Diagnostic Value of Plasma Septin9 Methylation Combined with Multi-indicator Detection in the Progression of Primary Liver Cancer

Diagnostic Value of Plasma Septin9 Methylation Combined with Multi-indicator Detection in the Progression of Primary Liver Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic Value of Plasma Septin9 Methylation Combined with Multi-indicator Detection in the Progression of Primary Liver Cancer 颖新 田, Dandan liu, dongdong xi, Fengtian Li, yu Wang, Congzhe Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9038335/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objectives To evaluate the diagnostic value of plasma methylated Septin9 (mSEPT9) combined with tumor markers and immunohistochemical (IHC) markers in the identification of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Methods A retrospective analysis was conducted on a cohort of patients who had undergone mSEPT9 gene testing. Statistical analyses were performed using SPSS26.0 statistical software. The mSEPT9 and IHC indexes were analyzed using ggplot2 in the R package. Results The positivity rate of mSEPT9 in the HCC group (60.47%) was significantly higher than that in ICC groups [ICC (48.33%) vs the benign liver lesion group (2.04%) vs the HC group (1.30%)]. Moreover, mSEPT9 positivity rates in stages I, II, and III of the HCC group were 40.57%, 66.41% and 81.25%, respectively ( P < 0.05). The results of IHC suggested that co-expression of Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive HCC subgroup compared to the mSEPT9-negative subgroup (12.5% vs. 3.4%, P < 0.001). Likewise, in the ICC group, co-expression of GPC-3, Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive subgroup compared to the mSEPT9-negative subgroup (8.4% vs. 1.6%, P < 0.001). Conclusion mSEPT9 demonstrated higher sensitivity in HCC patients, followed by AFP. In ICC patients, CA19-9 exhibited superior diagnostic value than mSEPT9. Additionally, the positivity rate of mSEPT9 was associated with HCC severity but not with ICC staging. MSEPT9 expression was also associated with IHC indicators, suggesting that epigenetic modifications may influence the expression of various proteins. Taking together, these findings provide a new direction for more accurate diagnosis and personalized treatment strategies for HCC, although further in-depth studies are warranted. mSEPT9 Hepatocellular carcinoma Intrahepatic cholangiocarcinoma Alpha-fetoprotein Carbohydrate antigen19-9 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction As is well documented, primary liver cancer, a prevalent gastrointestinal malignancy, has been termed the "king of cancer" [1] . Pathologically, liver cancer can be classified into hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular cholangiocarcinoma (CHC) [2] . At present, imaging techniques, including ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), are utilized for the detection and evaluation of liver cancer. However, due to atypical early lesions, imaging equipment discrepancies, and variations in diagnostic proficiency among clinicians, misdiagnosis and missed diagnosis remain common [3] . Contrarily, serum-based examinations primarily assess liver function and the levels of tumor markers, such as alpha-fetoprotein (AFP), carbohydrate antigen 125 (CA125), CA19-9, and carcinoembryonic antigen (CEA). Furthermore, AFP, as a specific marker for HCC, were widely utilized in clinical practice [4] . However, according to the American Association for the Study of Liver Diseases (AASLD) guidelines, AFP is not sufficiently sensitive and specific for screening purposes [5] . Currently, the diagnosis and treatment of hepatocellular carcinoma rely on IHC markers, including HepPar-1, GPC3, Arg-1, and others, to assist in diagnostic confirmation. However, due to the small size of fine-needle aspiration specimens and low tumor cellularity, together with the potential impact of tumor differentiation on IHC marker expression, the diagnostic sensitivity and specificity are suboptimal [6–7] . An earlier study has demonstrated that DNA methylation occurring during early carcinogenesis and cancer precursor stages is associated with tumor formation, positioning it as an ideal biomarker for early cancer diagnosis [8–10] . As a member of the tumor suppressor gene family, Septin9 undergoes aberrant DNA methylation in various malignancies [11–13] , eventually leading to dysregulated expression, reduced transcriptional activity, impaired physiological functions, and ultimately carcinogenesis [14–16] . Notably, the Septin9 methylation (mSEPT9) test represents the first epigenetic tumor liquid biopsy test approved by the FDA globally. It is one of the most essential diagnostic biomarkers for colorectal cancer [17–18] , displaying a correlation with cancer staging and exhibiting increasing sensitivity with cancer progression [19] . Recent investigations have unveiled correlations between mSEPT9 and other tumor types, including lung cancer and liver cancer [20] . In this study, a retrospective analysis was conducted among liver cancer patients to evaluate the diagnostic performance of mSEPT9 and its correlation with TNM stage. mSEPT9 was compared with conventional tumor markers, and their combined diagnostic value was assessed. In addition, the associations between mSEPT9 and relevant IHC indicators were systematically analyzed to provide a theoretical reference for the precise diagnosis and personalized treatment of liver cancer. Materials and Methods 1. Case selection and general characteristics A retrospective analysis was conducted on cases with primary liver cancer treated at the First Medical Center of Chinese People's Liberation Army General Hospital from March 2018 to December 2021. This study was approved by the Ethics Committee of the Chinese PLA General Hospital (Approval Number: S2025‐246‐01). After rigorous and detailed screening, 230 healthy examinees at the same hospital during the corresponding period were included as controls, all of whom had no underlying diseases. Moreover, 49 cases of benign liver lesions were included (hepatic cyst 12/49, hepatolithiasis and choledocholithiasis 13/49, focal nodular hyperplasia 10/49, hepatic hemangioma 7/49, hepatic echinococcosis 1/49, hepatitis 5/49, hepatic fibrolipoma 1/49). The malignancy cohorts consisted of 301 cases with HCC and 120 cases with ICC. The screening and exclusion procedures are illustrated in Fig.1. TNM staging in the HCC and ICC groups were conducted according to the TNM system of the American Joint Cancer Staging Consortium (AJCC) Cancer Staging Manual (8th edition). Inclusion criteria: 1) patients aged ≥ 18 years with complete clinical data and no PRIOR history of chemoradiotherapy, 2) patients pathologically diagnosed with benign liver lesions by imaging examinations, and those with malignant tumors definitively diagnosed with HCC or ICC according to the AJCC Staging Manual (8th edition). 3) Healthy subjects who underwent routine physical examinations at the hospital. 4) Liver function tests, blood routine tests, and tumor marker tests were performed in all enrolled cases prior to surgical intervention. Exclusion criteria included: 1) pregnant women or subjects with a history of immune system diseases; 2) patients with incomplete clinicopathological data or other data; 3) Patients with other malignancies liver cancer metastasis to other organs (excluding cases in stage TⅣ); 4) Patients with gastrointestinal diseases diagnosed by gastroscopy. 2. Test methods 2.1 Data collection Patient data, including sex, age, imaging examination findings, pre-treatment laboratory indicators, and pathological diagnoses, were collected from the electronic medical record system. Liver function was assessed using a Roche C701 analyzer, and tumor markers were detected using a Roche e601 analyzer. All reagents were sourced from Roche Diagnostics GmbH. The Sysmex XN2000 blood cell analyzer was employed to assess routine blood indexes, with proprietary reagents procured from Sysmex Corporation. 2.2 mSEPT9 methylation quantification In this study, a SEPT9 methylation detection kit (BioChain, Beijing, China) was employed. Firstly, free DNA was extracted from plasma using the Beijing "BioChain" Methylation Detection Kit 1 (Plasma Processing Kit). Unmethylated cytosines were then converted into uracil via bisulfite deamidation reaction through incubation with bisulfite reagent, following which the bisulfite-converted (Bis-DNA) was amplified via PCR using the "BioChain" Methylation Detection Kit 2 (PCR kit) with specific primers. Afterward, the target fragment in the SEPT9 gene was detected using a fluorescent probe specific to the methylated sequence. Plasma mSEPT9 levels were detected by SLAN fluorescent PCR using the β-Actin gene as an internal reference. A Ct value of ≤41.0 was defined as positive, whereas a Ct value of >41.0 or the absence of a detectable Ct value was interpreted as negative. 3 Statistical analyses SPSS 26.0 software was utilized for statistical analyses. P-P plots were used to test the normality of continuous variables. Normally distributed variables were presented as (x±s). were expressed as M (P25, P75). Categorical variables were expressed as frequencies and rates. To minimize bias caused by differences in sample size, Fisher's exact test was applied for comparisons of categorical variables. For continuous variables following a normal distribution, one-way ANOVA was used for comparisons among multiple groups, and Tamhane's T2 test was applied for post hoc pairwise comparisons due to heterogeneity of variances. For non-normally distributed variables, the Kruskal-Wallis non-parametric test was used. Binary logistic regression analysis was performed for mSEPT9 and other tumor markers, followed by ROC curve analysis to determine AUC, sensitivity, and specificity. The DeLong test was used to compare AUCs, with P <0.05, considered statistically significant. The correlation between mSEPT9 and IHC markers was analyzed using the ggplot2 package in R to assess potential associations between mSEPT9 expression and IHC indicators. Results and Analysis 1. Baseline Patient Characteristics One-way ANOVA was used to analyze the complete blood count (CBC), liver function parameters, and tumor marker levels of the four groups of data, and Tamhane's T2 test was used for post hoc pairwise comparisons. No significant difference in age was observed among groups ( P =0.548), whereas differences in the remaining variables were statistically significant. For instance, significant differences were observed in sex, Hemoglobin (Hb), red blood cell count (RBC), alanine aminotransferase levels (ALP), γ-glutamyl transferase levels (GGT), total bilirubin levels (TBIL), direct bilirubin levels (DBIL), CA19-9, and CA724 between the HC and benign liver lesion groups. Similarly, all indicators showed significant differences between the HC and HCC groups. Between the HC and ICC groups, significant differences were noted in Hb, RBC, WBC, alkaline phosphatase levels(ALT), aspartate aminotransferase levels (AST), GGT, TBIL, DBIL, CEA, CA125, and CA19-9. Between the benign lesion group and the HCC group, significant differences were observed in sex, WBC, PLT, ALT, AST, ALP, DBIL, AFP, and CA724. Between the benign lesion group and the ICC group, significant differences were identified in Hb, RBC, WBC, ALT, AST, ALP, DBIL, CEA, CA125, and CA19-9. Lastly, between the ICC and HCC groups, all indicators except WBC and AST showed significant differences (Table 1). Table 1. Comparative analysis of baseline characteristics, complete blood count, liver function parameters, and tumor marker levels among the four groups Indicator HC group Benign lesion group HCC group ICC group P value Demographics Male/Female 153/77 24/25# 260/40#O 79/43❖ ＜0.05 age 59.85±19.79 58.14±14.28 59.6±10.73 60.64±9.37 0.548 CBC Hb (g/L) 145.7±16.2 128.6±26.3# 125.5±19.9# 109.2±22.8#O❖ ＜0.05 RBC (10 12 /L) 4.8±0.52 4.2±0.59# 4.0±0.66# 3.5±0.78#O❖ ＜0.05 WBC (10 9 /L) 5.9±1.48 5.9±1.74 8.5±3.91#O 8.9±4.01#O ＜0.05 PLT (10 9 /L) 230.5±54.55 209.3±72.22 149.5±67.78#O 220.0±85.41❖ ＜0.05 Liver function ALT (U/L) 27.2 (17.3,49.9) 57.7 (25.3,185.05) 59.3 (36.6,98.0) O 119.0 (64.2,192.9) O❖ ＜0.05 AST (U/L) 17.8 (15.1,22.1) 21.6 (15.4,34.5) 39.2 (25.6,64.8) O 31.7 (21.1,51.8) O ＜0.05 ALP (U/L) 63.1 (51.6,74.8) 83.15 (62.2.1,136.7) # 18.9 (8.9,66.6) O 70.8 (16.5,136) O❖ ＜0.05 GGT (U/L) 41.6±43.85 169.5±262.28# 77.57±62.88# 205.94±286.83#❖ ＜0.05 TBIL (μmol/L) 11.2±4.7 18.7±15.6# 21.7±20.7# 51.1±59.8#O❖ ＜0.05 DBIL (μmol/L) 3.75 (2.9,4.8) 5.35 (3.6,9.4) # 7.85(5.4,11.1) O 14.9 (5.4,46.9) O❖ ＜0.05 Tumor marker CEA (U/L) 1.54 (1.0,2.34) 1.68 (0.99,2.68) 2.1 (1.37,3.19) # 3.28 (1.93,6.06) O❖ ＜0.05 AFP (U/L) 2.92 (2.08,4.53) 2.80 (1.89,3.79) 16.36 (3.62,339.6) O 2.99 (2.41,4.69) ❖ ＜0.05 CA125 (U/L) 8.97 (6.81,11.71) 10.58 (6.96,16.81) 11.43 (8.09,17.92) # 15.73 (10.36,27.66) O❖ ＜0.05 CA199 (U/L) 8.85 (5.45,13.71) 14.02 (7.74,35.11) # 13.28 (8.83,23.58) # 106.75 (24.02,524.1) O❖ ＜0.05 CA724 (U/L) 2.92 (1.47,6.56) 1.20 (0.90,1.91) # 1.5 (1.02,2.85) O 2.71 (1.38,6.27) O❖ ＜0.05 Note: #: Compared with the HC group, P < 0.05; o: Compared with the benign liver lesion group, P < 0.05; ❖: Compared with the HCC group, P < 0.05; #O: Compared with the HC group and benign liver lesion group, P < 0.05; O❖ : Compared with the HC group and HCC group, P < 0.05; #O❖: Compared with the HC group, benign liver lesion group, and HCC group, P < 0.05. 2. mSEPT9 detection results in each group Group-wise comparisons of positive rates were performed using Fisher's exact test. The positivity rate of mSEPT9 was 60.47% (182/301) in the HCC group, 48.33% (58/120) in the ICC group, 2.04% (1/49) in the Benign liver lesion group, and 1.30% (3/230) in the HC group (Fig.2). Statistically significant differences were noted between the HCC and HC groups ( P <0.001), between the ICC and HC groups ( P <0.001), and between the HCC group and the ICC group ( P 0.05). 3. Changes in mSEPT9 positivity rates across sex and age groups in the HCC and ICC groups MSEPT9 positivity rates were compared using Fisher's exact test in the HCC and ICC groups. As anticipated, no statistically significant difference was noted in the positivity rate of mSEPT9 between males and females, nor among patients across different age groups (Table 2). 4. Differential diagnostic performance of mSEPT9 and serum tumor markers in the HCC group 4.1 Diagnostic value of mSEPT9 and conventional tumor biomarkers in HCC In the HCC group, ROC curve analysis and DeLong's test were conducted to assess the diagnostic performance of mSEPT9 and conventional tumor markers, namely AFP, CA125, CA19-9, CEA, and CA724 (Fig.3). In the HCC group, mSEPT9, AFP, and CA19-9 levels were significantly higher compared with the HC group ( P <0.05). Interestingly, the AUC for mSEPT9 was 0.80 [95% confidence interval (CI): 0.75~0.84], which was higher than that for AFP (AUC=0.74, 95% CI: 0.69~0.79) and significantly higher than that for CA125 (AUC=0.54, 95% CI: 0.48~0.59), CA19-9 (AUC=0.56, 95% CI: 0.51~0.62), CEA (AUC=0.53, 95%CI: 0.48~0.59), and CA724 (AUC= 0.46, 95% CI: 0.41~0.51) P ＜0.001. The combined assessment of plasma mSEPT9 and AFP yielded an AUC of 0.89 for surpassing the individual performance of plasma mSEPT9 (AUC=0.80) and AFP (AUC=0.74) P ＜0.001. These findings collectively highlight the superiority of plasma mSEPT9 plus AFP and may provide key evidence supporting its use for the diagnosis of HCC. 4.2 Analysis of significant indicators (mSEPT9, AFP, and CA19-9) across HCC stages The positivity rates of mSEPT9 in stage I, II & III in the HCC group were 40.57% (43/106), 66.41% (87/131) and 81.25% (52/64), respectively, which were statistically significant by chi-square analysis ( P <0.05), indicating a positive correlation between mSEPT9 positivity and HCC progression. Meanwhile, the positivity rate of AFP differed significantly between stage I and II HCC [40.95% (43/105) vs. 55.38% (72/130), ( P 0.05). No significant differences were noted in the positivity rate of CA19-9 among patients with stage I, II, and III HCC (9.52% vs 12.5% vs 19.05%, P >0.05). As anticipated, significant differences were observed in the positivity rate of combined detection of mSEPT9 plus AFP between stage I and II HCC (63.81% vs 81.54%, P 0.05, 63.81% vs 91.94%, P <0.001), indicating that combined mSEPT9 and AFP positivity significantly increased with HCC progression (Fig4). 5. Differential diagnostic value of mSEPT9 and tumor biomarkers in the ICC cohort In the ICC group, ROC curve analysis and DeLong's test were conducted to compare the diagnostic performance of mSEPT9 and tumor biomarkers (AFP, CA125, CA19-9, CEA, and CA724). CA19-9, mSEPT9, CEA, and CA125 showed statistically significant differences compared with the HC group ( P 0.05). The AUC for CA19-9 was 0.87 (95% CI: 0.81–0.92), significantly outperforming mSEPT9 (AUC 0.73, 95% CI: 0.67–0.80), CEA (AUC 0.62, 95% CI: 0.55–0.69), CA125 (AUC 0.60, 95% CI: 0.53–0.67), AFP (AUC 0.53, 95% CI: 0.46–0.59), and CA724 (AUC 0.51, 95% CI: 0.45–0.58) ( P < 0.001). These results conjointly signal that CA19-9 is the most effective biomarker for ICC, followed by mSEPT9. Further analysis of mSEPT9 and CA19-9 in the ICC group revealed that the positivity rate of mSEPT9 was 48.33% (58/120), while that of CA19-9 was 67.5% (81/120). Notably, ROC curve analysis for the combined detection of mSEPT9 and CA19-9 demonstrated an AUC of 0.91, which was significantly higher than that of mSEPT9 alone (AUC 0.73) or CA19-9 alone (AUC 0.87) ( P < 0.001). Consequently, the combination of mSEPT9 and CA19-9 provides crucial diagnostic value for ICC (Fig. 5). 6. Analysis of statistically significant indicators (CA19-9, mSEPT9, CEA, and CA125) across ICC stages The positivity rates of mSEPT9 in stages I, II, and III of the ICC group were 38.64% (17/44), 40.91% (9/22), and 59.25% (32/54), respectively, with no statistically significant differences observed by chi-square analysis ( P > 0.05). Similarly, the positivity rates of CA19-9 across the three stages were 70.45% (31/44), 54.55% (12/22), and 70.37% (38/54), showing no significant difference ( P > 0.05). Of note, neither CEA (25.00% vs. 27.27% vs. 29.63%) nor CA125 (13.64% vs. 18.18% vs. 20.37%) exhibited statistically significant differences ( P > 0.05). The combined detection of mSEPT9 and CA19-9 also showed no significant differences in positivity rates across stages (88.64% vs. 68.18% vs. 81.48%, P > 0.05). Taken together, these findings suggest that the detection rates of mSEPT9, CA19-9, and other tumor markers do not significantly correlate with ICC progression (Fig. 6). 7. Correlation Analysis Between mSEPT9 and IHC Markers To further investigate the relationship between mSEPT9 and protein expression in primary liver cancer, both the HCC and ICC groups were stratified into mSEPT9-positive and mSEPT9-negative subgroups. Next, correlations between mSEPT9 and commonly used liver cancer IHC markers, including GPC-3, Arg, Hep, and Ki-67, were examined. In the HCC group, no significant differences were observed between the mSEPT9-positive and mSEPT9-negative subgroups for individual markers: GPC-3 (76.03% vs 72.29%), Arg (70.43% vs 69.29%), Hep (72.13% vs 74.60%), and Ki-67 (81.69% vs 79.54%) ( P ＞0.05). Likewise, in the ICC group, no significant differences were found for individual markers: GPC-3 (14.29% vs 1.01%), Arg (32.14% vs 50.50%), Hep (13.64% vs 0.60%), and Ki-67 (78.69% vs 72.21%) ( P ＞0.05). However, combined IHC marker expression demonstrated significant associations with mSEPT9 status. Specifically, in the HCC group, co-expression of Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive subgroup compared to the mSEPT9-negative subgroup (12.5% vs. 3.4%, P < 0.001) ( Fig. 7 ). In the ICC group, co-expression of GPC-3, Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive subgroup than in the mSEPT9-negative subgroup (8.4% vs. 1.6%, P < 0.001) ( Fig. 8 ). These results suggest that while individual markers showed no correlation with mSEPT9 status, specific combinations of IHC markers exhibited significant associations with mSEPT9 expression in both HCC and ICC. Discussion High sensitivity and specificity of mSEPT9 in liver cancer have been verified in previous research. Building on these observations, we further explored that mSEPT9 was associated with HCC severity but not with ICC staging. Combined detection of mSEPT9 and tumor markers significantly improved the diagnostic rate. Additionally, mSEPT9-positive cases showed higher positive rates of Hep, Arg, Ki67 in HCC and higher co-expression of GPC-3, Hep, Arg, Ki67 in ICC. To evaluate its diagnostic value in progressive liver cancer, mSEPT9 detection was retrospectively analyzed in patients with different stages of liver cancer. Of note, the positivity rate of mSEPT9 in HCC (60.47%) was significantly higher than in ICC (48.33%), benign liver lesions (2.04%), and HC (1.30%) ( P < 0.01). Additionally, the detection rate showed no significant differences across age groups or sexes, consistent with the findings of previous studies [21–22] .ROC curve analysis of mSEPT9 and tumor markers (AFP, CA19-9, CEA, and CA125) revealed that mSEPT9 demonstrated superior diagnostic performance in HCC compared to AFP and other markers (AUC 0.80 vs 0.74). Noteworthily, mSEPT9 positive exhibited a significant TNM stage-dependent increase, highlighting its crucial role in HCC development and progression. In contrast, AFP exhibited different detection patterns, demonstrating a significant difference between stages I-II ( P 0.05). AFP sensitivity ranged 41%-65% at a cutoff value of 20ng/mL [23] . Moreover, AFP levels are influenced by other benign liver conditions (such as cirrhosis and hepatitis), especially in the early stages, which may compromise their diagnostic specificity for liver cancer. However, it is worthwhile emphasizing that the positivity rate of AFP decreases in stage III, potentially due to hepatocyte destruction and reduced protein production. In addition, extremely elevated AFP levels may exceed the assay detection range, resulting in false negatives. In contrast, mSEPT9 is less affected by these shortcomings. Therefore, mSEPT9 exhibits higher sensitivity compared to AFP in the diagnosis of HCC and also holds a significant reference value for the evaluation and prognosis of HCC. Compared to individual detection of mSEPT9 (AUC 0.79) and AFP (AUC 0.75), combined detection of mSEPT9 and AFP improved diagnostic performance for liver cancer (AUC 0.88). Indeed, the combined detection of mSEPT9 and AFP achieved a positivity rate of 63.81% in stage I HCC and higher positivity rates in stages II and III (81.54%, 91.94%), compensating for the limited detection rate of AFP alone in advanced-stage HCC patients. In the ICC group, CA19-9 exhibited the highest positive rate at 69.2%. In ICC patients, cancer cells secrete substantial amounts of CA19-9 into the bloodstream, which is primarily associated with bile duct inflammation, biliary obstruction, and increased permeability of the bile duct wall [24] . Nevertheless, combined detection of mSEPT9 and CA19-9 yielded an AUC of 0.91 for ICC, which was significantly superior to mSEPT9 (AUC 0.73) and CA19-9 alone (AUC 0.84). These findings indicate that the combined detection of mSEPT9 and CA19-9 holds substantial diagnostic relevance for ICC, compensating for the limitations of single-marker testing and reducing the risk of missed diagnoses. Both CA19-9 and mSEPT9, as well as their combined detection, showed an upward trend across TNM stages of ICC, even though no statistically significant differences were noted. This may be attributed to the relatively small sample size and the imbalance among the three stages. Further expansion of the case cohort may be required to enable more comprehensive analyses. Herein, the positivity rate of mSEPT9 progressively increased with liver cancer progression, suggesting that it may play a critical role in the pathogenesis of HCC. On the other hand, IHC remains a principal method for assessing altered gene transcription products in human tissues, enabling the localization and quantification of protein or antigen expression in tissues while concurrently incorporating morphological analysis. No statistically significant differences were observed in individual IHC markers (GPC-3, Arg, Hep, and Ki-67) between the mSEPT9-positive and mSEPT9-negative subgroups. Tumor differentiation may influence the expression of immunomarkers, with that of Arg-1 and HepPar-1 decreasing and GPC3 expression increasing. This further suggests that combined markers detection holds significant diagnostic value for HCC, particularly in the diagnosis of poorly differentiated HCC [25] . Therefore, a combined analysis of IHC markers was conducted, unveiling that the mSEPT9-positive group exhibited significantly higher positivity rates for Hep, Arg, and Ki67 compared with the mSEPT9-negative group. In the ICC group, co-expression of GPC-3, Hep, Arg, and Ki67 was higher in mSEPT9-positive patients compared to mSEPT9-negative patients. These findings suggest that epigenetic alterations in Septin9 may be associated with downstream protein expression and related pathways involving Hep, Arg, and Ki67, playing a pivotal role in HCC pathogenesis. Michel et al. identified a positive correlation between Septin9 staining intensity and SATB2 expression, further supporting this hypothesis [26] . Nevertheless, the limited sample size, short follow-up duration, and the scarcity of studies evaluating the relationship between Septin9 expression and IHC markers limited the ability to draw definitive conclusions. These results should be considered preliminary, and the mechanisms by which Septin9 promotes HCC remain elusive. Further studies are warranted to explore the potential mechanisms underlying the association between mSEPT9 and the functional roles of these IHC markers. Limitations: This study investigated the diagnostic value of mSEPT9 combined with multiple indicators in liver cancer through a retrospective analysis. However, the benign lesion group with confirmed pathological diagnosis and complete case information was relatively small (only 49 cases) compared to the HC, HCC, and ICC groups. Although Fisher's exact test was applied to account for this discrepancy, the limited sample size may still have influenced the interpretation of the results. Further expansion of the cohort is needed for more robust analyses. This retrospective analysis did not include SEPTIN9-related data from patients with liver cirrhosis or chronic hepatitis, which may impact the specificity of SEPTIN9 as a diagnostic marker. Further expansion of the case cohort is required for more robust analyses. Furthermore, given that all cases were selected from a single institution, the results may not be fully generalizable to the general population. Conclusion mSEPT9 demonstrates high diagnostic sensitivity for both HCC and ICC, with positivity rates correlating with tumor progression stages. Herein, the benign lesion group required pathological confirmation, resulting in a relatively small sample size (49 cases) compared to the HCC and ICC groups, which represent a study limitation. Nevertheless, mSEPT9 demonstrated clear diagnostic utility in distinguishing HCC from benign liver diseases. Furthermore, the combined assessment of mSEPT9 with conventional tumor markers significantly improved detection rates for both HCC and ICC, thereby enhancing diagnostic accuracy, reducing the rate of missed diagnosis. Overall, this integrated approach may provide valuable guidance for clinical differential diagnosis. Yingxin Tian: Experimental data collection, statistical analysis, figure preparation, manuscript writing; Dandan Liu: Experimental data collection, statistical analysis, research supervision; Dongdong Xi: Statistical analysis and figure preparation; Fengtian Li: Experimental data collection; Yu Wang: Experimental data collection; Congzhe Chen: Statistical analysis; Jinxin Zhao: Data analysis; Hongli Tong: Research supervision; Ting Wen: Statistical analysis; Ying Zhang: Study design, experimental data collection, research supervision, manuscript review. Declarations Acknowledgments The authors sincerely thank departmental leaders and colleagues for their invaluable guidance and support throughout the conduct of this research and preparation of the manuscript. Funding This study was supported by the Youth Independent Innovation Science Foundation - Support Project (No. 22QNFC057). CONFLICT OF INTEREST STATEMENT All authors declare no potential competing interests. Ethics Statement The studies involving human participants were reviewed and approved by the First Medical Center of Chinese PLA General Hospital (Approval Number: S2025‐246‐01) Consent This study utilized medical records obtained from previous clinical diagnoses and treatments, with full protection of patient privacy and personal information. Moreover, the waiver of informed consent was approved by the Ethics Committee, considering that it did not compromise the rights or health of the participants. Data Availability Statement The datasets generated or analyzed in this study are available from the corresponding author upon reasonable request. References Nakano A, Hirabayashi K, Yamamuro H, et al. Combined primary hepatic neuroendocrine carcinoma and hepatocellular carcinoma: case report and literature review. World J Surg Oncol. 2021; 19(1):78. doi:10.1186/s12957-021 -02187-5 Liu H, Wei D, Yan Z, et.al. Analyzing the Role of Septin9 Gene Methylation in the Diagnosis and Treatment of Primary Liver Cancer in the Elderly. Altern Ther Health Med. 2023 May;29(4):194-199. PMID: 36947654 Zhao X, Xia Y, Li C, Wang D. Efficacy evaluation on the color doppler ultrasound, multislice spiral CT Combined with serum markers in diagnosis of primary hepatic carcinoma. Iran J Public Health. 2021;50(8):1603-1612. doi:10.18502 Lili W, Xuanxuan S, Man Zhang, et al. Clinical value of combined detection of serum tumor markers in diagnosis of primary hepatocellular carcinoma [J]. Laboratory Medicine and Clinic，2015.DOI: CNKI: SUN: JYYL.0.2015-06-014. Oussalah A, Rischer S, Bensenane M, et al. Plasma mSEPT9: A novel circulating cell-free DNA-based epigenetic biomarker to diagnose hepatocellular carcinoma[J]. Journal of Hepatology, 2018, 68: S113-S114. DOI:10.1016/S0168 -8278(18)30444-6. Takahashi Y, Dungubat E, Kusano H, et al. Ganbat D, Tomita Y, Odgerel S, Fukusato T. Application of Immunohistochemistry in the Pathological Diagnosis of Liver Tumors. Int J Mol Sci. 2021 May 28;22(11):5780. doi: 10.3390/ijms22115780. Hammad G, Magdy M, Aboushousha T, et al. HEPPAR1 and PIWIL2 as Panel Markers for Hepatocellular Carcinoma. Asian Pac J Cancer Prev. 2024 Jun 1;25(6):2123-2131. doi: 10.31557/APJCP.2024.25.6.2123. Xiaolong Z, Qiuju W. The role of DNA methylation in the diagnosis and treatment of auditory system diseases and research progress[J]. Chinese Medical Journal,2025,105(04):326-330. Dai X, Ren T, Nan N, et al. Methylation multiplicity and its clinical values in cancer. Expert Rev Mol Med. 2021 Mar 31;23: e2. doi: 10.1017/erm.2021.4. Shen Z, Feng J, Wei J, et al. Clinical value of plasma Septin9 methylation in the differential diagnosis of hepatocellular carcinoma[J]. International Journal of Laboratory Medicine,2020,41(23):2831-2834+2838 Abuhassan Q, Allela OQB, Kareem RA, et al. Liver cancer stem cells as novel diagnostic biomarkers. Clin Chim Acta. 2026 Jan 30; 580:120739. doi: 10.1016/j.cca.2025.120739. Ren M, Tao J, Sun Q, et al. SEPT9 as a therapeutic target for enhancing radiotherapy efficacy in esophageal squamous cell carcinoma. Discov Oncol. 2026 Jan 3. doi: 10.1007/s12672-025-03969-z. Peng H, Sun L, Zhao J, et al. Electrochemical detection of circulating-free DNA methylation: A new indicator for early cancer screening. Talanta. 2025 Sep 1; 292:127925. doi: 10.1016/j.talanta.2025.127925. Wang D, Dai Z, Bai M, et al. Integrating cell-free DNA methylation of SEPT9 and SFRP2 into a machine learning model for early diagnosis of HCC. Biomark Med. 2025 Aug;19(16):737-745. doi: 10.1080/17520363.2025.2541574. Arora J, Nassar M, Baraka B. Unravelling the potential of plasma DNA methylation in the detection and surveillance of esophageal cancer. World J Gastrointest Oncol. 2025 Jun 15;17(6):103333. doi: 10.4251/wjgo. v17.i6.103333. Miaomiao Z, Hui Yu, Qing Chen, et al. Progress of SEPT9 gene in tumours[J]. International Journal of Laboratory Medicine,2019,40(04):453-458 Li Q, Jiang W, Zhang Y, et al. Methylation of Septin9, SRSF1, and PAX8 in Early Screening of Colorectal Cancer in the Population Undergoing Physical Examinations. Clin Lab. 2023 Dec 1;69(12). doi: 10.7754/Clin.Lab.2023.230426. Wu Y, Tong Y, Zhang H, et al. A novel dual-target Septin9 methylation assay for improved detection of early-stage colorectal cancer and high-grade intraepithelial neoplasia. BMC Cancer. 2024 Jul 30; 24(1):916. doi:10.1186/s12885024126454. Song L, Jia J, Peng X, et.al. The performance of the SEPT9 gene methylation assay and a comparison with other CRC screening tests: A meta-analysis. Sci Rep. 2017 Jun 8;7(1):3032. doi: 10.1038/s41598-017-03321-8. Song L, Li Y. Progress on the clinical application of the SEPT9 gene methylation assay in the past 5 years. Biomark Med. 2017 May;11(6):415-418. doi: 10.2217/bmm-2017-0091. Tran YH, Dao TT, Nguyen UD, et al. Sensitive detection of circulating methylated SEPT9 in hepatocellular carcinoma patients using a novel quantitative PCR assay. Anal Methods. 2025 Feb 27;17(9):2181-2190. doi: 10.1039/d4ay02168a. Jin D, Qian L, Chen J, et al. Diagnostic accuracy y of methylated SEPT 9 for primary liver cancer: a systematic review and meta-analysis. Front Endocrinol (Lausanne). 2025 Feb 13; 16:1434174. doi: 10.3389/fendo.2025.1434174. He N, Feng G, Zhang C, et al. Plasma levels of methylated septin 9 are capable of detecting hepatocellular carcinoma and hepatic cirrhosis[J]. Molecular medicine reports, 22(4): 2705-2714[2023-07-18]. DOI:10.3892/mmr.2020.11356. SAJIANG Lin, ZHANG Yina, FU Jun. Diagnostic value of multiple tumour markers combined application for hepatocellular carcinoma[J] Cancer Progress, 2018,16(02):199-201+238. Lei L, Hao L, Hongtai B, et al. Expression of CD34 and Ki67 in Predicting Postoperative Recurrence Risk of Liver Cancer [J]. Chinese Hepatology, 2020, 25(2):3.DOI:CNKI:SUN:ZUAN.0.2020-02-034.DOI： 10.14000/jcnk i. issn. 1008-1704.2 Kmeid M, Park YN, Chung T, et al. SEPT9 Expression in Hepatic Nodules: An Immunohistochemical Study of Hepatocellular Neoplasm and Metastasis. Appl Immunohistochem Mol Morphol. 2023 May-Jun 01;31(5):278-287. doi: 10.1097. Table Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 26 Mar, 2026 Editor invited by journal 13 Mar, 2026 Editor assigned by journal 10 Mar, 2026 Submission checks completed at journal 10 Mar, 2026 First submitted to journal 05 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9038335","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612390708,"identity":"76a85335-299f-4f94-9739-0cf728187d39","order_by":0,"name":"颖新 田","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACZhBhwMBj38x88EFCRQ3xWuQM2NmSDR6cOUa8ZcYG/Dxmkg9bmAkrNTjOe/g1T8GdxO3MPGYViQ1sDPzt3Qn4tRzmS7OcYfAscWczW9mNxB0yDBJnzm4goIXHzOCDweHEhsPM224knmFjMJDIJUJLAlgLg1lBYhszUVqMHwBtMTY4zGLGQJQWSaAtjDMMDstJNrMlSyScOcZD0C98588Yf+b5c5iHn//wwY8/Kmrk+Nt78WtROMDAJoEswINXOQjINzAwfyCoahSMglEwCkY2AACZuUne+jj0KAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"颖新","middleName":"","lastName":"田","suffix":""},{"id":612390709,"identity":"550d1c4d-2708-4529-8b7f-c32a2e4ce097","order_by":1,"name":"Dandan liu","email":"","orcid":"","institution":"Department of Information, Medical Supplies Center of PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"liu","suffix":""},{"id":612390710,"identity":"84acd292-cfc5-45b7-aa80-efbd703d8fb9","order_by":2,"name":"dongdong xi","email":"","orcid":"","institution":"Hebei Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"dongdong","middleName":"","lastName":"xi","suffix":""},{"id":612390711,"identity":"39d9f785-3d1e-4a07-b63f-1dd4895ffdc7","order_by":3,"name":"Fengtian Li","email":"","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fengtian","middleName":"","lastName":"Li","suffix":""},{"id":612390712,"identity":"fdc0b14d-2825-45be-a90d-b88827295bc5","order_by":4,"name":"yu Wang","email":"","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"yu","middleName":"","lastName":"Wang","suffix":""},{"id":612390713,"identity":"ef5e8feb-0089-47ee-bd7f-ba6c62f74d67","order_by":5,"name":"Congzhe Chen","email":"","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Congzhe","middleName":"","lastName":"Chen","suffix":""},{"id":612390714,"identity":"7285e747-6d57-4e93-bf47-1e0425496f08","order_by":6,"name":"jinxin Zhao","email":"","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"jinxin","middleName":"","lastName":"Zhao","suffix":""},{"id":612390715,"identity":"3e174d8e-9477-4e1e-bafe-16baf59d19d1","order_by":7,"name":"Hongli Tong","email":"","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongli","middleName":"","lastName":"Tong","suffix":""},{"id":612390716,"identity":"c191220b-d993-4152-b4b2-d6ce146ddc41","order_by":8,"name":"ting wen","email":"","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"ting","middleName":"","lastName":"wen","suffix":""},{"id":612390717,"identity":"ce48f057-b32c-4ab8-9395-00051780cfc0","order_by":9,"name":"ying zhang","email":"","orcid":"","institution":"Department of Laboratory Medical Centre, the first Medical centre,Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"ying","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2026-03-05 09:10:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9038335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9038335/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105575431,"identity":"588436a3-17eb-4258-9fc5-55438aeb76e0","added_by":"auto","created_at":"2026-03-27 13:39:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172898,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Case Enrollment and Exclusion\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/78bec9b09d773756611ce1b0.png"},{"id":105575386,"identity":"e3ce5681-49f1-4aae-a471-b00023cb976f","added_by":"auto","created_at":"2026-03-27 13:38:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40137,"visible":true,"origin":"","legend":"\u003cp\u003ePositivity rate of mSEPT9 in each group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05，**\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01，***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; 60.47% in HCC group, 48.33% in ICC, 2.04% in Benign Liver Lesions group and 1.30% in HC Group. HCC group vs the HC group, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; ICC group vs the HC group, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; HCC group vs the ICC group, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/789f9881ce2d9098b864942b.png"},{"id":105575313,"identity":"c290bebf-5d39-47f6-8128-2d91f61466da","added_by":"auto","created_at":"2026-03-27 13:38:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119726,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of mSEPT9 and traditional tumor markers in the HCC group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e The diagnostic efficacy of mSEPT9+AFP (0.89) exceeded that of mSEPT9 (0.79) and AFP (0.74) alone, followed by CA19-9 (0.56), CA125 (0.54), CEA (0.53), and CA724 (0.46). Compared with the HC group, significant differences were observed in mSEPT9, AFP, and CA19-9 levels (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), whereas no significant differences were noted in the remaining indicators (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/e560420662e89884424dbd42.png"},{"id":105575425,"identity":"27d1e84a-4b5c-41b9-b110-cb39e72c92d5","added_by":"auto","created_at":"2026-03-27 13:38:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51577,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of mSEPT9, AFP, CA9-9 and mSEPT9 + AFP across HCC stages\u003c/p\u003e\n\u003cp\u003eNote：*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. Significant differences were observed in mSEPT9 positivity across stages I, II, and III, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001; A significant difference was observed in AFP positivity between stages I and II HCC, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; No significant differences were detected in CA19-9 positivity across stages I, II, and III HCC, \u003cem\u003eP\u003c/em\u003e＞0.05; For combined mSEPT9 and AFP positivity, significant differences were identified between stages I and II and between I and III（stage I vs stage III, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, stage I vs stage II, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, stage II vs III, \u003cem\u003eP\u003c/em\u003e＞0.05）\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/c834b879abfaf06e7cc567fb.png"},{"id":105575663,"identity":"36b2efea-11f2-4804-a2df-b4927f7d8afd","added_by":"auto","created_at":"2026-03-27 13:40:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120173,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of mSEPT9 and tumor markers in the ICC group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e In the ICC group, the AUC values of the markers ranked as follows: mSEPT9+CA19-9 (0.91) \u0026gt; CA19-9 (0.87) \u0026gt; mSEPT9 (0.73) \u0026gt; CEA (0.62) \u0026gt; CA125 (0.60) \u0026gt; AFP (0.53) \u0026gt; CA724 (0.51). Among these, AFP and CA724 showed no significant difference compared with the HC group (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05), whereas the remaining markers demonstrated statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/f44c0469f44c146e7c14658c.png"},{"id":105575316,"identity":"cfd664ba-6e24-4fea-bf09-dc5b2993a2e9","added_by":"auto","created_at":"2026-03-27 13:38:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":39040,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of mSEPT9, CA9-9, CA125, CEA, and mSEPT9 combined with CA9-9 in ICC staging.\u003c/p\u003e\n\u003cp\u003eNote: No statistically significant differences were observed across groups.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/65152e9d9013478aff7ea231.png"},{"id":105575312,"identity":"8b4db462-0051-4a16-8e35-2cc8f920772a","added_by":"auto","created_at":"2026-03-27 13:38:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":364908,"visible":true,"origin":"","legend":"\u003cp\u003eCombined analysis of mSEPT9 and IHC markers in the HCC group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e In the HCC group, co-expression of Hep, Arg, and Ki67 showed a significant difference between the mSEPT9(+) and mSEPT9(-) subgroups (12.5% vs. 3.4%, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/6a682566cd43732e10b7b327.png"},{"id":105575315,"identity":"16d5f33f-7e63-4224-b5d4-60331824e42a","added_by":"auto","created_at":"2026-03-27 13:38:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":260903,"visible":true,"origin":"","legend":"\u003cp\u003eCombined analysis of mSEPT9 and IHC markers in the ICC group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e In the ICC group, co-expression of GPC-3, Hep, Arg, and Ki-67 showed a significant difference between mSEPT9(+) and mSEPT9(-) subgroups (8.4% vs. 1.6%, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/895d85864591d936a1035f12.png"},{"id":106723631,"identity":"e677e8ae-c1bf-4fe0-8b9f-265a81f7c804","added_by":"auto","created_at":"2026-04-12 18:09:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1544724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/f952fc60-29c6-4091-93a0-b40c48557fc7.pdf"},{"id":105575815,"identity":"3c2b217e-7a04-4206-8fb3-e84fbef49ed5","added_by":"auto","created_at":"2026-03-27 13:41:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15106,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9038335/v1/48eee5a9802b48de2b290f7a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Value of Plasma Septin9 Methylation Combined with Multi-indicator Detection in the Progression of Primary Liver Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs is well documented, primary liver cancer, a prevalent gastrointestinal malignancy, has been termed the \"king of cancer\"\u003csup\u003e[1]\u003c/sup\u003e. Pathologically, liver cancer can be classified into hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular cholangiocarcinoma (CHC) \u003csup\u003e[2]\u003c/sup\u003e. At present, imaging techniques, including ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), are utilized for the detection and evaluation of liver cancer. However, due to atypical early lesions, imaging equipment discrepancies, and variations in diagnostic proficiency among clinicians, misdiagnosis and missed diagnosis remain common \u003csup\u003e[3]\u003c/sup\u003e. Contrarily, serum-based examinations primarily assess liver function and the levels of tumor markers, such as alpha-fetoprotein (AFP), carbohydrate antigen 125 (CA125), CA19-9, and carcinoembryonic antigen (CEA). Furthermore, AFP, as a specific marker for HCC, were widely utilized in clinical practice \u003csup\u003e[4]\u003c/sup\u003e. However, according to the American Association for the Study of Liver Diseases (AASLD) guidelines, AFP is not sufficiently sensitive and specific for screening purposes \u003csup\u003e[5]\u003c/sup\u003e. Currently, the diagnosis and treatment of hepatocellular carcinoma rely on IHC markers, including HepPar-1, GPC3, Arg-1, and others, to assist in diagnostic confirmation. However, due to the small size of fine-needle aspiration specimens and low tumor cellularity, together with the potential impact of tumor differentiation on IHC marker expression, the diagnostic sensitivity and specificity are suboptimal \u003csup\u003e[6\u0026ndash;7]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn earlier study has demonstrated that DNA methylation occurring during early carcinogenesis and cancer precursor stages is associated with tumor formation, positioning it as an ideal biomarker for early cancer diagnosis \u003csup\u003e[8\u0026ndash;10]\u003c/sup\u003e. As a member of the tumor suppressor gene family, Septin9 undergoes aberrant DNA methylation in various malignancies \u003csup\u003e[11\u0026ndash;13]\u003c/sup\u003e, eventually leading to dysregulated expression, reduced transcriptional activity, impaired physiological functions, and ultimately carcinogenesis \u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e. Notably, the Septin9 methylation (mSEPT9) test represents the first epigenetic tumor liquid biopsy test approved by the FDA globally. It is one of the most essential diagnostic biomarkers for colorectal cancer \u003csup\u003e[17\u0026ndash;18]\u003c/sup\u003e, displaying a correlation with cancer staging and exhibiting increasing sensitivity with cancer progression \u003csup\u003e[19]\u003c/sup\u003e. Recent investigations have unveiled correlations between mSEPT9 and other tumor types, including lung cancer and liver cancer \u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, a retrospective analysis was conducted among liver cancer patients to evaluate the diagnostic performance of mSEPT9 and its correlation with TNM stage. mSEPT9 was compared with conventional tumor markers, and their combined diagnostic value was assessed. In addition, the associations between mSEPT9 and relevant IHC indicators were systematically analyzed to provide a theoretical reference for the precise diagnosis and personalized treatment of liver cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e1. Case selection and general characteristics\u003c/p\u003e\n\u003cp\u003eA retrospective analysis was conducted on cases with primary liver cancer treated at the First Medical Center of Chinese People\u0026apos;s Liberation Army General Hospital from March 2018 to December 2021.\u0026nbsp;This study was approved by the Ethics Committee of the Chinese PLA General Hospital (Approval Number: S2025‐246‐01).\u003c/p\u003e\n\u003cp\u003eAfter rigorous and detailed screening, 230 healthy examinees at the same hospital during the corresponding period were included as controls, all of whom had no underlying diseases. Moreover, 49 cases of benign liver lesions were included (hepatic cyst 12/49, hepatolithiasis and choledocholithiasis 13/49, focal nodular hyperplasia 10/49, hepatic hemangioma 7/49, hepatic echinococcosis 1/49, hepatitis 5/49, hepatic fibrolipoma 1/49). The malignancy cohorts consisted of 301 cases with HCC and 120 cases with ICC. The screening and exclusion procedures are illustrated in Fig.1. TNM staging in the HCC and ICC groups were conducted according to the TNM system of the American Joint Cancer Staging Consortium (AJCC) Cancer Staging Manual (8th edition).\u003c/p\u003e\n\u003cp\u003eInclusion criteria: 1) patients aged \u0026ge; 18 years with complete clinical data and no PRIOR history of chemoradiotherapy, 2) patients pathologically diagnosed with benign liver lesions by imaging examinations, and those with malignant tumors definitively diagnosed with HCC or ICC according to the AJCC Staging Manual (8th edition). 3) Healthy subjects who underwent routine physical examinations at the hospital. 4) Liver function tests, blood routine tests, and tumor marker tests were performed in all enrolled cases prior to surgical intervention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExclusion criteria included: 1) pregnant women or subjects with a history of immune system diseases; 2) patients with incomplete clinicopathological data or other data; 3) Patients with other malignancies liver cancer metastasis to other organs (excluding cases in stage TⅣ); 4) Patients with gastrointestinal diseases diagnosed by gastroscopy.\u003c/p\u003e\n\u003cp\u003e2. Test methods\u003c/p\u003e\n\u003cp\u003e2.1 Data collection\u003c/p\u003e\n\u003cp\u003ePatient data, including sex, age, imaging examination findings, pre-treatment laboratory indicators, and pathological diagnoses, were collected from the electronic medical record system.\u003c/p\u003e\n\u003cp\u003eLiver function was assessed using a Roche C701 analyzer, and tumor markers were detected using a Roche e601 analyzer. All reagents were sourced from Roche Diagnostics GmbH. The Sysmex XN2000 blood cell analyzer was employed to assess routine blood indexes, with proprietary reagents procured from Sysmex Corporation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2 mSEPT9 methylation quantification\u003c/p\u003e\n\u003cp\u003eIn this study, a SEPT9 methylation detection kit (BioChain, Beijing, China) was employed. Firstly, free DNA was extracted from plasma using the Beijing \u0026quot;BioChain\u0026quot; Methylation Detection Kit 1 (Plasma Processing Kit). Unmethylated cytosines were then converted into uracil via bisulfite deamidation reaction through incubation with bisulfite reagent, following which the bisulfite-converted (Bis-DNA) was amplified via PCR using the \u0026quot;BioChain\u0026quot; Methylation Detection Kit 2 (PCR kit) with specific primers. Afterward, the target fragment in the SEPT9 gene was detected using a fluorescent probe specific to the methylated sequence. Plasma mSEPT9 levels were detected by SLAN fluorescent PCR using the\u0026nbsp;\u0026beta;-Actin gene as an internal reference. A Ct value of\u0026nbsp;\u0026le;41.0 was defined as positive, whereas a Ct value of \u0026gt;41.0 or the absence of a detectable Ct value was interpreted as negative.\u003c/p\u003e\n\u003cp\u003e3 Statistical analyses\u003c/p\u003e\n\u003cp\u003eSPSS 26.0 software was utilized for statistical analyses. P-P plots were used to test the normality of continuous variables. Normally distributed variables were presented as (x\u0026plusmn;s). were expressed as M (P25, P75). Categorical variables were expressed as frequencies and rates. To minimize bias caused by differences in sample size, Fisher\u0026apos;s exact test was applied for comparisons of categorical variables. For continuous variables following a normal distribution, one-way ANOVA was used for comparisons among multiple groups, and Tamhane\u0026apos;s T2 test was applied for post hoc pairwise comparisons due to heterogeneity of variances. For non-normally distributed variables, the Kruskal-Wallis non-parametric test was used.\u0026nbsp;Binary logistic regression analysis was performed for mSEPT9 and other tumor markers, followed by ROC curve analysis to determine AUC, sensitivity, and specificity. The DeLong test was used to compare AUCs, with\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.05, considered statistically significant. The correlation between\u0026nbsp;mSEPT9\u0026nbsp;and IHC markers was analyzed using the\u0026nbsp;ggplot2\u0026nbsp;package in\u0026nbsp;R to assess potential associations between\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emSEPT9 expression and IHC indicators.\u003c/p\u003e"},{"header":"Results and Analysis","content":"\u003cp\u003e1. Baseline Patient Characteristics\u003c/p\u003e\n\u003cp\u003eOne-way ANOVA was used to analyze the complete blood count (CBC), liver function parameters, and tumor marker levels of the four groups of data, and Tamhane\u0026apos;s T2 test was used for post hoc pairwise comparisons.\u0026nbsp;No significant difference in age\u0026nbsp;was observed among groups (\u003cem\u003eP\u003c/em\u003e=0.548), whereas differences in the remaining variables were statistically significant. For instance, significant differences were observed in\u0026nbsp;sex, Hemoglobin (Hb), red blood cell count (RBC), alanine aminotransferase levels (ALP), \u0026gamma;-glutamyl transferase levels (GGT), total bilirubin levels (TBIL), direct bilirubin levels (DBIL), CA19-9, and CA724 between the HC and benign liver lesion groups.\u0026nbsp;Similarly,\u0026nbsp;all indicators showed significant differences between the HC and HCC groups. Between the HC and ICC groups, significant differences were noted in\u0026nbsp;Hb, RBC, WBC, alkaline phosphatase levels(ALT), aspartate aminotransferase levels (AST), GGT, TBIL, DBIL, CEA, CA125, and CA19-9. Between the\u0026nbsp;benign lesion group and the HCC group, significant differences were observed in\u0026nbsp;sex, WBC, PLT, ALT, AST, ALP, DBIL, AFP, and CA724.\u0026nbsp;Between the benign lesion group and the ICC group, significant differences were identified in\u0026nbsp;Hb, RBC, WBC, ALT, AST, ALP, DBIL, CEA, CA125, and CA19-9. Lastly, between the ICC and HCC groups, all indicators except\u0026nbsp;WBC and AST\u0026nbsp;showed significant differences (Table 1).\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable style=\"width: 4.8e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"6\"\u003e\n \u003cp\u003eTable 1. Comparative analysis of baseline characteristics, complete blood count, liver function parameters, and tumor marker levels among the four groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHC group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBenign lesion group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHCC group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eICC group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMale/Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e153/77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24/25#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e260/40#O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e79/43❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e59.85\u0026plusmn;19.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e58.14\u0026plusmn;14.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e59.6\u0026plusmn;10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e60.64\u0026plusmn;9.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHb (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e145.7\u0026plusmn;16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e128.6\u0026plusmn;26.3#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e125.5\u0026plusmn;19.9#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e109.2\u0026plusmn;22.8#O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eRBC (10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.8\u0026plusmn;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.2\u0026plusmn;0.59#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.0\u0026plusmn;0.66#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.5\u0026plusmn;0.78#O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.9\u0026plusmn;1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.9\u0026plusmn;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.5\u0026plusmn;3.91#O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.9\u0026plusmn;4.01#O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e230.5\u0026plusmn;54.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e209.3\u0026plusmn;72.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e149.5\u0026plusmn;67.78#O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e220.0\u0026plusmn;85.41❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e27.2 (17.3,49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e57.7 (25.3,185.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e59.3 (36.6,98.0) O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e119.0 (64.2,192.9) O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.8 (15.1,22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21.6 (15.4,34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e39.2 (25.6,64.8) O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e31.7 (21.1,51.8) O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e63.1 (51.6,74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e83.15 (62.2.1,136.7) #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18.9 (8.9,66.6) O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e70.8 (16.5,136) O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e41.6\u0026plusmn;43.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e169.5\u0026plusmn;262.28#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e77.57\u0026plusmn;62.88#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e205.94\u0026plusmn;286.83#❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eTBIL (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.2\u0026plusmn;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18.7\u0026plusmn;15.6#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21.7\u0026plusmn;20.7#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e51.1\u0026plusmn;59.8#O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eDBIL (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.75 (2.9,4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e5.35 (3.6,9.4) #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7.85(5.4,11.1) O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.9 (5.4,46.9) O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor marker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCEA (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.54 (1.0,2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.68 (0.99,2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.1 (1.37,3.19) #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.28 (1.93,6.06) O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAFP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.92 (2.08,4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.80 (1.89,3.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e16.36 (3.62,339.6) O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.99 (2.41,4.69) ❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCA125 (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.97 (6.81,11.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.58 (6.96,16.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.43 (8.09,17.92) #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.73 (10.36,27.66) O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCA199 (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.85 (5.45,13.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.02 (7.74,35.11) #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.28 (8.83,23.58) #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e106.75 (24.02,524.1) O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eCA724 (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.92 (1.47,6.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.20 (0.90,1.91) #\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.5 (1.02,2.85) O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.71 (1.38,6.27) O❖\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e＜0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote: #:\u003c/strong\u003e Compared with the HC group, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; o: Compared with the benign liver lesion group,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.05; ❖: Compared with the HCC group,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.05; #O: Compared with the HC group and benign liver lesion group, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05;\u0026nbsp;O❖\u003cem\u003e:\u003c/em\u003e Compared with the HC group and HCC group, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05; #O❖: Compared with the HC group, benign liver lesion group, and HCC group, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e2. mSEPT9 detection results in each group\u003c/p\u003e\n\u003cp\u003eGroup-wise comparisons of positive rates were performed using Fisher\u0026apos;s exact test. The positivity rate of mSEPT9 was 60.47% (182/301) in the HCC group, 48.33% (58/120) in the ICC group, 2.04% (1/49) in the Benign liver lesion group, and 1.30% (3/230) in the HC group (Fig.2). Statistically significant differences were noted between the HCC and HC groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), between the ICC and HC groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and between the HCC group and the ICC group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). In contrast, no statistically significant difference was observed between the benign liver lesion group and the HC group (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e3. Changes in mSEPT9 positivity rates across sex and age groups in the HCC and ICC groups\u003c/p\u003e\n\u003cp\u003eMSEPT9 positivity rates were compared using Fisher\u0026apos;s exact test in the HCC and ICC groups. As anticipated, no statistically significant difference was noted in the positivity rate of mSEPT9 between males and females, nor among patients across different age groups (Table 2).\u003c/p\u003e\n\u003cp\u003e4. Differential diagnostic performance of mSEPT9 and serum tumor markers in the HCC group\u003c/p\u003e\n\u003cp\u003e4.1 Diagnostic value of mSEPT9 and conventional tumor biomarkers in HCC\u003c/p\u003e\n\u003cp\u003eIn the HCC group, ROC curve analysis and DeLong\u0026apos;s test were conducted to assess the diagnostic performance of mSEPT9 and conventional tumor markers, namely AFP, CA125, CA19-9, CEA, and CA724 (Fig.3). In the HCC group, mSEPT9, AFP, and CA19-9 levels were significantly higher compared with the HC group\u0026nbsp;(\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Interestingly, the AUC for mSEPT9 was 0.80 [95% confidence interval (CI): 0.75~0.84], which was higher than that for AFP (AUC=0.74, 95% CI: 0.69~0.79) and significantly higher than that for CA125 (AUC=0.54, 95% CI: 0.48~0.59), CA19-9 (AUC=0.56, 95% CI: 0.51~0.62), CEA (AUC=0.53, 95%CI: 0.48~0.59), and CA724 (AUC= 0.46, 95% CI: 0.41~0.51) \u003cem\u003eP\u003c/em\u003e＜0.001. The combined assessment of plasma mSEPT9 and AFP yielded an AUC of 0.89 for surpassing the individual performance of plasma mSEPT9 (AUC=0.80) and AFP (AUC=0.74) \u003cem\u003eP\u003c/em\u003e＜0.001. These findings collectively highlight the superiority of plasma mSEPT9 plus AFP and may provide key evidence supporting its use for the diagnosis of HCC.\u003c/p\u003e\n\u003cp\u003e4.2 Analysis of significant indicators (mSEPT9, AFP, and CA19-9) across HCC stages\u003c/p\u003e\n\u003cp\u003eThe positivity rates of mSEPT9 in stage I, II \u0026amp; III in the HCC group were 40.57% (43/106), 66.41% (87/131) and 81.25% (52/64), respectively, which were statistically significant by chi-square analysis (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), indicating a positive correlation between mSEPT9 positivity and HCC progression. Meanwhile, the positivity rate of AFP differed significantly between stage I and II HCC [40.95% (43/105) vs. 55.38% (72/130), (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05)], whereas no significant difference was observed in stage III HCC [49.21% (31/65)] compared with that in stages I and II (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). \u0026nbsp;No significant differences were noted in the positivity rate of CA19-9 among patients with stage I, II, and III HCC (9.52% vs 12.5% vs 19.05%, \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). As anticipated, significant differences were observed in the positivity rate of combined detection of mSEPT9 plus AFP between stage I and II HCC (63.81% vs 81.54%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, 81.54%vs. 91.94%, \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05, 63.81% vs 91.94%,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.001),\u0026nbsp;indicating that combined mSEPT9 and AFP positivity significantly increased with HCC progression (Fig4).\u003c/p\u003e\n\u003cp\u003e5. Differential diagnostic value of mSEPT9 and tumor biomarkers in the ICC cohort\u003c/p\u003e\n\u003cp\u003eIn the ICC group, ROC curve analysis and DeLong\u0026apos;s test were conducted to compare the diagnostic performance of mSEPT9 and tumor biomarkers (AFP, CA125, CA19-9, CEA, and CA724). CA19-9, mSEPT9, CEA, and CA125 showed statistically significant differences compared with the HC group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), whereas AFP and CA724 showed no significant differences (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). The AUC for CA19-9 was 0.87 (95% CI: 0.81\u0026ndash;0.92), significantly outperforming mSEPT9 (AUC 0.73, 95% CI: 0.67\u0026ndash;0.80), CEA (AUC 0.62, 95% CI: 0.55\u0026ndash;0.69), CA125 (AUC 0.60, 95% CI: 0.53\u0026ndash;0.67), AFP (AUC 0.53, 95% CI: 0.46\u0026ndash;0.59), and CA724 (AUC 0.51, 95% CI: 0.45\u0026ndash;0.58) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). These results conjointly signal that CA19-9 is the most effective biomarker for ICC, followed by mSEPT9. Further analysis of mSEPT9 and CA19-9 in the ICC group revealed that the positivity rate of mSEPT9 was 48.33% (58/120), while that of CA19-9 was 67.5% (81/120). Notably, ROC curve analysis for the combined detection of mSEPT9 and CA19-9 demonstrated an AUC of 0.91, which was significantly higher than that of mSEPT9 alone (AUC 0.73) or CA19-9 alone (AUC 0.87) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Consequently, the combination of mSEPT9 and CA19-9 provides crucial diagnostic value for ICC (Fig. 5).\u003c/p\u003e\n\u003cp\u003e6. Analysis of statistically significant indicators (CA19-9, mSEPT9, CEA, and CA125) across ICC stages\u003c/p\u003e\n\u003cp\u003eThe positivity rates of mSEPT9 in stages I, II, and III of the ICC group were\u0026nbsp;38.64% (17/44), 40.91% (9/22), and 59.25% (32/54), respectively, with no statistically significant differences observed by chi-square analysis (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Similarly, the positivity rates of CA19-9 across the three stages were 70.45% (31/44), 54.55% (12/22), and 70.37% (38/54), showing no significant difference (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Of note, neither CEA (25.00% vs. 27.27% vs. 29.63%) nor CA125 (13.64% vs. 18.18% vs. 20.37%) exhibited statistically significant differences (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). The combined detection of mSEPT9 and CA19-9 also showed no significant differences in positivity rates across stages (88.64% vs. 68.18% vs. 81.48%, \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Taken together, these findings suggest that the detection rates of mSEPT9, CA19-9, and other tumor markers do not significantly correlate with ICC progression (Fig. 6).\u003c/p\u003e\n\u003cp\u003e7. Correlation Analysis Between mSEPT9 and IHC Markers\u003c/p\u003e\n\u003cp\u003eTo further investigate the relationship between mSEPT9 and protein expression in primary liver cancer, both the HCC and ICC groups were stratified into mSEPT9-positive and mSEPT9-negative subgroups. Next, correlations between mSEPT9 and commonly used liver cancer IHC markers, including GPC-3, Arg, Hep, and Ki-67, were examined. In the HCC group, no significant differences were observed between the mSEPT9-positive and mSEPT9-negative subgroups for individual markers: GPC-3 (76.03% vs 72.29%), Arg (70.43% vs 69.29%), Hep (72.13% vs 74.60%), and Ki-67 (81.69% vs 79.54%)\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cem\u003eP\u003c/em\u003e＞0.05). Likewise, in the ICC group, no significant differences were found for individual markers: GPC-3 (14.29% vs\u0026nbsp;1.01%),\u0026nbsp;Arg (32.14% vs\u0026nbsp;50.50%), Hep (13.64% vs\u0026nbsp;0.60%), and Ki-67 (78.69% vs\u0026nbsp;72.21%) (\u003cem\u003eP\u003c/em\u003e＞0.05).\u003c/p\u003e\n\u003cp\u003eHowever, combined IHC marker expression demonstrated significant associations with mSEPT9 status. Specifically, in the HCC group, co-expression of Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive subgroup compared to the mSEPT9-negative subgroup (12.5% vs. 3.4%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (\u003cem\u003eFig. 7\u003c/em\u003e). In the ICC group, co-expression of GPC-3, Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive subgroup than in the mSEPT9-negative subgroup (8.4% vs. 1.6%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (\u003cem\u003eFig. 8\u003c/em\u003e). These results suggest that while individual markers showed no correlation with mSEPT9 status, specific combinations of IHC markers exhibited significant associations with mSEPT9 expression in both HCC and ICC.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHigh sensitivity and specificity of mSEPT9 in liver cancer have been verified in previous research. Building on these observations, we further explored that mSEPT9 was associated with HCC severity but not with ICC staging. Combined detection of mSEPT9 and tumor markers significantly improved the diagnostic rate. Additionally, mSEPT9-positive cases showed higher positive rates of Hep, Arg, Ki67 in HCC and higher co-expression of GPC-3, Hep, Arg, Ki67 in ICC.\u003c/p\u003e \u003cp\u003eTo evaluate its diagnostic value in progressive liver cancer, mSEPT9 detection was retrospectively analyzed in patients with different stages of liver cancer. Of note, the positivity rate of mSEPT9 in HCC (60.47%) was significantly higher than in ICC (48.33%), benign liver lesions (2.04%), and HC (1.30%) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, the detection rate showed no significant differences across age groups or sexes, consistent with the findings of previous studies \u003csup\u003e[21\u0026ndash;22]\u003c/sup\u003e.ROC curve analysis of mSEPT9 and tumor markers (AFP, CA19-9, CEA, and CA125) revealed that mSEPT9 demonstrated superior diagnostic performance in HCC compared to AFP and other markers (AUC 0.80 vs 0.74). Noteworthily, mSEPT9 positive exhibited a significant TNM stage-dependent increase, highlighting its crucial role in HCC development and progression. In contrast, AFP exhibited different detection patterns, demonstrating a significant difference between stages I-II (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but not stage III vs other stages (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). AFP sensitivity ranged 41%-65% at a cutoff value of 20ng/mL \u003csup\u003e[23]\u003c/sup\u003e. Moreover, AFP levels are influenced by other benign liver conditions (such as cirrhosis and hepatitis), especially in the early stages, which may compromise their diagnostic specificity for liver cancer. However, it is worthwhile emphasizing that the positivity rate of AFP decreases in stage III, potentially due to hepatocyte destruction and reduced protein production. In addition, extremely elevated AFP levels may exceed the assay detection range, resulting in false negatives. In contrast, mSEPT9 is less affected by these shortcomings. Therefore, mSEPT9 exhibits higher sensitivity compared to AFP in the diagnosis of HCC and also holds a significant reference value for the evaluation and prognosis of HCC. Compared to individual detection of mSEPT9 (AUC 0.79) and AFP (AUC 0.75), combined detection of mSEPT9 and AFP improved diagnostic performance for liver cancer (AUC 0.88). Indeed, the combined detection of mSEPT9 and AFP achieved a positivity rate of 63.81% in stage I HCC and higher positivity rates in stages II and III (81.54%, 91.94%), compensating for the limited detection rate of AFP alone in advanced-stage HCC patients.\u003c/p\u003e \u003cp\u003eIn the ICC group, CA19-9 exhibited the highest positive rate at 69.2%. In ICC patients, cancer cells secrete substantial amounts of CA19-9 into the bloodstream, which is primarily associated with bile duct inflammation, biliary obstruction, and increased permeability of the bile duct wall \u003csup\u003e[24]\u003c/sup\u003e. Nevertheless, combined detection of mSEPT9 and CA19-9 yielded an AUC of 0.91 for ICC, which was significantly superior to mSEPT9 (AUC 0.73) and CA19-9 alone (AUC 0.84). These findings indicate that the combined detection of mSEPT9 and CA19-9 holds substantial diagnostic relevance for ICC, compensating for the limitations of single-marker testing and reducing the risk of missed diagnoses. Both CA19-9 and mSEPT9, as well as their combined detection, showed an upward trend across TNM stages of ICC, even though no statistically significant differences were noted. This may be attributed to the relatively small sample size and the imbalance among the three stages. Further expansion of the case cohort may be required to enable more comprehensive analyses.\u003c/p\u003e \u003cp\u003eHerein, the positivity rate of mSEPT9 progressively increased with liver cancer progression, suggesting that it may play a critical role in the pathogenesis of HCC. On the other hand, IHC remains a principal method for assessing altered gene transcription products in human tissues, enabling the localization and quantification of protein or antigen expression in tissues while concurrently incorporating morphological analysis. No statistically significant differences were observed in individual IHC markers (GPC-3, Arg, Hep, and Ki-67) between the mSEPT9-positive and mSEPT9-negative subgroups. Tumor differentiation may influence the expression of immunomarkers, with that of Arg-1 and HepPar-1 decreasing and GPC3 expression increasing. This further suggests that combined markers detection holds significant diagnostic value for HCC, particularly in the diagnosis of poorly differentiated HCC \u003csup\u003e[25]\u003c/sup\u003e. Therefore, a combined analysis of IHC markers was conducted, unveiling that the mSEPT9-positive group exhibited significantly higher positivity rates for Hep, Arg, and Ki67 compared with the mSEPT9-negative group. In the ICC group, co-expression of GPC-3, Hep, Arg, and Ki67 was higher in mSEPT9-positive patients compared to mSEPT9-negative patients. These findings suggest that epigenetic alterations in Septin9 may be associated with downstream protein expression and related pathways involving Hep, Arg, and Ki67, playing a pivotal role in HCC pathogenesis. Michel et al. identified a positive correlation between Septin9 staining intensity and SATB2 expression, further supporting this hypothesis \u003csup\u003e[26]\u003c/sup\u003e. Nevertheless, the limited sample size, short follow-up duration, and the scarcity of studies evaluating the relationship between Septin9 expression and IHC markers limited the ability to draw definitive conclusions. These results should be considered preliminary, and the mechanisms by which Septin9 promotes HCC remain elusive. Further studies are warranted to explore the potential mechanisms underlying the association between mSEPT9 and the functional roles of these IHC markers.\u003c/p\u003e \u003cp\u003eLimitations: This study investigated the diagnostic value of mSEPT9 combined with multiple indicators in liver cancer through a retrospective analysis. However, the benign lesion group with confirmed pathological diagnosis and complete case information was relatively small (only 49 cases) compared to the HC, HCC, and ICC groups. Although Fisher's exact test was applied to account for this discrepancy, the limited sample size may still have influenced the interpretation of the results. Further expansion of the cohort is needed for more robust analyses. This retrospective analysis did not include SEPTIN9-related data from patients with liver cirrhosis or chronic hepatitis, which may impact the specificity of SEPTIN9 as a diagnostic marker. Further expansion of the case cohort is required for more robust analyses. Furthermore, given that all cases were selected from a single institution, the results may not be fully generalizable to the general population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003emSEPT9 demonstrates high diagnostic sensitivity for both HCC and ICC, with positivity rates correlating with tumor progression stages. Herein, the benign lesion group required pathological confirmation, resulting in a relatively small sample size (49 cases) compared to the HCC and ICC groups, which represent a study limitation. Nevertheless, mSEPT9 demonstrated clear diagnostic utility in distinguishing HCC from benign liver diseases. Furthermore, the combined assessment of mSEPT9 with conventional tumor markers significantly improved detection rates for both HCC and ICC, thereby enhancing diagnostic accuracy, reducing the rate of missed diagnosis. Overall, this integrated approach may provide valuable guidance for clinical differential diagnosis.\u003c/p\u003e \u003cp\u003eYingxin Tian: Experimental data collection, statistical analysis, figure preparation, manuscript writing; Dandan Liu: Experimental data collection, statistical analysis, research supervision; Dongdong Xi: Statistical analysis and figure preparation; Fengtian Li: Experimental data collection; Yu Wang: Experimental data collection; Congzhe Chen: Statistical analysis; Jinxin Zhao: Data analysis; Hongli Tong: Research supervision; Ting Wen: Statistical analysis; Ying Zhang: Study design, experimental data collection, research supervision, manuscript review.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank departmental leaders and colleagues for their invaluable guidance and support throughout the conduct of this research and preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study was supported by the Youth Independent Innovation Science Foundation - Support Project (No. 22QNFC057).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no potential competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the First Medical Center of Chinese PLA General Hospital (Approval Number: S2025‐246‐01)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized medical records obtained from previous clinical diagnoses and treatments, with full protection of patient privacy and personal information. Moreover, the waiver of informed consent was approved by the Ethics Committee, considering that it did not compromise the rights or health of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNakano A, Hirabayashi K, Yamamuro H, et al. Combined primary hepatic neuroendocrine carcinoma and hepatocellular carcinoma: case report and literature review. World J Surg Oncol. 2021; 19(1):78. doi:10.1186/s12957-021 -02187-5\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu H, Wei D, Yan Z, et.al. Analyzing the Role of Septin9 Gene Methylation in the Diagnosis and Treatment of Primary Liver Cancer in the Elderly. Altern Ther Health Med. 2023 May;29(4):194-199. 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Biomark Med. 2025 Aug;19(16):737-745. doi: 10.1080/17520363.2025.2541574.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArora J, Nassar M, Baraka B. Unravelling the potential of plasma DNA methylation in the detection and surveillance of esophageal cancer. World J Gastrointest Oncol. 2025 Jun 15;17(6):103333. doi: 10.4251/wjgo. v17.i6.103333.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMiaomiao Z, Hui Yu, Qing Chen, et al. Progress of SEPT9 gene in tumours[J]. International Journal of Laboratory Medicine,2019,40(04):453-458\u003c/li\u003e\n \u003cli\u003eLi Q, Jiang W, Zhang Y, et al. Methylation of Septin9, SRSF1, and PAX8 in Early Screening of Colorectal Cancer in the Population Undergoing Physical Examinations. Clin Lab. 2023 Dec 1;69(12). doi: 10.7754/Clin.Lab.2023.230426.\u003c/li\u003e\n \u003cli\u003eWu Y, Tong Y, Zhang H, et al. A novel dual-target Septin9 methylation assay for improved detection of early-stage colorectal cancer and high-grade intraepithelial neoplasia. 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Front Endocrinol (Lausanne). 2025 Feb 13; 16:1434174. doi: 10.3389/fendo.2025.1434174.\u003c/li\u003e\n \u003cli\u003eHe N, Feng G, Zhang C, et al. Plasma levels of methylated septin 9 are capable of detecting hepatocellular carcinoma and hepatic cirrhosis[J]. Molecular medicine reports, 22(4): 2705-2714[2023-07-18]. DOI:10.3892/mmr.2020.11356.\u003c/li\u003e\n \u003cli\u003eSAJIANG Lin, ZHANG Yina, FU Jun. Diagnostic value of multiple tumour markers combined application for hepatocellular carcinoma[J] Cancer Progress, 2018,16(02):199-201+238.\u003c/li\u003e\n \u003cli\u003eLei L, Hao L, Hongtai B, et al.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eExpression of CD34 and Ki67 in Predicting Postoperative Recurrence Risk of Liver Cancer [J].\u0026nbsp;Chinese Hepatology, 2020, 25(2):3.DOI:CNKI:SUN:ZUAN.0.2020-02-034.DOI： 10.14000/jcnk i. issn. 1008-1704.2\u003c/li\u003e\n \u003cli\u003eKmeid M, Park YN, Chung T, et al. SEPT9 Expression in Hepatic Nodules: An Immunohistochemical Study of Hepatocellular Neoplasm and Metastasis. Appl Immunohistochem Mol Morphol. 2023 May-Jun 01;31(5):278-287. doi: 10.1097.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mSEPT9, Hepatocellular carcinoma, Intrahepatic cholangiocarcinoma, Alpha-fetoprotein, Carbohydrate antigen19-9","lastPublishedDoi":"10.21203/rs.3.rs-9038335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9038335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo evaluate the diagnostic value of plasma methylated Septin9 (mSEPT9) combined with tumor markers and immunohistochemical (IHC) markers in the identification of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on a cohort of patients who had undergone \u003cem\u003emSEPT9\u003c/em\u003e gene testing. Statistical analyses were performed using SPSS26.0 statistical software. The mSEPT9 and IHC indexes were analyzed using ggplot2 in the R package.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe positivity rate of mSEPT9 in the HCC group (60.47%) was significantly higher than that in ICC groups [ICC (48.33%) vs the benign liver lesion group (2.04%) vs the HC group (1.30%)]. Moreover, mSEPT9 positivity rates in stages I, II, and III of the HCC group were 40.57%, 66.41% and 81.25%, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results of IHC suggested that co-expression of Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive HCC subgroup compared to the mSEPT9-negative subgroup (12.5% vs. 3.4%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Likewise, in the ICC group, co-expression of GPC-3, Hep, Arg, and Ki67 was significantly higher in the mSEPT9-positive subgroup compared to the mSEPT9-negative subgroup (8.4% vs. 1.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003emSEPT9 demonstrated higher sensitivity in HCC patients, followed by AFP. In ICC patients, CA19-9 exhibited superior diagnostic value than mSEPT9. Additionally, the positivity rate of mSEPT9 was associated with HCC severity but not with ICC staging. MSEPT9 expression was also associated with IHC indicators, suggesting that epigenetic modifications may influence the expression of various proteins. Taking together, these findings provide a new direction for more accurate diagnosis and personalized treatment strategies for HCC, although further in-depth studies are warranted.\u003c/p\u003e","manuscriptTitle":"Diagnostic Value of Plasma Septin9 Methylation Combined with Multi-indicator Detection in the Progression of Primary Liver Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 13:26:31","doi":"10.21203/rs.3.rs-9038335/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-26T04:58:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-13T11:56:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-10T11:32:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-10T11:32:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-05T09:02:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9c4d292-8fab-48ec-85c0-2fb109e39a29","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T13:26:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 13:26:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9038335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9038335","identity":"rs-9038335","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}