molecular profiles, therapies targeting CRELD1 or KDEL2 could be tailored to enhance efficacy and minimize off-target effects\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. The role of CRELD1 in immune regulation also highlights its potential as a biomarker for predicting responses to immunotherapy. Patients with high CRELD1 expression may benefit more from immune checkpoint inhibitors or combination therapies aimed at enhancing adaptive immune responses. These findings could inform the development of predictive models to guide treatment decisions and optimize outcomes for ccRCC patients\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including the use of robust MR analysis to establish causal relationships, the integration of colocalization and expression data to validate findings, and the exploration of immune regulatory functions to provide mechanistic insights. The large sample sizes and high-quality datasets used in the analysis further enhance the reliability and generalizability of the results\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Despite these strengths, our study has several limitations: (1) Population-Specific GWAS Data: The GWAS data used in our Mendelian randomization analysis are predominantly derived from European populations. This may limit the generalizability of our findings to other ethnic groups, particularly given the genetic and environmental heterogeneity of ccRCC across populations. To address this limitation, future studies should aim to validate our findings in more diverse populations, including cohorts of Asian and African ancestry. Collaborative efforts integrating multi-ethnic GWAS datasets, such as the Pan-UKB or PAGE consortium, will be essential to determine whether the identified associations are consistent across ancestries. Additionally, functional validation studies using diverse patient-derived samples will help assess the relevance of CRELD1 and KDEL2 in different genetic backgrounds, ultimately improving the clinical applicability of our findings. Future studies should incorporate diverse cohorts to validate these results. (2) Preliminary Nature of Molecular Docking Results: While molecular docking identified Gentamicin as a promising compound targeting CRELD1, these results are preliminary and require experimental validation. Further in vitro and in vivo studies are necessary to confirm the binding affinity and functional effects of Gentamicin on CRELD1 expression and activity. (3) Dual Role of KDEL2 and Its Context-Dependent Effects: Our findings suggest that KDEL2 may have a dual role in ccRCC, acting as a protective factor at the genetic level (OR\u0026thinsp;=\u0026thinsp;0.747) while exhibiting upregulation in tumor tissues (TPM: 50 vs. 25). One possible explanation is that KDEL2 functions as part of an adaptive ER stress response\u0026mdash;protecting cells under normal conditions but potentially aiding tumor survival in later stages. Further functional studies are needed to determine the precise role of KDEL2 in ccRCC progression. However, several limitations should be noted. First, the study primarily focused on European populations, which may limit the generalizability of the findings to other ethnic groups\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Future studies should validate these results in more diverse populations to ensure broader applicability. Second, while molecular docking identified Gentamicin as a promising compound, experimental validation in preclinical and clinical settings is necessary to confirm its therapeutic potential. Finally, the functional roles of KDEL2 in ccRCC remain unclear, requiring further investigation to resolve the discrepancies between its expression patterns and inferred protective effects.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides robust evidence for the causal roles of CRELD1 and KDEL2 in ccRCC pathogenesis, with CRELD1 emerging as a promising biomarker and therapeutic target. The protective role of CRELD1, its immune regulatory functions, and its downregulation in ccRCC underscore its potential as a tumor suppressor. Molecular docking analysis identified Gentamicin as a lead compound for targeting CRELD1, highlighting opportunities for drug development. These findings advance our understanding of ccRCC biology and offer new avenues for precision oncology, with implications for improving diagnosis, treatment, and outcomes for ccRCC patients. Future research should focus on validating these findings in diverse populations, particularly cohorts of non-European ancestry, and exploring the therapeutic potential of CRELD1-targeting compounds. Expanding the analysis to include Asian and African datasets will be crucial in assessing the broader relevance of these genetic associations and ensuring that potential therapeutic applications are applicable across populations. Such efforts will contribute to a more comprehensive understanding of ccRCC pathogenesis and facilitate the development of globally relevant precision medicine approaches.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eGentamicin have shown potential in upregulating CRELD1 expression, suggesting their possible role as therapeutic agents in ccRCC treatment. Given CRELD1\u0026rsquo;s function in modulating the tumor immune microenvironment and its association with favorable clinical outcomes, targeting this protein could lead to innovative therapeutic strategies. Our study highlights CRELD1 as both a biomarker for ccRCC prognosis and a candidate for future therapeutic intervention. The integration of CRELD1-targeting strategies with existing immunotherapies or targeted therapies could improve patient outcomes, particularly for those with limited response to current treatments. Further research, including biochemical validation, cell-based functional assays, preclinical validation, and clinical trials, is necessary to explore the full therapeutic potential of CRELD1 modulation in ccRCC. These studies will be crucial to determine whether Gentamicin\u0026rsquo;s predicted interaction with CRELD1 translates into a functional effect in a biological system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eccRCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClear cell renal cell carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMendelian randomization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenome-wide association studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVHL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVon Hippel-Lindau\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIFs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypoxia-inducible factors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVEGF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVascular endothelial growth factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTME\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor microenvironment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003epQTLs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein quantitative trait loci\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle nucleotide polymorphisms\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVW\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInverse-variance weighted\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePosterior probabilities\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePDB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein Data Bank\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTPM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscripts per million\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXue Hu: Investigation, Visualization, Writing - manuscript. Bosheng Luo: Data processing, Software, Methodology. Yingzhuo Li and Yang Wang: Data processing, Formal analysis and Supervision. Jiaping Wang: Fund preparation, Writing - editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research is financially supported by Science and technology plan project of Science and Technology Department of Yunnan Province (No:202401AY070001-005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used in this study are publicly available. The GWAS data for circulating protein levels were obtained from the published literature (PMID: 34857953) and the GWAS Catalog (GCST ID: 90320058), accessible at https://www.ebi.ac.uk/gwas/. The transcriptomic data for ccRCC were accessed from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/, cohort ID: [TCGA-KIRC]) and The Human Protein Atlas (https://www.proteinatlas.org). Molecular docking analyses utilized protein structures from the Protein Data Bank (PDB, https://www.rcsb.org) and compound structures from PubChem (https://pubchem.ncbi.nlm.nih.gov). Detailed results of the Mendelian randomization and colocalization analyses are included in the supplementary materials. Additional data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that she has no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBAHADORAM S, DAVOODI M, HASSANZADEH S, et al. 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Nature genetics, 2015, 47(12): 1457-64.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clear cell renal cell carcinoma, Mendelian randomization, CRELD1, immune infiltration, proteomics, therapeutic targets","lastPublishedDoi":"10.21203/rs.3.rs-5881486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5881486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClear cell renal cell carcinoma (ccRCC) is a highly aggressive kidney cancer subtype with poor survival rates, particularly in metastatic cases. While proteomics and immune dysregulation are implicated in ccRCC, the causal relationships between circulating proteins and ccRCC remain poorly understood. This study investigates the causal roles of circulating proteins in ccRCC pathogenesis and identifies potential therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample Mendelian randomization (MR) analysis using cis-pQTL data from genome-wide association studies (GWAS) to identify causal relationships between circulating proteins and ccRCC. Colocalization analysis was performed to validate shared genetic loci influencing both protein levels and ccRCC susceptibility. Transcriptomic data and immune infiltration analysis explored protein expression and immune regulatory roles. Molecular docking analysis identified compounds targeting key proteins.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwo proteins, CRELD1 and KDEL2, were identified as significantly associated with ccRCC (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CRELD1 emerged as a protective factor (OR\u0026thinsp;=\u0026thinsp;0.909, 95% CI: 0.879\u0026ndash;0.940), with consistent downregulation in ccRCC tissues. KDEL2 also demonstrated a protective association (OR\u0026thinsp;=\u0026thinsp;0.747), though it was paradoxically upregulated in tumor tissues, suggesting a possible compensatory response to cellular stress. Colocalization analysis confirmed shared causal variants for CRELD1 and ccRCC susceptibility (PPH3\u0026thinsp;+\u0026thinsp;PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.9). CRELD1 positively correlated with adaptive immune cells, including T-helper and regulatory T cells, highlighting its role in modulating the tumor immune microenvironment. Molecular docking identified Gentamicin as a promising compound targeting CRELD1, with a binding energy of -6.2 kcal/mol.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCRELD1 is a novel tumor suppressor and immune regulator in ccRCC, with potential as a diagnostic biomarker and therapeutic target. Gentamicin may offer a therapeutic strategy to upregulate CRELD1, improving immune responses and tumor suppression. These findings provide actionable insights for precision oncology in ccRCC.\u003c/p\u003e","manuscriptTitle":"Potential Therapeutic Strategies Targeting CRELD1: Regulation of the Immune Microenvironment in Clear Cell Renal Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 10:11:13","doi":"10.21203/rs.3.rs-5881486/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":"6b9f0e43-ee26-4684-9ab9-6ecdcb01211e","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-30T13:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 10:11:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5881486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5881486","identity":"rs-5881486","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}