CD4 as a Potential Biomarker for Differentiating Cirrhosis with Dysplastic Nodules from Well-Differentiated Hepatocellular Carcinoma

CD4 as a Potential Biomarker for Differentiating Cirrhosis with Dysplastic Nodules from Well-Differentiated Hepatocellular Carcinoma | 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 Article CD4 as a Potential Biomarker for Differentiating Cirrhosis with Dysplastic Nodules from Well-Differentiated Hepatocellular Carcinoma Hongkun Wang, Xiaorong Li, Xiaojun Liu, Huili Wan, Huixia Zheng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6598830/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 Distinguishing high-grade dysplastic nodules in cirrhotic livers from well-differentiated hepatocellular carcinoma (HCC) remains difficult due to overlapping histological features and limited biomarker specificity. In this study, 85 liver tissues were analyzed, including 54 HCCs of varying differentiation and 31 cirrhotic livers (10 with high-grade dysplastic nodules). Logistic regression (LR) and k-nearest neighbors (KNN) models were applied using leave-one-out cross-validation to classify (1) cirrhosis vs. HCC and (2) cirrhosis with dysplastic nodules vs. well-differentiated HCC. Immunohistochemistry showed progressive CD4 loss with lesion severity. Adding CD4 to the conventional panel [GPC-3, HSP-70, CD10] improved classification accuracy from 92.3–98.82% for cirrhosis vs. HCC, and from 79.31–93.11% for cirrhosis with dysplastic nodules vs. well-differentiated HCC. Functional pathway and protein–protein interaction (PPI) analyses identified CD4 as a central hub, most strongly linked to HSP-70 (p = 0.0041), supporting its synergistic diagnostic value. These findings underscore CD4's utility in enhancing classification accuracy and its potential as a robust marker for differentiating liver lesion subtypes. Further validation in larger, diverse cohorts is warranted. Biological sciences/Cancer Biological sciences/Cancer/Tumour biomarkers hepatocellular carcinoma cirrhosis early diagnosis pathway analysis PPI Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, primarily arising in the context of chronic liver disease, particularly cirrhosis due to hepatitis B and C infections, as well as alcohol-related liver disease 1 . HCC is a significant global health concern, with an estimated incidence of over 800,000 new cases annually, making it the sixth most common cancer and the third leading cause of cancer-related deaths worldwide 2 . Dysplastic nodules are atypical liver lesions that can arise in the context of chronic liver disease, particularly in patients with cirrhosis, and are considered precursors to HCC 3 . The prevalence of dysplastic nodules varies, with studies indicating that they can be found in approximately 10–20% of cirrhotic livers 4 . Their incidence increases with the severity of liver disease, necessitating early detection and management to prevent progression to malignancy 5 . Liver cirrhosis with dysplastic nodules and well-differentiated HCC present overlapping histological features that complicate their differentiation. Both conditions can exhibit similar cellular changes and architectural patterns, making accurate diagnosis challenging. Studies have shown that high-grade dysplastic nodules can be present in cirrhotic patients, indicating a potential pathway from liver cirrhosis to HCC 6 . Advanced imaging techniques, such as Gd-EOB-DTPA-enhanced MRI T1 mapping, have demonstrated high accuracy in distinguishing dysplastic nodules from HCC, highlighting significant differences in T1 values 7 . Current biomarkers like GPC-3, HSP70, GS, CD10, and CD34 are utilized in the diagnosis of HCC, but they often exhibit limitations in both specificity and sensitivity, which can lead to false positives or negatives 8 – 10 . This underscores the need for the development of more reliable biomarkers to improve diagnostic accuracy and patient outcomes in liver cancer. Multiple recent studies suggested that CD4 plays a significant role in the immune response and disease progression 11 , 12 . CD4 + T cells, as tumor-infiltrating lymphocytes, significantly influence HCC progression and therapeutic outcomes 13 . However, miRNA-425-5p, upregulated in XELOX-resistant HCC patients, promotes regulatory T cell expansion in CD4 + T cells, contributing to chemoresistance and immune evasion 14 . These findings suggest that while CD4 expression is intricately involved in HCC progression and treatment response, its role as a diagnostic marker requires further investigation to clarify its dual influence on disease outcomes. This study aims to explore CD4 as a potential biomarker to improve the differentiation between cirrhosis with dysplastic nodules and well-differentiated HCC. We hypothesize that CD4 expression levels can add effectiveness in distinguishing between these conditions, as variations in immune cell profiles can reflect the underlying tumor microenvironment. Identifying CD4 as a reliable biomarker could significantly enhance diagnostic accuracy and improve patient management in liver disease, offering insights into personalized treatment strategies and improved patient outcomes. Materials and Methods Specimen Collection and Classification Formalin-fixed, paraffin-embedded liver specimens were retrieved from the Department of Pathology at the First Hospital of Shanxi Medical University (January 2020–December 2023). The cohort comprised: 1) Hepatocellular carcinoma (HCC): n = 54 (19 well-differentiated, 33 moderately differentiated, 2 moderately to poorly differentiated); 2) Liver cirrhosis: n = 31, including 10 cases with co-existent high-grade dysplastic nodules (selected from regions within the cirrhotic and HCC specimens). All diagnoses were established by routine morphological evaluation and confirmed via immunohistochemistry for GPC-3, HSP70, GS, CD10, and CD34. Two senior pathologists independently reviewed every case; specimens failing to meet diagnostic criteria were excluded. Immunohistochemical Analysis Immunohistochemical staining was performed using the REAL EnVision™ Detection System (K5007, Dako, Glostrup, Denmark). Primary antibodies and their working dilutions are listed in Supplementary Table X. Hematoxylin and eosin (H&E)–stained and immunohistochemically stained sections for each case were evaluated. CD4 expression was assessed in liver sinusoidal endothelial cells and scored as follows: score 3, complete and continuous expression; score 2, relatively complete expression; score 1, incomplete or irregular expression; and score 0, absent or minimal expression. CD4 expression was considered negative when a score of 0 was recorded. Positivity criteria for other markers were defined as follows: 1) GPC-3 and HSP70: Positive when ≥ 5% of tumor cells exhibited staining of any intensity. 2) Glutamine synthetase (GS): Positive when ≥ 30% of tumor cells displayed moderate or strong staining. 3) CD10 and CD34: Positive when ≥ 5% of cells exhibited staining at any intensity. Statistical and Bioinformatics Analysis Classification analyses were conducted in Python (version 3.8) using the scikit-learn library. Two supervised learning algorithms—logistic regression (LR) and k-nearest neighbors (KNN)—were implemented to assess the discriminatory power of each protein marker panel. Leave-one-out cross-validation (LOOCV) was used to estimate model performance, and accuracy, sensitivity, and specificity were computed for each marker combination. Two diagnostic tasks were performed: 1) Cirrhosis versus HCC Individual markers (CD4, GPC-3, GS, HSP70, CD10, CD34) and all possible combinations were evaluated for their ability to distinguish liver cirrhosis (n = 21) from hepatocellular carcinoma (n = 54). The incremental contribution of CD4 was quantified by comparing performance metrics with and without CD4 in each panel. 2) High-Grade Dysplastic Nodules versus Well-Differentiated HCC The same marker sets were tested to differentiate cirrhosis with high-grade dysplastic nodules (n = 10) from well-differentiated HCC (n = 19). Comparative analysis against the cirrhosis versus HCC task assessed the consistency of each panel’s diagnostic value across clinical contexts. Functional Pathway and Protein–Protein Interaction Analysis Literature-based functional pathway and protein–protein interaction (PPI) analyses were performed using the AIC Bioinformatics Toolbox (ABT), a natural language processing–driven platform ( https://www.gousinfo.com/cnabt/userguide.html ). ABT mines relationships among biological entities—such as genes, diseases, and cellular processes—from over 35 million PubMed abstracts and full-text articles. For each of the six marker proteins (CD4, GPC-3, GS, HSP70, CD10, CD34), ABT extracted upstream regulators, downstream targets, and documented interaction partners, supported by curated reference sentences. The resulting pathways and PPI networks were visualized to elucidate potential molecular mechanisms and marker interconnectivity in cirrhosis and hepatocellular carcinoma differentiation. Results CD4 Expression in Liver Tissues Figure 1 demonstrates the varying patterns of CD4 expression across different liver tissue types. In normal liver tissue (Fig. 1 A), CD4 is expressed in an orderly and slender pattern. In liver cirrhosis (Fig. 1 B), expression becomes more disordered, short, and small. Liver cirrhosis with high-grade dysplastic nodules (HGDNs) (Fig. 1 C) shows a coarse, irregular, and disordered expression, with some areas of fusion enlargement and reduction. Well-differentiated HCC (Fig. 1 D) displays a rough, slightly thickened expression, while moderately differentiated HCC (Fig. 1 E) presents with significant reduction or loss of expression. In moderately to poorly differentiated HCC (Fig. 1 F), CD4 expression is severely reduced or absent, with only sparse and disordered staining in sinusoidal endothelial cells. These results highlight the progressive loss of CD4 expression as liver lesions progress from normal tissue to cirrhosis, dysplastic nodules, and HCC. Table 1 shows the distribution of CD4 expression levels (0 to 3) across various liver tissue types. In normal liver tissues, all 20 cases exhibited a CD4 expression level of 3. In liver cirrhosis, all 21 cases had a CD4 expression level of 2. For liver cirrhosis with high-grade dysplastic nodules, 2 cases had no CD4 expression (level 0), 8 cases showed low expression (level 1), and no cases exhibited higher levels of expression (levels 2 or 3). In HCC, 30 of 54 cases displayed no CD4 expression (level 0), and 24 cases exhibited low expression (level 1). No cases showed higher levels of CD4 expression (levels 2 or 3). Table 1 Expression of CD4 in normal liver tissue and various types of liver lesions Tissue type Number of cases CD4 0 1 2 3 normal liver tissues 20 0 0 0 20 liver cirrhosis 21 0 0 21 0 liver cirrhosis with high-grade dysplastic nodules 10 2 8 0 0 HCC 54 30 24 0 0 Put Table above here Differentiating cirrhosis from HCC This study first evaluated the effect of incorporating CD4 protein expression alongside other established markers—GPC-3, GS, HSP70, CD10, and CD34—to differentiate cirrhosis from HCC. The goal was to validate the diagnostic performance of these markers individually and in combination, and to assess the added value of CD4 in improving classification accuracy for general HCC diagnosis. Leave-one-out cross-validation (LOOCV) was performed using various combinations of protein markers to differentiate cirrhosis from HCC. Among the combinations tested, the inclusion of CD4 consistently contributed to high classification accuracy. Specifically, the combination of CD4, GPC-3, HSP-70, and CD10 achieved the highest accuracy, reaching 98.82% with the KNN classifier and 97.65% with logistic regression. Notably, combinations including CD4 generally outperformed those without it. While CD4 alone achieved moderate accuracy (69.41% KNN, 88.24% LR), its synergistic use with other markers, particularly GPC-3, HSP-70, and CD10, significantly enhanced diagnostic performance. These findings underscore the valuable role of CD4 in improving the differentiation between cirrhosis and HCC when used alongside established markers. Put Table 2 above here Table 2 LOOCV Results for Differentiating Cirrhosis from HCC Feature combination K-Nearest Neighbors (KNN) Logistic Regression Average AC AC SE SP AC SE SP CD4, GPC-3, HSP-70, CD10 98.82 98.15 100 97.65 98.15 97 98.235 CD4, GPC-3, HSP-70, CD10, CD34 97.65 96.3 100 97.65 98.15 97 97.65 CD4, GS, HSP-70, CD10 97.65 98.15 97 97.65 98.15 97 97.65 CD4, GS, HSP-70, CD10, CD34 96.47 98.15 94 97.65 98.15 97 97.06 GS, CD10 96.47 96.3 97 97.65 98.15 97 97.06 CD4, GPC-3, HSP-70 96.47 98.15 94 96.47 98.15 94 96.47 CD4, GS, GPC-3, CD10 97.65 98.15 97 95.29 96.3 94 96.47 GS, GPC-3, CD10 96.47 96.3 97 96.47 96.3 97 96.47 HSP-70, CD10 96.47 96.3 97 96.47 96.3 97 96.47 CD4, GS, CD10 96.47 96.3 97 95.29 96.3 94 95.88 CD4, GS, GPC-3, CD10, CD34 96.47 96.3 97 95.29 96.3 94 95.88 GPC-3, HSP-70, CD10 96.47 94.44 100 95.29 94.44 97 95.88 GS, HSP-70, CD10 95.29 94.44 97 96.47 96.3 97 95.88 GS, GPC-3, HSP-70, CD10, CD34 94.12 92.59 97 96.47 96.3 97 95.295 CD4, GS, GPC-3, HSP-70, CD10, CD34 95.29 94.44 97 95.29 96.3 94 95.29 CD4, HSP-70, CD10, CD34 95.29 94.44 97 95.29 96.3 94 95.29 GPC-3, CD10 95.29 94.44 97 95.29 94.44 97 95.29 GPC-3, HSP-70, CD10, CD34 95.29 92.59 100 95.29 94.44 97 95.29 GS, HSP-70, CD10, CD34 95.29 94.44 97 95.29 94.44 97 95.29 CD4, GPC-3, CD10, CD34 95.29 96.3 94 94.12 94.44 94 94.705 CD4, GS, CD10, CD34 94.12 94.44 94 95.29 96.3 94 94.705 CD4, GS, GPC-3, HSP-70, CD10 95.29 94.44 97 94.12 94.44 94 94.705 GPC-3, CD10, CD34 94.12 90.74 100 95.29 94.44 97 94.705 CD4, GPC-3, HSP-70, CD34 92.94 92.59 94 96.47 98.15 94 94.705 GS, GPC-3, CD10, CD34 92.94 90.74 97 96.47 96.3 97 94.705 GS, GPC-3, HSP-70, CD10 92.94 90.74 97 96.47 96.3 97 94.705 HSP-70, CD10, CD34 92.94 88.89 100 96.47 96.3 97 94.705 CD4, CD10, CD34 94.12 94.44 94 94.12 94.44 94 94.12 GS, CD10, CD34 90.59 87.04 97 97.65 98.15 97 94.12 CD4, HSP-70, CD10 92.94 90.74 97 95.29 96.3 94 94.115 CD4, GPC-3, CD10 92.94 90.74 97 94.12 94.44 94 93.53 CD10 92.94 90.74 97 92.94 90.74 97 92.94 CD10, CD34 92.94 90.74 97 92.94 90.74 97 92.94 CD4, GS, GPC-3, HSP-70 91.76 88.89 97 94.12 94.44 94 92.94 CD4, GS, GPC-3, HSP-70, CD34 91.76 88.89 97 94.12 94.44 94 92.94 GPC-3, HSP-70, CD34 92.94 88.89 100 91.76 88.89 97 92.35 GS, GPC-3, HSP-70 91.76 87.04 100 92.94 92.59 94 92.35 CD4, CD10 89.41 94.44 81 94.12 94.44 94 91.765 GPC-3, HSP-70 91.76 88.89 97 91.76 88.89 97 91.76 GS, GPC-3, HSP-70, CD34 91.76 87.04 100 91.76 92.59 90 91.76 CD4, GS, GPC-3, CD34 89.41 87.04 94 90.59 92.59 87 90 GS, GPC-3, CD34 90.59 85.19 100 89.41 88.89 90 90 CD4, GPC-3 89.41 87.04 94 89.41 87.04 94 89.41 CD4, GS, GPC-3 85.88 85.19 87 91.76 94.44 87 88.82 GS, GPC-3 87.06 83.33 94 89.41 88.89 90 88.235 CD4, GPC-3, CD34 83.53 79.63 90 89.41 87.04 94 86.47 CD4, HSP-70, CD34 85.88 87.04 84 84.71 85.19 84 85.295 CD4, HSP-70 84.71 79.63 94 84.71 79.63 94 84.71 CD4, GS, HSP-70 82.35 81.48 84 87.06 88.89 84 84.705 CD4, GS, HSP-70, CD34 85.88 85.19 87 83.53 88.89 74 84.705 CD4, GS 80 77.78 84 88.24 100 68 84.12 CD4, CD34 78.82 79.63 77 88.24 100 68 83.53 GPC-3, CD34 84.71 79.63 94 80 70.37 97 82.355 GS, HSP-70 82.35 79.63 87 81.18 79.63 84 81.765 CD4, GS, CD34 78.82 81.48 74 83.53 90.74 71 81.175 GPC-3 80 70.37 97 80 70.37 97 80 GS, HSP-70, CD34 78.82 70.37 94 81.18 79.63 84 80 CD4 69.41 55.56 94 88.24 100 68 78.825 GS, CD34 74.12 66.67 87 74.12 81.48 61 74.12 HSP-70 74.12 59.26 100 74.12 59.26 100 74.12 HSP-70, CD34 80 72.22 94 67.06 59.26 81 73.53 GS 64.71 53.7 84 64.71 53.7 84 64.71 CD34 58.82 38.89 94 63.53 100 0 61.175 Note : AC: accuracy; SE: sensitivity; SP: specificity The classification performance for distinguishing cirrhosis from HCC is summarized in Fig. 2 and Table 2 . Among individual markers, GPC-3 demonstrated the highest accuracy (80.0%), while CD34 exhibited the lowest (61.18%). The conventional clinical panel [GS, GPC-3, HSP-70] achieved an average accuracy of 92.35%. Notably, the inclusion of CD4 markedly enhanced diagnostic performance: the [CD4, GPC-3, HSP-70, CD10] combination yielded the highest average accuracy of 98.24% (KNN: 98.82%, LR: 97.65%), whereas extending this panel with CD34 produced 95.29% accuracy. Even the full six-marker panel [CD4, GS, GPC-3, HSP-70, CD10, CD34] maintained high performance (95.29%). Although CD4 alone showed moderate accuracy (78.83%), its incorporation into multi-marker panels consistently resulted in superior classification metrics, underscoring its critical role in improving the discrimination between cirrhotic and malignant liver tissues. Differentiating High-Grade Dysplastic Nodules from Well-Differentiated HCC We further evaluated the role of CD4 in distinguishing cirrhosis with high-grade dysplastic nodules from well-differentiated HCC, a clinically critical and challenging differentiation. The LOOCV results are presented in Table 3 . Table 3 LOOCV Results for Differentiating High-Grade Dysplastic Nodules from Well-Differentiated HCC Feature combination K-Nearest Neighbors (KNN) Logistic Regression Average AC AC SE SP AC SE SP CD4, GPC-3, HSP-70, CD10 96.55 94.74 100 89.66 94.74 80 93.105 CD4, GS, HSP-70, CD10 89.66 89.47 90 93.1 94.74 90 91.38 CD4, GS, HSP-70, CD10, CD34 93.1 94.74 90 89.66 94.74 80 91.38 CD4, GPC-3, HSP-70 89.66 94.74 80 89.66 94.74 80 89.66 CD4, GPC-3, HSP-70, CD10, CD34 89.66 84.21 100 89.66 94.74 80 89.66 CD4, GS, GPC-3, CD10, CD34 89.66 89.47 90 89.66 94.74 80 89.66 GS, HSP-70, CD10 89.66 89.47 90 89.66 89.47 90 89.66 GS, HSP-70, CD10, CD34 89.66 89.47 90 89.66 89.47 90 89.66 HSP-70, CD10 89.66 89.47 90 89.66 89.47 90 89.66 CD4, GS, GPC-3, CD10 86.21 89.47 80 89.66 94.74 80 87.935 CD4, GS, GPC-3, HSP-70, CD10, CD34 89.66 89.47 90 86.21 89.47 80 87.935 GPC-3, HSP-70, CD10 86.21 78.95 100 89.66 94.74 80 87.935 GS, CD10 86.21 89.47 80 89.66 94.74 80 87.935 GS, GPC-3, CD10 86.21 89.47 80 89.66 89.47 90 87.935 CD4, GS, CD10 82.76 84.21 80 89.66 94.74 80 86.21 CD4, HSP-70, CD10 86.21 78.95 100 86.21 89.47 80 86.21 GS, GPC-3, HSP-70, CD10 86.21 84.21 90 86.21 84.21 90 86.21 GS, GPC-3, CD10, CD34 79.31 78.95 80 93.1 100 80 86.205 CD4, GPC-3, CD10 86.21 78.95 100 82.76 84.21 80 84.485 CD4, GS, CD10, CD34 79.31 84.21 70 89.66 94.74 80 84.485 CD4, GS, GPC-3, HSP-70, CD10 86.21 84.21 90 82.76 84.21 80 84.485 CD4, HSP-70, CD10, CD34 82.76 78.95 90 86.21 89.47 80 84.485 GPC-3, HSP-70, CD10, CD34 82.76 78.95 90 86.21 89.47 80 84.485 GS, GPC-3, HSP-70, CD10, CD34 82.76 78.95 90 86.21 89.47 80 84.485 CD4, GPC-3, CD10, CD34 82.76 84.21 80 82.76 89.47 70 82.76 HSP-70, CD10, CD34 82.76 78.95 90 82.76 89.47 70 82.76 CD4, GPC-3, HSP-70, CD34 79.31 78.95 80 82.76 89.47 70 81.035 GPC-3, CD10, CD34 82.76 78.95 90 79.31 84.21 70 81.035 GS, CD10, CD34 79.31 89.47 60 82.76 94.74 60 81.035 CD4, GPC-3 79.31 78.95 80 79.31 78.95 80 79.31 GPC-3, CD10 75.86 68.42 90 82.76 84.21 80 79.31 GS, GPC-3, HSP-70 79.31 68.42 100 79.31 84.21 70 79.31 CD4, CD10, CD34 75.86 73.68 80 79.31 84.21 70 77.585 GPC-3, HSP-70 79.31 68.42 100 75.86 68.42 90 77.585 GPC-3, HSP-70, CD34 72.41 68.42 80 82.76 84.21 80 77.585 CD4, CD10 68.97 57.89 90 82.76 84.21 80 75.865 CD4, HSP-70 75.86 73.68 80 75.86 73.68 80 75.86 CD4, GPC-3, CD34 65.52 63.16 70 82.76 89.47 70 74.14 CD4, GS, GPC-3, HSP-70, CD34 68.97 63.16 80 79.31 84.21 70 74.14 CD4, GS, GPC-3, HSP-70 72.41 63.16 90 75.86 84.21 60 74.135 GPC-3, CD34 79.31 78.95 80 65.52 78.95 40 72.415 CD4, GS 72.41 78.95 60 72.41 84.21 50 72.41 CD10 79.31 73.68 90 62.07 73.68 40 70.69 GS, GPC-3 72.41 68.42 80 68.97 78.95 50 70.69 GS, GPC-3, HSP-70, CD34 68.97 68.42 70 72.41 78.95 60 70.69 CD4, GS, GPC-3 65.52 68.42 60 72.41 78.95 60 68.965 CD4, GS, GPC-3, CD34 65.52 73.68 50 72.41 84.21 50 68.965 GS, HSP-70 65.52 57.89 80 68.97 73.68 60 67.245 HSP-70, CD34 65.52 63.16 70 68.97 84.21 40 67.245 CD10, CD34 62.07 57.89 70 72.41 84.21 50 67.24 CD4, GS, HSP-70, CD34 65.52 73.68 50 65.52 78.95 40 65.52 GS, GPC-3, CD34 62.07 68.42 50 68.97 78.95 50 65.52 GS, HSP-70, CD34 55.17 52.63 60 75.86 89.47 50 65.515 CD34 68.97 68.42 70 58.62 73.68 30 63.795 CD4 62.07 52.63 80 65.52 100 0 63.795 CD4, HSP-70, CD34 68.97 63.16 80 58.62 73.68 30 63.795 GS 62.07 52.63 80 65.52 100 0 63.795 GS, CD34 58.62 63.16 50 68.97 89.47 30 63.795 HSP-70 62.07 42.11 100 65.52 100 0 63.795 CD4, GS, HSP-70 58.62 57.89 60 65.52 73.68 50 62.07 CD4, CD34 51.72 42.11 70 68.97 89.47 30 60.345 CD4, GS, CD34 55.17 63.16 40 65.52 84.21 30 60.345 GPC-3 65.52 47.37 100 31.03 47.37 0 48.275 Note : AC: accuracy; SE: sensitivity; SP: specificity Put Table 3 above here The diagnostic performance for differentiating cirrhosis with high-grade dysplastic nodules from well-differentiated HCC is summarized in Table 3 . Among individual markers, CD10 achieved the highest accuracy (70.69%), while GS and GPC-3 performed poorly (63.80% and 48.28%, respectively). The conventional clinical panel [GS, GPC-3, HSP-70] yielded a modest average accuracy of 79.31%. In contrast, the inclusion of CD4 substantially improved discrimination: the [CD4, GPC-3, HSP-70, CD10] combination attained the highest average accuracy of 93.11% (KNN: 96.55%, LR: 89.66%), whereas expanding this set to [CD4, GS, GPC-3, HSP-70, CD10, CD34] achieved 87.94% accuracy. Although CD4 alone demonstrated moderate performance (63.80%), its integration into multi-marker panels consistently produced superior classification metrics, highlighting its pivotal role in accurately distinguishing high-grade dysplasia from well-differentiated carcinoma. In summary, the two classification tasks yielded consistent findings regarding the diagnostic value of CD4 when combined with established markers. In the cirrhosis versus HCC analysis, the conventional panel [GS, GPC-3, HSP-70] achieved 92.3% average accuracy, whereas the [CD4, GPC-3, HSP-70, CD10] combination reached 98.2% accuracy. Similarly, for differentiating high-grade dysplastic nodules from well-differentiated HCC, the same clinical panel attained 76.8% accuracy, while the inclusion of CD4 in [CD4, GPC-3, HSP-70, CD10] elevated performance to 93.1%. Across both tasks, CD4 alone showed only moderate accuracy (78.8% and 63.8%, respectively), yet its addition to multi-marker panels consistently produced the highest classification metrics. This concordance underscores CD4’s pivotal role in enhancing the discrimination of both general cirrhotic versus malignant liver tissues and the more nuanced distinction between dysplastic nodules and well-differentiated carcinoma. Functional Pathway and PPI Analysis We conducted literature-based functional pathway analysis and protein-protein interaction (PPI) analysis to explore the relationships among the six markers and their association with Cirrhosis and HCC. The pathway analysis (Fig. 4 A) reveals important relationships between the six markers and the two liver diseases, Cirrhosis and Hepatocellular Carcinoma (HCC). Notably, CD10, CD34, and GPC-3 were significantly associated with both diseases, with CD10 and GPC-3 showing strong positive relationships with HCC and CD34 showing a significant association with Cirrhosis. Specifically, CD10 demonstrated a significant link to HCC (#ref = 8, polarity = 0, p-value = 0.0162), while GPC-3 showed strong associations with both diseases, with a positive relationship to Cirrhosis (#ref = 8, polarity = 0, p-value = 0.0162) and HCC (#ref = 10, polarity = 1, p-value = 0.0132). Similarly, CD4 showed a notable association with HCC (#ref = 6, polarity = 1, p-value = 0.019), but its relationship with Cirrhosis was not significant (#ref = 1, p-value = 0.1116). GS, while associated with Cirrhosis (#ref = 2, polarity = -1, p-value = 0.0124), was not significantly related to HCC. HSP-70 showed weaker associations with both diseases, with significant positive associations with HCC (#ref = 3, polarity = 1, p-value = 0.1008), but no significant relationship with Cirrhosis. These findings support the importance of multiple markers in differentiating between Cirrhosis and HCC, with CD10, CD34, and GPC-3 playing particularly notable roles. In the PPI analysis, all markers exhibited notable interactions, suggesting potential synergies for more accurate differentiation between liver diseases (Fig. 4 B). Specifically, CD4 showed interactions with all five other markers (CD10, GS, HSP-70, GPC-3, and CD34), with a particularly strong interaction with HSP-70 (#ref = 7, polarity = -1, p-value = 0.0041). This underscores the critical role of CD4 in the diagnostic process and supports the use of marker combinations, rather than single markers, for more accurate differentiation. For example, the combination of CD4, GPC-3, HSP-70, and CD10 demonstrated the highest accuracy in both differentiating general Cirrhosis from general HCC and distinguishing High-Grade Dysplastic Nodules from Well-Differentiated HCC (Table 2 and Table 3 ). Notably, CD4 emerged as a central hub in the PPI network, interacting with all other markers and exhibiting its strongest association with HSP-70 (polarity − 1, p = 0.0041). This network topology mirrors our classification results, where multi-marker panels including CD4 achieved the highest diagnostic accuracies (98.2% for cirrhosis vs. HCC and 93.1% for dysplastic nodules vs. well-differentiated HCC). The extensive connectivity of CD4 suggests it captures complementary biological signals—immune regulation via CD4, proteostasis via HSP-70, and oncogenic signaling via GPC-3 and CD10—that are not fully represented by any single marker. This synergy underpins the superior performance of CD4-inclusive panels in distinguishing liver lesion subtypes Discussion This study investigates the role of CD4 as a biomarker to differentiate between liver cirrhosis with dysplastic nodules and well-differentiated hepatocellular carcinoma (HCC). The hypothesis is that CD4 expression levels can enhance diagnostic accuracy by reflecting variations in the immune cell profiles of the tumor microenvironment. Results show that CD4 expression progressively decreases from normal liver tissue to cirrhosis, dysplastic nodules, and HCC. When combined with established markers like GPC-3, HSP-70, and CD10, CD4 significantly improves classification accuracy, achieving up to 98.82% in distinguishing cirrhosis from HCC and 93.11% in differentiating high-grade dysplastic nodules from well-differentiated HCC. These findings highlight CD4's potential to enhance diagnostic precision and inform personalized treatment strategies in liver disease. Across both classification tasks, CD4 emerged as a consistently powerful discriminator of malignant and pre-malignant liver lesions. When differentiating cirrhosis from HCC, adding CD4 to the conventional panel of GPC-3, HSP-70, and CD10 increased accuracy from 92.3–98.82%, markedly boosting both sensitivity and specificity. Likewise, in the more challenging distinction between cirrhosis with high-grade dysplastic nodules and well-differentiated HCC, inclusion of CD4 raised accuracy from 79.31–93.11%. These improvements underscore CD4’s pivotal role: beyond its known functions in T-cell–mediated immune surveillance and tumor microenvironment modulation 11 , CD4 expression patterns capture critical immunological differences that, when integrated into multi-marker panels and analyzed via robust classifiers such as KNN and logistic regression, substantially enhance diagnostic precision for HCC and its precursors. The six markers participate in a tightly interconnected network that spans immune regulation, metabolic control, and stress response, providing a mechanistic basis for their synergistic diagnostic performance. CD4, a key T-cell co-receptor, is positively modulated by GPC-3 within the hepatocellular carcinoma microenvironment, enhancing anti-tumor immune activation 15 , and shows context-dependent associations with CD10 (positive in T-follicular helper-cell lymphoma 16 ) and CD34 (positive in HCC immunotherapy settings 17 ). GPC-3 and CD34 together improve HCC differentiation accuracy 18 , while HSP-70 bolsters CD4 + T-cell responses under certain conditions 19 and collaborates with GS in hepatocarcinogenesis 20 . Although each marker alone yields only moderate accuracy, their integration captures multiple pathological facets—immune dysregulation via CD4, oncogenic signaling via GPC-3 and GS, proteostasis stress via HSP-70, and tissue remodeling via CD10 and CD34—which explains why combining CD4 with GPC-3, HSP-70, and CD10 elevates classification accuracy to 98.2% for cirrhosis versus HCC and 93.1% for high-grade dysplastic nodules versus well-differentiated HCC. This multi-marker strategy leverages complementary biological insights to achieve robust discrimination between liver lesion types. In addition to CD4, the other five markers each contribute distinct biological insights that aid in differentiating HCC from cirrhosis. GPC-3 is highly overexpressed in HCC but minimally detectable in cirrhotic liver, reflecting its role in promoting tumor growth and poor prognosis 21 , 22 . Glutamine synthetase (GS) is similarly upregulated in many HCCs—often in pericentral tumor regions—whereas GS expression remains low or focal in cirrhosis, providing metabolic discrimination between neoplastic and fibrotic tissue 23 . CD34, a marker of microvascular density, highlights the angiogenic switch characteristic of HCC, with extensive CD34⁺ capillarization in tumor sinusoids versus limited endothelial staining in cirrhosis 24 . HSP-70, a stress-inducible chaperone, is frequently elevated in HCC and correlates with aggressive phenotypes, whereas lower HSP-70 levels are associated with reduced fibrosis and cirrhosis severity 19 , 25 . By contrast, CD10—though present in a subset of HCC cases—lacks the specificity to reliably distinguish malignant from benign cirrhotic nodules when used alone 26 . Together, these complementary markers capture oncogenic signaling (GPC-3, GS), angiogenesis (CD34), stress response (HSP-70), and tissue remodeling (CD10), which—when integrated with CD4’s immune‐related information—enhance the robustness of multi‐marker panels for accurate liver lesion classification. CD4, a critical component of the immune system, plays a significant role in modulating immune responses, which can influence tumor progression and immune evasion in HCC 13 . The interaction between CD4 + T cells and HCC is complex, involving various pathways and mechanisms. At genetic level, CD4 + T cells exert a multifaceted genetic-level influence on hepatocellular carcinoma (HCC) by orchestrating immune‐regulatory and tumor‐suppressive pathways within the tumor microenvironment. As key tumor‐infiltrating lymphocytes, CD4 + T cells enhance anti‐tumor immunity—evidenced by improved responses to Lenvatinib plus anti–PD-1 therapy through increased systemic CD4 + proportions 11 —and modulate pro‐inflammatory cytokines such as IL-6, which correlates with post‐transplant HCC recurrence risk 27 , and TNF-α, which can bolster anti‐tumor responses 28 . Furthermore, CD4 + T cells regulate immune checkpoints (CTLA-4, PD-1) to maintain T‐cell activation and tolerance 29 , influence HLA molecule expression for enhanced tumor antigen presentation, and engage the TGF-β signaling axis—tumor suppressive early on but protumorigenic in advanced disease 30 . Through these interconnected pathways, CD4 + T cells modulate gene expression profiles that either restrain or facilitate HCC progression, underscoring their pivotal role in shaping therapeutic outcomes. At the cellular process level, CD4 + T helper cells profoundly shape the HCC tumor microenvironment by orchestrating cytokine- and chemokine‐driven signaling that governs the recruitment and activation of effector (e.g., CD8+) and regulatory T cell subsets. Through their secreted factors, CD4 + cells can either enhance anti‐tumor immunity or, in chronic liver disease settings, exacerbate immunosuppression and inflammation, thereby promoting tumor progression. Notably, a high prevalence of CD203a + Th17 cells—a CD4 + subset—increases post‐surgical HCC recurrence risk 29 , and protein–protein interaction analyses position CD4 at the nexus of key immune pathways in HCC 31 . Furthermore, clinical interventions such as Lenvatinib plus anti–PD-1 therapy have been shown to boost systemic CD4 + T‐cell levels, correlating with improved treatment responses 11 Collectively, these findings underscore CD4’s central role in modulating immune–tumor dynamics and highlight CD4‐targeted strategies as promising avenues for HCC immunotherapy. At the tissue and organ level, CD4⁺ T cells orchestrate the immune landscape of hepatocellular carcinoma (HCC) by shaping both local and systemic responses. Intrahepatic CD4⁺ T cells modulate the tumor microenvironment through cytokine-mediated activation of cytotoxic CD8⁺ T cells, macrophages, and dendritic cells, thereby influencing tissue architecture and anti‐tumor immunity 13 . Conversely, specific CD4⁺ subsets—such as CD203a⁺ Th17 cells—can promote HCC recurrence, with post‐surgical elevations correlating with a sixfold higher relapse risk 29 . Immunohistochemical studies further reveal that regulators of CD4⁺ function, such as SOCS2, can suppress Treg activity and inhibit tumor growth and metastasis when overexpressed 32 , while markers like METTL16 highlight pathways of therapeutic resistance and disease progression 33 . Together, these findings underscore CD4’s dualistic role in liver tissue—simultaneously driving anti‐tumor defenses and, in certain contexts, facilitating HCC recurrence—positioning CD4⁺ T cells as both biomarkers and potential targets for immunomodulatory strategies in HCC. In summary, the study effectively demonstrates the utility of CD4 as a valuable biomarker in differentiating liver conditions, particularly cirrhosis and hepatocellular carcinoma (HCC). By integrating CD4 with established markers like GPC-3, HSP-70, and CD10, the study achieves high diagnostic accuracy, significantly enhancing classification performance compared to traditional panels. Beyond diagnostic improvements, we also initiated the exploration of the underlying mechanisms of these multi-marker panels through PPI and pathway analysis, providing biological insight into their synergistic potential. This approach highlights both the clinical and mechanistic value of CD4-inclusive panels for robust liver lesion differentiation. Despite the promising results, the study is limited by its reliance on immunohistochemistry (IHC) staining, which may not capture the full complexity of CD4 expression patterns across diverse patient populations. Additionally, the study's sample size, particularly for certain liver conditions, may not be sufficient to generalize the findings broadly. The study also does not explore the potential variability in CD4 expression due to factors such as age, gender, or underlying health conditions, which could impact the generalizability and applicability of the results in diverse clinical settings. Conclusion This study identifies CD4 as a powerful adjunct biomarker for distinguishing cirrhosis (with or without dysplastic nodules) from well-differentiated HCC, markedly improving diagnostic accuracy when combined with GPC-3, HSP-70, and CD10. The progressive decline in CD4 expression—from normal liver through cirrhosis to HCC—reflects underlying immune alterations that can be harnessed in multi-marker panels to achieve robust classification performance. By integrating CD4’s immunological insights with traditional tumor markers, clinicians can more reliably differentiate early malignant changes from benign liver lesions, enabling timely intervention and personalized management. Further validation in larger, diverse cohorts will be essential to confirm these findings and support the clinical adoption of CD4-inclusive diagnostic algorithms. Declarations Data Availability Statement The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Funding Basic Research Program of the Shanxi Provincial Natural Science Foundation, Grant Number: 20210302123254. Beijing Fengtai Hospital Research Fund, Grant Number: 2024-2. Author information Authors and Affiliations Hongkun Wang Department of Pathology, Beijing Fengtai Hospital, Beijing, 100071, China Xiaorong Li Department of Pathology, Xi'an People's Hospital (Fourth Hospital of Xi'an) , Xi'an City, Shanxi, 710004, China Xiaojun Liu, Huili Wan and Huixia Zheng Department of Pathology, First Hospital of Shanxi Medical University, Taiyuan City, Shanxi, 030001, China Yuze Zhao Department of Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China. Contributions Hongkun Wang and Yuze Zhao designed the study, conducted data acquisition, organization, and analysis, and drafted the initial version of the manuscript. Xiaojun Liu, and Huixia Zheng contributed to data analysis and manuscript writing. Xiaorong Li and Huili Wan contributed to the study design and manuscript writing. All authors approved the manuscript for submission to the journal. Corresponding Author Correspondence to Yuze Zhao Ethics declarations Competing interest The authors declare no competing interests. References Balogh, J. et al. Hepatocellular carcinoma: a review. J Hepatocell Carcinoma 3 , 41-53, doi:10.2147/JHC.S61146 (2016). Foglia, B., Turato, C. & Cannito, S. Hepatocellular Carcinoma: Latest Research in Pathogenesis, Detection and Treatment. Int J Mol Sci 24 , doi:10.3390/ijms241512224 (2023). Anthony, P. P., Vogel, C. L. & Barker, L. F. Liver cell dysplasia: a premalignant condition. J Clin Pathol 26 , 217-223 (1973). Bennett, G. L. et al. 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Diagnostic accuracy and prognostic significance of Glypican-3 in hepatocellular carcinoma: A systematic review and meta-analysis. Front Oncol 12 , 1012418, doi:10.3389/fonc.2022.1012418 (2022). He, Y. et al. The Role of Glutamine Synthetase on the Sensitivity to Radiotherapy of Hepatocellular Carcinoma. Radiat Res , doi:10.1667/RADE-22-00181.1 (2025). Yu, Y. et al. Spectral computed tomography parameters for predicting vessels encapsulating tumor clusters (VETC) pattern in hepatocellular carcinoma: a pilot study. Quant Imaging Med Surg 15 , 3285-3297, doi:10.21037/qims-24-2077 (2025). Abdelrahman, R. S., Elnfarawy, A. A., Nashy, A. E., Abdelsalam, R. A. & Zaghloul, M. S. Targeting angiogenic and proliferative mediators by montelukast & trimetazidine Ameliorates thioacetamide-induced liver fibrosis in rats. Toxicol Appl Pharmacol 495 , 117208, doi:10.1016/j.taap.2024.117208 (2025). Wen, C. et al. Membranous Staining of CD10 Is Related to Steatosis Changes in Hepatocellular Carcinoma: An Investigation of CD10 Stainning in Hepatocellular Carcinoma, Focal Nodular Hyperplasia, and Intrahepatic Cholangiocarcinoma. Appl Immunohistochem Mol Morphol , doi:10.1097/PAI.0000000000001249 (2025). Kornberg, A., Seyfried, N. & Friess, H. Clinically Evident Portal Hypertension Is an Independent Risk Factor of Hepatocellular Carcinoma Recurrence Following Liver Transplantation. J Clin Med 14 , doi:10.3390/jcm14062032 (2025). Liu, F. et al. Neoepitope BTLA(P267L)-specific TCR-T cell immunotherapy unlocks precision treatment for hepatocellular carcinoma. Cancer Biol Med 22 , 412-432, doi:10.20892/j.issn.2095-3941.2024.0434 (2025). Babigian, J. et al. Extracellular NAD(+) levels are associated with CD203a expression on Th17 cells and predict long-term recurrence-free survival in hepatocellular carcinoma. J Cancer Res Clin Oncol 151 , 115, doi:10.1007/s00432-025-06155-4 (2025). Chaudhary, R., Weiskirchen, R., Ehrlich, M. & Henis, Y. I. Dual signaling pathways of TGF-beta superfamily cytokines in hepatocytes: balancing liver homeostasis and disease progression. Front Pharmacol 16 , 1580500, doi:10.3389/fphar.2025.1580500 (2025). Cakir, Y., Lebe, B., Toper, M. H. & Sarioglu, S. Genetic and Epigenetic Changes in Melanoma Progression: A TCGA-based Study. Appl Immunohistochem Mol Morphol , doi:10.1097/PAI.0000000000001257 (2025). Lan, X. et al. Suppressor of cytokine signaling 2 modulates regulatory T cell activity to suppress liver hepatocellular carcinoma growth and metastasis. World J Gastroenterol 31 , 100566, doi:10.3748/wjg.v31.i13.100566 (2025). Cao, L. & Bi, W. METTL16/IGF2BP2 axis enhances malignant progression and DDP resistance through up-regulating COL4A1 by mediating the m6A methylation modification of LAMA4 in hepatocellular carcinoma. Cell Div 20 , 9, doi:10.1186/s13008-025-00152-2 (2025). Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":2421374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of CD4 in liver tissues. \u003c/strong\u003eA: The expression of CD4 in normal liver tissue.; B: The expression of CD4 in liver cirrhosis.; C: The expression of CD4 in liver cirrhosis with High-Grade Dysplastic Nodules; D: The expression of CD4 in well-differentiated HCC; E: The expression of CD4 in moderately differentiated HCC; F: The expression of CD4 in medium to low-differentiated HCC. All images are based on immunohistochemistry (IHC) staining, magnification 100× (10× objective × 10× eyepiece).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6598830/v1/f0f39697245857772528c556.png"},{"id":92064465,"identity":"5c61e3f6-720b-43b6-a676-d2ea7c3da1cd","added_by":"auto","created_at":"2025-09-24 08:39:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of classification accuracy for differentiating cirrhosis from HCC using individual markers, the commonly used clinical combination ['GS', 'GPC-3', 'HSP-70'], the combinations with the highest average accuracy, and all six markers. \u003c/strong\u003eThe results highlight that combinations including CD4 achieved superior accuracy compared to traditional markers alone.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6598830/v1/73e2856d580d370736dd0720.png"},{"id":92064460,"identity":"ce0dcd9a-b9f2-4dc4-9d43-6151a335d62a","added_by":"auto","created_at":"2025-09-24 08:39:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of classification accuracy for differentiating cirrhosis with high-grade dysplastic nodules from well-differentiated HCC using individual markers, commonly used clinical markers (GS, GPC-3, and HSP-70), the two highest-performing marker combinations, and all six markers.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6598830/v1/19f7f6a041459a882c51219b.png"},{"id":92064462,"identity":"977f3f2d-6734-44f9-afa3-0d99e7fb7465","added_by":"auto","created_at":"2025-09-24 08:39:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":433536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway and Protein-Protein Interaction (PPI) Networks of Key Markers in Cirrhosis and Hepatocellular Carcinoma. \u003c/strong\u003e(A) Pathway analysis showing the relationships between six markers (CD10, CD34, CD4, GPC-3, GS, and HSP-70) and their association with Cirrhosis and HCC. (B) PPI network illustrating the interactions among these markers, highlighting their potential synergies in the differentiation of liver diseases.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6598830/v1/a1cb960ed8baf3af661bf282.png"},{"id":93201244,"identity":"cfcae774-3de6-4794-9f17-61baa23b9ade","added_by":"auto","created_at":"2025-10-10 07:02:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5444810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6598830/v1/e972d030-013f-45ab-a9a1-8e8c67e01d6e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CD4 as a Potential Biomarker for Differentiating Cirrhosis with Dysplastic Nodules from Well-Differentiated Hepatocellular Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the most common type of primary liver cancer, primarily arising in the context of chronic liver disease, particularly cirrhosis due to hepatitis B and C infections, as well as alcohol-related liver disease \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. HCC is a significant global health concern, with an estimated incidence of over 800,000 new cases annually, making it the sixth most common cancer and the third leading cause of cancer-related deaths worldwide \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDysplastic nodules are atypical liver lesions that can arise in the context of chronic liver disease, particularly in patients with cirrhosis, and are considered precursors to HCC \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The prevalence of dysplastic nodules varies, with studies indicating that they can be found in approximately 10\u0026ndash;20% of cirrhotic livers \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Their incidence increases with the severity of liver disease, necessitating early detection and management to prevent progression to malignancy \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLiver cirrhosis with dysplastic nodules and well-differentiated HCC present overlapping histological features that complicate their differentiation. Both conditions can exhibit similar cellular changes and architectural patterns, making accurate diagnosis challenging. Studies have shown that high-grade dysplastic nodules can be present in cirrhotic patients, indicating a potential pathway from liver cirrhosis to HCC \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Advanced imaging techniques, such as Gd-EOB-DTPA-enhanced MRI T1 mapping, have demonstrated high accuracy in distinguishing dysplastic nodules from HCC, highlighting significant differences in T1 values \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrent biomarkers like GPC-3, HSP70, GS, CD10, and CD34 are utilized in the diagnosis of HCC, but they often exhibit limitations in both specificity and sensitivity, which can lead to false positives or negatives \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This underscores the need for the development of more reliable biomarkers to improve diagnostic accuracy and patient outcomes in liver cancer.\u003c/p\u003e\u003cp\u003eMultiple recent studies suggested that CD4 plays a significant role in the immune response and disease progression \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. CD4\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026gt;+\u0026lt;/sup\u0026thinsp;\u0026gt;\u0026thinsp;T cells, as tumor-infiltrating lymphocytes, significantly influence HCC progression and therapeutic outcomes \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, miRNA-425-5p, upregulated in XELOX-resistant HCC patients, promotes regulatory T cell expansion in CD4\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026gt;+\u0026lt;/sup\u0026thinsp;\u0026gt;\u0026thinsp;T cells, contributing to chemoresistance and immune evasion\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These findings suggest that while CD4 expression is intricately involved in HCC progression and treatment response, its role as a diagnostic marker requires further investigation to clarify its dual influence on disease outcomes.\u003c/p\u003e\u003cp\u003eThis study aims to explore CD4 as a potential biomarker to improve the differentiation between cirrhosis with dysplastic nodules and well-differentiated HCC. We hypothesize that CD4 expression levels can add effectiveness in distinguishing between these conditions, as variations in immune cell profiles can reflect the underlying tumor microenvironment. Identifying CD4 as a reliable biomarker could significantly enhance diagnostic accuracy and improve patient management in liver disease, offering insights into personalized treatment strategies and improved patient outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSpecimen Collection and Classification\u003c/h2\u003e\u003cp\u003eFormalin-fixed, paraffin-embedded liver specimens were retrieved from the Department of Pathology at the First Hospital of Shanxi Medical University (January 2020\u0026ndash;December 2023). The cohort comprised: 1) Hepatocellular carcinoma (HCC): n\u0026thinsp;=\u0026thinsp;54 (19 well-differentiated, 33 moderately differentiated, 2 moderately to poorly differentiated); 2) Liver cirrhosis: n\u0026thinsp;=\u0026thinsp;31, including 10 cases with co-existent high-grade dysplastic nodules (selected from regions within the cirrhotic and HCC specimens).\u003c/p\u003e\u003cp\u003eAll diagnoses were established by routine morphological evaluation and confirmed via immunohistochemistry for GPC-3, HSP70, GS, CD10, and CD34. Two senior pathologists independently reviewed every case; specimens failing to meet diagnostic criteria were excluded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImmunohistochemical Analysis\u003c/h3\u003e\n\u003cp\u003eImmunohistochemical staining was performed using the REAL EnVision\u0026trade; Detection System (K5007, Dako, Glostrup, Denmark). Primary antibodies and their working dilutions are listed in Supplementary Table X. Hematoxylin and eosin (H\u0026amp;E)\u0026ndash;stained and immunohistochemically stained sections for each case were evaluated.\u003c/p\u003e\u003cp\u003eCD4 expression was assessed in liver sinusoidal endothelial cells and scored as follows: score 3, complete and continuous expression; score 2, relatively complete expression; score 1, incomplete or irregular expression; and score 0, absent or minimal expression. CD4 expression was considered negative when a score of 0 was recorded.\u003c/p\u003e\u003cp\u003ePositivity criteria for other markers were defined as follows: 1) GPC-3 and HSP70: Positive when \u0026ge;\u0026thinsp;5% of tumor cells exhibited staining of any intensity. 2) Glutamine synthetase (GS): Positive when \u0026ge;\u0026thinsp;30% of tumor cells displayed moderate or strong staining. 3) CD10 and CD34: Positive when \u0026ge;\u0026thinsp;5% of cells exhibited staining at any intensity.\u003c/p\u003e\n\u003ch3\u003eStatistical and Bioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eClassification analyses were conducted in Python (version 3.8) using the scikit-learn library. Two supervised learning algorithms\u0026mdash;logistic regression (LR) and k-nearest neighbors (KNN)\u0026mdash;were implemented to assess the discriminatory power of each protein marker panel. Leave-one-out cross-validation (LOOCV) was used to estimate model performance, and accuracy, sensitivity, and specificity were computed for each marker combination.\u003c/p\u003e\u003cp\u003eTwo diagnostic tasks were performed:\u003c/p\u003e\u003cp\u003e1) Cirrhosis versus HCC\u003c/p\u003e\u003cp\u003eIndividual markers (CD4, GPC-3, GS, HSP70, CD10, CD34) and all possible combinations were evaluated for their ability to distinguish liver cirrhosis (n\u0026thinsp;=\u0026thinsp;21) from hepatocellular carcinoma (n\u0026thinsp;=\u0026thinsp;54). The incremental contribution of CD4 was quantified by comparing performance metrics with and without CD4 in each panel.\u003c/p\u003e\u003cp\u003e2) High-Grade Dysplastic Nodules versus Well-Differentiated HCC\u003c/p\u003e\u003cp\u003eThe same marker sets were tested to differentiate cirrhosis with high-grade dysplastic nodules (n\u0026thinsp;=\u0026thinsp;10) from well-differentiated HCC (n\u0026thinsp;=\u0026thinsp;19). Comparative analysis against the cirrhosis versus HCC task assessed the consistency of each panel\u0026rsquo;s diagnostic value across clinical contexts.\u003c/p\u003e\n\u003ch3\u003eFunctional Pathway and Protein–Protein Interaction Analysis\u003c/h3\u003e\n\u003cp\u003eLiterature-based functional pathway and protein\u0026ndash;protein interaction (PPI) analyses were performed using the AIC Bioinformatics Toolbox (ABT), a natural language processing\u0026ndash;driven platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gousinfo.com/cnabt/userguide.html\u003c/span\u003e\u003cspan address=\"https://www.gousinfo.com/cnabt/userguide.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). ABT mines relationships among biological entities\u0026mdash;such as genes, diseases, and cellular processes\u0026mdash;from over 35\u0026nbsp;million PubMed abstracts and full-text articles. For each of the six marker proteins (CD4, GPC-3, GS, HSP70, CD10, CD34), ABT extracted upstream regulators, downstream targets, and documented interaction partners, supported by curated reference sentences. The resulting pathways and PPI networks were visualized to elucidate potential molecular mechanisms and marker interconnectivity in cirrhosis and hepatocellular carcinoma differentiation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCD4 Expression in Liver Tissues\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates the varying patterns of CD4 expression across different liver tissue types. In normal liver tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), CD4 is expressed in an orderly and slender pattern. In liver cirrhosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), expression becomes more disordered, short, and small. Liver cirrhosis with high-grade dysplastic nodules (HGDNs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) shows a coarse, irregular, and disordered expression, with some areas of fusion enlargement and reduction. Well-differentiated HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) displays a rough, slightly thickened expression, while moderately differentiated HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) presents with significant reduction or loss of expression. In moderately to poorly differentiated HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), CD4 expression is severely reduced or absent, with only sparse and disordered staining in sinusoidal endothelial cells. These results highlight the progressive loss of CD4 expression as liver lesions progress from normal tissue to cirrhosis, dysplastic nodules, and HCC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of CD4 expression levels (0 to 3) across various liver tissue types. In normal liver tissues, all 20 cases exhibited a CD4 expression level of 3. In liver cirrhosis, all 21 cases had a CD4 expression level of 2. For liver cirrhosis with high-grade dysplastic nodules, 2 cases had no CD4 expression (level 0), 8 cases showed low expression (level 1), and no cases exhibited higher levels of expression (levels 2 or 3). In HCC, 30 of 54 cases displayed no CD4 expression (level 0), and 24 cases exhibited low expression (level 1). No cases showed higher levels of CD4 expression (levels 2 or 3).\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\u003eExpression of CD4 in normal liver tissue and various types of liver lesions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTissue type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eCD4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enormal liver tissues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eliver cirrhosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eliver cirrhosis with high-grade dysplastic nodules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\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\u003ePut Table above here\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDifferentiating cirrhosis from HCC\u003c/h2\u003e\u003cp\u003eThis study first evaluated the effect of incorporating CD4 protein expression alongside other established markers\u0026mdash;GPC-3, GS, HSP70, CD10, and CD34\u0026mdash;to differentiate cirrhosis from HCC. The goal was to validate the diagnostic performance of these markers individually and in combination, and to assess the added value of CD4 in improving classification accuracy for general HCC diagnosis.\u003c/p\u003e\u003cp\u003eLeave-one-out cross-validation (LOOCV) was performed using various combinations of protein markers to differentiate cirrhosis from HCC. Among the combinations tested, the inclusion of CD4 consistently contributed to high classification accuracy. Specifically, the combination of CD4, GPC-3, HSP-70, and CD10 achieved the highest accuracy, reaching 98.82% with the KNN classifier and 97.65% with logistic regression. Notably, combinations including CD4 generally outperformed those without it. While CD4 alone achieved moderate accuracy (69.41% KNN, 88.24% LR), its synergistic use with other markers, particularly GPC-3, HSP-70, and CD10, significantly enhanced diagnostic performance. These findings underscore the valuable role of CD4 in improving the differentiation between cirrhosis and HCC when used alongside established markers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePut Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e above here\u003c/h2\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\u003eLOOCV Results for Differentiating Cirrhosis from HCC\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFeature combination\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eK-Nearest Neighbors (KNN)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLogistic Regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAverage AC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e97.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.115\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e91.765\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e88.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e88.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e86.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e85.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e82.355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e81.765\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e81.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e78.825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e73.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e61.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: AC: accuracy; SE: sensitivity; SP: specificity\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe classification performance for distinguishing cirrhosis from HCC is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among individual markers, GPC-3 demonstrated the highest accuracy (80.0%), while CD34 exhibited the lowest (61.18%). The conventional clinical panel [GS, GPC-3, HSP-70] achieved an average accuracy of 92.35%. Notably, the inclusion of CD4 markedly enhanced diagnostic performance: the [CD4, GPC-3, HSP-70, CD10] combination yielded the highest average accuracy of 98.24% (KNN: 98.82%, LR: 97.65%), whereas extending this panel with CD34 produced 95.29% accuracy. Even the full six-marker panel [CD4, GS, GPC-3, HSP-70, CD10, CD34] maintained high performance (95.29%). Although CD4 alone showed moderate accuracy (78.83%), its incorporation into multi-marker panels consistently resulted in superior classification metrics, underscoring its critical role in improving the discrimination between cirrhotic and malignant liver tissues.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDifferentiating High-Grade Dysplastic Nodules from Well-Differentiated HCC\u003c/h2\u003e\u003cp\u003eWe further evaluated the role of CD4 in distinguishing cirrhosis with high-grade dysplastic nodules from well-differentiated HCC, a clinically critical and challenging differentiation. The LOOCV results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eLOOCV Results for Differentiating High-Grade Dysplastic Nodules from Well-Differentiated HCC\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFeature combination\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eK-Nearest Neighbors (KNN)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eLogistic Regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAverage AC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e93.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e91.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e91.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e87.935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e87.935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e87.935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e87.935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e87.935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e86.205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e84.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e89.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e84.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e84.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e84.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e84.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e84.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e81.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e81.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e81.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, CD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e77.585\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e77.585\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e77.585\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, CD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e75.865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e74.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e74.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e74.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e72.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e70.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e70.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e70.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e68.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, GPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e68.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e67.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e67.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD10, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e67.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, GPC-3, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e65.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, HSP-70, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, HSP-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e62.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4, GS, CD34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e48.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: AC: accuracy; SE: sensitivity; SP: specificity\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePut Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e above here\u003c/h2\u003e\u003cp\u003eThe diagnostic performance for differentiating cirrhosis with high-grade dysplastic nodules from well-differentiated HCC is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among individual markers, CD10 achieved the highest accuracy (70.69%), while GS and GPC-3 performed poorly (63.80% and 48.28%, respectively). The conventional clinical panel [GS, GPC-3, HSP-70] yielded a modest average accuracy of 79.31%. In contrast, the inclusion of CD4 substantially improved discrimination: the [CD4, GPC-3, HSP-70, CD10] combination attained the highest average accuracy of 93.11% (KNN: 96.55%, LR: 89.66%), whereas expanding this set to [CD4, GS, GPC-3, HSP-70, CD10, CD34] achieved 87.94% accuracy. Although CD4 alone demonstrated moderate performance (63.80%), its integration into multi-marker panels consistently produced superior classification metrics, highlighting its pivotal role in accurately distinguishing high-grade dysplasia from well-differentiated carcinoma.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn summary, the two classification tasks yielded consistent findings regarding the diagnostic value of CD4 when combined with established markers. In the cirrhosis versus HCC analysis, the conventional panel [GS, GPC-3, HSP-70] achieved 92.3% average accuracy, whereas the [CD4, GPC-3, HSP-70, CD10] combination reached 98.2% accuracy. Similarly, for differentiating high-grade dysplastic nodules from well-differentiated HCC, the same clinical panel attained 76.8% accuracy, while the inclusion of CD4 in [CD4, GPC-3, HSP-70, CD10] elevated performance to 93.1%. Across both tasks, CD4 alone showed only moderate accuracy (78.8% and 63.8%, respectively), yet its addition to multi-marker panels consistently produced the highest classification metrics. This concordance underscores CD4\u0026rsquo;s pivotal role in enhancing the discrimination of both general cirrhotic versus malignant liver tissues and the more nuanced distinction between dysplastic nodules and well-differentiated carcinoma.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Pathway and PPI Analysis\u003c/h2\u003e\u003cp\u003eWe conducted literature-based functional pathway analysis and protein-protein interaction (PPI) analysis to explore the relationships among the six markers and their association with Cirrhosis and HCC.\u003c/p\u003e\u003cp\u003eThe pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) reveals important relationships between the six markers and the two liver diseases, Cirrhosis and Hepatocellular Carcinoma (HCC). Notably, CD10, CD34, and GPC-3 were significantly associated with both diseases, with CD10 and GPC-3 showing strong positive relationships with HCC and CD34 showing a significant association with Cirrhosis. Specifically, CD10 demonstrated a significant link to HCC (#ref\u0026thinsp;=\u0026thinsp;8, polarity\u0026thinsp;=\u0026thinsp;0, p-value\u0026thinsp;=\u0026thinsp;0.0162), while GPC-3 showed strong associations with both diseases, with a positive relationship to Cirrhosis (#ref\u0026thinsp;=\u0026thinsp;8, polarity\u0026thinsp;=\u0026thinsp;0, p-value\u0026thinsp;=\u0026thinsp;0.0162) and HCC (#ref\u0026thinsp;=\u0026thinsp;10, polarity\u0026thinsp;=\u0026thinsp;1, p-value\u0026thinsp;=\u0026thinsp;0.0132). Similarly, CD4 showed a notable association with HCC (#ref\u0026thinsp;=\u0026thinsp;6, polarity\u0026thinsp;=\u0026thinsp;1, p-value\u0026thinsp;=\u0026thinsp;0.019), but its relationship with Cirrhosis was not significant (#ref\u0026thinsp;=\u0026thinsp;1, p-value\u0026thinsp;=\u0026thinsp;0.1116). GS, while associated with Cirrhosis (#ref\u0026thinsp;=\u0026thinsp;2, polarity = -1, p-value\u0026thinsp;=\u0026thinsp;0.0124), was not significantly related to HCC. HSP-70 showed weaker associations with both diseases, with significant positive associations with HCC (#ref\u0026thinsp;=\u0026thinsp;3, polarity\u0026thinsp;=\u0026thinsp;1, p-value\u0026thinsp;=\u0026thinsp;0.1008), but no significant relationship with Cirrhosis. These findings support the importance of multiple markers in differentiating between Cirrhosis and HCC, with CD10, CD34, and GPC-3 playing particularly notable roles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the PPI analysis, all markers exhibited notable interactions, suggesting potential synergies for more accurate differentiation between liver diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Specifically, CD4 showed interactions with all five other markers (CD10, GS, HSP-70, GPC-3, and CD34), with a particularly strong interaction with HSP-70 (#ref\u0026thinsp;=\u0026thinsp;7, polarity = -1, p-value\u0026thinsp;=\u0026thinsp;0.0041). This underscores the critical role of CD4 in the diagnostic process and supports the use of marker combinations, rather than single markers, for more accurate differentiation. For example, the combination of CD4, GPC-3, HSP-70, and CD10 demonstrated the highest accuracy in both differentiating general Cirrhosis from general HCC and distinguishing High-Grade Dysplastic Nodules from Well-Differentiated HCC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNotably, CD4 emerged as a central hub in the PPI network, interacting with all other markers and exhibiting its strongest association with HSP-70 (polarity \u0026minus;\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.0041). This network topology mirrors our classification results, where multi-marker panels including CD4 achieved the highest diagnostic accuracies (98.2% for cirrhosis vs. HCC and 93.1% for dysplastic nodules vs. well-differentiated HCC). The extensive connectivity of CD4 suggests it captures complementary biological signals\u0026mdash;immune regulation via CD4, proteostasis via HSP-70, and oncogenic signaling via GPC-3 and CD10\u0026mdash;that are not fully represented by any single marker. This synergy underpins the superior performance of CD4-inclusive panels in distinguishing liver lesion subtypes\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigates the role of CD4 as a biomarker to differentiate between liver cirrhosis with dysplastic nodules and well-differentiated hepatocellular carcinoma (HCC). The hypothesis is that CD4 expression levels can enhance diagnostic accuracy by reflecting variations in the immune cell profiles of the tumor microenvironment. Results show that CD4 expression progressively decreases from normal liver tissue to cirrhosis, dysplastic nodules, and HCC. When combined with established markers like GPC-3, HSP-70, and CD10, CD4 significantly improves classification accuracy, achieving up to 98.82% in distinguishing cirrhosis from HCC and 93.11% in differentiating high-grade dysplastic nodules from well-differentiated HCC. These findings highlight CD4's potential to enhance diagnostic precision and inform personalized treatment strategies in liver disease.\u003c/p\u003e\u003cp\u003eAcross both classification tasks, CD4 emerged as a consistently powerful discriminator of malignant and pre-malignant liver lesions. When differentiating cirrhosis from HCC, adding CD4 to the conventional panel of GPC-3, HSP-70, and CD10 increased accuracy from 92.3\u0026ndash;98.82%, markedly boosting both sensitivity and specificity. Likewise, in the more challenging distinction between cirrhosis with high-grade dysplastic nodules and well-differentiated HCC, inclusion of CD4 raised accuracy from 79.31\u0026ndash;93.11%. These improvements underscore CD4\u0026rsquo;s pivotal role: beyond its known functions in T-cell\u0026ndash;mediated immune surveillance and tumor microenvironment modulation \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, CD4 expression patterns capture critical immunological differences that, when integrated into multi-marker panels and analyzed via robust classifiers such as KNN and logistic regression, substantially enhance diagnostic precision for HCC and its precursors.\u003c/p\u003e\u003cp\u003eThe six markers participate in a tightly interconnected network that spans immune regulation, metabolic control, and stress response, providing a mechanistic basis for their synergistic diagnostic performance. CD4, a key T-cell co-receptor, is positively modulated by GPC-3 within the hepatocellular carcinoma microenvironment, enhancing anti-tumor immune activation \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and shows context-dependent associations with CD10 (positive in T-follicular helper-cell lymphoma \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e) and CD34 (positive in HCC immunotherapy settings \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e). GPC-3 and CD34 together improve HCC differentiation accuracy \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, while HSP-70 bolsters CD4\u0026thinsp;+\u0026thinsp;T-cell responses under certain conditions \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003eand collaborates with GS in hepatocarcinogenesis \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Although each marker alone yields only moderate accuracy, their integration captures multiple pathological facets\u0026mdash;immune dysregulation via CD4, oncogenic signaling via GPC-3 and GS, proteostasis stress via HSP-70, and tissue remodeling via CD10 and CD34\u0026mdash;which explains why combining CD4 with GPC-3, HSP-70, and CD10 elevates classification accuracy to 98.2% for cirrhosis versus HCC and 93.1% for high-grade dysplastic nodules versus well-differentiated HCC. This multi-marker strategy leverages complementary biological insights to achieve robust discrimination between liver lesion types.\u003c/p\u003e\u003cp\u003eIn addition to CD4, the other five markers each contribute distinct biological insights that aid in differentiating HCC from cirrhosis. GPC-3 is highly overexpressed in HCC but minimally detectable in cirrhotic liver, reflecting its role in promoting tumor growth and poor prognosis \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Glutamine synthetase (GS) is similarly upregulated in many HCCs\u0026mdash;often in pericentral tumor regions\u0026mdash;whereas GS expression remains low or focal in cirrhosis, providing metabolic discrimination between neoplastic and fibrotic tissue \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. CD34, a marker of microvascular density, highlights the angiogenic switch characteristic of HCC, with extensive CD34⁺ capillarization in tumor sinusoids versus limited endothelial staining in cirrhosis \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. HSP-70, a stress-inducible chaperone, is frequently elevated in HCC and correlates with aggressive phenotypes, whereas lower HSP-70 levels are associated with reduced fibrosis and cirrhosis severity \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. By contrast, CD10\u0026mdash;though present in a subset of HCC cases\u0026mdash;lacks the specificity to reliably distinguish malignant from benign cirrhotic nodules when used alone \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Together, these complementary markers capture oncogenic signaling (GPC-3, GS), angiogenesis (CD34), stress response (HSP-70), and tissue remodeling (CD10), which\u0026mdash;when integrated with CD4\u0026rsquo;s immune‐related information\u0026mdash;enhance the robustness of multi‐marker panels for accurate liver lesion classification.\u003c/p\u003e\u003cp\u003eCD4, a critical component of the immune system, plays a significant role in modulating immune responses, which can influence tumor progression and immune evasion in HCC \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The interaction between CD4\u0026thinsp;+\u0026thinsp;T cells and HCC is complex, involving various pathways and mechanisms.\u003c/p\u003e\u003cp\u003eAt genetic level, CD4\u0026thinsp;+\u0026thinsp;T cells exert a multifaceted genetic-level influence on hepatocellular carcinoma (HCC) by orchestrating immune‐regulatory and tumor‐suppressive pathways within the tumor microenvironment. As key tumor‐infiltrating lymphocytes, CD4\u0026thinsp;+\u0026thinsp;T cells enhance anti‐tumor immunity\u0026mdash;evidenced by improved responses to Lenvatinib plus anti\u0026ndash;PD-1 therapy through increased systemic CD4\u0026thinsp;+\u0026thinsp;proportions \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u0026mdash;and modulate pro‐inflammatory cytokines such as IL-6, which correlates with post‐transplant HCC recurrence risk \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and TNF-α, which can bolster anti‐tumor responses \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Furthermore, CD4\u0026thinsp;+\u0026thinsp;T cells regulate immune checkpoints (CTLA-4, PD-1) to maintain T‐cell activation and tolerance \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, influence HLA molecule expression for enhanced tumor antigen presentation, and engage the TGF-β signaling axis\u0026mdash;tumor suppressive early on but protumorigenic in advanced disease \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Through these interconnected pathways, CD4\u0026thinsp;+\u0026thinsp;T cells modulate gene expression profiles that either restrain or facilitate HCC progression, underscoring their pivotal role in shaping therapeutic outcomes.\u003c/p\u003e\u003cp\u003eAt the cellular process level, CD4\u0026thinsp;+\u0026thinsp;T helper cells profoundly shape the HCC tumor microenvironment by orchestrating cytokine- and chemokine‐driven signaling that governs the recruitment and activation of effector (e.g., CD8+) and regulatory T cell subsets. Through their secreted factors, CD4\u0026thinsp;+\u0026thinsp;cells can either enhance anti‐tumor immunity or, in chronic liver disease settings, exacerbate immunosuppression and inflammation, thereby promoting tumor progression. Notably, a high prevalence of CD203a\u0026thinsp;+\u0026thinsp;Th17 cells\u0026mdash;a CD4\u0026thinsp;+\u0026thinsp;subset\u0026mdash;increases post‐surgical HCC recurrence risk \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and protein\u0026ndash;protein interaction analyses position CD4 at the nexus of key immune pathways in HCC \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Furthermore, clinical interventions such as Lenvatinib plus anti\u0026ndash;PD-1 therapy have been shown to boost systemic CD4\u0026thinsp;+\u0026thinsp;T‐cell levels, correlating with improved treatment responses \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Collectively, these findings underscore CD4\u0026rsquo;s central role in modulating immune\u0026ndash;tumor dynamics and highlight CD4‐targeted strategies as promising avenues for HCC immunotherapy.\u003c/p\u003e\u003cp\u003eAt the tissue and organ level, CD4⁺ T cells orchestrate the immune landscape of hepatocellular carcinoma (HCC) by shaping both local and systemic responses. Intrahepatic CD4⁺ T cells modulate the tumor microenvironment through cytokine-mediated activation of cytotoxic CD8⁺ T cells, macrophages, and dendritic cells, thereby influencing tissue architecture and anti‐tumor immunity \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Conversely, specific CD4⁺ subsets\u0026mdash;such as CD203a⁺ Th17 cells\u0026mdash;can promote HCC recurrence, with post‐surgical elevations correlating with a sixfold higher relapse risk \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Immunohistochemical studies further reveal that regulators of CD4⁺ function, such as SOCS2, can suppress Treg activity and inhibit tumor growth and metastasis when overexpressed \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, while markers like METTL16 highlight pathways of therapeutic resistance and disease progression \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Together, these findings underscore CD4\u0026rsquo;s dualistic role in liver tissue\u0026mdash;simultaneously driving anti‐tumor defenses and, in certain contexts, facilitating HCC recurrence\u0026mdash;positioning CD4⁺ T cells as both biomarkers and potential targets for immunomodulatory strategies in HCC.\u003c/p\u003e\u003cp\u003eIn summary, the study effectively demonstrates the utility of CD4 as a valuable biomarker in differentiating liver conditions, particularly cirrhosis and hepatocellular carcinoma (HCC). By integrating CD4 with established markers like GPC-3, HSP-70, and CD10, the study achieves high diagnostic accuracy, significantly enhancing classification performance compared to traditional panels. Beyond diagnostic improvements, we also initiated the exploration of the underlying mechanisms of these multi-marker panels through PPI and pathway analysis, providing biological insight into their synergistic potential. This approach highlights both the clinical and mechanistic value of CD4-inclusive panels for robust liver lesion differentiation.\u003c/p\u003e\u003cp\u003eDespite the promising results, the study is limited by its reliance on immunohistochemistry (IHC) staining, which may not capture the full complexity of CD4 expression patterns across diverse patient populations. Additionally, the study's sample size, particularly for certain liver conditions, may not be sufficient to generalize the findings broadly. The study also does not explore the potential variability in CD4 expression due to factors such as age, gender, or underlying health conditions, which could impact the generalizability and applicability of the results in diverse clinical settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies CD4 as a powerful adjunct biomarker for distinguishing cirrhosis (with or without dysplastic nodules) from well-differentiated HCC, markedly improving diagnostic accuracy when combined with GPC-3, HSP-70, and CD10. The progressive decline in CD4 expression\u0026mdash;from normal liver through cirrhosis to HCC\u0026mdash;reflects underlying immune alterations that can be harnessed in multi-marker panels to achieve robust classification performance. By integrating CD4\u0026rsquo;s immunological insights with traditional tumor markers, clinicians can more reliably differentiate early malignant changes from benign liver lesions, enabling timely intervention and personalized management. Further validation in larger, diverse cohorts will be essential to confirm these findings and support the clinical adoption of CD4-inclusive diagnostic algorithms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasic Research Program of the Shanxi Provincial Natural Science Foundation, Grant Number: 20210302123254.\u003c/p\u003e\n\u003cp\u003eBeijing Fengtai Hospital Research Fund, Grant Number: 2024-2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHongkun Wang\u0026nbsp;\u003c/strong\u003eDepartment of Pathology, Beijing Fengtai Hospital, Beijing, 100071, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaorong Li\u0026nbsp;\u003c/strong\u003eDepartment of Pathology, Xi'an People's Hospital (Fourth Hospital of Xi'an) , Xi'an City, Shanxi, 710004, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaojun Liu, Huili Wan\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Huixia Zheng\u0026nbsp;\u003c/strong\u003eDepartment of Pathology, First Hospital of Shanxi Medical University, Taiyuan City, Shanxi, 030001, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYuze Zhao\u003c/strong\u003eDepartment of Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHongkun Wang\u0026nbsp;and Yuze Zhao designed the study, conducted data acquisition, organization, and analysis, and drafted the initial version of the manuscript. Xiaojun Liu, and Huixia Zheng contributed to data analysis and manuscript writing. Xiaorong Li and Huili Wan contributed to the study design and manuscript writing. All authors approved the manuscript for submission to the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrespondence to Yuze Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalogh, J.\u003cem\u003e et al.\u003c/em\u003e Hepatocellular carcinoma: a review. \u003cem\u003eJ Hepatocell Carcinoma\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 41-53, doi:10.2147/JHC.S61146 (2016).\u003c/li\u003e\n\u003cli\u003eFoglia, B., Turato, C. \u0026amp; Cannito, S. 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METTL16/IGF2BP2 axis enhances malignant progression and DDP resistance through up-regulating COL4A1 by mediating the m6A methylation modification of LAMA4 in hepatocellular carcinoma. \u003cem\u003eCell Div\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 9, doi:10.1186/s13008-025-00152-2 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hepatocellular carcinoma, cirrhosis, early diagnosis, pathway analysis, PPI","lastPublishedDoi":"10.21203/rs.3.rs-6598830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6598830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDistinguishing high-grade dysplastic nodules in cirrhotic livers from well-differentiated hepatocellular carcinoma (HCC) remains difficult due to overlapping histological features and limited biomarker specificity. In this study, 85 liver tissues were analyzed, including 54 HCCs of varying differentiation and 31 cirrhotic livers (10 with high-grade dysplastic nodules). Logistic regression (LR) and k-nearest neighbors (KNN) models were applied using leave-one-out cross-validation to classify (1) cirrhosis vs. HCC and (2) cirrhosis with dysplastic nodules vs. well-differentiated HCC. Immunohistochemistry showed progressive CD4 loss with lesion severity. Adding CD4 to the conventional panel [GPC-3, HSP-70, CD10] improved classification accuracy from 92.3\u0026ndash;98.82% for cirrhosis vs. HCC, and from 79.31\u0026ndash;93.11% for cirrhosis with dysplastic nodules vs. well-differentiated HCC. Functional pathway and protein\u0026ndash;protein interaction (PPI) analyses identified CD4 as a central hub, most strongly linked to HSP-70 (p\u0026thinsp;=\u0026thinsp;0.0041), supporting its synergistic diagnostic value. These findings underscore CD4's utility in enhancing classification accuracy and its potential as a robust marker for differentiating liver lesion subtypes. Further validation in larger, diverse cohorts is warranted.\u003c/p\u003e","manuscriptTitle":"CD4 as a Potential Biomarker for Differentiating Cirrhosis with Dysplastic Nodules from Well-Differentiated Hepatocellular Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 08:38:13","doi":"10.21203/rs.3.rs-6598830/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":"17bb2e1f-3211-437f-b081-2f0dc32debf4","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54962692,"name":"Biological sciences/Cancer"},{"id":54962693,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2025-10-10T06:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 08:38:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6598830","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6598830","identity":"rs-6598830","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}