Genetically predicted causal associations between 152 blood-related exposures and pan-cancer in the framework of prediction, prevention and personalized medicine: a study integrating Mendelian randomization and bioinformatics

Genetically predicted causal associations between 152 blood-related exposures and pan-cancer in the framework of prediction, prevention and personalized medicine: a study integrating Mendelian randomization and bioinformatics | 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 Genetically predicted causal associations between 152 blood-related exposures and pan-cancer in the framework of prediction, prevention and personalized medicine: a study integrating Mendelian randomization and bioinformatics Xinhao tang, Xinyu tian, Jingjing Wu, Sainan Hao, Bowen Chu, Jun Shi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3774776/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 Objective Blood serves as a powerful tool for monitoring the intricate landscape of cancer development. Previous studies have emerged, suggesting that hematologic indicators hold promise in predicting the onset of malignancy. This present investigation aims to delve into the underlying causal connections between blood-related indicators and pan-cancer, further elucidating the potential impact of diseases and medication utilization reflected in these indicators on cancer, within the realm of predictive, preventive and personalised medicine(PPPM). Methods To embark on this scientific endeavor, we procured summary-level data from a genome-wide association studies (GWAS) encompassing blood-related indicators and cis-eQTLs of drug target genes, from the esteemed IEU OpenGWAS. Additionally, we obtained GWAS summary-level data encapsulating pan-cancer (consisting of an impressive cohort of 659,582 cases and 12,186,911 controls), along with diseases annotated by their correlation to blood-related indicators, from esteemed sources such as IEU OpenGWAS, UK Biobank, FinnGen, and Biobank Japan. In order to unravel the direct causal associations between blood-related indicators and pan-cancer, as well as the causal implications between the diseases manifested by these indicators and cancer, we initiated a robust analysis employing the two-sample Mendelian randomization(MR) method. Furthermore, utilizing bioinformatics methodologies, we went on to explore the potential effects of drug target genes on pan-cancer. Results Preliminary findings from our MR analysis provided compelling evidence of a significant link between blood-related exposures and pan-cancer. Drawing upon the intriguing interplay observed between blood pressure and tumors, it was postulated that monitoring hypertension (HTN) may offer notable advantages in the prevention of colorectal adenocarcinoma (COAD), breast carcinoma (BRCA), and esophageal carcinoma (ESCA). Similarly, considering the captivating relationship between blood glucose, insulin levels, and tumors, it was hypothesized that closely monitoring diabetes mellitus (DM) could prove beneficial in the prevention of stomach adenocarcinoma (STAD) and COAD. In consonance with the intriguing connection discovered between red blood cell counts, distribution width, and tumors, our findings supported the notion that monitoring anemia could impart advantageous effects in the prevention of lung adenocarcinoma (LUAD). Remarkably, drawing upon the intriguing relationship observed between deep vein thrombosis (DVT) and tumors, it was hypothesized that surveillance of DVT might prove valuable in the prevention of COAD. Additionally, we noted a disparity in risk for various cancers, including lung, breast, colorectal, ovarian, prostate, and pancreatic, consequent to the utilization of drugs for these aforementioned diseases. Among our identified drug targets, we carefully sifted through and diligently analyzed three pivotal genes, namely HMGCR, INSR, and NR3C1, fostering the prospect of formulating novel, tumor-targeted therapeutics. However, our investigation yielded insufficient evidence to confirm any mediating effects of glycated hemoglobin (HbA1c), hemoglobin-gastric, D-dimer, and renin on the associations between HTN, anemia, DVT, DM, and pan-cancer. Conclusions The present study unveils the intricate web of causal associations between blood-related indicators, the diseases they manifest, and medication utilization, all of which significantly impact the development of cancer. Notably, the potential for utilizing blood-related indicators as pioneering biomarkers for cancer prediction and prevention is underscored, showcasing a remarkable avenue for advancing PPPM strategies in the field of oncology. This seminal investigation serves as a beacon of novel insight, engendering the construction of refined and tailored approaches to combat the formidable challenge of cancer. Predictive preventive and personalised medicine(PPPM) pan-cancer Mendelian randomization(MR) Bioinformatics Tumorigenesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Introduction Unveiling the potential impact of blood-related indicators on pan-cancer could augment cancer prediction, prevention, and personalized medicine Cancer, a major global health concern, continues to impose escalating incidence and mortality rates worldwide. With an estimated 1,958,310 new cases and 609,820 deaths projected in the United States alone for 2023[ 1 ], the urgency to identify individuals at high risk of developing tumors and implement effective preventive measures becomes increasingly paramount. Recognizing blood as a valuable domain for disease surveillance, the utilization of blood-specific biomarkers holds significant potential in monitoring cancer progression[ 2 ].Within the repository of IEU OpenGWAS ( https://gwas.mrcieu.ac.uk/ ), a wide array of blood-related indicators, encompassing blood pressure, glucose levels, white blood cells, red blood cells, and medication usage, are captured, yielding insights into the overall health status of the human body. Notably, prior studies have unraveled associations between these indicators, diseases, and medications with tumor formation. For instance, patients with hypertension (HTN) display an elevated risk of developing breast carcinoma (BRCA)[ 3 ], while the renin-angiotensin system(RAS) axes intricately contribute to the pathogenesis of pediatric malignancies[ 4 ]. Notably, DM and the use of associated medications, such as insulin, have shown significant associations with an augmented susceptibility to several cancer types[ 5 ]. Similarly, elevated white blood cell counts serve as a biomarker indicative of heightened risk for lung adenocarcinoma (LUAD), as well as increased mortality and incidence rates for colorectal adenocarcinoma (COAD)[ 6 , 7 ]. Moreover, the red blood cell distribution width is considered a potential prognostic predictor for various cancers[ 8 , 9 ], and low red blood cell counts hold predictive value concerning early myometrial invasion in individuals with endometrioid endometrial carcinoma[ 10 ]. Furthermore, histo-blood group ABO system transferases have been markedly associated with diffuse-type and intestinal-type stomach adenocarcinoma (STAD)[ 11 ], whereas the use of cholesterol-lowering medications has been linked to an elevated risk of COAD[ 12 ].In summary, the exploration of blood-related indicators presents significant potential in identifying individuals at high risk of cancer, thereby informing them with appropriate medication advice to mitigate tumor occurrence. The establishment of causal associations between blood-related indicators and pan-cancer would undoubtedly facilitate the development of pioneering cancer surveillance and preventive strategies within the framework of predictive, preventive and personalised medicine (PPPM). Fueling PPPM - Mendelian randomization and bioinformatics methods The transition from reactive, disease-specific treatments to proactive, patient-centered prevention and treatment[ 13 , 14 ], epitomized by PPPM, necessitates the advent of novel technologies and analytical tools[ 15 ]. Mendelian randomization (MR), an innovative research methodology employing genetic variants as instrumental variables to examine disease etiology, stands as a powerful instrument for deciphering causal relationships while effectively accounting for confounding factors. In comparison to traditional epidemiological research methods, MR offers unique advantages, excluding the potential influence of confounders and providing a robust framework to investigate associations between blood-related markers, disease risk, and medication usage in the realm of oncology[ 16 ]. Complementing MR's strength, bioinformatics emerges as a formidable tool in constructing clinical prediction models and tailoring precision medicine strategies specific to tumor malignancies. As an analytical discipline, bioinformatics facilitates the discovery of novel biomarkers and therapeutic targets for cancer, ultimately furthering the promotion of PPPM within the field of oncology[ 17 ]. Working hypothesis within the PPPM framework The primary objective of this study is to refine cancer prediction and prevention advice by identifying individuals at high risk of developing cancer. By integrating MR and bioinformatics, this investigation aims to elucidate the casual associations between blood-related indicators and cancer, as well as explore the impact of disease and medication utilization on cancer development. Notably, the majority of blood-related indicators employed in this study stem from convenient, non-invasive techniques, such as blood pressure and glucose monitoring, along with routine blood examinations commonly accessible through community hospitals and family physicians. Should the preliminary verification of causal associations between blood-related indicators and cancer be established, this study holds the potential to shed new light on identifying individuals at high risk of cancer within the PPPM framework. Furthermore, it presents an easy and cost-effective method for cancer prediction and prevention, while highlighting the significance of personalized and precision medicine in addressing the global cancer burden. Materials and methods Study design In order to enhance the scientific and writing standardization of the analysis of the MR study, we employed the STROBE-MR guidelines to design our study [ 18 ]. Firstly, we implemented a two-sample MR method to investigate the association between 152 blood-related exposures and pan-cancer. Subsequently, we employed the mediating MR method to explore potential associations between the disease indicated by the significant outcome and tumors. For medications that potentially impact tumors as indicated by the significant outcome, we conducted a two-sample MR analysis to examine the causal associations between cis-eQTLs of drug target genes and pan-cancer. Finally, we employed bioinformatic tools to explore the potential impact of drug target genes on tumorigenesis. The flowchart depicting the overall study design is presented in Fig. 1 . Data sources We conducted a comprehensive search on the IEU OpenGWAS ( https://gwas.mrcieu.ac.uk/ ) platform, utilizing the keyword "blood," to acquire the genome-wide association studies (GWAS) summary-level data pertaining to blood-related exposures. Additionally, we acquired the GWAS summary-level data focusing on pan-cancer (26 different types of cancer) from esteemed sources including IEU OpenGWAS, UK Biobank ( https://www.ukbiobank.ac.uk/ ), FinnGen ( https://www.finngen.fi/en ), and Biobank Japan ( https://biobankjp.org/en/ ). A comprehensive overview of the GWAS cohorts involved in our study is presented in Table S2 of the Supplementary Material. With regards to the drug target gene information, we made use of the well-established DrugBank ( https://go.drugbank.com/ ) repository. Furthermore, the drug target gene cis-eQTL data were obtained from the IEU OpenGWAS. To leverage pan-cancer bulk RNA-seq data and relevant phenotypic data, we referred to the UCSC Xena platform ( https://xena.ucsc.edu/ ). Selection of instrumental variables (IVs) To ensure the robustness and reliability of our study, we incorporated 152 data points from blood-related GWAS as exposures. However, we excluded GWAS data points with an insufficient number of single nucleotide polymorphisms (SNPs), utilizing a locus-wide significance threshold of P < 5e-6, thus retaining only SNPs that exhibit a significant association with the blood-related exposures. To ensure the inclusion of independent SNPs while excluding those in strong linkage disequilibrium (LD), we conducted a careful LD clumping procedure for all instrumental variables (IVs), employing an LD threshold of r2 = 0.001 and a clumping window of 10,000 kb. Similar LD clumping strategies were employed for the drug target gene cis-eQTL, maintaining consistency with the aforementioned parameters (r2 = 0.001, clumping window: 10,000 kb). MR analysis MR analysis was performed utilizing several R packages including "ieugwasr," "plinkbinr," "gwasglue," and "TwoSampleMR" within R version 4.3.1, adhering to best practices for conducting rigorous and reliable analyses. Initially, we investigated the causal relationship between blood-related exposures and pan-cancer using two distinct MR approaches: inverse variance weighted (IVW), MR Egger, and weighted mode. The MR Egger method was employed to assess horizontal pleiotropy and evaluate the robustness of our findings. Robustness was deemed satisfactory when the p-value derived from IVW or weighted mode was less than 0.05, and the p-value obtained from MR Egger was greater than 0.05. In order to provide preliminary assessment of causal associations, results derived from the IVW method were utilized due to its superior estimation precision and test efficacy in the absence of horizontal pleiotropy among instrumental variables (IVs)[ 19 ]. Subsequently, we evaluated the potential impact of diseases and disease-related mediators on tumors, which may be reflected by blood-related exposures. This was accomplished by carrying out three separate two-sample MR analyses. In this context, we employed five MR methods: IVW, MR Egger, weighted mode, weighted median, and simple mode. Conclusively robust results were established when at least two MR methods (excluding MR Egger) yielded associations with p-values of less than 0.05, and those derived from MR Egger were greater than 0.05. Lastly, we explored the causal associations between drug target gene cis-eQTLs and pan-cancer using six Mendelian randomization methods: IVW, IVW (fixed effects), IVW radial, MR Egger, weighted median, and simple median. This comprehensive analysis aimed to uncover potential novel therapeutic targets for pan-cancer. In light of our study's focus on identifying new therapeutic avenues for pan-cancer, we refrained from conducting multiple tests for MR and colocalization results[ 20 ]. Our approach draws wisdom from previous MR literature, providing valuable references for future investigations in this field[ 21 , 22 ]. In all of our MR analyses, it is imperative that the single nucleotide polymorphisms (SNPs) satisfy three key assumptions in order to qualify as IVs. Firstly, the genetic variants must exhibit a robust and statistically significant association with the exposure under investigation, thereby fulfilling the requirement of the relevance assumption. Secondly, it is essential that these genetic variants are independent of any potential confounding factors, thus adhering to the principle of the independence assumption. Lastly, it is crucial to establish that the genetic variants solely influence the outcome of interest through their impact on the exposure, thereby upholding the principle of the exclusion restriction assumption. Analysis using bioinformatics We extracted the expression data of previously unreported drug target genes for bioinformatics studies, utilizing pan-cancer bulk RNA-seq data from each sample. Samples with no drug target gene expression were excluded, and the drug target gene expression in each sample was subjected to log2 transformation (log2(x + 0.001)). Cancer types with fewer than 3 samples were also excluded, resulting in a final set of expression data from 26 cancer types. Differential expression analysis between normal and tumor samples was performed using the "limma" R package. Significance analyses were conducted using unpaired Wilcoxon rank sum and signed-rank tests. To enhance the robustness of our findings, we obtained the TCGA prognostic dataset from a previously published study by Liu et al.[ 23 ]. Samples lacking drug target gene expression and samples with a follow-up time shorter than 30 days were excluded. Additionally, cancers with fewer than 10 samples were omitted, resulting in a final set of expression and overall survival data from 39 cancer types. To investigate the relationship between gene expression and prognosis in each tumor type, we employed the "survival" R package (version 3.2-7) to build Cox proportional hazards regression models. Prognostic significance was assessed using the log-rank test. Gene set enrichment analysis was performed using the "clusterProfiler" R package (version 4.8.3). Furthermore, we explored the correlation between drug target gene expression and immune cell infiltration using the "CIBERSORT" and "EPIC" R packages. We then extracted the expression data of drug target genes as well as 44 marker genes representing three types of RNA modifications (m1A(10), m5C(13), m6A(21)) in each sample. Samples with zero expression of drug target genes and RNA modification marker genes, as well as all normal samples, were filtered out. The remaining expression data underwent log2 transformation (log2(x + 0.001)). Pearson correlation analysis was applied to examine the relationship between drug target genes and marker genes of the three types of RNA modifications. For comprehensive genomic analysis, we downloaded the Simple Nucleotide Variation dataset of all TCGA samples processed by the MuTect2 software from the TCGA GDC ( https://portal.gdc.cancer.gov/ ). The tumor mutational burden (TMB) of each tumor was calculated using the "maftools" R package (version 2.8.05). Subsequently, we integrated the TMB and gene expression data and calculated the Pearson correlation between drug target gene expression and TMB in each tumor type. Results Relationship between blood indicators and pan-cancer Associations of blood pressure with pan-cancer Systolic and diastolic blood pressure associated with HTN had a potential relationship with COAD (P IVW =4.63×10 − 2 ,OR IVW =1.13,95%,CI IVW (1.00-1.28)), BRCA (P IVW =1.05×10 − 2 ,OR IVW =1.14,95% CI IVW (1.03–1.26)), esophageal carcinoma (ESCA) (P IVW =4.62×10 − 2 ,OR IVW = 1.72 95%,CI IVW (1.00-2.93)), pancreatic adenocarcinoma(PAAD) (P IVW =3.21×10 − 3 ,OR IVW =1.73 95%,CI IVW (1.06–2.83)), and STAD (P IVW =2.96×10 − 3 ,OR IVW =0.29,95% CI IVW (0.13–0.66)) (Fig. 2 ). High blood pressure itself was associated with ovarian cancer(OV) (P IVW =2.88×10 − 2 ,OR IVW =0.20,95% CI IVW (0.07–0.63)) and ESCA(P IVW =4.68×10 − 2 ,OR IVW = 0.04,95% CI IVW (0.00-0.94))(Fig. 3 ). Associations of glucose monitoring and insulin levels with pan-cancer Glucose and insulin levels associated with DM were potentially related to STAD (P IVW =1.02×10 − 2 ,OR IVW =1.32,95%CI IVW (1.07–1.62)), COAD (P IVW =4.94×10 − 3 OR IVW =1.30,95% CI IVW (1.08–1.57)) and ESCA (P IVW =2.66×10 − 2 ,OR IVW =0.79 95%, CI IVW (0.64–0.97)) (Fig. 4 ). Associations of medication for Cholesterol-lowering medication with pan-cancer Cholesterol-lowering medication was potentially associated with COAD (P IVW =4.14×10 − 2 ,OR IVW =1.08,95%CI IVW (1.00-1.17)), PAAD(P IVW =3.78×10 − 3 ,OR IVW =0.04,95%,CI IVW (0.00-0.35))(Fig. 5 ). Associations of red blood indicators related to red blood cells with pan-cancer Blood cell count and red blood cell distribution width associated with anemia were potentially related to LUAD (P IVW =2.94×10 − 3 ,OR IVW =1.11,95% CI IVW (1.04–1.19)), Brain glioblastoma (P IVW =5.17×10 − 3 ,OR IVW =0.16,95% CI (0.04–0.57)) and COAD (P IVW =4.64×10 − 3 ,OR IVW =0.47,95% CI IVW (0.28–0.79)) (Fig. 6 ). Ass o ciations of blood indicators related to white blood cells with pan-cancer There was a potential relationship between white blood cell count and PAAD (P IVW =8.30×10 − 3 ,OR IVW =1.94 95% CI IVW (1.19–3.19)), and Brain glioblastoma (P IVW =2.49×10 − 2 ,OR IVW =0.43 95% CI IVW (0.20–0.90)) (Fig. 7 ). Associations of diseases of blood and blood-forming organs with pan-cancer Diseases of blood and blood-forming organ were potentially associated with ESCA (P IVW =2.08×10 − 2 ,OR IVW =0.91,95% CI IVW (0.84–0.99)) (Fig. 8 ). Associations of tissue blood group ABO system transferase with pan-cancer ABO systemic transferase was not significantly associated with BRCA or thyroid carcinoma(THCA)(Fig. 9 ). Associations of other blood indicators with pan-cancer There is a potential relationship between deep vein thrombosis(DVT) and COAD (P IVW =5.85×10 − 3 ,OR IVW =1.34,95% CI IVW (1.09–1.65))(Fig. 10 ). Relationship between serum- exposure-related diseases and pan-cancer By testing again, we obtained 3 valid results. According to the results obtained, the mediating effects of glycated hemoglobin (HbA1c), hemoglobin-gastric, D-dimer, and renin on HTN, anemia, DVT, and DM with pan-cancer were apparently not significant (Fig. 11 ). Relationship between drugs selected and pan-cancer The role of lipid-lowering drugs in pan-cancer Lipid-lowering drugs are correlated with COAD and PAAD. In European populations, taking lipid-lowering drugs has a protective effect against COAD(P IVW =1.28×10 − 2 ,OR IVW =0.88,95%CI IVW (0.80–0.97)) and PAAD (P IVW =6.78×10 − 3 ,OR IVW =0.81,95%CI IVW (0.69–0.94)) by target gene HMGCR. Moreover, we found that the administration of lipid-lowering drugs was not significantly associated with the risk of cancer in Asian populations(Fig. 12 ). The casual association between oral contraceptive drugs and pan-cancer Oral contraceptive drugs correlated with COAD, BRCA and OV. Taking oral contraceptive drugs is protective against OV(OR IVW =0.68,P IVW =2.72×10 − 2 ,95%CI IVW (0.49–0.96))by target gene ESR1、NR3C1 and increases risk for BRCA(OR IVW =1.08,P IVW =4.34×10 − 2 ,95%CI IVW (0.93–1.65))by target gene BECN1 (Fig. 13 ). The casual association between insulin and pan-cancer Insulin is correlated with COAD, BRCA, and LUNG. Insulin use raises risk of LUAD(P IVW =4.78×10 − 3 ,OR IVW =1.16,95%CI IVW (1.00-1.35)), COAD(P IVW =1.02×10 − 3 ,OR IVW =1.16,OR IVW 1.32,95%CI IVW (1.12–1.56))by gene target INSR(Fig. 14 ). The casual association between hormones and pan-cancer Related hormones are associated with COAD, BRCA, and LUAD. Estrogen increased the risk of LUAD (P IVW =1.11×10 − 3 ,OR IVW =1.33,95%CI IVW (1.07–1.66)), decreased the risk of prostate adenocarcinoma(PRAD) (P IVW =3.51×10 − 2 ,OR IVW =0.81,95%CI IVW (0.66–0.99)) through the target gene ESR1. Progesterone increased the risk of BRCA (P IVW =3.90×10 − 2 ,OR IVW =1.18,95%CI IVW (1.01–1.37))through the gene target NR3C1(Fig. 15 ). The Potential Impact of Drug Target Genes on Pan-Cancer In light of the MR findings on drug target genes and their association with pan-cancer, we identified 4 frequently implicated target genes: HMGCR, ESR1, NR3C, and INSR. Considering that ESR1 has already been extensively studied using RNA-seq techniques [ 24 ], our investigation focused on HMGCR, NR3C, and INSR. HMGCR and its Relation to Pan-Cancer HMGCR demonstrated significant differential expression across 17 tumor types. Specifically, it was upregulated in 6 cancer types, notably in cervical squamous cell carcinoma (CESC) (Tumor: 4.22 ± 0.88, Normal: 2.84 ± 0.30, p = 0.01), and downregulated in COAD (Tumor: 4.91 ± 0.63, Normal: 5.28 ± 0.56, p = 4.3e-4), as well as 11 other cancer types (Fig. 16A). Prognostically, HMGCR was identified as a high-risk factor in 4 cancer types, including CESC (N = 273, p = 0.04, HR = 1.33 (1.02, 1.75)), and a protective factor in rectum adenocarcinoma (READ) (N = 368, p = 6.6e-3, HR = 0.67 (0.51, 0.89)), along with 4 additional cancer types (Fig. 16B). Additionally, HMGCR expression levels showed positive correlations with the androgen_response and protein_secretion pathways in pan-cancer. Notably, the expression patterns of HMGCR were closely associated with the development of CESC, COAD, and THCA based on observed differences in expression and prognostic outcomes. Within ESCA, HMGCR expression exhibited significant positive correlations with g2m_checkpoint, e2f_targets, and mitotic_spindle, and a significant negative correlation with tnfa_signaling_via_nfkb. In THCA, hmgcr expression was significantly positively correlated with g2m_checkpoint and epithelial_mesenchymal_transition, negatively correlated with myogenesis, and showed no significant relationship in COAD (Fig. 16C). In terms of immune infiltration, the combined analysis of two algorithms demonstrated a significant positive correlation between HMGCR expression and the activation of M0 macrophages and mast cells in ESCA, while negatively correlating with eosinophils. In COAD, HMGCR displayed a significant positive correlation with NK cell activation and a significant negative correlation with neutrophils. However, there was no significant relationship observed in THCA (Fig. 16D). Examining the Pearson correlation between HMGCR and marker genes of 5 immune pathway classes in pan-cancer, we consistently observed a significant positive correlation within the m1A, m5C, and m6A groups. Specifically, within the m1A modification group, we identified 13 gene groups exhibiting negative correlations with TRMT61A and ALKBH3. Similarly, within the m5C modification group, we observed 5 gene groups with negative correlations involving NSUN5, NSUN7, and ALYREF. Finally, within the m6A modification group, 10 gene groups displayed negative correlations with KIAA1429, IGF2BP1, and LRPPRC (Fig. 16E). Furthermore, HMGCR gene expression exhibited a significant positive correlation with tumor mutation load in 2 tumor types, notably in STAD (N = 409) with a correlation coefficient (R) of 0.20 and a p-value of 4.20×10 − 5 (Fig. 16F). INSR and its Implications in Pan-Cancer The differential expression analysis revealed significant variations in INSR expression across 16 tumor types. Notably, it was upregulated in 7 cancer types, including STAD (Tumor: 4.00 ± 0.74, Normal: 3.14 ± 1.02, p = 2.0e-6), and downregulated in 9 cancer types, prominently in COAD (Tumor: 3.16 ± 0.71, Normal: 3.70 ± 0.59, p = 2.3e-7), as well as 9 other cancer types (Fig. 17 A). Moreover, INSR exhibited a prognostic protective role in 3 cancer types, notably lower-grade glioma and glioblastoma(GBMLGG) (N = 619, p = 7.0e-4, HR = 0.72 (0.60, 0.87)) (Fig. 17 B). Furthermore, INSR expression was found to be inversely correlated with interferon_alpha_response, interferon_gamma_response, and inflammatory_response pathways, in addition to COAD, THCA, and uterine corpus endometrial carcinoma(UCEC) (Fig. 17 C).With regard to immune infiltration, analysis using the EPIC algorithm demonstrated a significant positive correlation between INSR expression and endothelial cells, whereas a significant negative correlation was observed with macrophages and NK cells in pan-cancer. Utilizing the Cibersort algorithm, INSR expression showed a significant negative correlation with B.cells.naive in skin cutaneous melanoma (SKCM), a significant positive correlation with NK.cells.activated in SKCM, and a significant positive correlation with B.cells.naive in USR (Fig. 17 D). Pearson correlation analysis between INSR expression and immune pathway marker genes in pan-cancer displayed a consistent positive correlation within the m1A, m5C, and m6A groups. Specifically, within the m1A modification group, 12 gene groups exhibited negative correlations with TRMT61A and ALKBH3. Similarly, within the m5C modification group, 12 gene groups demonstrated negative correlations with NOP2, DNMT3B, NSUN5, and ALYREFT. Finally, within the m6A modification group, 4 gene groups showed negative correlations with LRPPRC (Fig. 17 E). Moreover, INSR gene expression exhibited a significant negative correlation with tumor mutation load in 5 tumors, with notable findings in SBRCA (N = 981) (R = -0.06, P = 4.30×10 − 3 ) (Fig. 17 F) NR3C1 and its Role in Pan-Cancer The expression of NR3C1 exhibited significant differential patterns across 20 tumor types. Specifically, it was found to be significantly upregulated in 5 cancer types, including lower-grade glioma (LGG) (Tumor: 4.21 ± 0.54, Normal: 3.78 ± 0.20, p = 0.03). Conversely, NR3C1 showed significant downregulation in 15 cancer types, most notably in COAD (Tumor: 1.10 ± 1.31, Normal: 2.93 ± 0.44, p = 1.1e-17) (Fig. 18 A). Furthermore, NR3C1 was identified as a prognostic high-risk factor in 4 cancer types, such as STAD (N = 372, p = 1.9e-3, HR = 1.27 (1.09, 1.47)). Conversely, it was deemed a prognostic protective factor in GBMLGG (N = 619, p = 4.0e-8, HR = 0.55 (0.44, 0.68)), along with three other cancer types (Fig. 18 B). Additionally, the expression level of NR3C1 demonstrated positive correlations with the tgf_beta_signaling and uv_response_dn pathways, while exhibiting a negative correlation with oxidative_phosphorylation in the context of pan-cancer. Notably, the expression of NR3C1 played a pivotal role in the development of kidney renal clear cell carcinoma(KIRC), kidney chromophobe༈KICH༉, and kidney renal papillary cell carcinoma༈KIRP༉. In KIRP, NR3C1 gene expression positively correlated with epithelial_mesenchymal_transition and demonstrated a negative correlation with oxidative_phosphorylation. Similarly, NR3C1 expression in KIRC exhibited positive correlations with epithelial_mesenchymal_transition and a negative correlation with oxidative_phosphorylation. Moreover, NR3C1 expression showed a significant positive correlation with g2m_checkpoint in KICH (Fig. 18 C). The assessment of immune infiltration, using two algorithms, revealed that NR3C1 expression displayed a significant negative correlation with eosinophils in KIRP. However, KICH and KIRC did not exhibit a consistent relationship with immune cells (Fig. 18 D). Furthermore, Pearson correlation analysis between NR3C1 expression and the immune pathway marker genes in PAAD demonstrated significant positive correlations within the m1A, m5C, and m6A groups. Notably, within the m1A modification group, 14 gene groups exhibited negative correlations with TRMT61A, ALKBH3, and other genes. Similarly, within the m5C modification, 18 gene groups displayed negative correlations with NSUN5, NSUN7, ALYREF, and other genes. Lastly, within the m6A modification, 18 gene groups demonstrated negative correlations with KIAA1429, IGF2BP1, LRPPRC, and other genes (Fig. 18 E). Furthermore, NR3C1 gene expression exhibited significant associations with heterogeneity in 8 tumor types. It also showed a significant positive association with mutational load in 2 tumors, notably COAD (N = 282) (R = 0.15, P = 9.68×10 − 3 ). Conversely, NR3C1 was found to have a significant negative association with mutational load in 6 tumors, including LUAD (N = 509) (R = -0.18, P = 3.06×10 − 5 ) (Fig. 18 F). Discussion Our study represents a prospective investigation into pan-cancer development, utilizing blood indicators. It stands as the most extensive study to date within this field. Not only do our findings validate the outcomes of preceding studies, but they also go beyond them, owing to our comprehensive population sample and unparalleled systematic approach. By employing MR and bioinformatics, we have discovered that blood monitoring of patients with HTN, anemia, DVT, and DM aids in unraveling cancer PPPM in this specific population. Moreover, our research unveils the consequences of long-term medication, encompassing lipid-lowering drugs, oral contraceptive drugs, insulin, and hormones, regarding pan-cancer progression. Thus, it offers novel perspectives pan-cancer prognostication and fosters innovative approaches in pan-cancer therapeutics. This seminal contribution has granted substantial dividends in terms of predicting and preventing pertinent malignancies, as well as providing new strategies for pan-cancer treatment. Notably, we have successfully identified 3 crucial targets within the aforementioned medications, thereby establishing a theoretical groundwork for the advancement of targeted pan-cancer therapy. Comprehensive monitoring of HTN may prove advantageous in the prevention of COAD, BRCA, ESCA, and PAAD Building upon the observed correlation between blood pressure and tumor development, we hypothesize that monitoring HTN could offer preventative benefits for COAD, BRCA, ESCA, and PAAD, which aligns with prior research findings. Specifically, studies have demonstrated a heightened risk of COAD in male individuals with HTN (95% CI: 1.06–1.20)[ 25 ]. Conversely, no significant correlation was found between HTN and COAD risk in the female population. It is widely acknowledged that HTN influences colorectal carcinogenesis and metastasis through its impact on the RAS, induction of oxidative stress, and promotion of chronic inflammation[ 26 ]. Thus, blood pressure monitoring in males may aid in predicting and preventing COAD. HTN is responsible for a 15% increased probability of BRCA in postmenopausal women, likely due to shared adipose pathways with BRCA that trigger inflammation and modulate apoptosis. However, HTN does not exhibit significant effects on BRCA risk in premenopausal or Asian women[ 27 ]. Consequently, blood pressure should be vigilantly monitored in postmenopausal women to mitigate BRCA risk. Furthermore, a Korean study indicated a higher incidence of ESCA in the HTN population[ 28 ]. Paradoxically, another study reported a decreased incidence of five gastrointestinal cancers (including ESCA, PAAD, and STAD) in an Asian population with HTN. These contrasting results may be attributed to inherent differences in human development indices (HDIs) across populations and the potential for distinct ecological correlations to produce varied mortality rates for HTN-related cancers[ 29 ]. Specifically, a 1% increase in the HTN factor was associated with a 13% surge in PAAD incidence[ 30 ], likely driven by elevated levels of angiotensin II type 1 receptor (AT1R) in the RAS regulating blood pressure. These heightened levels facilitate cancer cell proliferation and angiogenesis, eventually leading to PAAD metastasis[ 31 ]. Consequently, targeting hypertensive populations becomes imperative in the prevention and management of PAAD development and progression. However, our analyzed results did not reveal a significant mediating effect of renin on the relationship between HTN and pan-cancer. Nonetheless, it is vital to acknowledge the critical role renin plays in the regulation of blood pressure through the RAS. Some tumors are known to secrete renin[ 31 ], while some studies have shown that renin stimulates the growth of renal cancer cells[ 32 ]. Additionally, evidence indicates that utilizing a certain dose of renin inhibitors can reduce the risk of junctional cancers[ 33 ]. Consequently, renin concentration may serve as a useful monitoring indicator for related pan-cancer in preventive healthcare strategies. The regular surveillance of DM holds potential benefits for the prevention of COAD and STAD. Our hypothesis is based on the correlation between blood glucose, insulin, and tumor development. Our findings align with previous MR studies, demonstrating that DM increases the likelihood of gastrointestinal cancers, including STAD and COAD[ 35 ]. Both Type 1 and Type 2 DM elevate the incidence of gastric cancers. For Type 1 DM, the rise may be attributed to autoimmune comorbidities[ 36 ], whereas insulin stimulates STAD cell proliferation and inhibits chemotherapy sensitivity by affecting P-glycoprotein in Type 2 DM[ 37 ]. Furthermore, female DM patients exhibit a slightly higher risk of developing STAD, and while no definitive pathophysiological mechanism explains this gender difference, lifestyle behaviors (such as difficulties controlling sugar intake in women) may contribute. Some studies have revealed a weak or negligible negative correlation between DM and tumor incidence. This observation may be attributed to genetic variants present in certain tumors, leading to both hyperglycemia and hypoinsulinism, with the protective effects of low insulin levels potentially counteracting the carcinogenic effects of hyperglycemia[ 36 ]. DM raises the incidence of COAD in men, but the rise is not statistically significant in women[ 38 ]. Insulin activates cell proliferation and protein synthesis downstream of the tumor via the phosphatidylinositol 3-kinase-protein kinase B-mammalian target of rapamycin and Ras-mitogen-activated protein kinase pathways[ 39 ]. This increased risk, however, diminishes in the long-term diabetic population due to progressive pancreatic β-cell depletion and subsequent lower insulin levels. Within the DM context, hyperglycemia and hypoxia contribute to the production of various pro-inflammatory factors that fuel tumor cells, drive tumor cell proliferation, facilitate cell invasion, and impede apoptosis, thereby enhancing the risk of tumorigenesis[ 40 , 41 ]. Although HbA1c serves as a biomarker for chronic hyperglycemia, our study did not find HbA1c to mediate the relationship between DM and pan-cancer. Prior research has indicated that elevated HbA1c levels are associated with an increased risk of COAD, PAAD, respiratory cancers, and cancers affecting the female reproductive tract[ 42 ], while demonstrating protective effects against THCA[ 43 ] and prostate adenocarcinoma PRAD[ 44 ]. A study conducted in the UK showed that HbA1c may be useful for the early detection of PAAD[ 45 ]. Consequently, HbA1c still serves as a potential indicator for monitoring pan-cancer development, but further investigation is required to understand the intricate mechanisms underlying the impact of HbA1c on pan-cancer pathogenesis. The effect of anemia on cancer development is related to the type of anemia This study endeavors to unravel the intricate association between anemia and cancer development, discerning the paramount influence wielded by distinct anemia subtypes. By leveraging the compelling correlations between blood cell count, red blood cell distribution width, and tumor emergence, we postulate the potential benefits of monitoring anemia for preventing LUAD. However, amalgamating a compendium of prior studies, indications emerge that the intricate interplay between anemia and tumor incidence is modulated by the specific anemia subtype in question. Notably, somatic cell aberrations intrinsic to aplastic anemia engender an elevated prevalence of malignancies, particularly acute myeloid leukemia and solid tumors[ 46 ]. Iron deficiency anemia heightens susceptibility to STAD[ 47 ], ESCA[ 48 ], and LUAD[ 49 ], with the former two intrinsically linked to low serum ferritin levels, while the pathogenesis of the latter necessitates further exploration. In contrast, pernicious anemia exhibits a dichotomous impact, lowering the incidence of most tumors barring gastric cancer, while concomitantly escalating the risk of hematologic malignancies (precisely multiple myeloma), Hodgkin's lymphoma, and biliary tract cancer[ 50 ]. Although our study did not find hemoglobin to act as a mediator in the relationship between anemia and pan-cancer, it can serve as an indicator to monitor cancer development, as substantiated by prior investigations. Empirical evidence reveals that cancer patients with diminished hemoglobin levels face double the mortality risk compared to those with higher levels[ 51 ]. Within the confines of non-small-cell lung cancer (NSCLC), hemoglobin alpha and beta demonstrate a protective role in cancer development, and their diminished expression may reflect advanced NSCLC with a debilitating prognosis[ 52 ]. Conversely, for a majority of hematopoietic, lymphopoietic, and gastrointestinal malignancies, hemoglobin concentrations begin to decline 2–3 years preceding cancer diagnosis, warranting closer scrutiny as a potential early indicator[ 53 ]. The Potential of DVT monitoring in COAD prevention This study explores the promising prospects of monitoring DVT as a preventive strategy for COAD, building upon the significant associations observed between DVT and tumors. Our hypothesis aligns with previous research findings[ 54 ], suggesting that DVT monitoring could potentially offer preventive benefits in COAD, likely attributable to the hypercoagulable state observed in DVT patients. This state triggers the activation of the coagulation-fibrinolytic system, resulting in thrombocytosis, elevated fibrinogen levels, and increased D-dimer concentrations, all of which contribute, to varying extents, to tumor development. Among these factors, thrombocytosis plays a pivotal role by enhancing cancer cell dissemination, adhesion, and infiltration of endothelial walls. This process is facilitated by the TP/PD-ECGF-mediated induction of tumor angiogenesis[ 55 ]. Additionally, high fibrinogen levels facilitate tumor metastasis through the binding to ICAM-1 on endothelial cells, promoting stable tumor cell adhesion within target organs while aiding platelet-tumor cell interactions and providing protection against the innate immune system[ 56 ]. The specific mechanism linking elevated D-dimer levels to pan-cancer remains unclear, although D-dimer concentrations are likely indicative of the activation state of the coagulation-fibrinolytic system[ 57 ]. Concordant with previous studies, DVT has also been associated with a poor prognosis in STAD[ 55 , 56 ], OV[ 58 ], and LUAD[ 59 ] post-disease progression. Prospective investigations have revealed a tumor occultation rate of approximately 10% in patients with DVT[ 60 ], emphasizing the potential of DVT monitoring as a means to identify occult cancers, often at early stages[ 61 ]. This underscores the importance of extensive evaluation in DVT patients, as it may aid in early screening and curative treatment, particularly for occult cancers. While our study did not demonstrate D-dimer as a mediator in the relationship between pan-cancer and DVT, existing literature highlights the utility of D-dimer monitoring in cancer diagnosis and prognosis prediction. Notably, elevated D-dimer concentrations exceeding ten times the upper limit of normal have shown diagnostic value in cancer patients[ 62 ], while low D-dimer concentrations serve as robust negative predictors of malignancy[ 63 ]. Furthermore, D-dimer levels can serve as a staging marker for cancer and reliably anticipate tumor metastasis[ 64 ]. Effects of lipid-lowering, oral contraceptive, hormones, and insulin use on pan-cancer The development of new targeted drugs for cancer requires significant time and financial resources. However, a promising shortcut is the rational development of commercially recognized targeted drugs for other diseases that can be repurposed for cancer treatment. In this study, we employed MR analysis to narrow down the selection of targeted drugs by investigating the associations between specific blood-related exposures and pan-cancer. We specifically focused on lipid-lowering drugs, oral contraceptive drugs, hormones, and insulin as representative targeted drugs. Subsequently, we analyzed their relationship with pan-cancer within the context of current relevant studies. Interestingly, our analysis revealed that the consumption of specific targeted drugs may exhibit a protective effect against certain cancers, such as COAD, PAAD, OV, and PRAD. Specifically, we found that taking lipid-lowering drugs can reduce the risk of developing COAD and PAAD. This protective effect could be attributed to the close relationship between the development of COAD and PAAD and cholesterol metabolism[ 65 , 66 ]. Cancer stem cell growth is dependent on cholesterol production and protein prenylation. Combination therapy involving 5-FU and lipid-lowering agents like lovastatin or zoledronic acid may reduce drug resistance by targeting cancer stem cells[ 67 ]. The protective mechanism of lipid-lowering drugs on PAAD is not yet well understood, but it appears to be influenced by the specific drug used. Atorvastatin, in particular, demonstrates a more pronounced protective effect[ 68 ]. Additionally, the combined use of statins in the treatment of metastatic PAAD shows promising outcomes[ 69 ]. It is important to note that the protective effect of lipid-lowering drugs against cancer may be influenced by lifestyle changes in patients. Furthermore, our analysis indicates that taking oral contraceptive drugs reduces the risk of OV. The exact mechanism underlying this effect remains unclear; however, it is likely related to the regulation of sex hormones. Oral contraceptives primarily consist of sex hormones and progestins, which result in increased progesterone levels, reduced gonadotropin levels, and suppressed ovulation. OV cells with higher expression of the follicle-stimulating hormone (FSH) receptor exhibit greater invasive capacity and poorer prognoses[ 70 ]. Moreover, ovulation-induced damage to the ovarian epithelium and subsequent repair can lead to genetic damage in ovarian epithelial cells, ultimately contributing to OV development[ 71 ]. Progesterone has been found to induce apoptosis in human OV cell lines[ 72 ]. Combining these findings with previous studies suggesting dual effects of estrogen on PRAD (inhibition at high serum estrogen concentrations and promotion at lower concentrations) contributes to a comprehensive understanding of the impact of oral contraceptive drugs and sex hormones on cancer risk[ 73 ]. Conversely, our analysis indicates an increased risk of developing COAD, BRAD, and LUNG when taking certain targeted drugs. Specifically, taking oral contraceptive drugs increases the risk of BRCA, while taking human insulin increases the risk of LUAD and COAD. Additionally, taking estrogen or progesterone increases the risk of LUAD. The effects of contraceptives and hormone replacement therapy on BRCA and LUAD involve estrogen and progesterone-related pathways. Estrogen receptor β promotes NSCLC by inducing angiogenic mimicry and invasion of LUAD cells[ 74 ], while Estrogen receptor α promotes the development of BRCA and NSCLC at an early stage, upregulating signaling involved in CCL2 and CXCL12, thereby enhancing macrophage infiltration[ 75 ]. Progesterone can increase the 5alpha-pregnane:4-pregnene ratio, which facilitates increased cell proliferation and segregation, promoting BRCA[ 76 ]. Hormone replacement therapy utilizing progesterone and estrogen leads to abnormal mammary gland development and BRCA1-mediated BRCA through ductal hyperplasia[ 77 ]. Insulin exerts anti-apoptotic effects, stimulates mitosis via the Akt pathway, and reduces the expression of apoptosis-related proteins, all of which are positively associated with the development of several cancers[ 78 ]. Moreover, insulin resistance has been identified as a risk factor for various chronic diseases[ 79 ], including COAD, HTN, and several others. Elevated fasting insulin levels and increased insulin resistance are associated with an increased risk of LUAD[ 80 ]. Prevention of pan-cancer development through inhibition of HMGCR, INSR, and NR3C1 expression Altered genomic regions have been implicated in both promoting and protecting against tumor development. Through our bioinformatic analyses of relevant target genes, we aim to facilitate the development of novel cancer drug targeting pathways and revolutionize previous targeted therapies. Our results demonstrate the crucial roles played by HMGCR, NR3C1, and INSR in regulating mutation and heterogeneity in various cancers, as well as their close association with immune cells and immune responses. The mevalonate pathway, primarily regulated by HMG-CoA reductase (encoded by HMGCR), is a key anticancer pathway targeted by statins. Inhibition of HMGCR expression disrupts the mevalonate pathway through both cholesterol-mediated and non-cholesterol-mediated mechanisms, subsequently affecting GPX4 and enhancing tumor cell susceptibility to iron-induced cell death[ 81 ]. This results in significant antitumor effects. Our findings indicate a positive association between variants in the HMGCR gene region and pan-cancer, corroborating previous research[ 82 ]. Taken together with previous studies, drugs targeting HMGCR expression hold promise for treating a wide range of cancer types, including BRCA[ 83 ], STAD[ 84 ], and liver hepatocellular carcinoma(LIHC)[ 85 ]. The INSR gene encodes insulin receptor (IR), with its IR-A isoform being implicated in increased cancer risk, such as in BRCA and THCA, through pathways involving insulin-like growth factor I receptor (IGF-IR), mammalian target of rapamycin(mTOR), and the epidermal growth factor (EGF) families[ 86 , 87 , 88 ]. IR participates in the insulin signaling pathway and glycolysis, both of which mediate tumorigenesis. Glycolysis, in particular, plays a critical role in tumor survival, proliferation, and drug resistance[ 89 ]. Notably, the presence of multiple IR subtypes may contribute to the complexity of IR expression's impact on cancer. Furthermore, targeted therapies against IR may also influence mitosis, potentially giving rise to side effects in human subjects[ 90 ]. Caution should be exercised when utilizing IR expression inhibition for future cancer treatments. NR3C1 encodes the glucocorticoid receptor (GR) and is involved in the glucocorticoid pathway. On one hand, NR3C1 expression in tumor tissues, particularly in the CD8 TIL subpopulation, is elevated compared to normal tissues. The GR repetitively activates the expression of genes associated with T-cell-induced dysfunction in the presence of glucocorticoids, effectively suppressing immune responses and reducing the efficacy of immune checkpoint blockade, thereby promoting tumor progression[ 91 ]. On the other hand, NR3C1 expression is low in NSCLC. Glucocorticoids prevent the GR from limiting tumor growth by inhibiting RAS activation, consequently contributing to poor disease prognosis[ 92 ]. Additionally, the effect of NR3C1 on cancer is influenced by other genes, such as the inhibition of miR-1270 in PAAD modulating the impact of NR3C1 inhibition[ 93 ]. This suggests that targeted therapy against NR3C1 cannot simply rely on solely targeting NR3C1 or inhibiting its expression. Achieving an optimal balance of effects on different cancer types is an important avenue for further inquiry. Limitations and prospects Owing to the limited accessibility of disease sample repositories, we encountered a lack of data on diseases where blood-related indicators, namely leukopenia, hypotension, and hypoglycemia, have a strong correlation with pan-cancer. Consequently, our effort to establish a comprehensive PPPM network for monitoring pan-cancer through blood indicators remains incomplete. Furthermore, the age distribution of the included samples was not clearly defined, imposing limitations on conducting subgroup-specific analyses. The prolonged incidence of chronic conditions such as HTN and DM heightens patient visits, potentially leading to an artificial elevation in cancer detection rates because of detection bias, thereby overestimating the risk of pan-cancer.Additionally, our investigations into the connection between lipid-lowering drugs, oral contraceptive drugs, hormones, and insulin with pan-cancer risk imply that disparate drug targets and pathways may entail varying levels of risk for the development of the same malignancy. Animal experiments and mechanistic studies remain indispensable in complementing specific drug usage and target development. We aspire that future studies will enhance our comprehension of hematological index-related diseases and the associated risk of pan-cancer development by integrating gwas data from diverse diseases, culminating in the establishment of a detection and prediction system for pan-cancer through blood indicator . Conclusion and expert recommendations in the framework of PPPM Our study has revealed associations between blood-related indicator and various types of cancer, including esophageal, breast, lung adenocarcinoma, ovarian, and several gastrointestinal cancers. This valuable information can contribute to personalized cancer surveillance and prevention strategies for these specific populations. The observed links between HTN, DVT, DM and anemia highlight the importance of developing tailored cancer prevention and treatment programs. Understanding the connection between these diseases and cancer can help identify high-risk individuals who may be prone to developing cancer. This knowledge can be applied within the framework of PPPM to screen at-risk individuals and provide convenient ways to prevent cancer or detect it at an early stage. Furthermore, the drug targets identified within these four groups of diseases could be utilized for prophylactic treatment of related cancers or to improve the efficacy of existing targeted therapies. This offers new possibilities for enhancing cancer treatment and prevention strategies. In summary, our proposal to strengthen research and interpretation of human blood sample data, particularly those related to cancer, is a crucial step towards advancing personalized cancer care and early detection methods. It also has the potential to contribute to the development of individualized cancer prevention plans for individuals with diseases such as hypertension and diabetes mellitus. Predictive approach Prevention and early screening for pan-cancer play a crucial role in reducing the suffering and economic burden of patients. At the genetic level, by utilizing MR and bioinformatics analysis of blood-related markers, along with their associated diseases and drug interactions in pan-cancer, in combination with clinical data and molecular experiments, it is possible to establish a predictive diagnostic and prognostic model for pan-cancer based on blood markers. This approach allows for the implementation of PPPM for pan-cancer. Targeted prevention This study seeks to investigate the varying impacts of different blood indicators, associated medical conditions (including HTN, DM, DVT, and anemia), as well as drug usage on the overall incidence of pan-cancer. Through such an exploration, the study has successfully identified previously undisclosed subpopulations that are particularly vulnerable to the development of cancers, thus shedding light on the importance of tailored and targeted cancer prevention approaches for specific at-risk populations. For instance, it is imperative to implement preventative measures for men afflicted with hypertension to avoid the onset of colorectal adenocarcinoma (COAD), and to closely monitor insulin users for early signs of lung adenocarcinoma (LUAD). Personalization of medicine services It is widely recognized that genetic factors play a key role in determining one's risk of developing cancer, as well as the type of cancer to which they are most susceptible. Blood is a good sample that balances accessibility and informativeness. Based on the results of the analysis of blood at the genetic level in relation to pan-cancer, it is possible to provide a direction and theoretical basis for personalized preventive, diagnostic and therapeutic medical services for specific populations.Simultaneously, our research endeavors have successfully identified specific gene targets that play a crucial role in the intricate mechanisms underlying the development of novel and precisely targeted therapeutic pathways. Paradigm shifts from reactive medicine to PPPM and moving beyond the state of the art Indeed, cancer remains an ongoing and formidable challenge in the field of human medicine, with certain types of cancers showcasing a stealthy and aggressive progression. This reality has underscored the limitations of conventional reactive approaches to cancer treatment, driving the necessity to transition towards PPPM model, which is more proactive and personalized. The correlation discovered in this study between blood markers and pan-cancer serves as a valuable tool in the development of a simplified, standardized, and highly sensitive cancer surveillance system. Based on this tool, the new tumor susceptible populations identified in the study, as well as new targets for pharmacological interventions, further contribute to personalized medicine in oncology.By leveraging such findings, healthcare professionals can design tailored cancer prevention and treatment strategies that are specifically optimized for individual patients, ultimately enhancing the overall efficacy and outcomes of cancer management. Abbreviations AT1R: angiotensin II type 1 receptor TMB: tumor mutational burden RAS ： renin-angiotensin system READ ： rectum adenocarcinoma PPPM ： predictive, preventive and personalised medicine MR ： mendelian randomization LD: linkage disequilibrium IVW: inverse variance weighted IVs: instrumental variables IR ： insulin receptor HbA1c: glycated hemoglobin HTN ： hypertension HDIs: human development indices GWAS: genome-wide association studies GSEA: gene set enrichment analysis GR: glucocorticoid receptor IGF-IR ： insulin-like growth factor I receptor mTOR ： mammalian target of rapamycin EGF ： epidermal growth factor DVT: deep vein thrombosis DM: diabetes mellitus BRCA: breast carcinoma CESC: cervical squamous cell carcinoma COAD: colorectal adenocarcinoma ESCA: esophageal carcinoma GBMLGG: lower-grade glioma and glioblastoma KICH: kidney chromophobe KIRC: kidney renal clear cell carcinoma KIRP ： kidney renal papillary cell carcinoma LGG ： lower-grade glioma LIHC ： liver hepatocellular carcinoma LUAD ： lung adenocarcinoma NSCLC: non-small-cell lung cancer OV: ovarian cancer PAAD: pancreatic adenocarcinoma PRAD ： prostate adenocarcinoma SKCM ： skin cutaneous melanoma SNPs ： single nucleotide polymorphisms STAD: stomach adenocarcinoma THCA: thyroid carcinoma UCEC: uterine corpus endometrial carcinoma FSH: follicle-stimulating hormone Declarations Acknowledgments We would like to express our gratitude to FinnGen, the IEU Open GWAS, the UK Biobank, and Biobank Japan for publishing the GWAS summary statistics. Author contribution Design subject: Xinhao Tang , Bowen Chu , Wenbo Xu, Mianhua Wu and Jun Shi; Acquisition of data and data organization: Xinhao Tang, Xinyu Tian, Jingjing Wu, and Sainan Hao; MR analysis:Xinhao Tang , Xinyu Tian, Jingjing Wu, and Sainan Hao; analysis using bioinformatics tools: Xinhao Tang, Xinyu Tian, Shuai Shan, and Tinghao Dai; Paper writing: Xinhao Tang, Xinyu Tian, and Bowen Chu; Supervision:Guanmin Tang, Wenbo Xu, Mianhua Wu and Jun Shi; Sponsor: Xinhao Tang, Guanmin Tang, and Mianhua Wu. Funding This study was supported by the Jiaxing Public Welfare Research Program Project (2020AY30006), Suzhou Science and Technology Development Programme (SKYD202306), National Student Innovation Training Program (s20021038117, 202210315128Z), 2022 Luo Linxiu Cup Student Innovation Cultivation Project of Nanjing University of Chinese Medicine (3), and 2023 Chinese Medicine Artificial Intelligence Unveiling and Hanging Project of Nanjing University of Chinese Medicine (1). Data availability The data used and/or analyzed in this study are available from the corresponding author upon reasonable request. Code availability The software and code used in the study can be obtained through the corresponding author. Consent to participate Not applicable to this study. Competing interests All authors disclosed no competing interests in this study. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. Tian Q, Price ND, Hood L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J Intern Med. 2012;271(2):111-121. Han H, Guo W, Shi W, Yu Y, Zhang Y, Ye X, He J. hypertension and breast cancer risk: a systematic review and meta-analysis. Sci Rep. 2017;7:44877. de Paula Gonzaga ALAC, Palmeira VA, Ribeiro TFS, Costa LB, de Sá Rodrigues KE, Simões-E-Silva AC. ACE2/Angiotensin-(1-7)/Mas Receptor Axis in Human Cancer: Potential Role for Pediatric Tumors. Curr Drug Targets. 2020;21(9):892-901. Lega IC, Lipscombe LL. Review: diabetes, Obesity, and Cancer-Pathophysiology and Clinical Implications. Endocr Rev. 2020;41(1):bnz014. Lee YJ, Lee HR, Nam CM, Hwang UK, Jee SH. White blood cell count and the risk of colon cancer. Yonsei Med J. 2006;47(5):646-56. Wong JYY, Bassig BA, Loftfield E, Hu W, Freediabetesan ND, Ji BT, Elliott P, Silverman DT, Chanock SJ, Rothman N, Lan Q. White Blood Cell Count and Risk of Incident Lung Cancer in the UK Biobank. JNCI Cancer Spectr. 2019;4(2):pkz102. Xie X, Yao M, Chen X, Lu W, Lv Q, Wang K, Zhang L, Lu F. Reduced red blood cell count predicts poor survival after surgery in patients with primary liver cancer. Medicine (Baltimore). 2015;94(8):e577. Ma W, Mao S, Bao M, Wu Y, Guo Y, Liu J, Wang R, Li C, Zhang J, Zhang W, Yao X. Prognostic significance of red cell distribution width in bladder cancer. Transl Androl Urol. 2020;9(2):295-302. Tong Y, Xie X, Mao X, Lei H, Chen Y, Sun P. Low Red Blood Cell Count as an Early Indicator for Myometrial Invasion in Women with Endometrioid Endometrial Carcinoma with Metabolic Syndrome. Cancer Manag Res. 2020;12:10849-10859. Duell EJ, Bonet C, Muñoz X, Lujan-Barroso L, Weiderpass E, Boutron-Ruault MC, Racine A, Severi G, Canzian F, Rizzato C, Boeing H, Overvad K, Tjønneland A, Argüelles M, Sánchez-Cantalejo E, Chamosa S, Huerta JM, Barricarte A, Khaw KT, Wareham N, Travis RC, Trichopoulou A, Trichopoulos D, Yiannakouris N, Palli D, Agnoli C, Tumino R, Naccarati A, Panico S, Bueno-de-Mesquita HB, Siersema PD, Peeters PH, Ohlsson B, Lindkvist B, Johansson I, Ye W, Johansson M, Fenger C, Riboli E, Sala N, González CA. Variation at ABO histo-blood group and FUT loci and diffuse and intestinal gastric cancer risk in a European population. Int J Cancer. 2015 ;136(4):880-893. Yuan F, Wen W, Jia G, Long J, Shu XO, Zheng W. Serum Lipid Profiles and Cholesterol-Lowering Medication Use in Relation to Subsequent Risk of Colorectal Cancer in the UK Biobank Cohort. Cancer Epidemiol Biomarkers Prev. 2023;32(4):524-530. Nabbout R, Kuchenbuch M. Impact of predictive, preventive and precision medicine strategies in epilepsy. Nat Rev Neurol. 2020;16(12):674-688. Sagner M, McNeil A, Puska P, Auffray C, Price ND, Hood L, Lavie CJ, Han ZG, Chen Z, Brahmachari SK, McEwen BS, Soares MB, Balling R, Epel E, Arena R. The P4 Health Spectrum - A Predictive, Preventive, Personalized and Participatory Continuum for Promoting Healthspan. Prog Cardiovasc Dis. 2017;59(5):506-521. Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol. 2011;8(3):184-187. Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res Synth Methods. 2019;10(4):486-496. Bodrova TA, Kostiushev DS, Antonova EN, Gnatenko DA, Bocharova MO, Lopukhin IuM, Pal'tsev MA, Suchkov SV. Introduction into PPPM: experience of the past and tomorrow's reality. Vestn Ross Akad Med Nauk. 2013;(1):58-64. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326(16):1614-1621. Lin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet. 2021 ;17(11):e1009922. Zhang Y, Li D, Zhu Z, Chen S, Lu M, Cao P, Chen T, Li S, Xue S, Zhang Y, Zhu J, Ruan G, Ding C. Evaluating the impact of metformin targets on the risk of osteoarthritis: a mendelian randomization study. Osteoarthritis Cartilage. 2022 ;30(11):1506-1514. Yuan S, Titova OE, Zhang K, Gou W, Schillemans T, Natarajan P, Chen J, Li X, Åkesson A, Bruzelius M, Klarin D, Damrauer SM, Larsson SC. Plasma protein and venous thromboembolism: prospective cohort and mendelian randomisation analyses. Br J Haematol. 2023;201(4):783-792. Xiang Y, Zhang C, Wang J, Cheng Y, Wang L, Tong Y, Yan D. Identification of host gene-microbiome associations in colorectal cancer patients using mendelian randomization. J Transl Med. 2023;21(1):535.[21]Liu J, Lichtenberg T, Hoadley KA,et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400-416.e11. Liu J, Lichtenberg T, Hoadley KA,et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400-416.e11. Shen YT, Huang X, Zhang G, Jiang B, Li CJ, Wu ZS. Pan-Cancer Prognostic Role and Targeting Potential of the Estrogen-Progesterone Axis. Front Oncol. 2021 ;11:636365. ]Xuan K, Zhao T, Sun C, Patel AS, Liu H, Chen X, Qu G, Sun Y. The association between hypertension and colorectal cancer: a meta-analysis of observational studies. Eur J Cancer Prev. 2021;30(1):84-96. Childers WK. Interactions of the renin-angiotensin system in colorectal cancer and metastasis. Int J Colorectal Dis. 2015;30(6):749-52. Han H, Guo W, Shi W, Yu Y, Zhang Y, Ye X, He J. hypertension and breast cancer risk: a systematic review and meta-analysis. Sci Rep. 2017 ;7:44877. Seo JH, Kim YD, Park CS, Han KD, Joo YH. hypertension is associated with oral, laryngeal, and esophageal cancer: a nationwide population-based study. Sci Rep. 2020 Jun 24;10(1):10291. Loney T, Nagelkerke NJ. The individualistic fallacy, ecological studies and instrumental variables: a causal interpretation. Emerg Themes Epidemiol. 2014;11:18. Huang J, Lok V, Ngai CH, Zhang L, Yuan J, Lao XQ, Ng K, Chong C, Zheng ZJ, Wong MCS. Worldwide Burden of, Risk Factors for, and Trends in Pancreatic Cancer. Gastroenterology. 2021 Feb;160(3):744-754. Khoshghamat N, Jafari N, Toloue-Pouya V, Azami S, Mirnourbakhsh SH, Khazaei M, Ferns GA, Rajabian M, Avan A. The therapeutic potential of renin-angiotensin system inhibitors in the treatment of pancreatic cancer. Life Sci. 2021 ;270:119118. Corvol P, Pinet F, Plouin PF, Bruneval P, Menard J. Renin-secreting tumors. Endocrinol Metab Clin North Am. 1994 ;23(2):255-70. Hu J, Zhang L-C, Song X, et al. KRT6 interacting with notch1 contributes to progression of renal cell carcinoma, and aliskiren inhibits renal carcinoma cell lines proliferation in vitro. Int J Clin Exp Pathol. 2015;8(8):9182–8. Wegman-Ostrosky T, Soto-Reyes E, Vidal-Millán S, Sánchez-Corona J. The renin-angiotensin system meets the hallmarks of cancer. J Renin Angiotensin Aldosterone Syst. 2015 ;16(2):227-33. Goto A, Yamaji T, Sawada N, Momozawa Y, Kamatani Y, Kubo M, Shimazu T, Inoue M, Noda M, Tsugane S, Iwasaki M. diabetes and cancer risk: A Mendelian randomization study. Int J Cancer. 2020;146(3):712-719. Guo J, Liu C, Pan J, Yang J. Relationship between diabetes and risk of gastric cancer: A systematic review and meta-analysis of cohort studies. diabetes Res Clin Pract. 2022 ;187:109866. Wei Z, Liang L, Junsong L, Rui C, Shuai C, Guanglin Q, Shicai H, Zexing W, Jin W, Xiangming C, Shufeng W. The impact of insulin on chemotherapeutic sensitivity to 5-fluorouracil in gastric cancer cell lines SGC7901, MKN45 and MKN28. J Exp Clin Cancer Res. 2015 ;34(1):64. Sasazuki S, Charvat H, Hara A, Wakai K, Nagata C, Nakamura K, Tsuji I, Sugawara Y, Tamakoshi A, Matsuo K, Oze I, Mizoue T, Tanaka K, Inoue M, Tsugane S; Research Group for the Development and Evaluation of Cancer Prevention Strategies in Japan. diabetes mellitus and cancer risk: pooled analysis of eight cohort studies in Japan. Cancer Sci. 2013 ;104(11):1499-507. Murphy N, Song M, Papadimitriou N, Carreras-Torres R, Langenberg C, Martin RM, Tsilidis KK, Barroso I, Chen J, Frayling TM, Bull CJ, Vincent EE, Cotterchio M, Gruber SB, Pai RK, Newcomb PA, Perez-Cornago A, van Duijnhoven FJB, Van Guelpen B, Vodicka P, Wolk A, Wu AH, Peters U, Chan AT, Gunter MJ. Associations Between Glycemic Traits and Colorectal Cancer: A Mendelian Randomization Analysis. J Natl Cancer Inst. 2022 ;114(5):740-752. Li W, Zhang X, Sang H, Zhou Y, Shang C, Wang Y, Zhu H. Effects of hyperglycemia on the progression of tumor diseases. J Exp Clin Cancer Res. 2019 ;38(1):327. Yu GH, Li SF, Wei R, Jiang Z. diabetes and Colorectal Cancer Risk: Clinical and Therapeutic Implications. J diabetes Res. 2022 ;2022:1747326. Hope C, Robertshaw A, Cheung KL, Idris I, English E. Relationship between HbA1c and cancer in people with or without diabetes: a systematic review. Diabet Med. 2016;33(8):1013-25. Huang L, Feng X, Yang W, Li X, Zhang K, Feng S, Wang F, Yang X. Appraising the Effect of Potential Risk Factors on Thyroid Cancer: A Mendelian Randomization Study. J Clin Endocrinol Metab. 2022 Jun ;107(7):e2783-e2791 de Beer JC, Liebenberg L. Does cancer risk increase with HbA1c, independent of diabetes? Br J Cancer. 2014 Apr 29;110(9):2361-8. Lemanska A, Price CA, Jeffreys N, Byford R, Dambha-Miller H, Fan X, Hinton W, Otter S, Rice R, Stunt A, Whyte MB, Faithfull S, de Lusignan S. BMI and HbA1c are metabolic markers for pancreatic cancer: Matched case-control study using a UK primary care database. PLoS One. 2022;17(10):e0275369. Esteves AC, Freitas O, Almeida T, Rosado L. Aplasias medulares congénitas [Inherited aplastic anemias]. An Pediatr (Barc). 2010;73(2):84-7. Spanish. Akiba S, Neriishi K, Blot WJ, Kabuto M, Stevens RG, Kato H, Land CE. Serum ferritin and stomach cancer risk among a Japanese population. Cancer. 1991 ;67(6):1707-12. Zhang ZF, Kurtz RC, Yu GP, Sun M, Gargon N, Karpeh M Jr, Fein JS, Harlap S. Adenocarcinomas of the esophagus and gastric cardia: the role of diet. Nutr Cancer. 1997;27(3):298-309. Oh TK, Song IA. Anemia May Increase the Overall Risk of Cancer: Findings from a Cohort Study with a 12-Year Follow-up Period in South Korea. Cancer EpidemiolBiomarkersPrev.2021;30(7):1440-1448. Lahner E, Capasso M, Carabotti M, Annibale B. Incidence of cancer (other than gastric cancer) in pernicious anaemia: A systematic review with meta-analysis. Dig Liver Dis. 2018 ;50(8):780-786. Chi G, Lee JJ, Montazerin SM, Marszalek J. Prognostic value of hemoglobin-to-red cell distribution width ratio in cancer: a systematic review and meta-analysis. Biomark Med. 2022;16(6):473-482. Kang N, Qiu WJ, Wang B, Tang DF, Shen XY. Role of hemoglobin alpha and hemoglobin beta in non-small-cell lung cancer based on bioinformatics analysis. Mol Carcinog. 2022;61(6):587-602. Edgren G, Bagnardi V, Bellocco R, Hjalgrim H, Rostgaard K, Melbye M, Reilly M, Adami HO, Hall P, Nyrén O. Pattern of declining hemoglobin concentration before cancer diagnosis. Int J Cancer. 2010;127(6):1429-36. Kawai K, Watanabe T. Colorectal cancer and hypercoagulability. Surg Today. 2014 ;44(5):797-803. Wang L, Huang X, Chen Y, Jin X, Li Q, Yi TN. Prognostic value of TP/PD-ECGF and thrombocytosis in gastric carcinoma. Eur J Surg Oncol. 2012 Jul;38(7):568-73. Yamashita H, Kitayama J, Kanno N, Yatomi Y, Nagawa H. Hyperfibrinogenemia is associated with lymphatic as well as hematogenous metastasis and worse clinical outcome in T2 gastric cancer. BMC Cancer. 2006 Jun 1;6:147. Chen Y, Yu H, Wu C, Li J, Jiao S, Hu Y, Tao H, Wu B, Li A. Prognostic value of plasma D-dimer levels in patients with small-cell lung cancer. Biomed Pharmacother. Tas F, Kilic L, Bilgin E, Keskin S, Sen F, Ciftci R, Yildiz I, Yasasever V. Clinical and prognostic significance of coagulation assays in advanced epithelial ovarian cancer. Int J Gynecol Cancer. 2013 ;23(2):276-81. Chen Y, Yu H, Wu C, Li J, Jiao S, Hu Y, Tao H, Wu B, Li A. Prognostic value of plasma D-dimer levels in patients with small-cell lung cancer. Biomed Pharmacother. 2016 ;81:210-217. Piccioli A, Lensing AW, Prins MH, Falanga A, Scannapieco GL, Ieran M, Cigolini M, Ambrosio GB, Monreal M, Girolami A, Prandoni P; SOMIT Investigators Group. Extensive screening for occult malignant disease in idiopathic venous thromboembolism: a prospective randomized clinical trial. J Thromb Haemost. 2004 ;2(6):884-9. Monreal M, Lensing AW, Prins MH, Bonet M, Fernández-Llamazares J, Muchart J, Prandoni P, Jiménez JA. Screening for occult cancer in patients with acute deep vein thrombosis or pulmonary embolism. J Thromb Haemost. 2004;2(6):876-81. Gotta J, Gruenewald LD, Eichler K, Martin SS, Mahmoudi S, Booz C, Biciusca T, Reschke P, Bernatz S, Pinto Dos Santos D, Scholtz JE, Alizadeh LS, Nour-Eldin NA, Hammerstingl RM, Gruber-Rouh T, Mader C, Hardt SE, Sommer CM, Bucolo G, D'Angelo T, Onay M, Finkelmeier F, Leistner DM, Vogl TJ, Giannitsis E, Koch V. Unveiling the diagnostic enigma of D-dimer testing in cancer patients: Current evidence and areas of application. Eur J Clin Invest. 2023 ;53(10):e14060. Schutgens RE, Beckers MM, Haas FJ, Biesma DH. The predictive value of D-dimer measurement for cancer in patients with deep vein thrombosis. Haematologica. 2005 Feb;90(2):214-9. PMID: 15710574. Dai H, Zhou H, Sun Y, Xu Z, Wang S, Feng T, Zhang P. D-dimer as a potential clinical marker for predicting metastasis and progression in cancer. Biomed Rep. 2018 Nov;9(5):453-457,xg Gabitova-Cornell L, Surumbayeva A, Peri S, Franco-Barraza J, Restifo D, Weitz N, Ogier C, Goldman AR, Hartman TR, Francescone R, Tan Y, Nicolas E, Shah N, Handorf EA, Cai KQ, O'Reilly AM, Sloma I, Chiaverelli R, Moffitt RA, Khazak V, Fang CY, Golemis EA, Cukierman E, Astsaturov I. Cholesterol Pathway Inhibition Induces TGF-β Signaling to Promote Basal Differentiation in Pancreatic Cancer. Cancer Cell. 2020;38(4):567-583.e11. Jun SY, Brown AJ, Chua NK, Yoon JY, Lee JJ, Yang JO, Jang I, Jeon SJ, Choi TI, Kim CH, Kim NS. Reduction of Squalene Epoxidase by Cholesterol Accumulation Accelerates Colorectal Cancer Progression and Metastasis. Gastroenterology. 2021 Mar;160(4):1194-1207.e28. Gao S, Soares F, Wang S, Wong CC, Chen H, Yang Z, Liu W, Go MYY, Ahmed M, Zeng Y, O'Brien CA, Sung JJY, He HH, Yu J. CRISPR screens identify cholesterol biosynthesis as a therapeutic target on stemness and drug resistance of colon cancer. Oncogene. 2021 ;40(48):6601-6613. Archibugi L, Arcidiacono PG, Capurso G. Statin use is associated to a reduced risk of pancreatic cancer: A meta-analysis. Dig Liver Dis. 2019 ;51(1):28-37. Abdel-Rahman O. Statin treatment and outcomes of metastatic pancreatic cancer: a pooled analysis of two phase III studies. Clin Transl Oncol. 2019 ;21(6):810-816. Li H, Liu Y, Wang Y, Zhao X, Qi X. Hormone therapy for ovarian cancer: Emphasis on mechanisms and applications (Review). Oncol Rep. 2021 Oct;46(4):223. HUNN, JESSICA MD; RODRIGUEZ, GUSTAVO C. MD. Ovarian Cancer: Etiology, Risk Factors, and Epidemiology. Clinical Obstetrics and Gynecology 55(1):p 3-23, 2012 Phung MT, Lee AW, Wu AH, Berchuck A, Cho KR, Cramer DW, Doherty JA, Goodman MT, Hanley GE, Harris HR, McLean K, Modugno F, Moysich KB, Mukherjee B, Schildkraut JM, Terry KL, Titus LJ, Jordan SJ, Webb PM, Pike MC, Pearce CL; Ovarian Cancer Association Consortium; Australian Ovarian Cancer Study Group and the Ovarian Cancer Association Consortium; Ovarian Cancer Association Consortium. Depot-Medroxyprogesterone Acetate Use Is Associated with Decreased Risk of Ovarian Cancer: The Mounting Evidence of a Protective Role of Progestins. Cancer Epidemiol Biomarkers Prev. 2021 ;30(5):927-935. Liu WJ, Zhao G, Zhang CY, Yang CQ, Zeng XB, Li J, Zhu K, Zhao SQ, Lu HM, Yin DC, Lin SX. Comparison of the roles of estrogens and androgens in breast cancer and prostate cancer. J Cell Biochem. 2020;121(4):2756-2769. Yu W, Ding J, He M, Chen Y, Wang R, Han Z, Xing EZ, Zhang C and Yeh S (2018) Estrogen receptor beta promotes the vasculogenic mimicry (VM) and cell invasion via altering the lncRNA‐MALAT1/miR‐145‐5p/NEDD9 signals in lung cancer. Oncogene 38, 1225–1238. He M, Yu W, Chang C, Miyamoto H, Liu X, Jiang K, Yeh S. Estrogen receptor α promotes lung cancer cell invasion via increase of and cross-talk with infiltrated macrophages through the CCL2/CCR2/MMP9 and CXCL12/CXCR4 signaling pathways. Mol Oncol. 2020;14(8):1779-1799. Trabert B, Sherman ME, Kannan N, Stanczyk FZ. Progesterone and Breast Cancer. Endocr Rev. 2020 ;41(2):320–44. Poole AJ, Li Y, Kim Y, Lin SC, Lee WH, Lee EY. Prevention of Brca1-mediated mammary tumorigenesis in mice by a progesterone antagonist. Science. 2006 ;314(5804):1467-70. Lawlor MA, Alessi DR. PKB/Akt: a key mediator of cell proliferation, survival and insulin responses? J Cell Sci. 2001 ;114(Pt 16):2903-10.. Limburg PJ, Stolzenberg-Solomon RZ, Vierkant RA, Roberts K, Sellers TA, Taylor PR, Virtamo J, Cerhan JR, Albanes D. Insulin, glucose, insulin resistance, and incident colorectal cancer in male smokers. Clin Gastroenterol Hepatol. 2006 ;4(12):1514-21. Argirion I, Weinstein SJ, Männistö S, Albanes D, Mondul AM. Serum Insulin, Glucose, Indices of Insulin Resistance, and Risk of Lung Cancer. Cancer Epidemiol Biomarkers Prev. 2017;26(10):1519-1524. . Jiang W, Hu JW, He XR, Jin WL, He XY. Statins: a repurposed drug to fight cancer. J Exp Clin Cancer Res. 2021 Jul 24;40(1):241. doi: 10.1186/s13046-021-02041-2. Carter P, Vithayathil M, Kar S, Potluri R, Mason AM, Larsson SC, Burgess S. Predicting the effect of statins on cancer risk using genetic variants from a Mendelian randomization study in the UK Biobank. Elife. 2020 ;9:e57191. Freed-Pastor WA, Mizuno H, Zhao X, Langerød A, Moon SH, Rodriguez-Barrueco R, Barsotti A, Chicas A, Li W, Polotskaia A, et al. Mutant p53 disrupts mammary tissue architecture via the mevalonate pathway. Cell. 2012;148(1–2):244–258. Dessì S, Batetta B, Pulisci D, Spano O, Anchisi C, Tessitore L, Costelli P, Baccino FM, Aroasio E, Pani P. Cholesterol content in tumor tissues is inversely associated with high-density lipoprotein cholesterol in serum in patients with gastrointestinal cancer. Cancer. 1994;73(2):253–258. Du D, Liu C, Qin M, Zhang X, Xi T, Yuan S, Hao H, Xiong J. Metabolic dysregulation and emerging therapeutical targets for hepatocellular carcinoma. Acta Pharm Sin B. 2022 ;12(2):558-580. Belfiore A, Frasca F, Pandini G, Sciacca L, Vigneri R. Insulin receptor isoforms and insulin receptor/insulin-like growth factor receptor hybrids in physiology and disease. Endocr Rev. 2009;30(6):586-623. Vella V, Sciacca L, Pandini G, Mineo R, Squatrito S, Vigneri R, Belfiore A. The IGF system in thyroid cancer: new concepts. Mol Pathol. 2001;54(3):121-4. Papa V, Pezzino V, Costantino A, Belfiore A, Giuffrida D, Frittitta L, Vannelli GB, Brand R, Goldfine ID, Vigneri R. Elevated insulin receptor content in human breast cancer. J Clin Invest. 1990;86(5):1503-10. Ting M, Miao YE, Yu FX, Luo GC, Xu X, Xiao LX, Zhang GQ, Chang J. Correlation Study on the Expression of INSR, IRS-1, and PD-L1 in Nonsmall Cell Lung Cancer. J Oncol. 2022;2022:5233222 Huang G, Song C, Wang N, Qin T, Sui S, Obr A, Zeng L, Wood TL, Leroith D, Li M, Wu Y. RNA-binding protein CUGBP1 controls the differential INSR splicing in molecular subtypes of breast cancer cells and affects cell aggressiveness. Carcinogenesis. 2020;41(9):1294-1305. Acharya N, Madi A, Zhang H, Klapholz M, Escobar G, Dulberg S, Christian E, Ferreira M, Dixon KO, Fell G, Tooley K, Mangani D, Xia J, Singer M, Bosenberg M, Neuberg D, Rozenblatt-Rosen O, Regev A, Kuchroo VK, Anderson AC. Endogenous Glucocorticoid Signaling Regulates CD8+ T Cell Differentiation and Development of Dysfunction in the Tumor Microenvironment. Immunity. 2020 ;53(3):658-671.e6. Caratti B, Fidan M, Caratti G, Breitenecker K, Engler M, Kazemitash N, Traut R, Wittig R, Casanova E, Ahmadian MR, Tuckermann JP, Moll HP, Cirstea IC. The glucocorticoid receptor associates with RAS complexes to inhibit cell proliferation and tumor growth. Sci Signal. 2022;15(726):eabm4452 Zhang L, Song L, Xu Y, Xu Y, Zheng M, Zhang P, Wang Q. Midkine promotes breast cancer cell proliferation and migration by upregulating NR3C1 expression and activating the NF-κB pathway. Mol Biol Rep. 2022 ;49(4):2953-2961. Additional Declarations No competing interests reported. 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pan-cancer\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/880a7d21fb3b0160f4a96da5.png"},{"id":49089118,"identity":"94893ee0-5ace-4785-a554-e9ef82beed76","added_by":"auto","created_at":"2024-01-03 01:31:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1585047,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot shows primary results of the causal associations between blood indicators related to red blood cells and pan-cancer\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/27e8f3bc894dbba23f269e87.png"},{"id":49089116,"identity":"69268f58-8503-4704-85f2-cbb750e1c037","added_by":"auto","created_at":"2024-01-03 01:31:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1034341,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot shows primary results of the causal associations between blood indicators related to white blood cells and pan-cancer\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/1ede926ca4325b1bd630016c.png"},{"id":49089111,"identity":"c6bcecf9-0501-4c62-82cb-ddc9d68e759a","added_by":"auto","created_at":"2024-01-03 01:31:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":834101,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot shows the primary results of the causal associations between blood and blood-forming organs/special screening examination for diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism and pan-cancer\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/2f96e757d297142f8edca97c.png"},{"id":49089717,"identity":"65b38f4a-0203-4a9e-852b-511b998f03d8","added_by":"auto","created_at":"2024-01-03 01:39:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":110460,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot shows the primary results of the causal associations between tissue blood group ABO system transferase and pan-cancer\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/790f7274332ef9f617a8178b.png"},{"id":49089127,"identity":"6617af80-8252-41ec-ae82-5e97ee8db659","added_by":"auto","created_at":"2024-01-03 01:31:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":151803,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot showed primary results of the causal associations between DVT and pan-cancer\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/bd51c0d51f3df9dedfb06ccb.png"},{"id":49089120,"identity":"a1bbe58c-c73b-4297-bfd2-593c9f93e047","added_by":"auto","created_at":"2024-01-03 01:31:30","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":333339,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showed the relationship between serum exposure-related diseases, mediators, and pan-cancer\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/75890a4a028f15922f383562.png"},{"id":49089131,"identity":"37996b9f-cd00-4bf5-af1a-58a741a968a5","added_by":"auto","created_at":"2024-01-03 01:31:32","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":333335,"visible":true,"origin":"","legend":"\u003cp\u003eMR estimates for the effects of lipid-lowering drugs on pan-cancer risk.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/04ee5e5cd7a81d599e47cca4.png"},{"id":49089115,"identity":"f28ae38d-6f63-400e-9506-5d5991fd8e22","added_by":"auto","created_at":"2024-01-03 01:31:30","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":163831,"visible":true,"origin":"","legend":"\u003cp\u003eMR estimates for the effects of oral contraceptive drugs on pan-cancer risk\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/01961ed62e058a4d90616e95.png"},{"id":49089132,"identity":"36bbbad4-ee2d-4773-8b7f-9818051863cc","added_by":"auto","created_at":"2024-01-03 01:31:32","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":269455,"visible":true,"origin":"","legend":"\u003cp\u003eMR estimates for the effects of insulin on pan-cancer risk\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/7eef6c0db66a5236ba97262d.png"},{"id":49089119,"identity":"8960f64c-95f5-41a2-9eaf-f80d92a57fba","added_by":"auto","created_at":"2024-01-03 01:31:30","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":323950,"visible":true,"origin":"","legend":"\u003cp\u003eMR estimates for the effects of related hormonal drug targets on pan-cancer risk\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/4b05a758ebfb4cf76fe4d1d1.png"},{"id":49089122,"identity":"d5c6dbce-1b7e-4a19-bdf6-ffffa7c04fce","added_by":"auto","created_at":"2024-01-03 01:31:31","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":8900190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eDifferential expression of HMGCR between cancer and normal samples. \u003cstrong\u003eB\u003c/strong\u003e Cox regression analysis of the HMGCR gene and tumor prognosis. \u003cstrong\u003eC\u003c/strong\u003e gene set enrichment analysis(GSEA) of HMGCR in pan-cancer. \u003cstrong\u003eD\u003c/strong\u003e Correlation between immune infiltration and HMGCR expression in pan-cancer. \u003cstrong\u003eE\u003c/strong\u003e Pearson correlation between HMGCR gene expression and RNA modification (m6A, m5C, m1A). \u003cstrong\u003eF \u003c/strong\u003ePearson correlation between HMGCR expression and tumor mutation burden\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/5afc3c23682ff8b78aa2e3af.png"},{"id":49089138,"identity":"6acc7d34-3442-4559-b61e-d4cf305150a4","added_by":"auto","created_at":"2024-01-03 01:31:33","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":5749497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eDifferential expression of INSR between cancer and normal samples. \u003cstrong\u003eB\u003c/strong\u003e Cox regression analysis of the INSR gene and tumor prognosis. \u003cstrong\u003eC\u003c/strong\u003e GSEA of INSR in pan-cancer. \u003cstrong\u003eD\u003c/strong\u003e Correlation between immune infiltration and INSR expression in pan-cancer. \u003cstrong\u003eE\u003c/strong\u003e Pearson correlation between INSR gene expression and RNA modification (m6A, m5C, m1A). \u003cstrong\u003eF\u003c/strong\u003e Pearson correlation between INSR expression and tumor mutation burden\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/11b4a03a9990c3f14da26ad4.png"},{"id":49089113,"identity":"530c7095-4923-4de8-baba-01c6369a567a","added_by":"auto","created_at":"2024-01-03 01:31:30","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":8225383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Differential Expression of NR3C1 between cancer and normal sample. B COX regression analysis of NR3C1gene and tumor prognosis. \u003cstrong\u003eC\u003c/strong\u003e GSEA of NR3C1 in pan-cancer. \u003cstrong\u003eD\u003c/strong\u003e Correlation between immune infiltration and NR3C1 expression in pan-cancer. \u003cstrong\u003eE\u003c/strong\u003e Pearson correlation between NR3C1 gene expression and RNA modification（m6A,m5C,m1A). \u003cstrong\u003eF \u003c/strong\u003ePearson correlation between NR3C1 expression and tumor mutation burden\u003c/p\u003e","description":"","filename":"floatimage18.png","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/d394b5711fa7334e3af0b8d1.png"},{"id":49913875,"identity":"8b581b50-619b-45a3-971e-5ac0078bb300","added_by":"auto","created_at":"2024-01-20 14:27:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9312009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/546493af-d165-4ccf-96cd-11138a8dce0f.pdf"},{"id":49089136,"identity":"f219054b-4559-45ca-b4ce-7ac421dc4a57","added_by":"auto","created_at":"2024-01-03 01:31:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39011,"visible":true,"origin":"","legend":"","description":"","filename":"strobemrchecklistS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/b731e152f46623d5852944c4.docx"},{"id":49089114,"identity":"74fc5a8c-4711-45a4-8127-773bd61a6543","added_by":"auto","created_at":"2024-01-03 01:31:30","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23455,"visible":true,"origin":"","legend":"","description":"","filename":"tableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/78ade69290fb69bae7d45fd8.docx"},{"id":49089121,"identity":"1d208398-7d44-4389-8b2c-34741becb552","added_by":"auto","created_at":"2024-01-03 01:31:31","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12650,"visible":true,"origin":"","legend":"","description":"","filename":"PPPMInnovationHighlightsS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/750d912001ffbee99d2af990.docx"},{"id":49089135,"identity":"f3a7bed5-11fa-4fd1-9e3d-caea6e899335","added_by":"auto","created_at":"2024-01-03 01:31:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":68464,"visible":true,"origin":"","legend":"","description":"","filename":"AuthoragreementandauthororderstatementS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3774776/v1/96fd75ad2fb09850454a349d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetically predicted causal associations between 152 blood-related exposures and pan-cancer in the framework of prediction, prevention and personalized medicine: a study integrating Mendelian randomization and bioinformatics","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cb\u003eUnveiling the potential impact of blood-related indicators on pan-cancer could augment cancer prediction, prevention, and personalized medicine\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCancer, a major global health concern, continues to impose escalating incidence and mortality rates worldwide. With an estimated 1,958,310 new cases and 609,820 deaths projected in the United States alone for 2023[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the urgency to identify individuals at high risk of developing tumors and implement effective preventive measures becomes increasingly paramount. Recognizing blood as a valuable domain for disease surveillance, the utilization of blood-specific biomarkers holds significant potential in monitoring cancer progression[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Within the repository of IEU OpenGWAS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a wide array of blood-related indicators, encompassing blood pressure, glucose levels, white blood cells, red blood cells, and medication usage, are captured, yielding insights into the overall health status of the human body. Notably, prior studies have unraveled associations between these indicators, diseases, and medications with tumor formation. For instance, patients with hypertension (HTN) display an elevated risk of developing breast carcinoma (BRCA)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while the renin-angiotensin system(RAS) axes intricately contribute to the pathogenesis of pediatric malignancies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, DM and the use of associated medications, such as insulin, have shown significant associations with an augmented susceptibility to several cancer types[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, elevated white blood cell counts serve as a biomarker indicative of heightened risk for lung adenocarcinoma (LUAD), as well as increased mortality and incidence rates for colorectal adenocarcinoma (COAD)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, the red blood cell distribution width is considered a potential prognostic predictor for various cancers[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and low red blood cell counts hold predictive value concerning early myometrial invasion in individuals with endometrioid endometrial carcinoma[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, histo-blood group ABO system transferases have been markedly associated with diffuse-type and intestinal-type stomach adenocarcinoma (STAD)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], whereas the use of cholesterol-lowering medications has been linked to an elevated risk of COAD[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].In summary, the exploration of blood-related indicators presents significant potential in identifying individuals at high risk of cancer, thereby informing them with appropriate medication advice to mitigate tumor occurrence. The establishment of causal associations between blood-related indicators and pan-cancer would undoubtedly facilitate the development of pioneering cancer surveillance and preventive strategies within the framework of predictive, preventive and personalised medicine (PPPM).\u003c/p\u003e\n\u003ch3\u003eFueling PPPM - Mendelian randomization and bioinformatics methods\u003c/h3\u003e\n\u003cp\u003eThe transition from reactive, disease-specific treatments to proactive, patient-centered prevention and treatment[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], epitomized by PPPM, necessitates the advent of novel technologies and analytical tools[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Mendelian randomization (MR), an innovative research methodology employing genetic variants as instrumental variables to examine disease etiology, stands as a powerful instrument for deciphering causal relationships while effectively accounting for confounding factors. In comparison to traditional epidemiological research methods, MR offers unique advantages, excluding the potential influence of confounders and providing a robust framework to investigate associations between blood-related markers, disease risk, and medication usage in the realm of oncology[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Complementing MR's strength, bioinformatics emerges as a formidable tool in constructing clinical prediction models and tailoring precision medicine strategies specific to tumor malignancies. As an analytical discipline, bioinformatics facilitates the discovery of novel biomarkers and therapeutic targets for cancer, ultimately furthering the promotion of PPPM within the field of oncology[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eWorking hypothesis within the PPPM framework\u003c/h3\u003e\n\u003cp\u003eThe primary objective of this study is to refine cancer prediction and prevention advice by identifying individuals at high risk of developing cancer. By integrating MR and bioinformatics, this investigation aims to elucidate the casual associations between blood-related indicators and cancer, as well as explore the impact of disease and medication utilization on cancer development. Notably, the majority of blood-related indicators employed in this study stem from convenient, non-invasive techniques, such as blood pressure and glucose monitoring, along with routine blood examinations commonly accessible through community hospitals and family physicians. Should the preliminary verification of causal associations between blood-related indicators and cancer be established, this study holds the potential to shed new light on identifying individuals at high risk of cancer within the PPPM framework. Furthermore, it presents an easy and cost-effective method for cancer prediction and prevention, while highlighting the significance of personalized and precision medicine in addressing the global cancer burden.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to enhance the scientific and writing standardization of the analysis of the MR study, we employed the STROBE-MR guidelines to design our study [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Firstly, we implemented a two-sample MR method to investigate the association between 152 blood-related exposures and pan-cancer. Subsequently, we employed the mediating MR method to explore potential associations between the disease indicated by the significant outcome and tumors. For medications that potentially impact tumors as indicated by the significant outcome, we conducted a two-sample MR analysis to examine the causal associations between cis-eQTLs of drug target genes and pan-cancer. Finally, we employed bioinformatic tools to explore the potential impact of drug target genes on tumorigenesis. The flowchart depicting the overall study design is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eWe conducted a comprehensive search on the IEU OpenGWAS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003c/span\u003e) platform, utilizing the keyword \"blood,\" to acquire the genome-wide association studies (GWAS) summary-level data pertaining to blood-related exposures. Additionally, we acquired the GWAS summary-level data focusing on pan-cancer (26 different types of cancer) from esteemed sources including IEU OpenGWAS, UK Biobank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ukbiobank.ac.uk/\u003c/span\u003e\u003c/span\u003e), FinnGen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003c/span\u003e), and Biobank Japan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobankjp.org/en/\u003c/span\u003e\u003c/span\u003e). A comprehensive overview of the GWAS cohorts involved in our study is presented in Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e of the Supplementary Material. With regards to the drug target gene information, we made use of the well-established DrugBank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003c/span\u003e) repository. Furthermore, the drug target gene cis-eQTL data were obtained from the IEU OpenGWAS. To leverage pan-cancer bulk RNA-seq data and relevant phenotypic data, we referred to the UCSC Xena platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSelection of instrumental variables (IVs)\u003c/h3\u003e\n\u003cp\u003eTo ensure the robustness and reliability of our study, we incorporated 152 data points from blood-related GWAS as exposures. However, we excluded GWAS data points with an insufficient number of single nucleotide polymorphisms (SNPs), utilizing a locus-wide significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5e-6, thus retaining only SNPs that exhibit a significant association with the blood-related exposures. To ensure the inclusion of independent SNPs while excluding those in strong linkage disequilibrium (LD), we conducted a careful LD clumping procedure for all instrumental variables (IVs), employing an LD threshold of r2\u0026thinsp;=\u0026thinsp;0.001 and a clumping window of 10,000 kb. Similar LD clumping strategies were employed for the drug target gene cis-eQTL, maintaining consistency with the aforementioned parameters (r2\u0026thinsp;=\u0026thinsp;0.001, clumping window: 10,000 kb).\u003c/p\u003e\n\u003ch3\u003eMR analysis\u003c/h3\u003e\n\u003cp\u003eMR analysis was performed utilizing several R packages including \"ieugwasr,\" \"plinkbinr,\" \"gwasglue,\" and \"TwoSampleMR\" within R version 4.3.1, adhering to best practices for conducting rigorous and reliable analyses.\u003c/p\u003e\n\u003cp\u003eInitially, we investigated the causal relationship between blood-related exposures and pan-cancer using two distinct MR approaches: inverse variance weighted (IVW), MR Egger, and weighted mode. The MR Egger method was employed to assess horizontal pleiotropy and evaluate the robustness of our findings. Robustness was deemed satisfactory when the p-value derived from IVW or weighted mode was less than 0.05, and the p-value obtained from MR Egger was greater than 0.05. In order to provide preliminary assessment of causal associations, results derived from the IVW method were utilized due to its superior estimation precision and test efficacy in the absence of horizontal pleiotropy among instrumental variables (IVs)[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eSubsequently, we evaluated the potential impact of diseases and disease-related mediators on tumors, which may be reflected by blood-related exposures. This was accomplished by carrying out three separate two-sample MR analyses. In this context, we employed five MR methods: IVW, MR Egger, weighted mode, weighted median, and simple mode. Conclusively robust results were established when at least two MR methods (excluding MR Egger) yielded associations with p-values of less than 0.05, and those derived from MR Egger were greater than 0.05.\u003c/p\u003e\n\u003cp\u003eLastly, we explored the causal associations between drug target gene cis-eQTLs and pan-cancer using six Mendelian randomization methods: IVW, IVW (fixed effects), IVW radial, MR Egger, weighted median, and simple median. This comprehensive analysis aimed to uncover potential novel therapeutic targets for pan-cancer.\u003c/p\u003e\n\u003cp\u003eIn light of our study's focus on identifying new therapeutic avenues for pan-cancer, we refrained from conducting multiple tests for MR and colocalization results[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our approach draws wisdom from previous MR literature, providing valuable references for future investigations in this field[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn all of our MR analyses, it is imperative that the single nucleotide polymorphisms (SNPs) satisfy three key assumptions in order to qualify as IVs. Firstly, the genetic variants must exhibit a robust and statistically significant association with the exposure under investigation, thereby fulfilling the requirement of the relevance assumption. Secondly, it is essential that these genetic variants are independent of any potential confounding factors, thus adhering to the principle of the independence assumption. Lastly, it is crucial to establish that the genetic variants solely influence the outcome of interest through their impact on the exposure, thereby upholding the principle of the exclusion restriction assumption.\u003c/p\u003e\n\u003ch3\u003eAnalysis using bioinformatics\u003c/h3\u003e\n\u003cp\u003eWe extracted the expression data of previously unreported drug target genes for bioinformatics studies, utilizing pan-cancer bulk RNA-seq data from each sample. Samples with no drug target gene expression were excluded, and the drug target gene expression in each sample was subjected to log2 transformation (log2(x\u0026thinsp;+\u0026thinsp;0.001)). Cancer types with fewer than 3 samples were also excluded, resulting in a final set of expression data from 26 cancer types.\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis between normal and tumor samples was performed using the \"limma\" R package. Significance analyses were conducted using unpaired Wilcoxon rank sum and signed-rank tests. To enhance the robustness of our findings, we obtained the TCGA prognostic dataset from a previously published study by Liu et al.[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Samples lacking drug target gene expression and samples with a follow-up time shorter than 30 days were excluded. Additionally, cancers with fewer than 10 samples were omitted, resulting in a final set of expression and overall survival data from 39 cancer types.\u003c/p\u003e\n\u003cp\u003eTo investigate the relationship between gene expression and prognosis in each tumor type, we employed the \"survival\" R package (version 3.2-7) to build Cox proportional hazards regression models. Prognostic significance was assessed using the log-rank test. Gene set enrichment analysis was performed using the \"clusterProfiler\" R package (version 4.8.3). Furthermore, we explored the correlation between drug target gene expression and immune cell infiltration using the \"CIBERSORT\" and \"EPIC\" R packages.\u003c/p\u003e\n\u003cp\u003eWe then extracted the expression data of drug target genes as well as 44 marker genes representing three types of RNA modifications (m1A(10), m5C(13), m6A(21)) in each sample. Samples with zero expression of drug target genes and RNA modification marker genes, as well as all normal samples, were filtered out. The remaining expression data underwent log2 transformation (log2(x\u0026thinsp;+\u0026thinsp;0.001)). Pearson correlation analysis was applied to examine the relationship between drug target genes and marker genes of the three types of RNA modifications.\u003c/p\u003e\n\u003cp\u003eFor comprehensive genomic analysis, we downloaded the Simple Nucleotide Variation dataset of all TCGA samples processed by the MuTect2 software from the TCGA GDC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e). The tumor mutational burden (TMB) of each tumor was calculated using the \"maftools\" R package (version 2.8.05). Subsequently, we integrated the TMB and gene expression data and calculated the Pearson correlation between drug target gene expression and TMB in each tumor type.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eRelationship between blood indicators and pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations of blood pressure with pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSystolic and diastolic blood pressure associated with HTN had a potential relationship with COAD (P\u003csub\u003eIVW\u003c/sub\u003e=4.63\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.13,95%,CI \u003csub\u003eIVW\u003c/sub\u003e(1.00-1.28)), BRCA (P\u003csub\u003eIVW\u003c/sub\u003e=1.05\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.14,95% CI\u003csub\u003eIVW\u003c/sub\u003e (1.03\u0026ndash;1.26)), esophageal carcinoma (ESCA) (P\u003csub\u003eIVW\u003c/sub\u003e=4.62\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e= 1.72 95%,CI\u003csub\u003eIVW\u003c/sub\u003e (1.00-2.93)), pancreatic adenocarcinoma(PAAD) (P\u003csub\u003eIVW\u003c/sub\u003e=3.21\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.73 95%,CI \u003csub\u003eIVW\u003c/sub\u003e(1.06\u0026ndash;2.83)), and STAD (P\u003csub\u003eIVW\u003c/sub\u003e=2.96\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.29,95% CI \u003csub\u003eIVW\u003c/sub\u003e(0.13\u0026ndash;0.66)) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). High blood pressure itself was associated with ovarian cancer(OV) (P\u003csub\u003eIVW\u003c/sub\u003e=2.88\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.20,95% CI \u003csub\u003eIVW\u003c/sub\u003e(0.07\u0026ndash;0.63)) and ESCA(P\u003csub\u003eIVW\u003c/sub\u003e=4.68\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e= 0.04,95% CI\u003csub\u003eIVW\u003c/sub\u003e (0.00-0.94))(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociations of glucose monitoring and insulin levels with pan-cancer\u003c/h3\u003e\n\u003cp\u003eGlucose and insulin levels associated with DM were potentially related to STAD (P\u003csub\u003eIVW\u003c/sub\u003e=1.02\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.32,95%CI\u003csub\u003eIVW\u003c/sub\u003e (1.07\u0026ndash;1.62)), COAD (P\u003csub\u003eIVW\u003c/sub\u003e=4.94\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e OR\u003csub\u003eIVW\u003c/sub\u003e=1.30,95% CI\u003csub\u003eIVW\u003c/sub\u003e (1.08\u0026ndash;1.57)) and ESCA (P\u003csub\u003eIVW\u003c/sub\u003e=2.66\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.79 95%, CI\u003csub\u003eIVW\u003c/sub\u003e (0.64\u0026ndash;0.97)) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociations of medication for Cholesterol-lowering medication with pan-cancer\u003c/h3\u003e\n\u003cp\u003eCholesterol-lowering medication was potentially associated with COAD (P\u003csub\u003eIVW\u003c/sub\u003e=4.14\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.08,95%CI\u003csub\u003eIVW\u003c/sub\u003e(1.00-1.17)), PAAD(P\u003csub\u003eIVW\u003c/sub\u003e=3.78\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.04,95%,CI\u003csub\u003eIVW\u003c/sub\u003e(0.00-0.35))(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociations of red blood indicators related to red blood cells with pan-cancer\u003c/h3\u003e\n\u003cp\u003eBlood cell count and red blood cell distribution width associated with anemia were potentially related to LUAD (P\u003csub\u003eIVW\u003c/sub\u003e=2.94\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.11,95% CI \u003csub\u003eIVW\u003c/sub\u003e(1.04\u0026ndash;1.19)), Brain glioblastoma (P\u003csub\u003eIVW\u003c/sub\u003e=5.17\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.16,95% CI (0.04\u0026ndash;0.57)) and COAD (P\u003csub\u003eIVW\u003c/sub\u003e=4.64\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.47,95% CI\u003csub\u003eIVW\u003c/sub\u003e (0.28\u0026ndash;0.79)) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAss\u003c/strong\u003eo\u003cstrong\u003eciations of blood indicators related to white blood cells with pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was a potential relationship between white blood cell count and PAAD (P\u003csub\u003eIVW\u003c/sub\u003e=8.30\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.94 95% CI\u003csub\u003eIVW\u003c/sub\u003e (1.19\u0026ndash;3.19)), and Brain glioblastoma (P\u003csub\u003eIVW\u003c/sub\u003e=2.49\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.43 95% CI\u003csub\u003eIVW\u003c/sub\u003e (0.20\u0026ndash;0.90)) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociations of diseases of blood and blood-forming organs with pan-cancer\u003c/h3\u003e\n\u003cp\u003eDiseases of blood and blood-forming organ were potentially associated with ESCA (P\u003csub\u003eIVW\u003c/sub\u003e=2.08\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.91,95% CI\u003csub\u003eIVW\u003c/sub\u003e (0.84\u0026ndash;0.99)) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociations of tissue blood group ABO system transferase with pan-cancer\u003c/h3\u003e\n\u003cp\u003eABO systemic transferase was not significantly associated with BRCA or thyroid carcinoma(THCA)(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociations of other blood indicators with pan-cancer\u003c/h3\u003e\n\u003cp\u003eThere is a potential relationship between deep vein thrombosis(DVT) and COAD (P\u003csub\u003eIVW\u003c/sub\u003e=5.85\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.34,95% CI\u003csub\u003eIVW\u003c/sub\u003e (1.09\u0026ndash;1.65))(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRelationship between serum- exposure-related diseases and pan-cancer\u003c/h3\u003e\n\u003cp\u003eBy testing again, we obtained 3 valid results. According to the results obtained, the mediating effects of glycated hemoglobin (HbA1c), hemoglobin-gastric, D-dimer, and renin on HTN, anemia, DVT, and DM with pan-cancer were apparently not significant (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRelationship between drugs selected and pan-cancer\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eThe role of lipid-lowering drugs in pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLipid-lowering drugs are correlated with COAD and PAAD. In European populations, taking lipid-lowering drugs has a protective effect against COAD(P\u003csub\u003eIVW\u003c/sub\u003e=1.28\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.88,95%CI\u003csub\u003eIVW\u003c/sub\u003e(0.80\u0026ndash;0.97)) and PAAD (P\u003csub\u003eIVW\u003c/sub\u003e=6.78\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.81,95%CI\u003csub\u003eIVW\u003c/sub\u003e(0.69\u0026ndash;0.94)) by target gene HMGCR. Moreover, we found that the administration of lipid-lowering drugs was not significantly associated with the risk of cancer in Asian populations(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe casual association between oral contraceptive drugs and pan-cancer\u003c/h3\u003e\n\u003cp\u003eOral contraceptive drugs correlated with COAD, BRCA and OV. Taking oral contraceptive drugs is protective against OV(OR\u003csub\u003eIVW\u003c/sub\u003e=0.68,P\u003csub\u003eIVW\u003c/sub\u003e=2.72\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,95%CI\u003csub\u003eIVW\u003c/sub\u003e(0.49\u0026ndash;0.96))by target gene ESR1、NR3C1 and increases risk for BRCA(OR\u003csub\u003eIVW\u003c/sub\u003e=1.08,P\u003csub\u003eIVW\u003c/sub\u003e=4.34\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,95%CI\u003csub\u003eIVW\u003c/sub\u003e(0.93\u0026ndash;1.65))by target gene BECN1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe casual association between insulin and pan-cancer\u003c/h3\u003e\n\u003cp\u003eInsulin is correlated with COAD, BRCA, and LUNG. Insulin use raises risk of LUAD(P\u003csub\u003eIVW\u003c/sub\u003e=4.78\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.16,95%CI\u003csub\u003eIVW\u003c/sub\u003e(1.00-1.35)), COAD(P\u003csub\u003eIVW\u003c/sub\u003e=1.02\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.16,OR\u003csub\u003eIVW\u003c/sub\u003e1.32,95%CI\u003csub\u003eIVW\u003c/sub\u003e(1.12\u0026ndash;1.56))by gene target INSR(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe casual association between hormones and pan-cancer\u003c/h3\u003e\n\u003cp\u003eRelated hormones are associated with COAD, BRCA, and LUAD. Estrogen increased the risk of LUAD (P\u003csub\u003eIVW\u003c/sub\u003e=1.11\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.33,95%CI\u003csub\u003eIVW\u003c/sub\u003e(1.07\u0026ndash;1.66)), decreased the risk of prostate adenocarcinoma(PRAD) (P\u003csub\u003eIVW\u003c/sub\u003e=3.51\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=0.81,95%CI\u003csub\u003eIVW\u003c/sub\u003e(0.66\u0026ndash;0.99)) through the target gene ESR1. Progesterone increased the risk of BRCA (P\u003csub\u003eIVW\u003c/sub\u003e=3.90\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e,OR\u003csub\u003eIVW\u003c/sub\u003e=1.18,95%CI\u003csub\u003eIVW\u003c/sub\u003e(1.01\u0026ndash;1.37))through the gene target NR3C1(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe Potential Impact of Drug Target Genes on Pan-Cancer\u003c/h3\u003e\n\u003cp\u003eIn light of the MR findings on drug target genes and their association with pan-cancer, we identified 4 frequently implicated target genes: HMGCR, ESR1, NR3C, and INSR. Considering that ESR1 has already been extensively studied using RNA-seq techniques [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], our investigation focused on HMGCR, NR3C, and INSR.\u003c/p\u003e\n\u003ch3\u003eHMGCR and its Relation to Pan-Cancer\u003c/h3\u003e\n\u003cp\u003eHMGCR demonstrated significant differential expression across 17 tumor types. Specifically, it was upregulated in 6 cancer types, notably in cervical squamous cell carcinoma (CESC) (Tumor: 4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88, Normal: 2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30, p\u0026thinsp;=\u0026thinsp;0.01), and downregulated in COAD (Tumor: 4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63, Normal: 5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56, p\u0026thinsp;=\u0026thinsp;4.3e-4), as well as 11 other cancer types (Fig.\u0026nbsp;16A). Prognostically, HMGCR was identified as a high-risk factor in 4 cancer types, including CESC (N\u0026thinsp;=\u0026thinsp;273, p\u0026thinsp;=\u0026thinsp;0.04, HR\u0026thinsp;=\u0026thinsp;1.33 (1.02, 1.75)), and a protective factor in rectum adenocarcinoma (READ) (N\u0026thinsp;=\u0026thinsp;368, p\u0026thinsp;=\u0026thinsp;6.6e-3, HR\u0026thinsp;=\u0026thinsp;0.67 (0.51, 0.89)), along with 4 additional cancer types (Fig.\u0026nbsp;16B).\u003c/p\u003e\n\u003cp\u003eAdditionally, HMGCR expression levels showed positive correlations with the androgen_response and protein_secretion pathways in pan-cancer. Notably, the expression patterns of HMGCR were closely associated with the development of CESC, COAD, and THCA based on observed differences in expression and prognostic outcomes. Within ESCA, HMGCR expression exhibited significant positive correlations with g2m_checkpoint, e2f_targets, and mitotic_spindle, and a significant negative correlation with tnfa_signaling_via_nfkb. In THCA, hmgcr expression was significantly positively correlated with g2m_checkpoint and epithelial_mesenchymal_transition, negatively correlated with myogenesis, and showed no significant relationship in COAD (Fig.\u0026nbsp;16C).\u003c/p\u003e\n\u003cp\u003eIn terms of immune infiltration, the combined analysis of two algorithms demonstrated a significant positive correlation between HMGCR expression and the activation of M0 macrophages and mast cells in ESCA, while negatively correlating with eosinophils. In COAD, HMGCR displayed a significant positive correlation with NK cell activation and a significant negative correlation with neutrophils. However, there was no significant relationship observed in THCA (Fig.\u0026nbsp;16D).\u003c/p\u003e\n\u003cp\u003eExamining the Pearson correlation between HMGCR and marker genes of 5 immune pathway classes in pan-cancer, we consistently observed a significant positive correlation within the m1A, m5C, and m6A groups. Specifically, within the m1A modification group, we identified 13 gene groups exhibiting negative correlations with TRMT61A and ALKBH3. Similarly, within the m5C modification group, we observed 5 gene groups with negative correlations involving NSUN5, NSUN7, and ALYREF. Finally, within the m6A modification group, 10 gene groups displayed negative correlations with KIAA1429, IGF2BP1, and LRPPRC (Fig.\u0026nbsp;16E).\u003c/p\u003e\n\u003cp\u003eFurthermore, HMGCR gene expression exhibited a significant positive correlation with tumor mutation load in 2 tumor types, notably in STAD (N\u0026thinsp;=\u0026thinsp;409) with a correlation coefficient (R) of 0.20 and a p-value of 4.20\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (Fig.\u0026nbsp;16F).\u003c/p\u003e\n\u003ch3\u003eINSR and its Implications in Pan-Cancer\u003c/h3\u003e\n\u003cp\u003eThe differential expression analysis revealed significant variations in INSR expression across 16 tumor types. Notably, it was upregulated in 7 cancer types, including STAD (Tumor: 4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74, Normal: 3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02, p\u0026thinsp;=\u0026thinsp;2.0e-6), and downregulated in 9 cancer types, prominently in COAD (Tumor: 3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71, Normal: 3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59, p\u0026thinsp;=\u0026thinsp;2.3e-7), as well as 9 other cancer types (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eA). Moreover, INSR exhibited a prognostic protective role in 3 cancer types, notably lower-grade glioma and glioblastoma(GBMLGG) (N\u0026thinsp;=\u0026thinsp;619, p\u0026thinsp;=\u0026thinsp;7.0e-4, HR\u0026thinsp;=\u0026thinsp;0.72 (0.60, 0.87)) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eFurthermore, INSR expression was found to be inversely correlated with interferon_alpha_response, interferon_gamma_response, and inflammatory_response pathways, in addition to COAD, THCA, and uterine corpus endometrial carcinoma(UCEC) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eC).With regard to immune infiltration, analysis using the EPIC algorithm demonstrated a significant positive correlation between INSR expression and endothelial cells, whereas a significant negative correlation was observed with macrophages and NK cells in pan-cancer. Utilizing the Cibersort algorithm, INSR expression showed a significant negative correlation with B.cells.naive in skin cutaneous melanoma (SKCM), a significant positive correlation with NK.cells.activated in SKCM, and a significant positive correlation with B.cells.naive in USR (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis between INSR expression and immune pathway marker genes in pan-cancer displayed a consistent positive correlation within the m1A, m5C, and m6A groups. Specifically, within the m1A modification group, 12 gene groups exhibited negative correlations with TRMT61A and ALKBH3. Similarly, within the m5C modification group, 12 gene groups demonstrated negative correlations with NOP2, DNMT3B, NSUN5, and ALYREFT. Finally, within the m6A modification group, 4 gene groups showed negative correlations with LRPPRC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eMoreover, INSR gene expression exhibited a significant negative correlation with tumor mutation load in 5 tumors, with notable findings in SBRCA (N\u0026thinsp;=\u0026thinsp;981) (R = -0.06, P\u0026thinsp;=\u0026thinsp;4.30\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003eF)\u003c/p\u003e\n\u003ch3\u003eNR3C1 and its Role in Pan-Cancer\u003c/h3\u003e\n\u003cp\u003eThe expression of NR3C1 exhibited significant differential patterns across 20 tumor types. Specifically, it was found to be significantly upregulated in 5 cancer types, including lower-grade glioma (LGG) (Tumor: 4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54, Normal: 3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.03). Conversely, NR3C1 showed significant downregulation in 15 cancer types, most notably in COAD (Tumor: 1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31, Normal: 2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44, p\u0026thinsp;=\u0026thinsp;1.1e-17) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003eA). Furthermore, NR3C1 was identified as a prognostic high-risk factor in 4 cancer types, such as STAD (N\u0026thinsp;=\u0026thinsp;372, p\u0026thinsp;=\u0026thinsp;1.9e-3, HR\u0026thinsp;=\u0026thinsp;1.27 (1.09, 1.47)). Conversely, it was deemed a prognostic protective factor in GBMLGG (N\u0026thinsp;=\u0026thinsp;619, p\u0026thinsp;=\u0026thinsp;4.0e-8, HR\u0026thinsp;=\u0026thinsp;0.55 (0.44, 0.68)), along with three other cancer types (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eAdditionally, the expression level of NR3C1 demonstrated positive correlations with the tgf_beta_signaling and uv_response_dn pathways, while exhibiting a negative correlation with oxidative_phosphorylation in the context of pan-cancer. Notably, the expression of NR3C1 played a pivotal role in the development of kidney renal clear cell carcinoma(KIRC), kidney chromophobe༈KICH༉, and kidney renal papillary cell carcinoma༈KIRP༉. In KIRP, NR3C1 gene expression positively correlated with epithelial_mesenchymal_transition and demonstrated a negative correlation with oxidative_phosphorylation. Similarly, NR3C1 expression in KIRC exhibited positive correlations with epithelial_mesenchymal_transition and a negative correlation with oxidative_phosphorylation. Moreover, NR3C1 expression showed a significant positive correlation with g2m_checkpoint in KICH (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eThe assessment of immune infiltration, using two algorithms, revealed that NR3C1 expression displayed a significant negative correlation with eosinophils in KIRP. However, KICH and KIRC did not exhibit a consistent relationship with immune cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003eD). Furthermore, Pearson correlation analysis between NR3C1 expression and the immune pathway marker genes in PAAD demonstrated significant positive correlations within the m1A, m5C, and m6A groups. Notably, within the m1A modification group, 14 gene groups exhibited negative correlations with TRMT61A, ALKBH3, and other genes. Similarly, within the m5C modification, 18 gene groups displayed negative correlations with NSUN5, NSUN7, ALYREF, and other genes. Lastly, within the m6A modification, 18 gene groups demonstrated negative correlations with KIAA1429, IGF2BP1, LRPPRC, and other genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eFurthermore, NR3C1 gene expression exhibited significant associations with heterogeneity in 8 tumor types. It also showed a significant positive association with mutational load in 2 tumors, notably COAD (N\u0026thinsp;=\u0026thinsp;282) (R\u0026thinsp;=\u0026thinsp;0.15, P\u0026thinsp;=\u0026thinsp;9.68\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Conversely, NR3C1 was found to have a significant negative association with mutational load in 6 tumors, including LUAD (N\u0026thinsp;=\u0026thinsp;509) (R = -0.18, P\u0026thinsp;=\u0026thinsp;3.06\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003eF).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study represents a prospective investigation into pan-cancer development, utilizing blood indicators. It stands as the most extensive study to date within this field. Not only do our findings validate the outcomes of preceding studies, but they also go beyond them, owing to our comprehensive population sample and unparalleled systematic approach. By employing MR and bioinformatics, we have discovered that blood monitoring of patients with HTN, anemia, DVT, and DM aids in unraveling cancer PPPM in this specific population. Moreover, our research unveils the consequences of long-term medication, encompassing lipid-lowering drugs, oral contraceptive drugs, insulin, and hormones, regarding pan-cancer progression. Thus, it offers novel perspectives pan-cancer prognostication and fosters innovative approaches in pan-cancer therapeutics. This seminal contribution has granted substantial dividends in terms of predicting and preventing pertinent malignancies, as well as providing new strategies for pan-cancer treatment. Notably, we have successfully identified 3 crucial targets within the aforementioned medications, thereby establishing a theoretical groundwork for the advancement of targeted pan-cancer therapy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComprehensive monitoring of HTN may prove advantageous in the prevention of COAD, BRCA, ESCA, and PAAD\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBuilding upon the observed correlation between blood pressure and tumor development, we hypothesize that monitoring HTN could offer preventative benefits for COAD, BRCA, ESCA, and PAAD, which aligns with prior research findings. Specifically, studies have demonstrated a heightened risk of COAD in male individuals with HTN (95% CI: 1.06\u0026ndash;1.20)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Conversely, no significant correlation was found between HTN and COAD risk in the female population. It is widely acknowledged that HTN influences colorectal carcinogenesis and metastasis through its impact on the RAS, induction of oxidative stress, and promotion of chronic inflammation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Thus, blood pressure monitoring in males may aid in predicting and preventing COAD.\u003c/p\u003e \u003cp\u003eHTN is responsible for a 15% increased probability of BRCA in postmenopausal women, likely due to shared adipose pathways with BRCA that trigger inflammation and modulate apoptosis. However, HTN does not exhibit significant effects on BRCA risk in premenopausal or Asian women[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, blood pressure should be vigilantly monitored in postmenopausal women to mitigate BRCA risk. Furthermore, a Korean study indicated a higher incidence of ESCA in the HTN population[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Paradoxically, another study reported a decreased incidence of five gastrointestinal cancers (including ESCA, PAAD, and STAD) in an Asian population with HTN. These contrasting results may be attributed to inherent differences in human development indices (HDIs) across populations and the potential for distinct ecological correlations to produce varied mortality rates for HTN-related cancers[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Specifically, a 1% increase in the HTN factor was associated with a 13% surge in PAAD incidence[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], likely driven by elevated levels of angiotensin II type 1 receptor (AT1R) in the RAS regulating blood pressure. These heightened levels facilitate cancer cell proliferation and angiogenesis, eventually leading to PAAD metastasis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Consequently, targeting hypertensive populations becomes imperative in the prevention and management of PAAD development and progression.\u003c/p\u003e \u003cp\u003eHowever, our analyzed results did not reveal a significant mediating effect of renin on the relationship between HTN and pan-cancer. Nonetheless, it is vital to acknowledge the critical role renin plays in the regulation of blood pressure through the RAS. Some tumors are known to secrete renin[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], while some studies have shown that renin stimulates the growth of renal cancer cells[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, evidence indicates that utilizing a certain dose of renin inhibitors can reduce the risk of junctional cancers[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Consequently, renin concentration may serve as a useful monitoring indicator for related pan-cancer in preventive healthcare strategies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe regular surveillance of DM holds potential benefits for the prevention of COAD and STAD.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur hypothesis is based on the correlation between blood glucose, insulin, and tumor development. Our findings align with previous MR studies, demonstrating that DM increases the likelihood of gastrointestinal cancers, including STAD and COAD[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Both Type 1 and Type 2 DM elevate the incidence of gastric cancers. For Type 1 DM, the rise may be attributed to autoimmune comorbidities[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], whereas insulin stimulates STAD cell proliferation and inhibits chemotherapy sensitivity by affecting P-glycoprotein in Type 2 DM[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, female DM patients exhibit a slightly higher risk of developing STAD, and while no definitive pathophysiological mechanism explains this gender difference, lifestyle behaviors (such as difficulties controlling sugar intake in women) may contribute. Some studies have revealed a weak or negligible negative correlation between DM and tumor incidence. This observation may be attributed to genetic variants present in certain tumors, leading to both hyperglycemia and hypoinsulinism, with the protective effects of low insulin levels potentially counteracting the carcinogenic effects of hyperglycemia[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. DM raises the incidence of COAD in men, but the rise is not statistically significant in women[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Insulin activates cell proliferation and protein synthesis downstream of the tumor via the phosphatidylinositol 3-kinase-protein kinase B-mammalian target of rapamycin and Ras-mitogen-activated protein kinase pathways[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This increased risk, however, diminishes in the long-term diabetic population due to progressive pancreatic β-cell depletion and subsequent lower insulin levels. Within the DM context, hyperglycemia and hypoxia contribute to the production of various pro-inflammatory factors that fuel tumor cells, drive tumor cell proliferation, facilitate cell invasion, and impede apoptosis, thereby enhancing the risk of tumorigenesis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough HbA1c serves as a biomarker for chronic hyperglycemia, our study did not find HbA1c to mediate the relationship between DM and pan-cancer. Prior research has indicated that elevated HbA1c levels are associated with an increased risk of COAD, PAAD, respiratory cancers, and cancers affecting the female reproductive tract[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], while demonstrating protective effects against THCA[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and prostate adenocarcinoma PRAD[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A study conducted in the UK showed that HbA1c may be useful for the early detection of PAAD[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Consequently, HbA1c still serves as a potential indicator for monitoring pan-cancer development, but further investigation is required to understand the intricate mechanisms underlying the impact of HbA1c on pan-cancer pathogenesis.\u003c/p\u003e\n\u003ch3\u003eThe effect of anemia on cancer development is related to the type of anemia\u003c/h3\u003e\n\u003cp\u003eThis study endeavors to unravel the intricate association between anemia and cancer development, discerning the paramount influence wielded by distinct anemia subtypes. By leveraging the compelling correlations between blood cell count, red blood cell distribution width, and tumor emergence, we postulate the potential benefits of monitoring anemia for preventing LUAD. However, amalgamating a compendium of prior studies, indications emerge that the intricate interplay between anemia and tumor incidence is modulated by the specific anemia subtype in question. Notably, somatic cell aberrations intrinsic to aplastic anemia engender an elevated prevalence of malignancies, particularly acute myeloid leukemia and solid tumors[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Iron deficiency anemia heightens susceptibility to STAD[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], ESCA[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and LUAD[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], with the former two intrinsically linked to low serum ferritin levels, while the pathogenesis of the latter necessitates further exploration. In contrast, pernicious anemia exhibits a dichotomous impact, lowering the incidence of most tumors barring gastric cancer, while concomitantly escalating the risk of hematologic malignancies (precisely multiple myeloma), Hodgkin's lymphoma, and biliary tract cancer[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough our study did not find hemoglobin to act as a mediator in the relationship between anemia and pan-cancer, it can serve as an indicator to monitor cancer development, as substantiated by prior investigations. Empirical evidence reveals that cancer patients with diminished hemoglobin levels face double the mortality risk compared to those with higher levels[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Within the confines of non-small-cell lung cancer (NSCLC), hemoglobin alpha and beta demonstrate a protective role in cancer development, and their diminished expression may reflect advanced NSCLC with a debilitating prognosis[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Conversely, for a majority of hematopoietic, lymphopoietic, and gastrointestinal malignancies, hemoglobin concentrations begin to decline 2\u0026ndash;3 years preceding cancer diagnosis, warranting closer scrutiny as a potential early indicator[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eThe Potential of DVT monitoring in COAD prevention\u003c/h3\u003e\n\u003cp\u003eThis study explores the promising prospects of monitoring DVT as a preventive strategy for COAD, building upon the significant associations observed between DVT and tumors. Our hypothesis aligns with previous research findings[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], suggesting that DVT monitoring could potentially offer preventive benefits in COAD, likely attributable to the hypercoagulable state observed in DVT patients. This state triggers the activation of the coagulation-fibrinolytic system, resulting in thrombocytosis, elevated fibrinogen levels, and increased D-dimer concentrations, all of which contribute, to varying extents, to tumor development.\u003c/p\u003e \u003cp\u003eAmong these factors, thrombocytosis plays a pivotal role by enhancing cancer cell dissemination, adhesion, and infiltration of endothelial walls. This process is facilitated by the TP/PD-ECGF-mediated induction of tumor angiogenesis[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Additionally, high fibrinogen levels facilitate tumor metastasis through the binding to ICAM-1 on endothelial cells, promoting stable tumor cell adhesion within target organs while aiding platelet-tumor cell interactions and providing protection against the innate immune system[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The specific mechanism linking elevated D-dimer levels to pan-cancer remains unclear, although D-dimer concentrations are likely indicative of the activation state of the coagulation-fibrinolytic system[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConcordant with previous studies, DVT has also been associated with a poor prognosis in STAD[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], OV[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and LUAD[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] post-disease progression. Prospective investigations have revealed a tumor occultation rate of approximately 10% in patients with DVT[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], emphasizing the potential of DVT monitoring as a means to identify occult cancers, often at early stages[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This underscores the importance of extensive evaluation in DVT patients, as it may aid in early screening and curative treatment, particularly for occult cancers.\u003c/p\u003e \u003cp\u003eWhile our study did not demonstrate D-dimer as a mediator in the relationship between pan-cancer and DVT, existing literature highlights the utility of D-dimer monitoring in cancer diagnosis and prognosis prediction. Notably, elevated D-dimer concentrations exceeding ten times the upper limit of normal have shown diagnostic value in cancer patients[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], while low D-dimer concentrations serve as robust negative predictors of malignancy[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Furthermore, D-dimer levels can serve as a staging marker for cancer and reliably anticipate tumor metastasis[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEffects of lipid-lowering, oral contraceptive, hormones, and insulin use on pan-cancer\u003c/h3\u003e\n\u003cp\u003eThe development of new targeted drugs for cancer requires significant time and financial resources. However, a promising shortcut is the rational development of commercially recognized targeted drugs for other diseases that can be repurposed for cancer treatment. In this study, we employed MR analysis to narrow down the selection of targeted drugs by investigating the associations between specific blood-related exposures and pan-cancer. We specifically focused on lipid-lowering drugs, oral contraceptive drugs, hormones, and insulin as representative targeted drugs. Subsequently, we analyzed their relationship with pan-cancer within the context of current relevant studies.\u003c/p\u003e \u003cp\u003eInterestingly, our analysis revealed that the consumption of specific targeted drugs may exhibit a protective effect against certain cancers, such as COAD, PAAD, OV, and PRAD.\u003c/p\u003e \u003cp\u003eSpecifically, we found that taking lipid-lowering drugs can reduce the risk of developing COAD and PAAD. This protective effect could be attributed to the close relationship between the development of COAD and PAAD and cholesterol metabolism[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Cancer stem cell growth is dependent on cholesterol production and protein prenylation. Combination therapy involving 5-FU and lipid-lowering agents like lovastatin or zoledronic acid may reduce drug resistance by targeting cancer stem cells[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The protective mechanism of lipid-lowering drugs on PAAD is not yet well understood, but it appears to be influenced by the specific drug used. Atorvastatin, in particular, demonstrates a more pronounced protective effect[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Additionally, the combined use of statins in the treatment of metastatic PAAD shows promising outcomes[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. It is important to note that the protective effect of lipid-lowering drugs against cancer may be influenced by lifestyle changes in patients.\u003c/p\u003e \u003cp\u003eFurthermore, our analysis indicates that taking oral contraceptive drugs reduces the risk of OV. The exact mechanism underlying this effect remains unclear; however, it is likely related to the regulation of sex hormones. Oral contraceptives primarily consist of sex hormones and progestins, which result in increased progesterone levels, reduced gonadotropin levels, and suppressed ovulation. OV cells with higher expression of the follicle-stimulating hormone (FSH) receptor exhibit greater invasive capacity and poorer prognoses[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Moreover, ovulation-induced damage to the ovarian epithelium and subsequent repair can lead to genetic damage in ovarian epithelial cells, ultimately contributing to OV development[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Progesterone has been found to induce apoptosis in human OV cell lines[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Combining these findings with previous studies suggesting dual effects of estrogen on PRAD (inhibition at high serum estrogen concentrations and promotion at lower concentrations) contributes to a comprehensive understanding of the impact of oral contraceptive drugs and sex hormones on cancer risk[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConversely, our analysis indicates an increased risk of developing COAD, BRAD, and LUNG when taking certain targeted drugs.\u003c/p\u003e \u003cp\u003eSpecifically, taking oral contraceptive drugs increases the risk of BRCA, while taking human insulin increases the risk of LUAD and COAD. Additionally, taking estrogen or progesterone increases the risk of LUAD. The effects of contraceptives and hormone replacement therapy on BRCA and LUAD involve estrogen and progesterone-related pathways. Estrogen receptor β promotes NSCLC by inducing angiogenic mimicry and invasion of LUAD cells[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], while Estrogen receptor α promotes the development of BRCA and NSCLC at an early stage, upregulating signaling involved in CCL2 and CXCL12, thereby enhancing macrophage infiltration[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Progesterone can increase the 5alpha-pregnane:4-pregnene ratio, which facilitates increased cell proliferation and segregation, promoting BRCA[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Hormone replacement therapy utilizing progesterone and estrogen leads to abnormal mammary gland development and BRCA1-mediated BRCA through ductal hyperplasia[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Insulin exerts anti-apoptotic effects, stimulates mitosis via the Akt pathway, and reduces the expression of apoptosis-related proteins, all of which are positively associated with the development of several cancers[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Moreover, insulin resistance has been identified as a risk factor for various chronic diseases[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], including COAD, HTN, and several others. Elevated fasting insulin levels and increased insulin resistance are associated with an increased risk of LUAD[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePrevention of pan-cancer development through inhibition of HMGCR, INSR, and NR3C1 expression\u003c/h3\u003e\n\u003cp\u003eAltered genomic regions have been implicated in both promoting and protecting against tumor development. Through our bioinformatic analyses of relevant target genes, we aim to facilitate the development of novel cancer drug targeting pathways and revolutionize previous targeted therapies. Our results demonstrate the crucial roles played by HMGCR, NR3C1, and INSR in regulating mutation and heterogeneity in various cancers, as well as their close association with immune cells and immune responses.\u003c/p\u003e \u003cp\u003eThe mevalonate pathway, primarily regulated by HMG-CoA reductase (encoded by HMGCR), is a key anticancer pathway targeted by statins. Inhibition of HMGCR expression disrupts the mevalonate pathway through both cholesterol-mediated and non-cholesterol-mediated mechanisms, subsequently affecting GPX4 and enhancing tumor cell susceptibility to iron-induced cell death[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. This results in significant antitumor effects. Our findings indicate a positive association between variants in the HMGCR gene region and pan-cancer, corroborating previous research[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Taken together with previous studies, drugs targeting HMGCR expression hold promise for treating a wide range of cancer types, including BRCA[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], STAD[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], and liver hepatocellular carcinoma(LIHC)[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe INSR gene encodes insulin receptor (IR), with its IR-A isoform being implicated in increased cancer risk, such as in BRCA and THCA, through pathways involving insulin-like growth factor I receptor (IGF-IR), mammalian target of rapamycin(mTOR), and the epidermal growth factor (EGF) families[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. IR participates in the insulin signaling pathway and glycolysis, both of which mediate tumorigenesis. Glycolysis, in particular, plays a critical role in tumor survival, proliferation, and drug resistance[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Notably, the presence of multiple IR subtypes may contribute to the complexity of IR expression's impact on cancer. Furthermore, targeted therapies against IR may also influence mitosis, potentially giving rise to side effects in human subjects[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Caution should be exercised when utilizing IR expression inhibition for future cancer treatments.\u003c/p\u003e \u003cp\u003eNR3C1 encodes the glucocorticoid receptor (GR) and is involved in the glucocorticoid pathway. On one hand, NR3C1 expression in tumor tissues, particularly in the CD8 TIL subpopulation, is elevated compared to normal tissues. The GR repetitively activates the expression of genes associated with T-cell-induced dysfunction in the presence of glucocorticoids, effectively suppressing immune responses and reducing the efficacy of immune checkpoint blockade, thereby promoting tumor progression[\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. On the other hand, NR3C1 expression is low in NSCLC. Glucocorticoids prevent the GR from limiting tumor growth by inhibiting RAS activation, consequently contributing to poor disease prognosis[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Additionally, the effect of NR3C1 on cancer is influenced by other genes, such as the inhibition of miR-1270 in PAAD modulating the impact of NR3C1 inhibition[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. This suggests that targeted therapy against NR3C1 cannot simply rely on solely targeting NR3C1 or inhibiting its expression. Achieving an optimal balance of effects on different cancer types is an important avenue for further inquiry.\u003c/p\u003e\n\u003ch3\u003eLimitations and prospects\u003c/h3\u003e\n\u003cp\u003eOwing to the limited accessibility of disease sample repositories, we encountered a lack of data on diseases where blood-related indicators, namely leukopenia, hypotension, and hypoglycemia, have a strong correlation with pan-cancer. Consequently, our effort to establish a comprehensive PPPM network for monitoring pan-cancer through blood indicators remains incomplete. Furthermore, the age distribution of the included samples was not clearly defined, imposing limitations on conducting subgroup-specific analyses. The prolonged incidence of chronic conditions such as HTN and DM heightens patient visits, potentially leading to an artificial elevation in cancer detection rates because of detection bias, thereby overestimating the risk of pan-cancer.Additionally, our investigations into the connection between lipid-lowering drugs, oral contraceptive drugs, hormones, and insulin with pan-cancer risk imply that disparate drug targets and pathways may entail varying levels of risk for the development of the same malignancy. Animal experiments and mechanistic studies remain indispensable in complementing specific drug usage and target development.\u003c/p\u003e \u003cp\u003eWe aspire that future studies will enhance our comprehension of hematological index-related diseases and the associated risk of pan-cancer development by integrating gwas data from diverse diseases, culminating in the establishment of a detection and prediction system for pan-cancer through blood indicator .\u003c/p\u003e"},{"header":"Conclusion and expert recommendations in the framework of PPPM","content":"\u003cp\u003eOur study has revealed associations between blood-related indicator and various types of cancer, including esophageal, breast, lung adenocarcinoma, ovarian, and several gastrointestinal cancers. This valuable information can contribute to personalized cancer surveillance and prevention strategies for these specific populations.\u003c/p\u003e \u003cp\u003eThe observed links between HTN, DVT, DM and anemia highlight the importance of developing tailored cancer prevention and treatment programs. Understanding the connection between these diseases and cancer can help identify high-risk individuals who may be prone to developing cancer. This knowledge can be applied within the framework of PPPM to screen at-risk individuals and provide convenient ways to prevent cancer or detect it at an early stage.\u003c/p\u003e \u003cp\u003eFurthermore, the drug targets identified within these four groups of diseases could be utilized for prophylactic treatment of related cancers or to improve the efficacy of existing targeted therapies. This offers new possibilities for enhancing cancer treatment and prevention strategies.\u003c/p\u003e \u003cp\u003eIn summary, our proposal to strengthen research and interpretation of human blood sample data, particularly those related to cancer, is a crucial step towards advancing personalized cancer care and early detection methods. It also has the potential to contribute to the development of individualized cancer prevention plans for individuals with diseases such as hypertension and diabetes mellitus.\u003c/p\u003e\n\u003ch3\u003ePredictive approach\u003c/h3\u003e\n\u003cp\u003ePrevention and early screening for pan-cancer play a crucial role in reducing the suffering and economic burden of patients. At the genetic level, by utilizing MR and bioinformatics analysis of blood-related markers, along with their associated diseases and drug interactions in pan-cancer, in combination with clinical data and molecular experiments, it is possible to establish a predictive diagnostic and prognostic model for pan-cancer based on blood markers. This approach allows for the implementation of PPPM for pan-cancer.\u003c/p\u003e\n\u003ch3\u003eTargeted prevention\u003c/h3\u003e\n\u003cp\u003eThis study seeks to investigate the varying impacts of different blood indicators, associated medical conditions (including HTN, DM, DVT, and anemia), as well as drug usage on the overall incidence of pan-cancer. Through such an exploration, the study has successfully identified previously undisclosed subpopulations that are particularly vulnerable to the development of cancers, thus shedding light on the importance of tailored and targeted cancer prevention approaches for specific at-risk populations. For instance, it is imperative to implement preventative measures for men afflicted with hypertension to avoid the onset of colorectal adenocarcinoma (COAD), and to closely monitor insulin users for early signs of lung adenocarcinoma (LUAD).\u003c/p\u003e\n\u003ch3\u003ePersonalization of medicine services\u003c/h3\u003e\n\u003cp\u003eIt is widely recognized that genetic factors play a key role in determining one's risk of developing cancer, as well as the type of cancer to which they are most susceptible. Blood is a good sample that balances accessibility and informativeness. Based on the results of the analysis of blood at the genetic level in relation to pan-cancer, it is possible to provide a direction and theoretical basis for personalized preventive, diagnostic and therapeutic medical services for specific populations.Simultaneously, our research endeavors have successfully identified specific gene targets that play a crucial role in the intricate mechanisms underlying the development of novel and precisely targeted therapeutic pathways.\u003c/p\u003e\n\u003ch3\u003eParadigm shifts from reactive medicine to PPPM and moving beyond the state of the art\u003c/h3\u003e\n\u003cp\u003eIndeed, cancer remains an ongoing and formidable challenge in the field of human medicine, with certain types of cancers showcasing a stealthy and aggressive progression. This reality has underscored the limitations of conventional reactive approaches to cancer treatment, driving the necessity to transition towards PPPM model, which is more proactive and personalized.\u003c/p\u003e \u003cp\u003eThe correlation discovered in this study between blood markers and pan-cancer serves as a valuable tool in the development of a simplified, standardized, and highly sensitive cancer surveillance system. Based on this tool, the new tumor susceptible populations identified in the study, as well as new targets for pharmacological interventions, further contribute to personalized medicine in oncology.By leveraging such findings, healthcare professionals can design tailored cancer prevention and treatment strategies that are specifically optimized for individual patients, ultimately enhancing the overall efficacy and outcomes of cancer management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAT1R:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eangiotensin II type 1 receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTMB: \u003c/em\u003e\u003c/strong\u003etumor mutational burden\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRAS\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003e renin-angiotensin system\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eREAD\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e：\u003c/em\u003erectum adenocarcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePPPM\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003epredictive, preventive and personalised medicine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003emendelian randomization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLD:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003elinkage disequilibrium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIVW:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e inverse variance weighted\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIVs:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003einstrumental variables\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003e insulin receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHbA1c:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e glycated hemoglobin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHTN\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003e hypertension\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHDIs:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e human development indices\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGWAS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003egenome-wide association studies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGSEA:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003egene set enrichment analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eglucocorticoid receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIGF-IR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003einsulin-like growth factor I receptor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003emTOR\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003e mammalian target of rapamycin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEGF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003e epidermal growth factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDVT:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003edeep vein thrombosis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ediabetes mellitus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBRCA: \u003c/em\u003e\u003c/strong\u003ebreast carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCESC:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ecervical squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOAD:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e colorectal adenocarcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eESCA: \u003c/em\u003e\u003c/strong\u003eesophageal carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGBMLGG: \u003c/em\u003e\u003c/strong\u003elower-grade glioma and glioblastoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKICH: \u003c/em\u003e\u003c/strong\u003ekidney chromophobe\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKIRC:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ekidney renal clear cell carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKIRP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003ekidney renal papillary cell carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLGG\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003elower-grade glioma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLIHC\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eliver hepatocellular carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLUAD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003elung adenocarcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNSCLC:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003enon-small-cell lung cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOV:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eovarian cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePAAD:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003epancreatic adenocarcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePRAD\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eprostate adenocarcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSKCM\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eskin cutaneous melanoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSNPs\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003esingle nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSTAD:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003estomach adenocarcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTHCA:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ethyroid carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUCEC:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e uterine corpus endometrial carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFSH:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003efollicle-stimulating hormone\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to FinnGen, the IEU Open GWAS, the UK Biobank, and Biobank Japan for publishing the GWAS summary statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesign subject: Xinhao Tang , Bowen Chu , Wenbo Xu, Mianhua Wu and Jun Shi;\u003c/p\u003e\n\u003cp\u003eAcquisition of data and data organization: Xinhao Tang, Xinyu Tian, Jingjing Wu, and Sainan Hao;\u003c/p\u003e\n\u003cp\u003eMR analysis:Xinhao Tang , Xinyu Tian, Jingjing Wu, and Sainan Hao;\u003c/p\u003e\n\u003cp\u003eanalysis using bioinformatics tools: Xinhao Tang, Xinyu Tian, Shuai Shan, and Tinghao Dai;\u003c/p\u003e\n\u003cp\u003ePaper writing: Xinhao Tang, Xinyu Tian, and Bowen Chu;\u003c/p\u003e\n\u003cp\u003eSupervision:Guanmin Tang, Wenbo Xu, Mianhua Wu and Jun Shi;\u003c/p\u003e\n\u003cp\u003eSponsor: Xinhao Tang, Guanmin Tang, and Mianhua Wu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Jiaxing Public Welfare Research Program Project (2020AY30006), Suzhou Science and Technology Development Programme (SKYD202306),\u0026nbsp;National Student Innovation Training Program (s20021038117, 202210315128Z), 2022 Luo Linxiu Cup Student Innovation Cultivation Project of Nanjing University of Chinese Medicine (3), and 2023 Chinese Medicine Artificial Intelligence Unveiling and Hanging Project of Nanjing University of Chinese Medicine (1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used and/or analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe software and code used in the study can be obtained through the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors disclosed no competing interests in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. \u003c/li\u003e\n\u003cli\u003eTian Q, Price ND, Hood L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J Intern Med. 2012;271(2):111-121.\u003c/li\u003e\n\u003cli\u003eHan H, Guo W, Shi W, Yu Y, Zhang Y, Ye X, He J. hypertension and breast cancer risk: a systematic review and meta-analysis. Sci Rep. 2017;7:44877.\u003c/li\u003e\n\u003cli\u003ede Paula Gonzaga ALAC, Palmeira VA, Ribeiro TFS, Costa LB, de S\u0026aacute; Rodrigues KE, Sim\u0026otilde;es-E-Silva AC. ACE2/Angiotensin-(1-7)/Mas Receptor Axis in Human Cancer: Potential Role for Pediatric Tumors. Curr Drug Targets. 2020;21(9):892-901. \u003c/li\u003e\n\u003cli\u003eLega IC, Lipscombe LL. Review: diabetes, Obesity, and Cancer-Pathophysiology and Clinical Implications. Endocr Rev. 2020;41(1):bnz014.\u003c/li\u003e\n\u003cli\u003eLee YJ, Lee HR, Nam CM, Hwang UK, Jee SH. White blood cell count and the risk of colon cancer. Yonsei Med J. 2006;47(5):646-56. \u003c/li\u003e\n\u003cli\u003eWong JYY, Bassig BA, Loftfield E, Hu W, Freediabetesan ND, Ji BT, Elliott P, Silverman DT, Chanock SJ, Rothman N, Lan Q. White Blood Cell Count and Risk of Incident Lung Cancer in the UK Biobank. JNCI Cancer Spectr. 2019;4(2):pkz102.\u003c/li\u003e\n\u003cli\u003eXie X, Yao M, Chen X, Lu W, Lv Q, Wang K, Zhang L, Lu F. Reduced red blood cell count predicts poor survival after surgery in patients with primary liver cancer. Medicine (Baltimore). 2015;94(8):e577.\u003c/li\u003e\n\u003cli\u003eMa W, Mao S, Bao M, Wu Y, Guo Y, Liu J, Wang R, Li C, Zhang J, Zhang W, Yao X. Prognostic significance of red cell distribution width in bladder cancer. Transl Androl Urol. 2020;9(2):295-302.\u003c/li\u003e\n\u003cli\u003eTong Y, Xie X, Mao X, Lei H, Chen Y, Sun P. Low Red Blood Cell Count as an Early Indicator for Myometrial Invasion in Women with Endometrioid Endometrial Carcinoma with Metabolic Syndrome. Cancer Manag Res. 2020;12:10849-10859.\u003c/li\u003e\n\u003cli\u003eDuell EJ, Bonet C, Mu\u0026ntilde;oz X, Lujan-Barroso L, Weiderpass E, Boutron-Ruault MC, Racine A, Severi G, Canzian F, Rizzato C, Boeing H, Overvad K, Tj\u0026oslash;nneland A, Arg\u0026uuml;elles M, S\u0026aacute;nchez-Cantalejo E, Chamosa S, Huerta JM, Barricarte A, Khaw KT, Wareham N, Travis RC, Trichopoulou A, Trichopoulos D, Yiannakouris N, Palli D, Agnoli C, Tumino R, Naccarati A, Panico S, Bueno-de-Mesquita HB, Siersema PD, Peeters PH, Ohlsson B, Lindkvist B, Johansson I, Ye W, Johansson M, Fenger C, Riboli E, Sala N, Gonz\u0026aacute;lez CA. Variation at ABO histo-blood group and FUT loci and diffuse and intestinal gastric cancer risk in a European population. Int J Cancer. 2015 ;136(4):880-893.\u003c/li\u003e\n\u003cli\u003eYuan F, Wen W, Jia G, Long J, Shu XO, Zheng W. Serum Lipid Profiles and Cholesterol-Lowering Medication Use in Relation to Subsequent Risk of Colorectal Cancer in the UK Biobank Cohort. Cancer Epidemiol Biomarkers Prev. 2023;32(4):524-530.\u003c/li\u003e\n\u003cli\u003eNabbout R, Kuchenbuch M. Impact of predictive, preventive and precision medicine strategies in epilepsy. Nat Rev Neurol. 2020;16(12):674-688.\u003c/li\u003e\n\u003cli\u003eSagner M, McNeil A, Puska P, Auffray C, Price ND, Hood L, Lavie CJ, Han ZG, Chen Z, Brahmachari SK, McEwen BS, Soares MB, Balling R, Epel E, Arena R. The P4 Health Spectrum - A Predictive, Preventive, Personalized and Participatory Continuum for Promoting Healthspan. Prog Cardiovasc Dis. 2017;59(5):506-521.\u003c/li\u003e\n\u003cli\u003eHood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol. 2011;8(3):184-187.\u003c/li\u003e\n\u003cli\u003eBowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res Synth Methods. 2019;10(4):486-496.\u003c/li\u003e\n\u003cli\u003eBodrova TA, Kostiushev DS, Antonova EN, Gnatenko DA, Bocharova MO, Lopukhin IuM, Pal\u0026apos;tsev MA, Suchkov SV. Introduction into PPPM: experience of the past and tomorrow\u0026apos;s reality. Vestn Ross Akad Med Nauk. 2013;(1):58-64. \u003c/li\u003e\n\u003cli\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M, Richards JB. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326(16):1614-1621.\u003c/li\u003e\n\u003cli\u003eLin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet. 2021 ;17(11):e1009922.\u003c/li\u003e\n\u003cli\u003eZhang Y, Li D, Zhu Z, Chen S, Lu M, Cao P, Chen T, Li S, Xue S, Zhang Y, Zhu J, Ruan G, Ding C. Evaluating the impact of metformin targets on the risk of osteoarthritis: a mendelian randomization study. Osteoarthritis Cartilage. 2022 ;30(11):1506-1514. \u003c/li\u003e\n\u003cli\u003eYuan S, Titova OE, Zhang K, Gou W, Schillemans T, Natarajan P, Chen J, Li X, \u0026Aring;kesson A, Bruzelius M, Klarin D, Damrauer SM, Larsson SC. Plasma protein and venous thromboembolism: prospective cohort and mendelian randomisation analyses. Br J Haematol. 2023;201(4):783-792. \u003c/li\u003e\n\u003cli\u003eXiang Y, Zhang C, Wang J, Cheng Y, Wang L, Tong Y, Yan D. Identification of host gene-microbiome associations in colorectal cancer patients using mendelian randomization. J Transl Med. 2023;21(1):535.[21]Liu J, Lichtenberg T, Hoadley KA,et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400-416.e11.\u003c/li\u003e\n\u003cli\u003eLiu J, Lichtenberg T, Hoadley KA,et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018;173(2):400-416.e11.\u003c/li\u003e\n\u003cli\u003eShen YT, Huang X, Zhang G, Jiang B, Li CJ, Wu ZS. Pan-Cancer Prognostic Role and Targeting Potential of the Estrogen-Progesterone Axis. Front Oncol. 2021 ;11:636365.\u003c/li\u003e\n\u003cli\u003e]Xuan K, Zhao T, Sun C, Patel AS, Liu H, Chen X, Qu G, Sun Y. The association between hypertension and colorectal cancer: a meta-analysis of observational studies. Eur J Cancer Prev. 2021;30(1):84-96. \u003c/li\u003e\n\u003cli\u003eChilders WK. Interactions of the renin-angiotensin system in colorectal cancer and metastasis. Int J Colorectal Dis. 2015;30(6):749-52.\u003c/li\u003e\n\u003cli\u003eHan H, Guo W, Shi W, Yu Y, Zhang Y, Ye X, He J. hypertension and breast cancer risk: a systematic review and meta-analysis. Sci Rep. 2017 ;7:44877. \u003c/li\u003e\n\u003cli\u003eSeo JH, Kim YD, Park CS, Han KD, Joo YH. hypertension is associated with oral, laryngeal, and esophageal cancer: a nationwide population-based study. Sci Rep. 2020 Jun 24;10(1):10291.\u003c/li\u003e\n\u003cli\u003eLoney T, Nagelkerke NJ. The individualistic fallacy, ecological studies and instrumental variables: a causal interpretation. Emerg Themes Epidemiol. 2014;11:18. \u003c/li\u003e\n\u003cli\u003eHuang J, Lok V, Ngai CH, Zhang L, Yuan J, Lao XQ, Ng K, Chong C, Zheng ZJ, Wong MCS. Worldwide Burden of, Risk Factors for, and Trends in Pancreatic Cancer. Gastroenterology. 2021 Feb;160(3):744-754.\u003c/li\u003e\n\u003cli\u003eKhoshghamat N, Jafari N, Toloue-Pouya V, Azami S, Mirnourbakhsh SH, Khazaei M, Ferns GA, Rajabian M, Avan A. The therapeutic potential of renin-angiotensin system inhibitors in the treatment of pancreatic cancer. Life Sci. 2021 ;270:119118.\u003c/li\u003e\n\u003cli\u003eCorvol P, Pinet F, Plouin PF, Bruneval P, Menard J. Renin-secreting tumors. Endocrinol Metab Clin North Am. 1994 ;23(2):255-70. \u003c/li\u003e\n\u003cli\u003eHu J, Zhang L-C, Song X, et al. KRT6 interacting with notch1 contributes to progression of renal cell carcinoma, and aliskiren inhibits renal carcinoma cell lines proliferation in vitro. Int J Clin Exp Pathol. 2015;8(8):9182\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eWegman-Ostrosky T, Soto-Reyes E, Vidal-Mill\u0026aacute;n S, S\u0026aacute;nchez-Corona J. The renin-angiotensin system meets the hallmarks of cancer. J Renin Angiotensin Aldosterone Syst. 2015 ;16(2):227-33. \u003c/li\u003e\n\u003cli\u003eGoto A, Yamaji T, Sawada N, Momozawa Y, Kamatani Y, Kubo M, Shimazu T, Inoue M, Noda M, Tsugane S, Iwasaki M. diabetes and cancer risk: A Mendelian randomization study. Int J Cancer. 2020;146(3):712-719. \u003c/li\u003e\n\u003cli\u003eGuo J, Liu C, Pan J, Yang J. Relationship between diabetes and risk of gastric cancer: A systematic review and meta-analysis of cohort studies. diabetes Res Clin Pract. 2022 ;187:109866. \u003c/li\u003e\n\u003cli\u003eWei Z, Liang L, Junsong L, Rui C, Shuai C, Guanglin Q, Shicai H, Zexing W, Jin W, Xiangming C, Shufeng W. The impact of insulin on chemotherapeutic sensitivity to 5-fluorouracil in gastric cancer cell lines SGC7901, MKN45 and MKN28. J Exp Clin Cancer Res. 2015 ;34(1):64. \u003c/li\u003e\n\u003cli\u003eSasazuki S, Charvat H, Hara A, Wakai K, Nagata C, Nakamura K, Tsuji I, Sugawara Y, Tamakoshi A, Matsuo K, Oze I, Mizoue T, Tanaka K, Inoue M, Tsugane S; Research Group for the Development and Evaluation of Cancer Prevention Strategies in Japan. diabetes mellitus and cancer risk: pooled analysis of eight cohort studies in Japan. Cancer Sci. 2013 ;104(11):1499-507. \u003c/li\u003e\n\u003cli\u003eMurphy N, Song M, Papadimitriou N, Carreras-Torres R, Langenberg C, Martin RM, Tsilidis KK, Barroso I, Chen J, Frayling TM, Bull CJ, Vincent EE, Cotterchio M, Gruber SB, Pai RK, Newcomb PA, Perez-Cornago A, van Duijnhoven FJB, Van Guelpen B, Vodicka P, Wolk A, Wu AH, Peters U, Chan AT, Gunter MJ. Associations Between Glycemic Traits and Colorectal Cancer: A Mendelian Randomization Analysis. J Natl Cancer Inst. 2022 ;114(5):740-752.\u003c/li\u003e\n\u003cli\u003eLi W, Zhang X, Sang H, Zhou Y, Shang C, Wang Y, Zhu H. Effects of hyperglycemia on the progression of tumor diseases. J Exp Clin Cancer Res. 2019 ;38(1):327. \u003c/li\u003e\n\u003cli\u003eYu GH, Li SF, Wei R, Jiang Z. diabetes and Colorectal Cancer Risk: Clinical and Therapeutic Implications. J diabetes Res. 2022 ;2022:1747326.\u003c/li\u003e\n\u003cli\u003eHope C, Robertshaw A, Cheung KL, Idris I, English E. Relationship between HbA1c and cancer in people with or without diabetes: a systematic review. Diabet Med. 2016;33(8):1013-25.\u003c/li\u003e\n\u003cli\u003eHuang L, Feng X, Yang W, Li X, Zhang K, Feng S, Wang F, Yang X. Appraising the Effect of Potential Risk Factors on Thyroid Cancer: A Mendelian Randomization Study. J Clin Endocrinol Metab. 2022 Jun ;107(7):e2783-e2791\u003c/li\u003e\n\u003cli\u003ede Beer JC, Liebenberg L. Does cancer risk increase with HbA1c, independent of diabetes? Br J Cancer. 2014 Apr 29;110(9):2361-8. \u003c/li\u003e\n\u003cli\u003eLemanska A, Price CA, Jeffreys N, Byford R, Dambha-Miller H, Fan X, Hinton W, Otter S, Rice R, Stunt A, Whyte MB, Faithfull S, de Lusignan S. BMI and HbA1c are metabolic markers for pancreatic cancer: Matched case-control study using a UK primary care database. PLoS One. 2022;17(10):e0275369.\u003c/li\u003e\n\u003cli\u003eEsteves AC, Freitas O, Almeida T, Rosado L. Aplasias medulares cong\u0026eacute;nitas [Inherited aplastic anemias]. An Pediatr (Barc). 2010;73(2):84-7. Spanish.\u003c/li\u003e\n\u003cli\u003eAkiba S, Neriishi K, Blot WJ, Kabuto M, Stevens RG, Kato H, Land CE. Serum ferritin and stomach cancer risk among a Japanese population. Cancer. 1991 ;67(6):1707-12.\u003c/li\u003e\n\u003cli\u003eZhang ZF, Kurtz RC, Yu GP, Sun M, Gargon N, Karpeh M Jr, Fein JS, Harlap S. Adenocarcinomas of the esophagus and gastric cardia: the role of diet. Nutr Cancer. 1997;27(3):298-309.\u003c/li\u003e\n\u003cli\u003eOh TK, Song IA. Anemia May Increase the Overall Risk of Cancer: Findings from a Cohort Study with a 12-Year Follow-up Period in South Korea. Cancer EpidemiolBiomarkersPrev.2021;30(7):1440-1448.\u003c/li\u003e\n\u003cli\u003eLahner E, Capasso M, Carabotti M, Annibale B. Incidence of cancer (other than gastric cancer) in pernicious anaemia: A systematic review with meta-analysis. Dig Liver Dis. 2018 ;50(8):780-786. \u003c/li\u003e\n\u003cli\u003eChi G, Lee JJ, Montazerin SM, Marszalek J. Prognostic value of hemoglobin-to-red cell distribution width ratio in cancer: a systematic review and meta-analysis. Biomark Med. 2022;16(6):473-482.\u003c/li\u003e\n\u003cli\u003eKang N, Qiu WJ, Wang B, Tang DF, Shen XY. Role of hemoglobin alpha and hemoglobin beta in non-small-cell lung cancer based on bioinformatics analysis. Mol Carcinog. 2022;61(6):587-602.\u003c/li\u003e\n\u003cli\u003eEdgren G, Bagnardi V, Bellocco R, Hjalgrim H, Rostgaard K, Melbye M, Reilly M, Adami HO, Hall P, Nyr\u0026eacute;n O. Pattern of declining hemoglobin concentration before cancer diagnosis. Int J Cancer. 2010;127(6):1429-36.\u003c/li\u003e\n\u003cli\u003eKawai K, Watanabe T. Colorectal cancer and hypercoagulability. Surg Today. 2014 ;44(5):797-803.\u003c/li\u003e\n\u003cli\u003eWang L, Huang X, Chen Y, Jin X, Li Q, Yi TN. Prognostic value of TP/PD-ECGF and thrombocytosis in gastric carcinoma. Eur J Surg Oncol. 2012 Jul;38(7):568-73. \u003c/li\u003e\n\u003cli\u003eYamashita H, Kitayama J, Kanno N, Yatomi Y, Nagawa H. Hyperfibrinogenemia is associated with lymphatic as well as hematogenous metastasis and worse clinical outcome in T2 gastric cancer. BMC Cancer. 2006 Jun 1;6:147.\u003c/li\u003e\n\u003cli\u003eChen Y, Yu H, Wu C, Li J, Jiao S, Hu Y, Tao H, Wu B, Li A. Prognostic value of plasma D-dimer levels in patients with small-cell lung cancer. Biomed Pharmacother. \u003c/li\u003e\n\u003cli\u003eTas F, Kilic L, Bilgin E, Keskin S, Sen F, Ciftci R, Yildiz I, Yasasever V. Clinical and prognostic significance of coagulation assays in advanced epithelial ovarian cancer. Int J Gynecol Cancer. 2013 ;23(2):276-81.\u003c/li\u003e\n\u003cli\u003eChen Y, Yu H, Wu C, Li J, Jiao S, Hu Y, Tao H, Wu B, Li A. Prognostic value of plasma D-dimer levels in patients with small-cell lung cancer. Biomed Pharmacother. 2016 ;81:210-217.\u003c/li\u003e\n\u003cli\u003ePiccioli A, Lensing AW, Prins MH, Falanga A, Scannapieco GL, Ieran M, Cigolini M, Ambrosio GB, Monreal M, Girolami A, Prandoni P; SOMIT Investigators Group. Extensive screening for occult malignant disease in idiopathic venous thromboembolism: a prospective randomized clinical trial. J Thromb Haemost. 2004 ;2(6):884-9. \u003c/li\u003e\n\u003cli\u003eMonreal M, Lensing AW, Prins MH, Bonet M, Fern\u0026aacute;ndez-Llamazares J, Muchart J, Prandoni P, Jim\u0026eacute;nez JA. Screening for occult cancer in patients with acute deep vein thrombosis or pulmonary embolism. J Thromb Haemost. 2004;2(6):876-81. \u003c/li\u003e\n\u003cli\u003eGotta J, Gruenewald LD, Eichler K, Martin SS, Mahmoudi S, Booz C, Biciusca T, Reschke P, Bernatz S, Pinto Dos Santos D, Scholtz JE, Alizadeh LS, Nour-Eldin NA, Hammerstingl RM, Gruber-Rouh T, Mader C, Hardt SE, Sommer CM, Bucolo G, D\u0026apos;Angelo T, Onay M, Finkelmeier F, Leistner DM, Vogl TJ, Giannitsis E, Koch V. Unveiling the diagnostic enigma of D-dimer testing in cancer patients: Current evidence and areas of application. Eur J Clin Invest. 2023 ;53(10):e14060.\u003c/li\u003e\n\u003cli\u003eSchutgens RE, Beckers MM, Haas FJ, Biesma DH. The predictive value of D-dimer measurement for cancer in patients with deep vein thrombosis. Haematologica. 2005 Feb;90(2):214-9. PMID: 15710574.\u003c/li\u003e\n\u003cli\u003eDai H, Zhou H, Sun Y, Xu Z, Wang S, Feng T, Zhang P. D-dimer as a potential clinical marker for predicting metastasis and progression in cancer. Biomed Rep. 2018 Nov;9(5):453-457,xg\u003c/li\u003e\n\u003cli\u003eGabitova-Cornell L, Surumbayeva A, Peri S, Franco-Barraza J, Restifo D, Weitz N, Ogier C, Goldman AR, Hartman TR, Francescone R, Tan Y, Nicolas E, Shah N, Handorf EA, Cai KQ, O\u0026apos;Reilly AM, Sloma I, Chiaverelli R, Moffitt RA, Khazak V, Fang CY, Golemis EA, Cukierman E, Astsaturov I. Cholesterol Pathway Inhibition Induces TGF-\u0026beta; Signaling to Promote Basal Differentiation in Pancreatic Cancer. Cancer Cell. 2020;38(4):567-583.e11. \u003c/li\u003e\n\u003cli\u003eJun SY, Brown AJ, Chua NK, Yoon JY, Lee JJ, Yang JO, Jang I, Jeon SJ, Choi TI, Kim CH, Kim NS. Reduction of Squalene Epoxidase by Cholesterol Accumulation Accelerates Colorectal Cancer Progression and Metastasis. Gastroenterology. 2021 Mar;160(4):1194-1207.e28.\u003c/li\u003e\n\u003cli\u003eGao S, Soares F, Wang S, Wong CC, Chen H, Yang Z, Liu W, Go MYY, Ahmed M, Zeng Y, O\u0026apos;Brien CA, Sung JJY, He HH, Yu J. CRISPR screens identify cholesterol biosynthesis as a therapeutic target on stemness and drug resistance of colon cancer. Oncogene. 2021 ;40(48):6601-6613. \u003c/li\u003e\n\u003cli\u003eArchibugi L, Arcidiacono PG, Capurso G. Statin use is associated to a reduced risk of pancreatic cancer: A meta-analysis. Dig Liver Dis. 2019 ;51(1):28-37. \u003c/li\u003e\n\u003cli\u003eAbdel-Rahman O. Statin treatment and outcomes of metastatic pancreatic cancer: a pooled analysis of two phase III studies. Clin Transl Oncol. 2019 ;21(6):810-816. \u003c/li\u003e\n\u003cli\u003eLi H, Liu Y, Wang Y, Zhao X, Qi X. Hormone therapy for ovarian cancer: Emphasis on mechanisms and applications (Review). Oncol Rep. 2021 Oct;46(4):223.\u003c/li\u003e\n\u003cli\u003eHUNN, JESSICA MD; RODRIGUEZ, GUSTAVO C. MD. Ovarian Cancer: Etiology, Risk Factors, and Epidemiology. Clinical Obstetrics and Gynecology 55(1):p 3-23, 2012 \u003c/li\u003e\n\u003cli\u003ePhung MT, Lee AW, Wu AH, Berchuck A, Cho KR, Cramer DW, Doherty JA, Goodman MT, Hanley GE, Harris HR, McLean K, Modugno F, Moysich KB, Mukherjee B, Schildkraut JM, Terry KL, Titus LJ, Jordan SJ, Webb PM, Pike MC, Pearce CL; Ovarian Cancer Association Consortium; Australian Ovarian Cancer Study Group and the Ovarian Cancer Association Consortium; Ovarian Cancer Association Consortium. Depot-Medroxyprogesterone Acetate Use Is Associated with Decreased Risk of Ovarian Cancer: The Mounting Evidence of a Protective Role of Progestins. Cancer Epidemiol Biomarkers Prev. 2021 ;30(5):927-935. \u003c/li\u003e\n\u003cli\u003eLiu WJ, Zhao G, Zhang CY, Yang CQ, Zeng XB, Li J, Zhu K, Zhao SQ, Lu HM, Yin DC, Lin SX. Comparison of the roles of estrogens and androgens in breast cancer and prostate cancer. J Cell Biochem. 2020;121(4):2756-2769.\u003c/li\u003e\n\u003cli\u003eYu W, Ding J, He M, Chen Y, Wang R, Han Z, Xing EZ, Zhang C and Yeh S (2018) Estrogen receptor beta promotes the vasculogenic mimicry (VM) and cell invasion via altering the lncRNA‐MALAT1/miR‐145‐5p/NEDD9 signals in lung cancer. Oncogene 38, 1225\u0026ndash;1238.\u003c/li\u003e\n\u003cli\u003eHe M, Yu W, Chang C, Miyamoto H, Liu X, Jiang K, Yeh S. Estrogen receptor \u0026alpha; promotes lung cancer cell invasion via increase of and cross-talk with infiltrated macrophages through the CCL2/CCR2/MMP9 and CXCL12/CXCR4 signaling pathways. Mol Oncol. 2020;14(8):1779-1799. \u003c/li\u003e\n\u003cli\u003eTrabert B, Sherman ME, Kannan N, Stanczyk FZ. Progesterone and Breast Cancer. Endocr Rev. 2020 ;41(2):320\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003ePoole AJ, Li Y, Kim Y, Lin SC, Lee WH, Lee EY. Prevention of Brca1-mediated mammary tumorigenesis in mice by a progesterone antagonist. Science. 2006 ;314(5804):1467-70. \u003c/li\u003e\n\u003cli\u003eLawlor MA, Alessi DR. PKB/Akt: a key mediator of cell proliferation, survival and insulin responses? J Cell Sci. 2001 ;114(Pt 16):2903-10..\u003c/li\u003e\n\u003cli\u003eLimburg PJ, Stolzenberg-Solomon RZ, Vierkant RA, Roberts K, Sellers TA, Taylor PR, Virtamo J, Cerhan JR, Albanes D. Insulin, glucose, insulin resistance, and incident colorectal cancer in male smokers. Clin Gastroenterol Hepatol. 2006 ;4(12):1514-21. \u003c/li\u003e\n\u003cli\u003eArgirion I, Weinstein SJ, M\u0026auml;nnist\u0026ouml; S, Albanes D, Mondul AM. Serum Insulin, Glucose, Indices of Insulin Resistance, and Risk of Lung Cancer. Cancer Epidemiol Biomarkers Prev. 2017;26(10):1519-1524. .\u003c/li\u003e\n\u003cli\u003eJiang W, Hu JW, He XR, Jin WL, He XY. Statins: a repurposed drug to fight cancer. J Exp Clin Cancer Res. 2021 Jul 24;40(1):241. doi: 10.1186/s13046-021-02041-2. \u003c/li\u003e\n\u003cli\u003eCarter P, Vithayathil M, Kar S, Potluri R, Mason AM, Larsson SC, Burgess S. Predicting the effect of statins on cancer risk using genetic variants from a Mendelian randomization study in the UK Biobank. Elife. 2020 ;9:e57191. \u003c/li\u003e\n\u003cli\u003eFreed-Pastor WA, Mizuno H, Zhao X, Langer\u0026oslash;d A, Moon SH, Rodriguez-Barrueco R, Barsotti A, Chicas A, Li W, Polotskaia A, et al. Mutant p53 disrupts mammary tissue architecture via the mevalonate pathway. Cell. 2012;148(1\u0026ndash;2):244\u0026ndash;258. \u003c/li\u003e\n\u003cli\u003eDess\u0026igrave; S, Batetta B, Pulisci D, Spano O, Anchisi C, Tessitore L, Costelli P, Baccino FM, Aroasio E, Pani P. Cholesterol content in tumor tissues is inversely associated with high-density lipoprotein cholesterol in serum in patients with gastrointestinal cancer. Cancer. 1994;73(2):253\u0026ndash;258.\u003c/li\u003e\n\u003cli\u003eDu D, Liu C, Qin M, Zhang X, Xi T, Yuan S, Hao H, Xiong J. Metabolic dysregulation and emerging therapeutical targets for hepatocellular carcinoma. Acta Pharm Sin B. 2022 ;12(2):558-580.\u003c/li\u003e\n\u003cli\u003eBelfiore A, Frasca F, Pandini G, Sciacca L, Vigneri R. Insulin receptor isoforms and insulin receptor/insulin-like growth factor receptor hybrids in physiology and disease. Endocr Rev. 2009;30(6):586-623. \u003c/li\u003e\n\u003cli\u003eVella V, Sciacca L, Pandini G, Mineo R, Squatrito S, Vigneri R, Belfiore A. The IGF system in thyroid cancer: new concepts. Mol Pathol. 2001;54(3):121-4. \u003c/li\u003e\n\u003cli\u003ePapa V, Pezzino V, Costantino A, Belfiore A, Giuffrida D, Frittitta L, Vannelli GB, Brand R, Goldfine ID, Vigneri R. Elevated insulin receptor content in human breast cancer. J Clin Invest. 1990;86(5):1503-10.\u003c/li\u003e\n\u003cli\u003eTing M, Miao YE, Yu FX, Luo GC, Xu X, Xiao LX, Zhang GQ, Chang J. Correlation Study on the Expression of INSR, IRS-1, and PD-L1 in Nonsmall Cell Lung Cancer. J Oncol. 2022;2022:5233222\u003c/li\u003e\n\u003cli\u003eHuang G, Song C, Wang N, Qin T, Sui S, Obr A, Zeng L, Wood TL, Leroith D, Li M, Wu Y. RNA-binding protein CUGBP1 controls the differential INSR splicing in molecular subtypes of breast cancer cells and affects cell aggressiveness. Carcinogenesis. 2020;41(9):1294-1305. \u003c/li\u003e\n\u003cli\u003eAcharya N, Madi A, Zhang H, Klapholz M, Escobar G, Dulberg S, Christian E, Ferreira M, Dixon KO, Fell G, Tooley K, Mangani D, Xia J, Singer M, Bosenberg M, Neuberg D, Rozenblatt-Rosen O, Regev A, Kuchroo VK, Anderson AC. Endogenous Glucocorticoid Signaling Regulates CD8+ T Cell Differentiation and Development of Dysfunction in the Tumor Microenvironment. Immunity. 2020 ;53(3):658-671.e6.\u003c/li\u003e\n\u003cli\u003eCaratti B, Fidan M, Caratti G, Breitenecker K, Engler M, Kazemitash N, Traut R, Wittig R, Casanova E, Ahmadian MR, Tuckermann JP, Moll HP, Cirstea IC. The glucocorticoid receptor associates with RAS complexes to inhibit cell proliferation and tumor growth. Sci Signal. 2022;15(726):eabm4452\u003c/li\u003e\n\u003cli\u003eZhang L, Song L, Xu Y, Xu Y, Zheng M, Zhang P, Wang Q. Midkine promotes breast cancer cell proliferation and migration by upregulating NR3C1 expression and activating the NF-\u0026kappa;B pathway. Mol Biol Rep. 2022 ;49(4):2953-2961.\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":"Predictive preventive and personalised medicine(PPPM), pan-cancer, Mendelian randomization(MR), Bioinformatics, Tumorigenesis","lastPublishedDoi":"10.21203/rs.3.rs-3774776/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3774776/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eBlood serves as a powerful tool for monitoring the intricate landscape of cancer development. Previous studies have emerged, suggesting that hematologic indicators hold promise in predicting the onset of malignancy. This present investigation aims to delve into the underlying causal connections between blood-related indicators and pan-cancer, further elucidating the potential impact of diseases and medication utilization reflected in these indicators on cancer, within the realm of predictive, preventive and personalised medicine(PPPM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo embark on this scientific endeavor, we procured summary-level data from a genome-wide association studies (GWAS) encompassing blood-related indicators and cis-eQTLs of drug target genes, from the esteemed IEU OpenGWAS. Additionally, we obtained GWAS summary-level data encapsulating pan-cancer (consisting of an impressive cohort of 659,582 cases and 12,186,911 controls), along with diseases annotated by their correlation to blood-related indicators, from esteemed sources such as IEU OpenGWAS, UK Biobank, FinnGen, and Biobank Japan. In order to unravel the direct causal associations between blood-related indicators and pan-cancer, as well as the causal implications between the diseases manifested by these indicators and cancer, we initiated a robust analysis employing the two-sample Mendelian randomization(MR) method. Furthermore, utilizing bioinformatics methodologies, we went on to explore the potential effects of drug target genes on pan-cancer.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePreliminary findings from our MR analysis provided compelling evidence of a significant link between blood-related exposures and pan-cancer. Drawing upon the intriguing interplay observed between blood pressure and tumors, it was postulated that monitoring hypertension (HTN) may offer notable advantages in the prevention of colorectal adenocarcinoma (COAD), breast carcinoma (BRCA), and esophageal carcinoma (ESCA). Similarly, considering the captivating relationship between blood glucose, insulin levels, and tumors, it was hypothesized that closely monitoring diabetes mellitus (DM) could prove beneficial in the prevention of stomach adenocarcinoma (STAD) and COAD. In consonance with the intriguing connection discovered between red blood cell counts, distribution width, and tumors, our findings supported the notion that monitoring anemia could impart advantageous effects in the prevention of lung adenocarcinoma (LUAD). Remarkably, drawing upon the intriguing relationship observed between deep vein thrombosis (DVT) and tumors, it was hypothesized that surveillance of DVT might prove valuable in the prevention of COAD. Additionally, we noted a disparity in risk for various cancers, including lung, breast, colorectal, ovarian, prostate, and pancreatic, consequent to the utilization of drugs for these aforementioned diseases. Among our identified drug targets, we carefully sifted through and diligently analyzed three pivotal genes, namely HMGCR, INSR, and NR3C1, fostering the prospect of formulating novel, tumor-targeted therapeutics. However, our investigation yielded insufficient evidence to confirm any mediating effects of glycated hemoglobin (HbA1c), hemoglobin-gastric, D-dimer, and renin on the associations between HTN, anemia, DVT, DM, and pan-cancer.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe present study unveils the intricate web of causal associations between blood-related indicators, the diseases they manifest, and medication utilization, all of which significantly impact the development of cancer. Notably, the potential for utilizing blood-related indicators as pioneering biomarkers for cancer prediction and prevention is underscored, showcasing a remarkable avenue for advancing PPPM strategies in the field of oncology. This seminal investigation serves as a beacon of novel insight, engendering the construction of refined and tailored approaches to combat the formidable challenge of cancer.\u003c/p\u003e","manuscriptTitle":"Genetically predicted causal associations between 152 blood-related exposures and pan-cancer in the framework of prediction, prevention and personalized medicine: a study integrating Mendelian randomization and bioinformatics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 01:31:05","doi":"10.21203/rs.3.rs-3774776/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":"eb102eef-d516-4e18-bcef-698fbd080bb4","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-20T14:19:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 01:31:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3774776","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3774776","identity":"rs-3774776","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}