HPG80 as a broad-spectrum cancer biomarker among newly diagnosed patients at Uganda Cancer Institute: An Exploratory Cross-sectional Study

HPG80 as a broad-spectrum cancer biomarker among newly diagnosed patients at Uganda Cancer Institute: An Exploratory Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HPG80 as a broad-spectrum cancer biomarker among newly diagnosed patients at Uganda Cancer Institute: An Exploratory Cross-sectional Study Naghib Bogere, Bridget Sharon Angucia, Geofrey Anguandia, Modern Akoragye, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7122941/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background In low- and middle-income countries (LMICs) such as Uganda, delayed cancer diagnosis due to limited access to diagnostic tools contributes to poor outcomes. Traditional tumour markers, such as CA15-3, CA125, CEA, and PSA, are widely used but have limited sensitivity and specificity across various cancer types. Circulating progastrin (hPG80) has emerged as a potential broad-spectrum biomarker detectable in the plasma of cancer patients. This study provides initial evidence on hPG80 positivity across different cancer types among newly diagnosed patients at the Uganda Cancer Institute (UCI) and compares it with conventional tumour markers. Methods We conducted a cross-sectional study among 210 newly diagnosed, treatment-naive adult cancer patients with histologically confirmed solid tumours at the Uganda Cancer Institute (UCI). Prior to treatment initiation, venous blood samples were collected and analysed for hPG80 using the DxPG80.lab ELISA kit. Concurrently, conventional tumour markers (e.g., CA15-3, CA125, CEA, PSA, CA19-9, AFP, LDH) were measured using the Roche COBAS 6000 analyser. hPG80 positivity was defined as plasma concentrations > 3.3 pM. We compared the proportion of patients testing positive for hPG80 versus traditional markers by cancer type. Spearman’s rank correlation was used to assess relationships between hPG80 and conventional markers. Results hPG80 was positive (> 3.3 pM) in 77.1% of all participants. The most frequent hPG80 positivity was seen in oesophageal (83%), cervical (79%), breast (72%), and prostate (77%) cancers, which are among the most diagnosed cancers in Uganda. In each of these cancers, hPG80 identified more patients than the corresponding conventional tumour markers (p < 0.001), except in prostate cancer, where it performed similarly to PSA (77% vs. 80%). Although high hPG80 positivity was also noted in pancreatic and colon cancers, the small number of cases in these groups limits interpretation. No significant correlations were observed between hPG80 and conventional tumour markers, suggesting that hPG80 may reflect different biological processes. Conclusion hPG80 was detectable in a high proportion of newly diagnosed cancer patients and showed higher positivity rates than conventional markers in several common cancers in Uganda. These findings support further evaluation of hPG80 as a complementary biomarker, with potential utility in cancer detection strategies in resource-limited settings. hPG80 Cancer Biomarkers Tumour Markers Uganda Cancer Institute Early Detection LMICs Background Cancer remains a major public health challenge, especially in low- and middle-income countries (LMICs) such as Uganda, where access to advanced diagnostic technologies and specialised care is often limited ( 1 – 3 ). This limited access contributes to delayed diagnosis, with most cancer patients in Uganda presenting at advanced stages, resulting in reduced treatment options and poorer patient outcomes ( 1 , 4 ). In such contexts, simple, broadly applicable diagnostic tools are urgently needed to support earlier identification and intervention. Blood-based tumour markers, including carcinoembryonic antigen (CEA), CA19-9, CA-125, and PSA, are commonly used due to their accessibility and low cost ( 5 , 6 ). However, these markers have limited sensitivity and specificity, especially for early detection, and their performance across different tumour types remains suboptimal ( 5 , 6 ). In LMICs, where diagnostic resources are already scarce, the limitations of these conventional markers can further delay timely diagnosis and management. This has created interest in novel biomarkers that could offer broader applicability and improved diagnostic performance, particularly in resource-constrained settings. Circulating progastrin (hPG80) has emerged as a promising candidate biomarker due to its association with tumorigenesis across multiple cancer types. Under normal physiological conditions, hPG80 is undetectable in peripheral blood due to rapid conversion to active gastrin ( 7 – 9 ). In cancer, activation of the β-catenin/Tcf4 pathway leads to overexpression of the GAST gene and detectable levels of hPG80 in plasma ( 9 ). Studies in high-income countries have reported elevated hPG80 levels in various malignancies, including lung, breast, hepatocellular carcinoma, and neuroendocrine tumours ( 7 , 8 , 10 – 13 ). These studies suggest that hPG80 may serve as a general cancer biomarker with potential applications in both diagnosis and prognosis ( 7 , 10 – 13 ), though most available evidence has come from well-resourced clinical settings. In LMICs like Uganda, where access to advanced diagnostics is limited, a single, broadly applicable biomarker could offer practical advantages. Unlike conventional tumour markers, which are typically restricted to specific cancer types, hPG80 may reflect more fundamental oncogenic processes that are similar across multiple malignancies. To date, no studies have explored hPG80 expression in sub-Saharan Africa or assessed its relevance in real-world oncology populations in LMICs. This represents an important evidence gap, particularly given the burden of late-stage presentation and limited diagnostic infrastructure in countries like Uganda. We therefore conducted a pilot study to assess hPG80 positivity among newly diagnosed, treatment-naive cancer patients at the Uganda Cancer Institute. The primary aim was to describe the proportion of patients with positive hPG80 levels across various solid tumour types. This exploratory work provides initial data on hPG80 expression in an African oncology population and lays the groundwork for future research on its clinical utility in resource-limited settings. Materials and Methods Study Design This cross-sectional study was conducted at the Uganda Cancer Institute (UCI), the national cancer referral center in Uganda. Participants were enrolled between May and July 2024, with data collected at a single time point prior to the initiation of any cancer treatment. The study aimed to provide a preliminary assessment of the diagnostic sensitivity of hPG80 in a diverse cohort of newly diagnosed cancer patients within a resource-limited setting. Study Population The study population consisted of newly diagnosed, treatment-naive cancer patients presenting at UCI during the study period. Common malignancies such as cervical and breast cancers were prioritised to reflect the cancer burden in Uganda, while rare cancers (such as pancreatic and lung) were also included to explore the broader diagnostic potential of hpG80. Participants were chosen through convenience sampling from the specialist oncology clinics at UCI. Trained research assistants screened participants for eligibility, and informed consent was obtained prior to enrolment. Inclusion and Exclusion Criteria Adults aged 18 years or older with a histologically confirmed diagnosis of solid malignancy and no prior treatment were eligible for inclusion. Exclusion criteria included patients who had received prior cancer treatment, hematologic malignancies, ECOG performance status of 4, the presence of dual malignancies, or pregnancy. Sample Collection and Laboratory Procedures Two venous blood samples were collected from each participant. The first sample was analysed at the UCI laboratory to quantify traditional tumour markers (CEA, CA19-9, CA125, PSA, AFP, CA15-3, and LDH) using the Roche COBAS 6000 analyser (Roche Diagnostics, Switzerland). The second sample was processed to obtain plasma and sent to LMK Laboratories for hpg80 quantification using the ELISA DxPG80 kit (BIODENA CARE, France), following the manufacturer's protocol. The assay has a lower limit of detection (LoD) of 1 pM and a limit of quantification (LoQ) of 3.3 pM, with inter- and intra-assay coefficients of variation below 10%. The kit demonstrates no cross-reactivity with other tumour markers and no interference from common clinical substances. Data Collection and Variables Demographic and clinical data, including age, sex, cancer type, cancer stage, ECOG performance status, and histological subtype, were abstracted from patient records using a standardised data collection tool. The primary outcome variable was hPG80 positivity, defined as hPG80 levels > 3.3 pM based on the manufacturer’s established cutoff values. Predictor variables and potential confounders were selected before based on biological plausibility and clinical relevance. Statistical Analysis Data analysis was performed using STATA (version 16.0). Descriptive statistics summarized patient demographics and clinical characteristics. The sensitivity of hPG80 and traditional tumor markers was calculated for each cancer type, and 95% confidence intervals were reported. Pearson correlation was used to assess the relationships between hPG80 positivity and traditional markers. Logistic regression models were used to explore the association between hpG80 positivity and clinical variables such as age, cancer stage, ECOG score, histology, and comorbidities. A p-value < 0.05 was considered statistically significant. Results Study Population A total of 210 newly diagnosed, treatment-naive cancer patients were enrolled between May and July 2024 (Table 1 ). The study population reflected the distribution of solid tumors commonly treated at UCI, with cervical cancer (31.4%) and breast cancer (23.8%) being the most prevalent. The median age of participants was 55.5 years (interquartile range 43–64) and the majority were female (68.1%). Most participants (62%) presented with advanced-stage disease (stage III or IV), while 2.9% were diagnosed at stage I. Table 1 Baseline Characteristics of Study Participants Characteristic Value / Frequency (n) Percentage (%) Age, Median (IQR) 55.5 (43–64) — hPG80 levels, Median (IQR) 6.25 (3.6 to 11.1) — Sex Male 67 31.9 Female 143 68.1 ECOG Performance Status 0 24 12.5 1 108 56.3 2 46 24.0 3 14 7.3 Cancer Stage I 6 2.9 II 38 18.1 III 72 34.3 IV 55 26.2 Missing 39 18.6 Histology type Squamous Cell Carcinoma 76 38.0 Non-Squamous Cell Carcinoma 124 62.0 Cancer type Cervical Cancer 66 31.4 Breast Cancer 50 23.8 Prostate Cancer 30 14.3 Esophageal Cancer 29 13.8 Colon Cancer 7 3.3 Rectal Cancer 6 2.9 Liver Cancer 5 2.4 Ovarian Cancer 5 2.4 Stomach Cancer 5 2.4 Pancreatic Cancer 4 1.9 Lung Cancer 3 1.4 Positivity of hPG80 across cancer types Overall, 77.1% of participants had elevated hPG80 levels, defined as plasma concentrations exceeding the assay threshold of 3.3 pM. Positivity rates varied by cancer type with gastrointestinal malignancies showing the highest rates. All patients with pancreatic cancer (n = 4) had elevated hPG80 (100%), followed by colon cancer (86%) and oesophageal cancer (83%) (Table 2 ). Cancers that are more common in Uganda also demonstrated substantial positivity: cervical cancer (79%), breast cancer (72%), and prostate cancer (77%). Lower rates were observed in rectal (67%), lung (67%), and stomach (60%) cancers. These findings are summarized in Table 2 . Estimates for less common cancers, such as pancreatic, lung, and stomach, must be interpreted with caution due to small sample sizes, which may affect the reliability of the reported rates. Table 2 Prevalence of Elevated hPG80 by Cancer Type Cancer Type Patients with Elevated hPG80 (n) Total patients (n) Prevalence of elevated hPG80 (%) Pancreatic Cancer 4 4 100% Colon Cancer 6 7 86% Esophageal Cancer 24 29 83% Liver Cancer 4 5 80% Cervical Cancer 52 66 79% Prostate Cancer 23 30 77% Breast Cancer 36 50 72% Rectal Cancer 4 6 67% Lung Cancer 2 3 67% Stomach Cancer 3 5 60% Comparison of hPG80 and Traditional Tumor Marker Positivity Rates We compared the positivity rates of hPG80 to those of commonly used tumor markers across different cancer types, using standard clinical cut-offs for each marker (Table 3 ). In several cancers, a higher proportion of patients had elevated hPG80 levels than traditional tumor markers. In breast cancer, hPG80 was elevated in 72% of cases, compared to 33% for CA15-3 (p < 0.001). Among cervical cancer patients, 79% had elevated hPG80 levels, while only 29% tested positive for CA125 (p < 0.001). Esophageal cancer showed a similar pattern, with hPG80 positivity at 83% versus only 7% for CEA (p 0.999). In other tumour types, including colon, liver, lung, and pancreatic cancers, hPG80 positivity was generally higher than that of corresponding traditional markers, but the differences were not statistically significant, likely due to small subgroup sizes. Table 3 Comparison of hPG80 and Traditional Tumor Marker Positivity Rates by Cancer Type Site Tumour marker Normal Abnormal Total Percentage p-value Breast CA15-3 33 16 49 33% < 0.001* hPG80 14 36 50 72% Cervix CA125 45 18 63 29% < 0.001* hPG80 14 52 66 79% Colon CEA 4 3 7 43% 0.266 hPG80 1 6 7 86% Oesophagus CEA 26 2 28 7% 0.999 hPG80 1 4 5 80% Lung CYFRA 2 0 2 0% 0.400 hPG80 1 2 3 67% Ovary CA125 1 4 5 80% > 0.999 hPG80 1 4 5 80% Pancreas CA19-9 2 1 3 33% 0.143 hPG80 0 4 4 100% Prostate PSA 6 24 30 80% > 0.999 hPG80 7 23 30 77% Rectum CEA 4 0 4 0% 0.076 hPG80 2 4 6 67% Stomach CEA 0 1 1 100% 0.250 CA19-9 3 0 3 0% 0.196 hPG80 2 3 5 60% Correlation Between hPG80 and Traditional Tumour Markers We assessed the relationship between hPG80 and traditional tumour markers using Spearman’s rank correlation. No statistically significant correlations were observed between hPG80 and any of the conventional markers tested (Table 4 ). This suggests that hPG80 may reflect distinct tumour biology not captured by existing serum tumour markers. Table 4 Spearman’s Correlation Between hPG80 and Traditional Tumour Markers Tumour Marker Spearman's ρ p-value N Interpretation CEA 0.075 0.6206 46 No correlation CA125 0.099 0.3474 93 No correlation CA15-3 0.206 0.1432 52 Weak, not significant CA19-9 0.191 0.2948 32 Weak, not significant PSA 0.216 0.1930 38 Weak, not significant AFP 0.090 0.6906 22 No correlation LDH NA NA 3 Insufficient data Exploratory Assessment of the Factors Associated with hPG80 Positivity As an exploratory analysis, we explored factors potentially associated with hPG80 positivity using modified Poisson regression to estimate both crude and adjusted prevalence ratios (PRs) (Table 5 ). In the crude analysis, hypertension (PR: 1.30; 95% CI: 1.16–1.45; p < 0.001) and diabetes mellitus (PR: 1.24; 95% CI: 1.08–1.44; p = 0.003) were significantly associated with a higher prevalence of detectable hPG80 levels. Other factors such as HIV status, BMI categories, ECOG performance status, sex, age, cancer stage, and histological subtype were not significantly associated with hPG80 positivity in the unadjusted models. In the adjusted multivariable model controlling for all covariates, only hypertension remained significantly associated with hPG80 positivity (adjusted PR: 1.26; 95% CI: 1.10–1.44; p = 0.001). The association with diabetes mellitus was attenuated and no longer statistically significant (adjusted PR: 1.11; 95% CI: 0.89–1.39; p = 0.342). No other clinical or demographic variables showed significant associations after adjustment. These findings suggest a potential link between hypertension and hPG80 expression, but given the exploratory nature of this analysis and the potential for residual confounding, further investigation is warranted in larger, hypothesis-driven studies. Table 5 Crude and Adjusted Prevalence Ratios for hPG80 Positivity Among Newly Diagnosed Cancer Patients Predictor Crude PR 95% CI (Crude) p-value (Crude) Adjusted PR 95% CI (Adjusted) p-value (Adjusted) Age (per year) 1.00 0.999–1.01 0.109 1.00 0.996–1.01 0.333 Male vs Female 1.07 0.918–1.24 0.396 1.09 0.867–1.37 0.457 ECOG 2–3 vs 0–1 1.10 0.948–1.28 0.209 1.02 0.811–1.29 0.841 Stage II vs I 1.07 0.584–1.94 0.835 1.05 0.584–1.90 0.861 Stage III vs I 1.15 0.641–2.05 0.646 1.17 0.678–2.01 0.578 Stage IV vs I 1.20 0.671–2.15 0.539 1.35 0.777–2.34 0.288 Squamous vs Non-squamous 1.12 0.966–1.29 0.136 1.13 0.925–1.38 0.231 Obese vs Normal BMI 0.95 0.715–1.26 0.708 0.96 0.684–1.33 0.787 Overweight vs Normal BMI 0.93 0.744–1.16 0.530 0.94 0.737–1.19 0.600 Underweight vs Normal BMI 1.15 0.977–1.36 0.091 1.15 0.906–1.46 0.253 HIV Positive vs Negative 0.88 0.681–1.14 0.323 0.84 0.619–1.14 0.270 Diabetes Present vs Absent 1.24 1.08–1.44 0.0028 1.11 0.891–1.39 0.342 Hypertension Present vs Absent 1.30 1.16–1.45 < 0.001 1.26 1.10–1.44 < 0.001 Discussion This pilot study assessed hPG80 positivity among newly diagnosed, treatment-naive cancer patients at the Uganda Cancer Institute. More than three-quarters of patients across various cancer types tested positive, highlighting the potential of hPG80 as a widely applicable biomarker in this context. These findings provide a foundational step towards understanding the role of hPG80 in cancer diagnostics within Uganda and similar resource-limited settings. HPG80 positivity rates varied significantly by cancer type, with notably high levels observed in cervical, breast, prostate, and esophageal cancers, all of which contribute considerably to the national cancer burden. For example, positivity rates for hPG80 reached 72% in breast cancer cases, 79% in cervical cancer, and 83% in esophageal cancer. These rates exceeded those of commonly used traditional markers, suggesting that hPG80 may identify a larger proportion of cancer cases, particularly among the most prevalent cancers in Uganda. These patterns indicate that hPG80 could enhance diagnostic yield in cancers where conventional markers like CA15-3 and CEA are less effective ( 6 ), ultimately improving early detection. In pancreatic and colon cancers, hPG80 also showed high positivity rates. However, these results should be interpreted cautiously due to the small sample sizes. Nevertheless, they align with existing literature that demonstrates hPG80 expression in gastrointestinal malignancies ( 7 , 14 ). This performance across various tumour types underscores hPG80's potential as a pan-cancer diagnostic marker. No statistically significant correlations were observed between hPG80 and conventional tumour markers such as CA15-3, CA125, CEA, or PSA. This suggests that hPG80 may reflect biological processes not captured by traditional markers, potentially related to its role in oncogenic signalling and cell proliferation. Unlike conventional markers that are often tissue-specific or indicative of tumour burden, hPG80 may provide a broader, tumour-agnostic signal across various types of cancer ( 6 , 8 , 15 ). Therefore, hPG80 could provide complementary diagnostic information when used in conjunction with existing markers. In settings like Uganda, where access to a full range of cancer-specific diagnostics is limited, such a marker could enhance overall diagnostic coverage if integrated thoughtfully into routine clinical workflows. Further research is needed to clarify the optimal positioning of hPG80 within multi-marker diagnostic strategies. Because all participants in this study had histologically confirmed cancer, specificity could not be assessed. This remains a key limitation, particularly in understanding the false-positive rate of hPG80 in non-malignant conditions. Although prior studies in non-cancer populations have demonstrated reasonable specificity ( 7 , 9 , 14 , 16 ), validation in local control populations is needed to establish its role in distinguishing benign from malignant disease in Uganda. In an exploratory secondary analysis, we assessed associations between select clinical variables and hPG80 positivity. Among these, hypertension showed a statistically significant association in both unadjusted and adjusted models. After controlling for age, ECOG performance status, BMI, and other covariates, hypertension remained independently associated with hPG80 positivity (adjusted PR = 1.26; 95% CI: 1.10–1.44; p = 0.0006). While this finding may suggest a biological or shared risk factor link between hypertension and tumour-related hPG80 expression, it should be interpreted cautiously given the study’s cross-sectional design and the secondary, hypothesis-generating nature of this analysis. Further research is needed to explore potential mechanisms or residual confounding. While the simplicity of a blood-based hPG80 test and its potential for broad application are appealing, implementation in real-world settings will require careful evaluation of its cost-effectiveness, accessibility, and compatibility with existing diagnostic workflows. In low-resource settings, a general-purpose biomarker could aid in early triage of patients with suspected cancer, guiding more targeted use of imaging or biopsy. However, this role remains hypothetical and should be formally assessed through operational research. Overall, these findings highlight the promise of hPG80 as a diagnostic aid in Uganda’s oncology landscape. Larger, controlled studies with non-cancer comparators, longitudinal follow-up, and economic evaluations are needed to define its place in cancer detection and management pathways. Declarations Ethics approval and consent to participate This study was conducted in accordance with all relevant guidelines and regulations. Ethical approval was obtained from the Uganda Cancer Institute Research and Ethics Committee (UCI-REC), which authorized the collection, analysis, and publication of the data. Informed consent was obtained from all patients before their enrolment in the study. Consent for publication Not Applicable Competing interests The authors declare that they have no competing interests. Funding This research was supported with funding from Phamacine Uganda Limited. The funders had no role in the design, data collection, analysis, interpretation of the study, nor in the writing of this manuscript. Author Contribution Conceptualization; NB, NN, AJM, SNN; Methodology; BA, NB, SNN; Data Collection; EN, MA, GA, LN, FN, JK, PO, JKL, AJM; Analysis, BA, NB; Original draft preparation and editing: All Authors Acknowledgement We acknowledge the staff at the Uganda Cancer Institute, including Ashraf Nkangi, and LMK Medical Laboratory and Consultancies LTD who helped with patient identification, screening, sample collection and analysis, chart retrieval and data abstraction. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Nakaganda A, Solt K, Kwagonza L, Driscoll D, Kampi R, Orem J. Challenges faced by cancer patients in Uganda: implications for health systems strengthening in resource limited settings. J Cancer Policy. 2021;27:100263. Omotoso O, Teibo JO, Atiba FA, Oladimeji T, Paimo OK, Ataya FS, et al. 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Acta Med Nagasaki. 2024;67(2):69–74. Cappellini M, Flaceliere M, Saywell V, Soule J, Blanc E, Belouin F, et al. A novel method to detect hPG80 (human circulating progastrin) in the blood. Anal Methods. 2021;13(38):4468–77. Prieur A, Harper A, Khan M, Vire B, Joubert D, Payen L, et al. Plasma hPG80 (Circulating Progastrin) as a Novel Prognostic Biomarker for early-stage breast cancer in a breast cancer cohort. BMC Cancer. 2023;23(1):305. Additional Declarations No competing interests reported. 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Anguandia","email":"","orcid":"","institution":"Uganda Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Geofrey","middleName":"","lastName":"Anguandia","suffix":""},{"id":498245547,"identity":"80cd072d-1c92-4867-bcfa-c4a47a6258d5","order_by":3,"name":"Modern Akoragye","email":"","orcid":"","institution":"Uganda Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Modern","middleName":"","lastName":"Akoragye","suffix":""},{"id":498245548,"identity":"d232ca9f-4942-48f7-8f8f-4f5046688629","order_by":4,"name":"Lydia Namukasa","email":"","orcid":"","institution":"Uganda Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Lydia","middleName":"","lastName":"Namukasa","suffix":""},{"id":498245549,"identity":"e91bb8b7-c3a0-46e5-92d0-5c036a24f9e7","order_by":5,"name":"Elizabeth Nampewo","email":"","orcid":"","institution":"LMK Medical Laboratory and Consultancies LTD","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Nampewo","suffix":""},{"id":498245550,"identity":"97361735-5085-4898-a42f-311ba3a15889","order_by":6,"name":"Florence Nalwoga","email":"","orcid":"","institution":"LMK Medical Laboratory and Consultancies LTD","correspondingAuthor":false,"prefix":"","firstName":"Florence","middleName":"","lastName":"Nalwoga","suffix":""},{"id":498245551,"identity":"e0a2a600-c946-421c-8094-62c44ec84309","order_by":7,"name":"Joseph Kiwewa","email":"","orcid":"","institution":"LMK Medical Laboratory and Consultancies LTD","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Kiwewa","suffix":""},{"id":498245552,"identity":"8ed914ed-63e6-481e-9f9c-aa259813f680","order_by":8,"name":"Pius Olupot","email":"","orcid":"","institution":"LMK Medical Laboratory and Consultancies LTD","correspondingAuthor":false,"prefix":"","firstName":"Pius","middleName":"","lastName":"Olupot","suffix":""},{"id":498245553,"identity":"ec63f44e-958c-44ba-aad1-042486f31087","order_by":9,"name":"John Kafuluma Lusiba","email":"","orcid":"","institution":"General Military Hospital Bombo","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Kafuluma","lastName":"Lusiba","suffix":""},{"id":498245554,"identity":"b7ccc9c7-482a-4ecb-a980-154d3bb66d86","order_by":10,"name":"Antony Jim Musoke","email":"","orcid":"","institution":"LMK Medical Laboratory and Consultancies LTD","correspondingAuthor":false,"prefix":"","firstName":"Antony","middleName":"Jim","lastName":"Musoke","suffix":""},{"id":498245555,"identity":"23481d2d-6d2c-4f17-858c-73a4fae0cb6c","order_by":11,"name":"Susan Ndidde Nabadda","email":"","orcid":"","institution":"LMK Medical Laboratory and Consultancies LTD","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"Ndidde","lastName":"Nabadda","suffix":""},{"id":498245556,"identity":"1c00bf2a-ea61-4ef4-a1d8-f60c2380bf9d","order_by":12,"name":"Nixon Niyonzima MBChB","email":"","orcid":"","institution":"Uganda Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Nixon","middleName":"Niyonzima","lastName":"MBChB","suffix":""}],"badges":[],"createdAt":"2025-07-14 15:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7122941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7122941/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89019171,"identity":"08462ada-ff1e-4a9e-9a25-5cec04206d46","added_by":"auto","created_at":"2025-08-13 19:33:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1077923,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7122941/v1/1326483e-76b1-4418-9831-41739da1ada8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HPG80 as a broad-spectrum cancer biomarker among newly diagnosed patients at Uganda Cancer Institute: An Exploratory Cross-sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer remains a major public health challenge, especially in low- and middle-income countries (LMICs) such as Uganda, where access to advanced diagnostic technologies and specialised care is often limited (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This limited access contributes to delayed diagnosis, with most cancer patients in Uganda presenting at advanced stages, resulting in reduced treatment options and poorer patient outcomes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In such contexts, simple, broadly applicable diagnostic tools are urgently needed to support earlier identification and intervention.\u003c/p\u003e\u003cp\u003eBlood-based tumour markers, including carcinoembryonic antigen (CEA), CA19-9, CA-125, and PSA, are commonly used due to their accessibility and low cost (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, these markers have limited sensitivity and specificity, especially for early detection, and their performance across different tumour types remains suboptimal (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In LMICs, where diagnostic resources are already scarce, the limitations of these conventional markers can further delay timely diagnosis and management. This has created interest in novel biomarkers that could offer broader applicability and improved diagnostic performance, particularly in resource-constrained settings.\u003c/p\u003e\u003cp\u003eCirculating progastrin (hPG80) has emerged as a promising candidate biomarker due to its association with tumorigenesis across multiple cancer types. Under normal physiological conditions, hPG80 is undetectable in peripheral blood due to rapid conversion to active gastrin (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In cancer, activation of the β-catenin/Tcf4 pathway leads to overexpression of the GAST gene and detectable levels of hPG80 in plasma (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Studies in high-income countries have reported elevated hPG80 levels in various malignancies, including lung, breast, hepatocellular carcinoma, and neuroendocrine tumours (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These studies suggest that hPG80 may serve as a general cancer biomarker with potential applications in both diagnosis and prognosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), though most available evidence has come from well-resourced clinical settings.\u003c/p\u003e\u003cp\u003eIn LMICs like Uganda, where access to advanced diagnostics is limited, a single, broadly applicable biomarker could offer practical advantages. Unlike conventional tumour markers, which are typically restricted to specific cancer types, hPG80 may reflect more fundamental oncogenic processes that are similar across multiple malignancies. To date, no studies have explored hPG80 expression in sub-Saharan Africa or assessed its relevance in real-world oncology populations in LMICs. This represents an important evidence gap, particularly given the burden of late-stage presentation and limited diagnostic infrastructure in countries like Uganda.\u003c/p\u003e\u003cp\u003eWe therefore conducted a pilot study to assess hPG80 positivity among newly diagnosed, treatment-naive cancer patients at the Uganda Cancer Institute. The primary aim was to describe the proportion of patients with positive hPG80 levels across various solid tumour types. This exploratory work provides initial data on hPG80 expression in an African oncology population and lays the groundwork for future research on its clinical utility in resource-limited settings.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cross-sectional study was conducted at the Uganda Cancer Institute (UCI), the national cancer referral center in Uganda. Participants were enrolled between May and July 2024, with data collected at a single time point prior to the initiation of any cancer treatment. The study aimed to provide a preliminary assessment of the diagnostic sensitivity of hPG80 in a diverse cohort of newly diagnosed cancer patients within a resource-limited setting.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study population consisted of newly diagnosed, treatment-naive cancer patients presenting at UCI during the study period. Common malignancies such as cervical and breast cancers were prioritised to reflect the cancer burden in Uganda, while rare cancers (such as pancreatic and lung) were also included to explore the broader diagnostic potential of hpG80. Participants were chosen through convenience sampling from the specialist oncology clinics at UCI. Trained research assistants screened participants for eligibility, and informed consent was obtained prior to enrolment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion and Exclusion Criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdults aged 18 years or older with a histologically confirmed diagnosis of solid malignancy and no prior treatment were eligible for inclusion. Exclusion criteria included patients who had received prior cancer treatment, hematologic malignancies, ECOG performance status of 4, the presence of dual malignancies, or pregnancy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Collection and Laboratory Procedures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTwo venous blood samples were collected from each participant. The first sample was analysed at the UCI laboratory to quantify traditional tumour markers (CEA, CA19-9, CA125, PSA, AFP, CA15-3, and LDH) using the Roche COBAS 6000 analyser (Roche Diagnostics, Switzerland). The second sample was processed to obtain plasma and sent to LMK Laboratories for hpg80 quantification using the ELISA DxPG80 kit (BIODENA CARE, France), following the manufacturer's protocol. The assay has a lower limit of detection (LoD) of 1 pM and a limit of quantification (LoQ) of 3.3 pM, with inter- and intra-assay coefficients of variation below 10%. The kit demonstrates no cross-reactivity with other tumour markers and no interference from common clinical substances.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection and Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemographic and clinical data, including age, sex, cancer type, cancer stage, ECOG performance status, and histological subtype, were abstracted from patient records using a standardised data collection tool. The primary outcome variable was hPG80 positivity, defined as hPG80 levels\u0026thinsp;\u0026gt;\u0026thinsp;3.3 pM based on the manufacturer\u0026rsquo;s established cutoff values. Predictor variables and potential confounders were selected before based on biological plausibility and clinical relevance.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData analysis was performed using STATA (version 16.0). Descriptive statistics summarized patient demographics and clinical characteristics. The sensitivity of hPG80 and traditional tumor markers was calculated for each cancer type, and 95% confidence intervals were reported. Pearson correlation was used to assess the relationships between hPG80 positivity and traditional markers. Logistic regression models were used to explore the association between hpG80 positivity and clinical variables such as age, cancer stage, ECOG score, histology, and comorbidities. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 210 newly diagnosed, treatment-naive cancer patients were enrolled between May and July 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study population reflected the distribution of solid tumors commonly treated at UCI, with cervical cancer (31.4%) and breast cancer (23.8%) being the most prevalent. The median age of participants was 55.5 years (interquartile range 43\u0026ndash;64) and the majority were female (68.1%). Most participants (62%) presented with advanced-stage disease (stage III or IV), while 2.9% were diagnosed at stage I.\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 Study Participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\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\u003eValue / Frequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.5 (43\u0026ndash;64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehPG80 levels, Median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.25 (3.6 to 11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.9\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG Performance Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistology type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquamous Cell Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Squamous Cell Carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCervical Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreast Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProstate Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEsophageal Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColon Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectal Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOvarian Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomach Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePancreatic Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4\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\u003cb\u003ePositivity of hPG80 across cancer types\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOverall, 77.1% of participants had elevated hPG80 levels, defined as plasma concentrations exceeding the assay threshold of 3.3 pM. Positivity rates varied by cancer type with gastrointestinal malignancies showing the highest rates. All patients with pancreatic cancer (n\u0026thinsp;=\u0026thinsp;4) had elevated hPG80 (100%), followed by colon cancer (86%) and oesophageal cancer (83%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCancers that are more common in Uganda also demonstrated substantial positivity: cervical cancer (79%), breast cancer (72%), and prostate cancer (77%). Lower rates were observed in rectal (67%), lung (67%), and stomach (60%) cancers. These findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Estimates for less common cancers, such as pancreatic, lung, and stomach, must be interpreted with caution due to small sample sizes, which may affect the reliability of the reported rates.\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\u003ePrevalence of Elevated hPG80 by Cancer Type\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatients with Elevated hPG80 (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal patients (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePrevalence of elevated hPG80 (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePancreatic Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColon Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEsophageal Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCervical Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProstate Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreast Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRectal Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomach Cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60%\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\u003cb\u003eComparison of hPG80 and Traditional Tumor Marker Positivity Rates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe compared the positivity rates of hPG80 to those of commonly used tumor markers across different cancer types, using standard clinical cut-offs for each marker (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In several cancers, a higher proportion of patients had elevated hPG80 levels than traditional tumor markers. In breast cancer, hPG80 was elevated in 72% of cases, compared to 33% for CA15-3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among cervical cancer patients, 79% had elevated hPG80 levels, while only 29% tested positive for CA125 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Esophageal cancer showed a similar pattern, with hPG80 positivity at 83% versus only 7% for CEA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In prostate cancer, positivity rates for hPG80 (77%) and PSA (80%) were similar, with no statistically significant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.999).\u003c/p\u003e\u003cp\u003eIn other tumour types, including colon, liver, lung, and pancreatic cancers, hPG80 positivity was generally higher than that of corresponding traditional markers, but the differences were not statistically significant, likely due to small subgroup sizes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of hPG80 and Traditional Tumor Marker Positivity Rates by Cancer Type\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=\"left\" 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=\"left\" 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\u003eSite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTumour marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePercentage\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBreast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA15-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33%\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=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCervix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29%\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=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eColon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOesophagus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7%\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=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAFP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLung\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCYFRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOvary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePancreas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA19-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eProstate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eStomach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA19-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehPG80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation Between hPG80 and Traditional Tumour Markers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe assessed the relationship between hPG80 and traditional tumour markers using Spearman\u0026rsquo;s rank correlation. No statistically significant correlations were observed between hPG80 and any of the conventional markers tested (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that hPG80 may reflect distinct tumour biology not captured by existing serum tumour markers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman\u0026rsquo;s Correlation Between hPG80 and Traditional Tumour Markers\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=\"char\" char=\".\" 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\u003eTumour Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpearman's ρ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA15-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeak, not significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA19-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeak, not significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeak, not significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInsufficient data\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\u003cb\u003eExploratory Assessment of the Factors Associated with hPG80 Positivity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs an exploratory analysis, we explored factors potentially associated with hPG80 positivity using modified Poisson regression to estimate both crude and adjusted prevalence ratios (PRs) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the crude analysis, hypertension (PR: 1.30; 95% CI: 1.16\u0026ndash;1.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and diabetes mellitus (PR: 1.24; 95% CI: 1.08\u0026ndash;1.44; p\u0026thinsp;=\u0026thinsp;0.003) were significantly associated with a higher prevalence of detectable hPG80 levels. Other factors such as HIV status, BMI categories, ECOG performance status, sex, age, cancer stage, and histological subtype were not significantly associated with hPG80 positivity in the unadjusted models.\u003c/p\u003e\u003cp\u003eIn the adjusted multivariable model controlling for all covariates, only hypertension remained significantly associated with hPG80 positivity (adjusted PR: 1.26; 95% CI: 1.10\u0026ndash;1.44; p\u0026thinsp;=\u0026thinsp;0.001). The association with diabetes mellitus was attenuated and no longer statistically significant (adjusted PR: 1.11; 95% CI: 0.89\u0026ndash;1.39; p\u0026thinsp;=\u0026thinsp;0.342). No other clinical or demographic variables showed significant associations after adjustment.\u003c/p\u003e\u003cp\u003eThese findings suggest a potential link between hypertension and hPG80 expression, but given the exploratory nature of this analysis and the potential for residual confounding, further investigation is warranted in larger, hypothesis-driven studies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCrude and Adjusted Prevalence Ratios for hPG80 Positivity Among Newly Diagnosed Cancer Patients\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\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude PR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI (Crude)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value (Crude)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted PR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI (Adjusted)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value (Adjusted)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (per year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.999\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.996\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale vs Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.918\u0026ndash;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.867\u0026ndash;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG 2\u0026ndash;3 vs 0\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.948\u0026ndash;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.811\u0026ndash;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II vs I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.584\u0026ndash;1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.584\u0026ndash;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III vs I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.641\u0026ndash;2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.678\u0026ndash;2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV vs I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.671\u0026ndash;2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.777\u0026ndash;2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquamous vs Non-squamous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.966\u0026ndash;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.925\u0026ndash;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese vs Normal BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.715\u0026ndash;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.684\u0026ndash;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight vs Normal BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.744\u0026ndash;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.737\u0026ndash;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight vs Normal BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.977\u0026ndash;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.906\u0026ndash;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV Positive vs Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.681\u0026ndash;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.619\u0026ndash;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Present vs Absent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.08\u0026ndash;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.891\u0026ndash;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension Present vs Absent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.16\u0026ndash;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.10\u0026ndash;1.44\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"},{"header":"Discussion","content":"\u003cp\u003eThis pilot study assessed hPG80 positivity among newly diagnosed, treatment-naive cancer patients at the Uganda Cancer Institute. More than three-quarters of patients across various cancer types tested positive, highlighting the potential of hPG80 as a widely applicable biomarker in this context. These findings provide a foundational step towards understanding the role of hPG80 in cancer diagnostics within Uganda and similar resource-limited settings.\u003c/p\u003e\u003cp\u003eHPG80 positivity rates varied significantly by cancer type, with notably high levels observed in cervical, breast, prostate, and esophageal cancers, all of which contribute considerably to the national cancer burden. For example, positivity rates for hPG80 reached 72% in breast cancer cases, 79% in cervical cancer, and 83% in esophageal cancer. These rates exceeded those of commonly used traditional markers, suggesting that hPG80 may identify a larger proportion of cancer cases, particularly among the most prevalent cancers in Uganda. These patterns indicate that hPG80 could enhance diagnostic yield in cancers where conventional markers like CA15-3 and CEA are less effective (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), ultimately improving early detection. In pancreatic and colon cancers, hPG80 also showed high positivity rates. However, these results should be interpreted cautiously due to the small sample sizes. Nevertheless, they align with existing literature that demonstrates hPG80 expression in gastrointestinal malignancies (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This performance across various tumour types underscores hPG80's potential as a pan-cancer diagnostic marker.\u003c/p\u003e\u003cp\u003eNo statistically significant correlations were observed between hPG80 and conventional tumour markers such as CA15-3, CA125, CEA, or PSA. This suggests that hPG80 may reflect biological processes not captured by traditional markers, potentially related to its role in oncogenic signalling and cell proliferation. Unlike conventional markers that are often tissue-specific or indicative of tumour burden, hPG80 may provide a broader, tumour-agnostic signal across various types of cancer (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Therefore, hPG80 could provide complementary diagnostic information when used in conjunction with existing markers. In settings like Uganda, where access to a full range of cancer-specific diagnostics is limited, such a marker could enhance overall diagnostic coverage if integrated thoughtfully into routine clinical workflows. Further research is needed to clarify the optimal positioning of hPG80 within multi-marker diagnostic strategies.\u003c/p\u003e\u003cp\u003eBecause all participants in this study had histologically confirmed cancer, specificity could not be assessed. This remains a key limitation, particularly in understanding the false-positive rate of hPG80 in non-malignant conditions. Although prior studies in non-cancer populations have demonstrated reasonable specificity (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), validation in local control populations is needed to establish its role in distinguishing benign from malignant disease in Uganda.\u003c/p\u003e\u003cp\u003eIn an exploratory secondary analysis, we assessed associations between select clinical variables and hPG80 positivity. Among these, hypertension showed a statistically significant association in both unadjusted and adjusted models. After controlling for age, ECOG performance status, BMI, and other covariates, hypertension remained independently associated with hPG80 positivity (adjusted PR\u0026thinsp;=\u0026thinsp;1.26; 95% CI: 1.10\u0026ndash;1.44; p\u0026thinsp;=\u0026thinsp;0.0006). While this finding may suggest a biological or shared risk factor link between hypertension and tumour-related hPG80 expression, it should be interpreted cautiously given the study\u0026rsquo;s cross-sectional design and the secondary, hypothesis-generating nature of this analysis. Further research is needed to explore potential mechanisms or residual confounding.\u003c/p\u003e\u003cp\u003eWhile the simplicity of a blood-based hPG80 test and its potential for broad application are appealing, implementation in real-world settings will require careful evaluation of its cost-effectiveness, accessibility, and compatibility with existing diagnostic workflows. In low-resource settings, a general-purpose biomarker could aid in early triage of patients with suspected cancer, guiding more targeted use of imaging or biopsy. However, this role remains hypothetical and should be formally assessed through operational research. Overall, these findings highlight the promise of hPG80 as a diagnostic aid in Uganda\u0026rsquo;s oncology landscape. Larger, controlled studies with non-cancer comparators, longitudinal follow-up, and economic evaluations are needed to define its place in cancer detection and management pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study was conducted in accordance with all relevant guidelines and regulations. Ethical approval was obtained from the Uganda Cancer Institute Research and Ethics Committee (UCI-REC), which authorized the collection, analysis, and publication of the data. Informed consent was obtained from all patients before their enrolment in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot Applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported with funding from Phamacine Uganda Limited. The funders had no role in the design, data collection, analysis, interpretation of the study, nor in the writing of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization; NB, NN, AJM, SNN; Methodology; BA, NB, SNN; Data Collection; EN, MA, GA, LN, FN, JK, PO, JKL, AJM; Analysis, BA, NB; Original draft preparation and editing: All Authors\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the staff at the Uganda Cancer Institute, including Ashraf Nkangi, and LMK Medical Laboratory and Consultancies LTD who helped with patient identification, screening, sample collection and analysis, chart retrieval and data abstraction.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNakaganda A, Solt K, Kwagonza L, Driscoll D, Kampi R, Orem J. Challenges faced by cancer patients in Uganda: implications for health systems strengthening in resource limited settings. J Cancer Policy. 2021;27:100263.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmotoso O, Teibo JO, Atiba FA, Oladimeji T, Paimo OK, Ataya FS, et al. Addressing cancer care inequities in sub-Saharan Africa: current challenges and proposed solutions. Int J Equity Health. 2023;22(1):189.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBogere N, Bongomin F, Katende A, Omaido BA, Namukwaya E, Mayanja-Kizza H, et al. A 10-year retrospective study of lung cancer in Uganda. 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Plasma hPG80 (circulating progastrin) as a novel prognostic biomarker for hepatocellular carcinoma at early to intermediate stages (BCLC 0 to B). 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYou B, Ceruse P, Couraud S, Duruisseaux M, Paparel P, Badet L et al. 136P Circulating hPG80 (WNT pathway activation) as a potential new prognostic/predictive factor of immunotherapy (ICI) efficacy: ONCOPRO prospective study. Abstr Book ESMO Congr. 2024 13\u0026ndash;17 Sept 2024. 2024;35:S270.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamasaki K, Tominaga T, Hidaka S, Hashimoto Y, Arai J ichi, Nonaka T et al. Plasma hPG80 (circulating progastrin) as a novel biomarker for detecting gastric cancer: a Japanese multicenter study. Acta Med Nagasaki. 2024;67(2):69\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCappellini M, Flaceliere M, Saywell V, Soule J, Blanc E, Belouin F, et al. A novel method to detect hPG80 (human circulating progastrin) in the blood. Anal Methods. 2021;13(38):4468\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrieur A, Harper A, Khan M, Vire B, Joubert D, Payen L, et al. Plasma hPG80 (Circulating Progastrin) as a Novel Prognostic Biomarker for early-stage breast cancer in a breast cancer cohort. BMC Cancer. 2023;23(1):305.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hPG80, Cancer Biomarkers, Tumour Markers, Uganda Cancer Institute, Early Detection, LMICs","lastPublishedDoi":"10.21203/rs.3.rs-7122941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7122941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIn low- and middle-income countries (LMICs) such as Uganda, delayed cancer diagnosis due to limited access to diagnostic tools contributes to poor outcomes. Traditional tumour markers, such as CA15-3, CA125, CEA, and PSA, are widely used but have limited sensitivity and specificity across various cancer types. Circulating progastrin (hPG80) has emerged as a potential broad-spectrum biomarker detectable in the plasma of cancer patients. This study provides initial evidence on hPG80 positivity across different cancer types among newly diagnosed patients at the Uganda Cancer Institute (UCI) and compares it with conventional tumour markers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional study among 210 newly diagnosed, treatment-naive adult cancer patients with histologically confirmed solid tumours at the Uganda Cancer Institute (UCI). Prior to treatment initiation, venous blood samples were collected and analysed for hPG80 using the DxPG80.lab ELISA kit. Concurrently, conventional tumour markers (e.g., CA15-3, CA125, CEA, PSA, CA19-9, AFP, LDH) were measured using the Roche COBAS 6000 analyser. hPG80 positivity was defined as plasma concentrations\u0026thinsp;\u0026gt;\u0026thinsp;3.3 pM. We compared the proportion of patients testing positive for hPG80 versus traditional markers by cancer type. Spearman\u0026rsquo;s rank correlation was used to assess relationships between hPG80 and conventional markers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ehPG80 was positive (\u0026gt;\u0026thinsp;3.3 pM) in 77.1% of all participants. The most frequent hPG80 positivity was seen in oesophageal (83%), cervical (79%), breast (72%), and prostate (77%) cancers, which are among the most diagnosed cancers in Uganda. In each of these cancers, hPG80 identified more patients than the corresponding conventional tumour markers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), except in prostate cancer, where it performed similarly to PSA (77% vs. 80%). Although high hPG80 positivity was also noted in pancreatic and colon cancers, the small number of cases in these groups limits interpretation. No significant correlations were observed between hPG80 and conventional tumour markers, suggesting that hPG80 may reflect different biological processes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ehPG80 was detectable in a high proportion of newly diagnosed cancer patients and showed higher positivity rates than conventional markers in several common cancers in Uganda. These findings support further evaluation of hPG80 as a complementary biomarker, with potential utility in cancer detection strategies in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"HPG80 as a broad-spectrum cancer biomarker among newly diagnosed patients at Uganda Cancer Institute: An Exploratory Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 19:17:20","doi":"10.21203/rs.3.rs-7122941/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-03T16:01:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174158525813732531188262034433497798958","date":"2025-08-22T13:33:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181619238619992034671363456743197575270","date":"2025-08-19T00:49:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285014935620524981676698211991074841273","date":"2025-08-18T14:01:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19667733540382547869268291486665847684","date":"2025-08-18T13:11:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134668175839598371796992872644220806361","date":"2025-08-08T11:41:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-08T06:36:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T18:09:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T09:56:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T09:55:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-07-14T15:50:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43cdf908-8723-40e3-a29d-5bb5df21a55c","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-13T19:17:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 19:17:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7122941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7122941","identity":"rs-7122941","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}