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. 2025 Aug 30;15:31971. doi: 10.1038/s41598-025-17606-w

Identification and experimental validation of biomarkers related to mitochondrial and programmed cell death in obsessive-compulsive disorder

Gaowei Mao 1,2, Weidong Cong 1,2,✉
PMCID: PMC12398541  PMID: 40885831

Abstract

Background Mitochondrial-related genes (MRGs) and programmed cell death-related genes (PCD-RGs) have been proven to play important roles in obsessive-compulsive disorder (OCD), and identifying their shared biomarkers is conducive to the diagnosis and research of OCD. Methods Differentially expressed genes (DEGs) between OCD and control samples were identified from the GSE78104 dataset. Differentially expressed MRGs (DE MRGs) and PCD-RGs (DE-PCD-RGs) were derived by intersecting with MRG and PCD-RG gene sets, respectively, resulting in DE mitochondrial-related PCD (DE-MPCD) genes. Key OCD-related genes were identified using weighted gene co-expression network analysis (WGCNA), and candidate genes for OCD were selected by intersecting these with DE-MPCD-RGs. Machine learning algorithms were applied to further screen potential biomarkers from the GSE78104 and GSE60190 datasets. The expression levels of selected biomarkers were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in samples from patients with OCD and healthy controls to assess the mRNA expression. Gene Set Enrichment Analysis (GSEA) was conducted to identify enriched pathways in these biomarkers. Immune cell infiltration patterns in OCD and potential therapeutic agents were also investigated. Results A total of 13 DE-MPCD-RGs were intersected with 374 key module genes, resulting in 12 candidate genes. Among these, eight potential biomarkers for OCD were identified. Notably, NDUFA1 and COX7C were significantly downregulated in OCD across both datasets and clinical samples, establishing them as reliable biomarkers for OCD. GSEA revealed that NDUFA1 and COX7C were significantly co-enriched in pathways such as “ribosome,” “oxidative phosphorylation,” “phosphorylation,” and “Parkinson’s disease.” Furthermore, activated CD8 T cells and neutrophils were identified as the differential immune cell types between OCD and control samples. Additionally, 73 potential therapeutic agents were predicted through drug-target interaction analysis. Conclusion This study identified two mitochondrial-related biomarkers in OCD, providing novel perspectives on the disorder’s pathogenesis. These findings hold promise for advancing early diagnosis and the development of targeted therapeutic strategies for OCD.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-17606-w.

Keywords: Obsessive-compulsive disorder, Mitochondria, Programmed cell death, Biomarkers, Immune infiltration

Subject terms: Cell biology, Computational biology and bioinformatics, Psychology, Biomarkers, Diseases, Medical research

Introduction

Obsessive-compulsive disorder (OCD) is a prevalent and debilitating neuropsychiatric condition, characterized by repetitive, irrational, and distressing obsessive-compulsive symptoms and thoughts1. OCD significantly impairs social functioning, affecting interpersonal relationships, work, and family life2. Current treatments primarily rely on selective serotonin reuptake inhibitors (SSRIs), but therapeutic responses remain suboptimal for many patients3. While the precise pathogenesis of OCD is not fully understood, research suggests that biological and genetic factors play critical roles in its onset, emphasizing the need to identify key genes that could improve diagnostic accuracy and therapeutic approaches.

Programmed cell death (PCD) is a crucial physiological process responsible for maintaining tissue homeostasis by eliminating damaged or unnecessary cells, thus ensuring tissue health4. PCD involves various mechanisms, including apoptosis, autophagy, immunogenic cell death, ferroptosis, lysosomal-dependent cell death, and necroptosis, among others5. Mitochondria, responsible for cellular energy production, are essential for normal cell function6. Dysfunctional mitochondria have been implicated in several neuropsychiatric disorders, including OCD and depression7,8. Moreover, mitochondria play central roles in various forms of PCD and biochemical signaling pathways that regulate these processes, which are vital for tissue development and homeostasis9. In the nervous system, PCD affects neural network regulation and neuroplasticity10. Mitochondrial dysfunction may contribute to OCD by influencing neurocellular apoptosis, participating in the onset and progression of this and other mental disorders11–13. However, the combined study of mitochondrial dysfunction and PCD in OCD remains underexplored, warranting further investigation.

This study employed transcriptomic data analysis, weighted gene co-expression network analysis (WGCNA), machine learning, and other methodologies to identify two key mitochondrial and PCD (MTPCD)-related genes, NDUFA1 and COX7C, as potential biomarkers for OCD. Functional analysis, molecular regulatory network exploration, and disease and drug analysis are performed to investigate the pathogenesis of OCD, offering new insights for clinical diagnosis and therapeutic development.

Materials and methods

Data source

To obtain datasets meeting the research criteria, we searched the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) using “obsessive-compulsive disorder” as the keyword. Datasets containing samples from patients with confirmed OCD and corresponding control samples were selected to ensure phenotypic consistency required for case-control analysis. Meanwhile, the chosen datasets were required to have sufficient sample sizes and consistent sequencing platforms. Finally, datasets GSE78104 and GSE60190 were included. The GSE78104 dataset (GPL19612) served as the training set, consisting of peripheral blood samples from 30 patients with OCD and 30 healthy controls. The GSE60190 dataset (GPL6947) was used as the validation set, including tissue samples from the dorsolateral prefrontal cortex of 16 patients with OCD and 102 healthy controls. A total of 1,136 mitochondrial-related genes (MRGs) were retrieved from the MitoCarta 3.0 database (https://www.broadinstitute.org/mitocarta/mitocarta30 -inventory-mammalian-mitochondrial-proteins-and-pathways)14, and 1,548 PCD-related genes (PCD-RGs) were extracted from the literature15.

Selection of the candidate genes

Differentially expressed genes (DEGs) between OCD and control samples were identified using the GSE78104 dataset with the ‘limma’ package (version 3.56.2)15, applying thresholds of |log2 fold-change (FC)| ≥ 0.5 and p ≤ 0.0516. Volcano and heatmaps were generated to visualize the DEGs using the ‘ggVolcano’ (version 0.0.2)17 and ‘ComplexHeatmap’ (version 2.16.0)18 packages, respectively. Differentially expressed MRGs (DE-MRGs) and PCD-RGs (DE-PCD-RGs) were determined by intersecting the DEGs with the 1,136 MRGs and 1,548 PCD-RGs. Spearman’s correlation analysis was used to assess the relationship between DE-MRGs and DE-PCD-RGs, with genes meeting the thresholds (p < 0.001, |cor| > 0.6) categorized as DE-MPCD-RGs. Next, WGCNA was applied to the GSE78104 dataset using the ‘WGCNA’ package (version 1.72.5)19 to identify key modules most related to OCD. Outlier samples were removed via cluster analysis, and the optimal soft threshold (β) was determined by setting the scale-free R2 to exceed 0.9, with the average connectivity approaching 0. Using the selected β-value, genes with similar expression profiles were grouped into modules via a dynamic tree-cutting method (minModuleSize = 30, mergeCutHeight = 0.25). Key modules significantly correlated with OCD were selected based on correlation coefficients (p < 0.05, |cor| > 0.3), and genes within these modules were considered key module genes. Candidate genes for OCD were then identified by intersecting the DE-MPCD-RGs with the key module genes using the ‘ggvenn’ package.

Functional analysis of candidate genes

To identify the biological functions and pathways associated with the candidate genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses (p.adjust < 0.05) were conducted using the ‘clusterProfiler’ package (version 4.8.2)20, with ‘org.Hs.eg.db’ (version 3.17.0)21 as the background gene set22–24. Further analysis of the candidate genes was performed using ToppCluster on the ‘ToppGene’ platform (version 2.3.1)25 (p < 0.05), focusing on key biological processes in GO and related diseases. The GO-disease network was visualized using ‘Cytoscape’ (version 3.7.1)26. Additionally, protein-protein interactions (PPI) among the candidate genes were explored by constructing a network using the Search Tool for Recurring Instances of Neighboring Genes (STRING database, https://string-db.org/) with an interaction score threshold set to 0.4.

Identification of biomarkers

In the GSE78104 dataset, the candidate genes were analyzed using the Support Vector Machine Recursive Feature Elimination (SVM-RFE) method via the ‘e1071’ package (version 1.7.13)19. This method identified signature genes by eliminating feature vectors generated by the SVM and selecting combinations associated with the lowest error rate. Additionally, univariate logistic regression was performed using the ‘rms’ package (version 6.7-1) to identify core genes significantly associated with OCD (p < 0.05). Signature and core genes were then intersected to identify potential biomarkers for OCD. Expression levels of these potential biomarkers were validated in both the GSE78104 and GSE60190 datasets, comparing OCD and control groups. Genes showing significant expression and consistent trends across both datasets were selected as OCD biomarkers. Correlations between biomarkers were analyzed using Spearman’s correlation.

Functional analysis of biomarkers and construction of gene interaction network

To further investigate the biological pathways associated with the identified biomarkers in OCD, Gene Set Enrichment Analysis (GSEA) was performed. Using the GSE78104 dataset, correlation coefficients between biomarkers and other genes were calculated via the ‘psych’ package (version 2.3.12)27. Genes were ranked according to these correlation coefficients, and GSEA (adj. p-value < 0.05, |NES| > 1) was conducted using the ‘clusterProfiler’ package (version 4.8.2)20. The pathway-enriched gene set file was downloaded from the GSEA website (http://www.gsea-CCigdb.org/gsea/CCigdb), with the KEGG pathway gene set ‘c2.cp.kegg_ legacy.v2023.2.Hs.entrez.gmt’ used as the reference. The top five pathways with the highest NES were displayed using the ‘enrichplot’ package (version 1.23.1)28. Additionally, interactions between biomarkers and other functionally similar genes were explored using GeneMANIA (http://genemania.org/), and co-expression networks were constructed to provide further insights into their functional relationships.

Immune infiltration analysis

To explore immune cell infiltration in OCD, the proportions of 28 immune cell types per sample in the GSE78104 dataset were calculated using the ‘GSEABase’ package (version 1.62.0)29 and the ‘mmc3.gmt gene set’. A heatmap was generated to visualize immune cell distribution across different samples using the ‘pheatmap’ package (version 1.0.12)30. Differences in immune cell proportions between OCD and control samples were assessed using Wilcoxon’s test (p < 0.05). Furthermore, correlations between differentially expressed immune cells and biomarkers were examined through Spearman’s correlation analysis.

Exploration of the molecular regulatory mechanisms of OCD

The target miRNAs for the biomarkers were predicted using the miRDB (https://mirdb.org/) and TargetScan (https://www.targetscan.org/vert_80/) databases. Shared miRNAs were identified by intersecting predictions from both databases. Upstream lncRNAs of the predicted miRNAs were analyzed using the miRNet database (https://www.mirnet.ca/).Additionally, upstream transcription factors (TFs) regulating the biomarkers were predicted in the miRNet database. The biomarker-miRNA-lncRNA and biomarker-TF regulatory networks were constructed using ‘Cytoscape’ software.

Correlation analysis of the N6-methyladenosine (m6A) and 5-methylcytosine (m5C) regulators

As key epigenetic modifications, m6A and m5C, through the expression patterns or interaction relationships of their regulatory factors, may participate in the core mechanism of “mitochondrial dysfunction-PCD imbalance” in OCD by affecting the transcriptional efficiency of PCD-related genes and the expression of mitochondrial function genes31. To investigate the role of m6A regulators in OCD, 20 known m6A regulatory factors (ALKBH5, FMR1, FTO, HNRNPA2B1, HNRNPC, IGFBP3, LRPPRC, METTL14, METTL16, METTL3, RBM15, RBM15B, RBMX, WTAP, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, and ZC3H13)32 were analyzed for differential expression between OCD and control samples using Wilcoxon’s test (p < 0.05) in the GSE78104 dataset. Spearman’s correlation analysis was performed to assess the relationships between biomarkers and the 20 m6A regulatory factors. Similarly, differences in the expression of 10 m5C regulators (NOP2, NSUN3, NSUN4, NSUN6, NSUN7, TRDMT1, TET1, TET2, YBX1, and YTHDF2)33 between OCD and control samples were evaluated using Wilcoxon’s test, followed by correlation analysis with biomarkers using Spearman’s correlation.

Disease association and drug prediction

Diseases strongly correlated with biomarkers were identified through the Disorders-Genes-Environment Network database (DisGeNET,http://www.disgenet.org/web/DisGeNET/menu), with a Gene-Disease Association Score (Score gda) > 0.3. Co-expression networks of biomarkers and diseases were visualized using Cytoscape. Drugs targeting these biomarkers were predicted via the Comparative Toxicogenomics Database (CTD, https://ctdbase.org/), and the biomarker-drug networks were also visualized using Cytoscape.

RNA extraction and reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Ethical approvalfor the study was granted by the Medical Ethics Committee of Fuzhou Neuropsychiatric Prevention and Treatment Hospital, Fujian Province (license number: 202411), and informed consent was obtained from all participants. RNA was extracted from 10 peripheral blood samples stored at −80℃ (5 control,5 OCD) using TRIzol reagent. The RNA was reverse-transcribed into cDNA using the SweScript First Strand cDNA Synthesis Kit (Servicebio). RT-qPCR was performed on a CFX96 real-time quantitative fluorescence PCR instrument, using a reaction system consisting of 2x Universal Blue SYBR Green qPCR Master Mix, primers, and cDNA samples. Primer sequences are provided in Supplementary Table 1. Relative mRNA expression, normalized to GAPDH levels, was calculated using the 2−Ct method.

Statistical analysis

All statistical analyses were performed using R programming language (version 4.1.3), with Wilcoxon’s test applied for comparisons between two groups. Statistical significance was determined with an adjusted p-value or p-value < 0.05.

The analytical workflow of this study is shown in Fig. 1.

Fig. 1.

Fig. 1

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The research process of this study.

Results

A total of 12 candidate genes were identified

In the GSE78104 dataset, a total of 70 DEGs were identified between OCD and control samples, including 18 upregulated and 52 downregulated DEGs (Fig. 2a, b). Based on relevance thresholds, the following DEGs were selected as DE-MPCD-RGs: TOMM7, NDUFA5, COX7C, NDUFA1, MRPS28, HINT1, NDUFA4, NDUFA6, RPL26, RPS7, AKR1C3, S100A8, and TPT1 (Figure. 2c). Outlier samples GSM2067409 and GSM2067439 were excluded from the GSE78104 dataset, leaving it suitable for further analysis (Fig. 2d). A β value of 12 was selected when R2 exceeded 0.9 and the average connectivity plateaued (Fig. 2e). This resulted in the identification of 12 co-expressed gene modules, each represented by different colors (Fig. 2f). The MEred module (cor = −0.44, p = 4e-04) and the MEbrown module (cor = 0.3, p = 0.02) showed the strongest associations with OCD (Fig. 2g). A total of 374 key module genes were identified in these modules. Subsequently, the intersection of the 13 DE-MPCD-RGs and 374 key module genes led to the identification of candidate genes for OCD, including AKR1C3, RPL26, RPS7, S100A8, TOMM7, MRPS28, NDUFA1, COX7C, NDUFA4, HINT1, NDUFA6, and TPT1 (Fig. 2h).

Fig. 2.

Fig. 2

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Analysis of the GSE78104 dataset comparing patients with OCD and controls: (a-b) Volcano and heatmaps depicting DEGs between OCD and normal samples. (c) Correlation analysis between DE-MRGs and DE-PCD-RGs. (d) Sample clustering diagram. (e) Selection of optimal power values. (f) Cluster dendrogram of modules. (g) Heatmap illustrating the correlation between modules and traits. (h) Venn diagram of MTCDD-DEGs and key module genes.

Biological functions and signaling pathways involved in candidate genes

GO analysis revealed that the candidate genes were enriched in 37 biological processes (BPs), 25 cellular components (CCs), and 41 molecular functions (MFs). Key biological functions and pathways related to mitochondria and PCD included ‘mitochondrial protein-containing complexes’, ‘mitochondrial inner membrane’, and ‘intrinsic apoptosis signaling pathway’ (Fig. 3a). KEGG pathway analysis identified several relevant pathways, including ‘chemical carcinogenesis-reactive oxygen species’, ‘oxidative phosphorylation’, and ‘non-alcoholic fatty liver disease’ (Fig. 3b). The GO-disease network linked the candidate genes to diseases such as congenital hypoplastic anemia, anemia, Diamond-Blackfan anemia, mitochondrial complex I deficiency, and radial ray abnormalities, with mitochondrial complex I deficiency showing the strongest association with the biological functions observed in the GO analysis (Fig. 3c). Finally, PPI network analysis demonstrated significant interactions between candidate genes, particularly COX7C, which showed close associations with NDUFA6, NDUFA1, and NDUFA4 (Fig. 3d).

Fig. 3.

Fig. 3

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Biological functions and signaling pathways of candidate genes: (a) GO enrichment analysis. (b) KEGG pathway enrichment analysis. (c) GO-Disease network diagram. (d) Protein-protein interaction (PPI) network of candidate genes.

NDUFA1 and COX7C were identified as biomarkers of OCD

RPS7, RPL26, S100A8, HINT1, NDUFA1, NDUFA6, COX7C, and NDUFA4 were identified as the most significant feature genes through the SVM-RFE algorithm (Fig. 4a). A total of 12 core genes were selected from the candidate genes (p < 0.05) (Table 1).The intersection of the 8 feature genes and 12 core genes led to the identification of potential biomarkers for OCD: RPS7, RPL26, S100A8, HINT1, NDUFA1, NDUFA6, COX7C, and NDUFA4 (Fig. 4b). Notably, the expression of NDUFA1 and COX7C was significantly different between OCD and control groups in both the GSE78104 and GSE60190 datasets, with consistent expression trends observed. These two genes were selected as biomarkers for OCD (Fig. 4c, d). Further RT-qPCR analysis of clinical samples validated the bioinformatics findings, revealing significantly lower expression levels of NDUFA1 and COX7C in the OCD group compared to the control group (Fig. 4e, f). Additionally, a strong positive correlation was observed between NDUFA1 and COX7C (cor = 0.8, p < 0.05) (Fig. 4g).

Fig. 4.

Fig. 4

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Identification and screening of biomarkers for OCD: (a) SVM-RFE algorithm for feature gene selection. (b) Intersection of signature and core genes. (c) Boxplot showing the expression of intersecting genes in the training set. (d) Boxplot showing the expression of intersecting genes in the validation set. (e) RT-qPCR analysis of NDUFA1 in clinical samples. (f) RT-qPCR analysis of COX7C in clinical samples. (g) Scatter plot showing the correlation between NDUFA1 and COX7C.

Table 1.

Information on the 12 core genes.

Gene Estimate P OR CIlower CIupper
AKR1C3 −0.600922775 0.020518617 0.54830544 0.316662025 0.886931497
RPL26 −1.359250955 0.000179658 0.2568531 0.115730138 0.487965083
RPS7 −1.54611211 0.000251319 0.213074777 0.083960619 0.447281041
S100A8 −0.652226463 0.023414604 0.520884754 0.283182597 0.887767365
TOMM7 −1.797521972 0.000362106 0.165709011 0.054373296 0.402561287
MRPS28 −0.967585603 0.010119489 0.379999401 0.168221111 0.750972189
NDUFA1 −1.200467687 0.006957046 0.30105338 0.115505423 0.667680879
COX7C −1.635651074 0.002022078 0.194825484 0.060535363 0.49401239
NDUFA4 −1.772087304 0.001419825 0.169977823 0.050167222 0.456904961
HINT1 −1.665477005 0.002227026 0.189100435 0.057366342 0.496066002
NDUFA6 −1.513048712 0.005051845 0.220237513 0.069029619 0.579458866
TPT1 −1.756944186 0.001897842 0.172571405 0.049426918 0.470100934
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Functional association of biomarkers and their functional annotation

Pathway enrichment analysis revealed that NDUFA1 and COX7C were co-enriched in several pathways, including ‘ribosome’, ‘oxidative phosphorylation’, and ‘Parkinson’s disease’ (Fig. 5a, b). GeneMANIA analysis indicated that NDUFA1 and COX7C were associated with the same biological pathways and functions as NDUFA2, NDUFB2, and NDUFA3 (Fig. 5c). These genes were primarily involved in the ‘mitochondrial inner membrane’ and ‘oxidative phosphorylation’ pathways, among others (Table 2).

Fig. 5.

Fig. 5

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Functional associations of biomarkers and their annotation: (a) GSEA enrichment analysis of NDUFA1. (b) GSEA enrichment analysis of COX7C. (c) GeneMANIA network for biomarkers.

Table 2.

Gene function prediction.

Function FDR Genes in network Genes in genome
mitochondrial inner membrane 5.98E-13 11 189
oxidative phosphorylation 6.05E-13 10 129
inner mitochondrial membrane protein complex 8.07E-13 10 138
mitochondrial protein complex 4.40E-12 11 256
ATP metabolic process 1.86E-10 10 248
ATP synthesis coupled proton transport 1.47E-07 5 24
energy coupled proton transport, down electrochemical gradient 1.94E-07 5 26
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Activated CD8 T cells and neutrophils were significantly different between samples

Immune cell distribution varied across the GSE78104 dataset (Fig. 6a), with significant differences observed in activated CD8 T cells and neutrophils between the OCD and control groups (Fig. 6b). Activated CD8 T cells were positively correlated with NDUFA1 (cor = 0.57, p = 3.3e-06) and COX7C (cor = 0.54, p = 9.8e-06), respectively (Fig. 6c, d).

Fig. 6.

Fig. 6

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Differential analysis of activated CD8 T cells and neutrophils across samples: (a) Distribution of immune cells in the GSE78104 dataset. (b) Differential expression of immune cells in OCD versus normal groups. (c, d) Correlation of biomarkers with immune cells and scatter plot.

Molecular regulatory mechanisms of OCD

Intersection analysis of predictions from 17 miRDB and 861 TargetScan led to the identification of 12 miRNAs. These miRNAs predicted a total of 90 lncRNAs, which were used to construct the NDUFA1-miRNA-lncRNA regulatory network for OCD (Fig. 7a). Additionally, 27 TFs were identified based on the biomarkers, with E2F1 and MYC predicted as co-regulated TFs by the biomarkers (Fig. 7b).

Fig. 7.

Fig. 7

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Molecular regulatory mechanisms in OCD: (a) Gene-miRNA-lncRNA regulatory network (pink for key genes, green for miRNAs, blue for lncRNAs). (b) Biomarker-TF regulatory network (purple for biomarkers, green for TF).

Expression of YTHDF3 and TET2 was significantly different between OCD and controls

Among the 20 m6A regulatory factors, only YTHDF3 showed significant differential expression between OCD and controls, with a notable upregulation in OCD (Fig. 8a). This suggested that it may drive the abnormal expression of downstream target genes through m6A modification during the pathogenesis of OCD. Correlation analysis revealed significant positive correlations between the biomarkers and the m6A regulators HNRNPC and METTL14 (Fig. 8b). This indicated that the m6A regulatory network may synergistically affect the expression of these markers, thereby influencing mitochondrial function and PCD processes in OCD. The expression of the m5C regulatory factor TET2 also differed significantly between OCD and controls (Fig. 8c), with COX7C positively correlated with NSUN6 and TRDMT1, and NDUFA1 inversely associated with NOP2 and TET1 (Fig. 8d). This suggests that m5C modification represents another layer of epigenetic mechanism involved in the pathological process of OCD.

Fig. 8.

Fig. 8

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Correlation analysis of m6A and m5C regulatory factors: (a) Boxplot of differential expression of m6A regulators in OCD and normal subjects. (b) Correlation analysis among m6A regulators. (c) Boxplot of differential expression of m5C regulators in OCD and normal subjects. (d) Correlation of m5C regulatory factors.

A total of 73 drugs were available for the treatment of OCD

The diseases most significantly associated with the biomarkers included mitochondrial complex I deficiency (Score gda = 0.7), deficiency of mitochondrial complex I of nuclear type 12 (Score gda = 0.6), mitochondrial disease (Score gda = 0.33), and Leigh disease (Score gda = 0.32) (Fig. 9a). Based on the biomarkers, 73 drugs were predicted for OCD treatment, with 14 drugs, including valproic acid and selenium, predicted for both biomarkers (Fig. 9b).

Fig. 9.

Fig. 9

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Disease biomarkers associated with drug interactions: (a) Biomarker-disease network diagram (purple for biomarkers, green for diseases). (b) Drug prediction network diagram (red for biomarkers, green for drugs).

Discussion

OCD is a prevalent condition that significantly impacts individuals’ quality of life. However, current treatment options remain suboptimal, highlighting the need for novel biomarkers to enable early diagnosis and improve therapeutic interventions1,3. In this study, 12 candidate genes were identified through differential analysis and the GEO database. Bioinformatics methods were employed to pinpoint the biomarkers NDUFA1 and COX7C, both associated with mitochondrial function and PCD in OCD. A diagnostic model for OCD was constructed and validated, offering a new approach to clinical diagnosis and treatment.

NDUFA1 is a critical 43 kDa protein located in the inner mitochondrial membrane, primarily involved in oxidative phosphorylation within the electron transport chain34. As an essential component of respiratory chain complex I, NDUFA1 facilitates electron transfer from NADH to ubiquinone, a crucial step in cellular energy production35. COX7C, another vital protein in the mitochondrial respiratory chain, participates in the formation of the cytochrome c oxidase complex36. This enzyme complex is positioned at the end of the mitochondrial electron transport chain, where it transfers electrons from cytochrome c to oxygen, generating water37, the final step in the cellular respiratory chain and essential for ATP synthesis38. The mechanisms through which NDUFA1 and COX7C contribute to disease pathogenesis are linked to their roles in maintaining mitochondrial function and cellular energy metabolism36,39. Impairment of these proteins can disrupt energy metabolism, potentially contributing to the development and progression of mental illnesses40. Impairment of NDUFA1 and COX7C function induces a reduction in ATP production, with this energy deficit disproportionately affecting high-metabolic brain regions implicated in obsessive-compulsive disorder (OCD), including the prefrontal cortex and hippocampus. Within these regions, insufficient energy supply may directly disrupt the neural circuits underlying obsessive thoughts and compulsive behaviors41. Additionally, these mitochondrial dysfunctions interfere with the synthesis and release of key neurotransmitters, such as dopamine and serotonin (5-hydroxytryptamine, 5-HT), which are central to the pathophysiological mechanisms of OCD42. Preclinical studies have provided further evidence for this mechanistic association, showing that decreased Complex IV (cytochrome c oxidase) activity in the striatum—a brain region critically involved in OCD pathophysiology—is correlated with abnormal dopamine transporter expression. These alterations may lead to enhanced hyperactivation of the dopamine system, thereby perpetuating the neurobiological cycle of compulsive behaviors43.However, the precise molecular mechanisms by which NDUFA1 and COX7C regulate OCD remain underexplored. Therefore, these proteins present potential as biomarkers for the diagnosis and treatment of OCD, offering a novel pathway for mitochondrial-targeted therapies.

Meanwhile, the findings of this study demonstrated that NDUFA1 and COX7C are co-enriched in pathways associated with oxidative phosphorylation, ribosome function, Alzheimer’s disease, Parkinson’s disease, neurodegenerative diseases, and other conditions. Several lines of evidence suggest that the dopamine pathway, in conjunction with mitochondrial function, plays a significant role in oxidative stress44. Mitochondrial dysfunction and disturbances in oxidative phosphorylation may contribute to the pathophysiology of various psychiatric disorders through complex neurotransmitter mechanisms45,46. Thus, further investigation into oxidative phosphorylation could provide critical insights to advance OCD treatment strategies.

Targeting NDUFA1 and COX7C to modulate oxidative stress and mitochondrial dysfunction in OCD presents a promising therapeutic strategy. Psychiatric disorders, including OCD, are often linked to abnormal functioning in specific brain regions, which may be further influenced by neurodegenerative diseases47. Notably, NDUFA1 and COX7C were found to be significantly positively correlated with activated CD8 T cells. These activated CD8 T cells, along with neutrophils, can infiltrate the brain, exacerbating microglia-mediated neuroinflammation, which contributes to neuronal damage48 and increases the risk of mental disorders49. Therefore, investigating the roles of NDUFA1 and COX7C in OCD is crucial for understanding the disorder’s etiology and developing novel immunotherapeutic strategies.

Additionally, the NDUFA1-miRNA-lncRNA regulatory network for OCD was constructed, highlighting E2F transcription factor 1 (E2F1) and Amyelocytomatosis viral oncogene homolog (MYC) as TFs co-predicted by the biomarkers. Both E2F1 and MYC have been primarily studied in tumor biology, where they play essential roles in regulating the cell cycle50. E2F1, a key regulator of the cell cycle, is particularly involved in the transition from the G1 phase to the S phase51. It has been identified as a critical gene in femoral head necrosis and is involved in regulating oxidative stress signaling pathways52. Furthermore, E2F1 contributes to schizophrenia pathogenesis by influencing key genes53. The biomarker-miRNA-lncRNA axis provides a novel avenue to explore the potential mechanisms underlying OCD.

MYC, a proto-oncogene, encodes a protein central to cell proliferation, growth, and apoptosis. E2F1 and MYC engage in complex, tightly regulated interactions during cell growth and death, with MYC promoting apoptosis54. For example, c-Myc can regulate E2F1 expression by activating a series of microRNAs that affect the stability and translation efficiency of E2F1 mRNA, thereby modulating E2F1 activity at both the transcriptional and translational levels55. This study is the first to uncover the regulatory relationship between E2F1, MYC, and NDUFA1/COX7C in the context of OCD, suggesting that these factors also play significant roles in the pathogenesis of the disorder. However, further investigation is necessary to fully elucidate the specific mechanisms involved.

In this study, both YTH domain family protein 3 (YTHDF3) in m6A regulatory factors and TET2 in m5C regulatory were significantly up-regulated in patients with OCD. YTHDF3 can regulate mRNA stability and autophagy, and its abnormal expression is associated with diseases related to cognitive dysfunction, particularly those involving neurodevelopment and neuroplasticity56.

Meanwhile, m5C can regulate the mRNA stability and translation efficiency of mitochondria-related genes, participating in the maintenance and dynamic balance of mitochondrial function57. Furthermore, biomarkers also show a significant correlation with m6A/m5C regulatory factors, indicating that PCD and mitochondrial function are regulated by regulatory factors at the mRNA level. These findings establish a functional association between the differential expression of m6A/m5C regulatory factors and the core biomarkers in this study, providing new evidence that the epigenetic modification network may serve as an upstream regulator of the ‘mitochondria-PCD axis’ in OCD.

A total of 73 drugs were predicted for OCD treatment based on the biomarkers, with 14 drugs, including valproic acid and selenium, identified by both NDUFA1 and COX7C. Valproic acid has demonstrated notable antipsychotic and antioxidative stress effects58. Selenium is essential for normal brain function, influencing enzyme activity, cellular oxidation processes, brain signaling, neurotransmitter function, and immune response. Selenium deficiency can impair neurotransmitter synthesis and release, potentially leading to cognitive deficits, whereas adequate selenium supplementation may improve mood and alleviate depressive symptoms59,60. These predicted drugs may hold therapeutic potential for OCD.

This research marks the first identification of biomarkers associated with mitochondria and PCD in OCD, providing new insights into the disorder’s diagnosis and treatment. However, the validation of these biomarkers in the present study is subject to certain limitations. First, the public datasets utilized in bioinformatics analyses may contain noise, missing values, or measurement errors, which could compromise the accuracy and reliability of the results. Second, during RT-qPCR validation, the small sample size employed may fail to comprehensively reflect the expression patterns of the biomarkers across the broader OCD population. Therefore, future studies will incorporate larger and more diverse cohorts, integrating multi-source data to enhance the generalizability of the findings. Additionally, we aim to expand the sample size for RT-qPCR validation, implement strict matching based on variables such as age, gender, and disease duration, and include patients from different clinical subgroups. Through stratified analyses, we will clarify the differential expression of biomarkers across diverse populations, thereby improving their clinical applicability.

Conclusion

In conclusion, this study utilized bioinformatics analysis to identify OCD biomarkers (NDUFA1 and COX7C) linked to MRGs and PCD-RGs, conducting correlation analysis to explore their mechanisms of action. These findings pave the way for further research into the underlying mechanisms of OCD and its potential treatments.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (10.4KB, xlsx)

Acknowledgements

Not Applicable.

Author contributions

G. M. was responsible for Conceptualization, Data management, Formal analysis, Investigation, Methodology, Software, Verification, Visualization, and Writing Original Draft. W. C. was responsible for Conceptualization, Data management, Formal analysis, Funding acquisition, Project management, Supervision, Verification, Writing, Review, and Editing. All authors reviewed and approved the final manuscript.

Funding

This work was supported by the “14th Five-Year” Clinical Specialty Training and Cultivation Construction Project of Fuzhou [grant number:20220106].

Data availability

The datasets analyzed for this study can be found in the [GEO, MitoCarta] [http://www.ncbi.nlm.nih.gov/geo/,https://www.broadinstitute.org/mitocarta/mitocarta30-inventory-mammalian-mitochondrial-proteins-and-pathways].

Declarations

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval

All procedures for the study are carried out in accordance with the relevant laws and institutional guidelines and have been approved by the relevant institutional committees. (Ethics Approval Number: 20220106; Date: 2024/3/7)

Consent to participate declaration

The privacy rights of human subjects have been observed and informed consent of human subjects has been obtained.

Consent to publish declaration

Not applicable.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (10.4KB, xlsx)

Data Availability Statement

The datasets analyzed for this study can be found in the [GEO, MitoCarta] [http://www.ncbi.nlm.nih.gov/geo/,https://www.broadinstitute.org/mitocarta/mitocarta30-inventory-mammalian-mitochondrial-proteins-and-pathways].

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