Circular RNAs ‘circHIPK3’ and ‘circTCF25’ as Blood-Based Biomarkers for SVR and Relapse After Therapy in Chronic Hepatitis C Virus Infection

Circular RNAs ‘circHIPK3’ and ‘circTCF25’ as Blood-Based Biomarkers for SVR and Relapse After Therapy in Chronic Hepatitis C Virus Infection | 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 Circular RNAs ‘circHIPK3’ and ‘circTCF25’ as Blood-Based Biomarkers for SVR and Relapse After Therapy in Chronic Hepatitis C Virus Infection Umm e Habiba, Muhammad Shahid, Mahnoor Fatima, Muhammad Ali Huzaifa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9145961/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Introduction: Hepatitis C infection (HCV) is one of the most significant health concerns in developing nations, such as Pakistan. Different programs under the WHO HCV elimination oath are working on eliminating the virus by 2030. Circular RNAs (circRNAs) are potential novel therapeutic targets and diagnostic biomarkers for HCV infection. Methodology: There were 171 patients with HCV infection included in this study. The patients were graded in 2 groups: sustained virological response (SVR) and Relapse (Re) based on the response to the therapy. Expression levels for circHIPK3 and circTCF25 were analyzed with quantitative real-time PCR, and a ROC curve was created to test their potential as diagnostic tools. Competing endogenous networks were built, and GO analysis was carried out according to differentially and significantly expressed genes. Results The outcomes of our study revealed that the level of circHIPK3 was upregulated in the SVR and Relapse groups. The level of circHIPK3 in the SVR group was much higher than in the Relapse group (FC:19.83 vs. FC: 1.73). Also, circTCF25 was upregulated in SVR patients (FC:6.86) but downregulated in relapsed patients (FC: -1.47). ROC analysis revealed that the AUC value of circHIPK3 was 0.818 (p-value: 0.0084), and the value of circTCF25 was 0.867 (p-value: 0.0024). Bioinformatic analysis revealed that the Ras signaling pathway, Kaposi sarcoma-associated herpesvirus, and Hepatitis C virus are key involved pathways. Conclusion This study is important to discover the role of circRNAs in the 3a genotype of HCV in the Pakistani population. circHIPK3 and circTCF25 may serve as useful biomarkers to discriminate SVR from Relapse in HCV infection. HCV circRNAs Direct-acting antiviral therapy ceRNA networks SVR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Hepatitis C virus (HCV) has infected 57 million individuals globally [ 1 ]. According to figures from the World Health Organization, this condition affects 3 to 4 million people per year worldwide. Its prevalence is 5% in developing countries [ 2 ]. Hepatitis is a disease state characterized by an inflammatory condition of the liver and often accompanied by edema and, in many cases, destruction of the liver tissue. The major cause of hepatopathies is the HCV [ 3 ]. Also, chronic hepatitis caused by HCV may lead to cirrhosis and transformation into cancer [ 4 ]. Apart from Hepatitis A and B, which have been well-known to cause acute liver failure (ALF), HCV has also emerged as a causative agent of ALF, which is a relatively rare disease [ 5 ]. HCV may reappear in patients with sustained virologic response (SVR) after completion of treatment; this has been referred to as recurrence. This may be due to reinfection or late relapse. HCV infection relapses after achieving SVR within 12–24 weeks is defined as relapse (Re). After SVR status is achieved in HCV, there are chances of both reinfection and late relapse to occur [ 6 ]. Detection of early diagnostic and prognostic factors to distinguish SVR from relapses in HCV patients is important to tackle this persistent threat to human health. Circular RNAs (circRNAs) are covalently self-looped continuous RNAs which are endogenous and native RNAs. They are transcribed predominantly from back-spliced exons or lariat intron and differentially spliced from exons or introns. In other words, a circular transcript is produced when a splice donor site in the primary transcript is spliced to a splice acceptor site located upstream of the splice donor site. Furthermore, the neighboring intronic complementary sequences dynamically determine the exon circularization [ 7 ]. Recent studies have explained the abundance of circRNA expression across many species, along with its tissue/cell type-specific expression profile [ 8 , 9 ]. They have been found to be extremely abundant, evolutionarily conserved, and highly stable in the cytoplasm, which is an interesting discovery [ 10 ]. Circular RNAs (circRNAs) have been proposed to be involved in the pathogenesis of diseases, and the implications of circRNAs encoded in host-virus interactions may also lead towards new diagnostic, prognostic, and therapeutic strategies against virus-associated diseases. It has been demonstrated that circRNAs are reliably found in bodily fluids, which provides a simple and non-invasive way for their application in the diagnosis of a range of disorders [ 11 ]. CircRNAs have lately demonstrated enormous potential as gene regulators in mammals. They can displace miRNA-targeted mRNA's inhibitory impact by competing for miRNA attachment, forming competing endogenous RNA networks (ceRNA networks). It has been established that circRNAs regulate tumor development and function as molecular biomarkers for lung cancer, glioblastoma (GBM), colorectal cancer (CRC), and HCC [ 12 ]. Multiple circRNAs show aberrant expression in HCC, revealing that they may be used as biomarkers for HCV infection diagnosis and therapy [ 13 ]. However, no work, to our knowledge, has previously reported the role of circHIPK3 and circTCF25 in HCV infection. This research focuses mainly on the expression profile of circRNAs in HCV-genotype 3a-infected patients (SVR and relapse groups) to identify dysregulated circRNAs with potential diagnostic relevance in the Pakistani population through quantitative real-time PCR. Materials and Methods Sample collection Blood samples were collected from 171 HCV patients (128 SVR patients and 43 relapse patients) in collaboration with the Genomics Center, Abdalians Cooperative Housing Society, 57000 Lahore, Pakistan. The study's focus was on the 3a genotype of HCV in the Pakistani population. Blood samples were also collected from 20 healthy controls who had never had HCV infection. The participants of the study solely had an HCV infection; they showed no signs of liver damage or other viral infections. The research did not include the following patients: (i) patients having other infections, (ii) pregnant women, and (iii) patients below the age of 18. The ethical guidelines of the 1975 Helsinki Agreement were followed in the conduct of this investigation. The study was approved by the ethical review committee of CEMB ( CEMB/IRB-11/24 December 19, 2024 ), University of the Punjab, Lahore. Patients who matched the study's requirements at the time of sampling were asked to fill out the consent form. Antiviral Therapy During our research period, we collected blood from patients who had completed their HCV treatment through Direct-Acting Antiviral (DAA) therapy. Sofosbuvir (400 mg) and Daclatasvir (60 mg) were given orally once daily for 12 weeks to all CHC patients in this study. After follow-up, one group was divided into SVR patients who showed a not-detectable HCV viral burden of < 20 IU/mL, and another group was relapse patients who had again developed higher levels of HCV. RNA Extraction and Quality Control Total RNA was isolated from whole blood using Thermo Scientific™ GeneJET RNA Purification Kit according to the manufacturer’s protocol. The RNA quantity was assessed with a NanoDrop ND-2000. Quantitative Real-time PCR (qRT-PCR) cDNA was synthesized using whole RNA with Thermo Scientific RevertAid First Strand cDNA Synthesis Kit (Lot no. 00839495) following the manufacturer’s instructions. Using a Maxima SYBR green/PCR master mix (ThermoFisher, USA), the expression levels of circRNAs were assessed using Real-Time Quantitative PCR (Smart Cycler II-Cepheid, USA). The circRNAs were intended to be amplified quantitatively using divergent primers as opposed to the more often utilized convergent primers. Ten minutes at 95°C, 40 cycles of 95°C for 15 seconds, 60°C for 60 seconds, and 70°C for 30 seconds were the amplification conditions. Internal control was implemented using GAPDH. The 2 −(∆∆Ct) approach was used to examine the data [ 14 ]. Bioinformatics Analysis Prediction of circRNA-miRNA-mRNA interactions CircInteractome ( https://circinteractome.nia.nih.gov/ )[ 15 ] and TargetScan ( https://www.targetscan.org/vert_80/ ) [ 16 ] were employed to predict the mRNAs and target miRNAs. Interaction networks of circRNA-miRNA-mRNA were depicted using Cytoscape [ 17 ]. Gene Ontology and Pathway Analysis The differentially expressed mRNAs involved in competing endogenous RNA networks (ceRNA) were analyzed by DAVID ( https://davidbioinformatics.nih.gov/ ) [ 18 ] for pathway analysis and gene ontology (GO) analysis to obtain their biological functions, the cellular components they are expressed, and their molecular functions. Enrichment scores were calculated by taking the –log10 of the p-values for the respective GO terms. Statistical Analysis All experimental data were analyzed using SPSS software (IBM SPSS 22.0). Mean ± S.D. was calculated to express baseline characteristics of patients. A descriptive analysis of different characteristics of patients was performed. The Kruskal-Wallis test was used to compare the gene expression level between the SVR, Relapse, and control groups. The diagnostic capability of statistically differently expressed circRNAs was evaluated using ROC curve analysis. A P value of less than 0.05 was employed as the cut-off point. Results Patients’ demographics and clinical data A total of 171 participants were included in this study, including 65 males and 106 females. The positive HCV patients involved in our study were divided into two groups: SVR and relapse. The characteristics of the study subjects are summarized in Table 1 . All HCV-positive patients were infected with the 3a genotype. The positive HCV patients involved in our study were divided into two groups: SVR (sustained virologic response) and relapse. SVR patients showed a mean viral load of less than 20 IU/ml, and the mean viral load in the Re-group was 250555 ± 798973 IU/ml. Among all HCV patients in our study, 128 were SVR patients and 43 were Relapse patients with a mean age of 46.13 ± 1.49 and 50.72 ± 2.9, respectively (Table 2 ). Table 1 Various parameters such as ALT = Alanine aminotransferases, ALP= Alkaline Phosphatases, and AST= Aspartate aminotransferase are expressed as mean ± S.D Characteristics HCV Group Healthy Group Age, years; mean ± SD 45 ± 9 Gender (M/F) 65/106 8/12 ALT 53.85 ± 30.99 26.99 AST 49.77 ± 25.18 27.12 ALP 234.74 ± 42.97 103.11 Table 2 Means of variables in the SVR and Relapse group Group Variables Age (Mean ± SEM) Gender (M/F) Viral Load (Mean) SVR (n = 128) 46.13 ± 1.49 35%/65% 20 IU/mL Relapse (n = 43) 50.72 ± 2.9 45%/55% 250555 IU/mL Blood-based gene expression profile of circHIPK3 and circTCF25 in HCV infection Blood-based expression profiles of circHIPK3 and circTCF25 were analyzed, which revealed a statistically significant difference among the groups. The expression of circHIPK3 and circTCF25 were up-regulated in the blood of HCV patients. The expression level of circHIPK3 (SVR FC: 19.83, Re FC: 1.73) was significantly up-regulated in the SVR and relapse groups, and the expression level of circTCF25 (SVR FC: 6.86, Re FC: -1.47) was significantly up-regulated in the SVR group and down-regulated in the relapse group. Also, the SVR group had a much higher level of circHIPK3 compared to the relapse group. Table 3 Fold change values of circHIPK3 and circTCF25 in SVR and Re groups Gene ID Host Gene Symbol FC Value (SVR Group) FC value (Re Group) P-Value has_circ_0021592 circHIPK3 19.83 1.73 0.0076 has_circ_0041077 circTCF25 6.86 -1.47 0.0021 Diagnostic potential of circHIPK3 and circTCF25 as biomarkers for HCV-positive patients ROC curves were computed to evaluate the diagnostic potential of circHIPK3 and circTCF25 for the discrimination of SVR and relapse groups. According to the findings, the AUC of circHIPK3 was 0.818 (95% CI: 0.6407 to 0.9956, p-value: 0.0084) and the AUC of circTCF25 was 0.867 (95% confidence interval [CI]: 0.7072 to 1.000, p -value: 0.0024), indicating that both circHIPK3 and circTCF25 expression in blood might be used to distinguish the SVR patients from relapse patients (Fig. 2 ). The AUC for circHIPK3 and circTCF25 (Fig. a,b) in SVR patients versus Re patients. CI, confidence interval; ROC, receiver operating characteristic; AUC, area under the curve Predicted competing endogenous RNA (circRNA-miRNA-mRNA) networks In order to study the potential post-transcriptional regulatory roles of circHIPK3 and circTCF25 in HCV pathogenesis, circRNA-miRNA-mRNA interaction networks were established. The putative mRNA response elements (MREs) of circHIPK3 and circTCF25 were predicted by using CircInteractome, and downstream mRNA targets of circHIPK3 and circTCF25 were predicted by using TargetScan. For each circRNA, the top 10 miRNAs were selected with Context + + score ≥ 80 and therefore high confidence of the miRNA binding affinity. The top 5 predicted mRNA targets were retrieved from TargetScan for each of these miRNAs, utilizing a cumulative weighted Context + + score <-0.5 as a cut-off threshold. The resulting interactions of two circRNA-centered competing endogenous RNA (ceRNA) networks were constructed using Cytoscape software. The networks of circHIPK3 and circTCF25 are presented in Figs. 3 and 4 . Gene Ontology analysis To understand the functional implications of mRNA targets associated with circHIPK3 and circTCF25, GO and KEGG pathways enrichment analysis were performed. GO enrichment analysis of genes associated with circTCF25 was observed, and distinct patterns in three domains representing molecular functions, cellular components, and biological processes were revealed (Fig. 5 ). In terms of molecular function, associated genes were significantly enriched in molecular biology related to transcription regulation and ion binding activity, and protein-protein interactions. Notable functions were transcription cis-regulatory region binding, DNA binding transcription activator activity, and protein tyrosine kinase binding, which suggests that circTCF25 plays a crucial role in gene expression modulation and intracellular signaling pathways. With respect to the cellular components, the analysis revealed that most of these genes were enriched at synaptic structures as well as at cell organelles associated with neuronal function and protein processing. An abundance of components like synaptic vesicles, extracellular exosomes, chromatin, synaptic membranes, and dendrites demonstrates the implication of the circTCF25 in neurobiological functions and cell communication, particularly in the nervous system. This was further substantiated in the biological process analysis that showed profound enrichment with the biological processes involved in the regulation of neuronal synaptic plasticity, signal transduction, as well as transcriptional control. Besides, other more general physiological processes were mentioned, such as regulation of systemic arterial blood pressure, skeletal muscle cell differentiation, embryonic patterning, and bone resorption. These findings suggest that circTCF25 is also likely to be acting outside of the neural milieu and participate in the various features of development and physiological regulation. The analysis of the pathways enrichment (Fig. 6 ) with the assistance of the KEGG database revealed that the genes that were linked to circTCF25 can be found in the crucial signaling and metabolic pathways. The most prominent were the Ras signaling pathway, which is the one that participates in the rudimentary cell proliferation and differentiation, and the glutathione metabolism pathway, which is the one that is involved in the redox homeostasis and the oxidative stress response. Additional pathways that were enriched were the endoplasmic reticulum-based protein processing and the natural killer cell-mediated cytotoxicity, which suggested that circTCF25 might be involved in protein homeostasis and immune surveillance. For circHIPK3, the protein binding term was the most enriched in the GO molecular function category, indicating that circHIPK3-related genes are highly engaged in protein-protein interactions and are controllers of cell network activities (Fig. 7 ). Other functions that were further enriched were protein homo-dimerization, signaling receptor inhibitor, phosphatidylserine and calcium-dependent phospholipid, and transforming growth factor beta receptor binding. These are directed to membrane-associated signaling, immune recognition, and transcriptional regulation roles. The sequence-specific DNA binding, voltage-gated potassium channel activity, and GTPase binding also highlight the participation in signal transduction, transcriptional regulation, and cell communication. There was a significant localization of cellular component enrichment to the mitochondria and endoplasmic reticulum, indicating the involvement of both in energy metabolism, calcium homeostasis, and protein quality control. It has also been observed that the genes were enriched also in lysosomes, axons, perikaryon, and blood microparticles, and this implies its roles in neuronal development and systemic signaling. It implies that circHIPK3-linked genes can play a role in maintaining the integrity of tissues, neuro-development, and immunity. Analysis of the biological processes showed a high level of enrichment of signal transduction and cell shape regulation, meaning that the process is involved in cellular responsiveness and structural dynamics. Other enriched processes were inflammatory response, protein peptidyl-prolyl isomerization, amino acid transport, and protein localization. KEGG pathway analysis identified significant enrichment in pathways related to viral infections, including Kaposi sarcoma-associated herpesvirus, Hepatitis C, Human cytomegalovirus, and Influenza A (Fig. 8 ). This suggests that circHIPK3-associated genes may play roles in antiviral defense or host-pathogen interactions. Pathways related to non-small cell lung cancer, pancreatic cancer, Toll-like receptor signaling, and chemokine signaling highlight involvement in oncogenesis and immune responses. Additionally, enrichment in lipid metabolism and prolactin signaling pathways expands their relevance to metabolic and endocrine regulation. Discussion HCV diagnosis and prognosis could be aided through analysis of gene expression upon infection, and variability of gene expression of blood-based genes in response to treatment. Resistance-associated substitutions (RASs) are currently significant obstacles for the treatment of HCV with direct-acting antivirals (DAAs) [ 19 ]. Circular RNAs are new key player and more research need in HCV infection. Blood-based gene expression was utilized in this study to examine the patterns of host gene expression in HCV 3a serotype patients from the Pakistani community. This research focused on circRNAs, which may act as the main players of HCV infection due to their expression levels, and has also attempted to explore the functional role played by these RNAs through their response to treatment. In our study, circHIPK3 (hsa_circ_0021592) is found to be significantly upregulated in the SVR and relapse group of HCV patients, and circHIPK3 showed higher expression in the SVR compared relapse group. In previous studies, circHIPK3 was found to be upregulated in several malignancies and is referred to as a promising cancerous circular RNA. In 2018, a study was reported that showed a role for circHIPK3 in colorectal cancer. It was markedly upregulated in CRC cell lines and tissues. Further, it was also positively correlated with the clinical and metastatic stages of the disease. The role of circHIPK3 in metastasis was also verified by the knockdown of circHIPK3, which significantly inhibited the proliferation and invasion of CRC cells [ 20 ]. circHIPK3 acts as a microRNA sponge in various human cancers [ 21 ]. CircHIPK3 was found to be associated with cell growth, through sponging 9 miRNAs [ 22 ]. ROC analysis using circHIPK3 provided an important value to distinguish SVR and relapse patients in our study, and it further highlights the viability of the circHIPK3 gene as a diagnostic biomarker for HCV infection. It has also been suggested as a potential biomarker of glioblastoma multiforme by ROC analysis [ 23 , 24 ]. CircTCF25 (hsa_circ_0041077) was identified to be elevated in SVR, and reduced in relapse sets in our research. Its role in HCV infection is not well documented, and it is reported to be linked with different carcinomas. A study conducted by Zhong et al. Shows that TCF25 is involved in bladder carcinoma, through sponging miR-103a-3p, which further leads to the upregulation of CDK6 levels and cell invasion and progression of carcinoma cells. This study has suggested circTCF25 as a strong biomarker for carcinoma [ 25 ]. CircTCF25 has also been upregulated in glioma cells to promote the progression and migration of glioma cells through sequestering miR-206. Its mechanism involves JAK2 and STAT3 pathways [ 26 ]. Our ROC curve analysis also demonstrated that circTCF25 could be employed as a significant diagnostic tool and a biomarker for HCV infection to differentiate SVR and relapse patients, with a significant AUC value. A major strength of this study is the combined use of bioinformatic analysis, which gave mechanistic insight into the role of circHIPK3 and circTCF25 in the context of HCV infection [ 27 , 28 ]. For circHIPK3, the results of GO enrichment revealed biological processes such as organelle organization, cellular response to chemical stress, etc., and the molecular function was cellular localization to mitochondria, endoplasmic reticulum, and focal adhesion sites. Its enrichment in the KEGG pathway included hepatitis B and C, and Toll-like receptor signaling, indicating the involvement of antiviral response and immune regulation. In contrast, circTCF25 was associated with chromatin and synaptic vesicle localization, and its enriched GO terms suggested regulation of protein serine-threonine kinase activity and protein autophosphorylation. Circulating TCF25 pathway annotation predicted the involvement in Ras signaling, glutathione metabolism, and natural killer (NK) cell-mediated cytotoxicity, indicating an immune surveillance role of circTCF25 in the ROS metabolism in HCV infection. Conclusion This study has revealed significantly high expression of circHIPK3 and circTCF25 in SVR patients versus Relapse cases and healthy controls, suggesting them as potential novel biomarkers for tracking their response to therapy for HCV infection. Bioinformatic analysis of their ceRNA network and GO/KEGG pathways indicated the functional roles related to immune regulation, signal transduction, and viral pathogenesis, which further confirmed their clinical relevance. These findings encourage the use of circRNA-based assays in HCV treatment management. However, further function validation, as well as longitudinal follow-up and exploration of their role in DAA resistance, needs to be done in order to ultimately determine their full translational potential in precision medicine for hepatitis. Declarations Contributions U., H., and M.S. and M.I.conceptualization, methodology, software, writing- original draft preparation. M.F., M.A., H., are reviewing, editing, validating, and supervising. All authors approved the final version of the manuscript. Conflict of interest All the authors declare that they have no conflicts of interest. Sources of funding No funding was received. Consent to participate Informed consent was obtained from all participants included in the study. 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Sci Rep 6(1):30919 Yin H, Wang H, Wang M, Yan Y, Dong Q, Li Q, Liu Y, Wang X, Guo T, Niu L (2022) CircTCF25 serves as a sponge for miR-206 to support proliferation, migration, and invasion of glioma via the Jak2/p‐Stat3/CypB axis. Mol Carcinog 61(6):558–571 Qin M, Liu G, Huo X, Tao X, Sun X, Ge Z, Yang J, Fan J, Liu L, Qin W (2016) Hsa_circ_0001649: a circular RNA and potential novel biomarker for hepatocellular carcinoma. Cancer Biomarkers 16(1):161–169 El Sharkawi FZ, El-Sherbiny M, Ali SAM, Nassif WM (2023) The potential value of plasma Circ-ITCH in Hepatocellular carcinoma patients with current Hepatitis C virus infection. Gastroenterol Hepatol 46(1):17–27 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 18 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 17 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-9145961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622081636,"identity":"a7a040f8-52c4-4bdf-9a93-5ccb6590dd2a","order_by":0,"name":"Umm e Habiba","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Umm","middleName":"e","lastName":"Habiba","suffix":""},{"id":622081637,"identity":"755ab320-db5b-43bd-b74e-ef1ae9f39569","order_by":1,"name":"Muhammad Shahid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie2RP0sDMRiHUw46RW4NXM9+AuGVwC0BP8sdhWa5SEdBwUzp4HDrfYxO6WopXJfcrnTpB1AQHSviW5GCEOufqZQ8kOSXkCd5QwgJBPYS+tF3NIaIQHSMky626HP8hcL/qpCo0D8pJ7WaPd+XZ6mO29nLaCRkNb4ZrsiFKHRicp+S3Z0PEmUHXDMMNUhVu3YKxMlC95pbv1ICKlgPo5BQmKsJU5Z1zBxXpP5G4WtlrwsdO75GRUL/EZW3nUqGt+AGggGVHNgRKnqjDP2FuYdMKLvghpWZoCBPa6emkDeSm17jf/6i5EtlL9MKC1vSV9GPx61dPV2JtEoM+JQtX/9gc3yX7RS8/EMJBAKBg+Qdratf73xosScAAAAASUVORK5CYII=","orcid":"","institution":"University of the Punjab","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Shahid","suffix":""},{"id":622081638,"identity":"75f73a44-c344-46e4-b965-fa4cb32ae84a","order_by":2,"name":"Mahnoor Fatima","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Mahnoor","middleName":"","lastName":"Fatima","suffix":""},{"id":622081639,"identity":"a97df617-ff50-4402-ae22-4ede060d7c75","order_by":3,"name":"Muhammad Ali Huzaifa","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ali","lastName":"Huzaifa","suffix":""},{"id":622081641,"identity":"86fe610d-be66-4623-82c0-4eba84666a7b","order_by":4,"name":"Muhammad Idrees","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Idrees","suffix":""}],"badges":[],"createdAt":"2026-03-17 08:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9145961/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9145961/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107482759,"identity":"932a6e37-9af2-4a62-a167-1bcfa6920faf","added_by":"auto","created_at":"2026-04-22 02:24:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelative fold change expression of circHIPK3 and circTCF25 in SVR and relapse groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/9db15f5fbe39ebfeb222ff36.png"},{"id":107257042,"identity":"0ad9b2d2-14fd-46dc-8b87-bb27842c66b6","added_by":"auto","created_at":"2026-04-19 12:25:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve analysis of circHIPK3 and circTCF25\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AUC for circHIPK3 and circTCF25 (Fig. a,b) in SVR patients versus Re patients. CI, confidence interval; ROC, receiver operating characteristic; AUC, area under the curve\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/1c075b31dd2df050ff1a26fc.png"},{"id":107484646,"identity":"6b386100-6219-44fc-8424-cfc274dd09a1","added_by":"auto","created_at":"2026-04-22 02:32:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":482359,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA network (circRNA-miRNA-mRNA) of circHIPK3; hexagon represents circRNA, octagons represent selected miRNAs, and diamonds represent target mRNAs\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/a3932633d71ff5e7824a12aa.png"},{"id":107257044,"identity":"46ed2a9a-c284-4ba6-8832-e7e0e57406a9","added_by":"auto","created_at":"2026-04-19 12:25:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":450595,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA network (circRNA-miRNA-mRNA) of circTCF25; hexagon represents circRNA, octagons represent selected miRNAs, and diamonds represent target mRNAs\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/4e3feca0532b656ad257f9c2.png"},{"id":107483056,"identity":"e6e65c8b-720e-44c9-97bd-70245911b80f","added_by":"auto","created_at":"2026-04-22 02:26:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":164203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology and Enrichment Analysis of genes associated with circTCF25\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/16e6162d59abe7ec73b4b8c9.png"},{"id":107483321,"identity":"af22a36c-f579-4523-9060-760cca8846de","added_by":"auto","created_at":"2026-04-22 02:27:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30245,"visible":true,"origin":"","legend":"\u003cp\u003ePathway Analysis of Genes Associated with circTCF25\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/89bf1721b7a8ec169db45514.png"},{"id":107482756,"identity":"b5db36f6-0876-4c96-a8c0-c012ec686d06","added_by":"auto","created_at":"2026-04-22 02:24:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":156562,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology and Enrichment Analysis of genes associated with circHIPK3\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/bf5759dd5c59d8ef50ac029f.png"},{"id":107257046,"identity":"981fb4a1-f24b-4cf5-abf1-162f99b1e4f6","added_by":"auto","created_at":"2026-04-19 12:25:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":58592,"visible":true,"origin":"","legend":"\u003cp\u003ePathway Analysis of Genes Associated with circHIPK3\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/de007502442d88072667993f.png"},{"id":107487009,"identity":"0ca1dd75-996c-45b6-a1d1-c66791aff524","added_by":"auto","created_at":"2026-04-22 02:39:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1322571,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9145961/v1/9eda8c28-2267-4834-9fad-97fafe8b0fed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circular RNAs ‘circHIPK3’ and ‘circTCF25’ as Blood-Based Biomarkers for SVR and Relapse After Therapy in Chronic Hepatitis C Virus Infection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis C virus (HCV) has infected 57\u0026nbsp;million individuals globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to figures from the World Health Organization, this condition affects 3 to 4\u0026nbsp;million people per year worldwide. Its prevalence is 5% in developing countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Hepatitis is a disease state characterized by an inflammatory condition of the liver and often accompanied by edema and, in many cases, destruction of the liver tissue. The major cause of hepatopathies is the HCV [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Also, chronic hepatitis caused by HCV may lead to cirrhosis and transformation into cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Apart from Hepatitis A and B, which have been well-known to cause acute liver failure (ALF), HCV has also emerged as a causative agent of ALF, which is a relatively rare disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. HCV may reappear in patients with sustained virologic response (SVR) after completion of treatment; this has been referred to as recurrence. This may be due to reinfection or late relapse. HCV infection relapses after achieving SVR within 12\u0026ndash;24 weeks is defined as relapse (Re). After SVR status is achieved in HCV, there are chances of both reinfection and late relapse to occur [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Detection of early diagnostic and prognostic factors to distinguish SVR from relapses in HCV patients is important to tackle this persistent threat to human health.\u003c/p\u003e \u003cp\u003eCircular RNAs (circRNAs) are covalently self-looped continuous RNAs which are endogenous and native RNAs. They are transcribed predominantly from back-spliced exons or lariat intron and differentially spliced from exons or introns. In other words, a circular transcript is produced when a splice donor site in the primary transcript is spliced to a splice acceptor site located upstream of the splice donor site. Furthermore, the neighboring intronic complementary sequences dynamically determine the exon circularization [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent studies have explained the abundance of circRNA expression across many species, along with its tissue/cell type-specific expression profile [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. They have been found to be extremely abundant, evolutionarily conserved, and highly stable in the cytoplasm, which is an interesting discovery [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCircular RNAs (circRNAs) have been proposed to be involved in the pathogenesis of diseases, and the implications of circRNAs encoded in host-virus interactions may also lead towards new diagnostic, prognostic, and therapeutic strategies against virus-associated diseases. It has been demonstrated that circRNAs are reliably found in bodily fluids, which provides a simple and non-invasive way for their application in the diagnosis of a range of disorders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. CircRNAs have lately demonstrated enormous potential as gene regulators in mammals. They can displace miRNA-targeted mRNA's inhibitory impact by competing for miRNA attachment, forming competing endogenous RNA networks (ceRNA networks). It has been established that circRNAs regulate tumor development and function as molecular biomarkers for lung cancer, glioblastoma (GBM), colorectal cancer (CRC), and HCC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Multiple circRNAs show aberrant expression in HCC, revealing that they may be used as biomarkers for HCV infection diagnosis and therapy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, no work, to our knowledge, has previously reported the role of circHIPK3 and circTCF25 in HCV infection.\u003c/p\u003e \u003cp\u003eThis research focuses mainly on the expression profile of circRNAs in HCV-genotype 3a-infected patients (SVR and relapse groups) to identify dysregulated circRNAs with potential diagnostic relevance in the Pakistani population through quantitative real-time PCR.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eBlood samples were collected from 171 HCV patients (128 SVR patients and 43 relapse patients) in collaboration with the Genomics Center, Abdalians Cooperative Housing Society, 57000 Lahore, Pakistan. The study's focus was on the 3a genotype of HCV in the Pakistani population. Blood samples were also collected from 20 healthy controls who had never had HCV infection.\u003c/p\u003e \u003cp\u003eThe participants of the study solely had an HCV infection; they showed no signs of liver damage or other viral infections. The research did not include the following patients: (i) patients having other infections, (ii) pregnant women, and (iii) patients below the age of 18. The ethical guidelines of the 1975 Helsinki Agreement were followed in the conduct of this investigation. The study was approved by the ethical review committee of CEMB (\u003cem\u003eCEMB/IRB-11/24 December 19, 2024\u003c/em\u003e), University of the Punjab, Lahore. Patients who matched the study's requirements at the time of sampling were asked to fill out the consent form.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAntiviral Therapy\u003c/h3\u003e\n\u003cp\u003eDuring our research period, we collected blood from patients who had completed their HCV treatment through Direct-Acting Antiviral (DAA) therapy. Sofosbuvir (400 mg) and Daclatasvir (60 mg) were given orally once daily for 12 weeks to all CHC patients in this study. After follow-up, one group was divided into SVR patients who showed a not-detectable HCV viral burden of \u0026lt;\u0026thinsp;20 IU/mL, and another group was relapse patients who had again developed higher levels of HCV.\u003c/p\u003e\n\u003ch3\u003eRNA Extraction and Quality Control\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated from whole blood using Thermo Scientific\u0026trade; GeneJET RNA Purification Kit according to the manufacturer\u0026rsquo;s protocol. The RNA quantity was assessed with a NanoDrop ND-2000.\u003c/p\u003e\n\u003ch3\u003eQuantitative Real-time PCR (qRT-PCR)\u003c/h3\u003e\n\u003cp\u003ecDNA was synthesized using whole RNA with Thermo Scientific RevertAid First Strand cDNA Synthesis Kit (Lot no. 00839495) following the manufacturer\u0026rsquo;s instructions. Using a Maxima SYBR green/PCR master mix (ThermoFisher, USA), the expression levels of circRNAs were assessed using Real-Time Quantitative PCR (Smart Cycler II-Cepheid, USA). The circRNAs were intended to be amplified quantitatively using divergent primers as opposed to the more often utilized convergent primers. Ten minutes at 95\u0026deg;C, 40 cycles of 95\u0026deg;C for 15 seconds, 60\u0026deg;C for 60 seconds, and 70\u0026deg;C for 30 seconds were the amplification conditions. Internal control was implemented using GAPDH. The 2\u003csup\u003e\u0026minus;(∆∆Ct)\u003c/sup\u003e approach was used to examine the data [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of circRNA-miRNA-mRNA interactions\u003c/h2\u003e \u003cp\u003eCircInteractome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://circinteractome.nia.nih.gov/\u003c/span\u003e\u003cspan address=\"https://circinteractome.nia.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and TargetScan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetscan.org/vert_80/\u003c/span\u003e\u003cspan address=\"https://www.targetscan.org/vert_80/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] were employed to predict the mRNAs and target miRNAs. Interaction networks of circRNA-miRNA-mRNA were depicted using Cytoscape [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene Ontology and Pathway Analysis\u003c/h3\u003e\n\u003cp\u003eThe differentially expressed mRNAs involved in competing endogenous RNA networks (ceRNA) were analyzed by DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://davidbioinformatics.nih.gov/\u003c/span\u003e\u003cspan address=\"https://davidbioinformatics.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] for pathway analysis and gene ontology (GO) analysis to obtain their biological functions, the cellular components they are expressed, and their molecular functions. Enrichment scores were calculated by taking the \u0026ndash;log10 of the p-values for the respective GO terms.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll experimental data were analyzed using SPSS software (IBM SPSS 22.0). Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D. was calculated to express baseline characteristics of patients. A descriptive analysis of different characteristics of patients was performed. The Kruskal-Wallis test was used to compare the gene expression level between the SVR, Relapse, and control groups. The diagnostic capability of statistically differently expressed circRNAs was evaluated using ROC curve analysis. A \u003cem\u003eP value\u003c/em\u003e of less than 0.05 was employed as the cut-off point.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u0026rsquo; demographics and clinical data\u003c/h2\u003e \u003cp\u003eA total of 171 participants were included in this study, including 65 males and 106 females. The positive HCV patients involved in our study were divided into two groups: SVR and relapse. The characteristics of the study subjects are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All HCV-positive patients were infected with the 3a genotype. The positive HCV patients involved in our study were divided into two groups: SVR (sustained virologic response) and relapse. SVR patients showed a mean viral load of less than 20 IU/ml, and the mean viral load in the Re-group was 250555\u0026thinsp;\u0026plusmn;\u0026thinsp;798973 IU/ml. Among all HCV patients in our study, 128 were SVR patients and 43 were Relapse patients with a mean age of 46.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 and 50.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVarious parameters such as ALT\u0026thinsp;=\u0026thinsp;Alanine aminotransferases, ALP= Alkaline Phosphatases, and AST= Aspartate aminotransferase are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCV Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65/106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.85\u0026thinsp;\u0026plusmn;\u0026thinsp;30.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.77\u0026thinsp;\u0026plusmn;\u0026thinsp;25.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234.74\u0026thinsp;\u0026plusmn;\u0026thinsp;42.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans of variables in the SVR and Relapse group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender (M/F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eViral Load (Mean)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVR (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e46.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35%/65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 IU/mL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelapse (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e50.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45%/55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250555 IU/mL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBlood-based gene expression profile of circHIPK3 and circTCF25 in HCV infection\u003c/h2\u003e \u003cp\u003eBlood-based expression profiles of circHIPK3 and circTCF25 were analyzed, which revealed a statistically significant difference among the groups. The expression of circHIPK3 and circTCF25 were up-regulated in the blood of HCV patients. The expression level of circHIPK3 (SVR FC: 19.83, Re FC: 1.73) was significantly up-regulated in the SVR and relapse groups, and the expression level of circTCF25 (SVR FC: 6.86, Re FC: -1.47) was significantly up-regulated in the SVR group and down-regulated in the relapse group. Also, the SVR group had a much higher level of circHIPK3 compared to the relapse group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFold change values of circHIPK3 and circTCF25 in SVR and Re groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHost Gene Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFC Value\u003c/p\u003e \u003cp\u003e(SVR Group)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFC value\u003c/p\u003e \u003cp\u003e(Re Group)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehas_circ_0021592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecircHIPK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehas_circ_0041077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecircTCF25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic potential of circHIPK3 and circTCF25 as biomarkers for HCV-positive patients\u003c/h2\u003e \u003cp\u003eROC curves were computed to evaluate the diagnostic potential of circHIPK3 and circTCF25 for the discrimination of SVR and relapse groups. According to the findings, the AUC of circHIPK3 was 0.818 (95% CI: 0.6407 to 0.9956, p-value: 0.0084) and the AUC of circTCF25 was 0.867 (95% confidence interval [CI]: 0.7072 to 1.000, \u003cem\u003ep\u003c/em\u003e-value: 0.0024), indicating that both circHIPK3 and circTCF25 expression in blood might be used to distinguish the SVR patients from relapse patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe AUC for circHIPK3 and circTCF25 (Fig. a,b) in SVR patients versus Re patients. CI, confidence interval; ROC, receiver operating characteristic; AUC, area under the curve\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredicted competing endogenous RNA (circRNA-miRNA-mRNA) networks\u003c/h2\u003e \u003cp\u003eIn order to study the potential post-transcriptional regulatory roles of circHIPK3 and circTCF25 in HCV pathogenesis, circRNA-miRNA-mRNA interaction networks were established. The putative mRNA response elements (MREs) of circHIPK3 and circTCF25 were predicted by using CircInteractome, and downstream mRNA targets of circHIPK3 and circTCF25 were predicted by using TargetScan. For each circRNA, the top 10 miRNAs were selected with Context\u0026thinsp;+\u0026thinsp;+\u0026thinsp;score\u0026thinsp;\u0026ge;\u0026thinsp;80 and therefore high confidence of the miRNA binding affinity. The top 5 predicted mRNA targets were retrieved from TargetScan for each of these miRNAs, utilizing a cumulative weighted Context\u0026thinsp;+\u0026thinsp;+\u0026thinsp;score \u0026lt;-0.5 as a cut-off threshold. The resulting interactions of two circRNA-centered competing endogenous RNA (ceRNA) networks were constructed using Cytoscape software. The networks of circHIPK3 and circTCF25 are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology analysis\u003c/h2\u003e \u003cp\u003eTo understand the functional implications of mRNA targets associated with circHIPK3 and circTCF25, GO and KEGG pathways enrichment analysis were performed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGO enrichment analysis of genes associated with circTCF25 was observed, and distinct patterns in three domains representing molecular functions, cellular components, and biological processes were revealed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In terms of molecular function, associated genes were significantly enriched in molecular biology related to transcription regulation and ion binding activity, and protein-protein interactions. Notable functions were transcription cis-regulatory region binding, DNA binding transcription activator activity, and protein tyrosine kinase binding, which suggests that circTCF25 plays a crucial role in gene expression modulation and intracellular signaling pathways. With respect to the cellular components, the analysis revealed that most of these genes were enriched at synaptic structures as well as at cell organelles associated with neuronal function and protein processing. An abundance of components like synaptic vesicles, extracellular exosomes, chromatin, synaptic membranes, and dendrites demonstrates the implication of the circTCF25 in neurobiological functions and cell communication, particularly in the nervous system. This was further substantiated in the biological process analysis that showed profound enrichment with the biological processes involved in the regulation of neuronal synaptic plasticity, signal transduction, as well as transcriptional control. Besides, other more general physiological processes were mentioned, such as regulation of systemic arterial blood pressure, skeletal muscle cell differentiation, embryonic patterning, and bone resorption. These findings suggest that circTCF25 is also likely to be acting outside of the neural milieu and participate in the various features of development and physiological regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the pathways enrichment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) with the assistance of the KEGG database revealed that the genes that were linked to circTCF25 can be found in the crucial signaling and metabolic pathways. The most prominent were the Ras signaling pathway, which is the one that participates in the rudimentary cell proliferation and differentiation, and the glutathione metabolism pathway, which is the one that is involved in the redox homeostasis and the oxidative stress response. Additional pathways that were enriched were the endoplasmic reticulum-based protein processing and the natural killer cell-mediated cytotoxicity, which suggested that circTCF25 might be involved in protein homeostasis and immune surveillance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor circHIPK3, the protein binding term was the most enriched in the GO molecular function category, indicating that circHIPK3-related genes are highly engaged in protein-protein interactions and are controllers of cell network activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Other functions that were further enriched were protein homo-dimerization, signaling receptor inhibitor, phosphatidylserine and calcium-dependent phospholipid, and transforming growth factor beta receptor binding. These are directed to membrane-associated signaling, immune recognition, and transcriptional regulation roles. The sequence-specific DNA binding, voltage-gated potassium channel activity, and GTPase binding also highlight the participation in signal transduction, transcriptional regulation, and cell communication. There was a significant localization of cellular component enrichment to the mitochondria and endoplasmic reticulum, indicating the involvement of both in energy metabolism, calcium homeostasis, and protein quality control. It has also been observed that the genes were enriched also in lysosomes, axons, perikaryon, and blood microparticles, and this implies its roles in neuronal development and systemic signaling. It implies that circHIPK3-linked genes can play a role in maintaining the integrity of tissues, neuro-development, and immunity. Analysis of the biological processes showed a high level of enrichment of signal transduction and cell shape regulation, meaning that the process is involved in cellular responsiveness and structural dynamics. Other enriched processes were inflammatory response, protein peptidyl-prolyl isomerization, amino acid transport, and protein localization.\u003c/p\u003e \u003cp\u003eKEGG pathway analysis identified significant enrichment in pathways related to viral infections, including Kaposi sarcoma-associated herpesvirus, Hepatitis C, Human cytomegalovirus, and Influenza A (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This suggests that circHIPK3-associated genes may play roles in antiviral defense or host-pathogen interactions. Pathways related to non-small cell lung cancer, pancreatic cancer, Toll-like receptor signaling, and chemokine signaling highlight involvement in oncogenesis and immune responses. Additionally, enrichment in lipid metabolism and prolactin signaling pathways expands their relevance to metabolic and endocrine regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHCV diagnosis and prognosis could be aided through analysis of gene expression upon infection, and variability of gene expression of blood-based genes in response to treatment. Resistance-associated substitutions (RASs) are currently significant obstacles for the treatment of HCV with direct-acting antivirals (DAAs) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Circular RNAs are new key player and more research need in HCV infection. Blood-based gene expression was utilized in this study to examine the patterns of host gene expression in HCV 3a serotype patients from the Pakistani community. This research focused on circRNAs, which may act as the main players of HCV infection due to their expression levels, and has also attempted to explore the functional role played by these RNAs through their response to treatment.\u003c/p\u003e \u003cp\u003eIn our study, circHIPK3 (hsa_circ_0021592) is found to be significantly upregulated in the SVR and relapse group of HCV patients, and circHIPK3 showed higher expression in the SVR compared relapse group. In previous studies, circHIPK3 was found to be upregulated in several malignancies and is referred to as a promising cancerous circular RNA. In 2018, a study was reported that showed a role for circHIPK3 in colorectal cancer. It was markedly upregulated in CRC cell lines and tissues. Further, it was also positively correlated with the clinical and metastatic stages of the disease. The role of circHIPK3 in metastasis was also verified by the knockdown of circHIPK3, which significantly inhibited the proliferation and invasion of CRC cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. circHIPK3 acts as a microRNA sponge in various human cancers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. CircHIPK3 was found to be associated with cell growth, through sponging 9 miRNAs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. ROC analysis using circHIPK3 provided an important value to distinguish SVR and relapse patients in our study, and it further highlights the viability of the circHIPK3 gene as a diagnostic biomarker for HCV infection. It has also been suggested as a potential biomarker of glioblastoma multiforme by ROC analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCircTCF25 (hsa_circ_0041077) was identified to be elevated in SVR, and reduced in relapse sets in our research. Its role in HCV infection is not well documented, and it is reported to be linked with different carcinomas. A study conducted by Zhong et al. Shows that TCF25 is involved in bladder carcinoma, through sponging miR-103a-3p, which further leads to the upregulation of CDK6 levels and cell invasion and progression of carcinoma cells. This study has suggested circTCF25 as a strong biomarker for carcinoma [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. CircTCF25 has also been upregulated in glioma cells to promote the progression and migration of glioma cells through sequestering miR-206. Its mechanism involves JAK2 and STAT3 pathways [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our ROC curve analysis also demonstrated that circTCF25 could be employed as a significant diagnostic tool and a biomarker for HCV infection to differentiate SVR and relapse patients, with a significant AUC value. A major strength of this study is the combined use of bioinformatic analysis, which gave mechanistic insight into the role of circHIPK3 and circTCF25 in the context of HCV infection [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For circHIPK3, the results of GO enrichment revealed biological processes such as organelle organization, cellular response to chemical stress, etc., and the molecular function was cellular localization to mitochondria, endoplasmic reticulum, and focal adhesion sites. Its enrichment in the KEGG pathway included hepatitis B and C, and Toll-like receptor signaling, indicating the involvement of antiviral response and immune regulation. In contrast, circTCF25 was associated with chromatin and synaptic vesicle localization, and its enriched GO terms suggested regulation of protein serine-threonine kinase activity and protein autophosphorylation. Circulating TCF25 pathway annotation predicted the involvement in Ras signaling, glutathione metabolism, and natural killer (NK) cell-mediated cytotoxicity, indicating an immune surveillance role of circTCF25 in the ROS metabolism in HCV infection.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has revealed significantly high expression of circHIPK3 and circTCF25 in SVR patients versus Relapse cases and healthy controls, suggesting them as potential novel biomarkers for tracking their response to therapy for HCV infection. Bioinformatic analysis of their ceRNA network and GO/KEGG pathways indicated the functional roles related to immune regulation, signal transduction, and viral pathogenesis, which further confirmed their clinical relevance. These findings encourage the use of circRNA-based assays in HCV treatment management. However, further function validation, as well as longitudinal follow-up and exploration of their role in DAA resistance, needs to be done in order to ultimately determine their full translational potential in precision medicine for hepatitis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eU., H., and M.S. and \u0026nbsp;M.I.conceptualization, methodology, software, writing- original draft preparation. M.F., M.A., H., are reviewing, editing, validating, and supervising. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants included in the study.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCunningham EB, Wheeler A, Hajarizadeh B, French CE, Roche R, Marshall AD, Fontaine G, Conway A, Bajis S, Valencia BM (2023) Interventions to enhance testing and linkage to treatment for hepatitis C infection for people who inject drugs: a systematic review and meta-analysis. 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Gastroenterol Hepatol 46(1):17\u0026ndash;27\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"current-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Current Microbiology","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"HCV, circRNAs, Direct-acting antiviral therapy, ceRNA networks, SVR","lastPublishedDoi":"10.21203/rs.3.rs-9145961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9145961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eHepatitis C infection (HCV) is one of the most significant health concerns in developing nations, such as Pakistan. Different programs under the WHO HCV elimination oath are working on eliminating the virus by 2030. Circular RNAs (circRNAs) are potential novel therapeutic targets and diagnostic biomarkers for HCV infection.\u003c/p\u003e\u003ch2\u003eMethodology:\u003c/h2\u003e \u003cp\u003eThere were 171 patients with HCV infection included in this study. The patients were graded in 2 groups: sustained virological response (SVR) and Relapse (Re) based on the response to the therapy. Expression levels for circHIPK3 and circTCF25 were analyzed with quantitative real-time PCR, and a ROC curve was created to test their potential as diagnostic tools. Competing endogenous networks were built, and GO analysis was carried out according to differentially and significantly expressed genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe outcomes of our study revealed that the level of circHIPK3 was upregulated in the SVR and Relapse groups. The level of circHIPK3 in the SVR group was much higher than in the Relapse group (FC:19.83 vs. FC: 1.73). Also, circTCF25 was upregulated in SVR patients (FC:6.86) but downregulated in relapsed patients (FC: -1.47). ROC analysis revealed that the AUC value of circHIPK3 was 0.818 (p-value: 0.0084), and the value of circTCF25 was 0.867 (p-value: 0.0024). Bioinformatic analysis revealed that the Ras signaling pathway, Kaposi sarcoma-associated herpesvirus, and Hepatitis C virus are key involved pathways.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study is important to discover the role of circRNAs in the 3a genotype of HCV in the Pakistani population. circHIPK3 and circTCF25 may serve as useful biomarkers to discriminate SVR from Relapse in HCV infection.\u003c/p\u003e","manuscriptTitle":"Circular RNAs ‘circHIPK3’ and ‘circTCF25’ as Blood-Based Biomarkers for SVR and Relapse After Therapy in Chronic Hepatitis C Virus Infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:25:28","doi":"10.21203/rs.3.rs-9145961/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-17T12:56:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T22:40:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89189029135037878232152745637438618571","date":"2026-04-13T08:34:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T20:51:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-18T18:42:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T09:12:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Current Microbiology","date":"2026-03-17T07:57:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"current-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Current Microbiology","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"69a603b7-f184-435f-a231-5938037c161b","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-17T12:56:50+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T13:28:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:25:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9145961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9145961","identity":"rs-9145961","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}