Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study

Abstract

Background

Cervical cancer (CC) is a leading cause of death among women worldwide, predominantly driven by high-risk human papillomavirus infection. MicroRNAs (miRNAs) regulate gene expression and may serve as noninvasive biomarkers for diagnosis, prognosis, and treatment monitoring in CC.

Objective

This study aimed to identify circulating miRNAs linked to HPV-related CC and evaluate their potential as biomarkers for prognosis and response to concurrent chemoradiotherapy (CRT).

Methods

In this prospective study, plasma samples from 36 CC patients were collected before and after treatment. Five miRNAs (hsa-miR-1-3p, hsa-miR-10a-5p, hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p) were selected via literature review and pathway analysis. miRNA levels were quantified by quantitative polymerase chain reaction and normalized to hsa-miR-16. Associations with clinicopathological features and survival were analyzed statistically.

Results

Pathway analysis confirmed the involvement of selected miRNAs in cancer and HPV-related pathways, including PI3K-Akt and MAPK signaling. Post-CRT, significant downregulation of hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p was observed (p < 0.05), indicating their potential as treatment response markers. Baseline hsa-miR-1-3p expression was significantly associated with Federation of Gynecology and Obstetrics stage (p = 0.043). No miRNAs showed significant associations with overall survival.

Conclusions

Circulating miRNAs, especially hsa-miR-34a-5p, hsa-miR-409-3p, and hsa-miR-34c-5p hold promise as noninvasive biomarkers for monitoring treatment efficacy in CC. Larger studies are needed to validate these findings and clarify their prognostic value.

Keywords

MicroRNA cervical cancer chemoradiotherapy plasma biomarkers

Introduction

Cervical cancer (CC) is a major cause of death among women globally and poses a significant health challenge.¹ According to the World Health Organization's most recent data, over 90% of CC-related deaths happened in low- or middle-income nations in 2018, and there were around 570,000 new instances of CC detected worldwide.^2,3 Cervical cancer is a significant public health issue, particularly in areas where women do not have sufficient access to comprehensive medical care. The uncontrolled growth and division of cervical tissue cells, which strays from the normal cell division process, can lead to cervical intraepithelial neoplasia (CIN), classified into stages CIN-I to CIN-III. CIN-I represents mild dysplasia, where some cells show abnormal changes. As the condition progresses to CIN-II, there is moderate dysplasia with a greater number of abnormal cells. Finally, CIN-III indicates severe dysplasia, where the abnormal cells cover a significant portion of the cervical epithelium. If left untreated, these changes can ultimately progress to CC, a form of malignant tumor.^4,5 Furthermore, individuals with advanced stages of CC experience a significant disease burden and have a poor prognosis.⁶

The development of CC is triggered by infection with high-risk human papillomavirus (HR-HPV). High-risk HPV infections, especially those caused by HPV types 16 and 18, have been recognized as major contributors to the development of CC in many individuals.⁷ The HPV genome encodes several oncoproteins, notably E6 and E7, which are crucial for HPV carcinogenesis. E6 interacts with E6-AP, leading to the degradation of the p53 tumor suppressor, while E7 binds to the retinoblastoma (Rb) protein, disrupting the Rb-E2F pathway. Together, these actions promote genetic instability and advance CC progression.⁶

Despite progress in treatment and early detection, there are still significant gaps in the prognosis and monitoring of CC.⁸ For example, research by Liu et al. underscored the significance of anemia and tumor size as key indicators of overall survival rates in patients undergoing surgery and concurrent chemoradiotherapy (CRT) for CC.⁹ Furthermore, a review by Arip et al. pointed out the necessity for extensive clinical studies to confirm the relevance of newly identified biomarkers.¹⁰ Additionally, Ding et al. created a machine learning model based on microRNAs (miRNAs) for survival prediction, revealing that existing clinical features do not provide adequate accuracy for prognosis predictions.⁸ Current methods for controlling and monitoring the effects of chemotherapy and radiotherapy in CC patients include imaging techniques such as MRI and CT scans, blood tests for tumor markers, and histopathological evaluations.¹¹

There is a growing focus on noninvasive liquid biopsy (LB) as a complementary and potentially alternative method to address existing limitations in current diagnostic approaches and disease management.^12,13 Liquid biopsy is a technique that extracts tumor-derived biomarkers from various body fluids, including blood, urine, saliva, feces, cerebrospinal fluid, and cervical-vaginal fluid. This method can detect several circulating tumor components, such as circulating tumor cells, circulating tumor DNA, messenger RNA (mRNA), noncoding RNA, extracellular vesicles, and tumor-educated platelets.¹⁴ Essentially, LB serves as a noninvasive diagnostic tool that facilitates the early detection and monitoring of cancer.^12,13,15 Noncoding RNAs play crucial roles in regulating gene expression and are classified into various types, with miRNAs.¹⁶ These molecules are short, single-stranded RNAs that are about 22 nucleotides long and play a role in negatively regulating posttranscriptional gene expression. They achieve this by targeting the 3′ untranslated regions of mRNAs, which results in either the degradation of the mRNAs or the inhibition of their translation.¹⁷ Changes in the expression levels of various miRNAs have been linked to the progression of CC.¹⁸ In recent years, numerous studies have identified alterations in these molecules across various diseases, underscoring their significance in understanding multiple health conditions. However, there have been relatively few investigations that provide a thorough analysis of the role of miRNAs in the management and treatment monitoring of CC.¹⁹

The primary aim of this study is to identify specific miRNAs associated with human HPV infection in CC. We will evaluate these miRNAs as potential biomarkers for prognosis and treatment monitoring. By establishing a link between specific miRNA profiles and clinical outcomes in CC patients, this research seeks to improve prognosis and inform personalized treatment strategies.

Materials and methods

Ethical approval and compliance

This prospective study was conducted in accordance with the Declaration of Helsinki (1975, revised 2024). The study protocol received approval from the Research Ethics Committee of Tabriz University of Medical Sciences (Tabriz, Iran; approval code: IR.TBZMED.REC.1400.438; date of approval: 2 August 2021). The reporting of this observational study conforms to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.²⁰ All patient details (including names, hospital IDs, and contact information) were de-identified prior to analysis to protect privacy. Clinical data were anonymized using study-specific codes, with the key stored separately in password-protected files accessible only to the principal investigator. Only de-identified aggregate data are presented in this publication. Clinical trial number: not applicable. During manuscript preparation, AI-assisted tools (Microsoft Copilot) were used exclusively for grammar checking and language refinement. All scientific content, data interpretation, and conclusions remain entirely the work of human authors.

Selection method of miRNAs related to HPV-related CC

This study conducted a comprehensive review of miRNAs dysregulated in CC patients (Supplemental Table 1). We identified five specific miRNAs: hsa-miR-1-3p,^10,21 hsa-miR-10a-5p,^22,23 hsa-miR-34a-5p,²⁴ hsa-miR-34c-5p,²⁵ and hsa-miR-409-3p,^26,27 based on their established roles in CC pathogenesis. To assess their association with CC and HPV infection pathways, we performed an in silico analysis using the Gene Union miRPath-v4 miRNA-centric analysis tool.

Human specimens

This prospective observational study compared miRNA expression before and after CRT in 36 newly diagnosed CC patients after obtaining informed consent. Patients were consecutively enrolled between September 2021 and September 2024. Inclusion criteria for the study required participants to have a histologically confirmed diagnosis of primary CC and to be scheduled for CRT without any prior treatment. Patients were excluded if they were pregnant or had concurrent malignancies. A 6 ml blood samples were collected from patients at three medical centers: Tabriz Madani Hospital (affiliated with Tabriz University of Medical Sciences), the Cancer Institute of Tehran University of Medical Sciences, and Ardabil Alavi Hospital (affiliated with Ardabil University of Medical Sciences). Pretreatment samples were obtained from all patients, and posttreatment samples were successfully collected from 25 patients. The remaining 11 patients did not provide posttreatment samples due to death during follow-up (primarily in advanced-stage cases), loss to contact, or withdrawal of consent.

Following collection, blood samples were centrifuged at 3000 g for 15 min to isolate plasma, which was stored at −80°C until RNA extraction.

RNA extraction and miRNA quantification by reverse transcription quantitative polymerase chain reaction

Total RNA including miRNAs was isolated from plasma samples using magnetic beads specifically optimized for small RNA extraction (Zybio, China), following the manufacturer's protocol to ensure efficient recovery of miRNAs. The concentration and purity of the isolated RNA were measured using a NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific), assessing absorbance ratios (A260/A280 and A260/A230) to confirm RNA integrity suitable for downstream applications.

Reverse transcription (RT) of miRNAs into complementary DNA (cDNA) was performed using a dedicated cDNA synthesis kit (Bon Yakhteh, Iran), optimized for short RNA templates like miRNAs. This RT step included the use of specific stem-loop or sequence-specific primers tailored for each miRNA of interest to enhance specificity.

Quantitative polymerase chain reaction (qPCR) analysis was performed using a SYBR Green fluorescence detection system (SYBR Green Premix Kit, Amplicon, Denmark) on a QIAquant 96 2plex real-time PCR instrument (QIAGEN). Five target miRNAs—hsa-miR-1-3p, hsa-miR-10a-5p, hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p—were amplified using specific forward primers paired with a universal reverse primer designed for miRNA amplification. Primer sequences are detailed in Supplemental Table 2.The relative expression levels of target miRNAs were calculated using the 2^ΔΔCt method, normalizing Ct values to hsa-miR-16 and comparing to control or baseline samples. Each qPCR reaction and RT reaction were performed in triplicate to ensure reproducibility.

Statistical analysis

Statistical analyses were performed using SPSS v28 (IBM) and Prism 8.3 (GraphPad). Continuous variables are presented as mean ± standard deviation, and categorical variables as frequencies (%). Normality was assessed using Shapiro–Wilk tests. For the paired cohort (n = 25), pre- and posttreatment miRNA expression (2^−ΔΔCt) were compared using paired t-tests. Associations between baseline ΔCt values and clinicopathological features in all patients (n = 36) were analyzed using independent t-tests or Mann–Whitney U tests (for binary variables), one-way ANOVA or Kruskal–Wallis tests (for multigroup comparisons), and Pearson/Spearman correlations (for continuous variables). Associations between baseline miRNA expression levels (ΔCt values) and clinicopathological features were assessed using multivariate general linear models.

For survival analysis, Kaplan–Meier curves with log-rank tests compared survival between high/low miRNA groups (stratified by median ΔCt), using first sampling date as time-zero for all patients (n = 36), while for the paired subset (n = 25), posttreatment 2^−ΔΔCt values were analyzed with survival time calculated from the second sampling date.

Given the biological and clinical heterogeneity between patients with and without posttreatment samples, all treatment-related miRNA analyses were restricted to the 25 patients with paired samples. The remaining 11 patients were excluded from these analyses due to the inability to assess treatment effects without posttreatment timepoints.

Results

Pathway analysis of selected miRNAs using DIANA-miRPath v40

The analysis utilized the DIANA-miRPath v4.0 webserver to identify pathways associated with the selected miRNAs. Results were exported to Excel for further analysis, organizing data by the number of target genes (Target Genes (n)) to prioritize pathways with the highest target counts. The top five pathways identified, based on the number of target genes, are:

Pathways in Cancer: 103 target genes, p = 1.14247E-08, FDR = 9.06192E-07.

PI3K-Akt Signaling Pathway: 70 target genes, p = 1.72057E-06, FDR = 2.17302E-05.

MAPK Signaling Pathway: 66 target genes, p = 3.22922E-07, FDR = 8.04066E-06.

MicroRNAs in Cancer: 63 target genes, p = 5.28243E-06, FDR = 5.45851E-05.

HPV: 63 target genes, p = 0.001678499, FDR = 0.006504183.

These results indicate a significant association between the selected miRNAs and key pathways involved in cancer development and HPV infection, suggesting their potential roles in CC pathogenesis.

Patient characteristics

The clinicopathological characteristics of these patients are summarized in Table 1. The cohort comprised 36 CC patients with a mean age of 50.85 ± 12.79 years, distributed across Federation of Gynecology and Obstetrics (FIGO) stages as follows: Stage I (38.8%, n = 14), Stage II (33.3%, n = 12), Stage III (22.2%, n = 8), and Stage IV (5.5%, n = 2). Histopathological analysis identified squamous cell carcinoma in 69.4% (n = 25) and adenocarcinoma in 30.5% (n = 11) of cases. Lymph node metastasis was present in 55.8% (n = 19/34), while distant metastases occurred in 44.4% (n = 16). Eighteen patients succumbed to advanced disease during follow-up. Primary treatments included CRT for 72.2%, radiotherapy alone for 25%, and chemotherapy alone for 2.7%. Treatment duration data for the 26 patients indicated the following distribution: less than 6 months (57.69%), between 6 and 12 months (38.46%), and greater than 12 months (3.85%). The complete baseline miRNA expression profiles (ΔCt values) across these clinical parameters are detailed in Table 1.

Table 1.

Associations between baseline miRNA level (ΔCt) and clinicopathological features.

Parameters	No. (%)	hsa-miR-1-3p		hsa-miR-10a-5p		hsa-miR-409-3p		hsa-miR-34a-5p		hsa-miR-34c-5p
Parameters	No. (%)	Mean ± SD	p value	Mean ± SD	p value	Mean ± SD	p value	Mean ± SD	p value	Mean ± SD	p value
Age (mean ± SD)	50.85		0.97		0.84		0.51		0.95		0.48
FIGO stage
Stage I	14 (38.8)	0.79 ± 3.07	0.043 ^#	−13.06 ± 1.57	0.42	1.36 ± 2.45	0.29	1.05 ± 1.77	0.40	2.55 ± 2.44	0.062
Stage II	12 (33.3)	−0.3 ± 2.84		−12.28 ± 2.24		2.32 ± 2.91		1.65 ± 2.56		3.54 ± 2.76
Stage III	8 (22.2)	1.84 ± 4.35		−12.58 ± 2.88		1.58 ± 2.86		1.61 ± 2.50		2.90 ± 2.25
Stage IV	2 (5.5)	−5.67 ± 0.74		14.87 ± 0.38		1.60 ± 0.32		−1.13 ± 0.02		−1.84 ± 1.36
Histology
Squamous cell carcinoma	25 (69.4)	−0.022 ± 3.90	0.42	−12.97 ± 2.02	0.45	1.08 ± 2.31	0.1	0.93 ± 1.96	0.2	2.57 ± 2.74	0.28
Adenocarcinoma	11 (30.5)	1.031 ± 2.58	0.42	−12.39 ± 2.39	0.45	2.67 ± 3.28	0.1	1.97 ± 2.64	0.2	3.02 ± 2.57	0.28
Lymph node metastasis (LNM)
Present	19 (55.8)	0.55 ± 3.95	0.82	−12.52 ± 2.32	0.58	1.85 ± 2.78	0.66	1.53 ± 2.32	0.58	2.70 ± 2.86	0.92
Absent	15 (44.1)	0.26 ± 3.27	0.82	−13.05 ± 2.02	0.58	1.43 ± 2.76	0.66	1.10 ± 2.18	0.58	2.79 ± 2.66	0.92
Metastasis
Present	16(44.4)	−0.15 ± 4.12	0.5	−12.82 ± 2.65	0.64	1.18 ± 2.98	0.3	1.55 ± 3.04	0.85	3.07 ± 3.5	0.77
Absent	20 (55.5)	0.69 ± 2.94	0.5	−12.47 ± 2.02	0.64	2.13 ± 2.60	0.3	1.39 ± 2.09	0.85	2.79 ± 2.42	0.77
Treatment type
Chemotherapy	1 (2.7)	0.40 ± 0.0	0.74	−14.10 ± 0.0	0.73	−3.5	0.11	−0.31 ± .0.0	0.77	1.81 ± 0.0	0.64
Radiotherapy	9 (25)	−.53 ± 2.52		−12.28 ± 2.44		2.51 ± 2.38		1.67 ± .3.16		3.7 ± 3.93
Chemoradiotherapy (CRT)	26 (72.2)	0.54 ± 3.84		−12.70 ± 2.33		1.62 ± 2.78		1.47 ± 2.40		2.71 ± 2.61
Duration of treatment (months)
<6 months	15 (57.69)	−1.26 ± 2.76	0.61	−12.81 ± 3.73	0.35	0.92 ± 2.68	0.21	1.12 ± 2.42	0.32	2.15 ± 2.97	0.36
6–12 months	10 (38.46)	−0.33 ± 2.56		−12.12 ± 2.73		1.99 ± 2.79		1.89 ± 3.04		3.42 ± 3.63
>12 months	1 (3.85)	0.5 ± 0.0		−9.29 ± 0.0		5.59 ± 0.0		5.17 ± 0.0		6.29 ± 0.0

The FIGO stage showed a statistically significant association with hsa-miR-1-3p expression (p = 0.043), with Stage IV demonstrating significantly lower value (higher expression) compared to Stage III. Data presented as n (%) for categorical variables and mean ± SD for continuous variables.

FIGO: International Federation of Gynecology and Obstetrics staging system; miRNA: microRNA. Bold value indicates statistical significance (p < 0.05).

Posttreatment changes in oncogenic miRNA expression profiles in cc patients

In a cohort of 25 cervical CC patients with both pretreatment and posttreatment samples, we observed significant alterations in the expression levels of specific oncogenic miRNAs following CRT. Notably, three miRNAs—hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p—exhibited statistically significant reductions in expression levels posttreatment (p < 0.05). In contrast, the expression levels of hsa-miR-1-3p and hsa-miR-10a-5p remained unchanged after treatment (p > 0.05). These findings suggest that hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p may serve as potential biomarkers for monitoring treatment responses in CC patients (Figure 1).

Figure 1.

Treatment-associated changes in plasma oncogenic microRNA (miRNA) expression following chemoradiotherapy (CRT) in cervical cancer (CC) patients (n = 25 matched pre/posttreatment pairs). Data represent mean relative expression ratio (fold change) ± SD, calculated as posttreatment/pretreatment levels normalized to hsa-miR-16-5p. *p < 0.05 by paired Student's t-test (all data passed Shapiro–Wilk normality testing, p > 0.05). Panels show: (A) hsa-miR-1-3p (p = 0.34), (B) hsa-miR-10a-5p (p = 0.24), (C) hsa-miR-34a-5p (p = 0.023), (D) hsa-miR-34c-5p (p = 0.039), and (E) hsa-miR-409-3p (p = 0.049).

Association between miRNA profiles and clinicopathological features

Our analysis revealed distinct miRNA expression profiles with specific clinical associations (Table 1). Most notably, baseline hsa-miR-1-3p levels showed significant variation by FIGO stage (p = 0.043), with Stage IV patients exhibiting markedly lower expression compared to Stage III (mean ΔCt difference: −7.51, 95% CI: −14.53 to −0.48). This suggests potential stage-dependent regulation of hsa-miR-1-3p in CC progression. No other significant associations were observed between the remaining miRNAs (hsa-miR-10a-5p and hsa-miR-34a-5p) and clinicopathological features (all p > 0.05).

Multivariate analysis revealed only marginal associations between clinicopathological factors and miRNA levels. Federation of Gynecology and Obstetrics stage showed a trend with miR-1-3p (p = 0.070, η² = 0.333), suggesting higher expression in advanced stages. A moderate interaction between histology and LNM was observed (multivariate p = 0.140, η² = 0.439), though no individual miRNAs reached significance. No other factors (age, metastasis status) showed significant associations. The low model fit (Adjusted R² ≤ 0.106) indicates limited explanatory power of these clinical variables on miRNA expression (see Supplementary Table 3).

Kaplan–Meier survival analysis of miRNA expression

Analysis of baseline (n = 36) and posttreatment (n = 25) miRNA expression levels revealed no statistically significant associations with patient survival outcomes. Kaplan–Meier analysis stratified by median miRNA expression (using ΔCt values for baseline and 2^−ΔΔCt for posttreatment) showed no significant survival differences between high- and low-expression groups for any miRNAs (log-rank p > 0.05) (see Supplementary Table 4 and Figures 2 and 3).

Discussion

Cervical cancer is a genetic and pathological condition where the dysregulation of miRNAs may significantly influence various characteristics associated with cancer.²⁸ Numerous techniques, such as the Pap smear, colposcopy, and biopsy, are currently widely utilized to diagnose precancerous cervical lesions.²⁹ Consequently, this malignancy continues to be one of the most significant health concerns globally, as most HR HPV infections are asymptomatic for one to two years.³⁰ The dysregulation of miRNAs has been well-documented across various stages in patients with underdeveloped CC, highlighting their essential role in the disease's progression.³¹ Current treatments for CC, including surgery, radiotherapy, chemotherapy, and immunotherapy, still result in 30%–60% of patients developing recurrence or metastasis due to limitations in follow-up monitoring.^32,33 Standard imaging may not detect minimal residual disease (MRD), risking undertreatment, while delayed tumor resolution after concurrent CRT can lead to overtreatment.³⁴ Liquid biopsy tools show promise in identifying MRD and managing treatment. Our study demonstrates the efficacy of concurrent CRT by analyzing changes in oncogenic HPV-related miRNA. These findings could enhance imaging assessments and support better monitoring of treatment efficacy and recurrence risk. Ding et al. study demonstrated the efficacy of machine learning models utilizing miRNA profiles for survival predictions in CC, reinforcing the potential of miRNAs to improve prognostic accuracy.⁸

Figure 2.

Kaplan–Meier survival analysis of posttreatment microRNA (miRNA) expression levels (n = 25 patients). Kaplan–Meier plots show overall survival stratified by median expression levels for five miRNAs (hsa-miR-1-3p, hsa-miR-10a-5p, hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p) in posttreatment samples from 25 patients. High and low expression groups were defined according to 2^−ΔΔCt values relative to the median for each miRNA. Censored observations are indicated by tick marks. No statistically significant differences in survival were observed between high- and low-expression groups for any of the miRNAs (log-rank p > 0.05 for all comparisons). These findings indicate that posttreatment miRNA expression levels were not associated with patient survival outcomes in this study.

Figure 3.

Kaplan–Meier survival analysis of baseline microRNA (miRNA) expression levels (n = 36 patients). Kaplan–Meier survival curves shows for overall survival stratified by median baseline expression levels of five mentioned miRNAs measured in 36 patients. Expression groups were defined using ΔCt values relative to the cohort median for each miRNA. Censored observations are marked with tick symbols on the survival curves. No statistically significant differences in survival were observed between the high- and low-expression groups for any of the miRNAs analyzed (log-rank p > 0.05). These data indicate that baseline miRNA expression levels do not significantly associate with patient survival outcomes in this study.

The pathway analysis conducted using DIANA-miRPath v4.0 highlights the significant roles that selected miRNAs play in critical biological pathways associated with CC and HPV infection. The identification of pathways in CC as the most enriched pathway, with 103 target genes, underscores the complexity of cancer biology and the multifaceted roles of miRNAs in regulating oncogenic processes. This finding suggests that these miRNAs may contribute to various mechanisms involved in tumorigenesis, including cell proliferation, apoptosis, and metastasis.

The PI3K-Akt signaling pathway and MAPK signaling pathway are also notable, with 70 and 66 target genes, respectively. These pathways are pivotal in cellular growth and survival, emphasizing the importance of miRNA dysregulation in cancer progression. The involvement of these pathways aligns with existing literature that links aberrant signaling to cancer aggressiveness and treatment resistance.

The significant reductions in the expression levels of hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p posttreatment suggest that these miRNAs may play a role in the response to treatment. The observed changes in expression levels indicate potential involvement in the mechanisms of treatment efficacy and resistance. These findings support the hypothesis that specific miRNAs could serve as biomarkers for monitoring treatment responses, allowing for personalized therapeutic approaches in CC management.

The distinct miRNA expression profiles associated with FIGO stage highlight the potential for miRNAs to serve as indicators of disease progression. The significant variation in hsa-miR-1-3p levels across stages suggests a role in tumor biology that warrants further exploration. The trend observed in multivariate analysis points to the complexity of interactions between FIGO stages and baseline hsa-miR-1-3p expression level, emphasizing the need for comprehensive models to elucidate these relationships.

The limited explanation of clinical variables on miRNA expression suggests that additional factors, possibly including genetic and epigenetic modifications, may play critical roles in miRNA regulation and should be considered in future studies.³⁵

The Kaplan–Meier survival analysis, revealing no significant associations between miRNA expression levels and patient survival outcomes, raises important questions regarding the prognostic utility of these miRNAs. While prior studies have indicated potential correlations between specific miRNAs and survival in various cancers, our findings suggest that miRNA expression alone may not be sufficient to predict outcomes in CC patients.^36,37 This underscores the necessity of integrating miRNA profiles with other clinical and molecular factors to develop more accurate prognostic models.

This study provides a robust foundational contribution to the field by identifying differentially expressed circulating miRNAs (notably hsa-miR-34a-5p, hsa-miR-409-3p, and hsa-miR-34c-5p) with clear biological plausibility based on prior functional evidence. Strengths include rigorous analytical methods, careful matching of clinical groups, and a focus on noninvasive biomarkers that have potential diagnostic and therapeutic relevance. Although further work is warranted (in vitro validation, 3′ UTR reporter assays to confirm direct targets, and correlation with matched tumor biopsies), our findings establish a strong rationale and prioritized targets for those follow-up studies.

A limitation of our study is the relatively small sample size, which may have resulted in reduced statistical power. Additionally, the relatively short follow-up period and the limited number of participants, combined with the absence of recurrence evaluation, constrain both the statistical power and the duration of the survival analyses presented. Further research is warranted to elucidate the underlying mechanisms by which these miRNAs influence or are influenced by the observed clinicopathological features. Prospective studies with larger patient cohorts are needed to validate these findings and assess the clinical utility of these miRNAs as biomarkers in CC management. Additionally, it is essential to specify the selection of miRNAs using extensive datasets and to conduct enrichment analyses alongside principal component analysis. Understanding the functional roles of these miRNAs in CC biology may reveal new therapeutic targets or strategies for improving patient outcomes.

Conclusion

Our findings suggest that plasma levels of hsa-miR-34a-5p, hsa-miR-34c-5p, and hsa-miR-409-3p may decrease following CRT in CC patients. While these miRNA changes could potentially serve as treatment response biomarkers, our observational study design and limited sample size preclude definitive conclusions. Further validation in larger, controlled cohorts is needed to establish their clinical utility.

Supplemental Material

sj-docx-1-sci-10.1177_00368504251380636 - Supplemental material for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study

Supplemental material, sj-docx-1-sci-10.1177_00368504251380636 for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study by Masoumeh Parvizi, Mohammad Taghizadeh-Teymorloei, Maryam Vaezi, Masoumeh Bakhshandeh, Noushin Mobaraki-Asl, Ebrahim Esmati, Reza Eghdam-Zamiri, Farhad Jeddi and Abbas Karimi in Science Progress

Supplemental Material

sj-docx-2-sci-10.1177_00368504251380636 - Supplemental material for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study

Supplemental material, sj-docx-2-sci-10.1177_00368504251380636 for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study by Masoumeh Parvizi, Mohammad Taghizadeh-Teymorloei, Maryam Vaezi, Masoumeh Bakhshandeh, Noushin Mobaraki-Asl, Ebrahim Esmati, Reza Eghdam-Zamiri, Farhad Jeddi and Abbas Karimi in Science Progress

Supplemental Material

sj-docx-3-sci-10.1177_00368504251380636 - Supplemental material for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study

Supplemental material, sj-docx-3-sci-10.1177_00368504251380636 for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study by Masoumeh Parvizi, Mohammad Taghizadeh-Teymorloei, Maryam Vaezi, Masoumeh Bakhshandeh, Noushin Mobaraki-Asl, Ebrahim Esmati, Reza Eghdam-Zamiri, Farhad Jeddi and Abbas Karimi in Science Progress

Supplemental Material

sj-docx-4-sci-10.1177_00368504251380636 - Supplemental material for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study

Supplemental material, sj-docx-4-sci-10.1177_00368504251380636 for Effects of chemoradiotherapy on plasma oncogenic miRNAs as biomarkers in cervical cancer patients: A prospective observational study by Masoumeh Parvizi, Mohammad Taghizadeh-Teymorloei, Maryam Vaezi, Masoumeh Bakhshandeh, Noushin Mobaraki-Asl, Ebrahim Esmati, Reza Eghdam-Zamiri, Farhad Jeddi and Abbas Karimi in Science Progress

Footnotes

Acknowledgment

The authors would like to thank the personnel of the Molecular Labs at the Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, for their support. During manuscript preparation, AI-assisted tools (Microsoft Copilot) were used exclusively for grammar checking and language refinement. All scientific content, data interpretation, and conclusions remain entirely the work of human authors.

ORCID iDs

Masoumeh Parvizi

Mohammad Taghizadeh-Teymorloei

Maryam Vaezi

Abbas Karimi

Ethics approval

The systematic review and meta-analysis utilized published data from indexed journals. The analysis was conducted under ethical approval IR.TBZMED.REC.1400.438.

Patient consent

Written informed consent was obtained from all patients participating in the study.

Author contributions

Masoumeh Parvizi contributed to data curation; investigation; methodology; software; validation; writing—original draft; and writing—review and editing. Mohammad Taghizadeh-Teymorloei contributed to data curation; investigation; methodology; software; validation; writing—original draft; and writing—review and editing. Masoumeh Bakhshandeh, Maryam Vaezi, Noushin Mobaraki-Asl, Ebrahim Esmati, and Reza Eghdam-Zamiri contributed to investigation and writing—review and editing. Farhad Jeddi contributed to investigation; data curation, supervision, writing—review and editing. Abbas Karimi contributed to conceptualization; data curation; funding acquisition; investigation; methodology; project administration; supervision; validation; and writing—review and editing.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is part of a PhD project supported by the Tabriz University of Medical Sciences under grant number 67334.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data that support the findings of this study are available from the corresponding author.

Supplemental material

Supplemental material for this article is available online.

References

Momenimovahed

Mazidimoradi

Maroofi

, et al. Global, regional and national burden, incidence, and mortality of cervical cancer. Cancer Rep (Hoboken) 2023; 6: e1756. 20221221.

Organization WH . Cervical cancer, https://www.who.int/news-room/fact-sheets/detail/cervical-cancer (accessed 5 March 2024).

Stelzle

Tanaka

Lee

, et al. Estimates of the global burden of cervical cancer associated with HIV. Lancet Glob Health 2021; 9: e161–e169.

Uddin

KMM

Sikder

Hasan

MN.

A comparative study on machine learning classifiers for cervical cancer prediction: a predictive analytic approach. EAI Endors Trans Internet Things 2025; 11: 1–12. Article.

Divine

Huh

. Tertiary prevention of cervical cancer. Clin Obstet Gynecol 2014; 57: 316–324.

Kusakabe

Taguchi

Sone

, et al. Carcinogenesis and management of human papillomavirus-associated cervical cancer. Int J Clin Oncol 2023; 28: 965–974. 20230609.

Lei

Ploner

Lagheden

, et al. High-risk human papillomavirus status and prognosis in invasive cervical cancer: a nationwide cohort study. PLoS Med 2018; 15: e1002666. 20181001.

Ding

Lang

Zou

, et al. Machine learning-based prediction of survival prognosis in cervical cancer. BMC Bioinformatics 2021; 22: 31.

Liu

Y-M

L-Q

Wang

S-S

, et al. Outcome and prognostic factors in cervical cancer patients treated with surgery and concurrent chemoradiotherapy: a retrospective study. World J Surg Oncol 2018; 16: 18.

10.

Arip

Tan

Jayaraj

, et al. Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review. Discov Oncol 2022; 13: 91.

11.

Peterson

Rose

Mink

, et al. Real-Time monitoring and evaluation of a visual-based cervical cancer screening program using a decision support job aid. Diagnostics 2016; 6: 20.

12.

Taghizadeh-Teymorloei

Alizadeh

Matin

, et al. Diagnostic and prognostic significance of ALU-based cell-free DNA in colorectal cancer: a systematic review and meta-analysis. Front Oncol 2024; 14: 1398062. Systematic Review.

13.

Taghizadeh

Jafari-Koshki

Jafarlou

, et al. The role of piRNAs in predicting and prognosing in cancer: a focus on piRNA-823 (a systematic review and meta-analysis). BMC Cancer 2024; 24: 484. 20240416.

14.

Taghizadeh-Teymorloei

Jafarlou

Matin

, et al. Clinical implications of alu-based cell-free DNA and serum onco-piRNA monitoring in colorectal cancer management. Clin Transl Oncol 2025; 27: 3449–3457.

15.

Batool

Yekula

Khanna

, et al. The liquid biopsy consortium: challenges and opportunities for early cancer detection and monitoring. Cell Rep Med 2023; 4: 101198. 20230915.

16.

Yadav

Mathan

Dubey

, et al. The emerging role of non-coding RNAs (ncRNAs) in plant growth, development, and stress response signaling. Noncoding RNA 2024; 10: 13.

17.

Larivera

Neumeier

Meister

. Post-transcriptional gene silencing in a dynamic RNP world. Biol Chem 2023; 404: 1051–1067.

18.

Feitosa

PSF

Fernandes

Pereira

, et al. Alterations in miRNA expression and their role in the pathogenesis of cervical cancer. Asian Pac J Cancer Prev 2024; 25: 3533–3541. Article.

19.

Condrat

Thompson

Barbu

, et al. miRNAs as biomarkers in disease: latest findings regarding their role in diagnosis and prognosis. Cells 2020; 9: 276 20200123.

20.

von Elm

Altman

Egger

, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 2008; 61: 344–349.

21.

Granados-López

Ruiz-Carrillo

Servín-González

, et al. Use of mature miRNA strand selection in miRNAs families in cervical cancer development. Int J Mol Sci 2017; 18: 407.

22.

Feng

Jin

, et al. Upregulation of miRNA-10a-5p promotes tumor progression in cervical cancer by suppressing UBE2I signaling. J Obstet Gynaecol 2023; 43: 2171283.

23.

Mitra

Elangovan

Cervical cancer development, chemoresistance, and therapy: a snapshot of involvement of microRNA. Mol Cell Biochem 2021; 476: 4363–4385. 20210828.

24.

Zhang

Wang

, et al. MicroRNA-152 acts as a tumor suppressor MicroRNA by inhibiting Krüppel-like factor 5 in human cervical cancer. Oncol Res 2019; 27: 335–340. 20180821.

25.

Córdova-Rivas

Fraire-Soto

Mercado-Casas Torres

, et al. 5p And 3p strands of miR-34 family members have differential effects in cell proliferation, migration, and invasion in cervical cancer cells. Int J Mol Sci 2019; 20: 545. 20190128.

26.

Zhang

, et al. MicroRNA-409 inhibits the proliferative ability of cervical carcinoma cells by regulating AKT. Eur Rev Med Pharmacol Sci 2018; 22: 936–942.

27.

Shukla

Varghese

Kabekkodu

, et al. Enumeration of deregulated miRNAs in liquid and tissue biopsies of cervical cancer. Gynecol Oncol 2019; 155: 135–143. 20190818.

28.

Causin

Freitas

AJA

Hidalgo Filho

CMT

, et al. A systematic review of MicroRNAs involved in cervical cancer progression. Cells 2021; 10: 668. 20210317.

29.

Wentzensen

Walker

Gold

, et al. Multiple biopsies and detection of cervical cancer precursors at colposcopy. J Clin Oncol 2015; 33: 83–89. 20141124.

30.

Arbyn

Simon

Peeters

, et al. 2020 List of human papillomavirus assays suitable for primary cervical cancer screening. Clin Microbiol Infect 2021; 27: 1083–1095. 20210508.

31.

Shen

Zhang

Liu

, et al. Potential role of microRNAs in the treatment and diagnosis of cervical cancer. Cancer Genet 2020; 248-249: 25–30. 20200922.

32.

Wright

Dizon

. Management of recurrent or metastatic cervical cancer. UpToDate 2020.

33.

Karimi

Jafari-Koshki

Zehtabi

, et al. Predictive impact of human papillomavirus circulating tumor DNA in treatment response monitoring of HPV-associated cancers; a meta-analysis on recurrent event endpoints. Cancer Med 2023; 12: 17592–17602. 20230726.

34.

Dong

Huang

, et al. Efficacy and safety of PEG-rhG-CSF in preventing chemoradiotherapy-induced neutropenia in patients with locally advanced cervical cancer. Biomol Biomed 2023; 23: 310–316. 20230316.

35.

Nalbant

Akkaya-Ulum

. Exploring regulatory mechanisms on miRNAs and their implications in inflammation-related diseases. Clin Exp Med 2024; 24: 42.

36.

Natarajan

Davies

, et al. miRNAs and long-term breast cancer survival: evidence from the WHEL study. Cancer Epidemiol Biomarkers Prev 2019; 28: 1525–1533. 20190611.

37.

Zografos

Zagouri

Kalapanida

, et al. Prognostic role of microRNAs in breast cancer: a systematic review. Oncotarget 2019: 10: 7156. breast cancer; microRNAs; prognosis; biomarkers.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.06 MB

0.01 MB

0.02 MB