ABO Blood Group-Based Disease Stratification as a Complementary Layer for p53-Driven Cancer Risk Models: A Meta-Analytic and Global Simulation Study

ABO Blood Group-Based Disease Stratification as a Complementary Layer for p53-Driven Cancer Risk Models: A Meta-Analytic and Global Simulation Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review ABO Blood Group-Based Disease Stratification as a Complementary Layer for p53-Driven Cancer Risk Models: A Meta-Analytic and Global Simulation Study Dev Sudersan Venkatesan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9515932/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background ABO blood group antigens are expressed on red blood cells, vascular endothelium, and epithelial surfaces, where they modulate inflammatory tone, immune cell adhesion, and angiogenic signalling. TP53 is the most frequently mutated gene in human cancer, with somatic alterations identified in over 40% of all malignancies across 20 cancer types in the NCI TP53 Database R21 (January 2025). Despite convergent biological plausibility - both systems regulating the inflammatory microenvironment and cellular stress response - no study has formally integrated ABO blood group stratification with TP53 mutational status into a unified cancer risk framework. I addressed this gap through a multi-source meta-analysis, GBD 2021-weighted global simulation, formal additive independence testing, and a novel composite biomarker index. Methods I conducted a random-effects meta-analysis (REML estimator) pooling published odds ratios from 31 studies covering approximately 2.5 million patients per comparison across three ABO contrasts (A vs O, B vs O, AB vs O). A GBD 2021-weighted simulation cohort of 10,000 patients was constructed across five global regions using ISBT blood group frequency data and NCI TP53 Database R21 cancer-specific mutation prevalences. Progressive logistic regression (four models), Cox proportional hazards with time-varying coefficients, 36-month landmark analysis, and restricted mean survival time (RMST) at tau = 60 months were applied. Additive independence was formally tested using the Rothman Synergy Index (SI) and relative excess risk due to interaction (RERI). A composite TP53 × ABO risk index was derived for 20 cancer types. Results Meta-analysis confirmed elevated cancer risk for blood groups A (OR = 1.23, 95% CI: 1.14–1.32, I²=62.7%, p < 0.0001), B (OR = 1.12, 95% CI: 1.04–1.20, I²=7.0%, p = 0.004), and AB (OR = 1.35, 95% CI: 1.19–1.53, I²=68.9%, p < 0.0001) versus group O. Meta-regression explained 100% of between-study heterogeneity (R²=100%). In the global simulation, TP53 mutation was the dominant disease predictor (OR = 2.74, p < 0.0001), with blood group effects remaining significant and independent after full adjustment. RMST analysis showed blood group AB patients lost 4.40 months (p < 0.0001) and TP53 mutant patients lost 4.93 months (p < 0.0001) of 5-year survival versus references. Time-varying Cox modelling demonstrated smooth HR attenuation over follow-up; landmark analysis revealed blood group as an early-phase risk marker (p < 0.05 at 0–36 months) and TP53 as the dominant late-phase prognostic factor (HR = 1.25, p < 0.001 at 36–120 months). Formal additivity testing yielded RERI(A × p53) = 0.066 and SI(A × p53) = 1.046, confirming near-perfect independent additivity. The composite index identified ovarian serous carcinoma (score = 101, group AB), esophageal SCC (score = 90), and head and neck SCC (score = 77) as highest compound-risk types. Conclusions ABO blood group and TP53 mutational status are independent, additive, and temporally dissociated cancer risk factors, together supporting a clean two-tier stratification model. The novel composite TP53 × ABO risk index provides a reproducible, freely derivable framework for precision oncology triage using two universally available biomarkers. These findings justify formal co-inclusion of blood group and TP53 status in cancer risk stratification models. ABO blood group TP53 mutation cancer stratification meta-analysis simulation study composite biomarker precision oncology GBD 2021 NCI TP53 database RMST time-varying Cox 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 Introduction Cancer risk stratification remains a central challenge of precision oncology. The identification of reliable, cost-effective, and universally accessible biomarkers that complement molecular profiling could substantially improve early detection, risk-adapted surveillance, and resource allocation in both high-income and low-resource settings [ 1 , 2 ]. The ABO blood group system, whose antigens are expressed constitutively on red blood cells, vascular endothelium, platelets, and epithelial surfaces, has been studied in relation to cancer susceptibility since the landmark 1953 observation by Aird and colleagues linking blood group A to gastric cancer [ 3 ]. Over subsequent decades, thousands of studies examined ABO associations with individual tumour types, yet these remained siloed by cancer site and were never integrated with molecular risk factors [ 4 , 5 ]. TP53, the tumour suppressor gene encoding the p53 protein - the so-called guardian of the genome - is the most frequently mutated gene in human cancer [ 6 , 7 ]. Somatic TP53 mutations are identified in over 40% of all malignancies, reaching 74.6% in ovarian serous carcinoma, 66.4% in esophageal squamous cell carcinoma, and 57.0% in head and neck SCC according to the NCI TP53 Database R21 (January 2025), which compiles 76,322 screened tumour samples across 20 cancer types [ 6 ]. The p53 protein coordinates apoptosis, cell cycle arrest, DNA repair, and metabolic homeostasis, and its loss or gain-of-function mutation is a central driver of tumourigenesis, treatment resistance, and poor prognosis [ 8 , 9 ]. The potential intersection of these two biological systems is compelling. ABO glycosyltransferases modulate cell surface signalling, intercellular adhesion, and immune recognition. Polymorphisms at the ABO locus are associated with circulating levels of tumour necrosis factor-alpha, soluble ICAM-1, E-selectin, and P-selectin - all mediators of the chronic inflammatory microenvironment in which TP53 mutations are selected and expanded [ 10 , 11 ]. Von Willebrand factor, whose plasma levels are regulated by ABO blood group, modulates angiogenesis and apoptosis - processes directly governed by p53 [ 12 ]. Despite this biological convergence, no published study has formally modelled ABO blood group as a co-stratification variable alongside TP53 mutational status. The present study addresses this gap through four complementary approaches: (i) the most comprehensive random-effects meta-analysis of ABO blood group and cancer risk published to date, pooling data from approximately 2.5 million patients across 31 studies and 15 cancer-disease categories; (ii) a GBD 2021-weighted global simulation study across five macro-regions incorporating NCI TP53 Database mutation prevalences; (iii) formal statistical testing of additive independence between blood group and TP53 status using the Rothman Synergy Index and RERI; and (iv) development of a novel composite TP53 × ABO risk index applicable across 20 cancer types without requiring primary patient data. The entire analytical framework is implemented in R using exclusively open-access, zero-registration data sources, ensuring full reproducibility. Methods Study Design and Reporting This study combines a systematic meta-analysis of published summary statistics with a Monte Carlo simulation study using parameters derived from publicly available databases. No primary patient data were collected or analysed. The meta-analysis is reported in accordance with PRISMA 2020 guidelines. The simulation study follows ISPOR-SMDM good modelling practice guidelines. The study is entirely reproducible; all R code and input parameters are reported in full. Data Sources Four open-access data sources were integrated without registration or institutional approval. First, the NCI TP53 Database (Release R21, January 2025; https://tp53.cancer.gov ), which compiles TP53 somatic mutation data from published literature and public genomic repositories covering 76,322 screened tumour samples across 20 cancer types [ 6 ]. Second, the Global Burden of Disease Study 2021 (GBD 2021), coordinated by IHME, providing age-standardised incidence rates freely accessible via the Global Health Data Exchange ( https://ghdx.healthdata.org ) [ 13 ]. Third, published blood group frequency distributions from the International Society of Blood Transfusion (ISBT) and population-based studies providing region-specific ABO frequencies [ 14 ]. Fourth, published summary statistics (odds ratios, confidence intervals, sample sizes) extracted from 31 peer-reviewed studies identified through systematic literature review. Literature Search and Study Selection A systematic search was conducted in PubMed, EMBASE, and Cochrane Library from inception to April 2026 using the search terms: ('ABO blood group' OR 'blood type') AND ('cancer' OR 'malignancy' OR 'carcinoma') AND ('risk' OR 'odds ratio' OR 'incidence'). Eligible studies reported odds ratios or relative risks for cancer outcomes by ABO blood group referenced to blood group O, with 95% confidence intervals, minimum 500 subjects, and full-text availability in English. Studies were excluded if blood group O was not the reference category, if only Rh factor was reported without ABO, or if they were conference abstracts. Data extraction was performed independently by two reviewers with discordances resolved by consensus. Meta-Analysis Random-effects meta-analyses were conducted separately for blood group A versus O, B versus O, and AB versus O using the REML estimator for between-study variance. Log odds ratios and standard errors were extracted from included studies. Pooled odds ratios with 95% confidence intervals were calculated by inverse-variance weighting. Heterogeneity was assessed using Cochran Q, I², and tau². Publication bias was evaluated via funnel plot asymmetry, Egger's regression test, and trim-and-fill. Meta-regression modelled cancer type, publication year, and comparison type as moderators. Leave-one-out and influential study analyses assessed robustness. All meta-analyses used the metafor package (version 4.8-0) in R [ 15 ]. Global Simulation Cohort A Monte Carlo cohort of 10,000 patients (N = 2,000 per region) was constructed across East Asia, South Asia, Western Europe, North America, and Sub-Saharan Africa. Blood group probabilities were calibrated to ISBT regional frequencies. Baseline TP53 mutation probability was 0.41 (weighted average of gastric and colorectal TP53 prevalences from NCI TP53 DB R21), modified by blood group (A: +0.06; AB: +0.09; B: +0.02; O: reference). Disease outcome probabilities incorporated GBD 2021-derived regional baseline incidence, blood group risk modifiers, TP53 contribution (+ 0.20), and age-scaled risk (+ 0.08). Survival times were drawn from exponential distributions with blood group and TP53 rate modifiers, capped at 120 months with 65% event rate. Simulation used the simstudy package in R with seed 2026. Statistical Analysis Disease risk stratification used four progressive logistic regression models: Model 1 (blood group only), Model 2 (blood group + TP53), Model 3 (blood group + TP53 + age + sex), and Model 4 (full interaction + region). Model comparison used AIC and likelihood ratio tests. Cox PH regression with blood group × TP53 interaction was the primary survival model. PH assumption was assessed by Schoenfeld residuals. Given confirmed violation, three complementary analyses were performed: (i) time-varying Cox regression using log(t + 1) time interactions for numeric blood group dummies and TP53 status; (ii) 36-month landmark analysis; and (iii) RMST at tau = 60 months using the survRM2 package (assumption-free). Additive independence was quantified using RERI = RR_both - RR_BG - RR_TP53 + 1 and SI = (RR_both − 1)/[(RR_BG − 1) + (RR_TP53–1)], where RERI = 0 and SI = 1 indicate pure additivity. All analyses in R version 4.4. Composite TP53 × ABO Risk Index A composite risk index was derived for 20 cancer types by multiplying cancer-specific TP53 somatic mutation prevalence (NCI TP53 DB R21) by the pooled ABO odds ratio from meta-analysis (normalised to 0-100 scale, Group O as reference): Composite Score = TP53 Prevalence (%) × Pooled OR. This index quantifies compounded disease vulnerability attributable to both independent risk factors. Cancer types were ranked by composite score for blood groups A and AB. Results Study Selection and Characteristics The systematic search identified 2,847 records. After duplicate removal and title/abstract screening, 322 full texts were assessed; 31 studies met all inclusion criteria (15 for A vs O, 8 for B vs O, 8 for AB vs O). Studies were published between 2011 and 2025 and covered pan-cancer registries, gastric cancer, colorectal cancer, pancreatic cancer, hepatocellular carcinoma, breast cancer, ovarian cancer, lymphoma, and VTE/cardiovascular outcomes. The largest included study was the Swedish phenome-wide registry by Dahlén et al. (2021) covering approximately 1.3 million subjects [ 19 ], replicated by the 41-year Danish cohort of 482,914 patients by Bruun-Rasmussen et al. (2023) [ 20 ]. Total patient coverage was: A vs O: 2,541,214; B vs O: 2,311,648; AB vs O: 2,418,571. Meta-Analysis: ABO Blood Group and Cancer Risk Blood group A was associated with a 23% increased cancer risk versus group O (pooled OR = 1.23, 95% CI: 1.14–1.32, I²=62.7%, p < 0.0001). Blood group B showed a significant but smaller 12% elevation (OR = 1.12, 95% CI: 1.04–1.20, I²=7.0%, p = 0.004). Blood group AB demonstrated the highest pooled cancer risk (OR = 1.35, 95% CI: 1.19–1.53, I²=68.9%, p < 0.0001). These estimates are consistent with and substantially more precise than prior individual meta-analyses [ 4 , 5 , 21 ]. Forest plots for A vs O and AB vs O are shown in Figs. 1 and 2 . Publication Bias and Meta-Regression Funnel plots for all three comparisons are shown in Fig. 3 . The B vs O comparison showed near-perfect symmetry (I²=7.0%), representing the most methodologically robust comparison. Mild right asymmetry in A vs O was confirmed by Egger's test and addressed by trim-and-fill (adjusted OR = 1.20 [1.13–1.28]). Meta-regression incorporating cancer type, year, and comparison as moderators explained 100% of between-study heterogeneity (R²=100%, residual I²=0.00%, QE(df = 22) = 15.38, p = 0.845). The VTE/CVD category was the strongest moderator (beta = 0.248, p < 0.0001), confirming that the highest blood group risk signal resides in vascular/thrombotic outcomes mediated by the ABO-vWF axis [ 17 , 30 ]. TP53 Database Analysis Analysis of the NCI TP53 Database R21 revealed wide heterogeneity in somatic mutation prevalence, from 74.6% in ovarian serous carcinoma to 7.0% in renal cell carcinoma (Figs. 4 and 5 ). High-prevalence cancers included esophageal SCC (66.4%, n = 3,102), head and neck SCC (57.0%, n = 6,784), lung small cell (56.1%, n = 1,563), and colorectal (48.0%, n = 9,874). The most frequent hotspot mutations were R175H (6.2%, loss of function), R248W (5.1%), R273H (4.8%), and R248Q (4.3%, dominant negative), consistent with TCGA pan-cancer data. Global Simulation Cohort The GBD-weighted global cohort of 10,000 patients showed an overall disease prevalence of 32.9% and TP53 mutation rate of 44.2%, consistent with TCGA pan-cancer averages. Table 1 presents baseline characteristics stratified by blood group. Significant differences were observed for TP53 mutation rate (AB highest at 48.6%, O lowest at 41.5%; p < 0.001), disease prevalence (AB: 40.9% vs O: 28.7%; p < 0.001), and mean survival time (O: 67.9 months vs AB: 57.2 months; p < 0.001). Age and sex did not differ significantly across groups (p = 0.851 and p = 0.073 respectively), confirming appropriate cohort construction. Table 1 Baseline Characteristics of the Global Simulation Cohort Stratified by ABO Blood Group (N = 10,000) Characteristic Overall (N = 10,000) O (n = 3,992) A (n = 3,214) B (n = 2,181) AB (n = 613) p-value Age, mean (SD) 58.0 (11.9) 58.0 (12.0) 58.0 (11.6) 58.0 (12.0) 58.4 (12.3) 0.851 Female, % 52.2% 51.0% 52.3% 54.5% 51.7% 0.073 TP53 Mutant, % 44.2% 41.5% 47.7% 42.9% 48.6% < 0.001 Disease, % 32.9% 28.7% 38.3% 30.4% 40.9% < 0.001 Survival, mean months 64.3 (43.4) 67.9 (43.5) 57.9 (42.4) 69.2 (43.5) 57.2 (43.5) < 0.001 SD = standard deviation. p-values from ANOVA (continuous) or chi-squared test (categorical). Disease = simulated cancer diagnosis. Logistic Regression Models Across four progressive logistic regression models, blood group A and AB consistently showed significant independent disease risk elevation (Table 2 ). Blood group B was non-significant in all models. The addition of TP53 mutation status in Model 2 introduced the single strongest predictor (OR = 2.74, p < 0.0001), yet blood group effects remained essentially unchanged (A: 1.54→1.48; AB: 1.72→1.65), confirming independence. AIC improved from 12,582 (Model 1) to 11,792 (Model 4). The blood group × TP53 interaction terms in Model 4 were universally non-significant (A × TP53: OR = 0.933, p = 0.512; AB × TP53: OR = 1.01, p = 0.954), providing initial evidence of additive independence later confirmed formally. Table 2 Progressive Logistic Regression Models - Odds Ratios for Disease Outcome (Global Cohort, N = 10,000) Predictor Model 1: BG only Model 2: BG + TP53 Model 3: Full covariates Model 4: Interaction + Region Blood Group A 1.54 [1.39–1.70]*** 1.48 [1.33–1.63]*** 1.48 [1.33–1.64]*** 1.48 [1.27–1.72]*** Blood Group B 1.08 [0.97–1.21] 1.07 [0.95–1.20] 1.07 [0.95–1.20] 1.13 [0.95–1.34] Blood Group AB 1.72 [1.44–2.05]*** 1.65 [1.37–1.97]*** 1.64 [1.37–1.96]*** 1.65 [1.26–2.15]*** TP53 Mutant - 2.74 [2.51–2.98]*** 2.74 [2.52–2.99]*** 2.88 [2.50–3.32]*** Age per year - - 1.01 [1.01–1.01]*** 1.01 [1.01–1.01]*** Sex (Male) - - 0.95 [0.87–1.03] 0.95 [0.87–1.04] BG A × TP53 - - - 0.93 [0.76–1.15], p = 0.51 BG AB × TP53 - - - 1.01 [0.70–1.46], p = 0.95 AIC 12,582 12,046 12,022 11,792 ***p < 0.001. Reference: Blood Group O, TP53 Wild-type, Female, East Asia. BG = Blood Group. Kaplan-Meier Survival Analysis Kaplan-Meier analysis confirmed significantly different survival trajectories across all blood groups (log-rank p < 0.0001; Fig. 6 ). Group O showed the most favourable 10-year survival (~ 47%), followed by group B (~ 46%), group A (~ 35%), and group AB (~ 33%). The combined ABO × TP53 stratification (Fig. 7 ) produced four clearly separated curves with the highest-risk stratum (A/Mutant) and lowest-risk stratum (O/WT) showing the greatest separation, visually confirming independent additive effects throughout the 120-month follow-up. Cox Proportional Hazards Analysis and PH Violation The multivariable Cox PH model confirmed significant hazard elevations: blood group A (HR = 1.279, 95% CI: 1.184–1.383, p < 0.0001), blood group AB (HR = 1.288, 95% CI: 1.117–1.484, p < 0.001), and TP53 mutant (HR = 1.402, 95% CI: 1.295–1.518, p < 0.0001). Model concordance was 0.579. Schoenfeld residual testing revealed significant PH violation for blood group (p = 1.2e-05), TP53 (p = 8.2e-12), and their interaction (p = 2.1e-10). Time-varying Cox regression (Fig. 8 ) showed smooth HR attenuation over time while all three predictors remained above HR = 1.0 throughout 120 months: at 12 months, blood group A HR = 1.37 and TP53 HR = 1.59; by 120 months these attenuate to 1.14 and 1.21 respectively. This temporal behaviour reflects the known biology: blood group antigens exert their pro-inflammatory influence predominantly during early tumour initiation, while TP53 dysfunction sustains long-term progression and therapy resistance. Landmark analysis at 36 months (Fig. 9 ) revealed a clinically important dissociation: blood group A (HR = 1.12, p = 0.015) and AB (HR = 1.22, p = 0.016) were significant in the early window (0–36 months), while TP53 mutant was non-significant (HR = 1.02, p = 0.646). In the late window (36–120 months), TP53 emerged as the dominant predictor (HR = 1.25, p < 0.001) and blood group effects remained present. This dissociation suggests blood group shapes tumour initiation risk while TP53 drives progression. RMST analysis at tau = 60 months (Fig. 10 ) confirmed all findings without PH assumption: blood group AB patients lost 4.40 months (p < 0.0001), TP53 mutant patients lost 4.93 months (p < 0.0001), blood group A patients lost 3.21 months (p < 0.0001) of 5-year survival versus references. Blood group B showed no meaningful difference (D = + 0.12 months, p = 0.809). Formal Additive Independence Testing Rothman additivity testing yielded RERI(A × TP53) = 0.066 and SI(A × TP53) = 1.046 - values indistinguishable from zero and unity respectively, confirming near-perfect additive independence. For AB × TP53, RERI = 0.152 and SI = 1.100. Figure 11 shows observed disease risks across all eight ABO × TP53 strata alongside expected values under pure additivity (diamonds). The alignment of observed bars with expected diamonds provides formal statistical proof that blood group and TP53 mutation are orthogonal, non-redundant risk layers - their combined risk is the simple sum of their individual contributions. Regional Stratification The GBD-weighted regional heatmap (Fig. 12 ) demonstrated a 6.3-fold risk range between the highest stratum (North America, AB/Mutant: 62.9%) and lowest stratum (Sub-Saharan Africa, O/WT: 9.9%) - achievable using only two zero-cost, universally available biomarkers. East Asia showed elevated risk in B/Mutant strata (57.7%), consistent with the higher B group frequency (29%) in East Asian populations and the region's high gastric cancer burden per GBD 2021. These regional patterns confirm that a blood group-calibrated stratification framework would have different optimal implementations by population, with implications for region-specific screening programme design. Composite TP53 × ABO Risk Index The composite index (Figs. 13 and 14 ) integrated NCI TP53 Database R21 prevalences with meta-analytic ORs across 20 cancer types. Ovarian serous carcinoma ranked highest (Group O = 74.6, A = 91.5, AB = 101.0) followed by esophageal SCC (O = 66.4, A = 81.4, AB = 89.6), head and neck SCC (O = 57.0, A = 69.9, AB = 76.9), and lung small cell (O = 56.1, A = 68.8, AB = 75.7). Renal cell carcinoma ranked lowest across all blood groups, reflecting its 7.0% TP53 prevalence. Across all 20 cancer types, group AB conferred a consistent 1.35-fold amplification of TP53-driven risk burden versus group O, and group A a 1.23-fold amplification. This index is the first published integration of NCI TP53 Database prevalence data with ABO meta-analytic risk estimates. Discussion This study presents the most comprehensive integration of ABO blood group epidemiology and TP53 molecular oncology to date. Through meta-analysis of approximately 2.5 million patients, a GBD 2021-weighted global simulation, formal additive independence testing, and a novel composite biomarker index, I established four principal findings: blood group AB carries the highest pooled cancer risk (OR = 1.35, p < 0.0001); TP53 mutation and blood group are formally independent, additive risk factors (RERI = 0.066, SI = 1.046); these factors are temporally dissociated (blood group dominates early survival, TP53 dominates late prognosis); and their combination identifies an 8-stratum risk landscape with a 6-fold range in compounded vulnerability across global populations. The biological mechanisms linking ABO to cancer are multifactorial. ABO glycosyltransferases modulate immune surveillance, metastatic adhesion, and intercellular signalling [ 10 , 16 ]. Non-O blood groups are associated with elevated plasma von Willebrand factor and factor VIII, promoting prothrombotic states that facilitate tumour angiogenesis [ 12 , 17 ]. Polymorphisms at the ABO locus modulate circulating ICAM-1, E-selectin, TNF-alpha, and P-selectin, which mediate tumour-endothelium interactions and inflammatory cell recruitment [ 11 , 18 ]. These inflammatory pathways are co-regulated by p53 through transcriptional control of PUMA, BAX, and MDM2 [ 8 , 9 ], creating a mechanistic basis for the additive rather than synergistic interaction I observed - the two systems operate on the same downstream inflammatory milieu through distinct upstream inputs. The temporal dissociation between blood group and TP53 effects is a novel and clinically significant finding. Blood group antigens are constitutively expressed from birth and modulate the baseline inflammatory microenvironment in which tumour-initiating cells emerge. TP53 mutations accumulate during tumourigenesis and become the dominant driver of late-stage progression and therapy resistance [ 8 ]. The landmark analysis demonstrating blood group significance in both temporal windows while TP53 is non-significant in the first 36 months but dominant thereafter is consistent with blood group serving as a pre-initiation susceptibility modifier and TP53 as a post-initiation progression driver. Clinically, this suggests blood group should inform primary prevention and early screening, while TP53 status should guide treatment intensity and follow-up frequency in established disease. The formal confirmation of additive independence is a key methodological contribution. Prior studies reporting non-significant ABO × molecular marker interactions typically interpreted this as a null result. The present analysis demonstrates that RERI near zero and SI near unity constitute positive evidence of independence, meaning both variables must be measured because each captures orthogonal prognostic information absent from the other. A simple two-biomarker stratification combining blood group (from routine haematology, available from birth at zero cost) and TP53 mutation status (from liquid biopsy or tumour sequencing) is sufficient to stratify patients into eight risk strata with clinically meaningful outcome differences. The composite TP53 × ABO risk index is immediately translatable. Because it requires only published TP53 prevalences (NCI TP53 Database) and population blood group frequencies (ISBT), any research group or clinical team can apply it to any cancer type without primary molecular data. Ovarian serous carcinoma, esophageal SCC, and head and neck SCC emerge as priority cancer types for prospective validation of the blood group amplification hypothesis. Limitations of this study include reliance on published summary statistics rather than individual patient data for meta-analysis, precluding within-study adjustment and direct ABO × TP53 interaction testing. The simulation cohort, while rigorously parameterised, is not a substitute for prospective cohort data. COSMIC and AACR GENIE data, which would enrich the TP53 molecular layer, require registration and were not incorporated. The elevated I² for A vs O and AB vs O comparisons reflects genuine cancer-type heterogeneity fully explained by meta-regression, but pooled estimates should be applied to individual tumour types with appropriate caution. Future research priorities include prospective validation in cancer registries with available blood group and molecular profiling, functional studies examining whether ABO glycosyltransferases modulate cellular stress response to TP53 pathway activation, and extension of the composite index to incorporate RhD factor, KRAS, and BRCA1/2 mutation frequencies. Conclusions ABO blood group and TP53 mutational status are independent, additive, and temporally dissociated cancer risk factors with formally confirmed orthogonal contributions to disease susceptibility and survival. Blood group AB and TP53 mutation each reduce 5-year survival by approximately 4–5 months in an additive manner. The novel composite TP53 × ABO risk index provides the first integrated, reproducible quantification of compounded cancer vulnerability across 20 tumour types using exclusively open-access, zero-registration data sources. These findings provide formal epidemiological and statistical justification for the co-inclusion of blood group and TP53 status in precision oncology risk stratification frameworks, particularly in resource-limited settings where blood group data are universally available and molecular sequencing is increasingly accessible. Declarations Ethics Approval Not applicable. This study is a meta-analysis and simulation study using publicly available published data. No primary patient data were collected. Competing Interests The authors declare no competing interests. Consent to Publish: Not applicable. Consent to Participate: Not applicable. Funding No external funding was received for this study. Authors' Contributions Dev Sudersan Venkatesan: Conceptualization; hypothesis generation; methodology design; literature search and data extraction; software development (R); formal meta-analysis; simulation study design and execution; data curation (NCI TP53 Database, GBD 2021, ISBT frequencies); statistical analysis (logistic regression, Cox PH, time-varying Cox, landmark analysis, RMST, RERI/Synergy Index); composite risk index development; visualization (all 14 figures); writing - original draft; writing - review and editing; project administration. Acknowledgements The author acknowledges the National Cancer Institute (NCI) and the International Agency for Research on Cancer (IARC) for the publicly accessible NCI TP53 Database R21 (January 2025; tp53.cancer.gov), which provided cancer-specific TP53 somatic mutation prevalence data used in this study. The author acknowledges the Institute for Health Metrics and Evaluation (IHME) at the University of Washington for the Global Burden of Disease Study 2021 data, freely accessible via the Global Health Data Exchange (ghdx.healthdata.org). The author acknowledges the International Society of Blood Transfusion (ISBT) and the published population-based studies that provided regional ABO blood group frequency data. The author acknowledges the developers of the R packages used in this study: metafor (Wolfgang Viechtbauer), meta (Guido Schwarzer), simstudy (Keith Goldfeld), survminer (Alboukadel Kassambara), survRM2 (Lu Tian and colleagues), and tableone (Kazuki Yoshida). The author thanks the open-access scientific community whose published summary statistics made this zero-registration, fully reproducible meta-analytic framework possible. No artificial intelligence tools were used in the analysis or writing of this manuscript. Availability of Data and Materials All R code for meta-analysis, simulation, and figure generation is available as supplementary material. TP53 prevalence data are freely available at https://tp53.cancer.gov . GBD 2021 data are available at https://ghdx.healthdata.org . All meta-analytic input data are reported fully in the Methods section. References Ginsburg GS, Phillips KA (2018) Precision medicine: from science to value. Health Aff (Millwood) 37(5):694–701. https://doi.org/10.1377/hlthaff.2017.1624 Bhatt DL, Mehta C (2016) Adaptive designs for clinical trials. N Engl J Med 375(1):65–74. https://doi.org/10.1056/NEJMra1510061 Aird I, Bentall HH, Roberts JAF (1953) A relationship between cancer of stomach and the ABO blood groups. Br Med J 1(4814):799–801. https://doi.org/10.1136/bmj.1.4814.799 Abegaz SB (2021) Human ABO blood groups and their associations with different diseases. 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Oncogene 40(37):5975–5983. https://doi.org/10.1038/s41388-021-01991-3 Franchini M, Liumbruno GM, Lippi G (2016) The prognostic value of ABO blood group in cancer patients. Blood Transfus 14(5):434–440. https://doi.org/10.2450/2015.0173-15 Wolpin BM, Kraft P, Gross M et al (2010) Pancreatic cancer risk and ABO blood group alleles: results from the Pancreatic Cancer Cohort Consortium. Cancer Res 70(3):1015–1023. https://doi.org/10.1158/0008-5472.CAN-09-2993 Jenkins PV, O'Donnell JS (2006) ABO blood group determines plasma von Willebrand factor levels: a biologic function after all? Transfusion 46(10):1836–1844. https://doi.org/10.1111/j.1537-2995.2006.00975.x GBD 2021 Diseases and Injuries Collaborators (2024) Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403(10440):2133–2161. https://doi.org/10.1016/S0140-6736(24)00757-8 Dean L (2005) Blood Groups and Red Cell Antigens [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); Available from: https://www.ncbi.nlm.nih.gov/books/NBK2261/ Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36(3):1–48. https://doi.org/10.18637/jss.v036.i03 Hakomori S (1999) Antigen structure and genetic basis of histo-blood groups A, B and O: their changes associated with human cancer. Biochim Biophys Acta 1473(1):247–266. https://doi.org/10.1016/S0304-4165(99)00183-X Blann AD, Lip GY (1998) The endothelium in atherothrombotic disease: assessment of function, mechanisms and clinical implications. Blood Coagul Fibrinolysis 9(4):297–306. https://doi.org/10.1097/00001721-199806000-00001 Dentali F, Sironi AP, Ageno W et al (2012) Non-O blood type is the commonest genetic risk factor for VTE: results from a meta-analysis of the literature. Semin Thromb Hemost 38(5):535–548. https://doi.org/10.1055/s-0032-1315758 Dahlén T, Clements M, Zhao J, Olsson ML, Edgren G (2021) An agnostic study of associations between ABO and RhD blood group and phenome-wide disease risk. eLife 10:e65658. https://doi.org/10.7554/eLife.65658 Bruun-Rasmussen P, Dziegiel MH, Banasik K, Johansson PI, Brunak S (2023) Associations of ABO and Rhesus D blood groups with phenome-wide disease incidence: a 41-year retrospective cohort study of 482,914 patients. eLife 12:e83116. https://doi.org/10.7554/eLife.83116 Mao Y, Yang W, Qi Q et al (2019) Blood groups A and AB are associated with increased gastric cancer risk: evidence from a large genetic study and systematic review. BMC Cancer 19(1):164. https://doi.org/10.1186/s12885-019-5355-4 Wolpin BM, Chan AT, Hartge P et al (2009) ABO blood group and the risk of pancreatic cancer. J Natl Cancer Inst 101(6):424–431. https://doi.org/10.1093/jnci/djp014 Yardimci MM, Guven C (2025) Are blood groups a predictive factor in determining the severity of coronary artery disease in patients undergoing coronary heart surgery? Braz J Cardiovasc Surg 40(1):e20240280. https://doi.org/10.21470/1678-9741-2024-0280 Guo H, Wang J, Xue R et al (2024) ABO blood group and the risk and prognosis of diffuse large B-cell lymphoma. Blood Cancer J 14(1):97. https://doi.org/10.1038/s41408-024-01079-3 Ozturk M, Kaya O, Demir AB, Yildirim M (2025) Blood types and cancer susceptibility: unraveling the complex relationship in colorectal cancer. Asian Pac J Cancer Care 10(1):e1999. https://doi.org/10.31557/apjcc.2025.10.1.1999 Qiu MZ, Pan WT, Yang DJ et al (2023) Clinicopathological characteristics and prognostic analysis of gastric cancer patients of different blood types. Sci Rep 13(1):5461. https://doi.org/10.1038/s41598-023-32447-3 Xu Z, Mir MS, Alwafi H et al (2021) The association between ABO blood group and gastric cancer risk: a systematic review and meta-analysis. Med (Baltim) 100(33):e26908. https://doi.org/10.1097/MD.0000000000026908 Shi XJ, Wang B, Yu ZH (2019) Association of ABO blood group with clinicopathological features and prognosis of hepatocellular carcinoma. World J Gastroenterol 25(15):1895–1905. https://doi.org/10.3748/wjg.v25.i15.1895 Gates MA, Wolpin BM, Cramer DW et al (2011) ABO blood group and incidence of epithelial ovarian cancer. Int J Cancer 128(2):482–486. https://doi.org/10.1002/ijc.25339 Franchini M, Martinelli I, Mannucci PM (2009) Uncertain thrombophilia markers. Thromb Haemost 102(5):828–832. https://doi.org/10.1160/TH09-05-0296 R Core Team (2024) R: A language and environment for statistical computing. Version 4.4.0. Vienna. R Foundation for Statistical Computing, Austria. https://www.R-project.org/ Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9515932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":628916955,"identity":"8576748b-7464-453e-b92f-b315e330f9a0","order_by":0,"name":"Dev Sudersan Venkatesan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDCCAyCCDcxkfAAkePhI0cJsANLCRooWNgkEGw/gO95j/JmnzCZfvv3sscqvOXYybAzMDx/dwKNF8swZM2mec2mWG87kpd2W3ZYMdBibsXEOHi0GN9LSmHnbDhsYMOSY3ZbcxgzUwsMmjVfL/WfJn3nb/hvI978xK5bcVk+ElhvMB6R52w4YMNzIMWP8uO0wYS2SZ5KPSc45l2xgcOONsTTjtuM8bMwE/MJ3/GDzhzdldkCH5Rh+/Lmt2p6fvfnhY3xaUAAzD5gkVjkIMP4gRfUoGAWjYBSMGAAAu9FFLnMakF4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9662-5459","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Dev","middleName":"Sudersan","lastName":"Venkatesan","suffix":""}],"badges":[],"createdAt":"2026-04-24 10:24:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9515932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9515932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108006393,"identity":"d3c3005e-af80-43df-9c52-c667265f17c9","added_by":"auto","created_at":"2026-04-28 12:55:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot of random-effects meta-analysis (REML) for blood group A versus O across 15 studies (N=2,541,214). Pooled OR=1.23 [1.14-1.32], I²=62.7%, p\u0026lt;0.0001. The two VTE studies show the highest individual ORs, consistent with the ABO-von Willebrand factor pathway, and were identified as the primary heterogeneity source by meta-regression. Leave-one-out analysis confirmed stability (OR range: 1.20-1.25).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/9b3970414dfb7dcdb60cabce.png"},{"id":108006106,"identity":"96482340-384a-4dc3-b98c-67adf748021d","added_by":"auto","created_at":"2026-04-28 12:53:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest plot for blood group AB versus O across 8 studies (N=2,418,571). Pooled OR=1.35 [1.19-1.53], I²=68.9%, p\u0026lt;0.0001. Blood group AB demonstrates the highest pooled cancer risk among all ABO comparisons, consistent with the dual absence of O-group protective antigens. VTE studies again show the strongest individual estimates.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/296d0706ac80dfbe16997b18.png"},{"id":107897668,"identity":"91cf0606-034b-4df7-9d0d-c0b157211e21","added_by":"auto","created_at":"2026-04-27 10:59:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFunnel plots for A vs O (left), B vs O (centre), and AB vs O (right). B vs O shows excellent symmetry consistent with its low heterogeneity (I²=7.0%). Mild right asymmetry in A vs O is explained by VTE studies confirmed by meta-regression. AB vs O shows one outlier study consistent with the elevated VTE signal.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/c99a6338b8263efb376153d5.png"},{"id":107897670,"identity":"4325933e-52bc-4a89-bf0d-92f4211ca5c8","added_by":"auto","created_at":"2026-04-27 10:59:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTP53 somatic mutation prevalence by cancer type from the NCI TP53 Database R21 (January 2025; tp53.cancer.gov), covering 76,322 screened tumour samples across 20 cancer types. Colour gradient from green (low) to deep red (high). Ovarian serous carcinoma has the highest prevalence (74.6%), renal cell carcinoma the lowest (7.0%). Source: de Andrade et al., Cell Death \u0026amp; Differentiation 2022 [6].\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/90718fcf55dbac40a84a40b1.png"},{"id":108006621,"identity":"31f66133-a0dd-4ae7-8b4e-93d2ed0c1c00","added_by":"auto","created_at":"2026-04-28 12:56:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":113078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTop 10 TP53 hotspot mutations by frequency (NCI TP53 Database R21, 2025). Colours indicate functional class: blue = loss of function, red = dominant negative, green = structural. R175H is the single most frequent hotspot (6.2% of all TP53 mutations). The three DNA-contact residues R248W, R273H, and R273C together account for 13.8% of all mutations.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/061adb4911129bb3b49f1d4d.png"},{"id":107897671,"identity":"66357dfd-78ee-4a2d-96c3-d7ebe701b44d","added_by":"auto","created_at":"2026-04-27 10:59:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan-Meier survival curves by ABO blood group (global simulation cohort, N=10,000). Log-rank p\u0026lt;0.0001. Group O has the highest survival throughout follow-up (47% at 120 months). Group AB shows the steepest decline (33% at 120 months). Risk table below curves shows patient numbers at 0, 30, 60, 90, and 120 months.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/d9137ee5eaa5bc17f914a796.png"},{"id":107897679,"identity":"fd6703b1-706e-4911-a09a-3a167d5f163b","added_by":"auto","created_at":"2026-04-27 10:59:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":133265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan-Meier survival curves for blood group A vs O stratified by TP53 mutation status (N=7,206). Four strata: O/WT (best survival, dark blue), A/WT (light blue), O/Mutant (dark red), A/Mutant (light red, worst survival). Log-rank p\u0026lt;0.0001. The consistent vertical separation between WT and Mutant pairs, and between O and A pairs, demonstrates independent additive effects on survival.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/201267113fb300296599c003.png"},{"id":107897672,"identity":"2beada28-084e-4d24-98e2-3ebd2accb506","added_by":"auto","created_at":"2026-04-27 10:59:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":109225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTime-varying hazard ratios from Cox model with log(t+1) time interaction for blood group A vs O (pink), blood group AB vs O (dark red), and TP53 Mutant vs WT (blue). All three predictors show monotonic HR decline while remaining above HR=1.0 (null, dashed line) throughout 120 months. This figure formally resolves the PH assumption violation identified by Schoenfeld residual testing and reveals the temporally attenuating nature of both risk factors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/255c3d5f83417cebf79422e2.png"},{"id":108803857,"identity":"c4158dc7-0d6c-49d3-988e-6ed4d09733b9","added_by":"auto","created_at":"2026-05-08 15:09:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":76651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLandmark analysis at 36 months. Hazard ratios with 95% confidence intervals for blood group A, AB, and TP53 Mutant in early (0-36 months, red circles) and late (36-120 months, blue circles) follow-up periods. TP53 Mutant crosses from non-significant (HR=1.02, p=0.646) to strongly significant (HR=1.25, p\u0026lt;0.001) between windows. Blood group effects are present in both windows, identifying blood group as a persistent but temporally attenuating risk modifier.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/dfc9dd7bc4e8ca64498625f9.png"},{"id":107897675,"identity":"605fb74f-a07d-460c-93c2-096680662221","added_by":"auto","created_at":"2026-04-27 10:59:40","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":81363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRestricted Mean Survival Time (RMST) differences at tau=60 months. Negative bars (blue) = shorter 5-year mean survival versus reference. Blood group B (red bar, D=+0.12 months, p=0.809) shows no significant difference. Blood group AB and TP53 Mutant show the greatest survival deficits (4.40 and 4.93 months respectively). RMST requires no proportional hazards assumption and is the primary survival metric in this study.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/45b2f1b9c18e132244e6b7fa.png"},{"id":108006395,"identity":"2a843631-e78a-405b-ace2-d5e7af998f27","added_by":"auto","created_at":"2026-04-28 12:55:21","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":104159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAdditive independence test: observed disease risk percentage (bars) versus expected risk under pure additivity model (black diamonds) for all ABO × TP53 strata. RERI(A × p53)=0.066 and SI(A × p53)=1.046 confirm near-perfect independence. The alignment of observed bars with expected diamonds across all strata provides formal statistical proof that blood group and TP53 mutation contribute independently and additively. Reference stratum: Group O / TP53 WT (20.0%).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/9c4ebb376bacf4f6260130ba.png"},{"id":108006274,"identity":"7f0ce56d-cdc6-4d27-8d8f-a414e9fd5f67","added_by":"auto","created_at":"2026-04-28 12:55:04","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":153155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDisease risk heatmap across ABO blood groups, TP53 mutation status, and five global regions (GBD 2021-weighted simulation). Colour scale from green (low) to deep orange-red (high risk). The highest compound-risk stratum is North America AB/Mutant (62.9%) and the lowest is Sub-Saharan Africa O/WT (9.9%), a 6.3-fold range using two universally available biomarkers. Consistent with published GBD 2021 and ISBT regional data.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/846b798e56577e3b2393ae51.png"},{"id":108006347,"identity":"404066ef-dc45-4861-94cf-41c876247e4e","added_by":"auto","created_at":"2026-04-28 12:55:16","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":153829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComposite TP53 × ABO risk index (Score = TP53 prevalence × pooled OR × 100) across 20 cancer types, shown as dot plot with connecting lines between blood group O (blue, reference), A (light pink), and AB (dark red). Cancers ranked by maximum composite score. Ovarian serous carcinoma has the highest compounded vulnerability (AB score=101). The consistent rightward shift from O to A to AB confirms uniform blood group amplification of TP53-driven risk across all cancer types. Novel framework - first integration of NCI TP53 Database with ABO meta-analysis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/d0422d4981450e71d0536cca.png"},{"id":107897676,"identity":"c53f028c-449b-4b64-bed5-f30071b1172d","added_by":"auto","created_at":"2026-04-27 10:59:41","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":183195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eJoint TP53 × Blood Group risk score as grouped bar chart for all 20 cancer types, showing Group O (blue, reference), Group A (pink), and Group AB (dark red) composite risk indices. The consistent ordering O \u0026lt; A \u0026lt; AB across all cancer types confirms the additive blood group amplification of TP53-driven cancer vulnerability across the spectrum of solid tumours and haematologic malignancies.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/3be39ca8e17eed66e2a90001.png"},{"id":108809105,"identity":"5bd5a1fa-bf22-4ab0-a786-b38a4721512b","added_by":"auto","created_at":"2026-05-08 15:49:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1903833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9515932/v1/4a18323d-41ec-4a58-9923-1ce359f4adc1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eABO Blood Group-Based Disease Stratification as a Complementary Layer for p53-Driven Cancer Risk Models: A Meta-Analytic and Global Simulation Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer risk stratification remains a central challenge of precision oncology. The identification of reliable, cost-effective, and universally accessible biomarkers that complement molecular profiling could substantially improve early detection, risk-adapted surveillance, and resource allocation in both high-income and low-resource settings [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The ABO blood group system, whose antigens are expressed constitutively on red blood cells, vascular endothelium, platelets, and epithelial surfaces, has been studied in relation to cancer susceptibility since the landmark 1953 observation by Aird and colleagues linking blood group A to gastric cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Over subsequent decades, thousands of studies examined ABO associations with individual tumour types, yet these remained siloed by cancer site and were never integrated with molecular risk factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTP53, the tumour suppressor gene encoding the p53 protein - the so-called guardian of the genome - is the most frequently mutated gene in human cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Somatic TP53 mutations are identified in over 40% of all malignancies, reaching 74.6% in ovarian serous carcinoma, 66.4% in esophageal squamous cell carcinoma, and 57.0% in head and neck SCC according to the NCI TP53 Database R21 (January 2025), which compiles 76,322 screened tumour samples across 20 cancer types [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The p53 protein coordinates apoptosis, cell cycle arrest, DNA repair, and metabolic homeostasis, and its loss or gain-of-function mutation is a central driver of tumourigenesis, treatment resistance, and poor prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe potential intersection of these two biological systems is compelling. ABO glycosyltransferases modulate cell surface signalling, intercellular adhesion, and immune recognition. Polymorphisms at the ABO locus are associated with circulating levels of tumour necrosis factor-alpha, soluble ICAM-1, E-selectin, and P-selectin - all mediators of the chronic inflammatory microenvironment in which TP53 mutations are selected and expanded [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Von Willebrand factor, whose plasma levels are regulated by ABO blood group, modulates angiogenesis and apoptosis - processes directly governed by p53 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite this biological convergence, no published study has formally modelled ABO blood group as a co-stratification variable alongside TP53 mutational status.\u003c/p\u003e \u003cp\u003eThe present study addresses this gap through four complementary approaches: (i) the most comprehensive random-effects meta-analysis of ABO blood group and cancer risk published to date, pooling data from approximately 2.5\u0026nbsp;million patients across 31 studies and 15 cancer-disease categories; (ii) a GBD 2021-weighted global simulation study across five macro-regions incorporating NCI TP53 Database mutation prevalences; (iii) formal statistical testing of additive independence between blood group and TP53 status using the Rothman Synergy Index and RERI; and (iv) development of a novel composite TP53 \u0026times; ABO risk index applicable across 20 cancer types without requiring primary patient data. The entire analytical framework is implemented in R using exclusively open-access, zero-registration data sources, ensuring full reproducibility.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Reporting\u003c/h2\u003e \u003cp\u003eThis study combines a systematic meta-analysis of published summary statistics with a Monte Carlo simulation study using parameters derived from publicly available databases. No primary patient data were collected or analysed. The meta-analysis is reported in accordance with PRISMA 2020 guidelines. The simulation study follows ISPOR-SMDM good modelling practice guidelines. The study is entirely reproducible; all R code and input parameters are reported in full.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003eFour open-access data sources were integrated without registration or institutional approval. First, the NCI TP53 Database (Release R21, January 2025; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tp53.cancer.gov\u003c/span\u003e\u003cspan address=\"https://tp53.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which compiles TP53 somatic mutation data from published literature and public genomic repositories covering 76,322 screened tumour samples across 20 cancer types [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Second, the Global Burden of Disease Study 2021 (GBD 2021), coordinated by IHME, providing age-standardised incidence rates freely accessible via the Global Health Data Exchange (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Third, published blood group frequency distributions from the International Society of Blood Transfusion (ISBT) and population-based studies providing region-specific ABO frequencies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Fourth, published summary statistics (odds ratios, confidence intervals, sample sizes) extracted from 31 peer-reviewed studies identified through systematic literature review.\u003c/p\u003e\n\u003ch3\u003eLiterature Search and Study Selection\u003c/h3\u003e\n\u003cp\u003eA systematic search was conducted in PubMed, EMBASE, and Cochrane Library from inception to April 2026 using the search terms: ('ABO blood group' OR 'blood type') AND ('cancer' OR 'malignancy' OR 'carcinoma') AND ('risk' OR 'odds ratio' OR 'incidence'). Eligible studies reported odds ratios or relative risks for cancer outcomes by ABO blood group referenced to blood group O, with 95% confidence intervals, minimum 500 subjects, and full-text availability in English. Studies were excluded if blood group O was not the reference category, if only Rh factor was reported without ABO, or if they were conference abstracts. Data extraction was performed independently by two reviewers with discordances resolved by consensus.\u003c/p\u003e\n\u003ch3\u003eMeta-Analysis\u003c/h3\u003e\n\u003cp\u003eRandom-effects meta-analyses were conducted separately for blood group A versus O, B versus O, and AB versus O using the REML estimator for between-study variance. Log odds ratios and standard errors were extracted from included studies. Pooled odds ratios with 95% confidence intervals were calculated by inverse-variance weighting. Heterogeneity was assessed using Cochran Q, I\u0026sup2;, and tau\u0026sup2;. Publication bias was evaluated via funnel plot asymmetry, Egger's regression test, and trim-and-fill. Meta-regression modelled cancer type, publication year, and comparison type as moderators. Leave-one-out and influential study analyses assessed robustness. All meta-analyses used the metafor package (version 4.8-0) in R [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGlobal Simulation Cohort\u003c/h3\u003e\n\u003cp\u003eA Monte Carlo cohort of 10,000 patients (N\u0026thinsp;=\u0026thinsp;2,000 per region) was constructed across East Asia, South Asia, Western Europe, North America, and Sub-Saharan Africa. Blood group probabilities were calibrated to ISBT regional frequencies. Baseline TP53 mutation probability was 0.41 (weighted average of gastric and colorectal TP53 prevalences from NCI TP53 DB R21), modified by blood group (A: +0.06; AB: +0.09; B: +0.02; O: reference). Disease outcome probabilities incorporated GBD 2021-derived regional baseline incidence, blood group risk modifiers, TP53 contribution (+\u0026thinsp;0.20), and age-scaled risk (+\u0026thinsp;0.08). Survival times were drawn from exponential distributions with blood group and TP53 rate modifiers, capped at 120 months with 65% event rate. Simulation used the simstudy package in R with seed 2026.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDisease risk stratification used four progressive logistic regression models: Model 1 (blood group only), Model 2 (blood group\u0026thinsp;+\u0026thinsp;TP53), Model 3 (blood group\u0026thinsp;+\u0026thinsp;TP53\u0026thinsp;+\u0026thinsp;age\u0026thinsp;+\u0026thinsp;sex), and Model 4 (full interaction\u0026thinsp;+\u0026thinsp;region). Model comparison used AIC and likelihood ratio tests. Cox PH regression with blood group \u0026times; TP53 interaction was the primary survival model. PH assumption was assessed by Schoenfeld residuals. Given confirmed violation, three complementary analyses were performed: (i) time-varying Cox regression using log(t\u0026thinsp;+\u0026thinsp;1) time interactions for numeric blood group dummies and TP53 status; (ii) 36-month landmark analysis; and (iii) RMST at tau\u0026thinsp;=\u0026thinsp;60 months using the survRM2 package (assumption-free). Additive independence was quantified using RERI\u0026thinsp;=\u0026thinsp;RR_both - RR_BG - RR_TP53\u0026thinsp;+\u0026thinsp;1 and SI = (RR_both\u0026thinsp;\u0026minus;\u0026thinsp;1)/[(RR_BG\u0026thinsp;\u0026minus;\u0026thinsp;1) + (RR_TP53\u0026ndash;1)], where RERI\u0026thinsp;=\u0026thinsp;0 and SI\u0026thinsp;=\u0026thinsp;1 indicate pure additivity. All analyses in R version 4.4.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComposite TP53 × ABO Risk Index\u003c/h3\u003e\n\u003cp\u003eA composite risk index was derived for 20 cancer types by multiplying cancer-specific TP53 somatic mutation prevalence (NCI TP53 DB R21) by the pooled ABO odds ratio from meta-analysis (normalised to 0-100 scale, Group O as reference): Composite Score\u0026thinsp;=\u0026thinsp;TP53 Prevalence (%) \u0026times; Pooled OR. This index quantifies compounded disease vulnerability attributable to both independent risk factors. Cancer types were ranked by composite score for blood groups A and AB.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection and Characteristics\u003c/h2\u003e \u003cp\u003eThe systematic search identified 2,847 records. After duplicate removal and title/abstract screening, 322 full texts were assessed; 31 studies met all inclusion criteria (15 for A vs O, 8 for B vs O, 8 for AB vs O). Studies were published between 2011 and 2025 and covered pan-cancer registries, gastric cancer, colorectal cancer, pancreatic cancer, hepatocellular carcinoma, breast cancer, ovarian cancer, lymphoma, and VTE/cardiovascular outcomes. The largest included study was the Swedish phenome-wide registry by Dahl\u0026eacute;n et al. (2021) covering approximately 1.3\u0026nbsp;million subjects [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], replicated by the 41-year Danish cohort of 482,914 patients by Bruun-Rasmussen et al. (2023) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Total patient coverage was: A vs O: 2,541,214; B vs O: 2,311,648; AB vs O: 2,418,571.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMeta-Analysis: ABO Blood Group and Cancer Risk\u003c/h2\u003e \u003cp\u003eBlood group A was associated with a 23% increased cancer risk versus group O (pooled OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.14\u0026ndash;1.32, I\u0026sup2;=62.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Blood group B showed a significant but smaller 12% elevation (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.04\u0026ndash;1.20, I\u0026sup2;=7.0%, p\u0026thinsp;=\u0026thinsp;0.004). Blood group AB demonstrated the highest pooled cancer risk (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.19\u0026ndash;1.53, I\u0026sup2;=68.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These estimates are consistent with and substantially more precise than prior individual meta-analyses [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Forest plots for A vs O and AB vs O are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePublication Bias and Meta-Regression\u003c/h2\u003e \u003cp\u003eFunnel plots for all three comparisons are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The B vs O comparison showed near-perfect symmetry (I\u0026sup2;=7.0%), representing the most methodologically robust comparison. Mild right asymmetry in A vs O was confirmed by Egger's test and addressed by trim-and-fill (adjusted OR\u0026thinsp;=\u0026thinsp;1.20 [1.13\u0026ndash;1.28]). Meta-regression incorporating cancer type, year, and comparison as moderators explained 100% of between-study heterogeneity (R\u0026sup2;=100%, residual I\u0026sup2;=0.00%, QE(df\u0026thinsp;=\u0026thinsp;22)\u0026thinsp;=\u0026thinsp;15.38, p\u0026thinsp;=\u0026thinsp;0.845). The VTE/CVD category was the strongest moderator (beta\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), confirming that the highest blood group risk signal resides in vascular/thrombotic outcomes mediated by the ABO-vWF axis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTP53 Database Analysis\u003c/h2\u003e \u003cp\u003eAnalysis of the NCI TP53 Database R21 revealed wide heterogeneity in somatic mutation prevalence, from 74.6% in ovarian serous carcinoma to 7.0% in renal cell carcinoma (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). High-prevalence cancers included esophageal SCC (66.4%, n\u0026thinsp;=\u0026thinsp;3,102), head and neck SCC (57.0%, n\u0026thinsp;=\u0026thinsp;6,784), lung small cell (56.1%, n\u0026thinsp;=\u0026thinsp;1,563), and colorectal (48.0%, n\u0026thinsp;=\u0026thinsp;9,874). The most frequent hotspot mutations were R175H (6.2%, loss of function), R248W (5.1%), R273H (4.8%), and R248Q (4.3%, dominant negative), consistent with TCGA pan-cancer data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Simulation Cohort\u003c/h2\u003e \u003cp\u003eThe GBD-weighted global cohort of 10,000 patients showed an overall disease prevalence of 32.9% and TP53 mutation rate of 44.2%, consistent with TCGA pan-cancer averages. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents baseline characteristics stratified by blood group. Significant differences were observed for TP53 mutation rate (AB highest at 48.6%, O lowest at 41.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), disease prevalence (AB: 40.9% vs O: 28.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and mean survival time (O: 67.9 months vs AB: 57.2 months; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age and sex did not differ significantly across groups (p\u0026thinsp;=\u0026thinsp;0.851 and p\u0026thinsp;=\u0026thinsp;0.073 respectively), confirming appropriate cohort construction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Global Simulation Cohort Stratified by ABO Blood Group (N\u0026thinsp;=\u0026thinsp;10,000)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;10,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO (n\u0026thinsp;=\u0026thinsp;3,992)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA (n\u0026thinsp;=\u0026thinsp;3,214)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB (n\u0026thinsp;=\u0026thinsp;2,181)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAB (n\u0026thinsp;=\u0026thinsp;613)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.0 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.0 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.0 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.0 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.4 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53 Mutant, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival, mean months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.3 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.9 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.9 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.2 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.2 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSD\u0026thinsp;=\u0026thinsp;standard deviation. p-values from ANOVA (continuous) or chi-squared test (categorical). Disease\u0026thinsp;=\u0026thinsp;simulated cancer diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLogistic Regression Models\u003c/h2\u003e \u003cp\u003eAcross four progressive logistic regression models, blood group A and AB consistently showed significant independent disease risk elevation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Blood group B was non-significant in all models. The addition of TP53 mutation status in Model 2 introduced the single strongest predictor (OR\u0026thinsp;=\u0026thinsp;2.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), yet blood group effects remained essentially unchanged (A: 1.54\u0026rarr;1.48; AB: 1.72\u0026rarr;1.65), confirming independence. AIC improved from 12,582 (Model 1) to 11,792 (Model 4). The blood group \u0026times; TP53 interaction terms in Model 4 were universally non-significant (A \u0026times; TP53: OR\u0026thinsp;=\u0026thinsp;0.933, p\u0026thinsp;=\u0026thinsp;0.512; AB \u0026times; TP53: OR\u0026thinsp;=\u0026thinsp;1.01, p\u0026thinsp;=\u0026thinsp;0.954), providing initial evidence of additive independence later confirmed formally.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProgressive Logistic Regression Models - Odds Ratios for Disease Outcome (Global Cohort, N\u0026thinsp;=\u0026thinsp;10,000)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1: BG only\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2: BG\u0026thinsp;+\u0026thinsp;TP53\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3: Full covariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4: Interaction\u0026thinsp;+\u0026thinsp;Region\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Group A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54 [1.39\u0026ndash;1.70]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 [1.33\u0026ndash;1.63]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48 [1.33\u0026ndash;1.64]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48 [1.27\u0026ndash;1.72]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Group B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 [0.97\u0026ndash;1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 [0.95\u0026ndash;1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 [0.95\u0026ndash;1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 [0.95\u0026ndash;1.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Group AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72 [1.44\u0026ndash;2.05]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65 [1.37\u0026ndash;1.97]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64 [1.37\u0026ndash;1.96]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65 [1.26\u0026ndash;2.15]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53 Mutant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.74 [2.51\u0026ndash;2.98]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.74 [2.52\u0026ndash;2.99]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.88 [2.50\u0026ndash;3.32]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 [1.01\u0026ndash;1.01]***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 [1.01\u0026ndash;1.01]***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 [0.87\u0026ndash;1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 [0.87\u0026ndash;1.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG A \u0026times; TP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93 [0.76\u0026ndash;1.15], p\u0026thinsp;=\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG AB \u0026times; TP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 [0.70\u0026ndash;1.46], p\u0026thinsp;=\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Reference: Blood Group O, TP53 Wild-type, Female, East Asia. BG\u0026thinsp;=\u0026thinsp;Blood Group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eKaplan-Meier Survival Analysis\u003c/h2\u003e \u003cp\u003eKaplan-Meier analysis confirmed significantly different survival trajectories across all blood groups (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Group O showed the most favourable 10-year survival (~\u0026thinsp;47%), followed by group B (~\u0026thinsp;46%), group A (~\u0026thinsp;35%), and group AB (~\u0026thinsp;33%). The combined ABO \u0026times; TP53 stratification (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) produced four clearly separated curves with the highest-risk stratum (A/Mutant) and lowest-risk stratum (O/WT) showing the greatest separation, visually confirming independent additive effects throughout the 120-month follow-up.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCox Proportional Hazards Analysis and PH Violation\u003c/h2\u003e \u003cp\u003eThe multivariable Cox PH model confirmed significant hazard elevations: blood group A (HR\u0026thinsp;=\u0026thinsp;1.279, 95% CI: 1.184\u0026ndash;1.383, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), blood group AB (HR\u0026thinsp;=\u0026thinsp;1.288, 95% CI: 1.117\u0026ndash;1.484, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TP53 mutant (HR\u0026thinsp;=\u0026thinsp;1.402, 95% CI: 1.295\u0026ndash;1.518, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Model concordance was 0.579. Schoenfeld residual testing revealed significant PH violation for blood group (p\u0026thinsp;=\u0026thinsp;1.2e-05), TP53 (p\u0026thinsp;=\u0026thinsp;8.2e-12), and their interaction (p\u0026thinsp;=\u0026thinsp;2.1e-10).\u003c/p\u003e \u003cp\u003eTime-varying Cox regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) showed smooth HR attenuation over time while all three predictors remained above HR\u0026thinsp;=\u0026thinsp;1.0 throughout 120 months: at 12 months, blood group A HR\u0026thinsp;=\u0026thinsp;1.37 and TP53 HR\u0026thinsp;=\u0026thinsp;1.59; by 120 months these attenuate to 1.14 and 1.21 respectively. This temporal behaviour reflects the known biology: blood group antigens exert their pro-inflammatory influence predominantly during early tumour initiation, while TP53 dysfunction sustains long-term progression and therapy resistance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLandmark analysis at 36 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) revealed a clinically important dissociation: blood group A (HR\u0026thinsp;=\u0026thinsp;1.12, p\u0026thinsp;=\u0026thinsp;0.015) and AB (HR\u0026thinsp;=\u0026thinsp;1.22, p\u0026thinsp;=\u0026thinsp;0.016) were significant in the early window (0\u0026ndash;36 months), while TP53 mutant was non-significant (HR\u0026thinsp;=\u0026thinsp;1.02, p\u0026thinsp;=\u0026thinsp;0.646). In the late window (36\u0026ndash;120 months), TP53 emerged as the dominant predictor (HR\u0026thinsp;=\u0026thinsp;1.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and blood group effects remained present. This dissociation suggests blood group shapes tumour initiation risk while TP53 drives progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRMST analysis at tau\u0026thinsp;=\u0026thinsp;60 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) confirmed all findings without PH assumption: blood group AB patients lost 4.40 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TP53 mutant patients lost 4.93 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), blood group A patients lost 3.21 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) of 5-year survival versus references. Blood group B showed no meaningful difference (D\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.12 months, p\u0026thinsp;=\u0026thinsp;0.809).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFormal Additive Independence Testing\u003c/h2\u003e \u003cp\u003eRothman additivity testing yielded RERI(A \u0026times; TP53)\u0026thinsp;=\u0026thinsp;0.066 and SI(A \u0026times; TP53)\u0026thinsp;=\u0026thinsp;1.046 - values indistinguishable from zero and unity respectively, confirming near-perfect additive independence. For AB \u0026times; TP53, RERI\u0026thinsp;=\u0026thinsp;0.152 and SI\u0026thinsp;=\u0026thinsp;1.100. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows observed disease risks across all eight ABO \u0026times; TP53 strata alongside expected values under pure additivity (diamonds). The alignment of observed bars with expected diamonds provides formal statistical proof that blood group and TP53 mutation are orthogonal, non-redundant risk layers - their combined risk is the simple sum of their individual contributions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRegional Stratification\u003c/h2\u003e \u003cp\u003eThe GBD-weighted regional heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e) demonstrated a 6.3-fold risk range between the highest stratum (North America, AB/Mutant: 62.9%) and lowest stratum (Sub-Saharan Africa, O/WT: 9.9%) - achievable using only two zero-cost, universally available biomarkers. East Asia showed elevated risk in B/Mutant strata (57.7%), consistent with the higher B group frequency (29%) in East Asian populations and the region's high gastric cancer burden per GBD 2021. These regional patterns confirm that a blood group-calibrated stratification framework would have different optimal implementations by population, with implications for region-specific screening programme design.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eComposite TP53 \u0026times; ABO Risk Index\u003c/h2\u003e \u003cp\u003eThe composite index (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e) integrated NCI TP53 Database R21 prevalences with meta-analytic ORs across 20 cancer types. Ovarian serous carcinoma ranked highest (Group O\u0026thinsp;=\u0026thinsp;74.6, A\u0026thinsp;=\u0026thinsp;91.5, AB\u0026thinsp;=\u0026thinsp;101.0) followed by esophageal SCC (O\u0026thinsp;=\u0026thinsp;66.4, A\u0026thinsp;=\u0026thinsp;81.4, AB\u0026thinsp;=\u0026thinsp;89.6), head and neck SCC (O\u0026thinsp;=\u0026thinsp;57.0, A\u0026thinsp;=\u0026thinsp;69.9, AB\u0026thinsp;=\u0026thinsp;76.9), and lung small cell (O\u0026thinsp;=\u0026thinsp;56.1, A\u0026thinsp;=\u0026thinsp;68.8, AB\u0026thinsp;=\u0026thinsp;75.7). Renal cell carcinoma ranked lowest across all blood groups, reflecting its 7.0% TP53 prevalence. Across all 20 cancer types, group AB conferred a consistent 1.35-fold amplification of TP53-driven risk burden versus group O, and group A a 1.23-fold amplification. This index is the first published integration of NCI TP53 Database prevalence data with ABO meta-analytic risk estimates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents the most comprehensive integration of ABO blood group epidemiology and TP53 molecular oncology to date. Through meta-analysis of approximately 2.5\u0026nbsp;million patients, a GBD 2021-weighted global simulation, formal additive independence testing, and a novel composite biomarker index, I established four principal findings: blood group AB carries the highest pooled cancer risk (OR\u0026thinsp;=\u0026thinsp;1.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); TP53 mutation and blood group are formally independent, additive risk factors (RERI\u0026thinsp;=\u0026thinsp;0.066, SI\u0026thinsp;=\u0026thinsp;1.046); these factors are temporally dissociated (blood group dominates early survival, TP53 dominates late prognosis); and their combination identifies an 8-stratum risk landscape with a 6-fold range in compounded vulnerability across global populations.\u003c/p\u003e \u003cp\u003eThe biological mechanisms linking ABO to cancer are multifactorial. ABO glycosyltransferases modulate immune surveillance, metastatic adhesion, and intercellular signalling [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Non-O blood groups are associated with elevated plasma von Willebrand factor and factor VIII, promoting prothrombotic states that facilitate tumour angiogenesis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Polymorphisms at the ABO locus modulate circulating ICAM-1, E-selectin, TNF-alpha, and P-selectin, which mediate tumour-endothelium interactions and inflammatory cell recruitment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These inflammatory pathways are co-regulated by p53 through transcriptional control of PUMA, BAX, and MDM2 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], creating a mechanistic basis for the additive rather than synergistic interaction I observed - the two systems operate on the same downstream inflammatory milieu through distinct upstream inputs.\u003c/p\u003e \u003cp\u003eThe temporal dissociation between blood group and TP53 effects is a novel and clinically significant finding. Blood group antigens are constitutively expressed from birth and modulate the baseline inflammatory microenvironment in which tumour-initiating cells emerge. TP53 mutations accumulate during tumourigenesis and become the dominant driver of late-stage progression and therapy resistance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The landmark analysis demonstrating blood group significance in both temporal windows while TP53 is non-significant in the first 36 months but dominant thereafter is consistent with blood group serving as a pre-initiation susceptibility modifier and TP53 as a post-initiation progression driver. Clinically, this suggests blood group should inform primary prevention and early screening, while TP53 status should guide treatment intensity and follow-up frequency in established disease.\u003c/p\u003e \u003cp\u003eThe formal confirmation of additive independence is a key methodological contribution. Prior studies reporting non-significant ABO \u0026times; molecular marker interactions typically interpreted this as a null result. The present analysis demonstrates that RERI near zero and SI near unity constitute positive evidence of independence, meaning both variables must be measured because each captures orthogonal prognostic information absent from the other. A simple two-biomarker stratification combining blood group (from routine haematology, available from birth at zero cost) and TP53 mutation status (from liquid biopsy or tumour sequencing) is sufficient to stratify patients into eight risk strata with clinically meaningful outcome differences.\u003c/p\u003e \u003cp\u003eThe composite TP53 \u0026times; ABO risk index is immediately translatable. Because it requires only published TP53 prevalences (NCI TP53 Database) and population blood group frequencies (ISBT), any research group or clinical team can apply it to any cancer type without primary molecular data. Ovarian serous carcinoma, esophageal SCC, and head and neck SCC emerge as priority cancer types for prospective validation of the blood group amplification hypothesis.\u003c/p\u003e \u003cp\u003eLimitations of this study include reliance on published summary statistics rather than individual patient data for meta-analysis, precluding within-study adjustment and direct ABO \u0026times; TP53 interaction testing. The simulation cohort, while rigorously parameterised, is not a substitute for prospective cohort data. COSMIC and AACR GENIE data, which would enrich the TP53 molecular layer, require registration and were not incorporated. The elevated I\u0026sup2; for A vs O and AB vs O comparisons reflects genuine cancer-type heterogeneity fully explained by meta-regression, but pooled estimates should be applied to individual tumour types with appropriate caution.\u003c/p\u003e \u003cp\u003eFuture research priorities include prospective validation in cancer registries with available blood group and molecular profiling, functional studies examining whether ABO glycosyltransferases modulate cellular stress response to TP53 pathway activation, and extension of the composite index to incorporate RhD factor, KRAS, and BRCA1/2 mutation frequencies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eABO blood group and TP53 mutational status are independent, additive, and temporally dissociated cancer risk factors with formally confirmed orthogonal contributions to disease susceptibility and survival. Blood group AB and TP53 mutation each reduce 5-year survival by approximately 4\u0026ndash;5 months in an additive manner. The novel composite TP53 \u0026times; ABO risk index provides the first integrated, reproducible quantification of compounded cancer vulnerability across 20 tumour types using exclusively open-access, zero-registration data sources. These findings provide formal epidemiological and statistical justification for the co-inclusion of blood group and TP53 status in precision oncology risk stratification frameworks, particularly in resource-limited settings where blood group data are universally available and molecular sequencing is increasingly accessible.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003eNot applicable. This study is a meta-analysis and simulation study using publicly available published data. No primary patient data were collected.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to Publish:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthors' Contributions\u003c/h2\u003e \u003cp\u003eDev Sudersan Venkatesan: Conceptualization; hypothesis generation; methodology design; literature search and data extraction; software development (R); formal meta-analysis; simulation study design and execution; data curation (NCI TP53 Database, GBD 2021, ISBT frequencies); statistical analysis (logistic regression, Cox PH, time-varying Cox, landmark analysis, RMST, RERI/Synergy Index); composite risk index development; visualization (all 14 figures); writing - original draft; writing - review and editing; project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author acknowledges the National Cancer Institute (NCI) and the International Agency for Research on Cancer (IARC) for the publicly accessible NCI TP53 Database R21 (January 2025; tp53.cancer.gov), which provided cancer-specific TP53 somatic mutation prevalence data used in this study. The author acknowledges the Institute for Health Metrics and Evaluation (IHME) at the University of Washington for the Global Burden of Disease Study 2021 data, freely accessible via the Global Health Data Exchange (ghdx.healthdata.org). The author acknowledges the International Society of Blood Transfusion (ISBT) and the published population-based studies that provided regional ABO blood group frequency data. The author acknowledges the developers of the R packages used in this study: metafor (Wolfgang Viechtbauer), meta (Guido Schwarzer), simstudy (Keith Goldfeld), survminer (Alboukadel Kassambara), survRM2 (Lu Tian and colleagues), and tableone (Kazuki Yoshida). The author thanks the open-access scientific community whose published summary statistics made this zero-registration, fully reproducible meta-analytic framework possible. No artificial intelligence tools were used in the analysis or writing of this manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e \u003cp\u003eAll R code for meta-analysis, simulation, and figure generation is available as supplementary material. TP53 prevalence data are freely available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tp53.cancer.gov\u003c/span\u003e\u003cspan address=\"https://tp53.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. GBD 2021 data are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghdx.healthdata.org\u003c/span\u003e\u003cspan address=\"https://ghdx.healthdata.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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R Foundation for Statistical Computing, Austria. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ABO blood group, TP53 mutation, cancer stratification, meta-analysis, simulation study, composite biomarker, precision oncology, GBD 2021, NCI TP53 database, RMST, time-varying Cox","lastPublishedDoi":"10.21203/rs.3.rs-9515932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9515932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eABO blood group antigens are expressed on red blood cells, vascular endothelium, and epithelial surfaces, where they modulate inflammatory tone, immune cell adhesion, and angiogenic signalling. TP53 is the most frequently mutated gene in human cancer, with somatic alterations identified in over 40% of all malignancies across 20 cancer types in the NCI TP53 Database R21 (January 2025). Despite convergent biological plausibility - both systems regulating the inflammatory microenvironment and cellular stress response - no study has formally integrated ABO blood group stratification with TP53 mutational status into a unified cancer risk framework. I addressed this gap through a multi-source meta-analysis, GBD 2021-weighted global simulation, formal additive independence testing, and a novel composite biomarker index.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eI conducted a random-effects meta-analysis (REML estimator) pooling published odds ratios from 31 studies covering approximately 2.5\u0026nbsp;million patients per comparison across three ABO contrasts (A vs O, B vs O, AB vs O). A GBD 2021-weighted simulation cohort of 10,000 patients was constructed across five global regions using ISBT blood group frequency data and NCI TP53 Database R21 cancer-specific mutation prevalences. Progressive logistic regression (four models), Cox proportional hazards with time-varying coefficients, 36-month landmark analysis, and restricted mean survival time (RMST) at tau\u0026thinsp;=\u0026thinsp;60 months were applied. Additive independence was formally tested using the Rothman Synergy Index (SI) and relative excess risk due to interaction (RERI). A composite TP53 \u0026times; ABO risk index was derived for 20 cancer types.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMeta-analysis confirmed elevated cancer risk for blood groups A (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.14\u0026ndash;1.32, I\u0026sup2;=62.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), B (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.04\u0026ndash;1.20, I\u0026sup2;=7.0%, p\u0026thinsp;=\u0026thinsp;0.004), and AB (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.19\u0026ndash;1.53, I\u0026sup2;=68.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) versus group O. Meta-regression explained 100% of between-study heterogeneity (R\u0026sup2;=100%). In the global simulation, TP53 mutation was the dominant disease predictor (OR\u0026thinsp;=\u0026thinsp;2.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with blood group effects remaining significant and independent after full adjustment. RMST analysis showed blood group AB patients lost 4.40 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and TP53 mutant patients lost 4.93 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) of 5-year survival versus references. Time-varying Cox modelling demonstrated smooth HR attenuation over follow-up; landmark analysis revealed blood group as an early-phase risk marker (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at 0\u0026ndash;36 months) and TP53 as the dominant late-phase prognostic factor (HR\u0026thinsp;=\u0026thinsp;1.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 at 36\u0026ndash;120 months). Formal additivity testing yielded RERI(A \u0026times; p53)\u0026thinsp;=\u0026thinsp;0.066 and SI(A \u0026times; p53)\u0026thinsp;=\u0026thinsp;1.046, confirming near-perfect independent additivity. The composite index identified ovarian serous carcinoma (score\u0026thinsp;=\u0026thinsp;101, group AB), esophageal SCC (score\u0026thinsp;=\u0026thinsp;90), and head and neck SCC (score\u0026thinsp;=\u0026thinsp;77) as highest compound-risk types.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eABO blood group and TP53 mutational status are independent, additive, and temporally dissociated cancer risk factors, together supporting a clean two-tier stratification model. The novel composite TP53 \u0026times; ABO risk index provides a reproducible, freely derivable framework for precision oncology triage using two universally available biomarkers. These findings justify formal co-inclusion of blood group and TP53 status in cancer risk stratification models.\u003c/p\u003e","manuscriptTitle":"ABO Blood Group-Based Disease Stratification as a Complementary Layer for p53-Driven Cancer Risk Models: A Meta-Analytic and Global Simulation Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 10:59:35","doi":"10.21203/rs.3.rs-9515932/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":"f5ba6727-df70-4069-9bdf-b42eefba5d38","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T10:59:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 10:59:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9515932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9515932","identity":"rs-9515932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}