Analysis of the Role of Alkaline Phosphatase and Gleason Score in Predicting Prognosis and Risk of Bone Metastasis in Prostate Cancer

Analysis of the Role of Alkaline Phosphatase and Gleason Score in Predicting Prognosis and Risk of Bone Metastasis in Prostate Cancer | 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 Analysis of the Role of Alkaline Phosphatase and Gleason Score in Predicting Prognosis and Risk of Bone Metastasis in Prostate Cancer Husna Dharma Putera, Muhammad Iqbal, Zairin Noor, Izaak Zoelkarnain Akbar, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7266645/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Prostate cancer ranks as one of the most common malignancies in men and is the second leading cause of cancer-related death. Bone metastasis is a frequent and severe complication, significantly worsening patient prognosis and quality of life. Early identification of bone metastasis risk is critical for guiding clinical decisions. Alkaline phosphatase (ALP) and Gleason score have been studied extensively as predictive markers, though their effectiveness remains debated. Objective: This study aimed to evaluate the predictive value of ALP levels and Gleason scores in assessing bone metastasis risk and prognosis in prostate cancer patients. Methods: A cross-sectional analytical observational study was conducted at Ulin Regional General Hospital, Banjarmasin, from January to June 2025. Forty-eight prostate cancer patients meeting the inclusion criteria were selected. Data including ALP, PSA levels, Gleason score, and radiologic findings were analyzed using logistic and ordinal regression. Predictive accuracy was assessed through ROC curve analysis. Results: Bone metastases were present in 58.3% of patients. ALP was a significant independent predictor of bone metastasis (OR: 1.56; p = 0.008) and showed a positive correlation with Gleason score (β = 0.07; p = 0.001). In contrast, the Gleason score alone was not significantly associated with metastasis (p > 0.05). The ROC curve for ALP demonstrated high diagnostic accuracy with an AUC of 0.923 and an optimal cut-off of 122.35 U/L (sensitivity 78.6%, specificity 95%). Conclusion: ALP outperforms Gleason score as a predictor of bone metastasis and may serve as a valuable prognostic marker in prostate cancer management. Alkaline Phosphatase Biomarker Prostate Cancer Bone Metastasis Prognosis Gleason Score Figures Figure 1 Figure 2 Introduction Prostate cancer is one of the most common malignancies in men and the second leading cause of cancer death worldwide. This disease has a broad clinical spectrum, ranging from indolent forms to aggressive forms with a tendency to metastasize, particularly to the bones [ 1 ] . Therefore, assessment of prognosis and risk of metastasis is an important aspect in the clinical management of prostate cancer. Currently, alkaline phosphatase (ALP) and the Gleason score are widely studied as potential indicators for assessing prostate cancer progression. ALP is an enzyme that is frequently elevated in osteoblastic processes, which are common in bone metastases [ 2 ] . Meanwhile, the Gleason score, which is based on the degree of histological differentiation of the tumor, has long been used to estimate tumor aggressiveness [ 3 ] . However, the combination of these two parameters as predictors of prognosis and bone metastasis remains a promising area of exploration [ 4 ] . Several recent studies indicate that increased ALP levels are associated with a higher risk of skeletal metastases and decreased patient survival [ 5 ] . Other studies have also shown that combining biomarkers such as ALP with prostate-specific antigen (PSA) or Gleason score can improve predictive accuracy compared to using them alone [ 6 ] . However, variations in sensitivity and specificity of each parameter are a challenge in clinical application [ 6 ] . This study aims to analyze the role of ALP and Gleason score as predictors of prognosis and risk of bone metastasis in prostate cancer patients. It is hoped that the results of this study will contribute to the selection of more appropriate diagnostic and therapeutic strategies, particularly in the early stages of disease management. Materials and Methods This is a quantitative, observational study with a cross-sectional approach conducted at Ulin Regional General Hospital, Banjarmasin. The study took place from January to June 2025 and used secondary data sourced from patient medical records. Population and sample The study population included all male patients diagnosed with prostate adenocarcinoma and undergoing relevant laboratory and radiological examinations during the study period. The sampling technique used was total sampling, including all patients who met the inclusion criteria. Patients included in the sample were those with complete records of alkaline phosphatase (ALP) levels, prostate-specific antigen (PSA) levels, Gleason score results from anatomic pathology, and bone radiology examinations used to detect possible metastases, such as X-rays, CT scans, or bone MRIs. Patients with a history of other metabolic bone diseases such as severe osteoporosis or Paget's disease, as well as those with incomplete medical records, were excluded from the analysis. Data Procedures and Analysis All collected data were coded and input into R statistical software version 4.2.2. Data analysis was conducted in two stages: descriptive analysis to describe general patient characteristics, and inferential analysis, which included logistic regression to examine the relationship between ALP and Gleason score and the incidence of bone metastases. An ordinal regression analysis was used to evaluate the contribution of predictors to prognostic classification. To assess predictive accuracy, a Receiver Operating Characteristic (ROC) curve was used to determine the optimal cut-off point for ALP levels in detecting metastases. A p-value < 0.05 was considered statistically significant. Research Ethics This research has received approval from the Health Research Ethics Committee of the Faculty of Medicine and Health Sciences, Lambung Mangkurat University, under letter number 050/KEPK-FKIK ULM/EC/VI/2025. The committee guarantees the confidentiality of patient data and guarantees its use only for scientific purposes. Results Table 1 Characteristics of prostate cancer patients in the study (N = 48) Characteristics N (%) Mean ± SD Median (Q1;Q3) Age - 67.1 ± 7.2 - PSA (ng/mL) - 70.5 ± 38.2 - ALP (U/L) - - 119.0 (85.0; 265.5) Gleason Score - 7.8 ± 1.3 - ≤ 6 9 (18.8%) - - 7 13 (27.1%) - - 8–10 26 (54.2%) - - Bone metastases 28 (58.3%) - - Note: SD = standard deviation; Q1 = first quartile; Q3 = third quartile; PSA = prostate-specific antigen; ALP = alkaline phosphatase. Table 1 shows the results of data collection on the characteristics of 48 prostate cancer patients. The mean age of patients was 67.1 years with a standard deviation of ± 7.2 years. The results of prostate-specific antigen (PSA) levels showed a mean value of 70.5 ng/mL (SD ± 38.2 ng/mL). The median alkaline phosphatase (ALP) level was recorded at 119 U/L with an interquartile range of 85.0 U/L to 265.5 U/L. The average Gleason score of all patients in this study was 7.8 with a variation of approximately ± 1.3 points. When categorized by tumor malignancy, more than half of the patients (54.2%) were classified as high or very high risk, with a Gleason score between 8 and 10. A total of 28 of the 48 patients (58.3%) were detected as having bone metastases based on radiological examination results. Table 2 Bone metastasis and prostate cancer prognosis (N = 48) Predictor Univariate Multivariate OR (95% CI) p OR (95% CI) p Bone metastasis outcome Age 0.95 (0.86; 1.05) 0.296 - PSA 1.07 (1.01; 1.13) 0.022 - ALP 1.53 (1.10; 2.13) 0.014 1.56 (1.12; 2.19) 0.008 Gleason 8–10 1.42 (0.50; 4.00) 0.502 0.64 (0.18; 2.23) 0.487 Predictor β (95% CI) p β (95% CI) p Gleason score outcome (prognosis) Age 0.02 (-0.06; 0.10) 0.621 - - PSA -0.01 (-0.03; 0.00) 0.072 -0.02 (-0.03; -0.01) 0.004 ALP 0.06 (0.02; 0.09) 0.009 0.07 (0.04; 0.10) 0.001 Note: CI = confidence interval, PSA = prostate-specific antigen, ALP = alkaline phosphatase. For ease of interpretation, the change in ALP is calculated per 10 U/L increase. Table 2 shows that alkaline phosphatase (ALP) is a strong and consistent predictor, both in univariate (OR = 1.53; p = 0.014) and multivariate (OR = 1.56; p = 0.008) analyses. This suggests that ALP is reliable as a single indicator and as part of a combined model in predicting bone metastasis in prostate cancer patients. In contrast, Gleason score did not show a significant association with bone metastasis in either model, with p > 0.05, thus limiting its use in the context of bone metastasis prediction. Meanwhile, PSA levels only showed a significant association in the univariate model (p = 0.022), but not in the multivariate model, possibly due to a more dominant interaction or collinearity with ALP. Age was also not significant in either model. For prognostic outcomes, it was shown that every 10 U/L increase in ALP levels was associated with a 0.07-point increase in Gleason score (p = 0.001) in the multivariate model. Furthermore, PSA was also significantly negatively correlated with Gleason score (β = − 0.02; p = 0.004), indicating that high PSA values tend to be associated with a worse tumor prognosis. Figure 1 shows that alkaline phosphatase (ALP) has excellent ability to differentiate prostate cancer patients with and without bone metastases, with an AUC of 0.923 (95% CI: 0.843–1.000; p < 0.001). The optimal cutoff point for ALP levels was 122.35 U/L, which provided a sensitivity of 78.6% and a specificity of 95%, reflecting a strong balance between detection ability and classification accuracy. The boxplot graph reinforces these results by showing that ALP levels in patients with bone metastases were consistently higher than in patients without metastases. Most ALP values in the metastatic group were above the cutoff point of 122.35 U/L, while those in the non-metastatic group were generally below it. One outlier in the negative group did not disrupt the clear distribution pattern, further confirming ALP's role as a reliable predictive indicator. In contrast to alkaline phosphatase, the results of this study do not support the use of the Gleason score as a predictor of bone metastasis. Table 2 shows that the odds ratio of Gleason 8–10 for bone metastasis was not statistically significant, both in univariate analysis (OR: 1.42; 95% CI: 0.50–4.00; p = 0.502) and multivariate analysis (OR: 0.64; 95% CI: 0.18–2.23; p = 0.487). Furthermore, ROC curve analysis showed that the area under the curve (AUC) of the Gleason score was only 60.4% with an insignificant p value of 0.221. This value indicates a low discriminatory ability in distinguishing patients with bone metastasis from those without. The optimal cutoff point for the Gleason score was 7.5, resulting in a sensitivity of 60.7% and a specificity of 55.0%, with an estimated accuracy of approximately 58%. Although the Gleason score plays an important role in determining prostate cancer prognosis, these results indicate that its ability to predict bone metastasis is not strong enough to be applied clinically as a single parameter. These findings are consistent with previous regression data, where ALP performed better in predicting both bone metastasis and prostate cancer prognosis. In the context of this study, each 10 U/L increase in ALP levels was associated with an increase in Gleason score of approximately 0.07 points (95% CI: 0.04–0.10; p = 0.001), thus confirming the role of alkaline phosphatase as a superior predictor of prognosis compared to Gleason score, particularly in the context of early detection of bone metastasis. Discussion This study shows that alkaline phosphatase (ALP) levels have a strong and significant predictive value for bone metastasis and prognosis in prostate cancer patients, while Gleason score does not show a significant association in predicting metastasis. These results confirm that ALP, either as a single biomarker or in combination with prostate-specific antigen (PSA), can be a more sensitive indicator in the early detection and assessment of prostate cancer progression. The Role of ALP in Predicting Bone Metastasis Regression test results showed that every 10 U/L increase in ALP significantly increased the risk of bone metastasis. This finding is consistent with previous literature showing that ALP is a marker of increased osteoblastic activity and bone remodeling in cases of bone metastasis [ 9 ] . ALP not only reflects the presence of metastases but also contributes to the severity of skeletal lesions. A study by Lee et al. found that high ALP was associated with a greater burden of bone metastases and poorer survival in metastatic prostate cancer [ 9 ] . A meta-analysis by Lin et al. also showed that increased ALP consistently correlated with decreased overall survival (OS) and cancer-specific survival (CSS) in patients with advanced prostate cancer [ 10 ] . This study strengthens our research findings that ALP is a stronger indicator than Gleason score in predicting metastasis. The use of ALP as an early predictor has advantages in prostate cancer management, especially in populations who have not undergone bone imaging. With a cut-off point of 122.35 U/L, ALP in this study demonstrated an AUC of 0.923, representing very high classification accuracy. This surpasses the performance of Gleason in the same context. Gleason Score in Predicting Metastasis The Gleason score is a standard parameter for assessing tumor aggressiveness based on histopathology. However, in this study, the Gleason score did not show a significant association with the incidence of bone metastases. This is supported by several recent studies showing that the Gleason score, while important in risk stratification, is not always a specific predictor of skeletal metastases [ 11 ] . For example, a study by Matsumoto et al. found that a Gleason score ≥ 8 did not always correlate with bone metastases at the time of initial diagnosis, especially in patients with normal PSA and ALP levels [ 11 ] . For example, a study by Matsumoto et al. found that a Gleason score ≥ 8 did not always correlate with bone metastases at the time of initial diagnosis, especially in patients with normal PSA and ALP levels. Furthermore, the Gleason AUC value in this study was only 60.4%, with low sensitivity and specificity. This means the Gleason score has limited ability to differentiate between patients with and without bone metastases. Therefore, the use of Gleason as the sole predictive parameter in clinical practice should be reconsidered, and it should be combined with other biomarkers such as ALP or PSA to improve predictive accuracy. PSA, Interaction with ALP, and Clinical Implications PSA is still considered a useful early biomarker in prostate cancer screening and monitoring. In this study, PSA was only significant in the univariate model but lost its significance in the multivariate model, suggesting possible collinearity or a mediating effect by ALP. Research by Gandhi et al. stated that PSA has a non-linear relationship to bone metastasis and that PSA sensitivity is decreased in highly differentiated cancers or rare histological variants. [ 12 ] . Therefore, the combination of PSA and ALP becomes important. A cohort study by Yuan et al. found that a prediction model combining PSA, ALP, and the neutrophil/lymphocyte ratio provided a higher AUC than using PSA alone in predicting bone metastases [ 13 ] . The clinical implications of these findings are significant. Identification of patients at high risk of bone metastases through inexpensive and easily performed biomarkers such as ALP could expedite clinical decisions regarding further imaging studies such as bone scans or PET-CT. Furthermore, ALP can be used to monitor response to therapies, such as hormone therapy or chemotherapy, as ALP values typically decrease with response to treatment [ 14 ] . The Relationship of ALP with Prognosis and Gleason Score Although the Gleason score was not significant in predicting metastasis, regression analysis showed that ALP had a positive correlation with the Gleason score, meaning that the higher the ALP, the higher the Gleason score. This suggests that ALP may indirectly reflect tumor aggressiveness. This finding is supported by a study by Hu et al., which stated that patients with high Gleason scores and high ALP levels had a greater risk of cancer death within two years of diagnosis [ 14 ] . Pathophysiologically, this can be explained by the fact that high-grade tumors are more likely to metastasize and cause increased osteoblastic activity, which is reflected in increased ALP levels. [ 15 ] . Additional Biomarkers and Predictive Model Combinations Beyond ALP and PSA, various other biomarkers are currently being developed to improve the accuracy of metastasis prediction, such as N-telopeptide, bone-specific ALP, lactate dehydrogenase (LDH), and circulating tumor cells (CTC) [ 16 , 17 ] . A study by Zhao et al. found that the combination of bone ALP, LDH, and Gleason score was able to increase the accuracy of metastasis classification by up to 91% [ 18 ] . Therefore, multibiomarker predictive models in the future may be a more reliable approach in the management of advanced prostate cancer. In the context of clinical use, the development of risk models based on quantitative biomarkers such as ALP also aligns with the trend of personalized medicine. Research by Kim et al. showed that patients with high ALP levels and Gleason grade ≥ 8 benefited more from early systemic therapy than from local therapy alone [ 16 ] . In addition to bone-specific ALP and LDH, several studies have also explored other biomarkers such as circulating tumor cells (CTCs) and miRNA as tools for early detection of bone metastasis. For example, O'Connor et al. in their meta-analysis reported that the integration of multiple serum biomarkers provided higher sensitivity and specificity than using a single biomarker alone [ 19 ] . This is important for patients with ambiguous imaging results or atypical PSA levels. Furthermore, Park et al. emphasized the prognostic role of LDH and ALP in determining the response to systemic therapy, particularly in patients undergoing hormone therapy [ 20 ] . LDH is considered to reflect the overall tumor burden and high metabolic activity of the neoplasm, so when combined with ALP it can improve the accuracy of risk stratification. Zhao et al. also reported that the prediction of bone metastasis was significantly improved when serum markers were combined with PET/CT imaging using the 18F-NaF tracer, with AUC reaching more than 0.90 [ 21 ] . These findings open up the possibility of using a hybrid approach between biomarkers and imaging for more precise diagnosis, especially in hospital settings with limited imaging resources. Meanwhile, a study by Song et al. developed a predictive model using the Gleason score combined with ALP and PSA that was shown to be able to classify patients into low, intermediate, and high risk groups for bone metastasis and premature death [ 22 ] . This model is suitable for application in risk stratification-based management schemes and is the basis of the precision medicine approach. Tang et al. have even developed a prognostic nomogram based on Gleason score, ALP, and bone metastasis status that is able to predict 3- and 5-year survival with a high degree of accuracy [ 23 ] . This nomogram is not only useful in daily clinical practice, but also in evidence-based therapeutic decision making. Study Limitations and Suggestions for Further Research This study is limited by its relatively small sample size and retrospective nature. Therefore, generalization of the results should be done with caution. Large-scale prospective studies integrating various biomarkers with machine learning-based prediction models are highly recommended to strengthen the validity of these findings. Furthermore, it is important to consider other parameters such as bone pain, ECOG status, and bone density in future prediction models. Conclusion This study demonstrates that alkaline phosphatase (ALP) levels are a significant predictor of bone metastasis and prognosis in prostate cancer patients. ALP demonstrated high predictive power, both as a single biomarker and in a multivariate model, with better classification accuracy than the Gleason score. Conversely, the Gleason score did not show a significant association with bone metastasis, although it remains relevant in the context of overall prognosis. These findings underscore the importance of integrating biochemical biomarkers, particularly ALP, into more personalized and precise prostate cancer risk prediction systems and management strategies. The ALP cutoff of 122.35 U/L can serve as a clinical reference for early bone metastasis risk assessment, helping expedite decisions about further testing or therapeutic intervention. This study supports the need for a multimodal approach combining ALP, PSA, and other clinical data to develop more accurate diagnostic and prognostic strategies for prostate cancer patients. Declarations Funding Statement This research was self-funded by the authors. No external financial support, grant, or sponsorship was received from any individual, institution, or organization during the planning, execution, or publication of this study. Author Contribution Husna D. Putera, Muhammad Iqbal, Zairin Noor, Sutan Agung L. Tobing, Andreas M. H. Siagian, Izaak Z. Akbar, Essy D. Damayanthi, Wongso Kesuma, and M. Rifqi F. Akbar conducted and prepared the research process.Candra Kusuma Negara and Ricky Prawira prepared the publication article and wrote the discussion.All authors reviewed the manuscript. Acknowledgement The authors would like to thank the Director of Ulin Regional General Hospital, Banjarmasin, and all staff from the medical records department and the clinical pathology laboratory for their permission and support in collecting data for this study. They also thank the Health Research Ethics Committee of the Faculty of Medicine and Health Sciences, Lambung Mangkurat University, for their ethical approval and guidance in conducting this study. Without their support, this research would not have been possible. References Mori K, Janisch F, Parizi MK, et al. Prognostic value of alkaline phosphatase in hormone-sensitive prostate cancer: a systematic review and meta-analysis. Int J Clin Oncol. 2020;25(2):247–57. https://doi.org/10.1007/s10147-019-01578-9 Tsuzuki S, Kawano S, Fukuokaya W, et al. 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Ann Med. 2022;54(1):2051–62. https://doi.org/10.1080/07853890.2022.2052533 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7266645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495641372,"identity":"8fccd858-2c44-457d-8456-47b7341a52f4","order_by":0,"name":"Husna Dharma Putera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACA4YECIOfgbkBzGCDieDVcgDIkGxgJFWLwQGoFgZCWszZc49Jf6i4J298I7F1A0ONHQMfOwEtlj3v0iQOnCk23HYjse0Gw7FkBjaeBwQcdiPHTOJgWwIjRAvbAQY2CUJ+AWv5l2C/eQZIyz+itTQkJG6QAGphbCNGy5l3yRZnjiUkzzjzsO1GYl8yD2G/HM89eKOiJsG2vz352I0P3+zk5NsJ2MLAwIPETkDlEqNlFIyCUTAKRgE2AABP1kizOrtkHgAAAABJRU5ErkJggg==","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":true,"prefix":"","firstName":"Husna","middleName":"Dharma","lastName":"Putera","suffix":""},{"id":495641376,"identity":"7667997a-d49f-4309-9abd-515f9480d22e","order_by":1,"name":"Muhammad Iqbal","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Iqbal","suffix":""},{"id":495641381,"identity":"0ff1d521-8dee-4af0-91fb-c1a7eaf4a9af","order_by":2,"name":"Zairin Noor","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Zairin","middleName":"","lastName":"Noor","suffix":""},{"id":495641383,"identity":"4575e0b2-f8b2-44f6-ad1e-9be533c5845e","order_by":3,"name":"Izaak Zoelkarnain Akbar","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Izaak","middleName":"Zoelkarnain","lastName":"Akbar","suffix":""},{"id":495641385,"identity":"ad1cf61f-740e-4931-9f06-d6d7ca05fb15","order_by":4,"name":"Andreas Marojahan Haratua Siagian","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"Marojahan Haratua","lastName":"Siagian","suffix":""},{"id":495641387,"identity":"37904c94-9c37-45ce-9d09-2924c192f6f9","order_by":5,"name":"Essy Dwi Damayanthi","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Essy","middleName":"Dwi","lastName":"Damayanthi","suffix":""},{"id":495641388,"identity":"2db338bf-1cf1-4a37-a1a6-f6153bcaf7be","order_by":6,"name":"Wongso Kesuma","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Wongso","middleName":"","lastName":"Kesuma","suffix":""},{"id":495641390,"identity":"8125f6da-0771-4067-ac20-b1c503858466","order_by":7,"name":"M. Rifqi Farizan Akbar","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Rifqi Farizan","lastName":"Akbar","suffix":""},{"id":495641391,"identity":"8ae19c63-f2c8-49ef-848d-0acbbaaf8476","order_by":8,"name":"Sutan Agung L. Tobing","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Sutan","middleName":"Agung L.","lastName":"Tobing","suffix":""},{"id":495641392,"identity":"fe58fa2a-20bd-4fbb-8946-0115c5ecfe6b","order_by":9,"name":"Candra Kusuma Negara","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Candra","middleName":"Kusuma","lastName":"Negara","suffix":""},{"id":495641393,"identity":"5bed7d6b-c880-4f87-83de-70f955339b30","order_by":10,"name":"Ricky Prawira","email":"","orcid":"","institution":"Universitas Lambung Mangkurat, Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Ricky","middleName":"","lastName":"Prawira","suffix":""}],"badges":[],"createdAt":"2025-08-01 02:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7266645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7266645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88414011,"identity":"150df0b0-5968-4ae5-9d1a-8e697efcdb00","added_by":"auto","created_at":"2025-08-06 08:43:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58079,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristics curve showing the predictive ability of alkaline phosphatase for bone metastasis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7266645/v1/20f58dbc12e94a3d316b7476.png"},{"id":88414014,"identity":"47c150e9-b75b-4357-8988-d068af8de9a1","added_by":"auto","created_at":"2025-08-06 08:43:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58119,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristics curve of the predictive ability of bone metastasis from the Gleason score\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7266645/v1/6bdbd213456a73ab30f2b1e6.png"},{"id":90356930,"identity":"211fb1ca-20cd-43e9-aba3-60cdb69c9ca2","added_by":"auto","created_at":"2025-09-01 21:31:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":787871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7266645/v1/99d14d33-e801-476a-9737-7b8444490e75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the Role of Alkaline Phosphatase and Gleason Score in Predicting Prognosis and Risk of Bone Metastasis in Prostate Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is one of the most common malignancies in men and the second leading cause of cancer death worldwide. This disease has a broad clinical spectrum, ranging from indolent forms to aggressive forms with a tendency to metastasize, particularly to the bones \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Therefore, assessment of prognosis and risk of metastasis is an important aspect in the clinical management of prostate cancer.\u003c/p\u003e\u003cp\u003eCurrently, alkaline phosphatase (ALP) and the Gleason score are widely studied as potential indicators for assessing prostate cancer progression. ALP is an enzyme that is frequently elevated in osteoblastic processes, which are common in bone metastases \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, the Gleason score, which is based on the degree of histological differentiation of the tumor, has long been used to estimate tumor aggressiveness \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. However, the combination of these two parameters as predictors of prognosis and bone metastasis remains a promising area of exploration \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSeveral recent studies indicate that increased ALP levels are associated with a higher risk of skeletal metastases and decreased patient survival \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Other studies have also shown that combining biomarkers such as ALP with prostate-specific antigen (PSA) or Gleason score can improve predictive accuracy compared to using them alone \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, variations in sensitivity and specificity of each parameter are a challenge in clinical application \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study aims to analyze the role of ALP and Gleason score as predictors of prognosis and risk of bone metastasis in prostate cancer patients. It is hoped that the results of this study will contribute to the selection of more appropriate diagnostic and therapeutic strategies, particularly in the early stages of disease management.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis is a quantitative, observational study with a cross-sectional approach conducted at Ulin Regional General Hospital, Banjarmasin. The study took place from January to June 2025 and used secondary data sourced from patient medical records.\u003c/p\u003e\u003cp\u003ePopulation and sample\u003c/p\u003e\u003cp\u003eThe study population included all male patients diagnosed with prostate adenocarcinoma and undergoing relevant laboratory and radiological examinations during the study period. The sampling technique used was total sampling, including all patients who met the inclusion criteria.\u003c/p\u003e\u003cp\u003ePatients included in the sample were those with complete records of alkaline phosphatase (ALP) levels, prostate-specific antigen (PSA) levels, Gleason score results from anatomic pathology, and bone radiology examinations used to detect possible metastases, such as X-rays, CT scans, or bone MRIs. Patients with a history of other metabolic bone diseases such as severe osteoporosis or Paget's disease, as well as those with incomplete medical records, were excluded from the analysis.\u003c/p\u003e\u003cp\u003eData Procedures and Analysis\u003c/p\u003e\u003cp\u003eAll collected data were coded and input into R statistical software version 4.2.2. Data analysis was conducted in two stages: descriptive analysis to describe general patient characteristics, and inferential analysis, which included logistic regression to examine the relationship between ALP and Gleason score and the incidence of bone metastases. An ordinal regression analysis was used to evaluate the contribution of predictors to prognostic classification. To assess predictive accuracy, a Receiver Operating Characteristic (ROC) curve was used to determine the optimal cut-off point for ALP levels in detecting metastases. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eResearch Ethics\u003c/p\u003e\u003cp\u003e This research has received approval from the Health Research Ethics Committee of the Faculty of Medicine and Health Sciences, Lambung Mangkurat University, under letter number 050/KEPK-FKIK ULM/EC/VI/2025. The committee guarantees the confidentiality of patient data and guarantees its use only for scientific purposes.\u003c/p\u003e"},{"header":"Results","content":"\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\u003eCharacteristics of prostate cancer patients in the study (N\u0026thinsp;=\u0026thinsp;48)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian (Q1;Q3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSA (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.0 (85.0; 265.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGleason Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (27.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (54.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone metastases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (58.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: SD\u0026thinsp;=\u0026thinsp;standard deviation; Q1\u0026thinsp;=\u0026thinsp;first quartile; Q3\u0026thinsp;=\u0026thinsp;third quartile; PSA\u0026thinsp;=\u0026thinsp;prostate-specific antigen; ALP\u0026thinsp;=\u0026thinsp;alkaline phosphatase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the results of data collection on the characteristics of 48 prostate cancer patients. The mean age of patients was 67.1 years with a standard deviation of \u0026plusmn;\u0026thinsp;7.2 years. The results of prostate-specific antigen (PSA) levels showed a mean value of 70.5 ng/mL (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;38.2 ng/mL). The median alkaline phosphatase (ALP) level was recorded at 119 U/L with an interquartile range of 85.0 U/L to 265.5 U/L. The average Gleason score of all patients in this study was 7.8 with a variation of approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 points. When categorized by tumor malignancy, more than half of the patients (54.2%) were classified as high or very high risk, with a Gleason score between 8 and 10. A total of 28 of the 48 patients (58.3%) were detected as having bone metastases based on radiological examination results.\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\u003eBone metastasis and prostate cancer prognosis (N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBone metastasis outcome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95 (0.86; 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003e1.07 (1.01; 1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53 (1.10; 2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.56 (1.12; 2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGleason 8\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.42 (0.50; 4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64 (0.18; 2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePredictor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGleason score outcome (prognosis)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02 (-0.06; 0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\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\u003e-0.01 (-0.03; 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.02 (-0.03; -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06 (0.02; 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07 (0.04; 0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: CI\u0026thinsp;=\u0026thinsp;confidence interval, PSA\u0026thinsp;=\u0026thinsp;prostate-specific antigen, ALP\u0026thinsp;=\u0026thinsp;alkaline phosphatase. For ease of interpretation, the change in ALP is calculated per 10 U/L increase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that alkaline phosphatase (ALP) is a strong and consistent predictor, both in univariate (OR\u0026thinsp;=\u0026thinsp;1.53; p\u0026thinsp;=\u0026thinsp;0.014) and multivariate (OR\u0026thinsp;=\u0026thinsp;1.56; p\u0026thinsp;=\u0026thinsp;0.008) analyses. This suggests that ALP is reliable as a single indicator and as part of a combined model in predicting bone metastasis in prostate cancer patients.\u003c/p\u003e\u003cp\u003eIn contrast, Gleason score did not show a significant association with bone metastasis in either model, with p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, thus limiting its use in the context of bone metastasis prediction. Meanwhile, PSA levels only showed a significant association in the univariate model (p\u0026thinsp;=\u0026thinsp;0.022), but not in the multivariate model, possibly due to a more dominant interaction or collinearity with ALP. Age was also not significant in either model.\u003c/p\u003e\u003cp\u003eFor prognostic outcomes, it was shown that every 10 U/L increase in ALP levels was associated with a 0.07-point increase in Gleason score (p\u0026thinsp;=\u0026thinsp;0.001) in the multivariate model. Furthermore, PSA was also significantly negatively correlated with Gleason score (β = \u0026minus;\u0026thinsp;0.02; p\u0026thinsp;=\u0026thinsp;0.004), indicating that high PSA values tend to be associated with a worse tumor prognosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that alkaline phosphatase (ALP) has excellent ability to differentiate prostate cancer patients with and without bone metastases, with an AUC of 0.923 (95% CI: 0.843\u0026ndash;1.000; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal cutoff point for ALP levels was 122.35 U/L, which provided a sensitivity of 78.6% and a specificity of 95%, reflecting a strong balance between detection ability and classification accuracy.\u003c/p\u003e\u003cp\u003eThe boxplot graph reinforces these results by showing that ALP levels in patients with bone metastases were consistently higher than in patients without metastases. Most ALP values in the metastatic group were above the cutoff point of 122.35 U/L, while those in the non-metastatic group were generally below it. One outlier in the negative group did not disrupt the clear distribution pattern, further confirming ALP's role as a reliable predictive indicator.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast to alkaline phosphatase, the results of this study do not support the use of the Gleason score as a predictor of bone metastasis. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the odds ratio of Gleason 8\u0026ndash;10 for bone metastasis was not statistically significant, both in univariate analysis (OR: 1.42; 95% CI: 0.50\u0026ndash;4.00; p\u0026thinsp;=\u0026thinsp;0.502) and multivariate analysis (OR: 0.64; 95% CI: 0.18\u0026ndash;2.23; p\u0026thinsp;=\u0026thinsp;0.487). Furthermore, ROC curve analysis showed that the area under the curve (AUC) of the Gleason score was only 60.4% with an insignificant p value of 0.221. This value indicates a low discriminatory ability in distinguishing patients with bone metastasis from those without.\u003c/p\u003e\u003cp\u003eThe optimal cutoff point for the Gleason score was 7.5, resulting in a sensitivity of 60.7% and a specificity of 55.0%, with an estimated accuracy of approximately 58%. Although the Gleason score plays an important role in determining prostate cancer prognosis, these results indicate that its ability to predict bone metastasis is not strong enough to be applied clinically as a single parameter.\u003c/p\u003e\u003cp\u003eThese findings are consistent with previous regression data, where ALP performed better in predicting both bone metastasis and prostate cancer prognosis. In the context of this study, each 10 U/L increase in ALP levels was associated with an increase in Gleason score of approximately 0.07 points (95% CI: 0.04\u0026ndash;0.10; p\u0026thinsp;=\u0026thinsp;0.001), thus confirming the role of alkaline phosphatase as a superior predictor of prognosis compared to Gleason score, particularly in the context of early detection of bone metastasis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study shows that alkaline phosphatase (ALP) levels have a strong and significant predictive value for bone metastasis and prognosis in prostate cancer patients, while Gleason score does not show a significant association in predicting metastasis. These results confirm that ALP, either as a single biomarker or in combination with prostate-specific antigen (PSA), can be a more sensitive indicator in the early detection and assessment of prostate cancer progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Role of ALP in Predicting Bone Metastasis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRegression test results showed that every 10 U/L increase in ALP significantly increased the risk of bone metastasis. This finding is consistent with previous literature showing that ALP is a marker of increased osteoblastic activity and bone remodeling in cases of bone metastasis \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. ALP not only reflects the presence of metastases but also contributes to the severity of skeletal lesions. A study by Lee et al. found that high ALP was associated with a greater burden of bone metastases and poorer survival in metastatic prostate cancer \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA meta-analysis by Lin et al. also showed that increased ALP consistently correlated with decreased overall survival (OS) and cancer-specific survival (CSS) in patients with advanced prostate cancer \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. This study strengthens our research findings that ALP is a stronger indicator than Gleason score in predicting metastasis. The use of ALP as an early predictor has advantages in prostate cancer management, especially in populations who have not undergone bone imaging. With a cut-off point of 122.35 U/L, ALP in this study demonstrated an AUC of 0.923, representing very high classification accuracy. This surpasses the performance of Gleason in the same context.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGleason Score in Predicting Metastasis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Gleason score is a standard parameter for assessing tumor aggressiveness based on histopathology. However, in this study, the Gleason score did not show a significant association with the incidence of bone metastases. This is supported by several recent studies showing that the Gleason score, while important in risk stratification, is not always a specific predictor of skeletal metastases \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor example, a study by Matsumoto et al. found that a Gleason score\u0026thinsp;\u0026ge;\u0026thinsp;8 did not always correlate with bone metastases at the time of initial diagnosis, especially in patients with normal PSA and ALP levels \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. For example, a study by Matsumoto et al. found that a Gleason score\u0026thinsp;\u0026ge;\u0026thinsp;8 did not always correlate with bone metastases at the time of initial diagnosis, especially in patients with normal PSA and ALP levels.\u003c/p\u003e\u003cp\u003eFurthermore, the Gleason AUC value in this study was only 60.4%, with low sensitivity and specificity. This means the Gleason score has limited ability to differentiate between patients with and without bone metastases. Therefore, the use of Gleason as the sole predictive parameter in clinical practice should be reconsidered, and it should be combined with other biomarkers such as ALP or PSA to improve predictive accuracy.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePSA, Interaction with ALP, and Clinical Implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePSA is still considered a useful early biomarker in prostate cancer screening and monitoring. In this study, PSA was only significant in the univariate model but lost its significance in the multivariate model, suggesting possible collinearity or a mediating effect by ALP.\u003c/p\u003e\u003cp\u003eResearch by Gandhi et al. stated that PSA has a non-linear relationship to bone metastasis and that PSA sensitivity is decreased in highly differentiated cancers or rare histological variants.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Therefore, the combination of PSA and ALP becomes important. A cohort study by Yuan et al. found that a prediction model combining PSA, ALP, and the neutrophil/lymphocyte ratio provided a higher AUC than using PSA alone in predicting bone metastases \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe clinical implications of these findings are significant. Identification of patients at high risk of bone metastases through inexpensive and easily performed biomarkers such as ALP could expedite clinical decisions regarding further imaging studies such as bone scans or PET-CT. Furthermore, ALP can be used to monitor response to therapies, such as hormone therapy or chemotherapy, as ALP values typically decrease with response to treatment \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Relationship of ALP with Prognosis and Gleason Score\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough the Gleason score was not significant in predicting metastasis, regression analysis showed that ALP had a positive correlation with the Gleason score, meaning that the higher the ALP, the higher the Gleason score. This suggests that ALP may indirectly reflect tumor aggressiveness.\u003c/p\u003e\u003cp\u003eThis finding is supported by a study by Hu et al., which stated that patients with high Gleason scores and high ALP levels had a greater risk of cancer death within two years of diagnosis \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Pathophysiologically, this can be explained by the fact that high-grade tumors are more likely to metastasize and cause increased osteoblastic activity, which is reflected in increased ALP levels. \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdditional Biomarkers and Predictive Model Combinations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBeyond ALP and PSA, various other biomarkers are currently being developed to improve the accuracy of metastasis prediction, such as N-telopeptide, bone-specific ALP, lactate dehydrogenase (LDH), and circulating tumor cells (CTC) \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. A study by Zhao et al. found that the combination of bone ALP, LDH, and Gleason score was able to increase the accuracy of metastasis classification by up to 91% \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, multibiomarker predictive models in the future may be a more reliable approach in the management of advanced prostate cancer.\u003c/p\u003e\u003cp\u003eIn the context of clinical use, the development of risk models based on quantitative biomarkers such as ALP also aligns with the trend of personalized medicine. Research by Kim et al. showed that patients with high ALP levels and Gleason grade\u0026thinsp;\u0026ge;\u0026thinsp;8 benefited more from early systemic therapy than from local therapy alone \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition to bone-specific ALP and LDH, several studies have also explored other biomarkers such as circulating tumor cells (CTCs) and miRNA as tools for early detection of bone metastasis. For example, O'Connor et al. in their meta-analysis reported that the integration of multiple serum biomarkers provided higher sensitivity and specificity than using a single biomarker alone \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. This is important for patients with ambiguous imaging results or atypical PSA levels.\u003c/p\u003e\u003cp\u003eFurthermore, Park et al. emphasized the prognostic role of LDH and ALP in determining the response to systemic therapy, particularly in patients undergoing hormone therapy \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. LDH is considered to reflect the overall tumor burden and high metabolic activity of the neoplasm, so when combined with ALP it can improve the accuracy of risk stratification.\u003c/p\u003e\u003cp\u003eZhao et al. also reported that the prediction of bone metastasis was significantly improved when serum markers were combined with PET/CT imaging using the 18F-NaF tracer, with AUC reaching more than 0.90 \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These findings open up the possibility of using a hybrid approach between biomarkers and imaging for more precise diagnosis, especially in hospital settings with limited imaging resources.\u003c/p\u003e\u003cp\u003eMeanwhile, a study by Song et al. developed a predictive model using the Gleason score combined with ALP and PSA that was shown to be able to classify patients into low, intermediate, and high risk groups for bone metastasis and premature death \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This model is suitable for application in risk stratification-based management schemes and is the basis of the precision medicine approach.\u003c/p\u003e\u003cp\u003eTang et al. have even developed a prognostic nomogram based on Gleason score, ALP, and bone metastasis status that is able to predict 3- and 5-year survival with a high degree of accuracy \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This nomogram is not only useful in daily clinical practice, but also in evidence-based therapeutic decision making.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Limitations and Suggestions for Further Research\u003c/b\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study is limited by its relatively small sample size and retrospective nature. Therefore, generalization of the results should be done with caution. Large-scale prospective studies integrating various biomarkers with machine learning-based prediction models are highly recommended to strengthen the validity of these findings. Furthermore, it is important to consider other parameters such as bone pain, ECOG status, and bone density in future prediction models.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that alkaline phosphatase (ALP) levels are a significant predictor of bone metastasis and prognosis in prostate cancer patients. ALP demonstrated high predictive power, both as a single biomarker and in a multivariate model, with better classification accuracy than the Gleason score. Conversely, the Gleason score did not show a significant association with bone metastasis, although it remains relevant in the context of overall prognosis. These findings underscore the importance of integrating biochemical biomarkers, particularly ALP, into more personalized and precise prostate cancer risk prediction systems and management strategies.\u003c/p\u003e\u003cp\u003eThe ALP cutoff of 122.35 U/L can serve as a clinical reference for early bone metastasis risk assessment, helping expedite decisions about further testing or therapeutic intervention. This study supports the need for a multimodal approach combining ALP, PSA, and other clinical data to develop more accurate diagnostic and prognostic strategies for prostate cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThis research was self-funded by the authors. No external financial support, grant, or sponsorship was received from any individual, institution, or organization during the planning, execution, or publication of this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHusna D. Putera, Muhammad Iqbal, Zairin Noor, Sutan Agung L. Tobing, Andreas M. H. Siagian, Izaak Z. Akbar, Essy D. Damayanthi, Wongso Kesuma, and M. Rifqi F. Akbar conducted and prepared the research process.Candra Kusuma Negara and Ricky Prawira prepared the publication article and wrote the discussion.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors would like to thank the Director of Ulin Regional General Hospital, Banjarmasin, and all staff from the medical records department and the clinical pathology laboratory for their permission and support in collecting data for this study. They also thank the Health Research Ethics Committee of the Faculty of Medicine and Health Sciences, Lambung Mangkurat University, for their ethical approval and guidance in conducting this study. Without their support, this research would not have been possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMori K, Janisch F, Parizi MK, et al. Prognostic value of alkaline phosphatase in hormone-sensitive prostate cancer: a systematic review and meta-analysis. Int J Clin Oncol. 2020;25(2):247\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10147-019-01578-9\u003c/span\u003e\u003cspan address=\"10.1007/s10147-019-01578-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsuzuki S, Kawano S, Fukuokaya W, et al. 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Ann Med. 2022;54(1):2051\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07853890.2022.2052533\u003c/span\u003e\u003cspan address=\"10.1080/07853890.2022.2052533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alkaline Phosphatase, Biomarker, Prostate Cancer, Bone Metastasis, Prognosis, Gleason Score","lastPublishedDoi":"10.21203/rs.3.rs-7266645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7266645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Prostate cancer ranks as one of the most common malignancies in men and is the second leading cause of cancer-related death. Bone metastasis is a frequent and severe complication, significantly worsening patient prognosis and quality of life. Early identification of bone metastasis risk is critical for guiding clinical decisions. Alkaline phosphatase (ALP) and Gleason score have been studied extensively as predictive markers, though their effectiveness remains debated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to evaluate the predictive value of ALP levels and Gleason scores in assessing bone metastasis risk and prognosis in prostate cancer patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional analytical observational study was conducted at Ulin Regional General Hospital, Banjarmasin, from January to June 2025. Forty-eight prostate cancer patients meeting the inclusion criteria were selected. Data including ALP, PSA levels, Gleason score, and radiologic findings were analyzed using logistic and ordinal regression. Predictive accuracy was assessed through ROC curve analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Bone metastases were present in 58.3% of patients. ALP was a significant independent predictor of bone metastasis (OR: 1.56; p = 0.008) and showed a positive correlation with Gleason score (β = 0.07; p = 0.001). In contrast, the Gleason score alone was not significantly associated with metastasis (p \u0026gt; 0.05). The ROC curve for ALP demonstrated high diagnostic accuracy with an AUC of 0.923 and an optimal cut-off of 122.35 U/L (sensitivity 78.6%, specificity 95%). \u003cstrong\u003eConclusion:\u003c/strong\u003e ALP outperforms Gleason score as a predictor of bone metastasis and may serve as a valuable prognostic marker in prostate cancer management.\u003c/p\u003e","manuscriptTitle":"Analysis of the Role of Alkaline Phosphatase and Gleason Score in Predicting Prognosis and Risk of Bone Metastasis in Prostate Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 08:43:54","doi":"10.21203/rs.3.rs-7266645/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"280bf075-0fdd-4920-8c0f-c793ba401fc7","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T21:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 08:43:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7266645","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7266645","identity":"rs-7266645","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}