APEX1 as a novel diagnostic and prognostic biomarker for hepatocellular carcinoma

APEX1 as a novel diagnostic and prognostic biomarker for hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article APEX1 as a novel diagnostic and prognostic biomarker for hepatocellular carcinoma Nourhan Assem, Kadry Mohammed El-Saeed, Sarah Ashraf Safwat, Dina Fathy Mohamed, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5815078/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Hepatocellular carcinoma (HCC) is the sixth prevailing cancer globally and is the second greatest reason for cancer-linked deaths in males, surpassed only by lung cancer. A significant number of HCC cases experiencing advanced stages are due to the frequent absence or inconspicuousness of early symptoms. Consequently, discovering reliable early diagnostic indicators and innovative treatment targets is essential to improve survival and overall outcomes for HCC patients. Herein, we aimed to evaluate the diagnostic potential of APEX1 in Egyptian HCC patients and explore its prognostic significance before treatment initiation. Study design and methodology: This research was conducted as a prospective comparative cohort study involving 60 Egyptian participants, age range more than 18 years selected from internal medicine and hepatology outpatient clinics and inpatient wards at Ain Shams University hospitals from January 2024 to July 2024, after informed consent was taken from the patients, who were allocated into: Group A (Cirrhotic) comprising 12 liver cirrhosis patients but without HCC, acting as the control group; Group B (HCC) included 48 patients diagnosed with HCC. Group B was further subdivided into four subgroups: Subgroup 1: 12 patients receiving palliative (supportive) care; Subgroup 2: 12 patients treated with Sorafenib, Subgroup 3: 12 patients treated with trans-arterial chemoembolization (TACE); and Subgroup 4: 12 patients treated with radiofrequency ablation (RFA). Data collection included anthropometric measurements, laboratory tests, radiological findings, CHILD-PUGH scores, BCLC scores, and patient performance metrics, gathered both before and after treatment. The data were analyzed utilizing IBM SPSS (ver. 27). Results: The study identified a serum APEX1 level cutoff of >13.72 ng/mL as a biomarker to differentiate between cirrhotic patients and those with HCC (sensitivity = 92.31%, specificity = 100.0%). The threshold for AFP was 20 ng/mL (sensitivity = 76.92%, specificity = 100.0%). Notably, significant differences in AFP and APEX1 levels were found between cirrhotic patients and HCC patients in the RF and TACE subgroups at baseline (p-value 0.004, < 0.001 for AFP and APEX1, respectively), indicating that APEX1 may serve as a more sensitive biomarker for early HCC detection. Both AFP and APEX1 exhibited a positive predictive value (PPV) of 100%. Furthermore, the study revealed that an AFP level exceeding 280 ng/mL at baseline could predict a poor prognosis (sensitivity = 57.1%, specificity = 81.8%, AUC = 0.679). In contrast, an APEX1 level greater than 9.96 ng/mL at baseline was found to be a noteworthy predictor of poor prognosis (specificity = 86.4%, sensitivity = 92.9%, AUC = 0.857). Conclusions : - APEX1 has proven to be a more sensitive and accurate serum biomarker than AFP for diagnosing HCC. - Serum APEX1 levels revealed a significant rise in HCC patients, even during the early stages (candidates for radiological/therapeutic intervention), making it a valuable tool for screening and early detection in surveillance programs. - APEX1 demonstrated the ability to predict patient prognosis even prior to the initiation of treatment. APEX1 diagnostic and prognostic biomarker Hepatocellular Carcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background In a global context, hepatocellular carcinoma (HCC) is the sixth prevailing cancer and the fourth prevailing cancer in Egypt [1]. HCC prevails with a percentage of approximately 85% among patients with cirrhosis. The HCC is predicted to have a five-year survival rate of approximately 18% [2]. Numerous hospital-based studies have documented a rising incidence of HCC. This increase may be linked to advancements in screening programs and diagnostic methods, improved survival rates among cirrhotic patients (which heightens their risk of developing HCC), and the growing pervasiveness and complications of hepatitis C virus (HCV), the primary liver cancer risk factor in Egypt [3]. Surveillance for HCC typically involves ultrasound imaging and alpha-fetoprotein (AFP) level assessment, particularly in high-risk populations such as individuals with cirrhosis or those with HCV or HBV infection, with or without cirrhosis [4]. During routine surveillance, the diagnosis of a suspicious lesion in a cirrhotic liver through ultrasound is typically followed by diagnostic validation utilizing contrast-enhanced triphasic CT or dynamic MRI [6]. A significant number of liver cancer instances are diagnosed at advanced stages, reducing the chances of curative treatment. This highlights the need for new biomarkers to enable earlier detection and effective monitoring of liver cancer patients [7]. Alpha-fetoprotein (AFP) is regarded as the main diagnostic indicator for HCC. Nevertheless, its specificity and sensitivity are limited, and its levels can be impacted by different HCC-linked factors [8]. Therefore, the development of novel, highly sensitive biomarkers is crucial for the early diagnosis of HCC and for enhancing clinical results. Apyrimidinic endodeoxyribonuclease 1 (APEX1) contributed to the DNA damage response and is widely expressed across various human tissues [9]. The dysregulated APEX1 expression can interfere with many physiological processes, encompassing the maintenance of cellular redox balance, cell cycle regulation, smooth muscle cell migration, mRNA stability, and apoptosis [10]. This study focuses on analyzing the APEX1 expression and clinical significance in HCC. Materials and Methods This is a prospective comparative cohort study aimed at evaluating the diagnostic and prognostic value of serum APEX1 biomarker. The study was performed on sixty Egyptian cirrhotic patients with or without HCC, their ages ranged from 44–91 years, selected from internal medicine and hepatology outpatient clinics and inpatient wards at Ain Shams University hospitals, from January 2024 to July 2024, after informed consent was taken from the patients and after obtaining the approval of the ethics committee. The patients were divided into two groups: Group A (Cirrhotic), comprising 12 liver cirrhosis patients without HCC, serving as the control group, and Group B (HCC), consisting of 48 cirrhotic patients with HCC who were further subdivided according to the BCLC staging criteria [ 5 ] for management (which takes into consideration the number and size of the nodules, the presence of vascular invasion, the liver functions and the performance of each case individually) and according to the EASL guidelines [ 5 ] into: 12 patients receiving palliative (supportive) care. 12 patients treated with Sorafenib (Nexavar). 12 patients treated with TACE. 12 patients treated Radiofrequency. Individuals under the age of 18, pregnant or nursing women, patients with malignancies other than HCC (such as RCC, cholangiocarcinoma, gastric carcinoma, or other cancers that may influence APEX1 levels), and participants with severe comorbid conditions (e.g., advanced cardiovascular or kidney disease) that could independently affect APEX1 levels, and those who declined to participate, were excluded from the study. All Patients were exposed to : history taking, clinical examination, and baseline laboratory tests, which involved a complete blood count, liver function tests (ALT, AST, total and direct bilirubin, serum albumin), coagulation profile (PT, PTT, INR), kidney function tests, viral biomarkers (HBs Ag, HCV Ab), AFP, and serum APEX1 level quantified from archived sera using an automated analyzer with a lectin-antibody sandwich immunoassay (where serum samples were collected from the patients with strict precautionary measures in order to ensure the maximum safety for all participants and working staff ). Additionally, radiological assessments included a pelvic-abdominal ultrasound, with any suspicious hepatic focal lesions further assessed with either contrast-enhanced triphasic CT or dynamic MRI for confirmation. These assessments were conducted both at the baseline and during the follow-up period, three months after treatment initiation . Following data collection, the CHILD-PUGH score, BCLC score, and ECOG performance status were calculated for each patient both before and after the intervention. Statistical analysis was conducted to evaluate the sensitivity and specificity of Apyrimidinic Endodeoxyribonuclease 1 (APEX1) in diagnosing HCC and monitoring patients with HCC after undergoing various interventions, including palliative care, Sorafenib (Nexavar), TACE, or RF. Additionally, comparisons were made between the groups receiving different treatments. Statistics: The Statistical Package for Social Sciences (IBM SPSS; Ver 27) was used to collect, review, code, and enter the data. The mean, standard deviation, and range were employed to represent quantitative data for parametric data, while for non-parametric data, the median and interquartile range (IQR) were deployed. Multiple statistical tests were employed to conduct group comparisons, including the Chi-square test, Fisher's exact test, Mann-Whitney test, paired t-test, Wilcoxon rank test, One-Way ANOVA followed by post hoc analysis via the LSD test, or the Kruskal-Wallis test followed by post hoc analysis with the Mann-Whitney test. Spearman correlation coefficients were also deployed. The confidence interval was established at 95%, with an accepted margin of error of 5%. P-values were interpreted as follows: P-values > 0.05, < 0.05, and < 0.01 were signified non-significance (NS), significant (S), and highly significant (HS), respectively. Results This prospective comparative cohort study included sixty Egyptian cirrhotic patients, with or without HCC, hospitalized in the internal medicine department at Ain Shams University Hospitals and Laboratories during a six-month interval from January 2024 to July 2024. The research population included 38 males (63.3%) and 22 females (36.7%), with ages spanning from 44 to 91 years and a mean age of 61.75 ± 9.03 years. Most participants [38 (63.3%)] resided in urban areas, while 21 patients (35.0%) were smokers, 14 (23.3%) revealed diabetes mellitus, and 24 (40.0%) had hypertension. Concerning the etiology of liver disease, the proportion of patients exhibiting HCV infection revealed a significant rise in the palliative and Sorafenib groups compared to the cirrhotic, TACE, and RF groups. The AFP levels manifested a significant rise in the palliative group, unlike the cirrhotic, TACE, and RF groups. Nonetheless, no significant differences in AFP levels were detected between the cirrhotic and treatment groups. Likewise, the Sorafenib group experienced a significant increase in AFP levels compared to that in TACE. A statistically significant difference was found between cirrhotic and all HCC patients at presentation for both AFP and APEX1 levels (p-value < 0.001). Moreover, there was a significant difference in AFP levels between cirrhotic and HCC patients who were to receive RF and TACE at presentation (p-value = 0.004). Regarding APEX1 levels, a highly significant difference was revealed between cirrhotic patients and early-stage HCC patients who were to be treated with RF and TACE (p-value 20 ng/ml (sensitivity = 76.92%, specificity = 100.0%, AUC = 0.930). For APEX1, the best threshold to differentiate between the two groups was > 13.72 ng/ml (sensitivity = 92.31%, specificity = 100.0%, AUC = 0.986). The APEX1 levels exhibited a significant positive connection with AFP, both at presentation and at follow-up. Since patients eligible for TACE or RF were considered to have relatively early-stage HCC compared to those receiving Sorafenib, who, in turn, were in earlier stages than those limited to palliative care (including management of ascites, edema, portosystemic hypertension complications, hepatic encephalopathy, and pain control), the study identified a statistically significant difference in AFP levels at presentation between cirrhotic patients and early HCC patients undergoing RF or TACE (p = 0.004). Additionally, a more significant difference was observed in APEX1 levels between cirrhotic patients and early HCC patients receiving RF or TACE, with a p-value of < 0.001.At presentation, AFP levels experienced significant rise in patients having worse prognosis [305 (21–769)] in comparison with those exhibiting good prognosis [17 (3.5–268)] (p-value = 0.0.024). The APEX1 levels at presentation were significantly greater in patients with poor prognosis [41.67 (15.6–56.27)] than in those with good prognosis [6.9 (5.3–9.1)] (p-value = 0.001). Herein, the prognosis was determined based on serological markers, imaging findings, lesion size, the presence of de novo lesions and the patient’s overall performance status. At presentation, AFP levels could predict patients with poor prognosis at a cutoff value of > 280 ng/ml (sensitivity = 57.1%, specificity = 81.8%, AUC = 0.679). While APEX1 levels at presentation could identify patients with poor prognosis at a cutoff value of > 9.96 ng/ml (sensitivity = 92.9%, specificity = 86.4%, AUC = 0.857). Discussion Herein, the average age of Egyptian patients with HCC was 61.75 ± 9.03 years (approximately in the 7th decade) and was primarily observed in individuals with liver cirrhosis. HCC and cirrhosis were more prevalent among males (63.3%), aligning with previous findings indicating that HCC commonly affects patients in their 6th to 7th decades. For instance, Fouad et al. [ 11 ] demonstrated a mean age of 62.7 years (SD: 9.25) among Egyptian HCC patients between 2010 and 2020, with 66.3% being over 60 years old and a male predominance of 73.6%. In this study, the prevalence of hypertension, diabetes mellitus, and smoking was 40%, 23%, and 35%, respectively. Comparatively, Kim et al. [ 12 ] investigated metabolic comorbidities and reported higher rates: hypertension in 60.6% of patients, diabetes in 37.1%, and a smoking history in 46.9%. In our research, the prevalence of HCV and HBV among Egyptian patients with liver disease was 78.3% and 18.3%, respectively. This aligns with findings by Ezzat et al. (2021) [ 13 ], which reported that HCV infection elevates the HCC risk by as much as 20 times, with 0.5–10% of HCV-related cirrhosis patients advancing to HCC each year. Moreover, Egypt exhibits the greatest worldwide incidence of HCV, although the HBV prevalence throughout the population is around 1.4% [ 13 ]. In our study, approximately 73.3% of virally infected patients underwent antiviral treatment. Similarly, Waked et al. (2020) [ 14 ] reported that in 2015, around 10% of screened individuals tested positive for HCV antibodies, and 7% had viremia, translating to 5.5 million people with chronic infection [ 14 ]. In our study, it was observed that a higher proportion of patients diagnosed early and seeking interventional or medical tumor therapy were from urban areas. Specifically, over two-thirds (63.3%) of the patients were urban residents. This can be attributed to the study being conducted in a tertiary hospital located in central Cairo. Similarly, a study by Zhou et al. (2020) [ 15 ] highlighted the influence of residence on diagnosis stage and treatment access. Their findings indicated that HCC patients from rural and suburban areas had an 18% and 5% greater risk of being diagnosed at an advanced stage, respectively, compared to urban patients, and were 12% less likely to receive treatment [ 15 ]. Furthermore, the palliative group exhibited a significantly greater proportion of patients with a performance status grade of 3 compared to other groups that were more likely to receive treatment. Conversely, the percentage of patients exhibiting a performance status grade of 0 was significantly greater in the RF, TACE, and Sorafenib groups compared to the palliative and cirrhotic groups (p-value 13.72 ng/ml (sensitivity = 92.31%, specificity = 100.0%, AUC = 0.986). In comparison to healthy control groups and non-tumor cirrhosis patients, a recent study demonstrated significantly elevated APEX1 levels in HCC patients (sensitivity: 81.4%, specificity: 94.9%) [ 9 ]. In this study, an AFP cutoff level of 20 ng/mL was deployed to distinguish between cirrhotic and HCC groups (sensitivity = 76.92%, specificity = 100%, AUC = 0.930). These findings align with studies showing that an AFP cutoff of 20 ng/mL provides a sensitivity spanning from 41–65% and a specificity between 80% and 94%, with diagnostic specificity improving when combined with ultrasound imaging [ 17 ]. A significant difference in AFP and APEX1 levels was observed between cirrhotic and all HCC patients (p < 0.001). Furthermore, there was a significant difference in AFP (p-value = 0.004) and APEX1 (p-value < 0.001) levels between cirrhotic patients and HCC patients in the RF and TACE groups, who represent the subsets with the lowest AFP and APEX1 values and are considered candidates for treatment. This indicated that APEX1 is a highly sensitive biomarker in serum and is used for early HCC diagnosis. The outcomes align with research by Cao et al. (2020) [ 18 ], which showed that APEX1 and AFP levels were significantly increased in HCC in comparison with cirrhotic samples. The AUC for APEX1 (0.823, p < 0.001) significantly exceeded the AUC for AFP (0.7869, p < 0.0001) in HCC tumor samples [ 18 ]. Here, the positive predictive value (PPV) was 100% for both AFP and APEX1. However, the findings of Cao et al. (2020) [ 18 ] indicated that APEX1 levels manifested a significant rise in early-stage HCC patients exhibiting minimal AFP expression. The PPV for APEX1 was significantly superior to that of AFP (67.91% vs. 55.22%) in HCC patients [ 18 ]. As the study tracked HCC patients from presentation through three months post-intervention, aiming to categorize them based on their response to treatment. This classification was guided by various factors, including laboratory markers, CHILD score, lesion number and size, vascular invasion, metastases, BCLC score, and ECOG performance status [ 19 ]. A statistically significant elevation in AFP levels at presentation was observed in individuals with a worse prognosis in comparison with those with a good prognosis (p = 0.024). Additionally, APEX1 levels showed an even more pronounced statistically significant increase in patients with poor prognosis, unlike those exhibiting good prognosis (p = 0.001). The outcomes indicate that APEX1 could function as a possible biomarker for prognostic prediction in HCC patients [ 20 ]. Herein, we indicated that AFP levels at presentation could predict unfavorable prognosis at a threshold of > 280 ng/ml (sensitivity = 57.1%, specificity = 81.8%, AUC = 0.679) [ 21 ]. In contrast, APEX1 levels at presentation showed superior predictive ability for poor prognosis at a cutoff value of > 9.96 ng/ml (sensitivity = 92.9%, specificity = 86.4%, AUC = 0.857) [ 18 ]. In conclusion, this study highlights APEX1 as a sensitive and accurate diagnostic and prognostic serum biomarker compared to AFP, making it a valuable tool for HCC diagnosis and follow-up. Limitation The limited sample size, which was further divided into smaller subgroups. Future research should address this issue by conducting studies on a larger scale. The short duration of follow-up, future studies should adopt a longer time scale for more comprehensive analysis. Conclusion Comparing the levels of APEX1 to those of AFP in cirrhotic and HCC patients: the APEX1 demonstrates greater sensitivity and accuracy compared to AFP in diagnosing HCC. - And as the study included early HCC patients eligible for radiological or therapeutic treatments (such as TACE, RF, or Sorafenib), comparing serum APEX1 levels with AFP levels in both cirrhotic and "early" HCC groups at the time of presentation. The results demonstrated a notable increase in APEX1 levels in HCC patients, even in the early stages of the disease. This highlights APEX1 as a promising biomarker for use in screening and early detection of HCC during surveillance efforts. - APEX1 proved to be a reliable indicator of patient prognosis by linking disease progression during follow-up to its serum levels at the time of presentation. Abbreviations AFP: Alphafetoprotein ALT: Alanine aminotransferase APEX1: Apyrimidinic endodeoxyribonuclease 1 AST: Aspartate aminotransferase AUC: Area under the curve BCLC: Barcelona-Clinic Liver Cancer CT: Computed tomography ECOG: Eastern cooperative oncology group HBV: Hepatitis B virus HCV: Hepatitis C virus MRI: Magnetic resonance imaging PT, PTT: Prothrombin time, Partial thromboplastin time, INR: International normalised ratio RCC: Renal cell carcinoma RF: Radiofrequency TACE: Transarterial chemoembolization Declarations Ethics approval and consent to participate: This research was conducted per ethical standards. The Ethical Committee of the Faculty of Medicine at Ain Shams University granted consent prior to the commencement of the study, which follow the ethical principles outlined in the 1975 Declaration of Helsinki. Written permission was acquired from each participant. Committee’s reference number: FWA 000017585 Consent for publication: not applicable Data and material availability: Data are available from the corresponding author upon request. Competing interests: No competing interests Funding: No fund. Acknowledgements: Gratitude is extended to the hepatology committee personnel at Ain Shams University for their assistance in enabling data collection. References Zheng, H., Qin, Z., Qiu, X., Zhan, M., Wen, F., & Xu, T. (2020). Cost-effectiveness analysis of ramucirumab treatment for patients with hepatocellular carcinoma who progressed on sorafenib with α-fetoprotein concentrations of at least 400 ng/ml. Journal of medical economics, 23(4), 347-352. Asafo-Agyei, K.O., Samant, H. (2022). Hepatocellular Carcinoma. 2021 Aug 8. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan–. PMID: 32644603. Demir, T. (2021). Systemic therapy of liver cancer. Adv Cancer Res.; 149:257–294. Tsang, J., Wong, J. S. L., Kwok, G. G. 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Bai, D., Zhang, C., Chen, P., Jin, S., & Jiang, G. (2017). The prognostic correlation of AFP level at diagnosis with pathological grade, progression, and survival of patients with hepatocellular carcinoma. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-12834-1 Tables Table 1: Comparison between cirrhotic patients and all the studied HCC patients regarding AFP and APEX1 levels at presentation: Cirrhotic At presentation HCC ALL At presentation Mann-Whitney Test Sig. N=12 N=48 Z P-value AFP Median (IQR) 2.65 (1.33-7.35) 52.8 (9.5-564) 4.085 <0.001 HS Range 0.5-20 1.8-12800 APEX1 Median (IQR) 7.08 (4.65-9.67) 28.87 (19.1-53.42) 4.676 0.05: Non significant (NS); P <0.05: Significant (S); P <0.01: Highly significant (HS) *: Chi-square test; ≠: Kruskall-Wallis test Table 2: Comparison between cirrhotic patients and HCC patients candidates for (RF or TACE) regarding AFP and APEX1 levels at presentation: Cirrhotic Patients HCC (RF / TACE) At presentation Mann-Whitney Test Sig. N=12 N=24 Z P-value AFP Median (IQR) 2.65 (1.33-7.35) 17 (2.9-76.9) 2.903 0.004 HS Range 0.5-20 1.8-972 APEX1 Median (IQR) 7.08 (4.65-9.67) 26.87 (14.28-28.87) 4.027 0.05: Non significant (NS); P <0.05: Significant (S); P <0.01: Highly significant (HS) *: Chi-square test; ≠: Kruskall-Wallis test Table 3: Sensitivity and specificity of AFP & APEX1: Table 4 : Correlation between APEX 1 and AFP at presentation: APEX1 R p-value AFP 0.677** 0.000 Table (4) shows that there was statistically significant positive correlation between APEX1 level at presentation and AFP. Table 5: Correlation between APEX 1 and AFP at follow up : APEX1 R p-value AFP 0.418* 0.011 Table (5) shows that there was statistically significant positive correlation between APEX1 level at follow up and AFP. Table 6: Comparison between patients with good and poor prognosis in (RF+TACE+Sorafenib) subgroups regarding AFP and APEX1 levels at presentation: Good Poor Test value P-value Sig. No. = 19 No. = 17 AFP Median (IQR) 17 (3.5 - 268) 305 (21 - 769) 2.168 0.024 S Range 0 – 874 1.8 – 972 APEX1 Median (IQR) 6.9 (5.3 – 9.1) 41.67 (15.6 – 56.27) 3.569 0.001 HS Range 2.24 – 72.48 1.3 – 73.85 P>0.05: Non significant (NS); P <0.05: Significant (S); P <0.01: Highly significant (HS) •: Mann-Whitney test Table 7: Sensitivity & specificity of AFP & APEX1 in predicting the poor prognosis of HCC: Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 08 Apr, 2025 Submission checks completed at journal 08 Apr, 2025 First submitted to journal 29 Mar, 2025 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-5815078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440246009,"identity":"2e7655cd-a276-457a-81e9-5add8a67d4db","order_by":0,"name":"Nourhan Assem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYJACZijNeIChAsRlbiBaC8MBhjMgLiMpWhjbwLbh12LO3mP2uaDmDgN/A/ODw7zzaqP524FaflRsw6nFsueM8ewZx54xSBxgMzjMu+147ozDjA2MPWdu49RicCPHmJmH7TDIIyAtx3IbgFqYGdsIafl3mEH+APuHw7xzjuXOJ0oLb9thBoMDPEBbGmpyNxDUcuZYMTNv32Eew8M8BQfnHDuQuxGo5SBevxxv3szM8+2wnNzx9o0P3tTU5c47f/jggx8VuLXAAA8odph4GA6DeQcIqocBxh8MdUQrHgWjYBSMgpEDAMHEWvAFbnaZAAAAAElFTkSuQmCC","orcid":"","institution":"Ain Shams University","correspondingAuthor":true,"prefix":"","firstName":"Nourhan","middleName":"","lastName":"Assem","suffix":""},{"id":440246010,"identity":"5880e0aa-fe55-49b2-8403-f3ddbbe4e67c","order_by":1,"name":"Kadry Mohammed El-Saeed","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Kadry","middleName":"Mohammed","lastName":"El-Saeed","suffix":""},{"id":440246011,"identity":"46dbf40e-33a1-4477-84dc-2936fafcf50b","order_by":2,"name":"Sarah Ashraf Safwat","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"Ashraf","lastName":"Safwat","suffix":""},{"id":440246012,"identity":"04bade1b-f2b8-4d0a-a9df-8d11e84b757f","order_by":3,"name":"Dina Fathy Mohamed","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Dina","middleName":"Fathy","lastName":"Mohamed","suffix":""},{"id":440246013,"identity":"35ff89c4-8f71-4a05-9cfa-4b686741e53c","order_by":4,"name":"Shereen Abdelmonaim Ibrahim","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Shereen","middleName":"Abdelmonaim","lastName":"Ibrahim","suffix":""},{"id":440246014,"identity":"124d77bb-97be-4472-8279-06981117ae86","order_by":5,"name":"Heba Ahmed Faheem","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Heba","middleName":"Ahmed","lastName":"Faheem","suffix":""}],"badges":[],"createdAt":"2025-01-12 18:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5815078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5815078/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80283843,"identity":"972f9375-f8d7-41e8-b4a3-d751018789b2","added_by":"auto","created_at":"2025-04-10 06:24:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21562,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between the studied groups regarding AFP level at presentation\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5815078/v1/6c42d736e7abe75214a6788b.jpg"},{"id":80283816,"identity":"0dba130e-8fc8-4c04-aa56-e0c35a8c121e","added_by":"auto","created_at":"2025-04-10 06:24:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20227,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between the studied groups regarding APEX1 level at presentation\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5815078/v1/437755c86b337dedadbbed07.jpg"},{"id":80283819,"identity":"1325f8b3-f116-4d86-9046-fd58abf8dbd4","added_by":"auto","created_at":"2025-04-10 06:24:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21301,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between APEX 1 and AFP levels at presentation among all the studied patients\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5815078/v1/2c59d7f33da92c2840305ed2.jpg"},{"id":80284457,"identity":"8eff16b0-dfac-4a15-ac23-7055b5e1a462","added_by":"auto","created_at":"2025-04-10 06:32:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19235,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between APEX 1 and AFP levels at follow up among all the studied patients\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5815078/v1/90ca18241fc57d2874990565.jpg"},{"id":80285490,"identity":"4ea71f4f-30b6-4fee-968e-93ec4f4c3e1f","added_by":"auto","created_at":"2025-04-10 06:40:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18323,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between patients with good and poor prognosis regarding AFP level at presentation\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5815078/v1/80c8e3ae5d1cfbc37a1df07a.jpg"},{"id":80283829,"identity":"34f9f20b-2759-4b59-ac2a-b47a24a9102f","added_by":"auto","created_at":"2025-04-10 06:24:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":18682,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between patients with good and poor prognosis regarding APEX1 level at presentation\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5815078/v1/f33c3a4041882212dfda223a.jpg"},{"id":80285777,"identity":"2a0dda9b-3855-4e51-9c71-78863ee173ff","added_by":"auto","created_at":"2025-04-10 06:48:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":827081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5815078/v1/2fd1feec-b7eb-434d-bcee-138313ae2475.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"APEX1 as a novel diagnostic and prognostic biomarker for hepatocellular carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eIn a global context, hepatocellular carcinoma (HCC) is the sixth prevailing cancer and the fourth prevailing cancer in Egypt [1]. HCC prevails with a percentage of approximately 85% among patients with cirrhosis. The HCC is predicted to have a five-year survival rate of approximately 18% [2]. Numerous hospital-based studies have documented a rising incidence of HCC. This increase may be linked to advancements in screening programs and diagnostic methods, improved survival rates among cirrhotic patients (which heightens their risk of developing HCC), and the growing pervasiveness and complications of hepatitis C virus (HCV), the primary liver cancer risk factor in Egypt [3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurveillance for HCC typically involves ultrasound imaging and alpha-fetoprotein (AFP) level assessment, particularly in high-risk populations such as individuals with cirrhosis or those with HCV or HBV infection, with or without cirrhosis [4].\u003c/p\u003e\n\u003cp\u003eDuring routine surveillance, the diagnosis of a suspicious lesion in a cirrhotic liver through ultrasound is typically followed by diagnostic validation utilizing contrast-enhanced triphasic CT or dynamic MRI [6]. A significant number of liver cancer instances are diagnosed at advanced\u0026nbsp;stages, reducing the chances of curative treatment. This highlights the need for new biomarkers to enable earlier detection and effective monitoring of liver cancer patients [7].\u003c/p\u003e\n\u003cp\u003eAlpha-fetoprotein (AFP) is regarded as the main diagnostic indicator for HCC. Nevertheless, its specificity and sensitivity are limited, and its levels can be impacted by different HCC-linked factors [8]. Therefore, the development of novel, highly sensitive biomarkers is crucial for the early diagnosis of HCC and for enhancing clinical results.\u003c/p\u003e\n\u003cp\u003eApyrimidinic endodeoxyribonuclease 1 (APEX1) contributed to the DNA damage response and is widely expressed across various human tissues [9]. The dysregulated APEX1 expression can interfere with many physiological processes, encompassing the maintenance of cellular redox balance, cell cycle regulation, smooth muscle cell migration, mRNA stability, and apoptosis [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study focuses on analyzing the APEX1 expression and clinical significance in HCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis is a prospective comparative cohort study aimed at evaluating the diagnostic and prognostic value of serum APEX1 biomarker. The study was performed on sixty Egyptian cirrhotic patients with or without HCC, their ages ranged from 44\u0026ndash;91 years, selected from internal medicine and hepatology outpatient clinics and inpatient wards at Ain Shams University hospitals, from January 2024 to July 2024, after informed consent was taken from the patients and after obtaining the approval of the ethics committee. The patients were divided into two groups: \u003cb\u003eGroup A\u003c/b\u003e (Cirrhotic), comprising 12 liver cirrhosis patients without HCC, serving as the control group, and \u003cb\u003eGroup B\u003c/b\u003e (HCC), consisting of 48 cirrhotic patients with HCC who were further subdivided according to the BCLC staging criteria [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] for management (which takes into consideration the number and size of the nodules, the presence of vascular invasion, the liver functions and the performance of each case individually) and according to the EASL guidelines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] into:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e12 patients receiving palliative (supportive) care.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e12 patients treated with Sorafenib (Nexavar).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e12 patients treated with TACE.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e12 patients treated Radiofrequency.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIndividuals under the age of 18, pregnant or nursing women, patients with malignancies other than HCC (such as RCC, cholangiocarcinoma, gastric carcinoma, or other cancers that may influence APEX1 levels), and participants with severe comorbid conditions (e.g., advanced cardiovascular or kidney disease) that could independently affect APEX1 levels, and those who declined to participate, were excluded from the study.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAll Patients were exposed to\u003c/span\u003e: history taking, clinical examination, and baseline laboratory tests, which involved a complete blood count, liver function tests (ALT, AST, total and direct bilirubin, serum albumin), coagulation profile (PT, PTT, INR), kidney function tests, viral biomarkers (HBs Ag, HCV Ab), AFP, and serum APEX1 level quantified from archived sera using an automated analyzer with a lectin-antibody sandwich immunoassay (where serum samples were collected from the patients with strict precautionary measures in order to ensure the maximum safety for all participants and working staff ).\u003c/p\u003e \u003cp\u003eAdditionally, radiological assessments included a pelvic-abdominal ultrasound, with any suspicious hepatic focal lesions further assessed with either contrast-enhanced triphasic CT or dynamic MRI for confirmation.\u003c/p\u003e \u003cp\u003eThese assessments were conducted both \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eat the baseline\u003c/span\u003e and during \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ethe follow-up period, three months after treatment initiation\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFollowing data collection, the CHILD-PUGH score, BCLC score, and ECOG performance status were calculated for each patient \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eboth before and after\u003c/span\u003e the intervention. Statistical analysis was conducted to evaluate the sensitivity and specificity of Apyrimidinic Endodeoxyribonuclease 1 (APEX1) in diagnosing HCC and monitoring patients with HCC after undergoing various interventions, including palliative care, Sorafenib (Nexavar), TACE, or RF. Additionally, comparisons were made between the groups receiving different treatments.\u003c/p\u003e\n\u003ch3\u003eStatistics:\u003c/h3\u003e\n\u003cp\u003eThe Statistical Package for Social Sciences (IBM SPSS; Ver 27) was used to collect, review, code, and enter the data. The mean, standard deviation, and range were employed to represent quantitative data for parametric data, while for non-parametric data, the median and interquartile range (IQR) were deployed.\u003c/p\u003e \u003cp\u003eMultiple statistical tests were employed to conduct group comparisons, including the Chi-square test, Fisher's exact test, Mann-Whitney test, paired t-test, Wilcoxon rank test, One-Way ANOVA followed by post hoc analysis via the LSD test, or the Kruskal-Wallis test followed by post hoc analysis with the Mann-Whitney test. Spearman correlation coefficients were also deployed.\u003c/p\u003e \u003cp\u003eThe confidence interval was established at 95%, with an accepted margin of error of 5%. P-values were interpreted as follows: P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05, \u0026lt; 0.05, and \u0026lt;\u0026thinsp;0.01 were signified non-significance (NS), significant (S), and highly significant (HS), respectively.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis prospective comparative cohort study included sixty Egyptian cirrhotic patients, with or without HCC, hospitalized in the internal medicine department at Ain Shams University Hospitals and Laboratories during a six-month interval from January 2024 to July 2024. The research population included 38 males (63.3%) and 22 females (36.7%), with ages spanning from 44 to 91 years and a mean age of 61.75\u0026thinsp;\u0026plusmn;\u0026thinsp;9.03 years. Most participants [38 (63.3%)] resided in urban areas, while 21 patients (35.0%) were smokers, 14 (23.3%) revealed diabetes mellitus, and 24 (40.0%) had hypertension.\u003c/p\u003e \u003cp\u003eConcerning the etiology of liver disease, the proportion of patients exhibiting HCV infection revealed a significant rise in the palliative and Sorafenib groups compared to the cirrhotic, TACE, and RF groups.\u003c/p\u003e \u003cp\u003eThe AFP levels manifested a significant rise in the palliative group, unlike the cirrhotic, TACE, and RF groups. Nonetheless, no significant differences in AFP levels were detected between the cirrhotic and treatment groups. Likewise, the Sorafenib group experienced a significant increase in AFP levels compared to that in TACE.\u003c/p\u003e \u003cp\u003eA statistically significant difference was found between cirrhotic and all HCC patients at presentation for both AFP and APEX1 levels (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eMoreover, there was a significant difference in AFP levels between cirrhotic and HCC patients who were to receive RF and TACE at presentation (p-value\u0026thinsp;=\u0026thinsp;0.004). Regarding APEX1 levels, a highly significant difference was revealed between cirrhotic patients and early-stage HCC patients who were to be treated with RF and TACE (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAt the time of presentation\u003c/span\u003e, the optimal cutoff value for AFP to distinguish between cirrhotic and HCC groups was \u0026gt;\u0026thinsp;20 ng/ml (sensitivity\u0026thinsp;=\u0026thinsp;76.92%, specificity\u0026thinsp;=\u0026thinsp;100.0%, AUC\u0026thinsp;=\u0026thinsp;0.930). For APEX1, the best threshold to differentiate between the two groups was \u0026gt;\u0026thinsp;13.72 ng/ml (sensitivity\u0026thinsp;=\u0026thinsp;92.31%, specificity\u0026thinsp;=\u0026thinsp;100.0%, AUC\u0026thinsp;=\u0026thinsp;0.986).\u003c/p\u003e \u003cp\u003eThe APEX1 levels exhibited a significant positive connection with AFP, both at presentation and at follow-up.\u003c/p\u003e \u003cp\u003eSince patients eligible for TACE or RF were considered to have relatively early-stage HCC compared to those receiving Sorafenib, who, in turn, were in earlier stages than those limited to palliative care (including management of ascites, edema, portosystemic hypertension complications, hepatic encephalopathy, and pain control), the study identified a statistically significant difference in AFP levels at presentation between cirrhotic patients and early HCC patients undergoing RF or TACE (p\u0026thinsp;=\u0026thinsp;0.004). Additionally, a more significant difference was observed in APEX1 levels between cirrhotic patients and early HCC patients receiving RF or TACE, with a p-value of \u0026lt;\u0026thinsp;0.001.At presentation, AFP levels experienced significant rise in patients having worse prognosis [305 (21\u0026ndash;769)] in comparison with those exhibiting good prognosis [17 (3.5\u0026ndash;268)] (p-value\u0026thinsp;=\u0026thinsp;0.0.024). The APEX1 levels at presentation were significantly greater in patients with poor prognosis [41.67 (15.6\u0026ndash;56.27)] than in those with good prognosis [6.9 (5.3\u0026ndash;9.1)] (p-value\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHerein, the prognosis was determined based on serological markers, imaging findings, lesion size, the presence of de novo lesions and the patient\u0026rsquo;s overall performance status.\u003c/p\u003e \u003cp\u003eAt presentation, AFP levels could predict patients with poor prognosis at a cutoff value of \u0026gt;\u0026thinsp;280 ng/ml (sensitivity\u0026thinsp;=\u0026thinsp;57.1%, specificity\u0026thinsp;=\u0026thinsp;81.8%, AUC\u0026thinsp;=\u0026thinsp;0.679). While APEX1 levels at presentation could identify patients with poor prognosis at a cutoff value of \u0026gt;\u0026thinsp;9.96 ng/ml (sensitivity\u0026thinsp;=\u0026thinsp;92.9%, specificity\u0026thinsp;=\u0026thinsp;86.4%, AUC\u0026thinsp;=\u0026thinsp;0.857).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHerein, the average age of Egyptian patients with HCC was 61.75\u0026thinsp;\u0026plusmn;\u0026thinsp;9.03 years (approximately in the 7th decade) and was primarily observed in individuals with liver cirrhosis. HCC and cirrhosis were more prevalent among males (63.3%), aligning with previous findings indicating that HCC commonly affects patients in their 6th to 7th decades. For instance, Fouad et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] demonstrated a mean age of 62.7 years (SD: 9.25) among Egyptian HCC patients between 2010 and 2020, with 66.3% being over 60 years old and a male predominance of 73.6%.\u003c/p\u003e \u003cp\u003eIn this study, the prevalence of hypertension, diabetes mellitus, and smoking was 40%, 23%, and 35%, respectively. Comparatively, Kim et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] investigated metabolic comorbidities and reported higher rates: hypertension in 60.6% of patients, diabetes in 37.1%, and a smoking history in 46.9%.\u003c/p\u003e \u003cp\u003eIn our research, the prevalence of HCV and HBV among Egyptian patients with liver disease was 78.3% and 18.3%, respectively. This aligns with findings by Ezzat et al. (2021) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], which reported that HCV infection elevates the HCC risk by as much as 20 times, with 0.5\u0026ndash;10% of HCV-related cirrhosis patients advancing to HCC each year. Moreover, Egypt exhibits the greatest worldwide incidence of HCV, although the HBV prevalence throughout the population is around 1.4% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, approximately 73.3% of virally infected patients underwent antiviral treatment. Similarly, Waked et al. (2020) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] reported that in 2015, around 10% of screened individuals tested positive for HCV antibodies, and 7% had viremia, translating to 5.5\u0026nbsp;million people with chronic infection [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, it was observed that a higher proportion of patients diagnosed early and seeking interventional or medical tumor therapy were from urban areas. Specifically, over two-thirds (63.3%) of the patients were urban residents. This can be attributed to the study being conducted in a tertiary hospital located in central Cairo. Similarly, a study by Zhou et al. (2020) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] highlighted the influence of residence on diagnosis stage and treatment access. Their findings indicated that HCC patients from rural and suburban areas had an 18% and 5% greater risk of being diagnosed at an advanced stage, respectively, compared to urban patients, and were 12% less likely to receive treatment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the palliative group exhibited a significantly greater proportion of patients with a performance status grade of 3 compared to other groups that were more likely to receive treatment. Conversely, the percentage of patients exhibiting a performance status grade of 0 was significantly greater in the RF, TACE, and Sorafenib groups compared to the palliative and cirrhotic groups (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings align with the results reported by Hsu et al. (2012) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the cutoff point for APEX1 level to differentiate between cirrhotic and HCC groups was \u0026gt;\u0026thinsp;13.72 ng/ml (sensitivity\u0026thinsp;=\u0026thinsp;92.31%, specificity\u0026thinsp;=\u0026thinsp;100.0%, AUC\u0026thinsp;=\u0026thinsp;0.986). In comparison to healthy control groups and non-tumor cirrhosis patients, a recent study demonstrated significantly elevated APEX1 levels in HCC patients (sensitivity: 81.4%, specificity: 94.9%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, an AFP cutoff level of 20 ng/mL was deployed to distinguish between cirrhotic and HCC groups (sensitivity\u0026thinsp;=\u0026thinsp;76.92%, specificity\u0026thinsp;=\u0026thinsp;100%, AUC\u0026thinsp;=\u0026thinsp;0.930). These findings align with studies showing that an AFP cutoff of 20 ng/mL provides a sensitivity spanning from 41\u0026ndash;65% and a specificity between 80% and 94%, with diagnostic specificity improving when combined with ultrasound imaging [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significant difference in AFP and APEX1 levels was observed between cirrhotic and all HCC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, there was a significant difference in AFP (p-value\u0026thinsp;=\u0026thinsp;0.004) and APEX1 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) levels between cirrhotic patients and HCC patients in the RF and TACE groups, who represent the subsets with the lowest AFP and APEX1 values and are considered candidates for treatment. This indicated that APEX1 is a highly sensitive biomarker in serum and is used for early HCC diagnosis. The outcomes align with research by Cao et al. (2020) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which showed that APEX1 and AFP levels were significantly increased in HCC in comparison with cirrhotic samples. The AUC for APEX1 (0.823, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) significantly exceeded the AUC for AFP (0.7869, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in HCC tumor samples [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, the positive predictive value (PPV) was 100% for both AFP and APEX1. However, the findings of Cao et al. (2020) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] indicated that APEX1 levels manifested a significant rise in early-stage HCC patients exhibiting minimal AFP expression. The PPV for APEX1 was significantly superior to that of AFP (67.91% vs. 55.22%) in HCC patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the study tracked HCC patients from presentation through three months post-intervention, aiming to categorize them based on their response to treatment. This classification was guided by various factors, including laboratory markers, CHILD score, lesion number and size, vascular invasion, metastases, BCLC score, and ECOG performance status [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A statistically significant elevation in AFP levels at presentation was observed in individuals with a worse prognosis in comparison with those with a good prognosis (p\u0026thinsp;=\u0026thinsp;0.024). Additionally, APEX1 levels showed an even more pronounced statistically significant increase in patients with poor prognosis, unlike those exhibiting good prognosis (p\u0026thinsp;=\u0026thinsp;0.001). The outcomes indicate that APEX1 could function as a possible biomarker for prognostic prediction in HCC patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHerein, we indicated that AFP levels at presentation could predict unfavorable prognosis at a threshold of \u0026gt;\u0026thinsp;280 ng/ml (sensitivity\u0026thinsp;=\u0026thinsp;57.1%, specificity\u0026thinsp;=\u0026thinsp;81.8%, AUC\u0026thinsp;=\u0026thinsp;0.679) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In contrast, APEX1 levels at presentation showed superior predictive ability for poor prognosis at a cutoff value of \u0026gt;\u0026thinsp;9.96 ng/ml (sensitivity\u0026thinsp;=\u0026thinsp;92.9%, specificity\u0026thinsp;=\u0026thinsp;86.4%, AUC\u0026thinsp;=\u0026thinsp;0.857) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights APEX1 as a sensitive and accurate diagnostic and prognostic serum biomarker compared to AFP, making it a valuable tool for HCC diagnosis and follow-up.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitation\u003c/strong\u003e \u003cp\u003eThe limited sample size, which was further divided into smaller subgroups. Future research should address this issue by conducting studies on a larger scale.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe short duration of follow-up, future studies should adopt a longer time scale for more comprehensive analysis.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eComparing the levels of APEX1 to those of AFP in cirrhotic and HCC patients: the APEX1 demonstrates greater sensitivity and accuracy compared to AFP in diagnosing HCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u0026nbsp;\u003c/strong\u003eAnd as the study included early HCC patients eligible for radiological or therapeutic treatments (such as TACE, RF, or Sorafenib), comparing serum APEX1 levels with AFP levels in both cirrhotic and \u0026quot;early\u0026quot; HCC groups at the time of presentation. The results demonstrated a notable increase in APEX1 levels in HCC patients, even in the early stages of the disease. This highlights APEX1 as a promising biomarker for use in screening and early detection of HCC during surveillance efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e APEX1 proved to be a reliable indicator of patient prognosis by linking disease progression during follow-up to its serum levels at the time of presentation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAFP: Alphafetoprotein\u003c/p\u003e\n\u003cp\u003eALT: Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAPEX1: Apyrimidinic endodeoxyribonuclease 1\u003c/p\u003e\n\u003cp\u003eAST: Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eAUC: Area under the curve\u003c/p\u003e\n\u003cp\u003eBCLC: Barcelona-Clinic Liver Cancer\u003c/p\u003e\n\u003cp\u003eCT: Computed tomography\u003c/p\u003e\n\u003cp\u003eECOG: Eastern cooperative oncology group\u003c/p\u003e\n\u003cp\u003eHBV: Hepatitis B virus\u003c/p\u003e\n\u003cp\u003eHCV: Hepatitis C virus\u003c/p\u003e\n\u003cp\u003eMRI: Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003ePT, PTT: \u0026nbsp;Prothrombin time, Partial thromboplastin time,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eINR: \u0026nbsp;International normalised ratio\u003c/p\u003e\n\u003cp\u003eRCC: Renal cell carcinoma\u003c/p\u003e\n\u003cp\u003eRF: Radiofrequency\u003c/p\u003e\n\u003cp\u003eTACE: Transarterial chemoembolization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: This research was conducted per ethical standards. The Ethical Committee of the Faculty of Medicine at Ain Shams University granted consent prior to the commencement of the study, which follow the ethical principles outlined in the 1975 Declaration of Helsinki. Written permission was acquired from each participant. Committee\u0026rsquo;s reference number: FWA 000017585\u003c/p\u003e\n\u003cp\u003eConsent for publication: not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData and material availability: Data are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: No competing interests\u003c/p\u003e\n\u003cp\u003eFunding: No fund.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: Gratitude is extended to the hepatology committee personnel at Ain Shams University for their assistance in enabling data collection. \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZheng, H., Qin, Z., Qiu, X., Zhan, M., Wen, F., \u0026amp; Xu, T. (2020). Cost-effectiveness analysis of ramucirumab treatment for patients with hepatocellular carcinoma who progressed on sorafenib with \u0026alpha;-fetoprotein concentrations of at least 400 ng/ml. Journal of medical economics, 23(4), 347-352.\u003c/li\u003e\n\u003cli\u003eAsafo-Agyei, K.O., Samant, H. (2022). Hepatocellular Carcinoma. 2021 Aug 8. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan\u0026ndash;. PMID: 32644603.\u003c/li\u003e\n\u003cli\u003eDemir, T. (2021). Systemic therapy of liver cancer. Adv Cancer Res.; 149:257\u0026ndash;294.\u003c/li\u003e\n\u003cli\u003eTsang, J., Wong, J. S. L., Kwok, G. G. W., Li, B. C. W., Leung, R., Chiu, J., Yau, T. (2021). Nivolumab+ Ipilimumab for patients with hepatocellular carcinoma previously treated with Sorafenib. Expert review of gastroenterology \u0026amp; hepatology, 15(6), 589-598.\u003c/li\u003e\n\u003cli\u003eEASL Clinical Practice Guidelines on the management of hepatocellular carcinoma, Sangro, Bruno et al. Journal of Hepatology, Volume 82, Issue 2, 315 - 374\u003c/li\u003e\n\u003cli\u003eJi, S., Wang, Z., Xia, S. (2021). Application of ultrasound combined with enhanced MRI by Gd-BOPTA in diagnosing hepatocellular carcinoma. Am J Transl Res.; 13(6):7172-7178. \u003c/li\u003e\n\u003cli\u003eRadjenović, B., Sabo, M., \u0026Scaron;oltes, L., Prnova, M., Čičak, P., \u0026amp; Radmilović-Radjenović, M. (2021). On efficacy of microwave ablation in the thermal treatment of an early-stage hepatocellular carcinoma. Cancers, 13(22), 5784.\u003c/li\u003e\n\u003cli\u003eDuan, H., Zhao, G., Xu, B. Y., Hu, S. W., \u0026amp; Li, J. (2017). Maternal Serum PLGF, PAPPA, \u0026beta;-hCG and AFP Levels in Early Second Trimester as Predictors of Preeclampsia. Clinical Laboratory; 63(5), 921-925.\u003c/li\u003e\n\u003cli\u003eKim, J. M., Yeo, M. K., Lim, J., Song, I. S., Chun, K., \u0026amp; Kim, K. H. (2019). APEX1 Expression as a Potential Diagnostic Biomarker of Clear Cell Renal Cell Carcinoma and Hepatobiliary Carcinomas. Journal of Clinical Medicine, 8(8), 1151.\u003c/li\u003e\n\u003cli\u003ePei, D. S., Jia, P. P., Luo, J. J., Liu, W., \u0026amp; Strauss, P. R. (2019). AP endonuclease 1 (Apex1) influences brain development linking oxidative stress and DNA repair. Cell death \u0026amp; disease, 10(5), 348.\u003c/li\u003e\n\u003cli\u003eFouad, Y., Gaber, Y., Alem, S. A., Abdallah, M., Abd-Elsalam, S. M., Nafady, S., Attia, D., \u0026amp; Eslam, M. (2023). Changes in the Etiologies of Liver Cancer in Upper Egypt over a Decade from 2010 to 2020: A Single Tertiary Care Center Study. South Asian Journal of Cancer, 13(01), 010\u0026ndash;016. \u003c/li\u003e\n\u003cli\u003eKim, H. Y., Lee, H. A., Radu, P., \u0026amp; Dufour, J. F. (2024). Association of modifiable metabolic risk factors and lifestyle with all-cause mortality in patients with hepatocellular carcinoma. Scientific Reports, 14(1). \u003c/li\u003e\n\u003cli\u003eEzzat, R., Eltabbakh, M., \u0026amp; El Kassas, M. (2021). Unique situation of hepatocellular carcinoma in Egypt: A review of epidemiology and control measures. World Journal of Gastrointestinal Oncology, 13(12), 1919.\u003c/li\u003e\n\u003cli\u003eWaked, I., Esmat, G., Elsharkawy, A., El-Serafy, M., Abdel-Razek, W., Ghalab, R., Elshishiney, G., Salah, A., Megid, S. A., Kabil, K., El-Sayed, M. H., Dabbous, H., Shazly, Y. E., Sliman, M. A., Hashem, K. A., Gawad, S. A., Nahas, N. E., Sobky, A. E., Sonbaty, S. E., Zaid, H. (2020). Screening and Treatment Program to Eliminate Hepatitis C in Egypt. New England Journal of Medicine/\u0026tilde;the \u0026oelig;New England Journal of Medicine, 382(12), 1166\u0026ndash;1174.\u003c/li\u003e\n\u003cli\u003eZhou, K., Pickering, T. A., Gainey, C. S., Cockburn, M., Stern, M. C., Liu, L., Unger, J. B., El-Khoueiry, A. B., \u0026amp; Terrault, N. A. (2020c). Presentation, Management, and Outcomes Across the Rural-Urban Continuum for Hepatocellular Carcinoma. JNCI Cancer Spectrum, 5(1).\u003c/li\u003e\n\u003cli\u003eHsu, C., Lee, Y., Hsia, C., Huang, Y., Su, C., Lin, H., Lee, R., Chiou, Y., Lee, F., \u0026amp; Huo, T. (2012). Performance status in patients with hepatocellular carcinoma: Determinants, prognostic impact, and ability to improve the Barcelona Clinic Liver Cancer system. Hepatology, 57(1), 112\u0026ndash;119. https://doi.org/10.1002/hep.25950\u003c/li\u003e\n\u003cli\u003eCen, P., Walther, C., Finkel, K. W., \u0026amp; Amato, R. J. (2014). Biomarkers in Oncology and Nephrology. In Elsevier eBooks (pp. 21\u0026ndash;38). \u003c/li\u003e\n\u003cli\u003eCao, L., Cheng, H., Jiang, Q., Li, H., \u0026amp; Wu, Z. (2020). APEX1 is a novel diagnostic and prognostic biomarker for hepatocellular carcinoma. Aging (Albany NY), 12(5), 4573.\u003c/li\u003e\n\u003cli\u003eMakary MS, Ramsell S, Miller E, Beal EW, Dowell JD. Hepatocellular carcinoma locoregional therapies: Outcomes and future horizons. World J Gastroenterol. 2021 Nov 21;27(43):7462-7479. doi: 10.3748/wjg.v27.i43.7462. PMID: 34887643; PMCID: PMC8613749\u003c/li\u003e\n\u003cli\u003ePascut, D., Sukowati, C. H. C., Antoniali, G., Mangiapane, G., Burra, S., Mascaretti, L. G., Tell, G. (2019). Serum AP-endonuclease 1 (sAPE1) as novel biomarker for hepatocellular carcinoma. Oncotarget, 10(3), 383.\u003c/li\u003e\n\u003cli\u003eBai, D., Zhang, C., Chen, P., Jin, S., \u0026amp; Jiang, G. (2017). The prognostic correlation of AFP level at diagnosis with pathological grade, progression, and survival of patients with hepatocellular carcinoma. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-12834-1\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eComparison between cirrhotic patients and all the studied HCC patients regarding AFP and APEX1 levels \u003cu\u003eat presentation:\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCirrhotic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAt presentation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHCC ALL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAt presentation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMann-Whitney\u0026nbsp;\u003cbr\u003e\u0026nbsp;Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN=12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN=48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.65 (1.33-7.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.8 (9.5-564)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e4.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8-12800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPEX1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.08 (4.65-9.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.87 (19.1-53.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e4.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4-13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.61-135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP\u0026gt;0.05: Non significant (NS); P \u0026lt;0.05: Significant (S); P \u0026lt;0.01: Highly significant (HS)\u003c/p\u003e\n\u003cp\u003e*: Chi-square test; \u0026ne;: Kruskall-Wallis test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Comparison between cirrhotic patients and HCC patients candidates for (RF or TACE) regarding AFP and APEX1 levels \u003cu\u003eat presentation:\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCirrhotic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePatients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;HCC (RF\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003eTACE)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAt presentation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMann-Whitney\u0026nbsp;\u003cbr\u003e\u0026nbsp;Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.65 (1.33-7.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e17 (2.9-76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.5-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e1.8-972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPEX1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e7.08 (4.65-9.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e26.87 (14.28-28.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.4-13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e5.07-69.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP\u0026gt;0.05: Non significant (NS); P \u0026lt;0.05: Significant (S); P \u0026lt;0.01: Highly significant (HS)\u003c/p\u003e\n\u003cp\u003e*: Chi-square test; \u0026ne;: Kruskall-Wallis test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Sensitivity and specificity of AFP \u0026amp; APEX1:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"676\" height=\"461\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eCorrelation between APEX 1 and AFP \u003cu\u003eat presentation:\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPEX1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.677**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable (4) shows that there was statistically significant positive correlation between APEX1 level at presentation and AFP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003eCorrelation between APEX 1 and AFP \u003cu\u003eat follow up\u003c/u\u003e:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPEX1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.418*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable (5) shows that there was statistically significant positive correlation between APEX1 level at follow up and AFP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6:\u0026nbsp;\u003c/strong\u003eComparison between patients with good and poor prognosis in (RF+TACE+Sorafenib) subgroups regarding AFP and APEX1 levels at presentation:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePoor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u0026nbsp;\u003cbr\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNo. = 19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNo. = 17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 (3.5 - 268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e305 (21 - 769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e2.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 \u0026ndash; 874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8 \u0026ndash; 972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAPEX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.9 (5.3 \u0026ndash; 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.67 (15.6 \u0026ndash; 56.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e3.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.24 \u0026ndash; 72.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3 \u0026ndash; 73.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP\u0026gt;0.05: Non significant (NS); P \u0026lt;0.05: Significant (S); P \u0026lt;0.01: Highly significant (HS)\u003c/p\u003e\n\u003cp\u003e\u0026bull;: Mann-Whitney test\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7:\u0026nbsp;\u003c/strong\u003eSensitivity \u0026amp; specificity of AFP \u0026amp; APEX1 in predicting the poor prognosis of HCC:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" 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Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"APEX1, diagnostic and prognostic biomarker, Hepatocellular Carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-5815078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5815078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHepatocellular carcinoma (HCC) is the sixth prevailing cancer globally and is the second greatest reason for cancer-linked deaths in males, surpassed only by lung cancer. A significant number of HCC cases experiencing advanced stages are due to the frequent absence or inconspicuousness of early symptoms. Consequently, discovering reliable early diagnostic indicators and innovative treatment targets is essential to improve survival and overall outcomes for HCC patients. Herein, we aimed to evaluate the diagnostic potential of APEX1 in Egyptian HCC patients and explore its prognostic significance before treatment initiation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design and methodology: \u003c/strong\u003eThis research was conducted as a prospective comparative cohort study involving 60 Egyptian participants, age range more than 18 years selected from internal medicine and hepatology outpatient clinics and inpatient wards at Ain Shams University hospitals from January 2024 to July 2024, after informed consent was taken from the patients, who were allocated into: Group A (Cirrhotic) comprising 12 liver cirrhosis patients but without HCC, acting as the control group; Group B (HCC) included 48 patients diagnosed with HCC. Group B was further subdivided into four subgroups: Subgroup 1: 12 patients receiving palliative (supportive) care; Subgroup 2: 12 patients treated with Sorafenib, Subgroup 3: 12 patients treated with trans-arterial chemoembolization (TACE); and Subgroup 4: 12 patients treated with radiofrequency ablation (RFA). Data collection included anthropometric measurements, laboratory tests, radiological findings, CHILD-PUGH scores, BCLC scores, and patient performance metrics, gathered both before and after treatment. The data were analyzed utilizing IBM SPSS (ver. 27).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study identified a serum APEX1 level cutoff of \u0026gt;13.72 ng/mL as a biomarker to differentiate between cirrhotic patients and those with HCC (sensitivity = 92.31%, specificity = 100.0%). The threshold for AFP was 20 ng/mL (sensitivity = 76.92%, specificity = 100.0%). Notably, significant differences in AFP and APEX1 levels were found between cirrhotic patients and HCC patients in the RF and TACE subgroups at baseline (p-value 0.004, \u0026lt; 0.001 for AFP and APEX1, respectively), indicating that APEX1 may serve as a more sensitive biomarker for early HCC detection.\u003c/p\u003e\n\u003cp\u003eBoth AFP and APEX1 exhibited a positive predictive value (PPV) of 100%.\u003c/p\u003e\n\u003cp\u003eFurthermore, the study revealed that an AFP level exceeding 280 ng/mL at baseline could predict a poor prognosis (sensitivity = 57.1%, specificity = 81.8%, AUC = 0.679). In contrast, an APEX1 level greater than 9.96 ng/mL at baseline was found to be a noteworthy predictor of poor prognosis (specificity = 86.4%, sensitivity = 92.9%, AUC = 0.857).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:\u003cstrong\u003e -\u003c/strong\u003eAPEX1 has proven to be a more sensitive and accurate serum biomarker than AFP for diagnosing HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003eSerum APEX1 levels revealed a significant rise in HCC patients, even during the early stages (candidates for radiological/therapeutic intervention), making it a valuable tool for screening and early detection in surveillance programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003eAPEX1 demonstrated the ability to predict patient prognosis even prior to the initiation of treatment.\u003c/p\u003e","manuscriptTitle":"APEX1 as a novel diagnostic and prognostic biomarker for hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 06:24:39","doi":"10.21203/rs.3.rs-5815078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-04-12T21:48:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146851494659609517555137650470198998054","date":"2025-04-10T17:38:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T10:02:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161950325210930718714944169004937931316","date":"2025-04-09T08:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-08T17:50:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-08T09:01:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Internal Medicine","date":"2025-03-29T17:12:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-internal-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Internal Medicine](https://ejim.springeropen.com/)","snPcode":"43162","submissionUrl":"https://submission.springernature.com/new-submission/43162/3","title":"The Egyptian Journal of Internal Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84e54d56-ea16-4052-a507-1b0b590dec98","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-20T09:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-10 06:24:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5815078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5815078","identity":"rs-5815078","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}