Introduction

Schizophrenia presents itself as a severe psychiatric disorder in early to late adolescence with a complex manifestation of positive symptoms (e.g., hallucinations and delusions), negative symptoms (e.g., social withdrawal, blunted affect) and pervasive cognitive deficits associated with functional decline [1, 2]. Though not as common as other psychiatric disorders with a lifetime prevalence of \(0.32\%\) [3] affected worldwide, it executes a high toll on society via multifaceted indices such as economic liabilities, human rights violations and suicides [4, 5]. Its pathophysiology is perplexing as uncovered through advanced genomic [6], developmental neurobiology [7], and systems biology investigations [8, 9] with irregularities in cellular, molecular, neuroanatomical, and neurophysiological domains [10] being implicated. This diverse milieu of factors and their enigmatic interplay contributes to its elusive etiology.

As far as the genetics of schizophrenia are concerned the strong links between schizophrenia and over \(100\) susceptibility loci, along with identified CNVs and SNVs, show promise. Thousands of common alleles with small effects collectively contribute substantially to schizophrenia risk. These findings may lead to new therapeutic insights. However, we must remember: (i) associations between genetic variants and schizophrenia do not necessarily imply causal pathways; (ii) many associations extend beyond schizophrenia to other mental disorders. The specifics of schizophrenia's origins and genotype-environment interactions are largely unknown, warranting caution in assessing the various genetic contributors to its development [11]. The evolution of pharmacological interventions to alleviate the patient's condition has been extremely slow since the advent of the first antipsychotics [12]. Efforts toward biomarker discovery for early identification of individuals at risk, improving diagnostic accuracy and precision, predicting treatment response, and to obtain new druggable targets are notable [13]. However, the prevalent body of work regarding this has mainly focused on the coding part of the genome but the mosaic manifestation of schizophrenia warrants a far more holistic understanding of the underlying pathophysiology.

Toward this end, integrating existing knowledge of schizophrenia etiology with the recent shift in our understanding of the ncRNAs as junk/black matter of the genome to being vital regulatory molecules is of prime importance [14,15,16]. By virtue of their ability to silence/alter expressions of multiple targets simultaneously, ncRNAs can affect entire signaling pathways [17]. Not surprisingly, understanding the role of ncRNAs in physiological and pathological conditions has significantly enriched our recognition and understanding of alternate/additional molecular pathways involved. More than a hundred miRNAs and though in its infancy \(\sim 30\) lncRNAs have been found dysregulated across the pre-frontal cortex, superior temporal gyrus, parietal cortex, amygdala, serum, and peripheral blood in schizophrenia [18]. SNPs in both miRNAs and lncRNAs have also been shown to have significant associations with the schizophrenia phenotype [19]. Collectively, substantial evidence has been generated as to the dysregulation of ncRNAs in schizophrenia and that the disruption of the networks they regulate is critical to schizophrenia as they are enriched with target genes pertaining to various neurophysiological processes [20, 21]. Considering all these, a new paradigm wherein the clinical utility of ncRNAs in the form of a next-generation is now being seriously assessed [22].

Nevertheless, making sense of the huge amount of data on ncRNAs in the public domain for targeted therapeutics is a herculean task [23,24,25,26]. Integrating the raw sequencing data from multiple sources and fitting them under logical models is one way of understanding how lncRNAs collectively interact with the coding genome. A possible route is the ceRNA theory, wherein ncRNAs such as lncRNAs that share common MREs with mRNAs can act as siphons and competitively sequester miRNAs, thus forming a complex regulatory network [27]. Any differential expression in these RNAs harboring common MREs could thereby lead to imbalances in the regulatory network and disease development [28,29,30]. ceRNA networks developed for diseases such as sarcopenia have identified lncRNAs, mRNAs and miRNAs which add to disease risk with very high accuracy [31]. Several lines of evidence have already shown a close association of ceRNA networks with several forms of cancer but very little is known for schizophrenia [\({\text{log}}_{2}(\text{fold change})\) values of all genes between schizophrenia and normal patients via limma package in R [41]. All p-values were also corrected via BH method in R. The genes were considered as differentially expressed corresponding to a \(\text{BH}-p-\text{value}<0.01\) and \(\left|{\text{log}}_{2}(\text{fold change})\right|>0.5\) [42]. DEGs having \({\text{log}}_{2}\left(\text{fold change}\right)>0.5\) and \({\text{log}}_{2}\left(\text{fold change}\right)<-0.5\) were designated as upregulated and downregulated, respectively. PCA method was utilized to assess the sample aggregation degree. It is an unsupervised method that can be used to understand the difference between two or more sample groups [43, 44]. Unsupervised PCA/dimensionality reduction was performed via R software based on the DEGs expression with respect to samples [3B–D). As observed, mRNA expression levels of all three hub genes were significant across control and schizophrenia samples.

Fig. 3
figure 3

A Overlap** hub genes between significant GO-BP, GO-MF, GO-CC, and pathway genesets. The red, blue, yellow, and green-colored areas signify KEGG, GO-BP, GO-MF, GO-CC genesets, respectively. Box-and-whisker plots showing expression intensity distribution of B HAP1, C HOMER3, D ADRA1A across control and schizophrenia patient samples. The top and bottom of the boxes signify 75th and 25th percentile of distribution. Horizontal lines within the boxes represent the median values while minimum and maximum values label the axes endpoints. P-values shown at the top of boxplots represent significance levels between sample groups for each hub gene

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Schizophrenia-associated 3-node ceRNA network construction and topological analysis

The schizophrenia-associated 3-node ceRNA network comprised \(767\) nodes and \(3030\) edges as shown in Fig. 4. The breakup of nodes and edge pairs is summarized in Table S6. Tables S7−S8 shows top \(3\) lncRNAs and miRNAs within ceRNA network ranked based on betweenness, degree, and closeness centralities. Within this network, degree of lncRNAs, miRNAs and mRNAs ranged from \(1\) to \(117\), \(2\) to \(50\), and \(24\) to \(90\),respectively. Average degrees of lncRNAs, miRNAs, and mRNAs were \(4.64\), \(21.18\), \(47.66\), respectively. As observed from these centralities, ADRA1A hub gene was repressed and regulated by maximum miRNAs while lncRNA NEAT1 interacted with the highest number of miRNAs. Numerous miRNAs, lncRNAs, and mRNAs participating in higher-order subnetwork motifs were observed and the top three higher-order subnetwork motifs based on highest centrality scores of betweenness, degree, and closeness have been reported. The first-ranked subnetwork motif comprised one miRNA (miR-3163), one lncRNA (NEAT1), and one hub gene (ADRA1A). The second-ranked subnetwork motif comprised one miRNA (miR-214-3p), one lncRNA (XIST), and one hub gene (HOMER3). And, the third-ranked subnetwork motif comprised one miRNA (miR-2467-3p), one lncRNA (KCNQ1OT1), and one hub gene (ADRA1A) (Fig. 5A–C). Figure S6 shows centrality distributions like betweenness, closeness, ND, TC, NC, and ASPL of \(3\)-node ceRNA network.

Fig. 4
figure 4

Schizophrenia-associated 3-node ceRNA network comprising 767 nodes and 3030 edges. Magenta-colored diamond nodes represent the lncRNAs, red circular nodes represents the miRNAs, and green-colored octagonal nodes represents the hub genes

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Fig. 5
figure 5

A Top higher-order subnetwork motif based on betweenness, degree, and closeness comprising one miRNA (miR-3163), one lncRNA (NEAT1), and one hub gene (ADRA1A). B The second higher-order subnetwork motif comprising one miRNA (miR-214-3p), one lncRNA (XIST), and one hub gene (HOMER3). C Third higher-order subnetwork motif comprising one miRNA (miR-2467-3p), one lncRNA (KCNQ1OT1), and one hub gene (ADRA1A). Magenta-colored diamond nodes represent the lncRNAs, red circular nodes represents the miRNAs, and green-colored octagonal nodes represents the hub genes

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Discussion

Rare/ultra-rare protein-coding variants de novo or captured through WES of familial forms of schizophrenia have provided insights into a few genes from dopaminergic and neurodevelopmental pathways in schizophrenia [69,70,71,72] but heritability and etiology remain unexplained. Regulatory variants such as ncRNAs are emerging to be essential players in our understanding of the biology/etiology of common conditions such as schizophrenia [73, 74]. As one of the most common types of ncRNAs, lncRNAs are believed to play a pivotal role in the ceRNA machinery and elicit a significant effect in both physiological and pathological mechanisms. Multiple lines of evidence have implicated them in various psychiatric disorders [75,76,77]. Their differential expression in tissue, cell types, and developmental levels indicates that lncRNA expression is tightly regulated [78,79,80]. These give further credence to the idea that lncRNA-associated ceRNA networks may play a crucial role in schizophrenia etiology. However, the dire lack of studies on lncRNAs in the public domain has made it difficult for bioinformatic analyses to annotate their role in disease biology sufficiently. This is exacerbated further by the dearth of HTSeq studies in schizophrenia, including lncRNAs and the lack of postmortem data. To account for these, we employed an approach to shortlist coding genes and their interacting miRNAs and then extract the lncRNA-miRNA interactions reported in the public domain, thus making the mRNA-lncRNA-disease interaction hypothesis-free.

Furthermore, recent studies have indicated the link between brain and periphery via the circulatory system, which contains secreted regulatory molecules and hormones produced in the diffused NES that impact the peripheral markers' gene expression pattern [81,82,83,84]. These findings confirm that schizophrenia is a systemic disorder and support the notion that biomarkers in peripheral samples such as WB, PBMCs, lymphoblasts and olfactory epithelium may be insightful. Another line of evidence that dictated the choice of blood expression profiles for the analysis was based on the current evidence wherein immune/inflammatory processes are located in the disorder [33,34,35] and the strong connections established between altered immunity and lncRNAs [36, 85]. Understanding the networks at play in the peripheral system might help generate a holistic view of the underlying connection.

In the enrichment analysis using the highest-order WGCNA module, three genes were found overlap** in all the significant pathway and GO term libraries tested.

ADRA1A was enriched for terms such as neuroactive ligand-receptor interaction, cytosolic \({\text{Ca}}^{2+}\) ion transport, positive regulation of GABAergic synaptic transmission. HOMER3 was enriched for glutamatergic synapse and G protein-coupled glutamate receptor binding and was a cellular component of dendrites. HAP1 was associated with pathways involved in neurodegeneration, neurogenesis, neurotrophin binding and similar to HOMER3 was found located in the dendrites. All of the pathways have either been directly reported in schizophrenia etiology before or are of substantial importance in the processes involved in schizophrenia pathophysiology [86,87,88,89,90,91]. Thereby, any perturbation in their expression could potentially disturb these pathways and initiate or maintain the disease phenotype. With this hypothesis, we assessed the lncRNAs and miRNAs that could putatively dysregulate these key mRNAs leading to disease manifestation.

Among the three lncRNAs, NEAT1 has recently been reported to be upregulated [\({\text{log}}_{2}(\text{fold change})\) \(>2\)] in Brodmann area 46, hippocampus and striatum. NEAT1 is highly enriched in the mammalian brain and is an indispensable structural component of paraspeckles which are membrane-less cellular bodies involved in several cellular processes such as splicing and transcriptional modulation through chromatin structure modifications with emerging evidence suggesting their altered abundance with several innate immune activating responses stimuli such as sequestering to IFNGR1 [92]. NEAT1 itself has been cited as lncRNA-type immunoregulator (i) affecting monocyte-macrophage functions and T cell differentiation [93], (ii) assembly of inflammasomes by recruitment, maturation, and stabilization of CASP1 in activated macrophages [94], (iii) elicits pro-proliferative and anti-apoptotic roles and migration, invasion, and inflammatory cytokines secretion [95], (iv) exhibits innate immunity responses against viral infections [96]. Furthermore, multiple studies have shown miRNA sequestering tendencies of NEAT1, thereby attenuating target gene activity [97,98,99,100,101]. Based on our bioinformatic analyses, we propose that NEAT1 could be sequestering miR-3163 because of its higher number of transcripts in the disease state, thereby elevating the repression of ADRA1A which was downregulated in cases in the DEA (Fig. 3B).

The second lncRNA, XIST has also been previously associated with multiple mental disorders [102,103,104]. XIST is involved in the inactivation of the X chromosome, which has a long-standing reputation for harboring genes important for brain development and function [105]. Outside of its silencing roles, XIST i) stimulates proliferation and differentiation of naive \({\text{CD}4}^{+}\) T cells [106], (ii) is delocalized in B cells of female-biased autoimmunity [107], (iii) in-part promotes \({\text{CD}11\text{c}}^{+}\) atypical B cell formation [107], and (iv) has been shown to perturb PDL1 levels by probable competitive binding of miR-34a-5p [108]. Even though the expression profile of HOMER3 in schizophrenia is unknown, HOMER1 (member of the three-member HOMER family) has been shown to be up and downregulated in schizophrenia depending on the tissue type with variants in both found to be associated with schizophrenia [109]. Overexpression of XIST has been reported in bipolar disorder and major depressive disorder (phenotypes closely associated with schizophrenia) as well but is highly tissue-specific [102]. Therefore, in a similar fashion as NEAT1, XIST could be competitively sequestering to miR-214-3p, a miRNA already known to target the Qki [110], thereby leading to the altered HOMER3 levels.

The third lncRNA, KCNQ1OT1, targeting KCNQ1, though actively involved in epigenetic phenomenon via chromatin modifications, HMT G9a, and PRC2 [111], has no direct association with schizophrenia yet. However, it does seem to sponge miR-15a, leading to immune evasion and malignant progression of prostate cancer via upregulating PDL1, an essential immune checkpoint [112]. This might explain its expression correlation with \({\text{CD}4}^{+}\), \({\text{CD}8}^{+}\), and cytotoxic T cell levels and several other immune cell subsets in another ceRNA reported in colorectal cancer [85]. Furthermore, it might be indirectly associated with increased sudden cardiac arrest in schizophrenia patients [113].The KCNQ1 protein forms functional potassium channels [114]. Multiple lines of evidence, structural variants and mice knockouts, have shown KCNQ1 to be associated with LQT1, a condition synonymous with increased adverse cardiac events [115,116,117]. It is established that all atypical antipsychotics affect the cardiac potassium pump and that about \(\sim 6-10\text{\%}\) of schizophrenia patients show a longer QT interval under treatment [118, 119]. We propose that the expression of KCNQ1 as dictated by altered KCNQ1OT1 levels could be a putative cause of these adverse drug reactions.

Further investigations into this aspect could potentially lead to pre-emptive treatment strategies. Though the levels of KCNQ1OT1 in schizophrenia are not known, we can extrapolate from available knowledge that rs8234 [120] leads to lower expression of KCNQ1 and is also associated with reduced processing speed, reduced white matter FA and higher risk for schizophrenia [121], thereby implying that lower levels of KCNQ1 are associated with these impaired phenotypes. Elevated KCNQ1OT1 levels could also propagate this scenario. Considering these derived associations, studies establishing KCNQ1OT1 levels in schizophrenia could be informative.

We could thereby imply that elevated KCNQ1OT1 transcripts could be competitively sequestering to miR-2467-3p and inhibiting the expression of ADRA1A, thereby leading to its downregulated state in the DEA. Interestingly, ADRA1A is also associated with several cardiac conditions [122,123,124]. This gives a glimpse into the intricate mechanism in which ceRNAs could be acting in the pathophysiology of schizophrenia.

In conclusion, this study identified lncRNAs NEAT1, XIST and KCNQ1OT1-associated ceRNA networks which could be potentially relevant to schizophrenia by interacting with schizophrenia-relevant genes, ADRA1A and HOMER3. Furthermore, the affinity of the mRNAs to neurodevelopmental processes and that of the lncRNAs to immune/inflammatory processes might indicate a mechanism to unite the two most significant models proposed in schizophrenia etiology. Of note, though the current analyses is based on data specific to schizophrenia, neuroinflammation and its effect on neurodynamics is a well-established phenomenon in a variety of psychiatric illnesses such as depression, bipolar depression and obsessive–compulsive disorder [125]. ceRNAs established through this study and new ones discovered by using similar methods have the potential of uncovering further such pathways. Further refinements in such prediction strategies have the potential of unveiling additional interactions in schizophrenia biology, which, eventually, systems biology approaches coupled with artificial intelligence and machine learning technologies can integrate into a holistic picture. However, it is also important to note that the prediction strategy deployed in this study does not take into account the miRNA and potential ceRNA expression levels. This is important as it is well-established that both miRNAs and ceRNAs have temporal, spatial, and disease-specific expression patterns. Furthermore, studies have shown that ceRNAs and miRNAs with concentrations within a particular range are capable of eliciting such crosstalks. Even though we have provided evidence to give strong credence to the highlighted ceRNA axes, these must still be validated by qRT-PCR, luciferase reporter systems and co-IP assays. Furthermore, we have discussed in favor of the standalone components of the ceRNA networks. We believe that additional investigations into their roles in the diseased state would be valuable in assessing their role as important biomarkers for schizophrenia. Further wet lab experimentations would be an asset in proving the efficacy and accuracy of the predicted biomarkers. Also, design of lead compounds as potential drugs post successful clinical trials could be helpful for the treatment of schizophrenia in near future.