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. 2025 Aug 23;29(6):707–719. doi: 10.1007/s40291-025-00805-6

Diagnostic Potential of Cross-Specimen microRNA Panels as Biomarkers for Colorectal Cancer: A Systematic Review and Meta-analysis

Atta Ullah Khan 1,✉, Maria Ali 2,#, Muhammad Aamir Wahab 1,#
PMCID: PMC12578759  PMID: 40848187

Abstract

Background and Objective

Colorectal cancer remains a major global health challenge, necessitating the development of accurate non-invasive diagnostic tools. Circulating and excretory microRNAs (miRNAs) are promising biomarkers owing to their stability and regulatory roles in tumorigenic pathways. While single miRNA assays often lack sufficient diagnostic accuracy, panels combining multiple miRNAs have shown enhanced performance. This systematic review and meta-analysis evaluated the diagnostic accuracy of multi-miRNA panels and explored their mechanistic relevance to colorectal cancer pathogenesis.

Methods

A comprehensive search of PubMed, Embase, Web of Science, and Scopus was conducted through March 2025 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The study protocol was registered with PROSPERO (CRD420251060655). Eligible studies assessed the diagnostic accuracy of multi-miRNA panels for colorectal cancer using extractable data on sensitivity, specificity, and area under the curve. Data were extracted independently by two reviewers. A bivariate random-effects model was used to calculate pooled diagnostic estimates. Study quality was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, and heterogeneity was evaluated using I2 statistics. Subgroup analyses were conducted by sample type (e.g., plasma, serum, stool) and panel size. Target genes of recurrent miRNAs were mapped to canonical colorectal cancer-related pathways.

Results

Twenty-nine studies comprising 5497 participants (3070 colorectal cancer cases and 2427 controls) and 35 multi-miRNA panels were included. Pooled sensitivity was 0.85 (95% confidence interval 0.80–0.88), specificity was 0.84 (95% confidence interval 0.80–0.88), and the area under the curve was 0.90, despite substantial heterogeneity (I2 > 77%). Panels derived from plasma samples showed the highest balanced performance (sensitivity 0.88; specificity 0.87), while three-miRNA panels exhibited the best diagnostic trade-offs. Mechanistic analysis of 42 recurrent miRNAs revealed consistent involvement in key colorectal cancer pathways, including PI3K/AKT, Wnt/β-catenin, epithelial-mesenchymal transition, angiogenesis, and immune regulation.

Conclusions

Multi-miRNA panels derived from diverse biospecimen sources demonstrate high diagnostic accuracy for colorectal cancer and are mechanistically linked to fundamental oncogenic pathways. Future efforts should focus on panel standardization, biospecimen-specific validation, and integration into clinical workflows to advance precision oncology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40291-025-00805-6.

Key Points

Tests with multiple markers can find colorectal cancer early; studies of nearly 5500 people showed these tests identify about 85% of cancers while rarely giving false positives in healthy people.
Three-marker blood tests showed the best performance among different panel sizes; when comparing panels with different numbers of markers, the three-marker combinations provided the most accurate results for detecting cancer.
These tests need to be made the same everywhere before doctors can use them; scientists must agree on which markers to test, how to handle blood samples, and how to fit these tests into regular cancer screening.
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Introduction

Colorectal carcinoma (CRC) constitutes the third most prevalent malignancy and represents the second leading etiology of cancer-related mortality globally [1]. While the contemporary therapeutic armamentarium continues to evolve, early detection remains the paramount determinant of clinical efficacy; however, conventional diagnostic modalities such as colonoscopy and fecal immunochemical testing are encumbered by inherent limitations including invasiveness, cost-effectiveness constraints, sampling variability, and suboptimal sensitivity profiles [2, 3]. Consequently, there exists a compelling unmet clinical need for non-invasive, highly sensitive, and specific diagnostic tools for early CRC detection.

MicroRNAs (miRNAs) represent a class of small (~22 nucleotide) non-coding RNA molecules that function as post-transcriptional regulators of gene expression through targeted messenger modulation. These molecules exhibit remarkable stability in systemic circulation, demonstrating RNase resistance and detectability in serum, plasma, and fecal specimens, thereby establishing their exceptional candidacy for liquid biopsy applications [4, 5]. In CRC pathogenesis, miRNA dysregulation constitutes a hallmark of tumorigenesis, orchestrating complex oncogenic networks including Wnt/β-catenin, PI3K/AKT, transforming growth factor-β/Smad, epidermal growth factor receptor signaling cascades, epithelial-mesenchymal transition, angiogenesis, apoptotic evasion, and DNA repair mechanisms [6, 7].

An expanding corpus of mechanistic literature has established distinct miRNAs as pivotal regulators of CRC oncogenesis. For instance, miR-21, the most frequently upregulated miRNA in CRC, exerts negative regulatory control over mismatch repair proteins MSH2 and MSH6, thereby promoting microsatellite instability and facilitating tumor progression [8]. miR-137 undergoes epigenetic silencing during early carcinogenesis and functions as a tumor suppressor through targeted inhibition of LSD1 and CDC42, subsequently attenuating proliferative capacity and invasive potential [9]. The let-7 family serves as a classical tumor suppressor by regulating critical oncogenes including RAS and HMGA2 and demonstrates consistent downregulation throughout CRC carcinogenesis [10]. Additionally, miR-125, miR-184, and miR-153 have been implicated in the modulation of cell-cycle progression, angiogenesis, invasion, and metastatic dissemination through direct targeting of key effectors such as insulin-like growth factor-1 receptor and matrix metalloproteinase-9, providing compelling evidence of their in vivo significance [6, 7, 9].

While miRNAs demonstrate considerable potential as non-invasive biomarkers for CRC, their individual diagnostic accuracy remains suboptimal for clinical implementation. In contrast, multi-miRNA panels have consistently demonstrated superior sensitivity and specificity across multiple independent studies. Among various biological matrices, serum-based miRNA panels have exhibited particularly robust diagnostic performance. Notably, specific miRNA combinations (e.g., miR-15b/miR-21, miR-31) have demonstrated enhanced diagnostic efficacy compared with individual markers. These findings substantiate the utility of multi-marker miRNA panels as precise methodologies for early CRC identification [11].

MicroRNA panels represent a promising frontier in CRC detection; however, the prevailing research paradigm has predominantly emphasized diagnostic performance metrics while inadequately integrating the underlying biological mechanisms. The incorporation of mechanistic insights into key oncogenic pathways is fundamental to validating the biological relevance of miRNA signatures, optimizing panel design, and enhancing clinical translational potential [12].

This systematic review and meta-analysis seeks to quantitatively assess the diagnostic accuracy of miRNA panels in CRC detection while contextualizing findings through a comprehensive analysis of dysregulated oncogenic pathways governing cellular proliferation, apoptotic evasion, invasion, and metastatic progression. The objective is to identify panels that demonstrate both diagnostic robustness and mechanistic coherence to facilitate clinical implementation.

Methods

Search Strategy and Study Selection

This systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for diagnostic test accuracy (PRISMA-DTA) guidelines [13]. The study protocol is registered with the PROSPERO international prospective register of systematic reviews bearing registration number CRD420251060655. We performed a systematic search across databases comprising PubMed, Embase, Web of Science, and Scopus. The search strategy was based on free words and MeSH terms, such as: (“microRNA” OR “miRNA” OR “miR”) AND (“colorectal cancer” OR “colon cancer” OR “rectal cancer” OR “CRC”) AND (“diagnosis” OR “biomarker” OR “sensitivity” OR “specificity”). Manual searches of the reference lists of relevant reviews articles were also conducted to find potentially eligible studies. All combined records were imported into a reference manager where de-duplication was performed. Two independent reviewers (MA and MAW) performed title/abstract screening for eligibility and full-text screening. Divergences were solved by consensus or a third reviewer. A detailed search strategy is presented in Table 1 of the Electronic Supplementary Material (ESM).

Table 1.

Mechanistic insight of miRNAs and their functional involvement in CRC-associated pathways

Biologic axis (representative genes) Recurrent miRNAs in ≥20 % of panels Canonical effects in CRC Illustrative panel(s)
1. Proliferation and survival (KRAS, PI3K/AKT, PTEN, BCL-2) miR-21, miR-92a, miR-1246, miR-15b

Activates PI3K/AKT and MAPK signaling

Suppresses tumor suppressors PTEN and PDCD4 [44–47]

Han 2021 (miR-15b/21/31),

Guo 2018 (miR-1246/21)
2. Invasion, EMT, and metastasis (Wnt/β-catenin, TGF-β/SMAD, MMPs) miR-223, miR-200c, miR-31, miR-203

Disrupts E-cadherin via p120-catenin

Activates Wnt/β-catenin and TGF-β pathways [48–51]

Chang 2016 (miR-223/92a),

Huang 2020 (miR-203a-3p/200c-3p)
3. Angiogenesis and hypoxia (VEGF-A, HIF-1α) miR-18a, miR-210, miR-19a/b Stabilizes HIF-1α and up-regulates VEGF-A, promoting neovascularization [52, 53] Marcuello 2019 (miR-19a/b/18a)
4. Immune modulation and inflammation (NF-κB, IL-6/STAT3) miR-24, miR-146a, miR-155

Skews macrophages towards the pro-tumor M2 phenotype

Sustains NF-κB-mediated cytokine loops [54–57].

 Fang 2015 (miR-24/320a/423-5p)
5. Stemness, chemoresistance, and apoptosis (ABCB1, NOTCH, TP53) let-7 family, miR-34, miR-375, miR-145

let-7/miR-34 restore TP53-dependent apoptosis and blunt KRAS [58, 59]

miR-375 and miR-145 suppress cancer stem‐cell self-renewal [60, 61]

 Silva 2021 (let-7e/miR-28-3p/106a/542-5p), Zhang 2018 (miR-34/16)
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CRC colorectal cancer, EMT epithelial-mesenchymal transition, HIF-1α hypoxia-inducible factor-α, IL-6 interleukin-6, MAPK mitogen activated protein kinase, miRNAs microRNAs, MMPs matrix metalloproteinases, TGF-β transforming growth factor-β, NF-κB nuclear factor-κB, VEGF-A vascular endothelial growth factor-A

Eligibility Criteria

Inclusion Criteria

Inclusion criteria were human-based peer-reviewed original research studies reporting sensitivity, specificity, and area under the curve (AUC) metrics or enough data on the diagnostic potential of miRNA as biomarkers for colorectal cancer (CRC) to be able to calculate true positive, true negative, false positive, and false negative and restricted to English language only.

Exclusion Criteria

Exclusion criteria were reviews, editorials, conference abstracts, or case reports, studies lacking control groups or full-text availability, and non-circulating miRNA studies or those focused solely on prognosis or therapy response.

Data Extraction

Two reviewers independently extracted data using a standardized form capturing study characteristics author, year, and country. MicroRNA profiles included the miRNA panel, and type of biospecimen. Diagnostic results were true positive, false positive, true negative, false negative, sensitivity, specificity, and AUC. Mechanistic information extracted included pathways, and biological functions of miRNAs.

Quality Assessment

The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was applied to evaluate the risk of bias and applicability concerns in four domains: patient selection, index test, reference standard, and flow/timing. All of the domains were judged to be at a low, high, or unclear risk of bias [14].

Statistical Analysis

Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and AUC were calculated by a bivariate random-effects model using OpenMetaAnalyst, which is an open-source program for performing a meta-analysis. Visualizations of diagnostic performance were created using hierarchical summary receiver operating characteristic curves. Heterogeneity was evaluated with I2 statistics, with I2 ≥ 50% and P < 0.05 considered significant and further assessed through meta-regression and subgroup analyses (e.g., type of biospecimen, miRNA panel size). Deeks’ funnel plot asymmetry test was used to assess the presence of publication bias.

Mechanistic Integration

For each miRNA panel, mechanistic annotations were compiled from the experimental literature to identify associated signaling pathways (e.g., Wnt/β-catenin, PI3K/AKT, epithelial-mesenchymal transition) and functional roles in CRC (e.g., tumor suppressor, oncogene). MicroRNA panels were categorized based on the depth of mechanistic validation to assess whether biological plausibility correlated with diagnostic accuracy.

Results

Study Selection

The electronic search identified 7375 records. Following the removal of 4000 duplicates, we screened 3375 by title/abstracts and assessed 972 full-text articles for eligibility. Twenty-nine studies met all rigorous criteria and contributed to the quantitative synthesis [15–43], as shown in the PRISMA flow diagram (Fig. 1). These consisted of a total of 5497 participants (3070 CRC cases and 2427 Healthy Control (HC)) and assessed 35 different miRNA panels. The characteristics of the included studies are presented in Table 2 of the ESM.

Fig. 1.

Fig. 1

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Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart of study selection process. MiRNAs microRNAs, NA not applicable

Study Quality Assessment

The QUADAS-2 evaluation of 29 studies reporting 34 distinct miRNA panels demonstrates substantial methodological deficiencies, predominantly in patient selection — a paramount consideration for CRC screening biomarker validation. Notably, 29 studies (85%) exhibited a high risk of patient selection bias, with merely five investigations (HerrerosVillanueva 2019, Vychytilova Faltejskova, Wikberg 2018, Bader El Din 2019, Nakamura 2022, and Marcuello 2019) achieving low-risk classifications. The preponderance of patient selection bias suggests inappropriate inclusion of symptomatic or advanced-stage populations, potentially inflating biomarker performance metrics and compromising clinical translatability.

An index test assessment revealed moderate quality with 11 low-risk studies, though 9 high-risk and 14 unclear-risk evaluations raise concerns regarding test conduct and interpretation. A reference standard evaluation demonstrated exceptional consistency, with 32 studies (94%) achieving low-risk ratings through an appropriate histopathological confirmation. Flow and timing assessments yielded heterogeneous results—22 low-risk versus 12 unclear-risk studies—indicating a potential temporal discordance between testing phases. Applicability concerns paralleled bias patterns, with 26 studies (76%) demonstrating problematic patient selection applicability, fundamentally undermining external validity for real-world CRC screening implementation.

Addressing methodological deficiencies requires multi-faceted reform. Patient selection must be standardized through exclusive recruitment from organized screening programs, emphasizing asymptomatic participants with clear stratification between screening-detected prevalent and symptom-prompted incident cases. Index test protocols necessitate consensus standardization of pre-analytical variables, assay conditions, and predefined thresholds, with mandatory blinding and inter-laboratory validation. Study design should prioritize prospective multicenter approaches with adequate sample sizes, consecutive enrollment, and standardized timing protocols. Regulatory frameworks must incorporate CRC screening-specific QUADAS-2 criteria with a weighted population appropriateness emphasis, supported by international consensus guidelines and mandatory study registration. Quality assurance should encompass independent statistical analysis plans, external quality assessment programs, and transparent STARD-compliant reporting. These coordinated interventions would substantially improve the validity and clinical applicability of CRC screening biomarker research, ensuring study populations accurately reflect intended screening contexts and facilitate reliable real-world translation (Fig. 2).

Fig. 2.

Fig. 2

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Summary of the study quality assessment. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) assessment of studies in terms of risk of bias and applicability concerns’ (a) summary, (b) and (c) graphs. CRC colorectal cancer, HC healthy control

MicroRNA Measurement Assay Assessment

The analytical framework demonstrated pronounced technical heterogeneity, with quantitative reverse transcription polymerase chain reaction serving as the predominant detection methodology. TaqMan-based platforms were most frequently employed, followed by SYBR Green chemistry, across diverse biospecimens including plasma, serum, stool, urine, and saliva. RNA extraction predominantly utilized commercial systems such as miRNeasy (Qiagen Sciences 19300 Germantown Rd Germantown MD 20874 USA) and TRIzol-based protocols, with select studies incorporating exosome isolation procedures. Normalization strategies varied substantially, encompassing synthetic spike-in controls (predominantly Caenorhabditis elegans miR-39), endogenous references (miR-16, U6 snRNA, miR-1228), and combinatorial approaches. Quality assurance included replicate measurements, melting curve validation for SYBR Green assays, and stringent cycle threshold criteria. This methodological diversity across detection platforms, sample processing, and normalization strategies significantly contributed to the observed between-study heterogeneity, underscoring the need for standardized approaches in miRNA biomarker research.

Overall Diagnostic Accuracy of miRNA Panels

The bivariate random-effects model yielded pooled estimates denoting strong diagnostic performance. Pooled sensitivity was 0.85 (95% confidence interval [CI] 0.80–0.88) and specificity was 0.84 (95% CI 0.80–0.88), indicating that the test detected 85% of the true positive cases and 84% of the true negative cases (Table 3 of the ESM). The positive likelihood ratio for a positive result was 4.90 (95% CI 3.93–6.11), which means that the odds of having the condition are close to five times higher for a positive result. Conversely, the negative likelihood ratio was 0.17 (95% CI 0.14–0.21), which was evidence of a significant reduction in the probability of disease after a negative test result (Fig. 3). The DOR was 30.4 (95% CI 21.5–42.9), indicating excellent overall discriminatory capability (Fig. 4). The hierarchical summary receiver operating characteristic curve was located in the upper-left quadrant of the receiver operating characteristic space and had an AUC of 0.90, which indicates high diagnostic accuracy (Fig. 5). However, substantial between-study heterogeneity was present, with I2 estimates of 81.0% for sensitivity and specificity, 83.9% for the negative likelihood ratio, 81.0% for the negative likelihood ratio, and 77.2% for DOR, which indicated an exploration of sources of heterogeneity remained necessary.

Fig. 3.

Fig. 3

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Positive and negative likelihood ratios. C.I. confidence interval, Di+ positive for the condition being tested, Di− negative for the condition being tested, FN false negative, FP false positive, TN true negative, TP true positive

Fig. 4.

Fig. 4

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Diagnostic odds ratio. C.I. confidence interval, FN false negative, FP false positive, TN true negative, TP true positive

Fig. 5.

Fig. 5

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Summary receiver operating characteristic curve

Mechanistic Interpretation of the Circulating Matrix-miRNA Panels

The 35 diagnostic panels assembled from the 29 studies (Table 1) converge on a handful of well-established oncogenic circuits in CRC. Below, the panels are mapped onto five biologic “axes” that collectively drive tumor initiation, progression, and treatment failure. Grouping the miRNAs this way both explains why seemingly disparate panels achieve comparable accuracy and suggests how future iterations might be rationally refined.

Subgroup and Sensitivity Analyses

The diagnostic accuracy differed among biospecimen types, presented in Table S4 of the ESM. Plasma-based panels (n = 16) had a better balance (sensitivity 0.86, specificity 0.83; DOR 31.1), and serum panels (n = 14) had the highest specificity (0.87), although sensitivity was slightly lower (0.79; DOR 33.3). Urine diagnostics (single study) yielded high sensitivity (0.89), albeit suboptimal specificity (0.76, DOR 25.6), while the salivary panels (single study) performed less well (sensitivity 0.72, specificity 0.66; DOR ≈ 5). Fecal-based assays were particularly sensitive (0.97) albeit for their lack of specificity (0.38) in comparison to a combined stool–plasma approach that exhibited greater sensitivity (0.97) and reasonable specificity (0.75) yielding a DOR of almost 90. Diagnostic measurements were also affected by panel size. Three miRNA combinations had the best sensitivity/specificity trade-offs (sensitivity 0.85; specificity 0.88; DOR ≈ 49.6). Panels of four miRNAs increased the sensitivity (0.87), but at the expense of specificity (0.81), while two miRNAs as well as larger (≥ 5 miRNA) sets displayed lower discriminatory power (DOR ≈ 24–26). Heterogeneity overall remained (I2 = 50–87%) in the majority of strata (Figs. 6 and 7).

Fig. 6.

Fig. 6

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Subgroup-based negative and positive likelihood ratios. C.I. confidence interval, Di+ positive for the condition, Di− negative for the condition, FN false negative, FP false positive, NA not applicable, TN true negative, TP true positive

Fig. 7.

Fig. 7

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Subgroup-based diagnostic odds ratios. C.I. confidence interval, FN false negative, FP false positive, NA not applicable, TN true negative, TP true positive

Leave-one-out diagnostics confirmed the robustness of pooled estimates. Sequential study exclusion produced minimal variation in DORs (range: 28.1–32.9), indicating no single study disproportionately influenced the overall effect (Fig. 6).

Publication Bias

Deeks’ funnel plot demonstrated symmetry, and the associated regression test was non-significant (p = 0.23), suggesting no substantive small-study effects or publication bias (Fig. 7).

Discussion

This systematic review and meta-analysis demonstrate excellent diagnostic performance of multi-miRNA panels for CRC detection, with pooled sensitivity and specificity of 0.85 and 0.84, respectively, and an AUC of 0.90. The superior performance of plasma-based panels (sensitivity 0.88, specificity 0.87) reflects the established stability of circulating miRNAs in plasma and standardized collection protocols, while the optimal diagnostic balance achieved by three-miRNA combinations (sensitivity 0.85, specificity 0.88, DOR 49.6) suggests a diagnostic “sweet spot” where additional biomarkers may introduce noise rather than signal enhancement (see Fig. 8).

Fig. 8.

Fig. 8

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Sensitivity analysis plot. C.I. confidence interval

The mechanistic validation revealing consistent involvement of identified miRNAs in key oncogenic pathways—particularly PI3K/AKT, Wnt/β-catenin, epithelial-mesenchymal transition, and immune regulation—provides robust biological rationale for their diagnostic utility. The recurrent presence of miR-21, miR-92a, and let-7 family members across multiple panels reflects their central roles in CRC pathogenesis, with miR-21’s regulation of mismatch repair proteins and let-7’s tumor suppressor function through RAS and HMGA2 modulation demonstrating that these panels capture fundamental disruptions in cellular homeostasis characteristic of colorectal carcinogenesis (see Fig. 9).

Fig. 9.

Fig. 9

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Deek’s asymmetry plot

The substantial between-study heterogeneity (I2 > 77% for most metrics) reflects the nascent state of miRNA biomarker research and highlights critical areas requiring standardization. The predominance of quantitative reverse transcription polymerase chain reaction-based detection methods introduces variability through different normalization strategies, sample processing protocols, and quality-control measures. Additionally, the high risk of patient selection bias in 85% of included studies, with many recruiting symptomatic or advanced-stage patients, potentially inflates diagnostic performance metrics and limits generalizability to screening populations. Despite these limitations, the leave-one-out sensitivity analysis confirmed result stability, and Deeks’ test showed no significant publication bias, providing evidence supporting the translational potential of miRNA panels for non-invasive CRC detection.

Limitations

The most significant limitation stems from predominant reliance on case-control studies with an inherent selection bias, as evidenced by the high risk of patient selection bias in 85% of included studies. Many studies recruited patients with symptomatic or advanced-stage disease, potentially inflating diagnostic performance metrics and creating a spectrum bias that may not reflect real-world screening scenarios where CRC prevalence is lower and the disease spectrum is broader. The substantial methodological diversity across studies represents another major limitation, with variety in RNA extraction methods, normalization strategies, detection platforms, and quality-control measures introducing technical heterogeneity that may obscure true biological signals. The absence of external validation for most miRNA panels and the predominant use of single-center designs with limited sample sizes reduces confidence in reproducibility across different laboratories and populations.

The available data precluded comprehensive subgroup analyses by important clinical variables such as tumor stage, location, molecular subtype, and patient demographics, preventing the assessment of whether panel performance varies across clinically relevant subgroups. Additionally, insufficient representation of diverse populations raises questions about generalizability across different ethnic groups and geographic regions, where genetic and environmental factors may influence miRNA expression patterns. While a mechanistic context was provided for identified miRNAs, the complex regulatory networks governing miRNA expression and their target interactions remain incompletely characterized, limiting our ability to predict panel performance in different clinical contexts or disease subtypes.

Conclusions

Multi-miRNA panels demonstrate excellent diagnostic performance for CRC with pooled sensitivity and specificity of 0.85 and 0.84, respectively, and an AUC of 0.90. The mechanistic validation revealing consistent involvement of identified miRNAs in fundamental oncogenic pathways provides a robust biological rationale supporting their diagnostic utility, while the superior performance of plasma-based panels and optimal diagnostic balance achieved by three-miRNA combinations offer practical guidance for clinical implementation. However, substantial methodological heterogeneity and predominant reliance on case-control designs highlight critical gaps requiring standardization initiatives, prospective validation in true screening populations, technical harmonization, and validation across diverse populations before clinical translation.

Key recommendations include developing international consensus guidelines for miRNA biomarker validation, conducting large-scale multicenter prospective studies within organized screening programs, establishing reference standards for detection platforms and normalization strategies, integrating pathway-informed biomarker selection strategies, and ensuring validation across diverse ethnic populations. The convergence of robust diagnostic performance with mechanistic coherence positions multi-miRNA panels as attractive candidates for integration into precision oncology workflows, with the potential to complement existing screening modalities through non-invasive, cost-effective early detection approaches that could significantly reduce CRC mortality through earlier diagnosis and treatment.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 698 KB) (698.4KB, pdf)

Funding

Open access funding provided by Università degli Studi della Campania Luigi Vanvitelli within the CRUI-CARE Agreement. This study received no external funding.

Declarations

Conflicts of Interest/Competing Interests

Atta Ullah Khan, Maria Ali, and Mohammad Aamir Wahab have no conflicts of interest that are directly relevant to the content of this article.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Availability of Data and Material

Not applicable.

Code Availability

Not applicable.

Authors’ Contributions

Conceptualization: AUK; methodology: AUK; formal analysis: AUK; investigation: AUK; data curation: MA, MAW; writing original draft preparation: AUK, MA, MAW; writing (review and editing): AUK, MA, MAW. All authors have read and agreed to the published version of the manuscript.

Footnotes

Maria Ali and Muhammad Aamir Wahab have contributed equally.

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