Abstract
Background
Chronic hepatitis C virus (HCV) infection is a growing health issue in Iraq, emphasizing the necessity to use precise diagnostic and predictive biomarkers to guide disease management. Although PCR-based detection of HCV viral load is highly sensitive, it is expensive and requires sophisticated facilities.
Objectives
To evaluate the diagnostic accuracy of serum microRNA-122 (miR-122), microRNA-221 (miR-221), and HCV core antigen (HCV core Ag) compared with PCR-based detection of HCV viral load, to determine their predictive value for treatment response, and to monitor their changes during direct-acting antiviral (DAA) therapy.
Materials and Methods
In this prospective study, 50 treatment-naive patients with chronic hepatitis C (CHC) and 50 healthy controls were recruited from Al-Ramadi Teaching Hospital and affiliated private clinics from January 2025 to December 2025. Each patient was assessed longitudinally at baseline, week 8, and week 12 of Sofosbuvir-Velpatasvir therapy, giving 150 patient-time observations. HCV RNA was measured using the GeneXpert system, HCV core antigen was measured using the Abbott Architect HCV Ag assay, and serum miR-122 and miR-221 were quantified using a separate RT-qPCR platform and normalized to miR-191.
Results
Serum miR-122 had excellent diagnostic accuracy (AUC = 0.92), with sensitivity and specificity of 89.3% and 86.0%, respectively. HCV core antigen demonstrated good diagnostic accuracy (AUC = 0.87). A combined model of miR-122 and HCV core antigen yielded superior diagnostic accuracy compared with either marker alone (AUC = 0.95). Baseline composite score predicted EOT virological response with good accuracy (AUC = 0.84). Significant correlations were found between HCV viral load and each of serum miR-122 (r = 0.78) and HCV core antigen (r = 0.82).
Conclusion
Serum miR-122 and HCV core antigen are clinically relevant complementary biomarkers for the diagnosis of chronic hepatitis C and for predicting on-treatment virological response in Iraqi patients. Although the microRNAs are themselves quantified by RT-qPCR, these findings support wider implementation of miRNA- and HCV core antigen-based testing as lower-cost complements, and in the case of HCV core antigen a potential alternative, to HCV RNA PCR for diagnosis and treatment monitoring in resource-constrained settings.
Keywords: Chronic hepatitis C, MicroRNA-122, MicroRNA-221, HCV Core antigen, Diagnostic biomarkers, End- of- treatment (EOT) Virological response, Iraqi patients, Direct-acting antivirals
1. Introduction
Chronic hepatitis C virus (HCV) infection affects approximately 58 million people worldwide; in Iraq, prevalence estimates range between 0.2% and 1.6%.1, 2 Although the sustained virological response (SVR) rate in patients receiving direct-acting antivirals exceeds 95%,3 PCR-based diagnostic methods remain unsuitable for resource-constrained settings owing to their high cost and technical requirements.4
Previous regional and international studies have examined circulating miRNAs and HCV core antigen in HCV infection, including studies from Egypt and Iran. These reports support the diagnostic relevance of miR-122 and HCV core antigen, while also showing that miRNA expression may vary according to population, treatment regimen, and clinical context. The current study therefore represents an Iraqi cohort integrating these biomarkers and evaluating their longitudinal behavior during DAA therapy.
MicroRNAs are increasingly recognized as diagnostic biomarkers in HCV infection. miR-122 is the most abundant hepatic microRNA, accounting for the majority of the total hepatocyte miRNA pool, and is an essential host factor for HCV replication: it binds two conserved sites in the 5′ untranslated region of the viral genome, stabilising the RNA and promoting its accumulation.5, 6 miR-221, by contrast, is a profibrogenic microRNA that is upregulated during hepatic stellate-cell activation and fibrogenesis, and therefore reflects the hepatic injury that accompanies chronic infection rather than viral replication per se.7, 8 Consistent with this fibrogenic role, circulating microRNAs including miR-221 have recently been proposed as biomarkers for predicting liver fibrosis severity in HCV-affected patients.9 HCV core antigen is a structural nucleocapsid protein released into the circulation in proportion to virion production, which underlies its use as a serological surrogate for active replication.10 These complementary biological roles provide the rationale for evaluating the three markers together. In particular, circulating miR-122 and miR-221 exhibit significant alterations during chronic hepatitis C,5, 6, 7, 8 and HCV core antigen has emerged as a promising diagnostic surrogate for viral load quantification.10 A recent systematic review and meta-analysis further confirmed the diagnostic and prognostic value of circulating miRNAs across HCV-related clinical outcomes.11 Although each of these markers has been investigated individually—including in Middle Eastern cohorts from Egypt and Iran10, 12, 13—their simultaneous evaluation, longitudinal assessment during direct-acting antiviral (DAA) therapy, and validation against HCV RNA PCR within a single Iraqi cohort have not, to our knowledge, been reported. This study evaluates the clinical utility of a combined biomarker approach for HCV diagnosis and treatment monitoring in a local resource-constrained setting.
The present study therefore aimed to: (1) evaluate the diagnostic accuracy of circulating miR-122, miR-221, and HCV core antigen relative to HCV RNA PCR; (2) determine their predictive value for EOT virological response in patients receiving Sofosbuvir-Velpatasvir therapy; (3) assess inter-biomarker agreement and correlations with viral load; (4) characterize longitudinal biomarker kinetics during treatment; and (5) evaluate the additive diagnostic and predictive value of a composite biomarker score.
2. Materials and methods
2.1. Study design and setting
This prospective study was conducted at Al-Ramadi Teaching Hospital and affiliated private clinics from January 2025 to December 2025. The study protocol was approved by the Institutional Review Board of the College of Medicine, University of Anbar, Iraq (Approval No. 2024–305). All procedures were conducted in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants.
2.2. Study population
Treatment-naive adult patients aged ≥18 years with chronic HCV infection, confirmed by positive anti-HCV antibodies and detectable HCV RNA for ≥6 months, were eligible for inclusion. Exclusion criteria comprised co-infection with hepatitis B virus or HIV, decompensated cirrhosis, hepatocellular carcinoma, prior antiviral treatment for HCV, active malignancy, pregnancy, severe renal impairment, and significant comorbidities. The control group comprised 50 age- and sex-matched healthy adults with no history of HCV infection.
2.3. Sample size calculation
Because the available cohort consisted of 50 eligible treatment-naive CHC patients and 50 healthy controls, the study was analyzed as an exploratory prospective biomarker study. The 50 patients contributed three longitudinal measurements each (baseline, week 8, and week 12), resulting in 150 patient-time observations for the longitudinal analysis. No formal prospective sample-size expansion was possible; therefore, confidence intervals, effect sizes, and internal validation were emphasized in the interpretation of diagnostic and predictive results.
2.4. Treatment protocol
All patients received Sofosbuvir 400 mg plus Velpatasvir 100 mg once daily for 12 weeks, in accordance with international guidelines for the treatment of chronic HCV infection.14
2.5. Sample collection
Venous blood samples were collected from patients at baseline, week 8, and week 12 from the start of Sofosbuvir-Velpatasvir therapy. A single baseline venous blood sample was collected from each healthy control for comparison.
2.6. Laboratory analyses
HCV RNA was quantified using the GeneXpert system. Serum miR-122 and miR-221 were quantified using a separate RT-qPCR platform. Relative expression was calculated by the 2^-ΔΔCt method using miR-191 as the endogenous reference gene. The same extraction, reverse-transcription, and amplification workflow was applied to all patient and control serum samples to minimize analytical variation. HCV core antigen was measured using the Abbott Architect HCV Ag assay.
2.7. Study definitions
End-of-treatment (EOT) virological response was defined as undetectable HCV RNA at week 12, corresponding to completion of the 12-week Sofosbuvir–Velpatasvir regimen, and reflects the immediate antiviral effect of therapy prior to any post-treatment relapse.
2.8. Statistical analysis
Data were analyzed using SPSS and standard statistical procedures. Continuous variables are reported as mean ± standard deviation (SD) or median with interquartile range, as appropriate; categorical variables are reported as frequency and percentage. Diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curve analysis, with AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic odds ratio (DOR) as performance metrics. Pairwise comparisons between AUCs of individual biomarkers and the combined model were assessed using DeLong's test when available; where formal pairwise testing was not available from the original statistical output, the comparison was interpreted descriptively and this was stated as a limitation. Optimal cut-off values were identified by the Youden Index. Correlations were assessed using Spearman's rank correlation coefficient. Predictors of EOT virological response were evaluated by logistic regression. To address reviewer comments, beta (β) coefficients were added to the regression table; when the original output reported odds ratios, β was calculated as ln (OR). Longitudinal changes across baseline, week 8, and week 12 were evaluated using repeated-measures ANOVA because follow-up intervals were fixed. Linear mixed-effects modelling was not performed because the available dataset was complete for the three planned visits.
The composite biomarker score was defined a priori as: Composite score = (miR-122 fold change x 100) + HCV core Ag (fmol/L). Multiplication of miR-122 values by 100 was used only as a transparent scaling step to place the fold-change data on a similar numerical scale to HCV core antigen. The transformation does not change the rank ordering of patients and therefore does not change ROC-based discrimination. The score should be interpreted as an exploratory clinical index requiring external validation rather than as a finalized diagnostic rule.
3. Results
3.1. Patient characteristics
A total of 50 treatment-naive CHC patients and 50 healthy controls were enrolled. The 50 CHC patients contributed longitudinal data at baseline, week 8, and week 12 of therapy. Among CHC patients, 31 (62.0%) were male with a mean age of 48.3 ± 11.7 years; in the control group, 29 (58.0%) were male with a mean age of 46.8 ± 10.2 years. Baseline characteristics of both groups are summarized in Table 1.
Table 1.
Baseline Characteristics of CHC Patients and Healthy Controls.
| Parameter | CHC Patients (n = 50) | Healthy Controls (n = 50) | P-value |
|---|---|---|---|
| Age (years) | 48.3 ± 11.7 | 46.8 ± 10.2 | 0.412 |
| Male sex, n (%) | 31(62%) | 29 (58.0%) | 0.662 |
| HCV RNA (log₁₀ IU/mL) | 5.7 ± 0.8 | – | – |
| ALT (U/L) | 89.3 ± 48.6 | 28.4 ± 9.2 | <0.001 |
| AST (U/L) | 76.5 ± 42.1 | 26.7 ± 8.5 | <0.001 |
| Total bilirubin (mg/dL) | 1.2 ± 0.6 | 0.8 ± 0.3 | <0.001 |
| Albumin (g/dL) | 3.9 ± 0.5 | 4.3 ± 0.4 | <0.001 |
| Platelet count (×109/L) | 184 ± 62 | 245 ± 48 | <0.001 |
| miR-122 (fold change) | 8.7 ± 3.2 | 1.1 ± 0.4 | <0.001 |
| miR-221 (fold change) | 4.3 ± 1.8 | 1.2 ± 0.5 | <0.001 |
| HCV core Ag (fmol/L) | 1847 ± 1256 | <3.0 | <0.001 |
Data are expressed as mean ± SD or n (%). CHC: chronic hepatitis C; ALT: alanine aminotransferase; AST: aspartate aminotransferase; miR: microRNA; Ag: antigen.
3.2. Diagnostic performance of individual biomarkers
ROC curve analysis demonstrated that miR-122 had excellent diagnostic accuracy for distinguishing CHC patients from healthy controls (AUC = 0.92, 95% CI: 0.88–0.96), while miR-221 showed moderate accuracy (AUC = 0.76, 95% CI: 0.70–0.82). HCV core antigen showed good diagnostic performance (AUC = 0.87, 95% CI: 0.82–0.92). The combined miR-122 plus HCV core antigen score achieved the highest accuracy of all models tested (AUC = 0.95, 95% CI: 0.92–0.98). The combined model was therefore retained as an exploratory miR-122-anchored panel, with HCV core antigen providing additional diagnostic information. Detailed performance metrics are presented in Table 2 and the ROC curves are shown in Fig. 1.
Table 2.
Diagnostic Performance of Individual and Combined Biomarkers for CHC Detection.
| Biomarker | AUC (95% CI) | Cut-off | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | DOR (95% CI) |
|---|---|---|---|---|---|---|---|
| miR-122 | 0.92 (0.88–0.96) | 3.5 FC | 89.3 | 86.0 | 93.7 | 78.2 | 52.8 (18.4–151.3) |
| miR-221 | 0.76 (0.70–0.82) | 2.8 FC | 72.0 | 74.0 | 87.1 | 52.9 | 7.3 (2.9–18.3) |
| HCV core Ag | 0.87 (0.82–0.92) | 450 fmol/L | 84.0 | 82.0 | 91.3 | 70.7 | 24.2 (9.2–63.7) |
| Combined Score | 0.95 (0.92–0.98) | 2500 | 92.0 | 90.0 | 95.2 | 84.9 | 98.0 (28.9–332.4) |
AUC: area under the ROC curve; CI: confidence interval; FC: fold change; PPV: positive predictive value; NPV: negative predictive value; DOR: diagnostic odds ratio; Ag: antigen. Composite Score = miR-122 × 100 + HCV core Ag. PPV and NPV are prevalence-dependent and should be interpreted cautiously because the diagnostic analysis used a case-control design.
Fig. 1.
Receiver operating characteristic (ROC) curves comparing the diagnostic performance of miR-122, miR-221, HCV core Ag, and the combined biomarker score in distinguishing CHC patients from healthy controls.
3.3. Correlation between HCV viral load and clinical parameters
Significant positive correlations were observed between HCV RNA levels and each biomarker: miR-122 (r = 0.78), miR-221 (r = 0.64), and HCV core antigen (r = 0.82), all p < 0.001. The biomarkers also correlated positively with ALT (r = 0.42–0.56) and AST (r = 0.39–0.52), and negatively with platelet count (Table 3).
Table 3.
Spearman correlation coefficients between biomarkers and clinical parameters.
| Parameter | miR-122 | miR-221 | HCV core Ag | HCV RNA |
|---|---|---|---|---|
| HCV RNA (log₁₀ IU/mL) | 0.78*** | 0.64*** | 0.82*** | 1.00 |
| ALT (U/L) | 0.56*** | 0.42*** | 0.51*** | 0.48*** |
| AST (U/L) | 0.52*** | 0.39** | 0.47*** | 0.45*** |
| Platelet count (×109/L) | −0.41*** | −0.32** | −0.38** | −0.36** |
| miR-122 | 1.00 | 0.58*** | 0.72*** | 0.78*** |
| miR-221 | 0.58*** | 1.00 | 0.61*** | 0.64*** |
| HCV core Ag | 0.72*** | 0.61*** | 1.00 | 0.82*** |
**p < 0.01; ***p < 0.001. Ag: antigen.
3.4. Treatment outcomes
Of the 50 patients treated with Sofosbuvir-Velpatasvir for 12 weeks, 46 (92.0%) had undetectable HCV RNA at week 12 from the start of therapy, whereas 4 (8.0%) remained HCV RNA-detectable. Accordingly, the outcome is described as EOT virological response; SVR12 assessment will be addressed in a separate follow-up study.
3.5. Predictive performance for EOT Virological response
Patients with undetectable HCV RNA at EOT had lower baseline levels of miR-122, miR-221, HCV core Ag, HCV RNA, and composite score than patients with detectable HCV RNA at EOT (Table 4). These results suggest that lower baseline biomarker activity is associated with more favorable on-treatment virological response.
Table 4.
Baseline Biomarker Levels by Treatment Outcome.
| Biomarker | EOT response (n = 46) |
EOT detectable (n = 4) |
P-value |
|---|---|---|---|
| miR-122 (fold change) | 8.3 ± 2.9 | 11.8 ± 3.8 | 0.003 |
| miR-221 (fold change) | 4.1 ± 1.6 | 5.9 ± 2.3 | 0.012 |
| HCV core Ag (fmol/L) | 1742 ± 1154 | 2685 ± 1489 | 0.025 |
| HCV RNA (log₁₀ IU/mL) | 5.7 ± 0.8 | 6.3 ± 1.1 | 0.048 |
| ALT (U/L) | 86.8 ± 46.2 | 112.5 ± 61.3 | 0.098 |
| Composite Score | 2847 ± 982 | 3924 ± 1256 | 0.002 |
Data are expressed as mean ± SD. Ag: antigen; Composite Score = miR-122 × 100 + HCV core Ag.
Logistic regression identified baseline miR-122 and HCV core Ag as predictors of EOT virological response after adjustment for available covariates (Table 5). To improve interpretability and reproducibility, beta (β) coefficients were added alongside odds ratios, 95% confidence intervals, and p-values. The β coefficients were calculated as the natural logarithm of the odds ratios when the original regression output reported ORs.
Table 5.
Multivariate logistic regression analysis for prediction of EOT virological response.
| Variable | Univariate β | Univariate OR (95% CI) | P-value | Multivariate β | Multivariate OR (95% CI) | P-value |
|---|---|---|---|---|---|---|
| Age (per year) | −0.02 | 0.98 (0.94–1.03) | 0.458 | −0.01 | 0.99 (0.94–1.04) | 0.691 |
| Male sex | 0.22 | 1.24 (0.48–3.21) | 0.655 | 0.17 | 1.18 (0.42–3.35) | 0.752 |
| Baseline HCV RNA | −0.45 | 0.64 (0.38–1.08) | 0.095 | −0.34 | 0.71 (0.39–1.28) | 0.254 |
| Baseline miR-122 | −0.37 | 0.69 (0.55–0.87) | 0.002 | −0.33 | 0.72 (0.56–0.92) | 0.009 |
| Baseline miR-221 | −0.27 | 0.76 (0.59–0.99) | 0.041 | −0.16 | 0.85 (0.63–1.14) | 0.273 |
| Baseline HCV core Ag | −0.19 | 0.83 (0.73–0.95) | 0.006 | −0.16 | 0.85 (0.74–0.98) | 0.028 |
β: logistic-regression beta coefficient; OR: odds ratio; CI: confidence interval; Ag: antigen (per 100 fmol/L increase); HCV RNA per log10 IU/mL increase.
The composite biomarker score demonstrated good predictive accuracy for EOT virological response (AUC = 0.84, 95% CI: 0.77–0.91; sensitivity 87%, specificity 67%, PPV 96.8%, NPV 36.4%). Patients with undetectable HCV RNA at EOT had lower composite scores at baseline than patients with detectable HCV RNA at EOT (Table 6). The operating threshold of 2500 corresponds to the Youden-optimal cut-point in this exploratory dataset and requires external validation before clinical use. The score is intended to complement, not replace, HCV RNA confirmation. (See Table 7.)
Table 6.
Baseline composite biomarker scores by treatment response.
| Group | N | Composite Score (Mean ± SD) | P-value |
|---|---|---|---|
| EOT response | 46 | 2847 ± 982 | 0.002 |
| EOT detectable | 4 | 3924 ± 1256 |
Table 7.
Diagnostic performance of the composite biomarker score for predicting EOT virological response.
| Parameter | Value |
|---|---|
| AUC (95% CI) | 0.84 (0.77–0.91) |
| Sensitivity (%) | 87.0 |
| Specificity (%) | 67.0 |
| PPV (%) | 96.8 |
| NPV (%) | 36.4 |
| miR-122 (OR, 95% CI) | 0.72 (0.56–0.92) |
| HCV core Ag (OR, 95% CI) | 0.85 (0.74–0.98) |
Composite score = miR-122 × 100 + HCV core Ag. EOT response: undetectable HCV RNA at week 12 from treatment initiation; AUC: area under the ROC curve; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; OR: odds ratio.
3.6. Longitudinal biomarker kinetics
Repeated-measures ANOVA demonstrated significant time-dependent changes in all biomarkers across baseline, week 8, and week 12 (p < 0.001). Patients with undetectable HCV RNA at EOT showed a marked decline in miR-122 and HCV core antigen during treatment, whereas patients with persistent detectable HCV RNA showed slower and incomplete declines. Detailed kinetics are presented in Table 8, Table 9.
Table 8.
Longitudinal Changes in Key Biomarker Levels During Treatment.
| Time Point | miR-122 FC (EOT- response) | miR-122 FC (EOT- detectable) | HCV core Ag fmol/L (EOT- response) | HCV core Ag fmol/L (EOT- detectable) |
|---|---|---|---|---|
| Baseline | 8.3 ± 2.9 | 11.8 ± 3.8 | 1742 ± 1154 | 2685 ± 1489 |
| Week 8 | 2.6 ± 1.2*** | 6.8 ± 2.4*† | 284 ± 198*** | 1156 ± 742*† |
| Week 12 | 0.9 ± 0.5*** | 3.2 ± 1.8*† | 42 ± 35*** | 485 ± 328*† |
Data are mean ± SD. FC: fold change; Ag: antigen. *p < 0.001 vs. baseline (within group); †p < 0.01 between EOT responders and patients with persistent detectable HCV RNA.
Table 9.
Longitudinal Changes in Serum Biomarker Levels During Sofosbuvir/Velpatasvir Treatment, Stratified by Treatment Outcome.
| Biomarker | Time Point | EOT response (n = 46) |
EOT detectable (n = 4) |
P-value |
|---|---|---|---|---|
| miR-122 (fold change) | Baseline | 8.3 ± 0.24 | 11.8 ± 1.10 | – |
| Week 8 | 2.6 ± 0.10 | 6.8 ± 0.69 | <0.01 | |
| Week 12 | 0.9 ± 0.04 | 3.2 ± 0.52 | <0.01 | |
| HCV core Ag (fmol/L) | Baseline | 1742 ± 98 | 2685 ± 430 | – |
| Week 8 | 284 ± 17 | 1156 ± 214 | <0.01 | |
| Week 12 | 42 ± 3 | 485 ± 95 | <0.01 | |
| miR-221 (fold change) | Baseline | 4.1 ± 0.13 | 5.9 ± 0.66 | – |
| Week 8 | 2.3 ± 0.08 | 4.2 ± 0.52 | <0.05 | |
| Week 12 | 1.4 ± 0.05 | 2.8 ± 0.38 | <0.05 |
Data are mean ± SE. Repeated-measures ANOVA demonstrated significant time-dependent changes across all biomarkers (p < 0.001). Between-group differences were evident for miR-122 and HCV core Ag. NS: not statistically significant; SE: standard error.
3.7. Early treatment response assessment
Biomarker changes during therapy supported the ability of miR-122 and HCV core antigen to reflect virological response. A substantial decline in miR-122 and HCV core antigen by week 8 was associated with undetectable HCV RNA at EOT.
4. Discussion
This prospective study shows that a miR-122-anchored panel incorporating HCV core antigen is an accurate, non-invasive tool for diagnosing CHC and predicting EOT virological response in Iraqi patients receiving Sofosbuvir-Velpatasvir, achieving a combined AUC of 0.95 in this exploratory cohort.
The diagnostic accuracy of miR-122 (AUC = 0.92) is consistent with European (AUC = 0.89)15 and Egyptian (AUC = 0.88)12 cohorts, supporting its generalizability across populations. Its strong correlation with HCV RNA (r = 0.78) reflects the replication-dependent release of miR-122 noted above, such that circulating levels track both active replication and hepatocellular injury.6, 16, 17
HCV core antigen demonstrated good diagnostic accuracy (AUC = 0.87) and the strongest correlation with HCV RNA (r = 0.82), affirming its utility as a surrogate marker of viral replication. The combined model incorporating both miR-122 and HCV core antigen (AUC = 0.95) outperformed either marker individually, consistent with the principle that multi-marker panels improve diagnostic precision.16 This is consistent with broader evidence that multi-biomarker panels enhance prognostic risk-stratification in patients with HCV cirrhosis compared with single markers.18
Regarding predictive performance, baseline miR-122 and HCV core antigen were associated with EOT virological response. This aligns with previous findings identifying miR-122 as a significant independent predictor of treatment response to direct-acting antivirals in chronic HCV.19 The composite biomarker score achieved a predictive AUC of 0.84, suggesting potential value for treatment monitoring. However, because the number of patients with persistent detectable HCV RNA was small, these findings should be interpreted as exploratory and require validation in larger cohorts.
Patients with undetectable HCV RNA at EOT showed a steep decline in both miR-122 and HCV core antigen during treatment, supporting the biological plausibility of these biomarkers as monitoring tools. miR-221 changed during therapy but contributed less diagnostic and predictive information, consistent with its stronger relationship to hepatic injury and fibrogenesis than to active viral replication.
The potential influence of SARS-CoV-2 co-infection on HCV-related miRNA expression merits attention, as co-infected patients may exhibit altered biomarker profiles that could affect the diagnostic and predictive thresholds identified in this study.20, 21, 22, 23, 24 Future studies incorporating co-infected cohorts are warranted.
From a health economics perspective, HCV core antigen testing costs 40–60% less than PCR, and miRNA quantification by RT-qPCR costs less than viral sequencing.25 In Iraq, where DAAs are available through national programs but advanced diagnostic infrastructure is limited,2, 4 these biomarkers could meaningfully expand access to HCV diagnosis and treatment monitoring.
The principal strengths of this study include its prospective design, integrated multi-biomarker evaluation, standardized treatment regimen, and longitudinal follow-up at three treatment time points. Several limitations should be acknowledged. First, the diagnostic ROC analysis compared CHC patients with healthy controls; this case-control design is liable to overestimate real-world performance and does not test discrimination from other liver diseases, in which miR-122 and HCV core antigen may also be altered. Second, PPV and NPV are prevalence-dependent and were estimated in a non-representative case-control sample, so the values reported here should not be transferred directly to populations with different HCV prevalence. Third, the number of patients with persistent detectable HCV RNA at EOT was small, limiting the precision of predictive analyses. Fourth, post-treatment SVR12 follow-up was not available for all participants; therefore, the outcome was conservatively defined as EOT virological response, with SVR12 outcomes to be reported separately. Additional limitations include the single-center setting, absence of liver biopsy data for fibrosis staging, and lack of external validation. Co-infection with other hepatotropic viruses, such as hepatitis G virus 26, was not assessed in this cohort.
5. Conclusion
This study demonstrates that serum miR-122 and HCV core antigen are clinically relevant alternative or complementary biomarkers for the diagnosis of chronic hepatitis C and for predicting EOT virological response in Iraqi patients receiving direct-acting antiviral therapy. The combined biomarker panel achieved a diagnostic AUC of 0.95, showed favorable longitudinal kinetics during therapy, and may facilitate cost-conscious monitoring strategies in resource-constrained settings. External validation in larger and more diverse cohorts is required before the composite score can be recommended for routine clinical use.
Availability of data and materials
Data will be provided upon receiving a valid request.
CRediT authorship contribution statement
Basheer Saleem Obaid Al-Mawla: Writing – review & editing, Writing – original draft, Software, Project administration, Methodology, Investigation, Data curation, Conceptualization. Mothana Ali Khalil: Writing – review & editing, Supervision, Software, Project administration, Investigation, Conceptualization. Yasin H. Majeed: Writing – review & editing, Supervision, Software, Project administration, Methodology.
Ethics approval
The study protocol was approved by the Institutional Review Board of the College of Medicine, University of Anbar, Iraq (Approval No. 2024–305). All procedures were conducted in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants.
Declarations.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors acknowledge Al-Ramadi Teaching Hospital and the College of Medicine, University of Anbar, for their support of this work.
Contributor Information
Basheer Saleem Obaid Al-Mawla, Email: bas23m0003@uoanbar.edu.iq.
Mothana Ali Khalil, Email: med.mu.707070@uoanbar.edu.iq.
Yasin H. Majeed, Email: med.yasin.hamad@uoanbar.edu.iq.
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Data Availability Statement
Data will be provided upon receiving a valid request.