A Multi-Dataset Meta-Analysis Identifies Core mRNA and lncRNA Networks Associated with Autism Spectrum Disorder

A Multi-Dataset Meta-Analysis Identifies Core mRNA and lncRNA Networks Associated with Autism Spectrum Disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Multi-Dataset Meta-Analysis Identifies Core mRNA and lncRNA Networks Associated with Autism Spectrum Disorder Kaleem Maqsood, Mahnoor Fatima, Zeshan Wadood, Humera Naveed, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8700783/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder accompanied by multifaceted genetic and molecular dysregulation. There is a need to identify reproducible gene expression patterns and regulatory networks by integrative meta-analysis methods. Methods Six autonomous ASD-related transcriptomic datasets were obtained from the Gene Expression Omnibus and consolidated analytically. Quality control, log2-transformation, and outlier removal were done before differential expression analysis between each dataset through the limma framework. Robust Rank Aggregation (RRA) was used to identify mRNAs and lncRNAs with consistent dysregulation. DAVID database was used for functional enrichment analysis. STRING was used to build protein-protein interaction (PPI) networks and MCODE and CytoHubba were used to identify hub genes. To create reproducible lncRNA-mRNA regulation networks among datasets, Pearson correlation was conducted. Results ASD-associated mRNAs and lncRNAs with consistent directionality across datasets were identified. Upregulated mRNAs were primarily involved in transcriptional regulation, chromatin remodeling, RNA processing, and intracellular signaling whereas downregulated mRNAs were enriched in synaptic signaling, neuronal communication, and ion-transport pathways. Densely interconnected modules enriched for translation initiation and RNA-binding functions, with EIF family members and heterogeneous nuclear ribonucleoproteins emerging as key hub genes. A reproducible regulatory network involving five lncRNAs and nineteen hub mRNAs was identified, with MALAT1 acting as a central regulatory hub. Conclusions This integrative transcriptomics analysis shows that coordinated transcriptional regulation and synaptic signaling dysregulation occur in ASD and identifies key hub genes and lncRNA-mediated regulatory networks. The results may bring new insights into the ASD molecular pathobiology and possible peripheral biomarkers and treatment targets. Autism spectrum Disorder Transcriptome lncRNA Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Autism spectrum disorder (ASD) is one the most common neurodevelopmental disorder which characterizes repetitive, restricted sensory behaviors and interests, and deterioration of social communication beginning early in life [ 1 ]. The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) has deemphasized ASDs classification because of the diverse clinical features and underlying biological causes [ 2 ]. In last four decades, the global ASD prevalence has been increasing steadily but with regional differences, with the disorder being 4–5 times more prevalent in boys than girls [ 3 , 4 ]. ASD is heterogeneous and has genetic, epigenetic, and environmental causes, but the exact etiology remains elusive. ASD has been linked with hundreds of genes till now [ 5 ]. For example, NLGN3, NLGN4X [ 6 ], SH3 and SHANK3 [ 7 ] which are synaptic genes were identified as the ASD genes through DNA sequence studies. But in numerous cases these genes cannot be used to identify the cause of ASD [ 8 ]. Transcriptomic studies provide a pivotal connection between measurement of protein levels and analysis of genetic information. Various biological processes have been shown to be implicated in ASD through systematic transcriptomic studies and their computational analyses. For example, computational analysis of transcriptomic datasets of different tissues from ASD patients demonstrated that differentially expressed genes in ASD patients were greatly enriched in immune/inflammation responses, oxidative phosphorylation and mitochondrion-linked functions [ 9 ]. Differential gene expression analysis from two microarray datasets consisting of 122 ASD and 89 control samples identified 1,862 significantly altered genes in blood of ASD patients which were mainly enriched in immune/infection-related pathways like Influenza A, Epstein–Barr virus infection and primary immunodeficiency pathways. Key hub genes highlighted were SUMO1, SP1, EGR1, EP300, and VHL among others with potential utility as blood-based biomarkers for prompt diagnosis and risk assessment of ASD [ 10 ]. It is evident that differentially expressed genes of blood could indicate ASD. But no study to date has integrated several human datasets to systematically compare gene expression status in the blood of ASD and healthy controls. An updated list of genes significantly dysregulated in individuals with ASD from all recent transcriptomic studies will help detect useful patterns for diagnosis and understanding the underlying mechanisms. Long non-coding RNAs (lncRNAs) are RNA molecules which remain untranslated, have a 3 -methyl-guanosine cap at their 5 end and poly(A) tail at their 3 end, are longer than 200 nucleotides, and are based on sense/antisense strands of protein-coding genes, pseudogenes, or intergenic regions. The lncRNAs may be located in cytoplasm or in the nucleus, though most of them are time specific and tissue/cell specific [ 11 ]. The mechanisms of action of lncRNAs include, but are not limited to, mRNA degradation, chromatin remodeling, splicing regulation, genomic imprinting, and cell-cycle regulation [ 12 , 13 ]. lncRNAs regulate gene expression at the level of transcription, posttranscription and epigenetics [ 14 ]. This diverse set of functions emphasizes that lncRNAs are key players in numerous cellular processes. LncRNAs dysregulation and mutations are involved in causing cancer, cardiovascular and neurological disorders [ 15 – 17 ]. They are also an important component in normal brain development [ 18 ] and their dysregulation can contribute to brain disorders like ASD [ 19 ]. A bioinformatics-based integrated analysis of multiple genomic datasets revealed several differentially expressed lncRNAs in the cortex of ASD patients, showed enriched expression in brain tissues, and also co-expression network with known ASD risk genes during cortical development. These lncRNAs were mainly involved in immune-related processes, synaptic signaling and transmission, and lipid transport pathways. The fact that these pathways are also dysregulated in ASD implicates lncRNAs in the pathogenesis of ASD [ 20 ]. Genome-wide expression study of lncRNAs in blood of ASD patients has produced several differentially expressed lncRNAs that are involved in neural mechanisms like trafficking of synaptic vesicle, extended synaptic potentiation, and persistent depression [ 21 ]. A significant upregulation of lncRNAs NEAT1 and TUG1 was reported in blood of ASD patients as compared to healthy controls [ 22 ]. The aim of the current study is to decipher the significance of lncRNAs in understanding ASD pathogenesis, and their potential use as biomarkers in ASD. Discovering critical genes in ASD can lead to early diagnosis and initiation of treatments. Gene Expression Omnibus (GEO) is a public database which has a collection of raw and processed datasets of genomics and high-throughput gene expression studies. In this study, we aimed to discover novel ASD-related genes by profiling the gene expression signatures in blood of ASD patients, by way of carrying out a series of computational and bioinformatics analyses on the transcriptomic datasets from GEO. This pipeline utilizes patterns of differential gene expression in blood of ASD and normal individuals. Functional enrichment, protein-protein interactions, and correlation analyses were carried out to elucidate potential mechanisms of ASD pathogenesis and to identify potential ASD-related lncRNAs. Correlation analysis can help identify links between formerly uncharacterized lncRNAs, ASD risk genes/hub genes and disturbed molecular pathways in ASD [ 20 ]. Methodology Data collection Gene expression data sets of ASD that were publicly available in the GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) database were accessed. The final analysis involved six microarray datasets that were independent of each other (GSE6575, GSE18123, GSE18123_6244, GSE25507, GSE42133, and GSE111175). Each of the datasets was obtained using peripheral blood or leukocyte samples of ASD and typically developing controls, which made it possible to investigate the existence of transcriptomic blood-based signatures of ASD. Details of all datasets summarized in Supplementary Table S1 . Data preprocessing and quality control GEOquery package in R-studio software (v4.3.3) ( https://www.R-project.org ) was used to download raw or processed expression matrices and log2 transform where necessary, depending on the assessment of distributions. The expression at the gene level was obtained by averaging the expression of several probes that target the same gene. Each of the datasets underwent principal component analysis (PCA), which evaluated the distribution of the samples and any possible outliers. The samples that exhibited deviant patterns of clusters were eliminated before the process of differential analysis. PCA plots were drawn to see how the samples in the ASD and the control groups separated. Differential expression analysis Each dataset was analyzed differently through the limma package (version 3.60.2) of Bioconductor ( https://www.bioconductor.org/ ) in R software to compare ASD samples to controls and linear models to analyze the relationships and to enhance the estimation of variance with empirical Bayes moderation. Those genes whose adjusted p-value was 0.5 were regarded as highly differentially expressed. Each dataset was plotted as volcano to represent the distribution of differentially expressed genes in terms of statistical significance and fold change. Robust rank aggregation meta-analysis Robust Rank Aggregation (RRA) analysis in terms of robustrankaggreg package was used to determine consistently dysregulated genes across datasets. In the case of every dataset, the differentially expressed genes were ranked based on the nominal p-values where up and down-regulated genes were ranked differently. The RRA algorithm took the form of ranking gene lists across all datasets. Differentially expressed genes were picked as genes having RRA p-value less than 0.01, and with similar direction of change across datasets. Ensembl gene biotype annotations categorized these genes further as either protein-coding mRNAs or lncRNAs. Functional enrichment analysis To examine the functions of the dysregulated genes, the mRNAs were functionally enriched by the DAVID (Database for Annotation, Visualization and Integrated Discovery) platform ( https://david.ncifcrf.gov/ ). The mRNAs upregulated and downregulated identified in the Robust Rank Aggregation (RRA) meta-analysis were tested individually to identify the direction-specific biological effects. The Gene Ontology (GO) analysis was used in GO categories of; Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted to find significantly enriched biological pathways. The significance of enrichment was analysed with the modified Fisher exact test provided in DAVID and terms with p < 0.05 were regarded as statistically significant. The scores were enriched as a function of -log10( p -value) and represented through bar plots. The main figures only showed the most enriched terms and pathways significantly and the full results of enrichment were given in the Supplementary Tables. Protein-protein interaction network and hub gene identification To perform meta-analysis of mRNAs, protein-to-protein interaction network (PPI) was built in STRING database ( https://string-db.org/ ) and an interaction confidence score of ≥ 0.9. The network that resulted was imported into Cytoscape (v3.10.2) ( https://www.cytoscape.org/ ). Identification of densely connected modules of PPI network was done using MCODE plug (version 2.0.3), and the two best clusters were picked to proceed with further analysis. The CytoHubba plugin was employed for hub genes identification based upon Maximal Clique Centrality (MCC) algorithm. The genes that had the best scores on MCC would be taken as the key hub genes. A topological analysis of the network was done using the tools of network analysis in Cytoscape, including node degree. lncRNA–mRNA correlation analysis To explore potential regulatory relationships between lncRNAs and hub mRNAs, Pearson correlation analysis was performed separately for each dataset using gene-level log2 expression matrices. Only significant lncRNAs identified from the RRA meta-analysis and hub mRNAs identified from the PPI network were included. Correlation coefficients were calculated for all lncRNA-mRNA pairs, and pairs with |r| ≥ 0.3 and p < 0.05 were considered significant. Correlation results from all datasets were integrated, and only lncRNA–mRNA pairs reproducible in at least two independent datasets were retained. Mean correlation coefficients across datasets were calculated for each reproducible pair. Construction of the lncRNA–mRNA regulatory network A reproducible lncRNA-mRNA regulatory network was created based on the integrated correlation analysis. The network was visualized in Cytoscape, with lncRNAs and mRNAs represented as distinct node shapes. Edge color indicated the direction of correlation (positive or negative), and edge width reflected correlation strength and dataset reproducibility. Key lncRNAs were identified based on network connectivity (degree). Meta-correlation heatmap visualization A meta-correlation heatmap was generated to visualize the mean correlation patterns between lncRNAs and hub mRNAs across datasets. The heatmap matrix was constructed using mean correlation coefficients of reproducible lncRNA-mRNA pairs and visualized using the pheatmap package in R. Hierarchical clustering was applied to both rows and columns to identify correlation patterns and functional groupings. Statistical analysis All analyses were conducted in R (Version 4.5.2). Pearson correlation coefficients were used for expression correlation analysis. Multiple testing correction was applied where required by using the Benjamini-Hochberg method. A p -value < 0.05 was considered statistically significant unless otherwise specified. Results Overview of included datasets and preprocessing The final analysis comprised 6 independent datasets of ASD-related transcriptomics based on peripheral blood or leukocytes (Supplementary Table S1 ). Before downstream studies, the expression data of both datasets were log2-transformed (where necessary) and quality-controlled so that the results of both samples are comparable. PCA plots showed that there is a consistent tendency of the separation of ASD and control samples and that there were no strong artifacts related to batch processing after processing. Any outlier samples that were identified were eliminated before the analysis of differential expression ( Supplementary Figure S1 ) . Each dataset was analyzed by means of differential expression analysis in the limma framework. Volcano plot was used to visualize dataset-level results to evaluate the distribution, directionality, and heterogeneity of change in mRNA and lncRNA expression between cohorts ( Supplementary Figure S2 and S3 ) . These analyses had a descriptive purpose, and the final ASD-associated genes were determined by means of Robust Rank Aggregation (RRA) meta-analysis. Identification of robust ASD-associated genes by RRA meta-analysis To identify genes consistently dysregulated across datasets, Robust Rank Aggregation (RRA) meta-analysis was performed. Genes were ranked separately based on upregulated and downregulated expression patterns within each dataset and integrated across studies (Supplementary Table S2 ). Genes with RRA p -value < 0.01 and consistent directionality were defined as meta-analysis differentially expressed genes. This approach yielded a robust set of ASD-associated mRNAs and lncRNAs that were subsequently used for functional enrichment and network analyses. Functional enrichment analysis of meta-analysis mRNAs Gene Ontology (GO) analysis Gene ontology enrichment of upregulated and downregulated meta-analysis mRNAs was conducted independently with the help of DAVID database (Fig. 1 ). The up regulated mRNAs were greatly enriched in biological processes that involve chromatin remodelling, transcription regulation, mRNA-processing and protein phosphorylation (Fig. 1 (A)). These genes were mostly related to cytosol, nucleoplasm, and nucleus at the cellular component level suggesting enhanced regulation in the nucleus and cytoplasm. Analysis of molecular function showed that it was enriched in RNA binding, DNA binding and protein binding, indicating that transcriptional and post-transcriptional regulation was improved in ASD. Conversely, mRNAs that were downregulated were substantially enriched in cell adhesion biological processes, ion transport, neuronal signals and synaptic regulation (Fig. 1 (B)). The cellular components plasma membrane, synaptic membrane, and cytoskeleton structures were highly enriched, whereas molecular functional analysis revealed the presence of the activity of ligand-gated ion channels, binding of neurotransmitter receptors and actin binding. These findings suggest a dysfunction in synaptic communication and membrane-related processes of ASD. Combined with the results of the GO enrichment, the findings indicate a dysregulation in ASD that is coordinated by hyperactivation of transcription and regulation and hyperdeactivation of neurons and synapses. KEGG pathway analysis To further describe the biological pathways of dysregulated mRNAs, KEGG pathway enrichment analysis was done with the help of DAVID (Fig. 2 ). The upregulated mRNAs were enriched significantly in intracellular signaling, cytoskeletal organization as well as cellular homeostasis pathways (Fig. 2 (A)). The most important enriched pathways were the regulation of actin cytoskeleton, autophagy, phosphatidylinositol signaling system, the thyroid hormone signaling pathway, and mRNA surveillance pathway. These are pathways of signal transduction, cytoskeleton dynamics, stress response, and post-transcriptional regulation. Downregulated mRNAs in contrast were mostly overrepresented in pathways involved in neuronal communication and synaptic signaling (Fig. 2 (B)). There were glutamatergic synapse, retrograde endocannabinoid signaling, focal adhesion, and efferocytosis, showing impairments in synaptic transmission, neuronal connection and cell-cell interactions in ASD. Altogether, the KEGG pathway analysis can be regarded as the complement to the GO results and indicates that ASD can be defined as the increased intracellular regulatory and stress-response pathways alongside the disruption of synaptic signaling and communication between neurons. The x-axis represents enrichment scores expressed as −log10(p-value). Upregulated genes were primarily enriched in pathways related to intracellular signaling, cytoskeletal regulation, autophagy, and hormone-related signaling, including regulation of actin cytoskeleton, autophagy, thyroid hormone signaling, and phosphatidylinositol signaling. In contrast, downregulated genes were mainly associated with synaptic and neuronal signaling pathways, including glutamatergic synapse and retrograde endocannabinoid signaling, highlighting complementary functional alterations in ASD. PPI network and hub gene identification Meta-analysis mRNAs based on high-confidence PPI network in the STRING database with an interaction score of 0.9 was constructed (Fig. 3 ). According to the network, it was observed that the clusters of genes are densely linked depicting coordinated functional relationships among the ASD-associated genes. With the MCODE module, two modules were recognized in the PPI network that are tightly connected to each other. The biggest module (Fig. 4 (A)) was mainly translated to the genes related to the process of translation and the second module (Fig. 4 (B)) was related to RNA binding and RNA processing functions. The CytoHubba plug-in identified hub genes with some of the members of the EIF family and heterogeneous nuclear ribonucleoproteins as the central ones (Supplementary Table S3). These clusters highlight distinct yet interconnected molecular processes dysregulated in ASD. lncRNA-mRNA correlation analysis and regulatory network construction Pearson correlation analysis was conducted with the gene-level log 2 expression data to identify possible regulatory relationship between lncRNAs and hub mRNAs on a dataset-by-dataset basis ( Supplementary Figure S4 ) . The thresholds of |r| ≥ 0.3 and p 0.05 were used to identify significant lncRNA-mRNA pairs (Supplementary Table S4). The results of correlation were combined between datasets and only interactions that could be reproduced in two or more independent datasets were kept. The resultant lncRNA-mRNA regulatory network contained five lncRNAs and nineteen hub mRNAs. One of them, MALAT1, turned out to be the most interconnected lncRNA with many positive correlations with several translation- and RNA-processing-related hub genes. Conversely, there were cases where DSCR8 and AQP4-AS1 have a negative correlation, indicating that they may be acting as inhibitors (Fig. 5 ). Meta-correlation heatmap analysis To visualize global correlation patterns across datasets, a meta-correlation heatmap was generated using mean correlation coefficients of reproducible lncRNA-mRNA pairs (Fig. 6 ). Hierarchical clustering revealed distinct regulatory modules, with MALAT1 clustering closely with translation-related genes, while DSCR8 and AQP4-AS1 formed separate clusters characterized by inverse correlations. Discussion The marked genetic heterogeneity of ASD highlights the value of identifying convergent pathways and molecular mechanisms rather than individual risk genes [ 23 ]. Previous transcriptomic studies of ASD have reported widespread but inconsistent dysregulation of synaptic, transcriptional, and RNA-processing pathways across different cohorts [ 24 , 25 ]. To address this, we performed a comprehensive meta-analysis of six independent peripheral blood–based transcriptomics datasets using Robust Rank Aggregation (RRA) to identify robust mRNA and lncRNA signatures associated with ASD. Overall, our results reveal a coordinated dysregulation of transcription and post-transcription regulatory mechanisms, alongside impaired neuronal and synaptic signaling pathways, consistent with earlier brain and blood transcriptome studies and supporting the provision of a systems-level model of ASD-associated molecular pathology [ 26 ]. Functional enrichment analysis showed that upregulated mRNAs in ASD were strongly enriched for chromatin remodeling, transcriptional regulation, mRNA processing, and protein phosphorylation, indicating a broad activation of gene regulatory and post-transcriptional machinery. Such coordinated upregulation of RNA metabolic and regulatory pathways has been spotted in brain and peripheral transcriptome studies of ASD, supporting the concept that disrupted control of gene expression is a fundamental feature of the disorder [ 27 ]. Notably, the enrichment of RNA-binding and translation-related functions aligns with previous studies reporting abnormal protein synthesis and RNA metabolism in ASD [ 28 ]. Dysregulation of these processes during early development may influence neuronal differentiation, synaptic maturation, and circuit formation, thereby contributing to ASD pathophysiology [ 29 ]. In contrast, downregulated mRNAs were predominantly enriched for neuronal communication, synaptic signaling, ion transport, and membrane-associated processes, indicating widespread impairment of neuronal connectivity in ASD [ 30 ]. Both GO and KEGG analyses consistently highlighted pathways such as glutamatergic synapse and retrograde endocannabinoid signaling, which are critical for synaptic plasticity and neuronal connectivity [ 31 ]. Disruption of these pathways may underlie the deficits in synaptic transmission, excitation–inhibition balance, and network connectivity commonly observed in ASD. The observed enrichment of membrane and cytoskeletal components further suggests alterations in synapse structure and stability, supporting the notion of ASD as a disorder of synaptic dysfunction [ 32 ]. Protein–protein interaction network analysis revealed densely connected gene clusters centered on EIF family members (EIF2S1, EIF3A, EIFAG1) and heterogeneous nuclear ribonucleoproteins (HNRNPA1, HNRNPC, HNRNPK), indicating that RNA metabolism and translation initiation are core dysregulated processes in ASD [ 33 ]. These hub genes are essential for regulating mRNA translation, stability, splicing, and intracellular transport, all of which are necessary for proper neuronal differentiation and synaptic development. Previous studies have shown that altered activity of EIF4E and EIF4G1 leads to excessive or imbalanced protein synthesis and is strongly associated with ASD-like phenotypes through mTOR-dependent translational dysregulation [ 34 ]. Likewise, HNRNPA1, HNRNPC, and HNRNPK have been implicated in controlling alternative splicing and localization of neurodevelopmental transcripts, and their disruption can cause widespread misexpression of synaptic and axon-guidance genes [ 35 ]. The identification of these genes as network hubs supports the growing evidence that defective RNA-binding and translation-control mechanisms drive ASD-related molecular pathology. Our lncRNA-mRNA correlation analysis revealed a reproducible regulatory network linking a small set of ASD-associated lncRNAs with key hub mRNAs, indicating that lncRNAs play a central role in coordinating ASD-related molecular program [ 36 ]. Among these, MALAT1 emerged as a central regulatory lncRNA, exhibiting strong positive correlations with multiple translation initiation and RNA-processing-related genes, suggesting that it may promote or stabilize the elevated RNA-metabolic state observed in ASD. MALAT1 has previously been implicated in transcriptional regulation, RNA splicing, and synaptic function, all of which are critical for neuronal development and plasticity, supporting its potential role in ASD [ 37 ]. Conversely, lncRNAs such as DSCR8 and AQP4-AS1 displayed predominantly negative correlations with hub mRNAs, suggesting inhibitory or modulatory regulatory roles within the ASD regulatory network. DSCR8, located in the Down Syndrome Critical Region, may link chromosomal dosage effects to RNA regulatory dysfunction [ 38 ], while AQP4-AS1 may reflect astrocyte-associated modulation of neuronal signaling [ 39 ]. These findings highlight the complexity of lncRNA-mediated regulation in ASD and suggest that both activating and repressive lncRNA mechanisms may contribute to disease-associated molecular dysregulation. The meta-correlation heatmap analysis demonstrated that key lncRNA–mRNA regulatory relationships were consistent across multiple independent ASD datasets, underscoring their robustness and biological significance [ 40 ]. The consistent co-clustering of MALAT1 with translation initiation and RNA-processing hub genes across datasets indicates that this lncRNA functions as a core integrator of the dysregulated translational machinery in ASD, rather than reflecting a context-dependent association [ 41 ]. Conversely, the reproducible inverse correlation patterns of DSCR8 and AQP4-AS1 signify consistent inhibitory regulatory modules of the transcriptomic environment of ASD. This type of reproducibility in datasets increases the biological significance of the reported regulatory interactions and their potential to serve as a biomarker and therapeutic targets. Together, our results indicate that ASD is associated with the systems-level imbalance, where hyperactivation of the transcriptional and post-transcriptional regulatory machinery and inhibition of neuronal and synaptic signaling pathways are observed. Such a molecular imbalance could lead to disturbed neurodevelopmental pathways and synaptic dysfunction and, thus, aberrant synaptic development, circuitry, and behavioral-phenotype in ASD [ 42 ]. Notably, the establishment of reproducible peripheral blood-based molecular networks creates the potential of establishing a non-invasive biomarker development that is accessible. The lncRNAs and hub genes revealed in the given research, especially MALAT1, EIF relatives and heterogeneous nuclear ribonucleoproteins are biologically justified targets of early ASD detection, molecular stratification and therapeutic monitoring, opening the path to precision medicine strategies in autism. Regardless of these strengths of this integrative analysis, there are a few limitations to be noted. First, peripheral blood samples might be an insufficient measure of brain-specific molecular changes, but more and more data indicate the applicability of blood-based transcriptomic signatures to neurodevelopmental disorders. Second, the lncRNA-mRNA correlation analysis fails to provide causality, and experimental validation will be needed to ascertain regulatory relationships. The research directions of future studies should be to corroborate the identified hub genes and lncRNAs in independent cohorts and experimental models and their functional roles in neural development and synaptic regulation Conclusion In summary, this study provides a comprehensive integrative analysis of ASD-associated transcriptomic alterations, revealing coordinated dysregulation of transcriptional regulation, RNA metabolism, and synaptic signaling pathways. The identification of key hub genes and lncRNA-mediated regulatory networks offers novel insights into ASD molecular mechanisms and provides a foundation for future biomarker development and therapeutic investigation. Declarations Competing interests : Authors have no competing interests to be declared. CRediT authorship contribution statement K.M: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft. M.F: Data curation, Formal analysis, Visualization, Writing – review & editing. Z.W. Methodology, Validation, Writing review & editing. H.N: Data curation, Investigation, Writing – review & editing. T.A and H.A: Writing review & editing. S.B: Conceptualization, Supervision, Project administration, Methodology, Writing review & editing. Acknowledgement : The authors have no acknowledgments to report. Ethics approval statement : Ethical approval was not required for this study as it involved the analysis of previously collected, anonymized data and did not involve any direct patient intervention. Data availability : The datasets presented in this study are accessible through an online database. You can locate them in the following database: (https://www.ncbi.nlm.nih.gov/geo/). Funding Declaration :This research received no external funding. Clinical Trial Registration Date (NLGN3) :Clinical trial number: not applicable. Clinical Trial Registration Details : Clinical trial number: not applicable. References Lord C et al (2020) Autism spectrum disorder. Nat Reviews Disease Primers 6(1):5 Abuse S, Administration MHS (2016) DSM-5 Child Mental Disorder Classification. DSM-5 Changes: Implications for Child Serious Emotional Disturbance [Internet]. 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Montoya JC et al (2014) Global differential expression of genes located in the Down Syndrome Critical Region in normal human brain. Colombia Médica 45(4):154–161 Cheng W et al (2020) Identification of a robust non-coding RNA signature in diagnosing autism spectrum disorder by cross-validation of microarray data from peripheral blood samples. Medicine 99(11):e19484 Xiong C et al (2024) Modelling cell type-specific lncRNA regulatory network in autism with Cycle. BMC Bioinformatics 25(1):307 Caglayan E, Liu Y, Konopka G (2022) Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets. bioRxiv, : p. 2022.03. 09.483658 Wang L et al (2023) Autism spectrum disorder: neurodevelopmental risk factors, biological mechanism, and precision therapy. Int J Mol Sci 24(3):1819 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures2.docx Supplementarytables.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 10 Feb, 2026 Editor assigned by journal 01 Feb, 2026 Submission checks completed at journal 01 Feb, 2026 First submitted to journal 26 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8700783","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590228067,"identity":"afe00caf-e1bd-4ad7-b3ae-e07308387c40","order_by":0,"name":"Kaleem Maqsood","email":"","orcid":"","institution":"Lahore Garrison University","correspondingAuthor":false,"prefix":"","firstName":"Kaleem","middleName":"","lastName":"Maqsood","suffix":""},{"id":590228068,"identity":"47d42e59-d33d-4c1e-a04a-63fdf89e4da3","order_by":1,"name":"Mahnoor Fatima","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Mahnoor","middleName":"","lastName":"Fatima","suffix":""},{"id":590228069,"identity":"f973c2af-7933-4dca-86a7-6340985976e9","order_by":2,"name":"Zeshan Wadood","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Zeshan","middleName":"","lastName":"Wadood","suffix":""},{"id":590228070,"identity":"e5f0a77c-849c-4473-a583-0f7dc833d781","order_by":3,"name":"Humera Naveed","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Humera","middleName":"","lastName":"Naveed","suffix":""},{"id":590228071,"identity":"53305f73-3f47-4067-bce1-de16b7a439f9","order_by":4,"name":"Turki Abualait","email":"","orcid":"","institution":"Imam Abdulrahman Bin Faisal University","correspondingAuthor":false,"prefix":"","firstName":"Turki","middleName":"","lastName":"Abualait","suffix":""},{"id":590228072,"identity":"a5b9ba2c-8a9c-40a2-b3db-d11518a90918","order_by":5,"name":"Hani Almohanna","email":"","orcid":"","institution":"King Fahad Specialist Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hani","middleName":"","lastName":"Almohanna","suffix":""},{"id":590228073,"identity":"0ca6ffc7-14aa-41e2-88ab-39dfef440af0","order_by":6,"name":"Shahid Bashir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCQY2hgS2AwwSzOwHHwD5PHzEa2HnSTYAaWEjSgsDSAs/g5kESICgFvnZzc8ePCi7kyfZzJBW+TXHToaNgfnhoxt4tBjcOWZukHDuWbE0M+Ox27LbkoEOYzM2zsGnRSLBTCKx7XDiPGaGtNuS25iBWnjYpPFpkZ+R/g2mxaxYcls9YS0MN3IgtswGamH8uO0wYS0GN3LKJEB+kWzmSZZm3Hach42ZgF+ADtsm+QMYYhLnjx/8+HNbtT0/e/PDx3gdBgUJIIKZB0wSoRyuhfEHkapHwSgYBaNgZAEAgb5HL0oLLDcAAAAASUVORK5CYII=","orcid":"","institution":"King Fahad Specialist Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shahid","middleName":"","lastName":"Bashir","suffix":""}],"badges":[],"createdAt":"2026-01-26 13:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8700783/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8700783/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102962118,"identity":"2dcae2d8-ad6a-40da-8c74-85b43167f075","added_by":"auto","created_at":"2026-02-19 04:03:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194037,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGene Ontology (GO) enrichment analysis of meta-analysis mRNAs performed using the DAVID database. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e GO enrichment results for upregulated mRNAs. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eGO enrichment results for downregulated mRNAs. Enriched GO terms are grouped into Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories. The y-axis represents enrichment scores expressed as −log10(p-value). Upregulated genes were predominantly enriched in transcriptional regulation, chromatin remodeling, mRNA processing, and protein phosphorylation, whereas downregulated genes were mainly associated with neuronal signaling, ion transport, synaptic regulation, and membrane-related functions, indicating distinct but complementary functional alterations in ASD.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/d6e33d9cc19bf772a89f1f49.png"},{"id":102595088,"identity":"b5e13290-f618-4c80-8e31-c9866273dfb4","added_by":"auto","created_at":"2026-02-13 12:03:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKEGG pathway enrichment analysis of meta-analysis mRNAs performed using the DAVID database.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Enriched KEGG pathways for upregulated mRNAs. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Enriched KEGG pathways for downregulated mRNAs.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe x-axis represents enrichment scores expressed as −log10(p-value). Upregulated genes were primarily enriched in pathways related to intracellular signaling, cytoskeletal regulation, autophagy, and hormone-related signaling, including regulation of actin cytoskeleton, autophagy, thyroid hormone signaling, and phosphatidylinositol signaling. In contrast, downregulated genes were mainly associated with synaptic and neuronal signaling pathways, including glutamatergic synapse and retrograde endocannabinoid signaling, highlighting complementary functional alterations in ASD.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/3d0685a278a9f36e7f136c19.png"},{"id":102595091,"identity":"2038bef9-7a52-40f0-b333-dc782bd06ffb","added_by":"auto","created_at":"2026-02-13 12:03:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":294696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHigh-confidence protein–protein interaction (PPI) network of meta-analysis mRNAs constructed using the STRING database (interaction score ≥ 0.9). Nodes represent protein-coding genes identified from the Robust Rank Aggregation meta-analysis, while edges represent known or predicted protein–protein interactions. The dense connectivity of the network indicates strong functional coordination among ASD-associated genes, particularly those involved in translation and RNA metabolism.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/a6638b7770ca538c7df3e83d.png"},{"id":102747275,"identity":"9e4a0aae-33cb-4c2b-81e3-4503e6ae86cc","added_by":"auto","created_at":"2026-02-16 09:04:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFunctional modules identified within the PPI network using the MCODE algorithm in Cytoscape.\u003cbr\u003e\n \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Cluster 1, the largest module, is enriched for translation initiation and ribosomal genes, including multiple EIF family members.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Cluster 2 is primarily composed of RNA-binding and RNA-processing genes, particularly heterogeneous nuclear ribonucleoproteins.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThese clusters highlight distinct yet interconnected molecular processes dysregulated in ASD.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/c0410c4a91ae8c3b96665ad9.png"},{"id":102595094,"identity":"0d480aa1-8040-4689-81b3-a2cbbfe9fd8b","added_by":"auto","created_at":"2026-02-13 12:03:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":242582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003elncRNA–mRNA regulatory network in ASD: \u003c/strong\u003eThe network illustrates reproducible correlations between ASD-associated lncRNAs (diamond-shaped nodes) and hub mRNAs (rectangular nodes) identified from protein–protein interaction analysis. Edges represent significant lncRNA–mRNA correlations observed in at least two independent datasets (|r| ≥ 0.3, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05). Red edges indicate positive correlations, while blue edges indicate negative correlations. Edge thickness corresponds to the strength and reproducibility of the correlations. MALAT1 shows the highest connectivity, suggesting a central regulatory role in ASD-related molecular pathways.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/e66c453797f4faca910a875e.png"},{"id":102595092,"identity":"205acfc9-c300-4980-9cb8-2b617542dad4","added_by":"auto","created_at":"2026-02-13 12:03:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeta lncRNA–mRNA correlation heatmap across ASD datasets: \u003c/strong\u003eThe heatmap presents the reproducible mean Pearson correlation coefficients of lncRNA-mRNA pairs of the multiple ASD datasets. The rows will consist of lncRNAs and the columns will consist of hub mRNAs. The intensity of color depicts the strength and the direction of correlation where red implies positive correlations and blue implies negative correlations. Hierarchical clustering was done to the rows and columns to display the global correlation pattern and regulatory modules. This visualization illustrates the similarity in the lncRNA-mRNA regulatory interactions in independent exchanges.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/2fff6567acfcb8901894e98f.png"},{"id":103049711,"identity":"0049482c-9691-45a1-8307-c8af43fa9567","added_by":"auto","created_at":"2026-02-20 07:45:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1893395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/f42536d2-8166-477f-ac1b-49083586d642.pdf"},{"id":102595096,"identity":"26165d21-c94b-4ee2-b90f-705e8f60856a","added_by":"auto","created_at":"2026-02-13 12:03:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1086278,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/41c25dad9f77efdd64a1ceaa.docx"},{"id":102747302,"identity":"9b01642f-6ad6-4230-9a68-ef8e97473021","added_by":"auto","created_at":"2026-02-16 09:04:29","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":103167,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8700783/v1/a6c0223bd632317c75968511.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Dataset Meta-Analysis Identifies Core mRNA and lncRNA Networks Associated with Autism Spectrum Disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism spectrum disorder (ASD) is one the most common neurodevelopmental disorder which characterizes repetitive, restricted sensory behaviors and interests, and deterioration of social communication beginning early in life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) has deemphasized ASDs classification because of the diverse clinical features and underlying biological causes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In last four decades, the global ASD prevalence has been increasing steadily but with regional differences, with the disorder being 4\u0026ndash;5 times more prevalent in boys than girls [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eASD is heterogeneous and has genetic, epigenetic, and environmental causes, but the exact etiology remains elusive. ASD has been linked with hundreds of genes till now [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For example, NLGN3, NLGN4X [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], SH3 and SHANK3 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] which are synaptic genes were identified as the ASD genes through DNA sequence studies. But in numerous cases these genes cannot be used to identify the cause of ASD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Transcriptomic studies provide a pivotal connection between measurement of protein levels and analysis of genetic information. Various biological processes have been shown to be implicated in ASD through systematic transcriptomic studies and their computational analyses. For example, computational analysis of transcriptomic datasets of different tissues from ASD patients demonstrated that differentially expressed genes in ASD patients were greatly enriched in immune/inflammation responses, oxidative phosphorylation and mitochondrion-linked functions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Differential gene expression analysis from two microarray datasets consisting of 122 ASD and 89 control samples identified 1,862 significantly altered genes in blood of ASD patients which were mainly enriched in immune/infection-related pathways like Influenza A, Epstein\u0026ndash;Barr virus infection and primary immunodeficiency pathways. Key hub genes highlighted were SUMO1, SP1, EGR1, EP300, and VHL among others with potential utility as blood-based biomarkers for prompt diagnosis and risk assessment of ASD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is evident that differentially expressed genes of blood could indicate ASD. But no study to date has integrated several human datasets to systematically compare gene expression status in the blood of ASD and healthy controls. An updated list of genes significantly dysregulated in individuals with ASD from all recent transcriptomic studies will help detect useful patterns for diagnosis and understanding the underlying mechanisms.\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) are RNA molecules which remain untranslated, have a 3 -methyl-guanosine cap at their 5 end and poly(A) tail at their 3 end, are longer than 200 nucleotides, and are based on sense/antisense strands of protein-coding genes, pseudogenes, or intergenic regions. The lncRNAs may be located in cytoplasm or in the nucleus, though most of them are time specific and tissue/cell specific [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The mechanisms of action of lncRNAs include, but are not limited to, mRNA degradation, chromatin remodeling, splicing regulation, genomic imprinting, and cell-cycle regulation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. lncRNAs regulate gene expression at the level of transcription, posttranscription and epigenetics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This diverse set of functions emphasizes that lncRNAs are key players in numerous cellular processes. LncRNAs dysregulation and mutations are involved in causing cancer, cardiovascular and neurological disorders [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. They are also an important component in normal brain development [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and their dysregulation can contribute to brain disorders like ASD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA bioinformatics-based integrated analysis of multiple genomic datasets revealed several differentially expressed lncRNAs in the cortex of ASD patients, showed enriched expression in brain tissues, and also co-expression network with known ASD risk genes during cortical development. These lncRNAs were mainly involved in immune-related processes, synaptic signaling and transmission, and lipid transport pathways. The fact that these pathways are also dysregulated in ASD implicates lncRNAs in the pathogenesis of ASD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Genome-wide expression study of lncRNAs in blood of ASD patients has produced several differentially expressed lncRNAs that are involved in neural mechanisms like trafficking of synaptic vesicle, extended synaptic potentiation, and persistent depression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A significant upregulation of lncRNAs NEAT1 and TUG1 was reported in blood of ASD patients as compared to healthy controls [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The aim of the current study is to decipher the significance of lncRNAs in understanding ASD pathogenesis, and their potential use as biomarkers in ASD. Discovering critical genes in ASD can lead to early diagnosis and initiation of treatments.\u003c/p\u003e \u003cp\u003eGene Expression Omnibus (GEO) is a public database which has a collection of raw and processed datasets of genomics and high-throughput gene expression studies. In this study, we aimed to discover novel ASD-related genes by profiling the gene expression signatures in blood of ASD patients, by way of carrying out a series of computational and bioinformatics analyses on the transcriptomic datasets from GEO. This pipeline utilizes patterns of differential gene expression in blood of ASD and normal individuals. Functional enrichment, protein-protein interactions, and correlation analyses were carried out to elucidate potential mechanisms of ASD pathogenesis and to identify potential ASD-related lncRNAs. Correlation analysis can help identify links between formerly uncharacterized lncRNAs, ASD risk genes/hub genes and disturbed molecular pathways in ASD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eGene expression data sets of ASD that were publicly available in the GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database were accessed. The final analysis involved six microarray datasets that were independent of each other (GSE6575, GSE18123, GSE18123_6244, GSE25507, GSE42133, and GSE111175). Each of the datasets was obtained using peripheral blood or leukocyte samples of ASD and typically developing controls, which made it possible to investigate the existence of transcriptomic blood-based signatures of ASD. Details of all datasets summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData preprocessing and quality control\u003c/h3\u003e\n\u003cp\u003eGEOquery package in R-studio software (v4.3.3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to download raw or processed expression matrices and log2 transform where necessary, depending on the assessment of distributions. The expression at the gene level was obtained by averaging the expression of several probes that target the same gene.\u003c/p\u003e \u003cp\u003eEach of the datasets underwent principal component analysis (PCA), which evaluated the distribution of the samples and any possible outliers. The samples that exhibited deviant patterns of clusters were eliminated before the process of differential analysis. PCA plots were drawn to see how the samples in the ASD and the control groups separated.\u003c/p\u003e\n\u003ch3\u003eDifferential expression analysis\u003c/h3\u003e\n\u003cp\u003eEach dataset was analyzed differently through the limma package (version 3.60.2) of Bioconductor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioconductor.org/\u003c/span\u003e\u003cspan address=\"https://www.bioconductor.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in R software to compare ASD samples to controls and linear models to analyze the relationships and to enhance the estimation of variance with empirical Bayes moderation. Those genes whose adjusted p-value was \u0026lt;\u0026thinsp;0.05, and log2 fold change (FC) was \u0026gt;\u0026thinsp;0.5 were regarded as highly differentially expressed.\u003c/p\u003e \u003cp\u003eEach dataset was plotted as volcano to represent the distribution of differentially expressed genes in terms of statistical significance and fold change.\u003c/p\u003e\n\u003ch3\u003eRobust rank aggregation meta-analysis\u003c/h3\u003e\n\u003cp\u003eRobust Rank Aggregation (RRA) analysis in terms of robustrankaggreg package was used to determine consistently dysregulated genes across datasets. In the case of every dataset, the differentially expressed genes were ranked based on the nominal p-values where up and down-regulated genes were ranked differently. The RRA algorithm took the form of ranking gene lists across all datasets. Differentially expressed genes were picked as genes having RRA p-value less than 0.01, and with similar direction of change across datasets. Ensembl gene biotype annotations categorized these genes further as either protein-coding mRNAs or lncRNAs.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eTo examine the functions of the dysregulated genes, the mRNAs were functionally enriched by the DAVID (Database for Annotation, Visualization and Integrated Discovery) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The mRNAs upregulated and downregulated identified in the Robust Rank Aggregation (RRA) meta-analysis were tested individually to identify the direction-specific biological effects.\u003c/p\u003e \u003cp\u003eThe Gene Ontology (GO) analysis was used in GO categories of; Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted to find significantly enriched biological pathways.\u003c/p\u003e \u003cp\u003eThe significance of enrichment was analysed with the modified Fisher exact test provided in DAVID and terms with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as statistically significant. The scores were enriched as a function of -log10(\u003cem\u003ep\u003c/em\u003e-value) and represented through bar plots. The main figures only showed the most enriched terms and pathways significantly and the full results of enrichment were given in the Supplementary Tables.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction network and hub gene identification\u003c/h2\u003e \u003cp\u003eTo perform meta-analysis of mRNAs, protein-to-protein interaction network (PPI) was built in STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and an interaction confidence score of \u0026ge;\u0026thinsp;0.9. The network that resulted was imported into Cytoscape (v3.10.2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Identification of densely connected modules of PPI network was done using MCODE plug (version 2.0.3), and the two best clusters were picked to proceed with further analysis.\u003c/p\u003e \u003cp\u003eThe CytoHubba plugin was employed for hub genes identification based upon Maximal Clique Centrality (MCC) algorithm. The genes that had the best scores on MCC would be taken as the key hub genes. A topological analysis of the network was done using the tools of network analysis in Cytoscape, including node degree.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003elncRNA–mRNA correlation analysis\u003c/h3\u003e\n\u003cp\u003eTo explore potential regulatory relationships between lncRNAs and hub mRNAs, Pearson correlation analysis was performed separately for each dataset using gene-level log2 expression matrices. Only significant lncRNAs identified from the RRA meta-analysis and hub mRNAs identified from the PPI network were included. Correlation coefficients were calculated for all lncRNA-mRNA pairs, and pairs with |r| \u0026ge; 0.3 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003cp\u003eCorrelation results from all datasets were integrated, and only lncRNA\u0026ndash;mRNA pairs reproducible in at least two independent datasets were retained. Mean correlation coefficients across datasets were calculated for each reproducible pair.\u003c/p\u003e\n\u003ch3\u003eConstruction of the lncRNA–mRNA regulatory network\u003c/h3\u003e\n\u003cp\u003eA reproducible lncRNA-mRNA regulatory network was created based on the integrated correlation analysis. The network was visualized in Cytoscape, with lncRNAs and mRNAs represented as distinct node shapes. Edge color indicated the direction of correlation (positive or negative), and edge width reflected correlation strength and dataset reproducibility. Key lncRNAs were identified based on network connectivity (degree).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeta-correlation heatmap visualization\u003c/h2\u003e \u003cp\u003eA meta-correlation heatmap was generated to visualize the mean correlation patterns between lncRNAs and hub mRNAs across datasets. The heatmap matrix was constructed using mean correlation coefficients of reproducible lncRNA-mRNA pairs and visualized using the pheatmap package in R. Hierarchical clustering was applied to both rows and columns to identify correlation patterns and functional groupings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R (Version 4.5.2). Pearson correlation coefficients were used for expression correlation analysis. Multiple testing correction was applied where required by using the Benjamini-Hochberg method. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant unless otherwise specified.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOverview of included datasets and preprocessing\u003c/h2\u003e \u003cp\u003eThe final analysis comprised 6 independent datasets of ASD-related transcriptomics based on peripheral blood or leukocytes (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Before downstream studies, the expression data of both datasets were log2-transformed (where necessary) and quality-controlled so that the results of both samples are comparable.\u003c/p\u003e \u003cp\u003ePCA plots showed that there is a consistent tendency of the separation of ASD and control samples and that there were no strong artifacts related to batch processing after processing. Any outlier samples that were identified were eliminated before the analysis of differential expression \u003cb\u003e(\u003c/b\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eEach dataset was analyzed by means of differential expression analysis in the limma framework. Volcano plot was used to visualize dataset-level results to evaluate the distribution, directionality, and heterogeneity of change in mRNA and lncRNA expression between cohorts \u003cb\u003e(\u003c/b\u003eSupplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and S3\u003cb\u003e)\u003c/b\u003e. These analyses had a descriptive purpose, and the final ASD-associated genes were determined by means of Robust Rank Aggregation (RRA) meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of robust ASD-associated genes by RRA meta-analysis\u003c/h2\u003e \u003cp\u003eTo identify genes consistently dysregulated across datasets, Robust Rank Aggregation (RRA) meta-analysis was performed. Genes were ranked separately based on upregulated and downregulated expression patterns within each dataset and integrated across studies (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Genes with RRA \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and consistent directionality were defined as meta-analysis differentially expressed genes. This approach yielded a robust set of ASD-associated mRNAs and lncRNAs that were subsequently used for functional enrichment and network analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of meta-analysis mRNAs\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eGene Ontology (GO) analysis\u003c/h2\u003e \u003cp\u003eGene ontology enrichment of upregulated and downregulated meta-analysis mRNAs was conducted independently with the help of DAVID database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe up regulated mRNAs were greatly enriched in biological processes that involve chromatin remodelling, transcription regulation, mRNA-processing and protein phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(A)). These genes were mostly related to cytosol, nucleoplasm, and nucleus at the cellular component level suggesting enhanced regulation in the nucleus and cytoplasm. Analysis of molecular function showed that it was enriched in RNA binding, DNA binding and protein binding, indicating that transcriptional and post-transcriptional regulation was improved in ASD.\u003c/p\u003e \u003cp\u003eConversely, mRNAs that were downregulated were substantially enriched in cell adhesion biological processes, ion transport, neuronal signals and synaptic regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(B)). The cellular components plasma membrane, synaptic membrane, and cytoskeleton structures were highly enriched, whereas molecular functional analysis revealed the presence of the activity of ligand-gated ion channels, binding of neurotransmitter receptors and actin binding. These findings suggest a dysfunction in synaptic communication and membrane-related processes of ASD.\u003c/p\u003e \u003cp\u003eCombined with the results of the GO enrichment, the findings indicate a dysregulation in ASD that is coordinated by hyperactivation of transcription and regulation and hyperdeactivation of neurons and synapses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eKEGG pathway analysis\u003c/h2\u003e \u003cp\u003eTo further describe the biological pathways of dysregulated mRNAs, KEGG pathway enrichment analysis was done with the help of DAVID (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe upregulated mRNAs were enriched significantly in intracellular signaling, cytoskeletal organization as well as cellular homeostasis pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(A)). The most important enriched pathways were the regulation of actin cytoskeleton, autophagy, phosphatidylinositol signaling system, the thyroid hormone signaling pathway, and mRNA surveillance pathway. These are pathways of signal transduction, cytoskeleton dynamics, stress response, and post-transcriptional regulation.\u003c/p\u003e \u003cp\u003eDownregulated mRNAs in contrast were mostly overrepresented in pathways involved in neuronal communication and synaptic signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B)). There were glutamatergic synapse, retrograde endocannabinoid signaling, focal adhesion, and efferocytosis, showing impairments in synaptic transmission, neuronal connection and cell-cell interactions in ASD.\u003c/p\u003e \u003cp\u003eAltogether, the KEGG pathway analysis can be regarded as the complement to the GO results and indicates that ASD can be defined as the increased intracellular regulatory and stress-response pathways alongside the disruption of synaptic signaling and communication between neurons.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe x-axis represents enrichment scores expressed as \u0026minus;log10(p-value). Upregulated genes were primarily enriched in pathways related to intracellular signaling, cytoskeletal regulation, autophagy, and hormone-related signaling, including regulation of actin cytoskeleton, autophagy, thyroid hormone signaling, and phosphatidylinositol signaling. In contrast, downregulated genes were mainly associated with synaptic and neuronal signaling pathways, including glutamatergic synapse and retrograde endocannabinoid signaling, highlighting complementary functional alterations in ASD.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePPI network and hub gene identification\u003c/h2\u003e \u003cp\u003eMeta-analysis mRNAs based on high-confidence PPI network in the STRING database with an interaction score of 0.9 was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to the network, it was observed that the clusters of genes are densely linked depicting coordinated functional relationships among the ASD-associated genes.\u003c/p\u003e \u003cp\u003eWith the MCODE module, two modules were recognized in the PPI network that are tightly connected to each other. The biggest module (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A)) was mainly translated to the genes related to the process of translation and the second module (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(B)) was related to RNA binding and RNA processing functions. The CytoHubba plug-in identified hub genes with some of the members of the EIF family and heterogeneous nuclear ribonucleoproteins as the central ones (Supplementary Table S3).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThese clusters highlight distinct yet interconnected molecular processes dysregulated in ASD.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003elncRNA-mRNA correlation analysis and regulatory network construction\u003c/h2\u003e \u003cp\u003ePearson correlation analysis was conducted with the gene-level log 2 expression data to identify possible regulatory relationship between lncRNAs and hub mRNAs on a dataset-by-dataset basis \u003cb\u003e(\u003c/b\u003eSupplementary Figure S4\u003cb\u003e)\u003c/b\u003e. The thresholds of |r| \u0026ge; 0.3 and p 0.05 were used to identify significant lncRNA-mRNA pairs (Supplementary Table S4).\u003c/p\u003e \u003cp\u003eThe results of correlation were combined between datasets and only interactions that could be reproduced in two or more independent datasets were kept. The resultant lncRNA-mRNA regulatory network contained five lncRNAs and nineteen hub mRNAs. One of them, MALAT1, turned out to be the most interconnected lncRNA with many positive correlations with several translation- and RNA-processing-related hub genes. Conversely, there were cases where DSCR8 and AQP4-AS1 have a negative correlation, indicating that they may be acting as inhibitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMeta-correlation heatmap analysis\u003c/h2\u003e \u003cp\u003eTo visualize global correlation patterns across datasets, a meta-correlation heatmap was generated using mean correlation coefficients of reproducible lncRNA-mRNA pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Hierarchical clustering revealed distinct regulatory modules, with MALAT1 clustering closely with translation-related genes, while DSCR8 and AQP4-AS1 formed separate clusters characterized by inverse correlations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe marked genetic heterogeneity of ASD highlights the value of identifying convergent pathways and molecular mechanisms rather than individual risk genes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Previous transcriptomic studies of ASD have reported widespread but inconsistent dysregulation of synaptic, transcriptional, and RNA-processing pathways across different cohorts [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To address this, we performed a comprehensive meta-analysis of six independent peripheral blood\u0026ndash;based transcriptomics datasets using Robust Rank Aggregation (RRA) to identify robust mRNA and lncRNA signatures associated with ASD. Overall, our results reveal a coordinated dysregulation of transcription and post-transcription regulatory mechanisms, alongside impaired neuronal and synaptic signaling pathways, consistent with earlier brain and blood transcriptome studies and supporting the provision of a systems-level model of ASD-associated molecular pathology [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFunctional enrichment analysis showed that upregulated mRNAs in ASD were strongly enriched for chromatin remodeling, transcriptional regulation, mRNA processing, and protein phosphorylation, indicating a broad activation of gene regulatory and post-transcriptional machinery. Such coordinated upregulation of RNA metabolic and regulatory pathways has been spotted in brain and peripheral transcriptome studies of ASD, supporting the concept that disrupted control of gene expression is a fundamental feature of the disorder [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Notably, the enrichment of RNA-binding and translation-related functions aligns with previous studies reporting abnormal protein synthesis and RNA metabolism in ASD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Dysregulation of these processes during early development may influence neuronal differentiation, synaptic maturation, and circuit formation, thereby contributing to ASD pathophysiology [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, downregulated mRNAs were predominantly enriched for neuronal communication, synaptic signaling, ion transport, and membrane-associated processes, indicating widespread impairment of neuronal connectivity in ASD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Both GO and KEGG analyses consistently highlighted pathways such as glutamatergic synapse and retrograde endocannabinoid signaling, which are critical for synaptic plasticity and neuronal connectivity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Disruption of these pathways may underlie the deficits in synaptic transmission, excitation\u0026ndash;inhibition balance, and network connectivity commonly observed in ASD. The observed enrichment of membrane and cytoskeletal components further suggests alterations in synapse structure and stability, supporting the notion of ASD as a disorder of synaptic dysfunction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProtein\u0026ndash;protein interaction network analysis revealed densely connected gene clusters centered on EIF family members (EIF2S1, EIF3A, EIFAG1) and heterogeneous nuclear ribonucleoproteins (HNRNPA1, HNRNPC, HNRNPK), indicating that RNA metabolism and translation initiation are core dysregulated processes in ASD [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These hub genes are essential for regulating mRNA translation, stability, splicing, and intracellular transport, all of which are necessary for proper neuronal differentiation and synaptic development. Previous studies have shown that altered activity of EIF4E and EIF4G1 leads to excessive or imbalanced protein synthesis and is strongly associated with ASD-like phenotypes through mTOR-dependent translational dysregulation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLikewise, HNRNPA1, HNRNPC, and HNRNPK have been implicated in controlling alternative splicing and localization of neurodevelopmental transcripts, and their disruption can cause widespread misexpression of synaptic and axon-guidance genes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The identification of these genes as network hubs supports the growing evidence that defective RNA-binding and translation-control mechanisms drive ASD-related molecular pathology.\u003c/p\u003e \u003cp\u003eOur lncRNA-mRNA correlation analysis revealed a reproducible regulatory network linking a small set of ASD-associated lncRNAs with key hub mRNAs, indicating that lncRNAs play a central role in coordinating ASD-related molecular program [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Among these, MALAT1 emerged as a central regulatory lncRNA, exhibiting strong positive correlations with multiple translation initiation and RNA-processing-related genes, suggesting that it may promote or stabilize the elevated RNA-metabolic state observed in ASD. MALAT1 has previously been implicated in transcriptional regulation, RNA splicing, and synaptic function, all of which are critical for neuronal development and plasticity, supporting its potential role in ASD [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConversely, lncRNAs such as DSCR8 and AQP4-AS1 displayed predominantly negative correlations with hub mRNAs, suggesting inhibitory or modulatory regulatory roles within the ASD regulatory network. DSCR8, located in the Down Syndrome Critical Region, may link chromosomal dosage effects to RNA regulatory dysfunction [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], while AQP4-AS1 may reflect astrocyte-associated modulation of neuronal signaling [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These findings highlight the complexity of lncRNA-mediated regulation in ASD and suggest that both activating and repressive lncRNA mechanisms may contribute to disease-associated molecular dysregulation.\u003c/p\u003e \u003cp\u003eThe meta-correlation heatmap analysis demonstrated that key lncRNA\u0026ndash;mRNA regulatory relationships were consistent across multiple independent ASD datasets, underscoring their robustness and biological significance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The consistent co-clustering of MALAT1 with translation initiation and RNA-processing hub genes across datasets indicates that this lncRNA functions as a core integrator of the dysregulated translational machinery in ASD, rather than reflecting a context-dependent association [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Conversely, the reproducible inverse correlation patterns of DSCR8 and AQP4-AS1 signify consistent inhibitory regulatory modules of the transcriptomic environment of ASD. This type of reproducibility in datasets increases the biological significance of the reported regulatory interactions and their potential to serve as a biomarker and therapeutic targets.\u003c/p\u003e \u003cp\u003eTogether, our results indicate that ASD is associated with the systems-level imbalance, where hyperactivation of the transcriptional and post-transcriptional regulatory machinery and inhibition of neuronal and synaptic signaling pathways are observed. Such a molecular imbalance could lead to disturbed neurodevelopmental pathways and synaptic dysfunction and, thus, aberrant synaptic development, circuitry, and behavioral-phenotype in ASD [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the establishment of reproducible peripheral blood-based molecular networks creates the potential of establishing a non-invasive biomarker development that is accessible. The lncRNAs and hub genes revealed in the given research, especially MALAT1, EIF relatives and heterogeneous nuclear ribonucleoproteins are biologically justified targets of early ASD detection, molecular stratification and therapeutic monitoring, opening the path to precision medicine strategies in autism.\u003c/p\u003e \u003cp\u003eRegardless of these strengths of this integrative analysis, there are a few limitations to be noted. First, peripheral blood samples might be an insufficient measure of brain-specific molecular changes, but more and more data indicate the applicability of blood-based transcriptomic signatures to neurodevelopmental disorders. Second, the lncRNA-mRNA correlation analysis fails to provide causality, and experimental validation will be needed to ascertain regulatory relationships. The research directions of future studies should be to corroborate the identified hub genes and lncRNAs in independent cohorts and experimental models and their functional roles in neural development and synaptic regulation\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study provides a comprehensive integrative analysis of ASD-associated transcriptomic alterations, revealing coordinated dysregulation of transcriptional regulation, RNA metabolism, and synaptic signaling pathways. The identification of key hub genes and lncRNA-mediated regulatory networks offers novel insights into ASD molecular mechanisms and provides a foundation for future biomarker development and therapeutic investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: Authors have no competing interests to be declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.M: Conceptualization, Data curation, Formal analysis, Methodology, Writing \u0026ndash; original draft. M.F: Data curation, Formal analysis, Visualization, Writing \u0026ndash; review \u0026amp; editing. Z.W. Methodology, Validation, Writing review \u0026amp; editing. H.N: Data curation, Investigation, Writing \u0026ndash; review \u0026amp; editing. T.A and H.A: Writing review \u0026amp; editing. S.B: Conceptualization, Supervision, Project administration, Methodology, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: The authors have no acknowledgments to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e: Ethical approval was not required for this study as it involved the analysis of previously collected, anonymized data and did not involve any direct patient intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The datasets presented in this study are accessible through an online database. You can locate them in the following database: (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e:This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration Date (NLGN3)\u003c/strong\u003e:Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration Details\u003c/strong\u003e: Clinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLord C et al (2020) Autism spectrum disorder. Nat Reviews Disease Primers 6(1):5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbuse S, Administration MHS (2016) DSM-5 Child Mental Disorder Classification. DSM-5 Changes: Implications for Child Serious Emotional Disturbance [Internet]. 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Int J Mol Sci 24(3):1819\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jomn","sideBox":"Learn more about [Journal of Molecular Neuroscience](https://www.springer.com/journal/12031)","snPcode":"12031","submissionUrl":"https://submission.nature.com/new-submission/12031/3","title":"Journal of Molecular Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Autism spectrum Disorder, Transcriptome, lncRNA, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-8700783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8700783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAutism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder accompanied by multifaceted genetic and molecular dysregulation. There is a need to identify reproducible gene expression patterns and regulatory networks by integrative meta-analysis methods.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSix autonomous ASD-related transcriptomic datasets were obtained from the Gene Expression Omnibus and consolidated analytically. Quality control, log2-transformation, and outlier removal were done before differential expression analysis between each dataset through the limma framework. Robust Rank Aggregation (RRA) was used to identify mRNAs and lncRNAs with consistent dysregulation. DAVID database was used for functional enrichment analysis. STRING was used to build protein-protein interaction (PPI) networks and MCODE and CytoHubba were used to identify hub genes. To create reproducible lncRNA-mRNA regulation networks among datasets, Pearson correlation was conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eASD-associated mRNAs and lncRNAs with consistent directionality across datasets were identified. Upregulated mRNAs were primarily involved in transcriptional regulation, chromatin remodeling, RNA processing, and intracellular signaling whereas downregulated mRNAs were enriched in synaptic signaling, neuronal communication, and ion-transport pathways. Densely interconnected modules enriched for translation initiation and RNA-binding functions, with EIF family members and heterogeneous nuclear ribonucleoproteins emerging as key hub genes. A reproducible regulatory network involving five lncRNAs and nineteen hub mRNAs was identified, with MALAT1 acting as a central regulatory hub.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis integrative transcriptomics analysis shows that coordinated transcriptional regulation and synaptic signaling dysregulation occur in ASD and identifies key hub genes and lncRNA-mediated regulatory networks. The results may bring new insights into the ASD molecular pathobiology and possible peripheral biomarkers and treatment targets.\u003c/p\u003e","manuscriptTitle":"A Multi-Dataset Meta-Analysis Identifies Core mRNA and lncRNA Networks Associated with Autism Spectrum Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 12:03:46","doi":"10.21203/rs.3.rs-8700783/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-10T08:31:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T04:50:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T04:47:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Neuroscience","date":"2026-01-26T13:17:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-neuroscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jomn","sideBox":"Learn more about [Journal of Molecular Neuroscience](https://www.springer.com/journal/12031)","snPcode":"12031","submissionUrl":"https://submission.nature.com/new-submission/12031/3","title":"Journal of Molecular Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"453b3435-47e1-4636-8d58-2e6dbf06de87","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-13T12:03:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 12:03:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8700783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8700783","identity":"rs-8700783","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}