miRNAs as biomarkers of autism spectrum disorder: a systematic review and meta-analysis

Vol.:(0123456789) 1 3 European Child & Adolescent Psychiatry https://doi.org/10.1007/s00787-023-02138-3 REVIEW miRNAs asbiomarkers ofautism spectrum disorder: asystematic review andmeta‑analysis NathaliaGarrido‑Torres1,2 · KaremGuzmán‑Torres3 · SusanaGarcía‑Cerro1,2 · GladysPinillaBermúdez3 · ClaudiaCruz‑Baquero3 · HanselOchoa4 · DiegoGarcía‑González1 · ManuelCanal‑Rivero1,2 · BenedictoCrespo‑Facorro1,2 · MiguelRuiz‑Veguilla1,2 Received: 30 November 2021 / Accepted: 5 January 2023 © The Author(s) 2023 Abstract Autism spectrum disorder (ASD) is a neurodevelopmental disorder with complex clinical manifestations that arise between 18 and 36months of age. Social interaction deficiencies, a restricted range of interests, and repetitive stereotyped behaviors are characteristics which are sometimes difficult to detect early. Several studies show that microRNAs (miRs/miRNAs) are strongly implicated in the development of the disorder and affect the expression of genes related to different neurological pathways involved in ASD. The present systematic review and meta-analysis addresses the current status of miRNA studies in different body fluids and the most frequently dysregulated miRNAs in patients with ASD. We used a combined approach to summarize miRNA fold changes in different studies using the mean values. In addition, we summarized p values for differential miRNA expression using the Fisher method. Our literature search yielded a total of 133 relevant articles, 27 of which were selected for qualitative analysis based on the inclusion and exclusion criteria, and 16 studies evaluating miRNAs whose data were completely reported were ultimately included in the metaanalysis. The most frequently dysregulated miRNAs across the analyzed studies were miR-451a, miR-144-3p, miR-23b, miR-106b, miR150-5p, miR320a, miR92a-2-5p, and miR486-3p. Among the most dysregulated miRNAs in individuals with ASD, miR-451a is the most relevant to clinical practice and is associated with impaired social interaction. Other miRNAs, including miR19a-3p, miR-494, miR-142-3p, miR-3687, and miR-27a-3p, are differentially expressed in various tissues and body fluids of patients with ASD. Therefore, all these miRNAs can be considered candidates for ASD biomarkers. Saliva may be the optimal biological fluid for miRNA measurements, because it is easy to collect from children compared to other biological fluids. Keywords Autism spectrum disorder· microRNA· Biomarker· Saliva Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a prevalence of approximately 1.5% in developed countries [1]. The aetiology of ASD has not been fully elucidated, since the disorder exhibits wide genetic variability that triggers both behavioral and phenotypic alterations at the level of brain structures. The clinical manifestations are complex and emerge between 18 and 36months of age. Social interaction deficiencies, a restricted range of interests, and repetitive stereotyped behaviors are the main characteristics of ASD, which are sometimes difficult to detect early [2]. Currently, the diagnosis is based on interviews with parents or caregivers using tools such as the Modified Checklist for Autism in Young Children, Revised (M-CHAT-R) [3], * Benedicto Crespo-Facorro [email protected] 1 Hospital Universitario Virgen Del Rocio, IBIS-CSIC, Department ofPsychiatry, University ofSevilla, Avda Manuel Siurot S/N, 41013Seville, Spain 2 Spanish Network forResearch inMental Health (CIBERSAM), Seville, Spain 3 Colegio Mayor de Cundinamarca, Bogotá, Colombia 4 Epidemiology Research Group (EpiAndes), Los Andes University, Bogotá, Colombia European Child & Adolescent Psychiatry 1 3 the Autism Diagnostic Observation Schedule (ADOS) [4], and the Autism Diagnostic Interview-Revised (ADI-R) [5]. Although the diagnostic reliability of these interviews is high, these tools require an evaluator with experience and specific training. Implementation of an intervention before the age of two can improve the prognosis. Early intervention can also lead to better neuronal maturation [6]. Therefore, a biomarker for early detection would be a good diagnostic supplement [7, 8]. Several biomarkers have been proposed for ASD detection, including functional connectivity observed on magnetic resonance imaging and calculated using machine learning algorithms [9], the genetic load [10], the increased CSF volume [11], transcriptomic signatures in blood, and levels of altered cytokines; however, more studies are required to corroborate whether these items can serve as ideal biomarkers [12]. The non-coding RNAs (ncRNAs) are recently emerging as novel promising biomarkers in medicine with great prognostic and predictive potential [13]. MiRNAs, the most well-studied ncRNA, are short non-coding RNAs of approximately 18–24 nucleotides that are responsible for regulating gene expression through epigenetic mechanisms [14] in approximately 60% of human genes [15]. As will be focused on this review, for ASD diagnoses, microRNAs (miRs/miR- NAs) are types of ncRNAs which have become an important research focus last years [15–17]. MiRNAs, the most well-studied ncRNA, are short noncoding RNAs of approximately 18–24 nucleotides that are responsible for regulating gene expression through epigenetic mechanisms [14] in approximately 60% of human genes [15]. In addition, miRNAs are heavily involved in neuronal plasticity and neuronal development [18], and their deregulation generates diverse neurological alterations, such as ASD. To be an accessible biomarker, miRNA should be able to be isolated using non-invasive protocols, easy to quantify, specific to the disorder, able to be translated from systems to human models [19] and reliable as an early indicator of disease onset [20]. Various investigations of miR- NAs as biomarkers for ASD have focused on biogenesis and measurement in different biofluids or tissues for detection [21], such as lymphoblastoid cells [22, 23], postmortem cerebral cortex tissues [2], serum or blood plasma [24], olfactory mucosa cells [18], and saliva [16]. Based on this knowledge, we conducted a systematic review to (1) identify which miRNAs can be used as biomarkers to support current diagnostic methods, (2) determine which body fluid may be ideal for miRNA measurement in children, and (3) clarify relationships between miRNAs and the genetic burden of ASD. Methodology The systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [25]. It also followed a protocol registered in PROSPERO CRD42021225956. Search strategy We searched the PubMed, Scopus, and Web of Science databases from 2010 until March 2021 utilizing Medical Subject Headings (MeSH) related to (autism spectrum disorder) AND (microRNA). We also cross-referenced relevant studies and previous reviews and contacted study authors and experts for data clarification. Eligibility To achieve a concise and precise review of the role of miR- NAs in ASD, some criteria were selected to systematize the search in the different databases and thus include only articles related to the subject. Inclusion criteria: (i) Studies on ASD patients; (ii) Studies in which ASD was diagnosed according to either the ADOS, ADI-R or any validated instrument; (iii) Studies measuring miRNAs in any peripheral tissue from patients with ASD; (iv) Studies measuring miRNAs in CNS from patients with ASD (iv) Studies with a control group. Exclusion criteria: (i) ASD studies focusing on biomarkers other than miRNAs; (ii) Clinical cases reports; (iii) Studies conducted 11 or more years ago; (iv) Studies conducted in languages other than English; (v) Review articles; and (vi) Studies conducted on animals. Study selection Three independent reviewers (KGT, NGT, and GPB) screened titles and abstracts to identify studies meeting the inclusion criteria outlined above using Rayyan [26] software. The same reviewers then reviewed the full texts of eligible articles, and the final list of included articles was established through consensus. The Kappa index was 0.931. Disagreements regarding the eligibility of studies were resolved through discussions with two additional reviewers (SGC and CCB). Meta‑analytic approach We implemented a meta-analytic approach to combine the differential gene expression results of the included studies. After study selection, the reviewers used a predefined spreadsheet to extract all relevant data from the included European Child & Adolescent Psychiatry 1 3 studies. The extracted data included general publication information and detailed information about the miRNAs analyzed in each study and their association with severity and clinical manifestations (impaired social behavior, repetitive behaviors, intelligence, memory and learning, and language impairment). We extracted fold change (fc) data or log2-fc values and their p values and confidence intervals when reported by the authors. We identified miRNAs that were assessed and reported in two or more studies to combine their log2-fc values. We used the R package MetaVolcanoR, which implements three strategies to combine and summarize differential gene expression data from different studies: a random-effects model approach, a combined p value approach, and a vote-counting approach [27]. Quality assessment The QUADAS-2 tool was developed to assess the quality of studies on diagnostic tests included in systematic reviews [28]. QUADAS-2 analyzes four domains: (1) patient selection, (2) index tests, (3) reference tests, and (4) flow and times. Each domain is evaluated in terms of its risk of bias, and the first three domains are also evaluated for their applicability. For more details, please see the supplementary documents. Results The literature search yielded a total of 133 articles. Then, 27 articles were selected for qualitative analysis based on the inclusion and exclusion criteria. Out of the 27, 16 studies were ultimately included in the meta-analysis and 218 miRNAs were identified (Fig.1). In all studies (Tables1, 2), miRNA measurements were performed in ASD patients and compared with miRNA measurements in a control group. The ages of the patients and controls ranged between 2 and 81years. miRNA was measured in lymphoblastoid cell lines in three studies [14, 22, 29], five studies measured miRNA in postmortem cerebral cortex tissues [2, 15, 30–32], one study used cells from the olfactory mucosa [18], thirteen studies used serum [7, 19, 24, 33–42], and five studies used saliva [8, 16, 43–45]. A recent narrative review [46] found that the miR-151a, miR-146a, and miR-27a-30 are dysregulated in people with ASD and are replicated in more than one tissue. Although 218 miRNAs were identified across the 16 studies included in the meta-analysis, only one (miR-451) was associated with a clinical manifestation of ASD in more than one study. Two studies [31, 45] reported that miR-451 is associated with impaired social interaction, one study [43] reported that miR-106 family is associated with repetitive behaviors, one study [41] reported that miR-486-3p is associated with intelligence, one study [33] reported that miR-140-3p is associated with memory and learning, and no studies reported that any miRNA was associated with language impairment. Regarding diagnoses, all studies used certain validated instruments to diagnose autism, including the ADOS, ADI-R-R, DSM, M-CHAT, and CARS. Two studies concluded that miRNAs are associated with severity. The expression of miR-6126 was significantly negative correlated with severity of the Social Response Scale (SRS) in adults with high functioning ASD [37] and miR-106a showed a positive correlation with autism severity evaluated by Childhood Autism Rating Scale (CARS) [42] in children aged under 14. For our meta-analytic approach, we included 16 studies and eight miRNAs (451-a, 144-3p, 23b, 106b, 150-5p, 320a, 92a-2-5p, and 486-3p) whose data were reported in more than one study. Since no studies reported the confidence interval for fc, we were unable to conduct all planned analyses. We used a combined approach to summarize the fold changes of the miRNAs in different studies according to the mean values. In addition, we summarized the p values for differential miRNA expression using the Fisher method. The combined results for the eight miRNAs are shown in Table3 and Fig.2a, b shows the 16 studies included in the meta-analysis and the fold change of the expression in every miRNA. Each circle represents a miRNA. 218 miRNAs were identified across 16 studies but only 8 miRNAs were repeated in more than one study. Figure3 summarizes all the target genes of the analyzed miRNAs and shows the miRNAs related to different clinical manifestations of ASD. Finally, Table4 shows target genes of the analyzed miRNAs; and Fig. S1. Quality assessment of individual studies is done using QUADAS tool. Discussion This review summarizes findings across 27 trials conducted in humans. To our knowledge, this is the first systematic review and meta-analysis of dysregulated miRNAs in ASD, their associations with ASD clinical manifestations, and miRNA measurement in biofluids from individuals with ASD. (i) The most frequently dysregulated miRNAs in patients with ASD were miR-451a, miR-144-3p, miR-23b, miR-106b, 150-5p, 320a, 92a-2-5p, and 486-3p, (ii) miR- 451 is one of the most frequently dysregulated miRNAs and is the only associated with impaired social interaction in more than one study. (iii) miR-451 has also been isolated in saliva and may be the most promising biofluid for miRNA measurement. (iv) miRNA 106 family is also one of the most frequently dysregulated miRNAs and is associated with European Child & Adolescent Psychiatry 1 3 repetitive behaviors in one of the studies. (v) miRNAs are associated with genes related to ASD. Although behavioral signs of ASD are present in many cases by the age of 18months, ASD is not typically diagnosed before 3 to 4years of age. To date, the only means to diagnose ASD is by observing children’s development through neurodevelopmental evaluations. No biological marker allows detection of the disease from birth; therefore, one of the current challenges for researchers is to determine whether miRNAs can truly be used as biomarkers to facilitate ASD diagnosis. We found that although research on miRNAs for the diagnosis of ASD started 12years ago, at present, no pattern of miRNAs specific to ASD clinical levels of severity has been identified, and specific sets of miRNA do not reveal a pattern, rather than big data emergent assessment across all miRNA. Only one paper [43] 2020 suggests that there is a specific pattern. MiRNA measurement in different body fluids, such as blood or saliva, may be useful for comparing the levels of a specific miRNA to control levels, which can be applied to the search for a biological marker of ASD. On the other hand, although transcriptomic or genetic analysis can be performed from birth, ASD is a disease with a heterogeneous genetic component, and no specific gene is universally affected in the entire population with ASD; therefore, performing a genetic or transcriptomic analysis is not feasible. However, as miRNAs are epigenetic modulators, the study of epigenetics can help identify one or more specific miRNAs that can be used as biomarkers. Fig. 1 PRISMA diagram noitacifitnedI ytilibigelE Screening Included Recordsidentified through database search Pubmed: 133 Scopus: 135 Web of science: 206 n= 474 Records after duplicates removed n=295 Records screened based on title and abstrac n=295 Records excluded n=262 *Wrong study desing: 202 *Wrong population: 61 *Wrong publication type: 60 Full text excluded n=6 *NomicroRNA measurement was done, only statistical analysis =1 *Wrong study design, not focused on microRNA as biomarker= 1 *Study conduced more than 10 years ago= 2 *The use of diagnosis for patiens is not specified=1 *The microRNA measurent that was made was not diferential=1 Articles included in quantitative synthesis (Meta-analysis) n=16 Articles included n=27 Full text articles assessed for elegibility n=33 Articles with insufficient information on microRNA measurement n=11 FC value is not specified= 11 European Child & Adolescent Psychiatry 1 3 Table 1 Findings of the 26 studies included in the systematic review Sample type Number of patients M:F age (years) Study type Other patient characteristics RNA extraction miRNA profile and validation Prediction of deregulated genes in ASD and micro- RNA target genes References ASD (n) CS (n) Cell lines 14 pts 14M: 0 F 4–14years old 14 pts (3 sets of brothers were monozygotic twins) 14M: 0 F 2–12years old Experimental with a control group Ethnicity of the patients: Hispanic or Latino Origin of the sample: Autism Genetics Resource Socioeconomic level: – Diagnostic criteria: ADI-R and ADOS mirVana kit Microarrays and qRT–PCR miRBase, software prediction software Ingenuity Pathway Analysis (IPA) version 6.0 y Pathway Studio version 5 (Ariadne Genomics, Rockville, MD, USA) Sarachana etal. [14] 20 pts 3M: 7 F Age: – 22 pts (brothers) 19M: 3 F Age: – Experimental with a control group Ethnicity of the patients: European descent Origin of the sample: – Socioeconomic level: – Diagnostic criteria: ADOS mirVana kit Microarrays and qRT–PCR Method of generalized estimation equations, to determine differentially expressed genes and network prediction software Ingenuity Pathway Analysis (IPA) Seno etal. [22] 10 pts 10M:0 F 3–13years old 17 pts 10 pts brothers 7 pts not brothers 17M:0 F 4–13years old Experimental with control group Ethnicity of the patients: EEUU Origin of the sample: Autism Genetic Resource Exchange and Coriell Cell Repository (Camden, NJ, USA) Socioeconomic level: – Diagnostic criteria: ADOS miRNeasy Mini Kit (Qiagen, Valencia, CA, USA), and RNeasy MinElute Cleanup Kit was used for miRNA isolation (Qiagen, Valencia, CA, USA) RNA-seq (Illumina Hiseq 4000) and qRT–PCR TargetScan and Miranda 3.3a Frye etal. [29] European Child & Adolescent Psychiatry 1 3 Table 1 (continued) Sample type Number of patients M:F age (years) Study type Other patient characteristics RNA extraction miRNA profile and validation Prediction of deregulated genes in ASD and micro- RNA target genes References ASD (n) CS (n) Postmortem Brodmann area 10 12 pts 10M:12 F 18–51years old 12 pts 11M:1F 16–44years old Experimental with a control group Ethnicity of the patients: – Origin of the sample: Harvard Brain Bank except for two brain samples, which were obtained from the UK Brain Bank for Autism Socioeconomic level: – Diagnostic criteria: ADI-R PMI: 26h miRNeasy kit treated with ADNasa free of ARNasa RNA-seq qRT–PCR software DIANA- lab and microrna. org Mor etal. [31] Postmortem cerebral cortex tissue (PAC) with Brodman areas 41 and 42 and (STS) with Brodmann area 22 10 pts 5M: 5 F 5–52years old 8 pts 6M:2 F 4–58years old Experimental with a control group Ethnicity of the patients: – Sample Source: Harvard Brain Tissue Resource Center Socioeconomic level: – Diagnostic criteria: ADI-R PMI: 23.5h for ASD PMI: 25h for MC Recover All Microarrays ements DIANA miRPath v2.0 Ander etal. [15] European Child & Adolescent Psychiatry 1 3 Table 1 (continued) Sample type Number of patients M:F age (years) Study type Other patient characteristics RNA extraction miRNA profile and validation Prediction of deregulated genes in ASD and micro- RNA target genes References ASD (n) CS (n) Cerebral cortex tissue: frontal cortex FC, Brodmann area 9, temporal cortex and cerebellar vermis 131 pts 106M: 25 F 8–81years old 105 pts 88M: 17 F 8–60years old Experimental with a control group Ethnicity of the patients: White Northern European, Caucasian, Asian, English-White, African American/Black Origin of the samples: Harvard, NICHD, BBA, and the Brain Bank of Neurodegenerative Disorders at MRC Socioeconomic level: – Diagnostic criteria: ADI-R confirmation and ASD diagnosis supported by other evidence, such as clinical history PMI: 24.1h miRNeasy kit RNA-seq qRT–PCR Linear mixed effect model (LME) to determine gene deregulation and TargetScan to determine target genes for microRNA Wu etal. [2] Postmortem brain cut area 21 of Brodmann 5 pts 3M: 2 F 4–9years old 6 pts 4M: 2 F 4–9years old Experimental with a control group Ethnicity of the patients: African American and Caucasian Origin of the samples: NIH Neuro- BioBank Socioeconomic level: – Diagnostic criteria: ADI-R, ADOS and DSM-IV PMI: (13–39) 14h miRVana™ miRNA isolation kit (AM1560, Thermo Fisher Scientific) TaqMan assays on Fluidigm 98.98qPCR miRTarBase miRDIP Nguyen etal. [32] European Child & Adolescent Psychiatry 1 3 Table 1 (continued) Sample type Number of patients M:F age (years) Study type Other patient characteristics RNA extraction miRNA profile and validation Prediction of deregulated genes in ASD and micro- RNA target genes References ASD (n) CS (n) Postmortem brain dorsolateral prefrontal cortex and Amygdala 15 pts 15M: 0 F 6–12years old 15 pts 15M: 0 F 6–12years old Experimental with a control group Ethnicity of the patients: – Origin of the samples: NIH NeuroBioBank at the University of Maryland, Baltimore MD Socioeconomic level: – Diagnostic criteria: – PMI: (10–24h) for ASD PMI: (12–18h) for MC themirVana™miRNA isolation kit (Ambion, Life Technologies) QRT-PCR – Almehmadi etal. [30] Stem cells of the olfactory mucous membrane 10 pts 7M: 3F 22–43 Years old 10 pts 7M: 3F 18–44 Years old Experimental with a control group Ethnicity of the patients: French Origin of the samples: Hospitals, medical social support centers for ASD and private practices in France Socioeconomic level: – Diagnostic criteria: DSM-5/ICD-10 mirVana kit qPCR high performance real time mir-DIP and software of network prediction Ingenuity Pathway Analysis (IPA) Nguyen etal. [18] Serum 55 pts 48M: 7 F 6–16years old 55 pts 48M: 7 F 6–16years old Experimental with a control group Ethnicity of the patients: Japanese Origin of the samples: – Socioeconomic level: – Diagnostic criteria: DSM-4 and ADI-R Qiagen miReasy serum/plasma kit miRNA PCR and qRT–PCR DIANA-mirPath v2.0 Vasu etal. [40] European Child & Adolescent Psychiatry 1 3 Table 1 (continued) Sample type Number of patients M:F age (years) Study type Other patient characteristics RNA extraction miRNA profile and validation Prediction of deregulated genes in ASD and micro- RNA target genes References ASD (n) CS (n) 20 pts 17M: 3 F 2.5–7years old 20 pts - M: -F 2.5–7years old Experimental with a control group Ethnicity of the patients: Chinese Origin of the samples: Xiangya Hospital China Socioeconomic level: – Diagnostic Criteria: DSM-4 TRIzol life Microarrays and qRT–PCR TargetScan, miRanda, CLIP- Seq and miRDB Huang etal. [34] 30 pts 24M: 6 F 3–11years old 30 pts 24M: 6 F 3–11years old Experimental with a control group Ethnicity of the patients: Bulgarian Origin of the samples: University Hospital of Plovdiv, Bulgaria Socioeconomic level: – Diagnostic criteria: ADI-R, CARS, GARS, and DSM-5 PAXgene blood miRNA kit qRT–PCR miRWalk Data base Kichukova etal. [36] 30 ASD 22M: 8 F 25 ASD + ST 16M: 9 F 24 ST 21M: 3 F 3–13years old 25 pts 25M: 0 F 3–13years old Experimental with a control group Ethnicity of the patients: – Origin of the samples: Section of Child and Adolescent Psychiatry, Department of Clinical and Experimental Medicine, University of Catania Socioeconomic level: Diverse Diagnostic criteria: ADOS, DMS-5, and ADI-R Qiagen miReasy kit serum/plasma TLDA and qRT– PCR DIANA-TarBase v7.0 y Cirnigliaro etal. [33] European Child & Adolescent Psychiatry 1 3 Table 2 Deregulated microRNAs in different studies Number of miRNAs studied Differential expression criteria miRNA with the highest differential expression Deregulated miRNA References Negatively deregulated miRNAs Positively deregulated miRNAs 1237 Pavlidis template matching analysis (PTM) miR-182-AS FC = − 1.54 P = 1.44E−03 miR-182AS, miR-136, miR-518, miR-153-1, miR-520b, miR- 455, miR-326, miR-199b, miR- 16-2, miR-133b, miR-148b, miR-211, miR-132, miR-495, miR-190, miR-189, miR-367, miR-139, miR-219- miR-185, miR-103, miR-107, miR-29b, miR-194, miR-524, miR-191, miR-376a-AS, miR-451, miR-23b, miR-195, miR-342, miR-23a, miR-25, miR-519c, miR-346, miR-205, miR-30c, miR-93, miR-186, miR-106b Sarachana etal. [14] 708 FC 1.5 or higher in at least 12 of 24 pair comparisons of brothers miR-199b-5p 1.81 FC p = 2.51E−05 miR-199b-5p, miR-548o miR-577, miR-486-3p miR-455-3p, miR-338-3p miR-199a-5p, miR-650 miR-486-5p, miR-125b miR-10a, miR-196a, miR-181c, miR-181a, miR-30a, miR-181b, miR-502-3p, Seno etal. [22] 269 control vs ASD 267 brothers vs ASD |log2 fold change| and p ≤ 0.05 miR-181a-5p − 1.1 FC p < 0.005 miR-320a − 1 FC p < 0.005 Controls vs ASD PC-5p-35875_59, mir-18b-p3, miR-451a_R-1, miR-181a-5p, miR-92a-2-5p_R + 1, PC-3p- 16340_153, miR-20b-5p, let-7i-3p_R-2, miR-4437_L + 2, mir-5100-p3_1ss17TC, PC-3p- 12325_216,miR-363-3p_R + 1, miR-10a-5p_R-1 Brothers vs ASD miR-451a_R-1, miR-99b-5p, PC-5p-8577_335, miR-320a, miR-96-5p_R-2, let-7e-5p, miR-4485-3p_L + 3R + 2, miR-4521_R + 3, miR-1270, miR-766-5p_R-1, miR- 106a-5p, miR-125a-5p_R-1, miR-1246_L-1R + 1 Controls vs ASD miR-1271-5p, miR-151a-5p Brothers vs ASD miR-5701_1ss2TG, miR-150-5p Frye etal. [29] European Child & Adolescent Psychiatry 1 3 Table 2 (continued) Number of miRNAs studied Differential expression criteria miRNA with the highest differential expression Deregulated miRNA References Negatively deregulated miRNAs Positively deregulated miRNAs 1104 FDR < 0. 05 and two-tailed t test for microRNA by real-time PCR miR-338-5p 4.4 FC p = 5.47E−81 miR-211-5p, miR-34a-5p, miR- 92b-3, miR-3960 miR-338-5p, miR-3168, miR- 7-5p, miR-21-3p, miR-19a-3p, miR-19b-3p, miR-219-5p, miR- 137, miR-146a-5p, miR-379-5p miR-494, miR-155-5p Por PCR in real time: miR- 142-5p, miR-21-5p, miR-451a, miR-142-3p miR-144-3p Mor etal. [31] 1733 p < 0.005 FC > 1.2 miR-297 − 1.24 FC p = 0.0012 miR-1, miR-297, miR-4742-3p miR-4753-5p, miR-554-3p miR-4709-3p, Ander etal. [15] 699 Mixed Effects Linear Model, FDR < 0.05 miR-3687 − 1.5 FC, FDR 1.5E−4 miR-1002, miR-1155, miR- 619-5p, miR-3687 miR-21-3p, miR-2277-5p, miR- 10a-5p, miR-424-5p, miR- 199b-3p, miR-148a-9p Wu etal. [2] 3Fold change > 1.2 and p < 0.001 by the Mann–Whitney test miR-146a – miR-146a Nguyen etal. [32] 1 Non-parametric Mann–Whitney U test miR-155p5 p < 0.0001 – miR-155p5 Almehmadi etal. [30] 667 p < 0.01 according to Wilcoxon’s signed rank test and p < 0.05 miR-146a p < 0.001 miR-221, miR-654-5p, miR-656 miR-146a Nguyen etal. [19] 125 Mann–Whitney p < 0.05 miR-572 p < 0.0001 miR-101-3p, miR-106b-5p,miR- 130a-3p, miR-195-5p, miR- 19b-3p, miR-27a-3p miR-151a-3p, miR-433, miR- 181b-5p, miR-328, miR-320a, miRR-572, miR-663a, miR- 469, Vasu etal. [40] 2578 Product range method p < 0.05 for micro-arrangements Wilcoxon range addition to test differences with p < 0.05 for qRT–PCR miR-451a p = 4.58E−5 miR-451a, miR-16-5p, miR- 574-3p, miR-7a-5p, miR-7d-5p, miR-7f-5p, miR-92a-3p, miR-3613-3p, miR-20a-5p, miR-3935, miR-4700-3p, miR-15b-5p, mir-15a-5p, miR-4436b-5p, miR-4665-5p, miR-19b-3p, miR-195-5p, miR-103a-3p, miR-1228-3p, miR-940, miR-1273c, miR-4299, miR- 5739, miR-6086, miR-494, miR-4270, miR-642a-3p, miR- 4516, miR-4436a, miR-1246, miR-575 miR-4721, miR- 483-5p, miR-1249, miR-4443, miR-921, miR-34b-3p, miR- 6125, miR-4669, miR-34c-3p, miR-4728-5p, miR-564, miR- 574-5p, miR-4788 Huang etal. [34] European Child & Adolescent Psychiatry 1 3 Table 2 (continued) Number of miRNAs studied Differential expression criteria miRNA with the highest differential expression Deregulated miRNA References Negatively deregulated miRNAs Positively deregulated miRNAs 42 FC > 0.5 miR-619-5p FC = 2.983 Preliminary studies: miR-589-3p, miR-6849-3p, miR-15a-5p, miR-183-5p, miR- 3674, miR-96-5p, miR-3687, miR-6799-3p, miR-587-3p, miR-504-5p, miR-576-5p, miR-486-3p, miR-3909, miR-29c-5p, miR-301a-3p, miR-3064-5p, miR-145-5p, miR-193b-3p, miR-487b-3p, miR- 664b-3p, miR-20b-3p, miR-671-3p, miR-199a-5p Confirmation by qRT–PCR: miR-3135a, miR-328-3p, miR- 197-5p, miR-500a-5p and miR-424-5p Preliminary studies: miR-4489, miR -8052, miR- 106b-5p, miR-142-3p, miR-3620-3p, miR-374b-5p, miR-18b-3p, miR-210-5p Confirmation by qRT–PCR: miR-365a-3p, miR-619-5p and miR-664a-3p Kichukova etal. [36] 754 Test of two classes not paired with FDR < 0.15 for microarrangements One-way ANOVA p ≤ 0.05 for qRT–PCR miR-140-3p p < 0.0001 FC = 1.42 miR-140-3p Cirnigliaro etal. [33] European Child & Adolescent Psychiatry 1 3 Table 2 (continued) Number of miRNAs studied Differential expression criteria miRNA with the highest differential expression Deregulated miRNA References Negatively deregulated miRNAs Positively deregulated miRNAs 702 Fisher’s exact test and Spearman’s test p < 0.05 miR-342 FC = 2.03 miR-30e, miR-30c-1, miR-101-1, miR-186, miR-197, miR-9-1, miR-199a-2, miR-181b-1, miR-181a-1, miR-29c, miR- 29b-2, miR-664a, miR-4433b, miR-9-2 miR-128-1, miR- 128-2, miR-425, miR-191, miR-15b, miR-16-2, miR-28, miR-582, miR-143 miR-22 miR-145, miR-378a, miR- 103a-1, miR-340, miR-30c-2, miR-339, miR-148a, miR-25, miR-93, miR-106b, miR-335, miR-29a, miR-29b-1, miR-671, miR-320a, miR-486, miR- 486-2, miR-30b, miR-151a, miR-101-2, miR-7-1, miR-23b, miR-27b, miR-24-1, miR- 181a-2, miR-181b-2 miR-505 miR-199b, miR-126, miR- 107, miR-146b, miR-130a, miR-6503, miR-139, miR- 326, miR-148b, miR-26a-2, miR-331, miR-16-1, miR-15a, miR-17, miR-19a, miR-19b-1, miR-92a-1, miR-625, miR-342, miR-345, miR-411, miR-329-1, miR-329-2, miR-494, miR-543, miR-495, miR-376c, miR-376b, miR-487b, miR-134, miR-485, miR-496, miR-409, miR-9-3, miR-484, miR-424 miR-324, miR-423, miR-142, miR-454, miR-301a, miR-21, miR-338, miR-7-3, miR- 199a-1, miR-24-2, miR-27a, miR-23a, miR-150, miR-99b, miR-125a, miR-103a-2, miR- 133a-2, miR-99a, mir-155, miR-185, miR-130b, mir-221, miR-222, miR-660, miR-223, miR-421, miR-374b, miR-652, miR-766, miR-92a-2, miR- 19b-2, miR-20b miR-1248, miR-4485, miR-7-2, miR-365a, miR-365b, miR-769, miR-98, miR-545 Jyonouchi etal. [35] European Child & Adolescent Psychiatry 1 3 Table 2 (continued) Number of miRNAs studied Differential expression criteria miRNA with the highest differential expression Deregulated miRNA References Negatively deregulated miRNAs Positively deregulated miRNAs 26 Student’s t test, Waller–Duncan test and Tukey HSD test with SPSS 17 – miR-19b-1-5p, miR-27a-3p, miR- 193a-5p miR-34c-5p, miR-145-5p, miR- 199a-5p Vaccaro etal. [39] 43 FC, p < 0.005 Student’s t test One-way analysis of variance miR-486-3p p < 0.001 FC = 2.566 Microarrays miR-6785-3p, miR-6819-3p Microarrays miR-6802-5p, miR-6808-5p, miR-6829-5p, miR-6865-5p, miR-5100, miR-6086, miR- 197-3p, miR-106b-5p, miR- 1185-1-3p, miR-1249-5p, miR- 140-3p, miR-1471, miR-188-5p hsa-miR-19a-3p, miR-24-3p, miR-296-5p, miR-30d-5p, miR- 3141, miR-3196, miR-342-3p, miR-3648, miR-3667-5p, miR-3945, miR-4429, miR- 4472, miR-4532, miR-4655-3p, miR-4728-5p, miR-4745-5p, miR-4778-5p, miR-4800-5p, miR-5006-5p, miR-5195-3p, miR-5585-3p, miR-6090 miR- 642b-3p, miR-6752-3p, miR- 6768-5p, miR-6785-5p Confirmation by qRT–PCR: miR-486-3p, miR-557 Yu etal. [41] 3 Analysis of variance (ANOVA) Student’s t test p < 0.05 – miR-3135a, miR-328-3p – Nt etal. [19] 2549 p < 0.01 FC > 1.5 miR-6126 p = 2.00E−05 FDR = 7.58E−03 miR-6126, miR-6780a-5p, miR-1227-5p, miR-3156-5p, miR-4716-3p, miR-144-3p, miR-3127-5p, miR-5581-5p, miR-6756-5p, miR-6767-5p, miR-7977, miR-4486, miR- 6734-5p, miR-4653-3p, miR- 6085, miR-874-3p miR-328-3p, miR-4515 Nakata etal. [37] 27 Mann–Whitney two-tailed p < 0.05 miR193a-5p miR-193a-5p, miR-576-3p, miR- 382-5p, miR-134-5p, miR-7-5p, miR-379-5p, miR-27a-5p, miR378a-3p, miR-574-3p, miR-223-5p, miR-433-3p, miR-3614-5p, mir-872-3p, miR-103a-3p miR-206, miR-4732-5p, miR-184 Jyonouchi etal. [24] European Child & Adolescent Psychiatry 1 3 Table 2 (continued) Number of miRNAs studied Differential expression criteria miRNA with the highest differential expression Deregulated miRNA References Negatively deregulated miRNAs Positively deregulated miRNAs 280 p < 0.05 miR-150-5p FC: − 8.68 miR-19a-3p, miR-361-5p, miR- 3613-3p, miR-150-5p, miR- 126-3p, miR-499a-5p – Ozkul etal. [38] 3 Kolmogorov–Smirnov test, Independent t test, Mann/Whitney U test, Kruskal–Wallis test, and ROC curve p < 0.01 miR-23a FC = 1.99 P = 0.18 miR-181b miR-23a, miR-181b Atwan etal. [7] 1 Statistical Package for Social Sciences version 20 (SPSS software, Chicago, USA) Chisquare tests Statistical significance was considered at p < 0.05, while p < 0.01 was considered highly statistically significant miR-106ap = 0.000 – miR-106a Zamil etal. [42] 246 Mann–Whitney FDR < 0.15 p < 0.05 miR-628-5p difference in Z 1,13, FDR 0.027 p < 0.0001 miR-23a-3p, miR-27a-3p, miR- 30e-5p, miR-32-5p, miR-7-5p, miR-28-5p, miR- 127-3p, miR-140-3p, miR-191-5p, miR-628-5p, miR-3529-3p, miR-2467-5, miR-218-50, miR-335-3p Hicks etal. [44] 11 Characteristic selection algorithm – – miR-92a-3p, miR-146b, miR-3916, miR-146b-5p, miR-378a-3p, miR-361-5p miR-125a-5p, miR-410 miR-106a-5p, miR-146a, miR- 10a Hicks etal. [16] 527 Non-parametric Kruskal–Wallis test and a partial least-squares discriminant analysis (PLS-DA) FDR < 0.05 PLS-DA ≥ 2.0 miR-28-3p χ2 = 34 FDR = 1.62E-5 miR-148a-5p, miR-151-3p miR-125b-2-3p, miR-7706, mir- 28-3p miR-28-3p, miR-665, miR-4705, miR-620, miR-1277-5p Hicks etal. [43] 14 Testing Grubbs through the Xlstat add-on with the program Microsoft Office Excel Z > 1.5Mann–Whitney test miR-32-5p miR-23a-3p, miR-32-5p, miR-140-3p, miR-628-5p miR-7-5p, miR-2467-5p Sehovic etal. [8] European Child & Adolescent Psychiatry 1 3 the levels of miRNAs are moderately low, as observed in the parents and siblings of patients with ASD. Rett syndrome is a monogenic disorder linked to the X chromosome and is caused by mutations in the MECP2 gene, which prevent its binding to methylated DNA, thus repressing gene translation and consequently the development of autistic behavior. MiR-199 exerts epigenetic regulation on MECP2 [39]. In addition, miR-132 also targets this gene [40], corroborating that miRNAs can play an important role in gene regulation. Peripheral blood studies show that miR-140-3p is differentially upregulated in patients with ASD compared to controls and in patients suffering from Tourette syndrome and ASD. Therefore, miR-140-3p may be a candidate biomarker for the differential diagnosis of ASD [33]. The FMR1 gene is widely expressed in neurons and is regulated by 14 miRNAs that are dysregulated in ASD [44]. This gene is altered in fragile X syndrome, which causes intellectual disability, and 40% of patients with this disease meet the diagnostic criteria for ASD. Hormones andepigenetic mechanisms miRNAs, as epigenetic modulators, affect the protein levels of the target mRNAs without modifying the gene sequences. Moreover, miRNA can also be regulated by epigenetic modifications [31, 47]. Therefore, epigenetics could play a role in dysregulation of miRNAs. Previous studies have found a dysregulation in methylation and acetylation patterns of miRNAs in the brain of humans with ASD. Mor etal. [31] showed that dysregulated miRNAs target biological pathways and specific genes, modifying their expression levels, that are highly relevant to the biology of autism. An interesting example is the oxytocin receptor (OXTR) gene, which codified for the receptor for the hormone and neurotransmitter oxytocin. OXTR acts as a vascular regulator or an inducer of uterine contractions during parturition. However, in the central nervous system [48–50], OXTR is associated with roles in social, cognitive, and emotional behavior. Regarding these functions, perturbations in OXTR have been implicated in subpopulations of individuals with ASD, including Asperger’s syndrome. miR-142 has important roles in the dopaminergic and monoamine pathways in the brain. Moreover, this miRNA can also target OXTR gene and modulate their expression. Mor etal. [31] evidenced that epigenetics plays a role in dysregulation of miR-142 in the brain of ASD patients. Particularly, they found a hypomethylation in five CpG sites in the promoter region of the gene coding for miR-142, which correlates with elevated levels of miR-142, resulting in effects on OXTR gene expression that may favor the pathogenesis of ASD. As epigenetic modulator, miR-142-5p negatively regulates the transcription of monoamine oxidase, thus Table 2 (continued) Number of miRNAs studied Differential expression criteria miRNA with the highest differential expression Deregulated miRNA References Negatively deregulated miRNAs Positively deregulated miRNAs 800 SAM (Significance of Microarrays Analysis) Fold change (FC) values The Mann–Whitney U test p < 0.05 miR-451a FC = − 3.58 P < 0.0001 miR-16-5p, miR-205-5p, miR- 451a, let-7b-5p miR-29a-3p, miR-141-3p, miR- 146a-5p, miR-200a-3p, miR- 200b-3p, miR-4454, miR-7975 Ragusa etal. [45] FC fold change, FDR Rogers discriminant function, One-way ANOVA single-factor analysis of variance European Child & Adolescent Psychiatry 1 3 influencing the metabolism of neurotransmitters such as serotonin and dopamine. Moreover, miR-142-5p targets and decreases the translation of dopamine D1 receptors. Therefore, this miRNA has important functions in the dopaminergic and monoamine pathways of the brain that are strongly related to ASD. In this line, miR-142-5p was found to be upregulated in patients with ASD and may be involved in the degradation of HDAC2 mRNA, generating alterations in cell differentiation and proliferation [31]. Neuronal maturation andASD ASD is characterized by its association with neuronal maturation, neuronal plasticity, neurogenesis, and neuronal functions [41]. A gene associated with stabilization of the neuronal cytoskeleton (MAPT) is a target of miR-34c-5p, which inhibits its expression and generates effects on neuronal maturation [39]. The studies by Nguyen etal. [32] show that miR-146a overexpression in patients with ASD induces negative regulation of the LIN28B gene, which encodes a protein whose function is to maintain neural progenitors in an early stage of neuroblast proliferation. Additionally, miR-146a participates in negative regulation of CDKN1A, CDKN3, and CDK1, which encode proteins responsible for controlling the duration of the G1 phase during the cell cycle and the balance between the maintenance of progenitor cells and the emergence of differentiated neurons, which may be involved in the pathophysiology of ASD. Social interaction andmiRNAs One of the functions of oxytocin is activation of the OXRT receptor, which, in the central nervous system, is mainly related to the biological signals of impaired social interaction. Overexpression of miR-21-5p and miR-451a is correlated with elevated OXRT mRNA expression, and, in turn, miR21-5p expression is correlated with low levels of the OXRT receptor. This hypothesis suggests that miR-21-5p inhibits translation of OXRT [31]. In addition, miR-6126 is closely related to oxytocin signaling pathways [37]. Changes induced in the OXRT pathway either by miRNA or by single-nucleotide polymorphisms have been associated with ASD, particularly in patients with Asperger’s syndrome. The deregulation of miR-21-5p, miR-451a and miR-6126 is related to alterations in social interactions, which are characteristic of ASD [31]. Repetitive behaviors Ten miRNAs [43] are related to the manifestation of restricted and repetitive behaviors in patients with ASD, highlighting miR-106a-5p, which is responsible for the regulation of genes that are candidates for ASD (SEMA5A, NTNG1, SRGAP3, and MAPK1). Therefore, the dysregulation of miR-106a-5p can target transcripts that are related to brain development and lead to restricted and repetitive behaviors. MicroRNAs involved inimmunity inASD Neuroimmune interactions can originate during embryogenesis. Accordingly, children with ASD present altered immune responses, including altered cytokine TH1/ TH2 profiles, low NK activity, chronic neuroinflammation generated by glutamatergic excitotoxicity and decreased GABAergic signals, and imbalances in serum immunoglobulins. The excitatory–inhibitory imbalance hypothesis postulates dysregulation of the GABA and glutamate neurotransmission is associated with deficits in individuals with ASD [51]. According to this theory, the imbalanced neurotransmission results in increased noise and hyperexcitability in the cerebral cortex of these patients. Later, Blaylock and Strunecka postulated a link between perturbed glutamatergic neurotransmission and pro-inflammatory changes in the ASD brain. The author introduced the term “immunoexcitotoxicity’ to describe neuronal injury hypothesized to result from microglial activation in ASD brain, since chronic activation of microglia derives in a predominant neurotoxic effect on the brain, with excitotoxic levels of glutamate being secreted [52]. In the peripheral blood of patients with ASD, miR- 34c-5p, which targets ZAP70, gene implicated in lymphocyte activation and NK acyivity, is profoundly dysregulated, reflecting a decrease in the CD4 + population and an imbalance of the Th1/Th2 subsets toward Th2 [39]. The cellular and differentiation functions of macrophages and monocytes, lymphocytes, and NK are partly regulated by miRNAs; for example, miR-181a was identified in patients with ASD and decreased IL-B and high IL-10 Table 3 Combined result of log2-fc from the meta-analysis FC fold change microRNA Meta-fc p value miR-451a − 2.4175 6.47E−25 miR-150-5p − 3.6757 5.12E−05 miR-486-3p 2.0780 1.16E−03 miR-144-3p 1.1675 1.93E−05 miR-320a − 1.2500 1.39E−03 miR-23b 1.1650 7.17E−03 miR-92a-2-5p 0.0500 4.91E−04 miR-106b − 0.0150 1.44E−02 European Child & Adolescent Psychiatry 1 3 Fig. 2 a Combined results of the fold change for the expression of eight miRNAs. b 16 studies included in the meta-analysis and the fold change of the expression in every miRNA European Child & Adolescent Psychiatry 1 3 profiles in the studies performed by Jyonouchi etal. [24, 35]. These studies showed a decrease in this miRNA. MiR-181a was also determined to directly regulate the inflammatory response mediated by macrophages and monocytes through negative regulation of pro-inflammatory cytokines and suppression of PTEN-related signaling pathways, thus affecting regulatory T-cell differentiation as well as mitochondrial functions. Therefore, IL-B and IL-10 profiles together with miRNA levels may be biomarkers for immunemediated inflammation in ASD. Moreover, recent research [29] found that all miR-181 family members, TNFa levels and NK profiles are dysregulated in ASD. MicroRNAs, intelligence, learning, language, andmemory inASD Yu etal. [41] demonstrated that miR-483-3p is dysregulated in ASD patients, leading to changes in the expression of c-Fos and Arc, which have an effect on dendritic and synaptic development, and thus contributing to the pathology of ASD, specifically impairments in intelligence and behavior. The study by Hicks etal. [44], which involved 14 miRNAs as possible biomarkers of ASD, shows that these miRNAs are expressed in different areas of the brain at different childhood ages. Analysis of the genes targeted by these miRNAs highlights the Forkhead P2 box protein (FOXP2), which has been heavily implicated in speech and language disorders. Mutations in this gene result in a very characteristic verbal apraxia of fragile X Syndrome. While verbal apraxia is typically found in fragile X Syndrome, this alteration is also a hallmark of ASD. Interestingly, about 40% of the children with Fragile X Syndrome meet the criteria for ASD [44]. The regulatory network mediated by miR-140-3p plays a role in the CNS, and its dysregulation leads to alteration of CD38 gene, which is involved in learning, postnatal glial development, and hippocampal-dependent memory [33]. Mir- 34b-3p, which is also dysregulated in patients with ASD, is related to neuronal development and long-term memory [34]. Therefore, these miRNAs, which are dysregulated in Cell Lines Lymphoblastoids Memory and learning Neuronal maturation Inteligence Language Social behavior Repetitive behavior Hormones Immunity Circadian cycle miR-16-2 ↓ miR-495 ↓ miR-326 ↓ miR-139 ↓ miR-148b ↓ miR-199b ↓ miR-146a-5p ↓ miR92a_5p ↓ miR-320a ↓ miR96-5p ↓ miR 150-5p ↓ miR-146 ↑ miR-106b ↑miR-23b ↑ miR-10a ↑ miR-34c-5p → MAPT miR-486-3p → ARID1 miR-199a-5p → SIRT1 miR-146a-5p → lin28B miR-140-3p ↓ CD38 miR-21-5p miR-6126 miR-451a ↓ OXRT miR-29b ↓ ID3 ATF2 VIP PER3 PER1 ARNTL DUSP2 miR-34c-5p ↓ ZAP70 miR-181a ↓ PTEN miR-142-5p ↓ Serotonin and dopamine miR-142-3p ↓ Dopamine D1 receptors Forkhead P2 (FOXP2) miR-486-3p ↓ c-Fos and Arc Saliva miR-16-5p ↑ miR-451a ↓ miR-146a-5p ↓ miR-27a-3p ↓ miR-151-3p ↓ miR-27a-3p ↓ miR-10a ↑ Post-mortem brain miR-19a-3p ↑ miR-142-3p ↑ miR-144-3p ↓ miR-451a ↑ miR-3687 ↓ miR-494 ↑ Serum miR-144-3p ↓miR-106 ↓ miR-451a ↓miR-23b ↓ miR-16-5p ↑ miR-16-2 ↓miR-495 ↓ miR-326 ↓ miR-139 ↓ miR-148b ↓ miR-199b ↓ miR-27a-3p ↓ miR-142-3p ↑ miR-3687 ↓ miR-151-3p ↓ miR-494 ↑ miR-19a-3p ↑ miR-451a ↑ miR-106a-5p NTGNG1 SRGAP3 MAPK1 SEMASA miR-106 b ↑ m i R-451 a m i R-451a ↑ m iR-144-3p ↓ m i R-106 ↓ m i R-144-3p ↓ m i R-451a ↓ m i R-451a ↓ miR-23b ↓ miR-23 b ↑ miR-146 ↑ Fig. 3 Target genes of the analyzed miRNAs and miRNAs related to different clinical manifestations of ASD European Child & Adolescent Psychiatry 1 3 agencies in the public, commercial, or not-for-profit sectors. NG-T received funds under Rio Hortega contract CM20/00015 with the Carlos III Health Institute. SG-C received funds under Sara Borrell contract CD19_00183 with the Carlos III Health Institute. Data availability The datasets generated for this study are available on request to the corresponding author. Declarations Conflict of interest No potential competing interest is reported by the authors. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. 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