Epigenetic Signature Enables Accurate Classification of Pleural and Peritoneal Effusions

Epigenetic Signature Enables Accurate Classification of Pleural and Peritoneal Effusions | 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 Epigenetic Signature Enables Accurate Classification of Pleural and Peritoneal Effusions Ting Liang, Ting Wang, Jian Liu, Hong Ge, Jia Feng, TingTing Bian, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7883390/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 This study demonstrates that a dual-marker DNA methylation test for SHOX2 and RASSF1A significantly improves the detection of cancer in pleural and peritoneal effusions compared to conventional cytology alone. In a prospective analysis of 696 patient samples, cytology by itself had a low sensitivity of only 34.7%. In contrast, the methylation assay achieved high accuracy with a sensitivity of approximately 80% and a specificity of over 89%. When the methylation results were combined with cytology, the diagnostic sensitivity dramatically increased to 91.7% without compromising specificity. This robust performance was consistent across various cancers, including lung, gastrointestinal, and gynecological malignancies. The findings strongly support integrating this minimally invasive molecular test into routine clinical workflows to enable faster, more accurate cancer diagnosis from fluid samples, potentially reducing the need for more invasive procedures. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Medical research Health sciences/Oncology DNA methylation profiling SHOX2 RASSF1A Epigenetics Malignant effusion Diagnosis and Differential Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hydrothorax and ascites—fluid accumulations in the pleural and peritoneal cavities—arise from a wide range of pathological conditions, including malignant tumors, infections, heart failure, tuberculosis, liver failure, and cirrhosis. In cytological evaluation, reactive mesothelial cell proliferation often exhibits marked atypia, which can closely mimic malignant morphology and lead to diagnostic ambiguity 1 , 2 . Accurately distinguishing benign from malignant effusions is therefore critical, as this determination directly influences clinical management, therapeutic decision-making, and prognosis. The etiologic spectrum of malignant effusions is broad. Lung cancer is the most frequent cause of malignant pleural effusions, followed by breast cancer, mesothelioma, lymphoma, and gastrointestinal malignancies 3 , 4 . In contrast, malignant ascites most commonly results from ovarian, hepatic, pancreatic, and gastric cancers 5 , 6 . Given this diversity, an ideal diagnostic assay must reliably detect malignancy across multiple tumor types, while maintaining high specificity to avoid unnecessary invasive procedures. However, conventional cytomorphology—though specific—has limited sensitivity, particularly in low-cellularity or morphologically equivocal samples. This diagnostic gap underscores the need for molecular adjuncts capable of improving detection rates without compromising specificity. DNA methylation profiling, particularly of tumor-associated genes such as SHOX2 and RASSF1A , has emerged as a promising approach for enhancing the accuracy of effusion cytology. Cytology and thoracoscopic pleural biopsy remain the gold standards for diagnosing pleural effusions 7 . However, their accuracy is often compromised by the complex cellular composition of hydrothorax and ascites. Abundant mesothelial cells, granulomatous inflammation, diverse lymphohematopoietic elements, and benign mesenchymal components can obscure the distinction between malignant mesothelioma, histiocytic tumors, lymphohematopoietic neoplasms, and sarcomas 8 , 9 . Liquid-based cytology testing (ThinPrep cytologic test, TCT) offers improved sample quality over conventional smear cytology but still exhibits limited sensitivity. Similarly, cell block preparation from effusion sediments can enhance detection rates; however, low cellularity and uneven cell distribution often hinder accurate interpretation. Given that the benign–malignant classification of pleural and peritoneal effusions directly informs therapeutic decisions, there is an urgent need for diagnostic strategies that combine high sensitivity with robust specificity 10 . DNA methylation is a key epigenetic mechanism in tumorigenesis and progression and has emerged as a promising molecular biomarker for cancer detection. Methylation profiling of SHOX2 and RASSF1A has been successfully applied to diagnose lung cancer using bronchoalveolar lavage fluid, pleural effusions, and biopsy tissue samples 11 . SHOX2 methylation has also been reported in multiple malignancies, including gastrointestinal, breast, and ovarian cancers 12 – 14 . Hypermethylation of the RASSF1A promoter, a tumor suppressor gene, is an established independent predictor of tumor invasion and metastasis in diverse cancers such as lung, gastrointestinal, breast, and head and neck tumors. The LungMe® assay—based on combined SHOX2 and RASSF1A methylation detection—has demonstrated high diagnostic accuracy for lung cancer, with reported sensitivity ranging from 71.5% to 84.0% and specificity from 80.7% to 90.0% in bronchoalveolar lavage fluid 11 , 15 . When applied to enriched exfoliated cells in pleural effusions, this dual-marker approach achieved even higher performance, with sensitivity of 71.4%–96% and specificity of 84.8%–100% for malignant effusion detection 16 , 17 . Despite these promising findings, the clinical utility of combined SHOX2 and RASSF1A methylation analysis has not been systematically evaluated across both pleural and peritoneal effusions from a broad spectrum of malignancies. Addressing this gap, the present study investigates the diagnostic performance of this dual-marker methylation signature—alone and in combination with cytomorphology—for differentiating benign from malignant effusions in a large, diverse patient cohort. In this study, we compared the diagnostic performance of traditional cytology (TCT), exfoliated cell paraffin block examination, and a dual-marker SHOX2/RASSF1A methylation assay (LungMe®) in differentiating benign from malignant pleural and peritoneal effusions. We further evaluated the clinical utility of integrating cytomorphologic and methylation analyses, and expanded the application of the SHOX2/RASSF1A methylation test beyond lung cancer to encompass multiple malignancy types. Results Patient Characteristics A total of 696 patients (407 males, 289 females) with pleural and peritoneal effusions were included, comprising 501 cases of hydrothorax and 230 cases of ascites. Among these, 435 cases were malignant and 261 benign. The proportion of benign hydrothorax was significantly higher in males than females (p < 0.05). The mean age of onset for benign and malignant hydrothorax ranged from 64.1 to 68.8 years. Notably, patients under 50 years had a significantly higher proportion of benign hydrothorax compared to malignant (19.9% vs. 6.0%, p < 0.05). The peak incidence of malignant hydrothorax occurred between ages 70–79 (31.7%), while benign hydrothorax was most common in the 60–69 age group (23.0%) (Table 1). Lung cancer was the predominant cause of malignant pleural effusions, accounting for 71.96% of cases, followed by gastric cancer (5.51%), lymphoma (4.80%), and breast cancer (3.32%) (Fig. 1a). The most frequent malignancies associated with malignant ascites were ovarian cancer (26.22%), gastric cancer (15.85%), liver cancer (15.85%), and pancreatic cancer (8.54%) (Fig. 1b). ROC analysis of SHOX2 and RASSF1A methylation Methylation-specific quantitative fluorescence PCR for SHOX2 and RASSF1A was performed on all samples, with ΔCt values calculated individually. These ΔCt values were used to generate ROC curves to differentiate benign from malignant pleural effusions and ascites. Youden’s index was applied to identify optimal cutoff points. For SHOX2, the optimal ΔCt cutoff values for diagnosing malignant pleural fluid and ascites were 8.88 and 8.46, respectively. Accordingly, a conservative cutoff of 9 was selected for both sample types. For RASSF1A, optimal cutoffs were 17.33 (pleural fluid) and 16.54 (ascites); however, based on assay stability considerations from preliminary studies [19], a maximum cutoff of 12 was chosen for RASSF1A methylation in both fluid types. The area under the curve (AUC) for SHOX2 methylation alone was 0.856, while that for RASSF1A was 0.671. Logistic regression combining raw ΔCt values for both markers significantly improved diagnostic accuracy, yielding an AUC of 0.948 (Fig. 2), demonstrating superior predictive performance of the dual-marker model. Scatter plots illustrating ΔCt values for SHOX2 and RASSF1A in benign versus malignant groups are shown in Fig. 3. The sensitivity of SHOX2 methylation for detecting malignant pleural effusions and ascites was 74.5% and 70.7%, respectively, with specificity of 90.7% and 89.3% (Fig. 3a, b). In contrast, RASSF1A methylation showed lower sensitivity—36.9% for pleural fluid and 26.2% for ascites—but achieved 100% specificity in both (Fig. 3c, d). Because lower ΔCt values correspond to higher methylation levels, statistical analysis determined that achieving 100% specificity for SHOX2 required a ΔCt threshold below 7.23 for pleural effusions and 8.51 for ascites. Consequently, ΔCt_SHOX2 < 7.23 was defined as a strong positive methylation signal in pleural fluid samples. Diagnostic efficacy of molecular methylation compared to cytology in differentiating benign and malignant pleural effusions and ascites To evaluate the diagnostic performance of molecular methylation assays relative to conventional morphology-based methods, pleural and peritoneal fluid samples were simultaneously analyzed using liquid-based TCT, LungMe® methylation assay, and paraffin-embedded cell block examination. Although cytology and histopathology remain the gold standards for diagnosing malignant effusions, definitive diagnosis often requires comprehensive clinical correlation 18,19 . In pleural effusions, LungMe® methylation demonstrated a sensitivity of 80.4%, significantly outperforming routine TCT (38.0%) and cell block histology (71.2%). The combined use of TCT, LungMe®, and paraffin block examination further enhanced sensitivity to 92.6%, with specificity maintained at 90.7% (Fig. 4a). In ascites, methylation sensitivity was slightly lower at 76.8%, compared to TCT (29.3%) and cell block (60.4%). However, combination testing increased sensitivity to 90.2% (Fig. 4b). Comparative analysis of cytology and methylation in pleural and peritoneal fluids (Table 2) revealed that cell block examination had significantly higher sensitivity than cytological TCT (67.1% vs. 34.7%). Both cytomorphology methods showed specificity and positive predictive values (PPV) near 100%. However, negative predictive values (NPV) were limited, ranging from 47.9% to 64.5%, reflecting reduced ability to rule out malignancy. Methylation assays demonstrated similarly high specificity (90.4%) and superior PPV (93.2%). Importantly, when methylation results exceeded stringent positivity thresholds (ΔCt_SHOX2 < 7.23 or ΔCt_RASSF1A ≤ 12), both specificity and PPV reached 100%. Sensitivity of the LungMe® assay (79.1%) surpassed that of TCT and cell block, indicating improved detection of true positives. The combination of LungMe® with cytology elevated sensitivity to 91.7% and PPV to 93.9%. Notably, when all three tests (TCT, cell block, LungMe®) were negative, NPV improved dramatically to 86.7%, markedly enhancing confidence in ruling out malignancy. Cytology often yielded indeterminate (“gray zone”) results, with atypical or heterogeneous cells observed in 39.2% of cases and a PPV of 68.5%. Integration of positive LungMe® methylation with these equivocal cytology results increased tumor specificity from 67.0% to 93.5% and raised PPV to 89.6%, providing a powerful tool for clarifying ambiguous diagnoses. Among 82 false-negative cytology cases, 74 were positive by methylation, with test specificity of 96.9% and PPV of 90.2%. When stratified by tumor origin (Table 3), LungMe® showed high sensitivity for detecting malignant effusions from diverse cancers: 100% for esophageal and breast cancer, >80% for lung, gastric, lymphoma, cholangiocarcinoma, and peritoneal tumors, and >70% for ovarian, pancreatic, and hepatocellular carcinomas. These results underscore the broad clinical applicability of LungMe® methylation testing for malignancy detection across multiple cancer types, supporting its potential role in routine diagnostic workflows. Diagnostic value of SHOX2 and RASSF1A methylation across tumor types Building on LungMe®’s high sensitivity across anatomical sites, we assessed the diagnostic performance of SHOX2 and RASSF1A methylation—individually and combined—in malignant pleural and peritoneal fluids from diverse cancers. SHOX2 methylation alone exhibited high sensitivity (74.6% to 100%) for most tumor types, including esophageal cancer, cholangiocarcinoma, gastric cancer, lymphoma, breast cancer, pancreatic cancer, and lung cancer. Sensitivity was modestly lower for hepatocellular carcinoma (61.3%), ovarian carcinoma (59.3%), and colorectal carcinoma (53.8%) (Fig. 5a). In contrast, RASSF1A methylation showed variable sensitivity: 78.6% in breast cancer, but lower rates in lung (38.8%) and ovarian cancer (38.5%). Overall positivity rates of RASSF1A in pleural and peritoneal fluids ranged between 11.1% and 28.6%, with gastric cancer showing the lowest rate at 7.3% (Fig. 5b). Notably, the combined dual-marker LungMe® assay demonstrated a clear complementary effect in breast, lung, ovarian, and hepatocellular carcinomas, significantly improving sensitivity to 100%, 80.1%, 73.1%, and 71.0%, respectively (Fig. 5c). Although RASSF1A positivity was relatively low overall, its methylation status is an independent biomarker linked to tumor invasiveness and poor prognosis across multiple malignancies 20 . This complementary relationship with SHOX2 underscores the added value of the dual-marker approach in enhancing diagnostic accuracy and providing prognostic insights. Discussion This study systematically compared conventional diagnostic methods—namely, liquid-based cytology (TCT) and paraffin block histopathology—with a novel combined SHOX2 and RASSF1A methylation assay (LungMe®) for differentiating benign from malignant hydrothorax and ascites across multiple cancer types. Our findings confirm the limited sensitivity of cytologic TCT in detecting malignant effusions, with rates of only 29.3% for ascites and 38.0% for pleural fluid. While paraffin-embedded cell block examination significantly improved sensitivity to 60.4% and 71.2% for ascites and pleural fluid, respectively, the overall diagnostic challenge of malignant ascites remained evident. The combined sensitivity of paraffin block and TCT was relatively modest—47.7% for ascites and 41.8% for pleural effusions—highlighting the need for enhanced diagnostic approaches. In contrast, the LungMe® methylation assay demonstrated markedly higher sensitivities of 80.4% for malignant pleural fluid and 76.8% for malignant ascites, alongside excellent specificity ranging from 89.3% to 90.7%. These results align well with previous reports, such as Liang et al., who documented a 76.5% positive detection rate for methylation markers in pleural fluid 21 , underscoring the robustness and clinical promise of methylation-based diagnostics. ROC curve analysis identified ΔCt thresholds of 9 for SHOX2 and 12 for RASSF1A as optimal cutoffs, consistent with findings from previous studies conducted at China-Japan Friendship Hospital, Zhujiang Medical University, and Southern Medical University 22 . The reproducibility of these cutoff values across multiple independent cohorts and institutions highlights the robustness and potential standardization of the SHOX2/RASSF1A methylation assay for pleural fluid analysis, supporting its broad applicability in clinical diagnostics. This study highlights the significant advantage of incorporating methylation analysis into routine pathological evaluation of hydrothorax and ascites. Several practical pathways exist for integrating this technique into clinical workflows: fresh cell samples can undergo simultaneous TCT and methylation testing, enabling rapid turnaround (typically within two working days) for initial screening. Positive or equivocal cases can then be further evaluated with paraffin-embedded cell block preparation, H&E staining, and immunohistochemistry. While morphologic diagnosis offers near-perfect specificity, its limited sensitivity results in high PPV but low NPV, meaning a negative cytology cannot reliably exclude malignancy. Incorporating methylation testing, which demonstrated an NPV of 86.7%, effectively addresses this diagnostic gap. Additionally, cytology reports categorized as “gray zone” or heterogeneous—accounting for 39.2% of cases in this study—can benefit from adjunct methylation analysis, facilitating high-risk triage and improving diagnostic confidence. When cytology results are negative, but methylation testing is positive, the positive predictive value (PPV) of methylation becomes critically important for clinical decision-making. In this study, a PPV of 90.2% highlights a strong likelihood of malignancy, underscoring the necessity for close clinical follow-up or repeat diagnostic confirmation. Growing evidence supports the integration of methylation analysis with conventional morphopathology to generate more comprehensive diagnostic reports, thereby enhancing both the sensitivity and accuracy of tumor detection. To our knowledge, this study is the first to expand the clinical application of the LungMe® methylation assay beyond lung cancer to multiple malignancies causing pleural and peritoneal effusions. Lung cancer remained the predominant cause of malignant pleural effusions (71.96%), followed by gastric cancer and lymphoma. Benign ascites was primarily attributed to hepatic cirrhosis, whereas malignant ascites mainly resulted from ovarian (26.22%), gastric (15.85%), and liver cancers (15.85%). Analysis of diagnostic sensitivity across tumor types revealed that SHOX2 methylation exhibited high sensitivity for most cancers, ranging from 53.8% to 100%. In contrast, RASSF1A showed lower overall sensitivity (7.3% to 38.8%), except in breast cancer, where it reached 78.6%, consistent with prior studies demonstrating the utility of circulating RASSF1A methylation for monitoring metastatic breast cancer 23 . The combined LungMe® assay markedly enhanced diagnostic sensitivity across a spectrum of tumors causing hydrothorax (100% for cholangiocarcinoma, esophageal, and breast cancers; >75% for gastric, lymphoma, lung, and ovarian cancers) and ascites (100% for esophageal and breast cancers; >75% for cholangiocarcinoma, lung, pancreatic, and peritoneal tumors). These findings underscore the value of combining multiple methylation markers to improve diagnostic accuracy. Future enhancements could incorporate additional markers such as SEPTIN9—a highly sensitive methylation biomarker for colorectal cancer detection 24 —and HOXA9, which is effective for identifying BRCA-mutated epithelial ovarian cancer 25 . For example, Turbine’s OncoMe™ Methylation Portfolio, which combines SHOX2, RASSF1A, SEPTIN9, and HOXA9, has been reported to increase hydrothorax detection sensitivity from 74.0% to 85.0% 22 . Such multi-marker panels hold great promise for advancing methylation-based diagnostics across diverse malignancies. Future epigenetic research should prioritize refining combined methylation assays by incorporating cell-free DNA (cfDNA) analysis to further enhance sensitivity and specificity. cfDNA originates from necrotic, fragmented, or secreted extracellular DNA released by normal cells, tumor cells, adjacent tissues, or both primary and metastatic lesions. In lung adenocarcinoma patients with malignant pleural effusions, cfDNA has demonstrated high clinical utility; mutation detection sensitivity in cfDNA reached 90.7%—surpassing even tissue biopsy sensitivity at 88% 26 . Determining the optimal source of DNA for methylation analysis—whether from the supernatant of pleural/peritoneal fluids or from cellular precipitates—will be critical to improving the positive predictive value (PPV) for tumor staging and prognostic evaluation. This distinction could unlock more accurate, minimally invasive tools for real-time cancer monitoring and personalized treatment strategies. Multi-marker methylation profiling represents a significant advance in the diagnostic evaluation of pleural and peritoneal effusions. Our study demonstrates that combined SHOX2 and RASSF1A methylation testing effectively discriminates benign from malignant effusions across diverse cancer types. When integrated with cytomorphological analysis, this approach markedly enhances tumor detection sensitivity, enabling a more accurate and minimally invasive diagnostic paradigm. This strategy holds broad clinical promise for early cancer detection, population screening, high-risk patient triage, adjunctive diagnostics, monitoring of recurrence, and personalized treatment decision-making. Methods Patient selection From June 2021 to October 2023, a total of 696 de-identified patients (age range: 15–97 years) with pleural or peritoneal effusions were consecutively enrolled at The Affiliated Hospital of Nantong University. The cohort comprised 271 patients with malignant pleural effusions, 205 with benign pleural effusions, 164 with malignant ascites, and 56 with benign ascites. Malignant or benign status was confirmed based on clinical, imaging, cytologic/histologic, and follow-up data. All procedures were conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of The Affiliated Hospital of Nantong University, and all experiments complied with institutional ethics committee regulations. All the patients/participants or their legal guardian(s) provided written informed consent to partake in this study. Collection of thoracic and peritoneal fluids For each patient, 50–200 mL of fresh pleural fluid or ascitic fluid was collected under sterile conditions. A 5–10 mL aliquot was centrifuged at 2,000 rpm for 10 minutes, and the supernatant was discarded. The resulting cell pellet was resuspended in ThinPrep cytology test (TCT) preservation solution or liquid-based cytology preservation solution for cytologic examination. The remaining fluid sample was similarly centrifuged at 2,000 rpm for 10 minutes, the supernatant was removed, and the cell pellet was processed into a formalin-fixed, paraffin-embedded (FFPE) cell block for histopathologic evaluation. Cytologic TCT of pleural and peritoneal fluids Cell sediments from pleural or peritoneal fluids were mixed with liquid-based cell preservation solution and processed using a ThinPrep cytology device. The prepared smears were air-dried and fixed in 95% ethanol for 10 minutes. Slides were then stained with hematoxylin–eosin (H&E), coverslipped, and examined microscopically by experienced cytopathologists. Cell block preparation and examination of pleural fluid cell sedimentation Cellular precipitates from pleural and peritoneal fluid specimens were collected by centrifugation and fixed in 95% ethanol for 6 hours. Fixed cell pellets were washed 3–5 times with phosphate-buffered saline to remove residual fixative, then dehydrated through a graded ethanol series (70% to 100%). Following dehydration, ethanol was replaced with xylene to facilitate paraffin infiltration. Samples were then immersed in melted paraffin to ensure full permeation, followed by rapid cooling on ice to solidify the paraffin and form wax blocks. The paraffin-embedded cell blocks were sectioned, stained with hematoxylin and eosin (H&E), and examined microscopically to assess cellular morphology and tissue architecture. Methylation-specific fluorescence quantitative PCR (MS-PCR) Genomic DNA was extracted from cell sediments using reagents provided by Shanghai Turboscan Life Science and Technology Co., which also supplied the methylation detection kit targeting human SHOX2 and RASSF1A genes (patent: Guomei Quanzhi 20173403354). Sulfite modification and purification of DNA were performed according to the manufacturer’s instructions. Real-time quantitative PCR was conducted on a Hongshi SLAN-96S system, with DNA concentration measured using an Ausheng Fluo-100B fluorescent dye DNA detector. Cell sediments were centrifuged at 2,000 rpm for 5 minutes, supernatant discarded, and pellets subjected to DNA extraction. DNA concentration was quantified, and approximately 200 ng of DNA was used for bisulfite conversion and purified into 20 μL eluent. For methylation analysis, 5 μL of modified DNA was used per reaction. β-actin served as an internal reference gene. Positive controls included plasmids containing methylated SHOX2 or RASSF1A DNA, while nuclease-free water was used as a negative control. During PCR, fluorescence signals were monitored on FAM, VIC, and CY5 channels. Successful amplification in positive controls was confirmed by signals across all channels, while negative controls showed no amplification. The cycle threshold (Ct) value for CY5 (β-actin) was required to be below 28, with an optimal range of 18–23, ensuring sample quality. For SHOX2, amplification in the VIC channel produced a characteristic sigmoidal (“S”-shaped) curve with Ct <32, and methylation levels were calculated as ΔCt_SHOX2 = Ct_SHOX2 – Ct_β-actin. For RASSF1A, amplification in the FAM channel yielded a similar curve with Ct <35, with methylation quantified as ΔCt_RASSF1A = Ct_RASSF1A – Ct_β-actin. DNA concentrations in fluid samples ranged from 0.23 to 106.00 ng/μL. A minimum of 50 ng DNA was added per PCR assay, with concentrations ≥10 ng/μL meeting assay requirements. The consistent β-actin Ct values (18–23) confirmed high-quality DNA and reliable assay performance. Receiver operating characteristics (ROC) curves Methylation ΔCt values of SHOX2 and RASSF1A served as test variables, while pathological diagnosis of pleural and peritoneal effusions combined with clinical data was used as the reference standard (state variable). ROC curves were generated to determine optimal cutoff values for each marker. Logistic regression models were constructed to estimate the probability of malignancy based on SHOX2 and RASSF1A methylation status, both individually and in combination. The discriminatory performance of these models was assessed by calculating the area under the ROC curve (AUC), evaluating their ability to differentiate benign from malignant effusions. Statistical analysis Data from three independent experiments are presented as mean ± standard deviation (SD). Comparisons between groups were performed using Student’s t-test. Statistical analyses were conducted using SPSS version 22.0 and GraphPad Prism 10 software. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for SHOX2 and RASSF1A methylation assays in pleural and peritoneal fluids were calculated and expressed as percentages. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of individual markers and their combination, with area under the curve (AUC) values calculated accordingly. A two-sided p-value < 0.05 was considered statistically significant. Abbreviations TCT Thinprep cytologic test ROC Receiver operator characteristic SHOX2 Short stature homeobox 2 RASSF1A Ras-association domain family member 1 A HOXA9 Homeobox A9 PPV Positive predictive value NPV Negative predictive value MS-PCR Methylation-specific PCR Declarations Author contributions T.L. -Writing - Original Draft, Visualization, Writing - Review & Editing; T.W. - Software, Data Curation, Writing- Reviewing and Editing; J. L. - Conceptualization, Methodology, Validation; H.G. - Writing- Reviewing and Editing; J.F. - Investigation, Validation; T.T.B. - Investigation, validation; L.L. - Investigation, validation; H.X. - Formal analysis, Software; H.S. - Investigation; B.S. - Software; X.S.R. - Software; J.L. - Editing and Supervision; Q.S.L. - Editing and Supervision. - Supervision, Project administration; X.Q.D. - Supervision, Project administration; Y.F.L. - Supervision, Project administration, Funding acquisition. Data availability statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Conflict of Interest The authors have no conflicts of interest to declare. Funding This study was supported by the Jiangsu Provincial Research Hospital (Grant No. YJXYY202204-YSB01), Nantong Basic Research Plan Project (Grant No. MS2023067), the National Natural Science Foundation of China (Grant No. 82273422). 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Tables Table 1 Basic patient information Characteristic Hydrothorax (N=476) Ascites (N=220) Hydrothorax and Ascites (N=696) Malignant (%) Benign (%) Malignant (%) Benign (%) Malignant (%) Benign (%) （n=271） （n=205） (n=164) (n=56) (n=435) (n=261) Gender Male 156 (57.6) 142 (69.3) 78 (47.6) 31 (55.4) 234 (53.8) 173 (66.3) Female 115 (42.4) 63 (30.7) 86 (52.4) 25 (41.6) 201 (46.2) 88 (33.7) Age (year) 68.8±11.7 65.1±16.8 64.6±12.5 64.1±16.2 67.2±12.1 64.8±16.6 <50 12 (4.4) 39 (19.0) 14 (8.5) 13 (23.2) 26 (6.0) 52 (19.9) 50-59 52 (19.2) 29 (14.1) 39 (23.8) 8 (14.3) 91(20.9) 37 (14.2) 60-69 61 (22.5) 47 (22.9) 49 (29.9) 13 (23.2) 110 (25.3) 60 (23.0) 70-79 97 (35.8) 47 (22.9) 41 (25.0) 12 (21.4) 138 (31.7) 59 (22.6) ≥80 49 (18.1) 43 (21.0) 21 (12.8) 10 (17.9) 70 (16.1) 53 (20.3) Table 2 Efficacy of cytology vs. methylation either alone and or in combination to diagnose benign vs. malignant pleural and peritoneal fluids Test item Testing result Overall positive rate Malignant H&A (Sensitivity) Benign H&A (Specificity) （ n=696 ） （ n=435 ） （ n=261 ） PPV NPV Cell paraffin block Cancer cells (+) 293 (42.1%) 292 (67.1%) 1 (99.6%) 99.7% 64.5% Cytological TCT Cancer cells (+) 151(21.7%) 151 (34.7%) 0 (100%) 100.0% 47.9% LungMe® methylation Positive（+） 369 (53.0%) 344 (79.1%) 25 (90.4%) 93.2% 72.2% LungMe ® methylation Positive（+） 123 (17.7%) 98 (22.5%) 25(90.4%) 79.7% 41.2% Strongly positive (++) 246(35.3%) 246(56.6%) 0 (100%) 100% 58.0% Combinatory Cell paraffin block + TCT + LungMe ® (+) 425 (61.1%) 399 (91.7%) 26 (90.0%) 93.9% 86.7% Methylation improves cytological gray area detection and prevents missed diagnosis Cytology Heterotypic cells (+) in cytology 273 (39.2%) 187 (43%) 86 (67.0%) 68.5% 41.4% Cytology + LungMe ® Heterotypic cells (+) in cytology + LungMe ® （+） 164 (23.6%) 147 (33.8%) 17 (93.5%) 89.6% 45.9% No cancer cells in cytology + LungMe ® （+） 82 (11.8%) 74 (17.0%) 8 (96.9%) 90.2% 41.2% H&A, Hydrothorax and Ascites; PPV, Positive predictive value; NPV, Negative predictive value. Table 3 Diagnostic value of combined methylation assay LungMe ® for malignant hydrothorax caused by different tumors Tumor type Hydrothorax (n=476) Ascetics (n=220) Hydrothorax & Ascetics (n=696) LungM ® (+) Sensitivity LungM ® (+) Sensitivity LungMe ® (+) Sensitivity n n （ % ） n n （ % ） n n （ % ） Lung cancer 195 156 (80.0) 6 5 (83.3) 201 161 (80.1) Ovarian cancer 9 7 (77.8) 43 31 (72.1) 52 38 (73.1) Gastric cancer 15 12 (80.0) 26 22 (84.6) 41 34 (82.9) Liver cancer 5 3 (60.0) 26 19 (73.1) 31 22 (71.0) Pancreatic cancer 4 2 (50.0) 14 12 (85.7) 18 14 (77.8) Lymphoma 13 11 (84.6) 4 3 (75.0) 17 14 (82.4) Breast cancer 9 9 (100) 5 5 (100) 14 14 (100) Colorectal cancer 5 3 (60.0) 8 4 (50.0) 13 7 (53.8) Cholangiocarcinoma 1 1 (100) 6 5 (83.3) 7 6 (85.7) Esophagus cancer 6 6 (100) 1 1 (100) 7 7 (100) Malignant celiac & peritoneal tumor 0 0 (0) 9 8 （88.9) 9 8 (88.9) Other malignancies 9 8 (88.9) 16 11 (68.8) 25 19 76.0) Total 271 218 (80.4) 164 126 (76.8) 435 344 (79.1) Additional Declarations No competing interests reported. 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08:31:57","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147289,"visible":true,"origin":"","legend":"","description":"","filename":"6f3fb0a089a4457da7ab74e4548ad0ed1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/8e012b20f00c12c74e0f4446.xml"},{"id":96157069,"identity":"ac323ef2-3a2b-4695-abd2-82f83a5399b8","added_by":"auto","created_at":"2025-11-18 08:31:56","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157069,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/0b8fcf2b0d761fe66bb91137.html"},{"id":96157046,"identity":"84db44c2-1e61-45d6-b1e9-f61e4070dc94","added_by":"auto","created_at":"2025-11-18 08:31:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":589861,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of tumor types causing malignant pleural effusions and ascites.\u003c/p\u003e\n\u003cp\u003e(a) Tumor types associated with malignant pleural effusions.\u003c/p\u003e\n\u003cp\u003e(b) Tumor types associated with malignant ascites.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/45429b9c72dc2446fb9bac75.png"},{"id":96251821,"identity":"4a6a4fd2-dafa-4802-a063-be2762de3f1e","added_by":"auto","created_at":"2025-11-19 07:40:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1038737,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic performance of SHOX2 and RASSF1A methylation in hydrothorax and ascites assessed by ROC curve analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/f4c21938faa95dfaecdcdcae.png"},{"id":96157049,"identity":"adf3a41b-42d8-4059-96c5-0f157993c637","added_by":"auto","created_at":"2025-11-18 08:31:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":550170,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots showing ΔCt distributions of SHOX2 and RASSF1A methylation in thoracic and peritoneal fluids.\u003c/p\u003e\n\u003cp\u003e(a) ΔCt distribution of SHOX2 in benign versus malignant pleural fluids.\u003c/p\u003e\n\u003cp\u003e(b) ΔCt distribution of SHOX2 in benign versus malignant ascites.\u003c/p\u003e\n\u003cp\u003e(c) ΔCt distribution of RASSF1A in benign versus malignant pleural fluids.\u003c/p\u003e\n\u003cp\u003e(d) ΔCt distribution of RASSF1A in benign versus malignant ascites.\u003c/p\u003e\n\u003cp\u003eA ΔCt value of 20 indicates absence of methylation signal. Statistical significance: ***, P \u0026lt; 0.001; *, P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/2300af645370bd1b5f7da3b2.png"},{"id":96157047,"identity":"9c21f537-402c-4cfa-924d-be65b92cbf6a","added_by":"auto","created_at":"2025-11-18 08:31:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":322385,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity and specificity of molecular methylation versus cytology in distinguishing benign and malignant pleural and peritoneal fluids.\u003c/p\u003e\n\u003cp\u003e(a) Comparison of sensitivity and specificity for methylation and cytology in diagnosing benign and malignant pleural fluids.\u003c/p\u003e\n\u003cp\u003e(b) Comparison of sensitivity and specificity for methylation and cytology in diagnosing benign and malignant ascites.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/53d23e1b37eb3757dbbefe4d.png"},{"id":96157060,"identity":"78a2fc13-2702-43a0-8140-9ba60e2e093f","added_by":"auto","created_at":"2025-11-18 08:31:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":493192,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic sensitivity of SHOX2 and RASSF1A, individually and combined, in malignant pleural and peritoneal fluids across various tumor types.\u003c/p\u003e\n\u003cp\u003e(a) Diagnostic sensitivity of SHOX2 for malignant hydrothorax caused by different tumors.\u003c/p\u003e\n\u003cp\u003e(b) Diagnostic sensitivity of RASSF1A for malignant hydrothorax caused by different tumors.\u003c/p\u003e\n\u003cp\u003e(c) Diagnostic sensitivity of LungMe® methylation assay (combined SHOX2 and RASSF1A) for malignant hydrothorax caused by different tumors.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/51953273ede30e43b668f7bb.png"},{"id":96367599,"identity":"f0e386a3-952f-4c35-9ad3-6ca99871cbcb","added_by":"auto","created_at":"2025-11-20 10:13:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3106262,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7883390/v1/22b136e5-0c6e-4bd8-a566-f54cebbad547.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epigenetic Signature Enables Accurate Classification of Pleural and Peritoneal Effusions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHydrothorax and ascites\u0026mdash;fluid accumulations in the pleural and peritoneal cavities\u0026mdash;arise from a wide range of pathological conditions, including malignant tumors, infections, heart failure, tuberculosis, liver failure, and cirrhosis. In cytological evaluation, reactive mesothelial cell proliferation often exhibits marked atypia, which can closely mimic malignant morphology and lead to diagnostic ambiguity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Accurately distinguishing benign from malignant effusions is therefore critical, as this determination directly influences clinical management, therapeutic decision-making, and prognosis.\u003c/p\u003e\u003cp\u003eThe etiologic spectrum of malignant effusions is broad. Lung cancer is the most frequent cause of malignant pleural effusions, followed by breast cancer, mesothelioma, lymphoma, and gastrointestinal malignancies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In contrast, malignant ascites most commonly results from ovarian, hepatic, pancreatic, and gastric cancers\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Given this diversity, an ideal diagnostic assay must reliably detect malignancy across multiple tumor types, while maintaining high specificity to avoid unnecessary invasive procedures. However, conventional cytomorphology\u0026mdash;though specific\u0026mdash;has limited sensitivity, particularly in low-cellularity or morphologically equivocal samples. This diagnostic gap underscores the need for molecular adjuncts capable of improving detection rates without compromising specificity. DNA methylation profiling, particularly of tumor-associated genes such as \u003cem\u003eSHOX2\u003c/em\u003e and \u003cem\u003eRASSF1A\u003c/em\u003e, has emerged as a promising approach for enhancing the accuracy of effusion cytology.\u003c/p\u003e\u003cp\u003eCytology and thoracoscopic pleural biopsy remain the gold standards for diagnosing pleural effusions\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, their accuracy is often compromised by the complex cellular composition of hydrothorax and ascites. Abundant mesothelial cells, granulomatous inflammation, diverse lymphohematopoietic elements, and benign mesenchymal components can obscure the distinction between malignant mesothelioma, histiocytic tumors, lymphohematopoietic neoplasms, and sarcomas\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Liquid-based cytology testing (ThinPrep cytologic test, TCT) offers improved sample quality over conventional smear cytology but still exhibits limited sensitivity. Similarly, cell block preparation from effusion sediments can enhance detection rates; however, low cellularity and uneven cell distribution often hinder accurate interpretation. Given that the benign\u0026ndash;malignant classification of pleural and peritoneal effusions directly informs therapeutic decisions, there is an urgent need for diagnostic strategies that combine high sensitivity with robust specificity\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDNA methylation is a key epigenetic mechanism in tumorigenesis and progression and has emerged as a promising molecular biomarker for cancer detection. Methylation profiling of \u003cem\u003eSHOX2\u003c/em\u003e and \u003cem\u003eRASSF1A\u003c/em\u003e has been successfully applied to diagnose lung cancer using bronchoalveolar lavage fluid, pleural effusions, and biopsy tissue samples\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. SHOX2 methylation has also been reported in multiple malignancies, including gastrointestinal, breast, and ovarian cancers\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Hypermethylation of the RASSF1A promoter, a tumor suppressor gene, is an established independent predictor of tumor invasion and metastasis in diverse cancers such as lung, gastrointestinal, breast, and head and neck tumors.\u003c/p\u003e\u003cp\u003eThe LungMe\u0026reg; assay\u0026mdash;based on combined SHOX2 and RASSF1A methylation detection\u0026mdash;has demonstrated high diagnostic accuracy for lung cancer, with reported sensitivity ranging from 71.5% to 84.0% and specificity from 80.7% to 90.0% in bronchoalveolar lavage fluid\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. When applied to enriched exfoliated cells in pleural effusions, this dual-marker approach achieved even higher performance, with sensitivity of 71.4%\u0026ndash;96% and specificity of 84.8%\u0026ndash;100% for malignant effusion detection\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Despite these promising findings, the clinical utility of combined SHOX2 and RASSF1A methylation analysis has not been systematically evaluated across both pleural and peritoneal effusions from a broad spectrum of malignancies. Addressing this gap, the present study investigates the diagnostic performance of this dual-marker methylation signature\u0026mdash;alone and in combination with cytomorphology\u0026mdash;for differentiating benign from malignant effusions in a large, diverse patient cohort.\u003c/p\u003e\u003cp\u003eIn this study, we compared the diagnostic performance of traditional cytology (TCT), exfoliated cell paraffin block examination, and a dual-marker \u003cem\u003eSHOX2/RASSF1A\u003c/em\u003e methylation assay (LungMe\u0026reg;) in differentiating benign from malignant pleural and peritoneal effusions. We further evaluated the clinical utility of integrating cytomorphologic and methylation analyses, and expanded the application of the SHOX2/RASSF1A methylation test beyond lung cancer to encompass multiple malignancy types.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 696 patients (407 males, 289 females) with pleural and peritoneal effusions were included, comprising 501 cases of hydrothorax and 230 cases of ascites. Among these, 435 cases were malignant and 261 benign. The proportion of benign hydrothorax was significantly higher in males than females (p \u0026lt; 0.05). The mean age of onset for benign and malignant hydrothorax ranged from 64.1 to 68.8 years. Notably, patients under 50 years had a significantly higher proportion of benign hydrothorax compared to malignant (19.9% vs. 6.0%, p \u0026lt; 0.05). The peak incidence of malignant hydrothorax occurred between ages 70\u0026ndash;79 (31.7%), while benign hydrothorax was most common in the 60\u0026ndash;69 age group (23.0%) (Table 1).\u003c/p\u003e\n\u003cp\u003eLung cancer was the predominant cause of malignant pleural effusions, accounting for 71.96% of cases, followed by gastric cancer (5.51%), lymphoma (4.80%), and breast cancer (3.32%) (Fig. 1a). The most frequent malignancies associated with malignant ascites were ovarian cancer (26.22%), gastric cancer (15.85%), liver cancer (15.85%), and pancreatic cancer (8.54%) (Fig. 1b).\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC analysis of \u003cem\u003eSHOX2\u003c/em\u003e and \u003cem\u003eRASSF1A\u003c/em\u003e methylation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethylation-specific quantitative fluorescence PCR for SHOX2 and RASSF1A was performed on all samples, with \u0026Delta;Ct values calculated individually. These \u0026Delta;Ct values were used to generate ROC curves to differentiate benign from malignant pleural effusions and ascites. Youden\u0026rsquo;s index was applied to identify optimal cutoff points. For SHOX2, the optimal \u0026Delta;Ct cutoff values for diagnosing malignant pleural fluid and ascites were 8.88 and 8.46, respectively. Accordingly, a conservative cutoff of 9 was selected for both sample types. For RASSF1A, optimal cutoffs were 17.33 (pleural fluid) and 16.54 (ascites); however, based on assay stability considerations from preliminary studies [19], a maximum cutoff of 12 was chosen for RASSF1A methylation in both fluid types. The area under the curve (AUC) for SHOX2 methylation alone was 0.856, while that for RASSF1A was 0.671. Logistic regression combining raw \u0026Delta;Ct values for both markers significantly improved diagnostic accuracy, yielding an AUC of 0.948 (Fig. 2), demonstrating superior predictive performance of the dual-marker model. Scatter plots illustrating \u0026Delta;Ct values for SHOX2 and RASSF1A in benign versus malignant groups are shown in Fig. 3. The sensitivity of SHOX2 methylation for detecting malignant pleural effusions and ascites was 74.5% and 70.7%, respectively, with specificity of 90.7% and 89.3% (Fig. 3a, b). In contrast, RASSF1A methylation showed lower sensitivity\u0026mdash;36.9% for pleural fluid and 26.2% for ascites\u0026mdash;but achieved 100% specificity in both (Fig. 3c, d). Because lower \u0026Delta;Ct values correspond to higher methylation levels, statistical analysis determined that achieving 100% specificity for SHOX2 required a \u0026Delta;Ct threshold below 7.23 for pleural effusions and 8.51 for ascites. Consequently, \u0026Delta;Ct_SHOX2 \u0026lt; 7.23 was defined as a strong positive methylation signal in pleural fluid samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic efficacy of molecular methylation compared to cytology in differentiating benign and malignant pleural effusions and ascites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the diagnostic performance of molecular methylation assays relative to conventional morphology-based methods, pleural and peritoneal fluid samples were simultaneously analyzed using liquid-based TCT, LungMe\u0026reg; methylation assay, and paraffin-embedded cell block examination. Although cytology and histopathology remain the gold standards for diagnosing malignant effusions, definitive diagnosis often requires comprehensive clinical correlation\u003csup\u003e18,19\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn pleural effusions, LungMe\u0026reg; methylation demonstrated a sensitivity of 80.4%, significantly outperforming routine TCT (38.0%) and cell block histology (71.2%). The combined use of TCT, LungMe\u0026reg;, and paraffin block examination further enhanced sensitivity to 92.6%, with specificity maintained at 90.7% (Fig. 4a). In ascites, methylation sensitivity was slightly lower at 76.8%, compared to TCT (29.3%) and cell block (60.4%). However, combination testing increased sensitivity to 90.2% (Fig. 4b).\u003c/p\u003e\n\u003cp\u003eComparative analysis of cytology and methylation in pleural and peritoneal fluids (Table 2) revealed that cell block examination had significantly higher sensitivity than cytological TCT (67.1% vs. 34.7%). Both cytomorphology methods showed specificity and positive predictive values (PPV) near 100%. However, negative predictive values (NPV) were limited, ranging from 47.9% to 64.5%, reflecting reduced ability to rule out malignancy.\u003c/p\u003e\n\u003cp\u003eMethylation assays demonstrated similarly high specificity (90.4%) and superior PPV (93.2%). Importantly, when methylation results exceeded stringent positivity thresholds (\u0026Delta;Ct_SHOX2 \u0026lt; 7.23 or \u0026Delta;Ct_RASSF1A \u0026le; 12), both specificity and PPV reached 100%. Sensitivity of the LungMe\u0026reg; assay (79.1%) surpassed that of TCT and cell block, indicating improved detection of true positives. The combination of LungMe\u0026reg; with cytology elevated sensitivity to 91.7% and PPV to 93.9%. Notably, when all three tests (TCT, cell block, LungMe\u0026reg;) were negative, NPV improved dramatically to 86.7%, markedly enhancing confidence in ruling out malignancy.\u003c/p\u003e\n\u003cp\u003eCytology often yielded indeterminate (\u0026ldquo;gray zone\u0026rdquo;) results, with atypical or heterogeneous cells observed in 39.2% of cases and a PPV of 68.5%. Integration of positive LungMe\u0026reg; methylation with these equivocal cytology results increased tumor specificity from 67.0% to 93.5% and raised PPV to 89.6%, providing a powerful tool for clarifying ambiguous diagnoses. Among 82 false-negative cytology cases, 74 were positive by methylation, with test specificity of 96.9% and PPV of 90.2%.\u003c/p\u003e\n\u003cp\u003eWhen stratified by tumor origin (Table 3), LungMe\u0026reg; showed high sensitivity for detecting malignant effusions from diverse cancers: 100% for esophageal and breast cancer, \u0026gt;80% for lung, gastric, lymphoma, cholangiocarcinoma, and peritoneal tumors, and \u0026gt;70% for ovarian, pancreatic, and hepatocellular carcinomas. These results underscore the broad clinical applicability of LungMe\u0026reg; methylation testing for malignancy detection across multiple cancer types, supporting its potential role in routine diagnostic workflows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic value of SHOX2 and RASSF1A methylation across tumor types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding on LungMe\u0026reg;\u0026rsquo;s high sensitivity across anatomical sites, we assessed the diagnostic performance of SHOX2 and RASSF1A methylation\u0026mdash;individually and combined\u0026mdash;in malignant pleural and peritoneal fluids from diverse cancers. SHOX2 methylation alone exhibited high sensitivity (74.6% to 100%) for most tumor types, including esophageal cancer, cholangiocarcinoma, gastric cancer, lymphoma, breast cancer, pancreatic cancer, and lung cancer. Sensitivity was modestly lower for hepatocellular carcinoma (61.3%), ovarian carcinoma (59.3%), and colorectal carcinoma (53.8%) (Fig. 5a). In contrast, RASSF1A methylation showed variable sensitivity: 78.6% in breast cancer, but lower rates in lung (38.8%) and ovarian cancer (38.5%). Overall positivity rates of RASSF1A in pleural and peritoneal fluids ranged between 11.1% and 28.6%, with gastric cancer showing the lowest rate at 7.3% (Fig. 5b). Notably, the combined dual-marker LungMe\u0026reg; assay demonstrated a clear complementary effect in breast, lung, ovarian, and hepatocellular carcinomas, significantly improving sensitivity to 100%, 80.1%, 73.1%, and 71.0%, respectively (Fig. 5c). Although RASSF1A positivity was relatively low overall, its methylation status is an independent biomarker linked to tumor invasiveness and poor prognosis across multiple malignancies\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e.\u0026nbsp;This complementary relationship with \u003cem\u003eSHOX2\u003c/em\u003e underscores the added value of the dual-marker approach in enhancing diagnostic accuracy and providing prognostic insights.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically compared conventional diagnostic methods\u0026mdash;namely, liquid-based cytology (TCT) and paraffin block histopathology\u0026mdash;with a novel combined SHOX2 and RASSF1A methylation assay (LungMe\u0026reg;) for differentiating benign from malignant hydrothorax and ascites across multiple cancer types.\u003c/p\u003e\u003cp\u003eOur findings confirm the limited sensitivity of cytologic TCT in detecting malignant effusions, with rates of only 29.3% for ascites and 38.0% for pleural fluid. While paraffin-embedded cell block examination significantly improved sensitivity to 60.4% and 71.2% for ascites and pleural fluid, respectively, the overall diagnostic challenge of malignant ascites remained evident. The combined sensitivity of paraffin block and TCT was relatively modest\u0026mdash;47.7% for ascites and 41.8% for pleural effusions\u0026mdash;highlighting the need for enhanced diagnostic approaches. In contrast, the LungMe\u0026reg; methylation assay demonstrated markedly higher sensitivities of 80.4% for malignant pleural fluid and 76.8% for malignant ascites, alongside excellent specificity ranging from 89.3% to 90.7%. These results align well with previous reports, such as Liang et al., who documented a 76.5% positive detection rate for methylation markers in pleural fluid\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, underscoring the robustness and clinical promise of methylation-based diagnostics.\u003c/p\u003e\u003cp\u003eROC curve analysis identified ΔCt thresholds of 9 for SHOX2 and 12 for RASSF1A as optimal cutoffs, consistent with findings from previous studies conducted at China-Japan Friendship Hospital, Zhujiang Medical University, and Southern Medical University\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The reproducibility of these cutoff values across multiple independent cohorts and institutions highlights the robustness and potential standardization of the SHOX2/RASSF1A methylation assay for pleural fluid analysis, supporting its broad applicability in clinical diagnostics.\u003c/p\u003e\u003cp\u003eThis study highlights the significant advantage of incorporating methylation analysis into routine pathological evaluation of hydrothorax and ascites. Several practical pathways exist for integrating this technique into clinical workflows: fresh cell samples can undergo simultaneous TCT and methylation testing, enabling rapid turnaround (typically within two working days) for initial screening. Positive or equivocal cases can then be further evaluated with paraffin-embedded cell block preparation, H\u0026amp;E staining, and immunohistochemistry. While morphologic diagnosis offers near-perfect specificity, its limited sensitivity results in high PPV but low NPV, meaning a negative cytology cannot reliably exclude malignancy. Incorporating methylation testing, which demonstrated an NPV of 86.7%, effectively addresses this diagnostic gap. Additionally, cytology reports categorized as \u0026ldquo;gray zone\u0026rdquo; or heterogeneous\u0026mdash;accounting for 39.2% of cases in this study\u0026mdash;can benefit from adjunct methylation analysis, facilitating high-risk triage and improving diagnostic confidence.\u003c/p\u003e\u003cp\u003eWhen cytology results are negative, but methylation testing is positive, the positive predictive value (PPV) of methylation becomes critically important for clinical decision-making. In this study, a PPV of 90.2% highlights a strong likelihood of malignancy, underscoring the necessity for close clinical follow-up or repeat diagnostic confirmation. Growing evidence supports the integration of methylation analysis with conventional morphopathology to generate more comprehensive diagnostic reports, thereby enhancing both the sensitivity and accuracy of tumor detection.\u003c/p\u003e\u003cp\u003eTo our knowledge, this study is the first to expand the clinical application of the LungMe\u0026reg; methylation assay beyond lung cancer to multiple malignancies causing pleural and peritoneal effusions. Lung cancer remained the predominant cause of malignant pleural effusions (71.96%), followed by gastric cancer and lymphoma. Benign ascites was primarily attributed to hepatic cirrhosis, whereas malignant ascites mainly resulted from ovarian (26.22%), gastric (15.85%), and liver cancers (15.85%).\u003c/p\u003e\u003cp\u003eAnalysis of diagnostic sensitivity across tumor types revealed that SHOX2 methylation exhibited high sensitivity for most cancers, ranging from 53.8% to 100%. In contrast, RASSF1A showed lower overall sensitivity (7.3% to 38.8%), except in breast cancer, where it reached 78.6%, consistent with prior studies demonstrating the utility of circulating RASSF1A methylation for monitoring metastatic breast cancer\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The combined LungMe\u0026reg; assay markedly enhanced diagnostic sensitivity across a spectrum of tumors causing hydrothorax (100% for cholangiocarcinoma, esophageal, and breast cancers; \u0026gt;75% for gastric, lymphoma, lung, and ovarian cancers) and ascites (100% for esophageal and breast cancers; \u0026gt;75% for cholangiocarcinoma, lung, pancreatic, and peritoneal tumors).\u003c/p\u003e\u003cp\u003eThese findings underscore the value of combining multiple methylation markers to improve diagnostic accuracy. Future enhancements could incorporate additional markers such as SEPTIN9\u0026mdash;a highly sensitive methylation biomarker for colorectal cancer detection\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u0026mdash;and HOXA9, which is effective for identifying BRCA-mutated epithelial ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For example, Turbine\u0026rsquo;s OncoMe\u0026trade; Methylation Portfolio, which combines SHOX2, RASSF1A, SEPTIN9, and HOXA9, has been reported to increase hydrothorax detection sensitivity from 74.0% to 85.0%\u003csup\u003e22\u003c/sup\u003e. Such multi-marker panels hold great promise for advancing methylation-based diagnostics across diverse malignancies.\u003c/p\u003e\u003cp\u003eFuture epigenetic research should prioritize refining combined methylation assays by incorporating cell-free DNA (cfDNA) analysis to further enhance sensitivity and specificity. cfDNA originates from necrotic, fragmented, or secreted extracellular DNA released by normal cells, tumor cells, adjacent tissues, or both primary and metastatic lesions. In lung adenocarcinoma patients with malignant pleural effusions, cfDNA has demonstrated high clinical utility; mutation detection sensitivity in cfDNA reached 90.7%\u0026mdash;surpassing even tissue biopsy sensitivity at 88%\u003csup\u003e26\u003c/sup\u003e. Determining the optimal source of DNA for methylation analysis\u0026mdash;whether from the supernatant of pleural/peritoneal fluids or from cellular precipitates\u0026mdash;will be critical to improving the positive predictive value (PPV) for tumor staging and prognostic evaluation. This distinction could unlock more accurate, minimally invasive tools for real-time cancer monitoring and personalized treatment strategies.\u003c/p\u003e\u003cp\u003eMulti-marker methylation profiling represents a significant advance in the diagnostic evaluation of pleural and peritoneal effusions. Our study demonstrates that combined SHOX2 and RASSF1A methylation testing effectively discriminates benign from malignant effusions across diverse cancer types. When integrated with cytomorphological analysis, this approach markedly enhances tumor detection sensitivity, enabling a more accurate and minimally invasive diagnostic paradigm. This strategy holds broad clinical promise for early cancer detection, population screening, high-risk patient triage, adjunctive diagnostics, monitoring of recurrence, and personalized treatment decision-making.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom June 2021 to October 2023, a total of 696 de-identified patients (age range: 15\u0026ndash;97 years) with pleural or peritoneal effusions were consecutively enrolled at The Affiliated Hospital of Nantong University. The cohort comprised 271 patients with malignant pleural effusions, 205 with benign pleural effusions, 164 with malignant ascites, and 56 with benign ascites. Malignant or benign status was confirmed based on clinical, imaging, cytologic/histologic, and follow-up data. All procedures were conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of The Affiliated Hospital of Nantong University, and all experiments complied with institutional ethics committee regulations. All the patients/participants or their legal guardian(s) provided written informed consent to partake in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollection of thoracic and peritoneal fluids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each patient, 50\u0026ndash;200 mL of fresh pleural fluid or ascitic fluid was collected under sterile conditions. A 5\u0026ndash;10 mL aliquot was centrifuged at 2,000 rpm for 10 minutes, and the supernatant was discarded. The resulting cell pellet was resuspended in ThinPrep cytology test (TCT) preservation solution or liquid-based cytology preservation solution for cytologic examination. The remaining fluid sample was similarly centrifuged at 2,000 rpm for 10 minutes, the supernatant was removed, and the cell pellet was processed into a formalin-fixed, paraffin-embedded (FFPE) cell block for histopathologic evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytologic TCT of pleural\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and peritoneal fluids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell sediments from pleural or peritoneal fluids were mixed with liquid-based cell preservation solution and processed using a ThinPrep cytology device. The prepared smears were air-dried and fixed in 95% ethanol for 10 minutes. Slides were then stained with hematoxylin\u0026ndash;eosin (H\u0026amp;E), coverslipped, and examined microscopically by experienced cytopathologists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell block preparation and examination of pleural fluid cell sedimentation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCellular precipitates from pleural and peritoneal fluid specimens were collected by centrifugation and fixed in 95% ethanol for 6 hours. Fixed cell pellets were washed 3\u0026ndash;5 times with phosphate-buffered saline to remove residual fixative, then dehydrated through a graded ethanol series (70% to 100%). Following dehydration, ethanol was replaced with xylene to facilitate paraffin infiltration. Samples were then immersed in melted paraffin to ensure full permeation, followed by rapid cooling on ice to solidify the paraffin and form wax blocks. The paraffin-embedded cell blocks were sectioned, stained with hematoxylin and eosin (H\u0026amp;E), and examined microscopically to assess cellular morphology and tissue architecture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethylation-specific fluorescence quantitative PCR (MS-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from cell sediments using reagents provided by Shanghai Turboscan Life Science and Technology Co., which also supplied the methylation detection kit targeting human SHOX2 and RASSF1A genes (patent: Guomei Quanzhi 20173403354). Sulfite modification and purification of DNA were performed according to the manufacturer\u0026rsquo;s instructions. Real-time quantitative PCR was conducted on a Hongshi SLAN-96S system, with DNA concentration measured using an Ausheng Fluo-100B fluorescent dye DNA detector.\u003c/p\u003e\n\u003cp\u003eCell sediments were centrifuged at 2,000 rpm for 5 minutes, supernatant discarded, and pellets subjected to DNA extraction. DNA concentration was quantified, and approximately 200 ng of DNA was used for bisulfite conversion and purified into 20 \u0026mu;L eluent. For methylation analysis, 5 \u0026mu;L of modified DNA was used per reaction. \u0026beta;-actin served as an internal reference gene. Positive controls included plasmids containing methylated SHOX2 or RASSF1A DNA, while nuclease-free water was used as a negative control.\u003c/p\u003e\n\u003cp\u003eDuring PCR, fluorescence signals were monitored on FAM, VIC, and CY5 channels. Successful amplification in positive controls was confirmed by signals across all channels, while negative controls showed no amplification. The cycle threshold (Ct) value for CY5 (\u0026beta;-actin) was required to be below 28, with an optimal range of 18\u0026ndash;23, ensuring sample quality.\u003c/p\u003e\n\u003cp\u003eFor SHOX2, amplification in the VIC channel produced a characteristic sigmoidal (\u0026ldquo;S\u0026rdquo;-shaped) curve with Ct \u0026lt;32, and methylation levels were calculated as \u0026Delta;Ct_SHOX2 = Ct_SHOX2 \u0026ndash; Ct_\u0026beta;-actin. For RASSF1A, amplification in the FAM channel yielded a similar curve with Ct \u0026lt;35, with methylation quantified as \u0026Delta;Ct_RASSF1A = Ct_RASSF1A \u0026ndash; Ct_\u0026beta;-actin.\u003c/p\u003e\n\u003cp\u003eDNA concentrations in fluid samples ranged from 0.23 to 106.00 ng/\u0026mu;L. A minimum of 50 ng DNA was added per PCR assay, with concentrations \u0026ge;10 ng/\u0026mu;L meeting assay requirements. The consistent \u0026beta;-actin Ct values (18\u0026ndash;23) confirmed high-quality DNA and reliable assay performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver operating characteristics (ROC) curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethylation \u0026Delta;Ct values of SHOX2 and RASSF1A served as test variables, while pathological diagnosis of pleural and peritoneal effusions combined with clinical data was used as the reference standard (state variable). ROC curves were generated to determine optimal cutoff values for each marker. Logistic regression models were constructed to estimate the probability of malignancy based on SHOX2 and RASSF1A methylation status, both individually and in combination. The discriminatory performance of these models was assessed by calculating the area under the ROC curve (AUC), evaluating their ability to differentiate benign from malignant effusions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from three independent experiments are presented as mean \u0026plusmn; standard deviation (SD). Comparisons between groups were performed using Student\u0026rsquo;s t-test. Statistical analyses were conducted using SPSS version 22.0 and GraphPad Prism 10 software. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for SHOX2 and RASSF1A methylation assays in pleural and peritoneal fluids were calculated and expressed as percentages. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of individual markers and their combination, with area under the curve (AUC) values calculated accordingly. A two-sided p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTCT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Thinprep cytologic test\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operator characteristic\u003c/p\u003e\n\u003cp\u003eSHOX2 \u0026nbsp; \u0026nbsp; \u0026nbsp;Short stature homeobox 2\u003c/p\u003e\n\u003cp\u003eRASSF1A \u0026nbsp;Ras-association domain family member 1 A\u003c/p\u003e\n\u003cp\u003eHOXA9 \u0026nbsp; \u0026nbsp; Homeobox A9\u003c/p\u003e\n\u003cp\u003ePPV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Positive predictive value\u003c/p\u003e\n\u003cp\u003eNPV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative predictive value\u003c/p\u003e\n\u003cp\u003eMS-PCR \u0026nbsp; \u0026nbsp; Methylation-specific PCR\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.L. -Writing - Original Draft, Visualization, Writing - Review \u0026amp; Editing; T.W. - Software, Data Curation, Writing- Reviewing and Editing; J. L. - Conceptualization, Methodology, Validation; H.G. - Writing- Reviewing and Editing; J.F. - Investigation, Validation; T.T.B. - Investigation, validation; L.L. - Investigation, validation; H.X. - Formal analysis, Software; H.S. - Investigation; B.S. - Software; X.S.R. - Software; J.L. - Editing and Supervision; Q.S.L. - Editing and Supervision. - Supervision, Project administration; X.Q.D. - Supervision, Project administration; Y.F.L. - Supervision, Project administration, Funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Jiangsu Provincial Research Hospital (Grant No. YJXYY202204-YSB01), Nantong Basic Research Plan Project (Grant No. MS2023067), the National Natural Science Foundation of China (Grant No. 82273422).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eElkins, M. 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Q.\u003cem\u003e et al.\u003c/em\u003e SEPTIN9-SDC2-VIM methylation signature as a biomarker for the early diagnosis of colorectal cancer. \u003cem\u003eAmerican journal of cancer research\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 3128-3140 (2022).\u003c/li\u003e\n\u003cli\u003eRusan, M., Andersen, R. F., Jakobsen, A. \u0026amp; Steffensen, K. D. Circulating HOXA9-methylated tumour DNA: A novel biomarker of response to poly (ADP-ribose) polymerase inhibition in BRCA-mutated epithelial ovarian cancer. \u003cem\u003eEuropean journal of cancer (Oxford, England : 1990)\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 121-129, doi:10.1016/j.ejca.2019.11.012 (2020).\u003c/li\u003e\n\u003cli\u003eDeng, Q.\u003cem\u003e et al.\u003c/em\u003e Predictive value of unmethylated RASSF1A on disease progression in non-small cell lung cancer patients receiving pemetrexed-based chemotherapy. \u003cem\u003eCancer biomarkers : section A of Disease markers\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 313-323, doi:10.3233/cbm-190258 (2020).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 579px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Basic patient information\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eHydrothorax\u0026nbsp;(N=476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eAscites (N=220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eHydrothorax\u0026nbsp;and\u0026nbsp;Ascites\u0026nbsp;(N=696)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eMalignant (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eBenign (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eMalignant (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eBenign (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMalignant (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eBenign (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e（n=271）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e（n=205）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e(n=164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e(n=56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(n=435)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e(n=261)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e156 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e142 (69.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e78 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e31 (55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e234 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e173 (66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e115 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e63 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e86 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25 (41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e201 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e88 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e68.8\u0026plusmn;11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e65.1\u0026plusmn;16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e64.6\u0026plusmn;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e64.1\u0026plusmn;16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e67.2\u0026plusmn;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e64.8\u0026plusmn;16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e12 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e39 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e14 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e52 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e52 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e29 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e39 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e91(20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e37 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e61 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e47 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e49 (29.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e110 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e60 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e97 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e47 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e41 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e12 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e138 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e59 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e49 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e43 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e21 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e70 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e53 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2 Efficacy of cytology vs. methylation either alone and or in combination to diagnose benign vs. malignant pleural and peritoneal fluids\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u0026nbsp;item\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTesting\u0026nbsp;result\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;positive\u0026nbsp;rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant\u0026nbsp;H\u0026amp;A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Sensitivity)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign\u0026nbsp;H\u0026amp;A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Specificity)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=696\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=435\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=261\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCell\u0026nbsp;paraffin\u0026nbsp;block\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eCancer\u0026nbsp;cells\u0026nbsp;(+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e293 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e292 (67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1 (99.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e99.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e64.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCytological\u0026nbsp;TCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eCancer\u0026nbsp;cells\u0026nbsp;(+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e151(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e151 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e47.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eLungMe\u0026reg;\u0026nbsp;methylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003ePositive（+）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e369 (53.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e344 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e25 (90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e93.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e72.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eLungMe\u003csup\u003e\u0026reg;\u003c/sup\u003e methylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003ePositive（+）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e123 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e98 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e25(90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e79.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e41.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eStrongly\u0026nbsp;positive (++)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e246(35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e246(56.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e58.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCombinatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eCell\u0026nbsp;paraffin\u0026nbsp;block\u0026nbsp;+\u0026nbsp;TCT\u0026nbsp;+ LungMe\u003csup\u003e\u0026reg;\u003c/sup\u003e (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e425 (61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e399 (91.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e26 (90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e93.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e86.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMethylation improves cytological gray area detection and prevents missed diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCytology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eHeterotypic\u0026nbsp;cells\u0026nbsp;(+) in\u0026nbsp;cytology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e273 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e187 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e86 (67.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e68.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e41.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCytology + LungMe\u003csup\u003e\u0026reg;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eHeterotypic cells (+) in cytology + LungMe\u003csup\u003e\u0026reg;\u003c/sup\u003e（+）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e164 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e147 (33.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e17 (93.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e89.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e45.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;cancer cells in cytology + LungMe\u003csup\u003e\u0026reg;\u003c/sup\u003e（+）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e82 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e74 (17.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e8 (96.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e90.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e41.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 100px;\"\u003e\n \u003cp\u003eH\u0026amp;A, Hydrothorax and Ascites; PPV, Positive predictive value; NPV, Negative predictive value.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"104%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 3 Diagnostic value of combined methylation assay LungMe\u003csup\u003e\u0026reg;\u003c/sup\u003e for malignant hydrothorax caused by different tumors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 26px;\"\u003e\n \u003cp\u003eHydrothorax (n=476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 26px;\"\u003e\n \u003cp\u003eAscetics (n=220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 25px;\"\u003e\n \u003cp\u003eHydrothorax \u0026amp; Ascetics (n=696)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLungM\u003csup\u003e\u0026reg;\u003c/sup\u003e (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLungM\u003csup\u003e\u0026reg;\u003c/sup\u003e (+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLungMe\u003csup\u003e\u0026reg;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e(+)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eLung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOvarian cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eGastric cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eLiver cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003ePancreatic cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eLymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eColorectal cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eCholangiocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eEsophagus cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e（88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eOther malignancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e(76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e(79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\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":"DNA methylation profiling, SHOX2, RASSF1A, Epigenetics, Malignant effusion, Diagnosis and Differential Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7883390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7883390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study demonstrates that a dual-marker DNA methylation test for SHOX2 and RASSF1A significantly improves the detection of cancer in pleural and peritoneal effusions compared to conventional cytology alone. In a prospective analysis of 696 patient samples, cytology by itself had a low sensitivity of only 34.7%. In contrast, the methylation assay achieved high accuracy with a sensitivity of approximately 80% and a specificity of over 89%. When the methylation results were combined with cytology, the diagnostic sensitivity dramatically increased to 91.7% without compromising specificity. This robust performance was consistent across various cancers, including lung, gastrointestinal, and gynecological malignancies. The findings strongly support integrating this minimally invasive molecular test into routine clinical workflows to enable faster, more accurate cancer diagnosis from fluid samples, potentially reducing the need for more invasive procedures.\u003c/p\u003e","manuscriptTitle":"Epigenetic Signature Enables Accurate Classification of Pleural and Peritoneal Effusions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 08:31:49","doi":"10.21203/rs.3.rs-7883390/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":"3e36db7c-3e97-47dd-8150-1b19698bd861","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58048973,"name":"Health sciences/Biomarkers"},{"id":58048974,"name":"Biological sciences/Cancer"},{"id":58048975,"name":"Health sciences/Medical research"},{"id":58048976,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-11-20T08:24:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 08:31:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7883390","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7883390","identity":"rs-7883390","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}