Preliminary Evidence of Systemic Inflammatory Markers in Differentiating Gleason 3+4 and 4+3 Prostate Cancer

Preliminary Evidence of Systemic Inflammatory Markers in Differentiating Gleason 3+4 and 4+3 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 Preliminary Evidence of Systemic Inflammatory Markers in Differentiating Gleason 3+4 and 4+3 Prostate Cancer Feng Guo, Aerken Maolake, Jianhua Zhao, Bide Liu, Wenlong Fan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9235683/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Gleason score 7 prostate cancer comprises two biologically distinct subgroups—Gleason 3 + 4 and Gleason 4 + 3—with differing prognoses and therapeutic implications. However, reliable non-invasive methods to distinguish these subtypes prior to treatment remain limited. This exploratory study evaluated whether systemic inflammatory markers and prostate volume (PV) provide preliminary signals differentiating Gleason 3 + 4 from 4 + 3 disease. We analyzed 26 patients with biopsy-confirmed Gleason score 7 prostate cancer (3 + 4, n = 15; 4 + 3, n = 11). Pre-biopsy complete blood counts were used to calculate inflammation-based indices, including neutrophil-to-lymphocyte-to-platelet ratio (NLPR), systemic immune-inflammation index (SII), and others. Prostate volume (PV) was measured by a transrectal ultrasonography. Group comparisons used non-parametric tests, and receiver operating characteristic (ROC) analyses were performed. NLPR and PV showed modest separation between groups (NLPR AUC = 0.715; PV AUC = 0.709). Most other inflammatory markers-including NLR, PLR, MLR, AISI and SIRI-showed limited discriminatory performance. A multivariable combining NLPR and PV yielded an AUC of 0.776, which improved to 0.82 after adding SII. Given the very small cohort size (n = 26), these findings should be regarded strictly as preliminary and hypothesis-generating. This exploratory analysis suggests that NLPR and PV may reflect biological difference between Gleason 3 + 4 and 4 + 3 tumors. These observations require validation in larger, prospective, multi-institutional cohorts before any clinical interpretation or application can be considered. Prostate cancer Gleason score inflammation NLPR prostate volume biomarkers exploratory study Figures Figure 1 Figure 2 Figure 3 Introduction Prostate cancer (PCa) exhibits wide biological heterogeneity, ranging from indolent disease to highly aggressive tumors capable of rapid progression and metastasis [ 1 ]. Among the available grading systems, the Gleason score remains the cornerstone for prognostic stratification and treatment decision-making in PCa [ 2 ]. Gleason score 7—which encompasses the subgroups 3 + 4 and 4 + 3—presents a particularly challenging category. Although both patterns sum to 7, they differ markedly in histological architecture and clinical behavior. Gleason 3 + 4 cancers are predominantly composed of lower-grade pattern 3 glands, whereas Gleason 4 + 3 tumors contain a greater proportion of the more aggressive pattern 4 component [ 3 ]. Multiple studies have demonstrated that Gleason 4 + 3 is associated with higher risk of biochemical recurrence, adverse pathological features, and worse long-term outcomes compared with Gleason 3 + 4 disease [ 4 , 5 ]. Therefore, distinguishing between these two subtypes prior to definitive treatment is clinically meaningful and may help refine management strategies. Given these prognostic differences, there is growing interest in identifying non-invasive biomarkers that may assist in preoperative risk stratification. Prostate-specific antigen (PSA)-related metrics such as PSA density (PSAD) and prostate volume (PV), as well as systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and others—have been investigated for their diagnostic or prognostic potential in PCa. These markers which can be obtained through routine clinical testing may reflect underlying tumor burden or the host inflammatory response associated with more aggressive disease. However, it remains unclear whether these readily available parameters can meaningfully differentiate Gleason 3 + 4 from 4 + 3 PCa, given their overlapping clinical characteristics and shared classification within intermediate-risk disease. To address this gap, we conducted a small exploratory analysis evaluating the diagnostic performance of PSAD, PV, and several systemic inflammatory indices in distinguishing these two Gleason 7 subtypes. Our aim was to determine whether a combination of simple, accessible biomarkers could generate preliminary evidence to support improved pre-treatment risk stratification within this clinically important group. Method and Materials Study Design and Patient Selection This retrospective study was derived from a consecutively maintained clinical cohort of 116 patients who underwent ultrasound-guided prostate biopsy between 2016 and 2022 at the Urology Department of the People’s Hospital of Xinjiang Uygur Autonomous Region, China. Among these, 66 patients were diagnosed with prostate cancer. For the present analysis, 26 patients with biopsy-confirmed Gleason score 7 prostate cancer were included and stratified into Gleason 3 + 4 (n = 15) and Gleason 4 + 3 (n = 11) subgroups for comparative evaluation. The study protocol was approved by the institutional ethics committee, and the requirement for informed consent was waived owing to the retrospective study design. Laboratory Parameters and Inflammatory Marker Calculation Peripheral blood samples were collected on the first day after hospital admission. Laboratory tests included total PSA, free PSA, and complete blood counts (CBC) components. Based on CBC results, the following systemic inflammatory markers were calculated: Neutrophil-to-lymphocyte ratio (NLR): neutrophil count ÷ lymphocyte count Platelet-to-lymphocyte ratio (PLR): platelet count ÷ lymphocyte count Monocyte-to-lymphocyte ratio (MLR): monocyte count ÷ lymphocyte count Systemic immune-inflammation index (SII): platelet count × neutrophil count ÷ lymphocyte count Aggregate index of systemic inflammation (AISI): neutrophil count × monocyte count × platelet count ÷ lymphocyte count Systemic inflammation response index (SIRI): neutrophil count × monocyte count ÷ lymphocyte count Neutrophil-to-lymphocyte ratio (NLR): neutrophil count ÷ (lymphocyte count × platelet count). Inflammatory indices were selected based on previous literature linking systemic inflammation with tumor aggressiveness. Prostate Volume and PSA Density Prostate volume (PV) was measured with transrectal ultrasound using the standard ellipsoid formula: PV = π/6 × width × length × height. PSA density (PSAD) was calculated as: PSAD= total PSA ÷ prostate volume. Statistical Analysis Given the small sample size and non-normal data distribution, group comparisons were performed using the Mann–Whitney U test. Receiver operating characteristic (ROC) curves were constructed to assess the ability of individual markers and combined models to distinguish Gleason 3 + 4 from 4 + 3 disease. Diagnostic metrics included area under the ROC curve (AUC), sensitivity, specificity, and 95% confidence intervals (CI). Multivariable logistic regression models were constructed to evaluate whether the combination of inflammatory markers and prostate volume improved discriminatory performance. All statistical analyses were performed using GraphPad Prism. A two-tailed p-value < 0.05 was considered statistically significant. Results Patient Characteristics Baseline clinical and hematological characteristics of the 26 patients with Gleason score 7 prostate cancer are summarized in Table 1 . Among them, 15 patients had Gleason 3 + 4 and 11 had Gleason 4 + 3. No statistically significant differences were observed in age, PSA, or basic hematologic parameters between the two groups. Table 1 Baseline Clinical and Laboratory Characteristics of Patients with Gleason 3 + 4 and Gleason 4 + 3 Prostate Cancer. Characteristic Gleason 3 + 4 (n = 15) Median (IQR) Gleason 4 + 3 (n = 11) Median (IQR) P value Mann-Whitney test Age, years 65.00 (10.50) 65.00 (9.00) 0.8282 WBC, ×10 9 /L 5.43 (1.69) 7.32 (3.57) 0.0892 Neutrophils, ×10 9 /L 3.40 (1.21) 4.73 (1.40) 0.1176 Lymphocytes,×10 9 /L 1.56 (0.42) 1.55 (0.78) 0.6925 Monocytes, ×10 9 /L 0.42 (0.30) 0.52 (0.22) 0.4352 Platelet, ×10 9 /L 217.00 (78.50) 187.00 (59.50) 0.1599 tPSA 11.10 (18.03) 27.57 (55.53) 0.4130 Inflammatory Markers and Prostate Volume Although most inflammatory indices showed no significant differences between groups, both the neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and prostate volume (PV) tended to be higher values in the Gleason 4 + 3 group, NLPR: p = 0.0687; PV: p = 0.0774. These trends did not reach statistical significance but suggest potential biological differences (Fig. 1 ). ROC Curve Analysis of Individual Markers and Combined Predictive Model ROC curve analysis demonstrated that NLPR had the highest individual discriminatory ability for differentiating Gleason 4 + 3 from 3 + 4, with an AUC of 0.7152 (95% CI: 0.5109–0.9194, P = 0.065), followed by PV with an AUC of 0.7091 (95% CI: 0.5077–0.9105, P = 0.073). In contrast, PSAD showed no discriminative value (AUC = 0.5030, P = 0.979) (Fig. 2 , Table 2 ). The inflammatory markers NLR, PLR, MLR, AISI, SII, and SIRI also showed poor discriminative performance, with AUCs 0.05.(Table 2 ). A multivariable logistic regression model combining PV and NLPR significantly improved diagnostic performance (AUC = 0.7758, 95% CI: 0.5941–0.9574, P = 0.0182), and the inclusion of SII further enhanced the model (AUC = 0.8121, 95% CI: 0.6468–0.9774, P = 0.0075) (Fig. 3 , Table 3 ). These results suggest that integrated inflammatory and anatomical information may better capture differences between Gleason 3 + 4 and 4 + 3 tumors compared with individual markers alone. Table 2 Diagnostic performance of inflammatory markers, PSAD and PV in Differentiating Gleason 3 + 4 from Gleason4 + 3 Prostate Cancer. Variables AUC 95% interval confidence P-value NLR 0.6242 0.3920 ~ 0.8565 0.287 MLR 0.5242 0.2978~ 0.7507 0.836 PLR 0.5091 0.2645~ 0.7536 0.938 SII 0.5818 0.3525 ~ 0.811 0.484 NLPR 0.7152 0.5109 ~ 0.9194 0.065 AISI 0.5030 0.2693 ~ 0.7367 0.979 SIRI 0.5697 0.3394 ~ 0.8000 0.551 PV 0.7091 0.5077 ~ 0.9105 0.073 PSAD 0.5030 0.2534 ~ 0.7526 0.979 Table 3 Multivariable Logistic Regression Models Combing Inflammatory Markers and Prostate Volume. Variables AUC PPV NPV 95% interval confidence P-value PV+NLPR 0.7758 73.33% 63.64% 0.5941 ~ 0.9574 0.0182 PV+NLPR + SII 0.8121 80.00% 72.73% 0.6468 ~ 0.9774 0.0075 PPV=Positive Predictive value; NPV=Negative Predictive value. Discussion In this exploratory analysis, PSA density (PSAD) showed minimal ability to discriminate between Gleason 3 + 4 and 4 + 3 prostate cancer, with an AUC of 0.5032—essentially equivalent to random classification. Although PSAD is widely used to differentiate prostate cancer from benign prostatic hyperplasia and other non-malignant conditions [ 6 – 8 ], its utility appears limited for finer risk stratification within Gleason score 7 disease. This limited performance may reflect overlapping PSA secretion patterns and similar prostate volume characteristics between Gleason 3 + 4 and 4 + 3 tumors, which reduce its discriminatory capacity in this specific clinical setting. In contrast, prostate volume (PV) and systemic inflammatory indices—particularly the neutrophil-to-lymphocyte-to-platelet ratio (NLPR)—showed moderate discriminatory signals, with AUCs of 0.7091 and 0.7152, respectively. When incorporated into a multivariable logistic regression model, PV and NLPR yielded an improved AUC of 0.7758, which further increased to 0.8121 after inclusion of the systemic immune-inflammation index (SII). While these findings suggest that systemic inflammatory responses and anatomical characteristics may capture biological differences between Gleason 3 + 4 and 4 + 3 tumors, the observed model performance should be interpreted with caution given the small sample size and potential for overfitting. From a clinical perspective, distinguishing these two Gleason 7 subtypes is important, as Gleason 4 + 3 cancers are associated with higher rates of biochemical recurrence, adverse pathological features, and worse long-term outcomes [ 9 , 10 ]. Therefore, identifying noninvasive biomarkers that may aid in pre-treatment risk stratification remains a clinically relevant objective. The present findings provide preliminary evidence that inflammation-based indices, particularly NLPR, in combination with prostate volume, may offer additional information beyond PSA-derived parameters. However, several limitations must be acknowledged. First, the study cohort was small (n = 26), substantially limiting statistical power and increasing the likelihood of overfitting. Second, the retrospective, single-center design may introduce selection bias and restrict generalizability. Third, systemic inflammatory markers can be influenced by non-cancer-related conditions—including subclinical infections, medications, and chronic inflammatory disorders—which were not fully controlled for in this study. Accordingly, these findings should be considered strictly hypothesis-generating rather than confirmatory. Future studies should validate these preliminary findings in larger, multi-institutional cohorts with standardized laboratories and imaging protocols. Integrating inflammatory indices into predictive nomograms—potentially alongside MRI-based parameters or genomic classifiers—may further improve risk stratification. Prospective investigations are also needed to determine whether these biomarkers correlate with long-term oncologic outcomes and whether they have clinical utility in guiding management decisions. In summary, PSAD does not appear to be useful for differentiating Gleason 3 + 4 from 4 + 3 prostate cancer in this cohort. In contrast, systemic inflammatory indices, particularly NLPR, in combination with prostate volume, demonstrated preliminary discriminatory signals. These findings suggest the potential role for inflammation-related biomarkers in refining risk stratification within intermediate-risk prostate cancer, although validation in large studies is essential before clinical application. Declarations Disclosure Statement The authors have nothing to disclose. Conflict of Interest No potential conflict of interest relevant to this article was reported. Funding This research received no external funding. Ethics Statement This retrospective study was approved by the Medical Ethics Committee of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang, China. Informed Consent Statement Informed consent was waived due to the retrospective nature of the study and the analysis used anonymous clinical data. Registry and the Registration No. of the study: N/A Generative AI Statement ChatGPT (OpenAI) was used for language editing only. All scientific content and conclusions are the responsibility of the authors. Data Availability Statement Data is available from the corresponding author upon reasonable request. Animal Studies: N/A Author Contribution Jiuzhi Li and Aerken Maolake: Conception and design, administrative support and provision of study materials. Feng Guo, Zecheng Ni, Jianhua Zhao, Bide Liu, and Wenlong Fan: Collection and assembly of data. Fen Guo and Aerken Maolake: Data analysis and interpretation. All authors wrote the manuscript and approved the final version of the manuscript. Data Availability Data is available from the corresponding author upon reasonable request. References Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. Epstein JI, Egevad L, Amin MB, et al. The 2014 ISUP Gleason grading system: Definition and implications. Am J Surg Pathol. 2016;40(2):244–52. Epstein JI, Allsbrook WC Jr, Amin MB, Egevad LL, ISUP Grading Committee. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol. 2005;29(9):1228–1242. Pierorazio PM, Walsh PC, Partin AW, Epstein JI. Contemporary Gleason grading and the risk of prostate cancer mortality: a population-based study. Eur Urol. 2013;63(3):428–35. Ross HM, Kryvenko ON, Cowan JE, Simko JP, Wheeler TM, Epstein JI. The prognostic significance of Gleason score 3 + 4=7 versus 4 + 3=7 prostate cancer: a retrospective analysis of radical prostatectomy outcomes. BJU Int. 2007;99(3):545–50. Brawer MK. Prostate-specific antigen: current status. CA Cancer J Clin. 1999;49(5):264–81. Loeb S, et al. The role of PSA density in the diagnosis of prostate cancer. Nat Rev Urol. 2012;9(7):369–77. Maolake A, et al. Combination of PSA density and MLR improves the diagnostic accuracy of prostate cancer. Front Oncol. 2025;15:1570584. 10.3389/fonc.2025.1570584 . Pierorazio PM, Walsh PC, Partin AW, Epstein JI. Prognostic Gleason grade grouping: data based on the modified Gleason scoring system. BJU Int. 2013;111(5):753–60. 10.1111/j.1464-410X.2012.11611 . Van Leenders GJ, et al. Gleason 4 + 3 versus Gleason 3 + 4 prostate cancer: implications for prognosis and treatment. Histopathology. 2016;69(6):874–81. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 May, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 29 Mar, 2026 Editor invited by journal 27 Mar, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 26 Mar, 2026 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. 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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-9235683","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614710711,"identity":"d291ad8c-2486-4243-8fa8-09b89000cec4","order_by":0,"name":"Feng Guo","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Guo","suffix":""},{"id":614710712,"identity":"8a3e3bf8-818e-4938-b5c0-ef1a774ea5a3","order_by":1,"name":"Aerken Maolake","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYHACZgjFDmJUgPjMDURqYQYxzoAYjKRoYWwDsQho4W/vfWzwccc2eX5mZiBjXm00fztQy4+KbTi1SJw5bpw488xtw5nNbEDGtuO5Mw4zNjD2nLmN25obacyHedtuM244zGB8mHfbsdwGoBZmxjbcWuTvP2M+/Lfttv2Gw+yfD/+dcyx3PiEtBjfYmJOBChI3HOYxTmZsqMndQEiL4Zk0ZsPettvJM5t5ig17jh3I3QjUchCfX+SOH2OW+Nl227afvX2zxI+autx55w8ffPCjAo/30cBhMHmAaPVAUEeK4lEwCkbBKBghAAA1/F1hc1a0mwAAAABJRU5ErkJggg==","orcid":"","institution":"Roswell Park Comprehensive Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Aerken","middleName":"","lastName":"Maolake","suffix":""},{"id":614710713,"identity":"29ae5b3f-a2d9-4ce3-9467-d711b92de6a8","order_by":2,"name":"Jianhua Zhao","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Zhao","suffix":""},{"id":614710714,"identity":"4fced6cb-9ff8-460d-bafb-5201feaaf8ab","order_by":3,"name":"Bide Liu","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Bide","middleName":"","lastName":"Liu","suffix":""},{"id":614710715,"identity":"41241420-bb8c-4c55-b52c-ef0202e40326","order_by":4,"name":"Wenlong Fan","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Wenlong","middleName":"","lastName":"Fan","suffix":""},{"id":614710716,"identity":"2712328b-7058-40de-9055-acb9db6e775d","order_by":5,"name":"Zecheng Ni","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Zecheng","middleName":"","lastName":"Ni","suffix":""},{"id":614710717,"identity":"ae72555b-8ccc-4fc9-8018-f0b2b4cc5421","order_by":6,"name":"Jiuzhi Li","email":"","orcid":"","institution":"People's Hospital of Xinjiang Uygur Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Jiuzhi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-03-26 14:56:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9235683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9235683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105982553,"identity":"851a3cfc-2923-4e95-93a7-c4dedbeda568","added_by":"auto","created_at":"2026-04-02 07:02:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106365,"visible":true,"origin":"","legend":"\u003cp\u003eViolin Plots of NLPR and Prostate Volume between Gleason 3+4 and 4+3 Prostate Cancer\u003c/p\u003e\n\u003cp\u003eDistribution of NLPR and PV in patients with Gleason 3+4 and 4+3 scores. Median and interquartile ranges are displayed. Statistical comparison was conducted using the Mann–Whitney U test. NLPR (P = 0.0687); PV (P = 0.0774).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9235683/v1/7ef512ebc58cb0aedfb7d971.jpeg"},{"id":105982555,"identity":"5c0f58b5-749b-4788-a58a-0a6482ff95b8","added_by":"auto","created_at":"2026-04-02 07:02:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186065,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curves for Individual Markers\u003c/p\u003e\n\u003cp\u003eROC curves showing the discriminatory ability of NLPR (AUC = 0.7152), PV (AUC = 0.7091), and SII (AUC = 0.5818) in distinguishing Gleason 3+4 from 4+3. NLPR and PV showed fair discrimination.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9235683/v1/fb5b18f14bc444f5322e76cf.jpeg"},{"id":105982554,"identity":"c18f0eba-d0b0-4132-90fb-013f83499de9","added_by":"auto","created_at":"2026-04-02 07:02:49","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189677,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve of Combined Predictive Model: PV + NLPR + SII\u003c/p\u003e\n\u003cp\u003eMultivariable model combining prostate volume, neutrophil-to-lymphocyte-platelet ratio (NLPR), and systemic immune-inflammation index (SII) achieved an AUC of 0.8121 (P = 0.0075), with improved discrimination between Gleason 3+4 and 4+3 tumors.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9235683/v1/404316fcdcb9f03bf829c314.jpeg"},{"id":106401609,"identity":"c3daa664-2eb7-498d-b1f4-f885a93ea5b8","added_by":"auto","created_at":"2026-04-08 09:08:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1126642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9235683/v1/d4798085-77a9-4ced-a455-025ef9930cff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preliminary Evidence of Systemic Inflammatory Markers in Differentiating Gleason 3+4 and 4+3 Prostate Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) exhibits wide biological heterogeneity, ranging from indolent disease to highly aggressive tumors capable of rapid progression and metastasis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the available grading systems, the Gleason score remains the cornerstone for prognostic stratification and treatment decision-making in PCa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Gleason score 7\u0026mdash;which encompasses the subgroups 3\u0026thinsp;+\u0026thinsp;4 and 4\u0026thinsp;+\u0026thinsp;3\u0026mdash;presents a particularly challenging category. Although both patterns sum to 7, they differ markedly in histological architecture and clinical behavior. Gleason 3\u0026thinsp;+\u0026thinsp;4 cancers are predominantly composed of lower-grade pattern 3 glands, whereas Gleason 4\u0026thinsp;+\u0026thinsp;3 tumors contain a greater proportion of the more aggressive pattern 4 component [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Multiple studies have demonstrated that Gleason 4\u0026thinsp;+\u0026thinsp;3 is associated with higher risk of biochemical recurrence, adverse pathological features, and worse long-term outcomes compared with Gleason 3\u0026thinsp;+\u0026thinsp;4 disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, distinguishing between these two subtypes prior to definitive treatment is clinically meaningful and may help refine management strategies.\u003c/p\u003e \u003cp\u003eGiven these prognostic differences, there is growing interest in identifying non-invasive biomarkers that may assist in preoperative risk stratification. Prostate-specific antigen (PSA)-related metrics such as PSA density (PSAD) and prostate volume (PV), as well as systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and others\u0026mdash;have been investigated for their diagnostic or prognostic potential in PCa. These markers which can be obtained through routine clinical testing may reflect underlying tumor burden or the host inflammatory response associated with more aggressive disease.\u003c/p\u003e \u003cp\u003eHowever, it remains unclear whether these readily available parameters can meaningfully differentiate Gleason 3\u0026thinsp;+\u0026thinsp;4 from 4\u0026thinsp;+\u0026thinsp;3 PCa, given their overlapping clinical characteristics and shared classification within intermediate-risk disease. To address this gap, we conducted a small exploratory analysis evaluating the diagnostic performance of PSAD, PV, and several systemic inflammatory indices in distinguishing these two Gleason 7 subtypes. Our aim was to determine whether a combination of simple, accessible biomarkers could generate preliminary evidence to support improved pre-treatment risk stratification within this clinically important group.\u003c/p\u003e"},{"header":"Method and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patient Selection\u003c/h2\u003e \u003cp\u003eThis retrospective study was derived from a consecutively maintained clinical cohort of 116 patients who underwent ultrasound-guided prostate biopsy between 2016 and 2022 at the Urology Department of the People\u0026rsquo;s Hospital of Xinjiang Uygur Autonomous Region, China.\u003c/p\u003e \u003cp\u003eAmong these, 66 patients were diagnosed with prostate cancer. For the present analysis, 26 patients with biopsy-confirmed Gleason score 7 prostate cancer were included and stratified into Gleason 3\u0026thinsp;+\u0026thinsp;4 (n\u0026thinsp;=\u0026thinsp;15) and Gleason 4\u0026thinsp;+\u0026thinsp;3 (n\u0026thinsp;=\u0026thinsp;11) subgroups for comparative evaluation.\u003c/p\u003e \u003cp\u003eThe study protocol was approved by the institutional ethics committee, and the requirement for informed consent was waived owing to the retrospective study design.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory Parameters and Inflammatory Marker Calculation\u003c/h3\u003e\n\u003cp\u003ePeripheral blood samples were collected on the first day after hospital admission. Laboratory tests included total PSA, free PSA, and complete blood counts (CBC) components. Based on CBC results, the following systemic inflammatory markers were calculated:\u003c/p\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio (NLR): neutrophil count\u0026thinsp;\u0026divide;\u0026thinsp;lymphocyte count\u003c/p\u003e \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR): platelet count\u0026thinsp;\u0026divide;\u0026thinsp;lymphocyte count\u003c/p\u003e \u003cp\u003eMonocyte-to-lymphocyte ratio (MLR): monocyte count\u0026thinsp;\u0026divide;\u0026thinsp;lymphocyte count\u003c/p\u003e \u003cp\u003eSystemic immune-inflammation index (SII): platelet count \u0026times; neutrophil count\u0026thinsp;\u0026divide;\u0026thinsp;lymphocyte count\u003c/p\u003e \u003cp\u003eAggregate index of systemic inflammation (AISI): neutrophil count \u0026times; monocyte count \u0026times; platelet count\u0026thinsp;\u0026divide;\u0026thinsp;lymphocyte count\u003c/p\u003e \u003cp\u003eSystemic inflammation response index (SIRI): neutrophil count \u0026times; monocyte count\u0026thinsp;\u0026divide;\u0026thinsp;lymphocyte count\u003c/p\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio (NLR): neutrophil count \u0026divide; (lymphocyte count \u0026times; platelet count).\u003c/p\u003e \u003cp\u003eInflammatory indices were selected based on previous literature linking systemic inflammation with tumor aggressiveness.\u003c/p\u003e\n\u003ch3\u003eProstate Volume and PSA Density\u003c/h3\u003e\n\u003cp\u003eProstate volume (PV) was measured with transrectal ultrasound using the standard ellipsoid formula: PV\u0026thinsp;=\u0026thinsp;π/6 \u0026times; width \u0026times; length \u0026times; height.\u003c/p\u003e \u003cp\u003ePSA density (PSAD) was calculated as: PSAD= total PSA\u0026thinsp;\u0026divide;\u0026thinsp;prostate volume.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eGiven the small sample size and non-normal data distribution, group comparisons were performed using the Mann\u0026ndash;Whitney U test. Receiver operating characteristic (ROC) curves were constructed to assess the ability of individual markers and combined models to distinguish Gleason 3\u0026thinsp;+\u0026thinsp;4 from 4\u0026thinsp;+\u0026thinsp;3 disease. Diagnostic metrics included area under the ROC curve (AUC), sensitivity, specificity, and 95% confidence intervals (CI).\u003c/p\u003e \u003cp\u003eMultivariable logistic regression models were constructed to evaluate whether the combination of inflammatory markers and prostate volume improved discriminatory performance.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using GraphPad Prism. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eBaseline clinical and hematological characteristics of the 26 patients with Gleason score 7 prostate cancer are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among them, 15 patients had Gleason 3\u0026thinsp;+\u0026thinsp;4 and 11 had Gleason 4\u0026thinsp;+\u0026thinsp;3. No statistically significant differences were observed in age, PSA, or basic hematologic parameters between the two groups.\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 Clinical and Laboratory Characteristics of Patients with Gleason 3\u0026thinsp;+\u0026thinsp;4 and Gleason 4\u0026thinsp;+\u0026thinsp;3 Prostate Cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGleason 3\u0026thinsp;+\u0026thinsp;4 (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGleason 4\u0026thinsp;+\u0026thinsp;3 (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003cp\u003eMann-Whitney test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.00 (10.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.00 (9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.43 (1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.32 (3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.40 (1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.73 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes,\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55 (0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e217.00 (78.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187.00 (59.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etPSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.10 (18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.57 (55.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInflammatory Markers and Prostate Volume\u003c/h3\u003e\n\u003cp\u003eAlthough most inflammatory indices showed no significant differences between groups, both the neutrophil-to-lymphocyte-to-platelet ratio (NLPR) and prostate volume (PV) tended to be higher values in the Gleason 4\u0026thinsp;+\u0026thinsp;3 group, NLPR: p\u0026thinsp;=\u0026thinsp;0.0687; PV: p\u0026thinsp;=\u0026thinsp;0.0774. These trends did not reach statistical significance but suggest potential biological differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eROC Curve Analysis of Individual Markers and Combined Predictive Model\u003c/h3\u003e\n\u003cp\u003eROC curve analysis demonstrated that NLPR had the highest individual discriminatory ability for differentiating Gleason 4\u0026thinsp;+\u0026thinsp;3 from 3\u0026thinsp;+\u0026thinsp;4, with an AUC of 0.7152 (95% CI: 0.5109\u0026ndash;0.9194, P\u0026thinsp;=\u0026thinsp;0.065), followed by PV with an AUC of 0.7091 (95% CI: 0.5077\u0026ndash;0.9105, P\u0026thinsp;=\u0026thinsp;0.073). In contrast, PSAD showed no discriminative value (AUC\u0026thinsp;=\u0026thinsp;0.5030, P\u0026thinsp;=\u0026thinsp;0.979) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The inflammatory markers NLR, PLR, MLR, AISI, SII, and SIRI also showed poor discriminative performance, with AUCs\u0026thinsp;\u0026lt;\u0026thinsp;0.65, and all P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05.(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A multivariable logistic regression model combining PV and NLPR significantly improved diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.7758, 95% CI: 0.5941\u0026ndash;0.9574, P\u0026thinsp;=\u0026thinsp;0.0182), and the inclusion of SII further enhanced the model (AUC\u0026thinsp;=\u0026thinsp;0.8121, 95% CI: 0.6468\u0026ndash;0.9774, P\u0026thinsp;=\u0026thinsp;0.0075) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results suggest that integrated inflammatory and anatomical information may better capture differences between Gleason 3\u0026thinsp;+\u0026thinsp;4 and 4\u0026thinsp;+\u0026thinsp;3 tumors compared with individual markers alone.\u003c/p\u003e \u003cp\u003e \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\u003eDiagnostic performance of inflammatory markers, PSAD and PV in Differentiating Gleason 3\u0026thinsp;+\u0026thinsp;4 from Gleason4\u0026thinsp;+\u0026thinsp;3 Prostate Cancer.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% interval confidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3920 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2978~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2645~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3525 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5109 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2693 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3394 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5077 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2534 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.979\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 \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\u003eMultivariable Logistic Regression Models Combing Inflammatory Markers and Prostate Volume.\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% interval confidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePV+NLPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5941 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePV+NLPR\u0026thinsp;+\u0026thinsp;SII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6468 ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePPV=Positive Predictive value; NPV=Negative Predictive value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this exploratory analysis, PSA density (PSAD) showed minimal ability to discriminate between Gleason 3\u0026thinsp;+\u0026thinsp;4 and 4\u0026thinsp;+\u0026thinsp;3 prostate cancer, with an AUC of 0.5032\u0026mdash;essentially equivalent to random classification. Although PSAD is widely used to differentiate prostate cancer from benign prostatic hyperplasia and other non-malignant conditions [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], its utility appears limited for finer risk stratification within Gleason score 7 disease. This limited performance may reflect overlapping PSA secretion patterns and similar prostate volume characteristics between Gleason 3\u0026thinsp;+\u0026thinsp;4 and 4\u0026thinsp;+\u0026thinsp;3 tumors, which reduce its discriminatory capacity in this specific clinical setting.\u003c/p\u003e \u003cp\u003eIn contrast, prostate volume (PV) and systemic inflammatory indices\u0026mdash;particularly the neutrophil-to-lymphocyte-to-platelet ratio (NLPR)\u0026mdash;showed moderate discriminatory signals, with AUCs of 0.7091 and 0.7152, respectively. When incorporated into a multivariable logistic regression model, PV and NLPR yielded an improved AUC of 0.7758, which further increased to 0.8121 after inclusion of the systemic immune-inflammation index (SII). While these findings suggest that systemic inflammatory responses and anatomical characteristics may capture biological differences between Gleason 3\u0026thinsp;+\u0026thinsp;4 and 4\u0026thinsp;+\u0026thinsp;3 tumors, the observed model performance should be interpreted with caution given the small sample size and potential for overfitting.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, distinguishing these two Gleason 7 subtypes is important, as Gleason 4\u0026thinsp;+\u0026thinsp;3 cancers are associated with higher rates of biochemical recurrence, adverse pathological features, and worse long-term outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, identifying noninvasive biomarkers that may aid in pre-treatment risk stratification remains a clinically relevant objective. The present findings provide preliminary evidence that inflammation-based indices, particularly NLPR, in combination with prostate volume, may offer additional information beyond PSA-derived parameters.\u003c/p\u003e \u003cp\u003eHowever, several limitations must be acknowledged. First, the study cohort was small (n\u0026thinsp;=\u0026thinsp;26), substantially limiting statistical power and increasing the likelihood of overfitting. Second, the retrospective, single-center design may introduce selection bias and restrict generalizability. Third, systemic inflammatory markers can be influenced by non-cancer-related conditions\u0026mdash;including subclinical infections, medications, and chronic inflammatory disorders\u0026mdash;which were not fully controlled for in this study. Accordingly, these findings should be considered strictly hypothesis-generating rather than confirmatory.\u003c/p\u003e \u003cp\u003eFuture studies should validate these preliminary findings in larger, multi-institutional cohorts with standardized laboratories and imaging protocols. Integrating inflammatory indices into predictive nomograms\u0026mdash;potentially alongside MRI-based parameters or genomic classifiers\u0026mdash;may further improve risk stratification. Prospective investigations are also needed to determine whether these biomarkers correlate with long-term oncologic outcomes and whether they have clinical utility in guiding management decisions.\u003c/p\u003e \u003cp\u003eIn summary, PSAD does not appear to be useful for differentiating Gleason 3\u0026thinsp;+\u0026thinsp;4 from 4\u0026thinsp;+\u0026thinsp;3 prostate cancer in this cohort. In contrast, systemic inflammatory indices, particularly NLPR, in combination with prostate volume, demonstrated preliminary discriminatory signals. These findings suggest the potential role for inflammation-related biomarkers in refining risk stratification within intermediate-risk prostate cancer, although validation in large studies is essential before clinical application.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors have nothing to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest relevant to this article was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Medical Ethics Committee of People\u0026apos;s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eInformed consent was waived due to the retrospective nature of the study and the analysis used anonymous clinical data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistry and the Registration No. of the study:\u003c/strong\u003e N/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChatGPT (OpenAI) was used for language editing only. All scientific content and conclusions are the responsibility of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal Studies:\u003c/strong\u003e N/A\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJiuzhi Li and Aerken Maolake: Conception and design, administrative support and provision of study materials. Feng Guo, Zecheng Ni, Jianhua Zhao, Bide Liu, and Wenlong Fan: Collection and assembly of data. Fen Guo and Aerken Maolake: Data analysis and interpretation. All authors wrote the manuscript and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpstein JI, Egevad L, Amin MB, et al. The 2014 ISUP Gleason grading system: Definition and implications. Am J Surg Pathol. 2016;40(2):244\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpstein JI, Allsbrook WC Jr, Amin MB, Egevad LL, ISUP Grading Committee. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol. 2005;29(9):1228\u0026ndash;1242.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierorazio PM, Walsh PC, Partin AW, Epstein JI. Contemporary Gleason grading and the risk of prostate cancer mortality: a population-based study. Eur Urol. 2013;63(3):428\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss HM, Kryvenko ON, Cowan JE, Simko JP, Wheeler TM, Epstein JI. The prognostic significance of Gleason score 3\u0026thinsp;+\u0026thinsp;4=7 versus 4\u0026thinsp;+\u0026thinsp;3=7 prostate cancer: a retrospective analysis of radical prostatectomy outcomes. BJU Int. 2007;99(3):545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrawer MK. Prostate-specific antigen: current status. CA Cancer J Clin. 1999;49(5):264\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoeb S, et al. The role of PSA density in the diagnosis of prostate cancer. Nat Rev Urol. 2012;9(7):369\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaolake A, et al. Combination of PSA density and MLR improves the diagnostic accuracy of prostate cancer. Front Oncol. 2025;15:1570584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2025.1570584\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2025.1570584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierorazio PM, Walsh PC, Partin AW, Epstein JI. Prognostic Gleason grade grouping: data based on the modified Gleason scoring system. BJU Int. 2013;111(5):753\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1464-410X.2012.11611\u003c/span\u003e\u003cspan address=\"10.1111/j.1464-410X.2012.11611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Leenders GJ, et al. Gleason 4\u0026thinsp;+\u0026thinsp;3 versus Gleason 3\u0026thinsp;+\u0026thinsp;4 prostate cancer: implications for prognosis and treatment. Histopathology. 2016;69(6):874\u0026ndash;81.\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":"Prostate cancer, Gleason score, inflammation, NLPR, prostate volume, biomarkers, exploratory study","lastPublishedDoi":"10.21203/rs.3.rs-9235683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9235683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGleason score 7 prostate cancer comprises two biologically distinct subgroups\u0026mdash;Gleason 3\u0026thinsp;+\u0026thinsp;4 and Gleason 4\u0026thinsp;+\u0026thinsp;3\u0026mdash;with differing prognoses and therapeutic implications. However, reliable non-invasive methods to distinguish these subtypes prior to treatment remain limited. This exploratory study evaluated whether systemic inflammatory markers and prostate volume (PV) provide preliminary signals differentiating Gleason 3\u0026thinsp;+\u0026thinsp;4 from 4\u0026thinsp;+\u0026thinsp;3 disease. We analyzed 26 patients with biopsy-confirmed Gleason score 7 prostate cancer (3\u0026thinsp;+\u0026thinsp;4, n\u0026thinsp;=\u0026thinsp;15; 4\u0026thinsp;+\u0026thinsp;3, n\u0026thinsp;=\u0026thinsp;11). Pre-biopsy complete blood counts were used to calculate inflammation-based indices, including neutrophil-to-lymphocyte-to-platelet ratio (NLPR), systemic immune-inflammation index (SII), and others. Prostate volume (PV) was measured by a transrectal ultrasonography. Group comparisons used non-parametric tests, and receiver operating characteristic (ROC) analyses were performed. NLPR and PV showed modest separation between groups (NLPR AUC\u0026thinsp;=\u0026thinsp;0.715; PV AUC\u0026thinsp;=\u0026thinsp;0.709). Most other inflammatory markers-including NLR, PLR, MLR, AISI and SIRI-showed limited discriminatory performance. A multivariable combining NLPR and PV yielded an AUC of 0.776, which improved to 0.82 after adding SII. Given the very small cohort size (n\u0026thinsp;=\u0026thinsp;26), these findings should be regarded strictly as preliminary and hypothesis-generating. This exploratory analysis suggests that NLPR and PV may reflect biological difference between Gleason 3\u0026thinsp;+\u0026thinsp;4 and 4\u0026thinsp;+\u0026thinsp;3 tumors. These observations require validation in larger, prospective, multi-institutional cohorts before any clinical interpretation or application can be considered.\u003c/p\u003e","manuscriptTitle":"Preliminary Evidence of Systemic Inflammatory Markers in Differentiating Gleason 3+4 and 4+3 Prostate Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:02:45","doi":"10.21203/rs.3.rs-9235683/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"286282872348569498171469889201675137195","date":"2026-05-14T02:27:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T09:10:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274570755361260489307818050607452510820","date":"2026-04-14T13:49:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T20:13:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-27T17:07:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T09:06:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T09:06:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-26T14:38:53+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":"ed3674fc-5ad5-4e10-85a9-644bb59bca4e","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"286282872348569498171469889201675137195","date":"2026-05-14T02:27:32+00:00","index":137,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T07:02:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:02:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9235683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9235683","identity":"rs-9235683","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}