A robust and comprehensive computational framework for circRNA cluster DE analysisMore than 50% of circRNAs are clustered due to alternative back splicing (ABS), forming groups of circRNAs that share one back splicing junction (BSJ) site (referred to as circRNA clusters hereafter) (Fig. 1A)11. The circRNAs within each cluster share common sequences and can potentially play additive roles in regulating the availability of miRNAs and RNA-binding proteins (RBPs)11. However, most current circRNA differential expression (DE) analytic tools and algorithms focus on individual circRNAs, which could overlook the significant additive gene regulatory roles of clustered circRNAs if individual circRNAs within clusters show insignificant changes. To address this challenge, we developed circMeta2, a computational tool specifically designed for circRNA DE analysis at the cluster level (Fig. 1). As illustrated in the flowchart, circMeta2 is designed to work with various output formats from several major algorithms for circRNA identification and quantification, including CIRI2, find_circ and CIRCexplorer2 (Fig. 1A)26,35,36. This flexible input compatibility maximizes the utility of circMeta2 for easy and seamless workflow integration. Clustered circRNAs with either alternative 3’ back splicing (A3BS, circRNAs sharing 3’ splicing sites) or alternative 5’ back splicing (A5BS, circRNAs sharing 5’ splicing sites) can be identified by circMeta2 based on shared BSJ sites (Fig. 1A). Importantly, circMeta2 incorporates either statistical tests embedded in circMeta for small sample size RNA-seq data or a GLM framework for large population-scale RNA-seq data22, which enables controlling for confounding variables such as age and gender in the DE analysis of individual circRNAs (Fig. 1B). This design makes circMeta2 versatile in handling various sample sizes. circMeta2 further adopts a weighted Stouffer’s method to combine the p-values of individual circRNAs into a single combined p-value for their residing circRNA clusters, reflecting both basal expression level and DE direction (Fig. 1B).Fig. 1: Overview of the circMeta2 framework.A Illustration of circRNA clusters formed by alternative back splicing and their additive roles in miRNA sequestration. B Algorithms used for differential expression analysis of circRNA clusters in datasets with a small number of samples and in larger population-level studies.Identification and functional characterization of brain region-specific circRNA clusters in the human frontal cortex versus cerebellumTo demonstrate the effectiveness of circMeta2 in identifying and functionally characterizing DE circRNA clusters from RNA-seq with a small sample size, we identify and compare circRNA profiles identified from RNA-seq data with two biological replicates in human frontal cortex and cerebellum from ENCODE37 respectively. Genome-wide circRNAs were first identified by CIRCexplorer2 and then used for comparison between the frontal cortex and cerebellum. As a result, 50,720 unique circRNAs were identified in total, with 4008 (7.9%) circRNAs specifically identified in the cerebellum and 30,373 (59.9%) in the frontal cortex (Fig. 2A). These findings suggest that the number of individual circRNAs expressed in the human frontal cortex is higher than in the cerebellum. Using circMeta2, we identified 12,274 and 11,694 of circRNA clusters in frontal cortex and cerebellum respectively, with 11,687 of these clusters expressed in both brain regions. Among all the human cortical and cerebellar circRNA clusters, circMeta2 found the majority (60.85%) of circRNA clusters were composed of 2–3 individual circRNAs with either A5BS or A3BS (Fig. 2B). From 12,281 total circRNA clusters, DE analysis by circMeta2 using the small sample size setting (Methods) discovered 3909 cortical-enriched and 2,856 cerebellar-enriched clusters, respectively (FDR < 0.05, Fig. 2C).Fig. 2: Identification of brain region-specific circRNA clusters and their functional roles in miRNA regulation.A Percentage of circRNAs commonly and specifically expressed in frontal cortex and cerebellum. B Numbers of circRNA clusters composed of different number of individual circRNAs in frontal cortex (blue) and cerebellum (orange). CircRNA clusters derived from A3BS and A5BS were counted separately. C Volcano plot showing 3,909 frontal cortical-enriched (red) and 2856 cerebellum-enriched (blue) circRNA clusters. D Schematic representation of expression changes in individual circRNAs within the PROM1-4323-A3BS cluster, with red arrows indicating upregulation and asterisk denoting significance. Strong binding affinity is shown between the cluster’s common sequence and miR-138-5p. E Cumulative plot indicates log2FC of miR-138-5p’s top 10% target genes is significantly lower than log2FC of random selected non-target genes (p-value = 0.049). F Gene Ontology (GO) analysis for miR-138-5p’s target genes upregulated in the frontal cortex compared to cerebellum.Among the 3909 clusters with higher expression profiles in the frontal cortex than in the cerebellum (log2 Fold Changes (log2FC) > 0), the expression of circRNA cluster PROM1-4323-A3BS differed drastically between the frontal cortex and cerebellum (FDR = 3.17 × 10−8, log2FC = 8.24, Fig. 2D). A close inspection of circRNA cluster PROM1-4323-A3BS revealed six unique circRNAs within this cluster. However, four out of six were not found to be significantly enriched in the frontal cortex by individual circRNA DE analyses, indicating the importance of functionally investigating clustered circRNAs as groups (Fig. 2D). Using a well-established algorithm, TargetScan, which predicts mRNA targets of miRNA based on conserved seed sequence31, we found that all six individual circRNAs derived from circRNA cluster PROM1-4323-A3BS contained a common sequence element harboring two miR-138-5p binding sites that could potentially interfere with the availability and silencing activities of miR-138-5p specifically in the cortex. The predicted miRNA binding sites were further confirmed by the high binding affinity between the cluster common sequence and miR-138-5p using STarMir (Fig. 2D)38.Consistent with the high expression of circRNA cluster PROM1-4323-A3BS, which could soak and limit the availability and silencing activity of miR-138-5p, its activities were indeed significantly deactivated, evidenced by a higher log2FC of miR-138-5p top 10% target genes than randomly selected non-target genes, suggesting a global activation of these target mRNAs (Fig. 2E, p-value = 0.049). We then focused on upregulated miR-138-5p target genes evaluated by mRNA DE analysis, which could be regulated by the PROM1-4323-A3BS/miR-138-5p axis. Gene Ontology (GO) analysis revealed these genes are highly related to brain functions, such as nervous system development, generation of neurons, brain development, regulation of neuron migration, and synaptic vesicle cycle (Fig. 2F)34. These findings are consistent with previous reports that miR-138-5p is critical in neuron differentiation and memory39,40. One of the miR-138-5p target, WW and C2 containing 1 (WWC1) is significantly upregulated in AD and has been reported to play an important role in memory function40,41. Our analysis demonstrates that circMeta2 is a practically useful tool for circRNA cluster DE analysis in biological experimental settings with a limited number of replicates. To the best of our knowledge, this study also systematically surveys the circRNA cluster landscape in different human brain regions for the first time, revealing their important roles in modulating the availability of regulatory mRNAs.Applying circMeta2 to identify tissue-specific circRNAs and circRNA clustersIn addition to the comparison between the frontal cortex and cerebellum, we extended our analysis by applying circMeta2 to a wide range collection of tissues and organs. Specifically, circMeta2 was applied to seven major organs, including the brain, spinal cord, heart, liver, lung, ovary, and spleen. Each organ, except for the brain, has two biological replicates of total RNA-seq samples. Within the brain, we further evaluated six distinct brain regions including the frontal cortex, cerebellum, diencephalon, occipital lobe, parietal lobe, and temporal lobe, each with two biological replicates (Supplementary Data 1). By calculating the distances among the seven tissues based on their circRNA expression profiles, we found that six brain regions were more closely related to each other than to the spinal cord, consistent with the difference between central nervous system and peripheral nervous system (Fig. 3A). Tissue-specific circRNA clusters were identified among seven tissues (FDR < 0.05 and a Fold Change > 1) by treating one tissue as a group and other tissues as the other group: cerebellum (1994 clusters), diencephalon (712 clusters), frontal cortex (839 clusters), occipital lobe (806 clusters), parietal lobe (642 clusters), spinal cord (1035 clusters), and temporal lobe (1017 clusters) (Fig. 3B). These findings illustrated circMeta2’s capacity to effectively identify circRNA clusters uniquely expressed in different tissues within the nervous system. To further investigate, we selected frontal cortex as a representative of the brain organ and compared it to five non-CNS organs. Using circMeta2, organ-specific DE circRNA cluster analysis was conducted by treating each organ as a group and using the remaining organs as the other group. Consequently, we identified organ-specific circRNA clusters for brain (2436 clusters), heart (751 clusters), liver (980 clusters), lung (569 clusters), ovary (554 clusters), and spleen (701 clusters) (Supplementary Fig. 1). We also used other brain regions to represent the brain organ and demonstrated the expression of organ-specific circRNA clusters (Supplementary Fig. 2). Collectively, these results confirmed circMeta2’s effectiveness in identifying both tissue- and organ-specific circRNA clusters.Fig. 3: Tissue-specific circRNA cluster analysis in ENCODE dataset.A Hierarchical tree of ENCODE data based on circRNA expression profiles. B Heatmap of normalized expression levels of tissue-specific circRNA clusters with FDR < 0.05 and Fold Change > 1. C Venn diagrams comparing individual circRNA and circRNA cluster from ENCODE, validation, and synthetic data for frontal cortex tissue.To validate the identified circRNAs and circRNA clusters, we used frontal cortex as an example and adopted another non-ENCODE related frontal cortex validation dataset, which consists of two total RNA-seq data42 to assess the reproducibility of the findings at both individual and cluster levels. Using CIRCexplorer2, we identified 46,712 individual circRNAs from the ENCODE frontal cortex dataset and 15,252 from the validation dataset, with 10,532 common circRNAs confirmed by the validation dataset (Fig. 3C1). To evaluate the significance of this overlap, we generated a synthetic background dataset by randomly sampling circRNAs from six different brain regions and the spinal cord within the ENCODE dataset, matching the number of circRNAs identified in the ENCODE frontal cortex. We then compared these circRNAs to those identified in the validation dataset (Fig. 3C2). The common circRNAs identified using the ENCODE frontal cortex were more numerous than those identified using the synthetic background dataset. Fisher’s exact test, considering numbers of common and uncommon circRNAs in both ENCODE frontal cortex and synthetic background datasets, revealed that the overlap between ENCODE frontal cortex and the validation was statistically significant, with an odds ratio of 1.57 and a -log10(p) value > 16. Additionally, circMeta2 identified 18,885 expressed circRNA clusters from ENCODE frontal cortex and 10,249 clusters from the validation dataset (expression level > 0.5), with 7,409 common circRNA clusters (Fig. 3C3). The number of common circRNA clusters was also statistically significant with an odds ratio of 1.28 and a -log10(p) value > 16 based on Fisher’s exact test (Fig. 3C4). These results validated circMeta2’s reliability and applicability in identifying tissue-specific circRNAs and circRNA clusters.Evaluation of brain region-specific landscape and functions of clustered circRNAs in a large cohort of AD samples using circMeta2Recent studies have established the critical contribution of individual circRNAs to AD pathogenesis9,10. However, the systematic evaluation of clustered circRNA as groups simultaneously contributing to AD progression has not been studied. Here, we applied circMeta2 to RNA-seq data from the MSBB study24, which contains hundreds of bulk RNA-seq profiled from four different cortex regions (Fig. 4A). Using CIRCexplorer2, we identified 24,373, 20,810, 20,436, and 23,599 individually expressed circRNAs in BM10, BM22, BM36 and BM44, respectively. Among these circRNAs, 17,195 were identified in all four brain regions, while each cortex region exhibited region-specific circRNAs (1998 in BM10, 507 in BM22, 458 in BM36, and 1693 in BM44), underscoring both common and region-specific roles of circRNAs (Fig. 4B). Compared with other regions, 1,998 circRNAs were specifically expressed in BM10, a region with unique cognitive and executive functions, including risk and decision-making and working memory43. These findings indicate the distinct roles of these circRNAs in the frontal pole. According to shared BSJ sites, circMeta2 identified 10,285, 8578, 8412, and 9835 circRNA clusters in BM10, BM22, BM36, and BM44, respectively. Importantly, more than 55% of circRNAs formed circRNA clusters in all four cortex regions (Supplementary Fig. 3). Consistent with the feature in ENCODE data from the cortex and cerebellum, most circRNA clusters were composed of two or three individual circRNAs (Fig. 4C). Using the Poisson GLM followed by weighted Stouffer’s method integrated in circMeta2, we identified hundreds of significantly AD-dysregulated circRNA clusters in each brain region (FDR < 0.05) (Fig. 4D).Fig. 4: Dysregulation of circRNA clusters in various cortex regions of Alzheimer’s Disease.A 3D brain map highlighting locations of four brain regions used in this study: BM10 (Frontal Pole), BM22 (Superior Temporal), BM36 (Parahippocampal), and BM44 (Pars Opercularis). B Venn diagram illustrating the overlap of circRNAs identified across the four cortex regions. C Bar plot depicting the number of A5BS circRNA clusters composed of different numbers of individual circRNAs. D Volcano plots showing dysregulated circRNA clusters in Alzheimer’s Disease identified in each cortex region.One highly expressed circRNA cluster in the BM10 region was circRNA cluster MAN2A1-11161-A5BS (chr5-109091029), which showed aberrant and upregulation in the AD BM10 region (FDR = 5.4 × 10−3, log2FC = 0.83). circRNA cluster MAN2A1-11161-A5BS was composed of three individual circRNAs, yet none of them exhibited significant upregulation by individual circRNA DE analysis, implying that their functional significance was neglected by DE analysis solely on individual circRNA (Fig. 5A, B). Functional prediction suggested that the common sequence shared among all three individual circRNAs within circRNA cluster MAN2A1-11161-A5BS possesses two potential binding sites for miR-9-5p (Fig. 5A), whose downregulation has been tightly linked to AD pathology44,45. Consistent with the upregulation of MAN2A1-11161-A5BS in AD, we also confirmed that the activity of miR-9-5p is significantly repressed by comparing its target genes with randomly selected miR-9-5p non-target genes (Fig. 5C, p-value = 1.4 × 10−7). GO analysis revealed AD-upregulated miR-9-5p target genes are closely related to brain functions that potentially influence AD pathology, including chemical synaptic transmission, trans-synaptic signaling, CNS development, neurogenesis, and synapse organization (Fig. 5D). Specifically, several AD risk genes, such as GSK3B, PTEN, and STMN, were upregulated in AD and involved in multiple GO terms (Fig. 5E)46.Fig. 5: Upregulated circRNA cluster in Alzheimer’s Disease and its molecular function.A Schematic illustration of individual circRNA in cluster MAN2A1-11161-A5BS, showing miR-9-5p binding sites in the common sequence. Expression change in AD is indicated by red arrows (upregulated) and blue arrow (downregulated). B Expression levels of individual circRNAs from the MAN2A1-11161-A5BS cluster in normal control and AD. C Boxplot illustrating that the log2FC of miR-9-5p target genes is significantly higher than that of randomly selected non-target genes (p-value = 1.4 × 10−7). D GO analysis of miR-9-5p targets upregulated in AD BM10 region. E AD risk genes targeted by miR-9-5p, upregulated in the AD BM10 region are involved in multiple GO terms. F GO terms for genes targeted by the top 10 dysregulated circRNA clusters via miRNA soaking in four different cortex regions. Each row represents a cluster while each column indicates a specific GO term. Commonly affected GO terms across all four cortex regions are listed.A similar strategy was used to predict biological processes that regulated by the top 10 dysregulated circRNA clusters from each cortex region (Fig. 5F). Interestingly, multiple GO terms were commonly affected in all four cortex regions by their top dysregulated circRNAs, including synapse organization, vesicle mediated transport in synapse and regulation of neuron projection development (Fig. 5F), all of which potentially contribute to AD pathogenesis.Coordinated roles of circRNA clusters in AD pathogenesis via miRNA spongingIn addition to the additive effects of different circRNAs from a specific cluster, it is plausible that different circRNA clusters may also soak the same miRNA to regulate its activity in a given brain region. For example, two circRNA clusters in the BM10 region, namely ARMC8-9535-A5BS and WDR17-10690-A3BS, both upregulated in AD, could coordinately modulate the same miRNA according to their shared miRNA binding sites (Fig. 6A, B). As shown in Fig. 6C, individual circRNAs ARMC8-6121 (chr3-137940767:137964025), ARMC8-9453 (chr3-137940767:137942575), and ARMC8-14822 (chr3-137940767:137947852) account for 40.9%, 27.6%, and 31.6% expression of circRNA cluster ARMC8-9535-A5BS (chr3-137940767) in the normal control group, respectively. In comparison, the expression of all three individual circRNA components in this cluster was increased, leading to a significantly increased overall expression of circRNA cluster ARMC8-9535-A5BS (Fig. 6C). Similarly, an upregulation of cluster WDR17-10690-A3BS was also recognized in the AD BM10 region (Fig. 6D).Fig. 6: Coordination of circRNA clusters in BM10 region in AD pathogenesis.A Illustration of two AD-upregulated circRNA clusters from BM10 brain region that coordinate to sequester miR-144-3p. Red arrow indicates upregulation of circRNA, and asterisk denote significant expression change. B Schematic representation of structure and components of ARMC8-9535-A5BS and WDR17-10690-A3BS clusters. C, D Expression levels of individual circRNAs from ARMC8-9535-A5BS cluster (C) and WDR17-10690-A3BS cluster (D) in normal control and AD patients. E Boxplot showing that the log2FC of miR-144-3p target genes is significantly higher than random selected non-targets genes (p-value = 4.14 × 10−11). F Gene Ontology analysis of miR-144-3p targets, upregulated in BM10 region of AD.Interestingly, the common sequences of both circRNA clusters are predicted to bind to 27 miRNAs (Supplementary Data 2). Among them, miR-144-3p could be simultaneously soaked by both clusters, suggesting that the upregulation of ARMC8-9535-A5BS and WDR17-10690-A3BS works coordinately to repress the activity of miR-144-3p in AD. This hypothesis is also supported by the global upregulation of miR-144-3p target genes compared with randomly selected miR-144-3p non-targeted genes (Fig. 6E, p-value = 4.14 × 10−11). GO analysis revealed that miR-144-3p target genes are enriched in multiple neuronal functions such as synapse organization, neuron differentiation, and trans-synaptic signaling, potentially contributing to AD pathology (Fig. 6F). Our analysis demonstrates the coordinated roles of distinctive circRNA clusters in the same brain regions to influence a common set of miRNAs, suggesting a sophisticated and fine-tuned network composed of circRNA clusters, miRNAs and mRNAs contributing to AD pathogenesis.Besides the coordinated roles of different circRNA clusters from the same cortex region, we also explored whether circRNA clusters in different cortex regions could work together to promote AD pathogenesis through the regulation of common AD-related miRNAs. We found that the downregulation of circRNA cluster ARHGAP26-11281-A5BS (chr5-142434003) in BM44 and ARHGAP26-10077-A5BS (chr5-142416760) in BM22 could both release miR-140-5p and enhance its activity in AD via their cluster common sequences, suggesting that different circRNA clusters could regulate the same downstream miRNA in different brain regions (Fig. 7A). Interestingly, miR-140-5p has not only been shown to be aberrantly upregulated in the AD brain but also to help ameliorate AD phenotype when silenced47,48. A significant binding affinity between the common sequences of both clusters and miR-140-5p was confirmed using STarMir (Fig. 7A)38.Fig. 7: Brain region-specific functions of circRNA clusters in AD pathogenesis.A Illustration of AD downregulated circRNA cluster ARHGAP26-11281-A5BS and its common function to sponge miR-140-5p in brain regions BM44 and BM22. Blue arrows indicate downregulation of circRNA, with asterisk denoting significant expression change. B Schematic representation of structure and components of ARHGAP26-11281-A5BS cluster, including two miR-140-5p binding sites in its common sequence. C Expression levels of individual circRNAs from ARHGAP26-11281-A5BS cluster in normal control and AD patients. D Boxplot showing that the log2FC of miR-140-5p target genes is significantly lower than that of random selected non-targets genes (p-value = 1.22 × 10−4). E Scatter plot showing common (orange), BM22 region-specific (light green) and BM44 region-specific (light blue) GO terms that regulated by ARHGAP26-11281-A5BS cluster.CircRNA cluster ARHGAP26-11281-A5BS (chr5:142434003) is composed of four individual circRNAs, ARHGAP26-969 (chr5-142434003:142513670), ARHGAP26-1341 (chr5-142434003:142437312), ARHGAP26-1342 (chr5-142434003:142526946) and ARHGAP26-15800 (chr5-142434003:142500712) (Fig. 7B). Each accounted for 16.8%, 49.6%, 30.4% and 3.2% of the total expression of the circRNA cluster in the normal control. The cumulative level of common sequence among all circRNAs in circRNA cluster ARHGAP26-11281-A5BS was significantly downregulated in AD, which is predicted to coordinately release their commonly bound miR-140-5p and enhance its activity (Fig. 7C). Similarly, circRNA cluster ARHGAP26-10077-A5BS was also significantly downregulated in AD, resulting in the release of miR-140-5p from its common sequence, which also harbors the miR-140-5p binding site (Supplementary Fig. 4A, B). Importantly, by comparing the expression change (log2FC) of miR-140-5p targets versus random non-target genes, we confirmed that the activity of miR-140-5p was truly enhanced in both BM44 and BM22 cortex regions (Fig. 7D, p-value = 1.22 x 10-4, Supplementary Fig. 4C, p-value = 1.08 × 10−6). Specifically, 61 and 51 miR-140-5p targets were significantly downregulated in BM22 and BM44 and were used to compare the functional impact of miR-140-5p release in these two brain regions by GO analysis (Fig. 7E). Interestingly, numerous overlapping biological processes were commonly affected in BM22 and BM44 regions, including nervous system development and central nervous system development (Fig. 7E), indicating that circRNA could play important and common roles across different brain regions. Collectively, our study suggests that different circRNA clusters from either the same or different cortex regions could work coordinately to sponge miRNA and regulate gene networks that involved in AD pathogenesis.Functional roles of circRNA clusters in promoting AD progressionRecent studies have identified the progressive dysregulation of multiple circRNAs that correlated with AD severity using the MSBB dataset, shedding light on their functional impact in disease progression and potential roles as biomarkers9,10. To investigate the roles of circRNA clusters in different AD stages, we first sub-grouped AD samples into normal healthy donors (NP.1 = 1), possible AD (NP.1 = 3 or 4), and definite AD (NP.1 = 2) groups based on neuropathology categories as measured by the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) and identified specific circRNA clusters associated with AD progression based on their continuous accumulation or depletion along the severity scale (Fig. 8A and Supplementary Data 3)49. One example of our findings was circRNA cluster ASAP1-12100-A5BS in BM36, which exhibited consistent depletion along with increased AD severity (Fig. 8B, FDR = 2.59 × 10-4).Fig. 8: Continuous dysregulation of circRNA clusters with Alzheimer’s Disease (AD) progression.A. Schematic illustrations of AD brain in condition of normal control, possible AD, and definite AD (Created in BioRender. Zhao, F. (2024) BioRender.com/f01j596). B Expression level of circRNA cluster ASAP1-12100-A5BS in BM36 brain region across normal control, possible AD and definite AD. C Log2FC comparison showing significant differences between miR-455-3p target and randomly selected non-target genes for possible AD vs. normal control (p-value = 0.0010, left) and definite AD vs. normal control (p-value = 4.05 × 10−6, right). D Bubble plot show GO analysis of miR-455-3p target genes, downregulated in definite AD. Bubble size represents the number of genes per GO term and Z-score indicates the likelihood of a decrease or increase in biological function. E Heatmap displaying log10FDR of top ranked AD risk genes across multiple GO terms that related to brain function and AD pathogenesis with FDR = 1 (blue) indicating gene absence in specific GO terms.The common sequence of ASAP1-12100-A5BS contains a binding site for miR-455-3p, which has been shown to be aberrantly upregulated in AD-affected brains50,51. The activity change of miR-455-3p was assessed by comparing the log2FC of its target genes and randomly selected non-target genes in Possible AD and Definite AD groups, respectively. Consistent with the progressive downregulation of circRNA cluster ASAP1-12100-A5BS, the activity of miR-455-3p was continuously enhanced in both Possible AD (p-value = 0.001) and Definite AD (p-value = 4.05 × 10−6) (Fig. 8C). To further ascertain the statistical significance of miRNA activity change between Possible AD and Definite AD, the Wilcoxon rank-sum test was applied to compare the differences in log2FC between Possible AD vs. Normal and Definite AD vs. Normal (p-value < 2.2×10–16)52, and the result strongly supports that downregulation of ASAP1-12100-A5BS would release miR-455-3p, which significantly contributes to the progression of AD.To investigate the molecular mechanism of the ASAP1-12100-A5BS/miR-455-3p pathway in promoting AD progression, GO analysis was performed for miR-455-3p targets that are progressively downregulated in AD (Fig. 8D). Multiple biological processes and pathways that could be disrupted in AD were identified, including neuronal development, synaptic signaling and nerve system development (Fig. 8D). A closer look also suggests that multiple AD risk genes are progressively downregulated in AD and involved in these biological pathways, such as SNCA, APOE, and PSEN1 (Fig. 8E). Our study identified a significant correlation between the dysregulation of circRNA clusters and the progression of Alzheimer’s disease. Specifically, we noted that the depletion of the circRNA cluster ASAP1-12100-A5BS correlates with increased activity of miR-455-3p in AD-affected brains. This finding highlights a potential regulatory interaction that may impact disease progression.
