The Potential microRNA Prognostic Signature in HNSCCs: A Systematic Review
by
, , , , , , and
Mario Dioguardi
1,*,
Francesca Spirito
1,
Giovanna Iacovelli
1,
Diego Sovereto
1,
Enrica Laneve
1,
Luigi Laino
2,
Giorgia Apollonia Caloro
3,
Ari Qadir Nabi
4,
Andrea Ballini
1,
Lorenzo Lo Muzio
1
and
Giuseppe Troiano
1 1
Department of Clinical and Experimental Medicine, University of Foggia, Via Rovelli 50, 71122 Foggia, Italy
2
Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania “Luigi Vanvitelli”, 80121 Naples, Italy
3
Unità Operativa Nefrologia e Dialisi, Presidio Ospedaliero Scorrano, ASL (Azienda Sanitaria Locale) Lecce, Via Giuseppina Delli Ponti, 73020 Scorrano, Italy
4
Biology Department, Salahaddin University-Erbil, Erbil 44001, Kurdistan, Iraq
*
Author to whom correspondence should be addressed.
Non-Coding RNA 2023, 9(5), 54; https://doi.org/10.3390/ncrna9050054
Submission received: 5 July 2023
/
Revised: 7 September 2023
/
Accepted: 12 September 2023
/
Published: 14 September 2023
(This article belongs to the Special Issue Current Strategies to Investigate the Role of ncRNAs in Carcinogenesis)
Abstract
:Head and neck squamous cell carcinomas (HNSCCs) are often diagnosed at advanced stages, incurring significant high mortality and morbidity. Several microRNAs (miRs) have been identified as pivotal players in the onset and advancement of HNSCCs, operating as either oncogenes or tumor suppressors. Distinctive miR patterns identified in tumor samples, as well as in serum, plasma, or saliva, from patients have significant clinical potential for use in the diagnosis and prognosis of HNSCCs and as potential therapeutic targets. The aim of this study was to identify previous systematic reviews with meta-analysis data and clinical trials that showed the most promising miRs in HNSCCs, enclosing them into a biomolecular signature to test the prognostic value on a cohort of HNSCC patients according to The Cancer Genome Atlas (TCGA). Three electronic databases (PubMed, Scopus, and Science Direct) and one registry (the Cochrane Library) were investigated, and a combination of keywords such as “signature microRNA OR miR” AND “HNSCC OR LSCC OR OSCC OR oral cancer” were searched. In total, 15 systematic literature reviews and 76 prognostic clinical reports were identified for the study design and inclusion process. All survival index data were extracted, and the three miRs (miR-21, miR-155, and miR-375) most investigated and presenting the largest number of patients included in the studies were selected in a molecular biosignature. The difference between high and low tissue expression levels of miR-21, miR-155, and miR-375 for OS had an HR = 1.28, with 95% CI: [0.95, 1.72]. In conclusion, the current evidence suggests that miRNAs have potential prognostic value to serve as screening tools for clinical practice in HNSCC follow-up and treatment. Further large-scale cohort studies focusing on these miRNAs are recommended to verify the clinical utility of these markers individually and/or in combination.
1. Introduction
Among the main tumors of the head and neck region, oral squamous cell carcinomas (OSCCs) represent the sixth malignant tumor in global incidence, with about 700,000 thousand new cases each year [1].
The risk factors most associated with the onset of head and neck squamous cell carcinoma (HNSCC) are alcohol and the consumption of smoked or chewed tobacco, and for laryngeal squamous cell carcinoma (LSCC), positivity to HPV subtypes 16 and 18 was considered a risk factor but with a favorable prognosis [2].
Survival at 5 years after diagnosis remains very low, as only one of two patients survives, and surgical resective therapy can be very debilitating, with a worsening of the quality of life, difficulty in swallowing and speech, and in general due to a perceived deterioration in the relationship with other people [3].
The identification of survival prognostic biomarkers remains a very open topic: In fact, the ability to predict a disease by estimating the clinical trend and survival time remains one of the diagnostic and prognostic objectives to be achieved. In recent decades, several prognostic biomarkers have been investigated in an attempt to create a predictive survival biomolecular signature.
Among the widely studied prognostic and diagnostic biomarkers associated with head and neck cancers, we have the non-coding sequences of RNA messenger (mRNA), and among these, microRNAs (miRNA/miRs) [4]. The latter group is a class of mature, non-coding, single-stranded RNAs with 21–23 nucleotides, which were proposed as promising biomarkers for patients with cancer diagnosis and follow-up [3,5].
Some previous systematic literature reviews have tried to identify individual miRs, aggregating the prognostic survival data of multiple studies, obtaining promising results only in some cases for HNSCCs, such as in the cases of miR-31 [6], miR-21 [7], miR-155 [8], and miR-195 [9].
Other studies tried to identify a biomolecular signature by aggregating miRNAs in HNSCC tissue expression, exploring their use as potential biomarkers for cancer detection and/or prognosis [10,11].
This systematic review aims to identify retrospective and prospective clinical studies investigating the prognostic value of miR expression in HNSCC patients, as well as including data from previous systematic reviews with meta-analyses. From these studies, we selected the most promising miRs, inserting them into a biomolecular signature to test the prognostic value on a cohort of HNSCC patients according to the “The Cancer Genome Atlas” (TCGA) [12].
2. Results
2.1. Study Selection
The following research question guided the selection of the studies: Are there biomolecular signatures consisting of non-coding mRNA sequences (especially miRs) in the scientific literature, whose differential expression in HNSCC tumor tissues was indicative of a different prognosis in patient survival?
The research phase was carried out by consulting and extracting the bibliographic references on three databases, SCOPUS (2455), Science Direct (1367), PubMed (2505), and on a Cochrane Library register (5), providing a total of 6332 articles.
Filters were applied on PubMed and Scopus to selectively include literature reviews and meta-analyses, together with clinical studies. Subsequently, the bibliographic references of Scopus and PubMed were reported on EndNote X8, and the duplicates were removed, while further overlap** of the references were manually removed. The articles obtained were selected by reading the abstract and the title; this phase was also performed for Science Direct and the Cochrane Library, and the articles selected from these two sources were added to those chosen from PubMed and Scopus, and thus 117 potentially eligible records were obtained.
A further search of the gray literature (Google Scholar and Open Gray) and previous systematic reviews did not identify additional manuscripts for inclusion in the present systematic review (Figure 1). Records were independently screened by two authors (M.D. and A.B.), while dubious situations were addressed at the end of the selection by involving a third author (F.S.) to resolve potential conflicts.
The last update of the literature search was conducted on 13 August 2023.
In total, 76 clinical studies and 15 systematic reviews were included at the end of the inclusion process. We designed our strategy to be optimized for a sensitive and broad search, and the results of this selection are reported in a flowchart (Figure 1).
2.2. Data Characteristics: Systematic Review
The systematic reviews included were the following: Dioguardi et al., 2023 [9]; Dioguardi et al., 2022 [13]; Dioguardi et al., 2022 [14]; Dioguardi et al., 2022 [8]; Dioguardi et. al, 2022 [6]; Dioguardi et al., 2022 [7]; Irimie-Aghiorghiese et al., 2019 [15]; Lubov et al., 2017 [16]; ** clinicians improve the quality of life and health conditions of HNSCC patients, providing useful information for oncologists in terms of the most appropriate therapeutic choice, according to life expectancy and neoplasm aggressiveness [107].
Knowledge of the prognostic potential of biosignatures could be useful for clinicians after the diagnosis of HNSCCs to define prognosis by formulating predictive models of individualized prognostic risk. Bringing this model back into clinical practice in patients with HNSCCs who have unfavorable prognostic biosignatures (with low RFS or OS), a more or less aggressive therapy or surgical treatment could be recommended, with a tailored therapeutic approach in the context of personalized medicine [108].
The discovery of the miRs’ prognostic value presents critical insights with potential biases that must be taken into consideration before, during, and after the execution of retrospective studies or clinical trials, but also during the data meta-analysis. The choice of variables can significantly affect the results as well as the overall validity of the analysis.
Factors such as sample size, the heterogeneity of patient populations, virus positivity (HPV and EBV), and the choice of statistical analysis can affect the results.
In the context of HNSCCs, taking, for instance, HPV positivity as a variable, the role of papillomavirus as a risk factor in a subset of head and neck cancers [109], mainly oropharyngeal and laryngeal cancers, has been established, with different epidemiological, clinical, and molecular characteristics compared with HNSCCs, starting with HPV positivity, which was associated with distinctly different and more favorable prognostic survival values [110].
Therefore, the inclusion or exclusion of some clinical variables (smoking, alcohol, age, and gender) may alter the results of prognostic values for the associations observed between miR signatures, including the related survival results [111].
The meta-analysis size of the sample can also be addressed by performing a trial sequential analysis (TSA) to verify the power of the results as a function of the sample, with an effect achieved in terms of RR [112].
In addition, some laboratory study phases can be biased, making the detection of biomolecular signatures in biological samples difficult. In fact, possible biases can be identified in the sample selection (e.g., fresh tissue, fixed tissue, and biological fluids), RNA extraction, and sample quality control. In addition, miR profiling can also be affected by variability in the technical platform (instruments and software), which is an important source of bias that affects not least the data analysis [113].
In addition, the results of tissue miR expression seem to be influenced by the tissue preservation technique (frozen or in formalin); in fact, to reduce the heterogeneity of the data, it is recommended to aggregate data during a meta-analysis by conducting a subgroup analysis also based on the category of tissue preservation [114].
The difficulty in determining the prognostic value of miRs is due to the complexity of biological systems and the multiple roles of miRs in the regulation of gene expression. It is important to remember that microRNA expression patterns can vary between different cancer types, and even within subtypes of the same cancer, making it difficult to establish universal prognostic markers [113].
Furthermore, many miR biosignatures are currently being developed using algorithms and machine learning based on the search for associations between expression and disease outcomes. Therefore, causality is often not considered, and algorithms can generate signatures that are not biologically expressive, despite their statistical significance [115].
In this context, the execution of systematic reviews with the inclusion of Phase 2 prognostic studies can lead to improvement in these studies by better highlighting the most reliable and predictable results while not overlooking data without statistical significance or evidence (publication bias). The present systematic review aims to refine the design, execution, and reporting of Phase 2 studies [116,117] and provide useful knowledge in guiding Phase 3 clinical studies aimed toward finding a prognostic model [118].
Jamail et al. indicated that the over- or underexpression of some miRs was related to the survival of patients with HNSCCs, reporting that the elevated expressions of miR-21, miR-18a, miR-134a, miR-210, miR-181a, miR-19a, and miR-155 were associated with a reduction in the survival of patients with HNSCCs, while the decreased expression of miR-153, miR-200c, miR-363, miR-203, miR-17, miR-205, miR-Let-7d, Let-7g, miR-34a, miR-126a, miR-375, miR-491-p5, miR-218, miR-451, and miR-125b was associated with a poor survival prognosis [22].
These results agree with the findings of Huang et al. (2021) [21], who provide evidence in their review of LSCC suggesting that miRNA-100, miR155, miR-21, miR-34a, miR-195, and miR-let-7 are potential tumor biomarkers.
In light of the data reported in the medical literature, and from the preliminary research conducted in the field [6,7,8,9,13,14,119], we carried out our review after registering it on Prospero, which was written following the indications of PRISMA. A meta-analysis of the data was not carried out due to the excessive heterogeneity of data and histological subtypes of HNSCCs, and thus a TCGA analysis was instead used to test possible microRNA biosignatures that emerged from the data extraction and qualitative analysis of the studies.
In this systematic review, we identified 64 miRs from 15 systematic reviews, whose altered expression was correlated with prognostic indices. The miRs mainly investigated were miR-21, miR-155, and miR-375. HR values for OS in miR-21 ranged from 1.29 to 1.72, and these values were 1.59 for miR 375 and 1.40 for miR-155 (considering only the results of meta-analyses reporting HR values aggregated for individual miRs); the HR values for several miR panels ranged from 2.65 to 1.10 (Table 1).
By selecting the three miRs that, based on our research, were the most investigated in HNSCCs, and performing a survival analysis using these three miRs on the patient cohorts present in the TCGA, an HR of OS equal to 1.28 was found. From these preliminary data, it is evident that the existing results in the literature are still insufficient to clearly define a prognostic microRNA biosignature, and the retrospective statistical analyses performed using the TCGA in an attempt to further validate the findings do not fully achieve this purpose. In fact, by considering only miR-21, three meta-analyses report an aggregate HR value of about 1.7 as the difference between high and low expression levels, while using the TGCA, considering a follow-up period of 60 months, miR-21 presented an HR (high and low expression) equal to 1.27 95% CI: [0.95, 1.71] (Figure 4). Considering instead the HR data of miR-21, miR-155, and miR-375 using the TCGA and combining them in a single prognostic signature, the value of HR was 1.28 95% CI: [0.95, 1.72].
The performance of miRs is more or less superimposable if we consider the miRs that are reportedly downregulated in the literature during HNSCCs; for instance, Jamali et al. (2015) [22] and Wang et al. (2019) [18] revealed that hsa-miR-153 (-), hsa-miR-200c (-), hsa-miR-363 (-), hsa-miR-17 (-), hsa-miR-205 (-), hsa-Let-7d (-), hsa-Let -7g (-), hsa-miR-34a (-), hsa-miR-375 (-), hsa-miR-491 (-), hsa-miR-218 (-), hsa-miR-125b (-), and hsa-mir-375 (-) were downregulated, with an HR = 0.66 95% CI: [0.46–0.88] (Figure 4). Considering the HR between low and high expression levels, an HR of 1.51 was observed. These results are largely reproducible using the Kaplan–Meier portal except for subsequent updates of the latter.
5. Conclusions
In conclusion, we can state that although prognostic survival biomarkers have been identified that possess a discrete potential consisting of a miR signature, in the current state of knowledge for head and neck tumors, there are no studies that fully validate the results. Nevertheless, it is crucial to emphasize that additional validation is necessary before we can definitively establish their practicality. While some miRNA studies have revealed noteworthy findings related to their influence on patient survival, the limited number of studies that have been agregaded to derive these results diminishes their relevance in clinical contexts. Hence, there is a clear need for more extensive and long-term patient studies that specifically investigate these miRs.
Supplementary Materials
The following supporting information can be downloaded at: https://mdpi.longhoe.net/article/10.3390/ncrna9050054/s1.
Author Contributions
Conceptualization, M.D., A.Q.N. and G.T.; methodology, M.D., G.I. and A.B.; software, F.S., M.D. and D.S.; validation, F.S., M.D. and A.B.; formal analysis, M.D.; investigation, M.D. and G.A.C.; data curation, M.D. and G.I.; bibliographic arch research, G.I. and A.B.; writing—original draft preparation, M.D. and A.B.; writing—review and editing, M.D. and A.B.; visualization, L.L.M., M.D. and E.L.; supervision, L.L., L.L.M., G.T. and M.D.; critical revision of the manuscript for important intellectual content, M.D., E.L. and A.B.; project administration, M.D. All authors have read and agreed to the published version of the manuscript.
Funding
This research received REFIN (Research for Innovation) funding: Title project: Analysis of Non-Coding RNA as Biomarkers in Patients with Squamous Carcinoma of the Oral Cavity; Project Code: (UNIFG222–CUP D74I19003340002).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Flowchart describing the mechanisms of screening miR studies and including several databases and records.
Figure 1.
Flowchart describing the mechanisms of screening miR studies and including several databases and records.
Figure 2.
Kaplan–Meier curve based on miR-21, miR-155, and miR-375 expression levels for overall survival (OS) of patients with HNSCC (TCGA cohort); false discovery rate (FDR): 100%. Kaplan–Meier curves created from a public database and Kaplan–Meier plotter web application (http://kmplot.com/analysis/, accessed on 10 May 2023).
Figure 2.
Kaplan–Meier curve based on miR-21, miR-155, and miR-375 expression levels for overall survival (OS) of patients with HNSCC (TCGA cohort); false discovery rate (FDR): 100%. Kaplan–Meier curves created from a public database and Kaplan–Meier plotter web application (http://kmplot.com/analysis/, accessed on 10 May 2023).
Figure 3.
Automatically generated cut-off plot using the Kaplan–Meier plotter web application, http://kmplot.com/analysis/, accessed on 10 May 2023. Significance vs. cut-off values between the lower and upper quartiles of expression are presented, with the red circle indicating the best cut-off.
Figure 3.
Automatically generated cut-off plot using the Kaplan–Meier plotter web application, http://kmplot.com/analysis/, accessed on 10 May 2023. Significance vs. cut-off values between the lower and upper quartiles of expression are presented, with the red circle indicating the best cut-off.
Figure 4.
(A) Kaplan–Meier curve based on miR-21 expression levels for overall survival (OS) of patients with HNSCC (TCGA cohort) FDR: 100%; (B) Kaplan–Meier curve based on hsa-miR-153 (-), hsa-miR-200c (-), hsa-miR-363 (-), hsa-miR-17 (-), hsa-miR-205 (-), hsa-Let-7d (-), hsa-Let -7g (-), hsa-miR-34a (-), hsa-miR-375 (-), hsa-miR-491 (-), hsa-miR-218 (-), and hsa-miR-125b (-); OS HR = 0.66 95% CI: [0.46–0.88]; FDR: over 50%; Kaplan–Meier curves created from public database and Kaplan–Meier plotter web application (http://kmplot.com/analysis/, accessed on 10 May 2023).
Figure 4.
(A) Kaplan–Meier curve based on miR-21 expression levels for overall survival (OS) of patients with HNSCC (TCGA cohort) FDR: 100%; (B) Kaplan–Meier curve based on hsa-miR-153 (-), hsa-miR-200c (-), hsa-miR-363 (-), hsa-miR-17 (-), hsa-miR-205 (-), hsa-Let-7d (-), hsa-Let -7g (-), hsa-miR-34a (-), hsa-miR-375 (-), hsa-miR-491 (-), hsa-miR-218 (-), and hsa-miR-125b (-); OS HR = 0.66 95% CI: [0.46–0.88]; FDR: over 50%; Kaplan–Meier curves created from public database and Kaplan–Meier plotter web application (http://kmplot.com/analysis/, accessed on 10 May 2023).
Table 1.
Main data sources extracted from systematic reviews: National Heart, Lung, and Blood Institute (NHLBI); The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK); Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2); Meta-Analysis of Observational Studies in Epidemiology (MOOSE); The Newcastle–Ottawa Scale (NOS); tongue squamous cell carcinoma (TSCC); oral squamous cell carcinoma (OSCC); oropharyngeal squamous cell carcinoma (OPSCC); CI (confidence interval); The Cancer Genome Atlas (TCGA);\ data not reported; overall survival (OS); disease-free survival (DFS); recurrence-free survival (RFS); cancer-specific survival (CSS); progression-free survival (PFS); relative risk (RR).
Table 1.
Main data sources extracted from systematic reviews: National Heart, Lung, and Blood Institute (NHLBI); The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK); Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2); Meta-Analysis of Observational Studies in Epidemiology (MOOSE); The Newcastle–Ottawa Scale (NOS); tongue squamous cell carcinoma (TSCC); oral squamous cell carcinoma (OSCC); oropharyngeal squamous cell carcinoma (OPSCC); CI (confidence interval); The Cancer Genome Atlas (TCGA);\ data not reported; overall survival (OS); disease-free survival (DFS); recurrence-free survival (RFS); cancer-specific survival (CSS); progression-free survival (PFS); relative risk (RR).
| First Author, Data | Country | miR | Studies and Reports Included | Patients/Sample Total Included | HR and RR Data Extract | Risk of Bias |
|---|---|---|---|---|---|---|
| Dioguardi et al., 2023 [9] | Italy | miR-195 | 3 | 81 TSCC, 304 LSCC | OS, RR = 0.36 95% CI: [0.25, 0.51]; | REMARK |
| Dioguardi et al., 2022 [13] | Italy | miR-197 | 1 + TCGA | 68 OSCC | OS, HR = 1.01, 95% CI: [1.00, 1.02]; | REMARK |
| Dioguardi et al., 2022 [14] | Italy | miR-196a, miR-196b | 5 | 417 HNSCC (105 TSCC, 3 OPSCC, 116 OSCC and others, 192 LSCC) | OS, HR = 1.67, 95% CI: [1.16, 2.49]; DFS, HR= 1.39, 95% CI: [0.33 5.52]; | REMARK |
| Dioguardi et al., 2022 [8] | Italy | miR-155 | 8 | 709 HNSCC (120 LSCC) | OS, HR = 1.40, 95% CI: [1.13, 1.75]; DFS, HR = 1.36, 95% CI: [0.65 2.83]; PFS, HR = 1.09, 95% CI: [0.53 5.15] | REMARK |
| Dioguardi et al., 2022 [6] | Italy | miR-31 | 4 | 240 HNSCC | OS, HR = 1.58, 95% CI: [1.21, 2.06] | REMARK |
| Dioguardi et al., 2022 [7] | Italy | miR-21 | 8 | 351 OSCC | OS, HR = 1.29, 95% CI: [1.16, 1.4]; DFS, HR = 2, 95% CI: [1.35, 2.95]; CSS, HR = 1.19, 95% CI: [0.72, 1.97]; RFS, HR = 1.41, 95% CI: [0.48–4.15] | REMARK |
| Irimie-Aghiorghiese et al., 2019 [15] | Romania | miR-21 | 7 | 757 HNSCC | OS, HR = 1.719, 95% CI: [1.402, 2.109] | \ |
| Lubov et al., 2017 [16] | Brazil, Canada | miR-21 | 4 | 456 HNSCC | OS, HR= 1.81, 95% CI: [0.66, 2.95] | QUADAS-2 |
| **e and Wu, 2017 [17] | China | miR-21 | 9 | 777 Oral Cancer | OS, HR = 1.71, 95% CI: [1.20, 2.44]; CSS, HR = 2.63, 95% CI: [1.25, 5.51]; RFS, HR = 2.04, 95% CI: [1.09, 3.80]; DFS, HR= 2.70, 95% CI: [1.08, 6.76] | MOOSE |
| Wang et al., 2019 [18] | China | miR-375 | 13 (5 HNSCC) | 1340 patients (294 HNSCC) | HNSCC; OS, HR= 1.59, 95% CI [1.16, 2.18] | NOS, MOOSE |
| Li et al., 2019 [19] | China | miR-146ª | 10 cancers (1 HNSCC + TGCA) | \ | HNSCC; OS, HR = 0.734, 95% CI: [0.572, 0.941] | \ |
| Qiu et al., 2021 [20] | China | miR-205, miR-429, miR-21, miR-331, miR-200a-3p, miR-19a, miR-21-5p, miR-151a, miR-17, miR-18b, miR-324, miR-96, miR-29c, miR-200b, miR-375, miRNA-204, miR−200c, miR-130a, miR-15b | 10 | 1093 HNSCC | RFS, HR= 2.51, 95% CI: [2.13, 2.96] | NOS, QUADAS-2 |
| Huang et al., 2021 [21] | China | miR-203, miR-195, miR-29c, miR-300, miR-146, miR-155, miR-200b, miR-16-2, miR-10a, miR-100, miR-101, miR-34c, miR-125, miR-149, miR-145, miR-181a, let-7a, miR-21, miR-494, miR-720, miR-675, miR-137, miR-31, miR-9, miR-19a, miR-424, miR-23a, miR-196b. | 36 | 3020 LSCC | OS, HR = 1.10 95% CI: [1.00,1.20] downregolator; OS, HR = 1.13, 95% CI: [1.06–1.20] upregolator; DFS, HR = 2.57, 95% CI: [1.56–4.23]. | NHLBI |
| Jamali et al., 2015 [22] | Iran | miR-34a, miR-375, mir-155, let-7g, miR-210, miR-20a, miR-126, miR-21, miR-205, miR-203, miR-19a, miR-134a, miR-200c, let-7b, miR-153, miR-18a, miR-17a, miR-451, miR-193b. | 21 | HNSCC | miR-21 OS, HR = 1.57, 95% CI [1.22–2.02]; | MOOSE |
| Troiano et al. [23] | Italy | miR-21, miR-455-5p, miiR-155-5p, miR-372, miR-373, miR29b, miR-1246, miR-196a, miR-181. miR-204, miR-101, miR-32, miR-20a, miR-16, miR-17, miR-125b. | 15 | 1200 OSCC | OS, HR = 2.65 95% CI: [2.07, 3.39]; DFS, HR 1.95 95% CI: [1.28, 2.98]. | NOS |
Table 2.
Data extracted from the 76 studies included, providing information regarding the type of tumor, the location of the tumor, the number of patients with data concerning the average age, the average or maximum follow-up, gender, and the common risk factors in the patients are reported to be smoking, alcohol, and HPV positivity; TNM (T: tumor size; N: regional lymph nodes; M: distant metastasis); pTNM, pathological TNM staging; cTNM, clinical TNM staging; N/A, not available; Ma (male); Fe (female); R (range); y (years); smoking (Sm); alcohol (Alc); SEM (standard error mean); PS (prospective study); RT (retrospective study); HPSCC (hypopharyngeal squamous cell carcinoma); OTSCC (oral tongue squamous cell carcinoma); BOTSCC (base of tongue squamous cell carcinoma); NPC (nasopharyngeal carcinoma). Data are not reported in a clear and explicit manner;\ data not present.
Table 2.
Data extracted from the 76 studies included, providing information regarding the type of tumor, the location of the tumor, the number of patients with data concerning the average age, the average or maximum follow-up, gender, and the common risk factors in the patients are reported to be smoking, alcohol, and HPV positivity; TNM (T: tumor size; N: regional lymph nodes; M: distant metastasis); pTNM, pathological TNM staging; cTNM, clinical TNM staging; N/A, not available; Ma (male); Fe (female); R (range); y (years); smoking (Sm); alcohol (Alc); SEM (standard error mean); PS (prospective study); RT (retrospective study); HPSCC (hypopharyngeal squamous cell carcinoma); OTSCC (oral tongue squamous cell carcinoma); BOTSCC (base of tongue squamous cell carcinoma); NPC (nasopharyngeal carcinoma). Data are not reported in a clear and explicit manner;\ data not present.
| First Author, Date | Country | Study Design | Tumor Type/Tumor Site | miR | Follow-Up Months | Patient (Ma, Fe) | Age (Years) | Smoking | Alcohol | HPV | Staging |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Jung et al., 2012 [24] | USA | RT | 17 OSCC (Tongue base 6, Tongue anterior 3, Tongue border 2, Tongue ventral, Mouth 1, Oropharynx 1, Tongue unspecified 2) | miR-7, miR-21, miR-424 | 180 | 17 Ma | 34–81 | \ | \ | HPV +10, −7 | pTNM stage I 1, II 5, IV 8, 3 N\A |
| Kawakita et al., 2014 [25] | Japan | RT | 79 OTSCC | miR-21 | 60 | 44 Ma, 35 Fe | ≥67 y 47, <67 y 32 | \ | \ | \ | T1 + T2 47, T3 + T4 32 |
| Hedbäck et al., 2014 [26] | Denmark | RT | 86 OSCC (Tongue 21, Mouth floor 65) | miR-21 | 60 | 63 Ma, 23 Fe | \ | Sm yes 86 | \ | \ | ? |
| Yu et al., 2017 [27] | Taiwan | RT | 100 OSCC (Buccal 37, Tongue 35, Mouth floor 12, Others 16) | miR-21 | 100 | 92 Ma, 8 Fe | 55.3, ≤55 y 56, >55 y 44 | \ | \ | \ | Stage I + II 23, III + IV 77 |
| Supic et al., 2018 [28] | Serbia | RT | 60 OTSCC | miR-183, miR-21 | 80 | 47 Ma, 13 Fe | 58, 43–82, <58 y 28, ≥58 y 32 | Sm Never/former 18, current 42; | Alc low 39, Alc high 21 | \ | Stage II 15, III 45 |
| Jakob et al., 2019 [29] | Germany | RT | 36 OSCC (Mouth floor 6, Tongue 25, Palate 5) | miR-21, miR-29, miR-31, miR-99a, miR-99b, miR-100, miR-143, miR-155 | 58 | 27 Ma, 9 Fe | 59, 23–84 | Sm yes 28 | Alc Yes 21 | \ | Stage I + II 10, III + IV 26 |
| Li et al., 2013 [30] | China | RT | 63 OSCC (Tongue) | miR-21 | 150 | 63 Ma | 54, 35–72 | \ | \ | \ | \ |
| Zheng et al., 2016 [31] | China | RT | 84 Tongue cancer (72 OTSCC) | miR-21 | 90 | \ | \ | \ | \ | \ | \ |
| Li et al., 2009 [32] | China | RT | 103 OTSCC | miR-21 | 70 | 56 Ma, 47 Fe | <50 y 47, ≥50 y 56 | \ | \ | \ | Clinical Stage I + II 60, III + IV 43 |
| Ganci et al., 2016 [33] | Italy | RT | 92 OSCC | miR-130b, miR-141, miR-21, miR-96 | 60 | 57 Ma, 35 Fe | <64 y 48, >64 y 44 | Sm never 22, Sm or ex 53 | Alc no 31, Alc yes 43 | HPV +1 | T1 + T2 50, T3 + T4 42 |
| Wang et al., 2018 [34] | China | RT | 118 HNSCC | miR-31 | 60 | 65 Ma, 53 Fe | <56 y 51, ≥56 y 67 | Sm yes 76, Sm no 42 | Alc no 46, Alc yes 71 | \ | TNM stage I + II 33, III + IV 85 |
| Qiang et al., 2019 [35] | China | RT | 56 HNSCC (21 Hypopharynx, 25 Larynx) | miR-31 | 60 | 32 Ma, 24 Fe | ≥60 y 29, <60 y 27 | \ | \ | \ | Clinical stage T1 + T2 21, T3 + T4 35 |
| Tu et al., 2021 [36] | Taiwan | RT | 40 OSCC | miR-31 | 160 | 36 Ma 4 Fe | 57.53 ± 1.58 y | Sm yes 30 | \ | \ | Stage I + III 12, IV 28 |
| Hess et al., 2017 [37] | Germany | RT | 149 HNSCC (Oropharynx 78, Hypopharynx 71) | miR-155, miR-200b, miR-146a | 61 | 123 Ma, 26 Fe | 57, 38–71 | Ex Sm+Sm no 54, Sm yes 92 | \ | HPV-16 +12 | \ |
| Zhao et al., 2018 [38] | China | RT | 120 LSCC (Glottic 74, Supraglottic 46) | miR-155 | 79 | 107 Ma, 13 Fe | ≥60 y 63, <60 y 57 | \ | \ | \ | T1 + T2 67, T3 + T4 53 |
| Baba et al., 2016 [39] | Japan | RT | 73 OSCC | miR-155 | 50 | 49 Ma, 24 Fe | <60 y 18, ≧60 y 55 | \ | \ | \ | pTNM stage I + II 29, III + IV 44 |
| Shi et al., 2015 [40] | China | RT | 30 OSCC | miR-155 | 50 | 19 Ma, 11 Fe | 40–75, 56.4 ± 8.6 | Sm yes 16, never Sm 14 | Alc no 16, Alc yes 14 | \ | stage I + II 8, III + IV 22 |
| Kim et al., 2018 [41] | Korea | RT | 68 OSCC (Oral Tongue 39, Buccal 13, Mouth floor 8, Retromolar trigone 7, Upper alveolar ridge 1) | miR-155 | 80 | 45 Ma, 23 Fe | 57.7, 23–84 | \ | \ | \ | pTNM stage I + II 35, III + IV 33 |
| Bersani et al., 2018 [42] | Sweden | RT | 168 OTSCC/BOTSCC | miR-155, miR-185, miR-193b | 34 | 126 Ma, 42 Fe | 61 | \ | \ | HPV +110 | Tumor stage I + II 17, III + IV 155 |
| Wu et al., 2020 [43] | China | RT | 62 OSCC | miR-155 | 60 | 42 Ma, 20 Fe | ≤50 y 39, >50 y 23 | \ | \ | \ | TNM stage I + II 46, III + IV 16 |
| Shuang et al., 2017 [44] | China | PS | 122 LSCC (Glottis 61, Supraglottis 42, Subglottis 19) | miR-195 | 60 | 80 Ma, 42 Fe | ≤60 y 69, >60 y 53 | Sm yes 99, Sm no 23 | \ | \ | Clinical stage I + II 23, III + IV 99 |
| Ding and Qi, 2019 [45] | China | PS | 182 LSCC (Supraglottic 50, Glottic 95, Subglottic 37) | miR-195 | 60 | 120 Ma, 62 Fe | <60 y 80, ≥60 y 102 | \ | \ | \ | Clinical stage I + II 130, III + IV 52 |
| Jia et al., 2013 [46] | China | PS | 81 OTSCC | miR-195 | 48 | 45 Ma, 36 Fe | <60 y 45, ≥60 y 36 | \ | \ | \ | Clinical stage I + II 48, III + IV 33 |
| Qin et al., 2019 [47] | China | PS | 80 OSCC (Tongue 30, Gingival 24, Cheek 13 Floor of Mouth 10, Oropharynx 3) | miR-196a | 80 | 43 Ma, 37 Fe | ≥60 y 39, <60 y 41 | Sm yes 30, Sm no 50 | Alc no 56, Alc yes 24 | \ | TNM stage I + II 33, III + IV 47 |
| Liu et al., 2013 [48] | Taiwan | PS | 95 OSCC (Buccal 34, Tongue 25, Others 36) | miR-196a, miR-196a2 | 85 | 90 Ma, 5 Fe | 53.6 | \ | \ | \ | Clinical stage I + III 26, IV 69 |
| Maruyama et al., 2018 [49] | Japan | PS | 50 OSCC (OTSCC 50) | miR-196a, miR-10a, miR-10b, miR-196b | 6o | 24 Ma, 26 Fe | <60 y 21, ≥60 y 29 | Sm yes 19, Sm no 31 | Alc no 25, Alc yes 22 | \ | Clinical stage I 32, II 18 |
| Zhao et al., 2018 [50] | China | PS | 113 LSCC (Glottic 70, Supraglottic 43) | miR-196b | 97 | 96 Ma, 17 Fe | <60 y 42, ≥60 y 71 | \ | \ | \ | Tumor stage II 47, III + IV 66 |
| Luo et al., 2019 [51] | China | PS | 79 LSCC | miR-196b | 60 | 66 Ma, 13 Fe | 60.58 | Sm yes 52, ex Sm 21, Sm no 6 | exAlc 17, Alc no 4, Alc yes 58 | \ | TNM stage I + II 23, III + IV 56 |
| Ahn et al., 2017 [52] | Korea | RT | 68 OSCC | miR-197 | 44.3 | 45 Ma, 23 Fe | 57.7, 23–84 | \ | \ | \ | pTNM I + II 35, III + IV 33 |
| Hudcova et al., 2016 [53] | Czech Republic | RT | 42 OSCC (34 patients included in the analysis) | miR-29c, miR-200b, miR-375 | 48 | 42 Ma | 63, 47–87 | \ | \ | \ | Tumor stage T1 + T2 18, T3 + T4 22 |
| Kang et al., 2021 [54] | China | RT | 80 OSCC | miR-198 | 60 | ? | ? | ? | ? | \ | ? |
| Bonnin et al., 2016 [55] | France | RT | 75 Oropharynx (Base of tongue 24, Soft palate 11, Tonsil 22, Pharyngeal wall 4, Vallecula 9, Other 5) | miR-422a | 50–120? | 61 Ma, 14 Fe | 54, 39–82 | Alc yes + Sm yes 56 | Alc yes + Sm yes 56 | HPV +13 | Staging III 13, S IV 62 |
| Ganci et al., 2013 [56] | Italia | RT | 121 HNSCC (Oral cavity 73, Larynx 29, Hypopharynx 9, Oropharynx 10) | miR-205, miR-429, miR-21, miR-331, miR-200a, miR-19a, miR-21, miR-151a, miR-17, miR-18b, miR-324, miR-96, miR-139, miR-21-5p, miR-17-3p | 73 | 89 Ma, 32 Fe | <62 y 60, >62 y 60 | Sm no 27, Sm yes or ex 94, Unknown 1 | Alc yes or ex 70, Alc no 50, Unknown 1 | HPV +114, −5, Unknown 2 | pTNM T1 + T2 56, T3 + T4 65 |
| Harris et al., 2012 [57] | USA | PS | 123 HNSCC (OSCC 43, OPSCC 37, LSCC 43) | miR-375 | 60 | 85 Ma, 38 Fe | <58 y 45, ≥67 y 40, 59–66, 38 | Sm never 18, ex Sm 57, Sm yes 48 | Alc no 89, Alc yes 34 | HPV +31, −74 | TNM stage I + II 24, III + VI 99 |
| Ahmad et al., 2019 [58] | Czech Republic | RT | 94 patients, 43 cancers, (Oral cavity 8, Hipo-pharynx 13, Larynx 30, Oropharynx 43) | miR-15b | 60 | 80 Ma, 14 Fe | 58 | \ | \ | \ | TNM stage I + II 22, III + VI 72 |
| Rajthala et al., 2021 [59] | Norway | RT | 160 OSCC (Tongue 71, Gingiva 42, Buccal 20, Floor of mouth 18) | miR-204 | 103 | 102 Ma, 58 Fe | 65.25, 27–93 | Sm no 49, Sm yes 75 | Alc low normal 51, Alc moderate-Hight 35 | HPV −160 | Stage Stage Ⅰ 43, stage Ⅱ 37, stage Ⅲ 25, Stage Ⅳ 57 |
| Song et al., 2020 [60] | Japan | RT | 204 OSCC (77 Tongue) | miR-200c | 40 | 146 Ma, 58 Fe | <60 y 82, ≥60 y 122 | Sm no 71, Sm yes 133 | Alc no 36, Alc yes 168 | \ | TNM stage I + II 113, III + IV 91 |
| Zhao et al., 2018 [61] | China | PS | 132 LSCC (Glottic 76, Supraglottic 56) | miR-145 | 70 | 114 Ma, 18 Fe | <60 y 48, ≥60 84 | \ | \ | \ | T stage T2 51, T3 + T4 81 |
| Li et al., 2013 [62] | China | RT | 80 LSCC | miR-101 | 60 | 56 Ma, 24 Fe | ≥60 y 48, <60 y 32 | Sm no 60, Sm yes 20 | \ | \ | Clinical stage I + II 38, III + IV 42 |
| de Jong et al., 2015 [63] | Finland | RT | 34 LSCC (supraglottic 18, glottic 16) | miR-452, miR-141, miR-203 | 60 | 20 Ma, 14 Fe | \ | \ | \ | \ | T stage 2–3 34 |
| Fang et al., 2019 [64] | China | RT | 66 LSCC (Supraglottic 19, Glottic 45, Subglottic 2) | miR-29c | 110 | 62 Ma, 4 Fe | ≤60 y 26, >60 y 40 | \ | Alc no 45, Alc yes 21 | \ | TNM stage I 7, II 13, III 14, IV 32 |
| He et al., 2017 [65] | China | RT | 133 LSCC | miR-300 | 60 | 87 Ma, 46 Fe | 61.33 ± 7.86 y | \ | \ | \ | TNM stage I + II 65, III + IV 68 |
| Re et al., 2015 [66] | Italy | RT | 99 LSCC (Supraglottic 19, Transglottic 66, Subglottic 5) | miR-34c | 120 | 87 Ma, 3 Fe | 66.51 ± 8.02 y | \ | \ | \ | \ |
| Xu et al., 2016 [67] | China | RT | 97 LSCC (Glottic 31, supraglottic 19) | miR-149 | 80 | 73 Ma, 24 Fe | <60 y 46, ≥60 y 51, 70–35, 63.8 | Sm (cigarette/day) 1–20 25, ≥20 36 | Alc grams of <50 45, ≥50 52 | \ | Stages I + II 59, III + IV 38 |
| Tian et al., 2014 [68] | China | RT | 56 LSCC (Supraglottic 26, Glottic 30) | miR-203 | 60 | 40 Ma, 16 Fe | ≥59 y 32, <59 y 24 | \ | \ | \ | Clinical stage I + II 24, III + IV 32 |
| Zhao et al., 2018 [69] | China | RT | 127 LSCC (Glottic 77, Supraglottic 50) | miR-181a | 69 | 114 Ma, 13 Fe | ≥60 y 79, <60 y 48 | Sm no 12, Sm yes 115, | Alc no 93, Alc yes 34 | \ | T stage T2 53, T3 + T4 74 |
| Guan et al., 2016 [70] | China | RT | 65 HNSCC (Larynx 46, Hypo-others 16) | miR-675 | 72 | 48 Ma, 14 Fe | 63.8, >64 y 33, ≥64 y 29 | \ | \ | \ | T stage T1 + T2 18, T3 + T4 44 |
| Avissar et al., 2009 [71] | USA | RT | 169 HNSCC (Oral 83, Pharyngeal 31, Laryngeal 19) | miR-375, miR-21 | 60 | 91 Ma 42 Fe | 61.5 ± 11.9 y | Pack-years sm, No (0) 22, <36.75 43, ≥36.75 68 | Drinks per week, No (0) 13, <18 50, ≥18 70 | HPV\16, + 16 | Stage I + I 46, III + IV 118 |
| Wu et al., 2014 [72] | China | PS | 103 LSCC (Supraglottic 66, Glottic 37) | miR-9 | 60 | 54 Ma, 49 Fe | <60 y 41, ≥60 y 62 | \ | \ | \ | TNM stage I + II 43, III + IV 60 |
| Wu, Zhang et al., 2014 [73] | China | PS | 83 LSCC | miR-19a | 80 | 57 Ma, 26 Fe | ≥56 y 42, <56 y 41 | \ | \ | \ | T stage T1 + T2 52, T3–T4 31 |
| Zhang et al., 2015 [74] | China | RT | 52 LSCC | miR-23a | 60 | 45 Ma, 7 Fe | <60 y 22, ≥60 y 30 | Sm no 7, Sm yes 45 | Alc no 15, Alc yes 37 | \ | Clinical stage I 6, II 12, III 31, IV 3 |
| Hu et al., 2015 [75] | China | RT | 46 LSCC (Glottic 33, Supraglottic 11, Subglottic 2) | miR-21, miR-375 | 60 | 42 Ma, 4 Fe | 59.2 ± 7.84 y | Sm no 12, Sm yes 31 | Alc no 22, Alc yes 19 | \ | TNM stage I + II 31 III + IV 15 |
| Re et al., 2017 [76] | Italy | RT | 43 LSCC (Supraglottic 8, Transglottic 33, Subglottic 2) | miR-21, let-7a, miR-34c | 55.7 | 42 Ma, 1 Fe | 66.51 ± 8.02 y | \ | \ | \ | TNM stage III 31 IV 12 |
| Shen et al., 2012 [77] | China | RT | 69 LSCC | miR-34a | 40 | \ | <60 y 33, ≥60 y 36 | \ | \ | \ | TNM stage I + II 42, III + IV 27 |
| Maia et al., 2017 [78] | Brazil, Singapore | RT | 34 LSCC (Supraglottic 7, Glottic 27) | miR-296 | 40 | 30 Ma, 4 Fe | ≤60 y 16, >60 y 18 | Sm yes 31, Sm no 3 | \ | \ | T stage I 16 II 18 |
| Ogawa et al., 2012 [79] | Japan | RT | 24 HNSCC (24 Sinonasal Squamous Cell Carcinomas) | miR-34a | 53 | 16 Ma, 8 Fe | >60 y 14, <60 y 10 | \ | \ | \ | T stage T2 1, T3 10, T4a 13 |
| Pantazis et al., 2020 [80] | Greece | RT | 105 LSCC | miR-20b | 84 | 60 Ma, 45 Fe | 62, 36–87 | \ | \ | \ | TNM stage I 15, II 16, III 38, IV 36 |
| Childs et al., 2009 [81] | USA | RT | 94 HNSCC (Oral cavity 31, Oropharynx 32, Hypopharynx, 9 Larynx 32) | Let-7, miR-205, miR-21 | 60 | 71 Ma, 33 Fe | <60 y 41, <60 y 63 | Sm current 46, Sm former 39, Sm never 17 | \ | HPV\16 −59, +37 | Tumor stage I + II 24, III IV 80 |
| Ko et al., 2014 [82] | Korea | RT | 167 HNSCC (Oropharynx 88, Oral cavity 79) | miR-21 | 72 | 136 Ma, 31 Fe | 56, 25–90 | Sm no 57, Sm yes 109 | \ | HPV −131, +31 | Stage I 26, II 35, III 20, IVa 86 |
| Arantes et al., 2017 [83] | Brazil | RT | 71 HNSCC (Oropharynx 35, Larynx/Hypopharynx 38) | miR-21 | 60 | 68 Ma, 3 Fe | 40–76 | Sm yes 57 | Alc yes 27 | HPV +6 | Clinical stage T2 + T3 46, T4 25 |
| Chang et al., 2013 [84] | Taiwan | RT | 98 OSCC (Buccal 43, tongue 29, Gingiva 21, Floor of the mouth 5) | miR-20a, miR-17 | 84 | 83 Ma, 15 Fe | <50 y 34, >50 y 64 | Sm yes 81, Sm no 17 | \ | \ | Clinical stage I + II 42, III + IV 56 |
| Gee et al., 2010 [85] | UK | RT | 46 HNSCC (Oral cavity 10, Oropharynx 21, Hypopharynx 9, Larynx 5, Paranasal sinus 1) | miR-210, miR-21, miR-10b | 60 | 37 Ma, 9 Fe | 63, 43–92 | Sm never 6, ex Sm 12, Sm current 28 | Alc no 10, never heavy 14, currently heavy 22 | \ | Stage I 2, II 2, III 6, IV 35 |
| Jia et al., 2014 [86] | China | RT | 76 TSCC | miR-26a | 48 | 40 Ma, 36 Fe | <60 y 41, ≥60 y 35 | \ | \ | \ | Clinical stage I+II 45, III+IV 31 |
| Liao et al., 2013 [87] | China | RT | 106 OSCC (Tongue 18 Floor of mouth 4 Buccal 12, Hard palate 4, Upper or lower gingival 11) | miR-1246 | 60 | 30 Ma, 19 Fe | <60 y 21, ≥60 y 28 | \ | \ | \ | TNM Stage I + II 25, III + IV 24 |
| Liu et al., 2013 [88] | China | RT | 280 NPC | miR-451 | 96 | 206 Ma 74 Fe | ≤45 y 136, >45 y 144 | \ | \ | \ | TNM stage I + II 91, III + IV 189 |
| Liu, Shen et al., 2013 [89] | Taiwan | RT | 96 HNSCC (Buccal 34, Tongue 26, Oral pharynx and Other 36) | miR-134 | 80 | 90 Ma, 6 Fe | 53.5 | \ | \ | \ | Stage I + III 27, IV 69 |
| Luo et al., 2014 [90] | China | PS | 168 NPC | miR-18a | 80 | 127 Me, Fe 41 | ≥50 y 99, <50 y 69 | \ | \ | \ | Clinical stage I + II 72, III + IV 96 |
| Peng et al., 2014 [91] | Taiwan | RT | 58 OSCC | miR-218, miR-125b, Let-7g | 60 | \ | \ | \ | \ | \ | \ |
| Sasahira et al., 2012 [92] | Japan | RT | 118 OSCC (Tongue 64, others 54) | miR-126 | 60 | 68 Me, 50 Fe | 67.4, 46–91 | \ | \ | \ | Clinical stage I + II 74, III + IV 44 |
| Tu et al., 2015 [93] | Taiwan | RT | 50 OSCC (Buccal 17, Tongue 24, others 9) | miR-372, miR-373 | 150 | 47 Ma, 3 Fe | 52.6 | \ | \ | \ | Stage I + II 8, III + IV 42 |
| Wu et al., 2014 [94] | Taiwan | RT | 115 OSCC (Tongue 60, Buccal 43, Lip\gingiva\plate 12) | miR-218 | 96 | 65 Ma, 50 Fe | <55 y 66, ≥55 y 49 | Sm no 50, Sm yes 65 | Alc no 63, Alc yes 53 | HPV16/18 −55, +60 | Stage I + II 61, III + IV 54 |
| Xu et al., 2013 [95] | China | RT | 65 OSCC | miR-153, miR-200c | 60 | \ | \ | \ | \ | \ | \ |
| Zhang et al., 2017 [96] | China | RT | 44 OSCC | miR-375 | 60 | \ | \ | \ | \ | \ | \ |
| Jia et al., 2015 [97] | China | RT | 105 TSCC | miR-375 | 50 | 49 Ma, 56 Fe | <60 y 65, ≥60 y 40 | \ | \ | \ | Clinical stage Ⅰ + Ⅱ 59, Ⅲ + Ⅳ 46 |
| Hu et al., 2014 [98] | China | 46 LSCC (Glottic 33, Supraglottic 11, Subglottic 2) | miR-375, miR-21 | 60 | 42 Ma, 4 Fe | <65 y 22, ≤65 y 24 | Sm no 12, Sm yes 31 | Alc no 22, Alc yes 19 | \ | Stage I + II 31 III + IV 15 | |
| Gu et al., 2018 [99] | China | 56 TSCC | miR-22 | 60 | 33 Ma, 23 Fe | >50 y 23, ≤50 y 33 | \ | \ | \ | Clinical stage II + IIIa 46, IIIb + IV 10 |
Table 3.
The values of HR (95% confidence interval) and RR for the different prognostic indices of survival are shown in the table; overall survival (OS); disease-free survival (DFS); recurrence-free survival (RFS); cancer-specific survival (CSS); progression-free survival (PFS); relative risk (RR); high versus low expression (H-L); low versus high expression (L-H); infinite (inf).
Table 3.
The values of HR (95% confidence interval) and RR for the different prognostic indices of survival are shown in the table; overall survival (OS); disease-free survival (DFS); recurrence-free survival (RFS); cancer-specific survival (CSS); progression-free survival (PFS); relative risk (RR); high versus low expression (H-L); low versus high expression (L-H); infinite (inf).
| First Author, Date | miR | OS | DFS | CSS | RFS | PFS | RR |
|---|---|---|---|---|---|---|---|
| Jung et al., 2012 [24] | miR-21 | 5.31 (1.39–20.38) H-L | |||||
| Kawakita et al., 2014 [25] | miR-21 | 1.19 (0.71–1.9) H-L | |||||
| Hedbäck et al., 2014 [26] | miR-21 | 2.70 (1.1–6.9) H-L | |||||
| Yu et al., 2017 [27] | miR-21 | 1.87 (1.21–2.87) H-L | |||||
| Supic et al., 2018 [28] | miR-21 | 2.002 (0.904–4.434) H-L | |||||
| miR-183 | 5.666 (1.708–18.791) H-L | 1.868 (0.924–3.776) H-L | |||||
| Jakob et al., 2019 [29] | miR-21 | 2.31 (0.62–8.58) H-L | 0.18 (0.02–1.39) H-L | 0.16 (0.02–1.22) H-L | |||
| miR-29b | 2,7726,7353.03 (0-inf) H-L | 4.09 (0.93–17.93) H-L | 4 (0.92–17.45) H-L | ||||
| miR-31 | 3.69 (1.07–12.79) H-L | 1.82 (0.66–5.05) H-L | 2.31 (0.94–5.69) H-L | ||||
| miR-99a | 0.31 (0.1–0.95) H-L | 0.69 (0.29–1.64) H-L | 0.64 (0.28–1.42) H-L | ||||
| miR-99b | 0.58 (0.17–1.94) H-L | 0.22 (0.07–0.76) H-L | 0.27 (0.09–0.79) H-L | ||||
| miR-100 | 3.14 (0.66–39.98) H-L | 2.49 (0.72–8.67) H-L | 2.85 (0.83–9.74) H-L | ||||
| miR-143 | 0.2 (0.04–0.92) H-L | 0.56 (0.22–1.45) H-L | 0.46 (0.18–1.17) H-L | ||||
| miR-155 | 2.94 (0.93–9.29) H-L | 2.04 (0.67–6.2) H-L | 1.92 (0.7–5.22) H-L | ||||
| Li et al., 2013 [30] | miR-21 | 2.13 (1.11–4.10) H-L | |||||
| Zheng et al., 2016 [31] | miR-21 | 1.22 (1.09–1.36) H-L | |||||
| Li et al., 2009 [32] | miR-21 | 2.06 (1.21–3.51) H-L | |||||
| Ganci et al., 2016 [33] | miR-21 | 4.2 (1.1–15.98) H-L | |||||
| miR-130b | 2.9 (0.8–11) H-L | ||||||
| miR-141 | 4 (1.26–13.9) H-L | ||||||
| miR-96 | 5.7 (1.52–21.3) H-L | ||||||
| Wang et al., 2018 [34] | miR-31 | 3.31 (1.42–5.36) H-L | 3.86 (1.53–6.05) H-L | ||||
| Qiang et al., 2019 [35] | miR-31 | 1.38 (1.02–1.87) H-L | |||||
| Tu et al., 2021 [36] | miR-31 | 1.68 (0.7747–3.6433) H-L | |||||
| Hess et al., 2017 [37] | miR-155 | 1.9 (1.0–3.7) H-L | |||||
| miR-200b | 1.4 (0.8–2.6) H-L | ||||||
| miR-146a | 2.2 (1.2–4.3) H-L | ||||||
| Zhao et al., 2018 [38] | miR-155 | 1.476 (0.983–1.916) H-L | |||||
| Baba et al., 2016 [39] | miR-155 | 5.156 H-L | 1.3300 H-L | ||||
| Shi et al., 2015 [40] | miR-155 | 1.748 (0.508–6.015) H-L | |||||
| Kim et al., 2018 [41] | miR-155 | 1.6300 p = 0.7592 H-L | |||||
| Bersani et al., 2018 [42] | miR-155 | 0.5760 p = 0.30 H-L | |||||
| Wu et al., 2020 [43] | miR-155 | 1.6600 p = 0.6780 H-L | 1.4900 p = 0.7861 H-L | ||||
| Shuang et al., 2017 [44] | miR-195 | RR 0.358 (0.134–0.959) | |||||
| Ding and Qi, 2019 [45] | miR-195 | RR 0.3616 (0.2409–0.5428) | |||||
| Jia et al., 2013 [46] | miR-195 | RR 0.322 (0.120–0.865 | |||||
| Qin et al., 2019 [47] | miR-196a | 2.175 (1.455–4.034) H-L | |||||
| Liu et al., 2013 [48] | miR-196a, miR-196a2 | 2.57(1.20–5.48) H-L | |||||
| Maruyama et al., 2018 [49] | miR-196a | 0.91 (0.12–7.19) H-L | 0.6 (0.18–2.06) H-L | ||||
| Zhao et al., 2018 [50] | miR-196b | 1.577 (0.989–2.516) H-L | |||||
| Luo et al., 2019 [51] | miR-196b | 1.80 (0.38–8.51) H-L | |||||
| Ahn et al., 2017 [52] | miR-197 | 1.01 (1.00–1.02)? | |||||
| Hudcova et al., 2016 [53] | miR-200b | 1.00 (0.42–2.38) H-L | 1.25 (0.51–3.08) H-L | 0.91 (0.14–5.23) H-L | |||
| miR-375 | 1.32 (0.76–2.27) H-L | 1.45 (0.74- 2.81) H-L | 1.77 (0.67–468) H-L | ||||
| miR-29c | 0.89 (0.47–1.70) H-L | 0.80 (0.37–1.75) H-L | 0.31 (0.10–0.91) H-L | ||||
| Kang et al., 2021 [54] | miR-198 | 3.996 (1.345–5.885) L-H | 3.609 (1.123–5.334) L-H | ||||
| Bonnin et al., 2016[55] | miR-422a | 1.99 (1.07–3.7) L-H | |||||
| Ganci et al., 2013 [56] | miR-205 | 4.98 (1.67–14.9) H-L | |||||
| miR-429 | 4.45 (1.59–12.45) H-L | ||||||
| miR-21-3p | 2.17 (0.98–4.83) H-L | 3.12 (1.28–7.6) H-L | |||||
| miR-331 | 3.45 (1.24–9.64) H-L | ||||||
| miR-200a | 3.1 (1.18–7.9) H-L | ||||||
| miR-19a | 2.86 (1.1–7.7) H-L | ||||||
| miR-21-5p | 2.41 (1.1–5.53) H-L | 2.77 (1.04–7.38) H-L | |||||
| miR-151a | 3 (1–8.97) H-L | ||||||
| miR-17 | 2.1 (0.91–4.71) H-L | 2.82 (0.98–8.14) H-L | |||||
| miR-18b | 2.54 (0.97–6.69) H-L | ||||||
| miR-324 | 2.62 (0.85–8) H-L | ||||||
| miR-96 | 2.19 (0.87–5.53) H-L | ||||||
| miR-139 | 0.33 (0.12–0.87) H-L | ||||||
| Harris et al., 2012 [57] | miR-375 | 12.8 (3.4–48.6) L-H | |||||
| Ahmad et al., 2019 [58] | miR-15b | 0.246 (0.053–0.787) H-L | |||||
| Rajthala et al., 2021 [59] | miR-204 | 0.668 (0.45–1.00) H-L | 0.56 (0.33–0.96) H-L | ||||
| Song et al., 2020 [60] | miR-200c | 1.669 (1.03–2.703) L-H | 1.705 (1.136–2.56) L-H | ||||
| Zhao et al., 2018 [61] | miR-145 | 0.662 (0.298–1.004) H-L | |||||
| Li et al., 2013 [62] | miR-101 | 1.13 (0.17–7.50) L-H | |||||
| de Jong et al., 2015 [63] | miR-452 | 0.5 p = 0.1 H-L | |||||
| miR-141 | 0.7 p = 0.4 H-L | ||||||
| miR-203 | 0.6 p = 0.4 H-L | ||||||
| Fang et al., 2019 [64] | miR-29c | 0.350 (0.129–0.949) H-L | |||||
| He et al., 2o17 [65] | miR-300 | 1.89 (0.66–2.33) L-H | |||||
| Re et al., 2015 [66] | miR-34c | 3.623 (1.911–6.86) L-H | 1.81 (1.02–3.25) L-H | ||||
| Xu et al., 2016 [67] | miR-149 | 1.57 (1.02–2.40) L-H | |||||
| Tian et al., 2014 [68] | miR-203 | p = 0.002 L-H | |||||
| Zhao et al., 2018 [69] | miR-181a | 0.559 (0.211–1.106) H-L | |||||
| Guan et al., 2016 [70] | miR-675 | 2.52 (1.75–8.45) H-L | 3.26 (0.94–10.71) H-L | ||||
| Avissar et al., 2009 [71] | miR-21 | 1.68 (1.04–2.77) H-L | |||||
| Wu et al., 2014 [72] | miR-9 | 3.18 (2.19–11.91) H-L | |||||
| Wu, Zhang et al., 2014 [73] | miR-19 | 2.260 p = 0.034 H-L | |||||
| Zhang et al., 2015 [57] | miR-23a | 6.712 (2.076–21.700) H-L | |||||
| Hu et al., 2015 [75] | Expression ratio of miRNA-21/miRNA-375 | p = 0.032 | |||||
| Re et al., 2017 [76] | miR-34c-5p | 7.32 (2.33–23.00) L-H | 7.830 (2.225–27.552) L-H | ||||
| Shen et al., 2012 [77] | miR-34a | 4.02 (1.67–9.69) L-H | |||||
| Maia et al., 2017 [78] | miR-296 | 8.6 (1.7–42.2) H-L | |||||
| Ogawa et al., 2012 [79] | miR-34a | 0.005 (0.00–0.29) L-H | 0 L-H? | ||||
| Pantazis et al., 2020 [80] | miR-20b | 11.62 (2.64–46.62) H-L | 4.23 (1.75–22.52) H-L | ||||
| Childs et al., 2009 [81] | miR-205 | 2.51 p 0.025 L-H | |||||
| miR-21 | 1.00 p 0.995 H-L | ||||||
| Le7d | 1.73 p 0.166 L-H | ||||||
| Ko et al., 2014 [82] | miR-21 | 2.972 (1.340–6.590) L-H? | 1.659 (0.824–3.343) L-H? | ||||
| Arantes et al., 2017 [83] | miR-21 | 2.05 (1.05–4.02) H-L | |||||
| Chang et al., 2013 [84] | miR-17 | 2.47 (1.37–4.44) L-H | |||||
| miR-20a | 3.44 (1.45–8.15) L-H | ||||||
| Gee et al., 2010 [85] | miR-210 | 6.88 (2.30–20.53) H-L | |||||
| Jia et al., 2014 [86] | miR-26a | RR 0.283 (0.118–0.682) | |||||
| Liao et al., 2013 [87] | miR-1246 | 2.82 (1.07–7.43) H-L | |||||
| Liu et al., 2013 [88] | miR-451 | 2.00 (1.18–3.41) L-H | 1.81 (1.16–2.83) L-H | ||||
| Liu, Shen et al., 2013 [89] | miR-134 | 2.17 (1.17–5.12) H-L | |||||
| Luo et al., 2014 [90] | miR-18a | 0.4147 (0.2208–0.7791) L-H | |||||
| Peng et al., 2014 [91] | Let-7g | 3.267 (1.164–9.174) L-H | 3.289 (1.059–10.204) L-H | ||||
| Sasahira et al., 2012 [92] | miR-126 | 2.631 (0.9886–7.9851) L-H | |||||
| Tu et al., 2015 [93] | miR-372 | 2.57 (1.20–5.48) H-L | |||||
| miR-373 | 2.62 (1.47–4.64) H-L | ||||||
| Wu et al., 2014 [77] | miR-218 | 2.51 (1.32–4.77) L-H | |||||
| Xu et al., 2013 [95] | miR-153 | 2.295 (1.168–4.508) L-H | |||||
| miR-200c | 2.202 (1.110–4.371) L-H | ||||||
| Zhang et al., 2017[96] | miR-375 | 1.61 (0.96–2.70) L-H | |||||
| Jia et al., 2015 [97] | miR-375 | 2.07 (1.02–4.20) L-H | RR 0.449 (0.207–0.978) | ||||
| Hu et al., 2014 [98] | miR-375 | 1.88 (0.56–6.31) L-H | |||||
| miR-21 | 1.1302 (0.34–3.757) H-L | ||||||
| Gu et al., 2018 [99] | miR-22 | p < 0.005 L-H |
Table 4.
Spearman’s correlations.
| miR | miR-21 | miR-155 | miR-375 |
|---|---|---|---|
| miR-21 | 1 (p < 1 × 10−4) | ||
| miR-155 | 0.1122 (p = 0.0152) | 1 (p < 1 × 10−4) | |
| miR-375 | −0.4331 (p < 1 × 10−4) | −0.0107 (p = 0.8177) | 1 (p < 1 × 10−4) |
Table 5.
Pearson’s correlations among different microRNAs.
| miR | miR-21 | miR-155 | miR-375 |
|---|---|---|---|
| miR-21 | 1 (p < 1 × 10−4) | ||
| miR-155 | 0.1426 (p = 0.002) | 1 (p < 1 × 10−4) | |
| miR-375 | −0.2241 (p < 1 × 10−4) | −0.0336 (p = 0.4684) | 1 (p < 1 × 10−4) |
Table 6.
Data on resistance to chemotherapy and radiotherapy in relation to altered expression of microRNAs.
Table 6.
Data on resistance to chemotherapy and radiotherapy in relation to altered expression of microRNAs.
| First Autor, Data | miR | Tumor Type | Adjuvant Therapy | Administered Chemotherapy Drug | Main Results of the Study |
|---|---|---|---|---|---|
| Zheng et al., 2016 [31] | miR-21 | OTSCC | chemotherapy | \ | miR-21 enhances chemo-resistance in OTSCC |
| Hess et al., 2017 [37] | miR-155, miR-200b, miR-146a | HNSCC | radiotherapy/chemotherapy | 5-fluorouracil/cisplatin, 5-fluorouracil/mitomycin C | MiR-146a was revealed as a prognostic marker for chemoradiation. MiR-155 and miR-146a were identified as markers for tumor-infiltrating lymphocytes. |
| Qin et al., 2019 [47] | miR-196a | HNSCC | chemotherapy | cisplatin | miR-196a may serve as a promising predictor of and potential therapeutic target for cisplatin resistance in HNC |
| Ahmad et al., 2019 [58] | miR-15b | HNSCC | radiotherapy | \ | miR-15b-5p represents a potentially helpful biomarker for individualized treatment decisions concerning the management of HNSCC patients treated with intensity-modulated radiotherapy |
| de Jong et al., 2015 [63] | miR-203 | HNSCC | radiotherapy | \ | miR-203 causes intrinsic radioresistance of HNSCC, which could enable the identification and treatment modification of radioresistant tumors. |
| Maia et al., 2017 [78] | miR-296 | LSCC | radiotherapy | \ | miR-296-5p expression is associated with resistance to radiotherapy and tumor recurrence in early-stage LSCC |
| Ogawa et al., 2012 [79] | miR-34a | HNSCC | chemotherapy | cisplatin | miR-34a expression can be an independent prognostic biomarker in patients with sinonasal squamous cell carcinoma who are undergoing treatment with cisplatin |
| Zhang et al., 2017 [96] | miR-375 | OSCC | radiotherapy | \ | miRNA-375 inhibits growth and enhances radiosensitivity in OSCC |
| Gu et al., 2018 [99] | miR-22 | TSCC | chemotherapy | cisplatin | strong correlation between miR-22 expression and chemosensitivity to cisplatin in TSCC patients |
Table 7.
Risk of bias, ROBIS scale: ok (low); ? (unclear).
| Phase 1 | Phase 2 | Phase 3 | ||||
|---|---|---|---|---|---|---|
| First Author, Data | PICO | Study Eligibility Criteria | Identification and Selection of Studies | Data Collection and Study Appraisal | Synthesis and Findings | Risk of Bias in the Review |
| Dioguardi et al., 2023 [9] | ok | ok | ok | ok | ? | ok |
| Dioguardi et al., 2022 [13] | ok | ok | ok | ok | ok | ok |
| Dioguardi et al., 2022 [14] | ok | ok | ok | ok | ok | ok |
| Dioguardi et al., 2022 [8] | ok | ok | ok | ok | ok | ok |
| Dioguardi et al., 2022 [6] | ok | ok | ok | ok | ok | ok |
| Dioguardi et al., 2022 [7] | ok | ok | ok | ok | ok | ok |
| Irimie-Aghiorghiese et al., 2019 [15] | ok | ? | ? | ok | ok | ok |
| Lubov et al., 2017 [16] | ok | ? | ? | ? | ok | ok |
| **e and Wu, 2017 [17] | ok | ? | ok | ok | ok | ok |
| Wang et al., 2019 [18] | ok | ? | ok | ok | ok | ok |
| Li et al., 2019 [19] | ok | ? | ? | ? | ok | ok |
| Qiu et al., 2021 [20] | ok | ok | ok | ok | ok | ok |
| Huang et al., 2021 [21] | ok | ? | ? | ok | ok | ok |
| Jamali et al., 2015 [22] | ok | ? | ok | ok | ok | ok |
| Troiano et al., 2018 [23] | ok | ? | ok | ok | ok | ok |
Table 8.
Assessment of the risk of bias; REMARK.
| First Author, Data | Sample | Clinical Data | Marker Quantification | Prognostication | Statistics | Classical Prognostic Factors | Score |
|---|---|---|---|---|---|---|---|
| Jung et al., 2012 [24] | 1 | 2 | 3 | 2 | 2 | 3 | 13 |
| Kawakita et al., 2014 [25] | 3 | 2 | 2 | 2 | 1 | 3 | 13 |
| Hedbäck et al., 2014 [26] | 3 | 2 | 2 | 3 | 3 | 2 | 15 |
| Yu et al., 2017 [27] | 3 | 2 | 3 | 3 | 3 | 2 | 16 |
| Supic et al., 2018 [28] | 2 | 3 | 3 | 3 | 3 | 3 | 17 |
| Jakob et al., 2019 [29] | 1 | 3 | 3 | 3 | 3 | 3 | 16 |
| Li et al., 2013 [30] | 2 | 2 | 3 | 2 | 2 | 3 | 14 |
| Zheng et al., 2016 [31] | 3 | 1 | 3 | 3 | 2 | 2 | 14 |
| Li et al., 2009 [32] | 3 | 3 | 3 | 3 | 3 | 3 | 18 |
| Ganci et al., 2016 [33] | 3 | 2 | 3 | 3 | 3 | 2 | 16 |
| Wang et al., 2018 [34] | 3 | 2 | 3 | 2 | 2 | 3 | 15 |
| Qiang et al., 2019 [35] | 2 | 3 | 3 | 2 | 2 | 2 | 14 |
| Tu et al., 2021 [36] | 1 | 3 | 3 | 2 | 2 | 2 | 13 |
| Hess et al., 2017 [37] | 3 | 2 | 3 | 2 | 2 | 2 | 14 |
| Zhao et al., 2018 [38] | 3 | 2 | 3 | 2 | 2 | 2 | 14 |
| Baba et al., 2016 [39] | 2 | 3 | 3 | 2 | 2 | 2 | 14 |
| Shi et al., 2015 [40] | 1 | 2 | 3 | 2 | 2 | 2 | 12 |
| Kim et al., 2018 [41] | 2 | 2 | 3 | 2 | 2 | 1 | 12 |
| Bersani et al., 2018 [42] | 3 | 3 | 3 | 2 | 2 | 1 | 14 |
| Wu et al., 2020 [43] | 2 | 2 | 3 | 3 | 3 | 2 | 15 |
| Shuang et al., 2017 [44] | 3 | 2 | 3 | 2 | 3 | 2 | 15 |
| Ding and Qi, 2019 [45] | 3 | 2 | 3 | 2 | 3 | 2 | 15 |
| Jia et al., 2013 [46] | 2 | 2 | 3 | 2 | 3 | 2 | 14 |
| Qin et al., 2019 [47] | 2 | 3 | 2 | 2 | 3 | 2 | 14 |
| Liu et al., 2013 [48] | 2 | 1 | 2 | 2 | 3 | 2 | 12 |
| Maruyama et al., 2018 [49] | 2 | 2 | 2 | 3 | 2 | 3 | 14 |
| Zhao et al., 2018 [50] | 3 | 1 | 2 | 2 | 3 | 2 | 13 |
| Luo et al., 2019 [51] | 2 | 3 | 3 | 2 | 2 | 2 | 14 |
| Ahn et al., 2017 [52] | 2 | 3 | 1 | 2 | 3 | 2 | 14 |
| Hudcova et al., 2016 [53] | 2 | 2 | 3 | 3 | 3 | 2 | 15 |
| Kang et al., 2021 [54] | 2 | 1 | 3 | 3 | 3 | 2 | 14 |
| Bonnin et al., 2016 [55] | 2 | 2 | 3 | 3 | 3 | 2 | 15 |
| Ganci et al., 2013 [56] | 3 | 3 | 3 | 3 | 3 | 2 | 17 |
| Harris et al., 2012 [57] | 3 | 3 | 2 | 3 | 3 | 2 | 16 |
| Ahmad et al., 2019 [58] | 2 | 2 | 3 | 3 | 3 | 2 | 15 |
| Rajthala et al., 2021 [59] | 3 | 3 | 3 | 3 | 3 | 2 | 17 |
| Song et al., 2020 [60] | 3 | 2 | 3 | 3 | 3 | 2 | 16 |
| Zhao et al., 2018 [61] | 3 | 2 | 3 | 2 | 3 | 2 | 15 |
| Li et al., 2013 [62] | 2 | 3 | 3 | 3 | 2 | 2 | 15 |
| de Jong et al., 2015 [63] | 1 | 1 | 3 | 2 | 3 | 3 | 13 |
| Fang et al., 2019 [64] | 2 | 3 | 3 | 3 | 3 | 2 | 16 |
| He et al., 2017 [65] | 3 | 2 | 2 | 3 | 3 | 2 | 15 |
| Re et al., 2015 [66] | 3 | 1 | 2 | 3 | 3 | 2 | 14 |
| Xu et al., 2016 [67] | 3 | 3 | 2 | 3 | 2 | 3 | 16 |
| Tian et al., 2014 [68] | 2 | 1 | 2 | 3 | 2 | 3 | 13 |
| Zhao et al., 2018 [69] | 3 | 3 | 2 | 3 | 3 | 2 | 16 |
| Guan et al., 2016 [70] | 2 | 2 | 2 | 3 | 3 | 3 | 15 |
| Avissar et al., 2009 [71] | 3 | 3 | 3 | 3 | 3 | 3 | 18 |
| Wu et al., 2014 [72] | 3 | 2 | 2 | 3 | 3 | 3 | 16 |
| Wu, Zhang et al., 2014 [73] | 2 | 3 | 2 | 3 | 3 | 3 | 16 |
| Zhang et al., 2015 [74] | 1 | 3 | 2 | 3 | 2 | 3 | 14 |
| Hu et al., 2015 [75] | 1 | 3 | 3 | 3 | 2 | 3 | 15 |
| Re et al., 2017 [76] | 1 | 2 | 3 | 3 | 2 | 3 | 14 |
| Shen et al., 2012 [77] | 2 | 1 | 2 | 3 | 2 | 3 | 13 |
| Maia et al., 2017 [78] | 1 | 3 | 2 | 3 | 2 | 2 | 13 |
| Ogawa et al., 2012 [79] | 1 | 1 | 2 | 3 | 2 | 3 | 12 |
| Pantazis et al., 2020 [80] | 3 | 2 | 2 | 3 | 3 | 3 | 16 |
| Childs et al., 2009 [81] | 2 | 3 | 3 | 3 | 3 | 3 | 17 |
| Ko et al., 2014 [82] | 3 | 3 | 2 | 3 | 3 | 3 | 17 |
| Arantes et al., 2017 [83] | 2 | 3 | 2 | 3 | 2 | 3 | 15 |
| Chang et al., 2013 [84] | 2 | 2 | 3 | 3 | 2 | 3 | 15 |
| Gee et al., 2010 [85] | 1 | 3 | 3 | 3 | 2 | 3 | 15 |
| Jia et al., 2014 [86] | 2 | 2 | 2 | 3 | 2 | 3 | 14 |
| Liao et al., 2013 [87] | 3 | 2 | 2 | 2 | 2 | 3 | 14 |
| Liu et al., 2013 [88] | 3 | 2 | 2 | 3 | 3 | 3 | 16 |
| Liu, Shen et al., 2013 [89] | 2 | 2 | 2 | 3 | 2 | 3 | 14 |
| Luo et al., 2014 [90] | 3 | 2 | 2 | 3 | 3 | 3 | 16 |
| Peng et al., 2014 [91] | 1 | 1 | 3 | 2 | 2 | 3 | 12 |
| Sasahira et al., 2012 [92] | 3 | 2 | 2 | 2 | 2 | 3 | 14 |
| Tu et al., 2015 [93] | 1 | 2 | 3 | 3 | 2 | 3 | 14 |
| Wu et al., 2014 [94] | 3 | 3 | 2 | 3 | 3 | 3 | 17 |
| Xu et al., 2013 [95] | 2 | 1 | 3 | 2 | 2 | 3 | 13 |
| Zhang et al., 2017 [96] | 1 | 1 | 2 | 2 | 2 | 3 | 11 |
| Jia et al., 2015 [97] | 3 | 2 | 2 | 2 | 2 | 3 | 14 |
| Hu et al., 2014 [98] | 1 | 3 | 3 | 2 | 3 | 3 | 15 |
| Gu et al., 2018 [99] | 2 | 2 | 2 | 2 | 3 | 3 | 14 |
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Dioguardi, M.; Spirito, F.; Iacovelli, G.; Sovereto, D.; Laneve, E.; Laino, L.; Caloro, G.A.; Nabi, A.Q.; Ballini, A.; Lo Muzio, L.; et al. The Potential microRNA Prognostic Signature in HNSCCs: A Systematic Review. Non-Coding RNA 2023, 9, 54. https://doi.org/10.3390/ncrna9050054
AMA Style
Dioguardi M, Spirito F, Iacovelli G, Sovereto D, Laneve E, Laino L, Caloro GA, Nabi AQ, Ballini A, Lo Muzio L, et al. The Potential microRNA Prognostic Signature in HNSCCs: A Systematic Review. Non-Coding RNA. 2023; 9(5):54. https://doi.org/10.3390/ncrna9050054
Chicago/Turabian StyleDioguardi, Mario, Francesca Spirito, Giovanna Iacovelli, Diego Sovereto, Enrica Laneve, Luigi Laino, Giorgia Apollonia Caloro, Ari Qadir Nabi, Andrea Ballini, Lorenzo Lo Muzio, and et al. 2023. "The Potential microRNA Prognostic Signature in HNSCCs: A Systematic Review" Non-Coding RNA 9, no. 5: 54. https://doi.org/10.3390/ncrna9050054
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