Biomarkers For Major Depressive Disorder Research Articles

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Elevated SCN11A concentrations associated with lower serum lipid levels in patients with major depressive disorder

The pathogenesis of major depressive disorder (MDD) involves lipid metabolism. Our earlier research also revealed that MDD patients had much lower total cholesterol (TC) concentrations than healthy controls (HCs). However, it is still unclear why TC decreased in MDD. Here, based on the Ingenuity Knowledge Base’s ingenuity pathway analysis, we found that sodium voltage-gated channel alpha subunit 11A (SCN11A) might serve as a link between low lipid levels and MDD. We analyzed the TC levels and used ELISA kits to measure the levels of SCN11A in the serum from 139 MDD patients, and 65 HCs to confirm this theory and explore the potential involvement of SCN11A in MDD. The findings revealed that TC levels were considerably lower and SCN11A levels were remarkably increased in MDD patients than those in HCs, while they were significantly reversed in drug-treatment MDD patients than in drug-naïve MDD patients. There was no significant difference in SCN11A levels among MDD patients who used single or multiple antidepressants, and selective serotonin reuptake inhibitors or other antidepressants. Pearson correlation analysis showed that the levels of TC and SCN11A were linked with the Hamilton Depression Rating Scales score. A substantial association was also found between TC and SCN11A. Moreover, a discriminative model made up of SCN11A was discovered, which produced an area under a curve of 0.9571 in the training set and 0.9357 in the testing set. Taken together, our findings indicated that SCN11A may serve as a link between low lipid levels and MDD, and showed promise as a candidate biomarker for MDD.

Identification of Potential Biomarkers for Major Depressive Disorder: Based on Integrated Bioinformatics and Clinical Validation

Identification of Potential Biomarkers for Major Depressive Disorder: Based on Integrated Bioinformatics and Clinical Validation

Gray matter volume and corresponding covariance connectivity are biomarkers for major depressive disorder

The major depressive disorder (MDD) is a common and severe mental disorder. To identify a reliable biomarker for MDD is important for early diagnosis and prevention. Given easy access and high reproducibility, the structural magnetic resonance imaging (sMRI) is an ideal method to identify the biomarker for depression. In this study, sMRI data of first episode, treatment-naïve 66 MDD patients and 54 sex-, age-, and education-matched healthy controls (HC) were used to identify the differences in gray matter volume (GMV), group-level, individual-level covariance connections. Finally, the abnormal GMV and individual covariance connections were applied to classify MDD from HC. MDD patients showed higher GMV in middle occipital gyrus (MOG) and precuneus (PCun), and higher structural covariance connections between MOG and PCun. In addition, the Allen Human Brain Atlas (AHBA) was applied and revealed the genetic basis for the changes of gray matter volume. Importantly, we reported that GMV in MOG, PCun and structural covariance connectivity between MOG and PCun are able to discriminate MDD from HC. Our results revealed structural underpinnings for MDD, which may contribute towards early discriminating for depression.

MICRO-RNA AS DIAGNOSTIC MODALITY OF MAJOR DEPRESSIVE DISORDER

Introduction – Major Depressive Disorder (MDD) is a chronic mental health disorder, with suicidal ideation, but until now an exact diagnostic approach has not been found. For the limitations of the current diagnosis, such as subjective reports of patients or their families with mental status examinations, there is still a lack of objective diagnostic results. In this case, it is still a challenge for researchers. Neutropic factors, hypothalamic-pituitary-adrenal hormones (HPA), indicators of inflammation, and metabolic factors, were found to be novel biomarkers for MDD. However, these biomarkers show limited sensitivity and specificity. The most recent diagnostic development effort is the use of microRNA as a diagnostic modality approach for MDD. Methods – This study uses a qualitative method by collecting valid journals with certain inclusion and exclusion criteria. The keywords used were MDD, microRNA, and Diagnostic. There are journals used as the source of this literature review which come from valid scientific sources such as NCBI, ScienceDirect, and PubMed which were published in the last 10 years. Results – The results of the miRNA-124-3p expression test showed a significant difference between the healthy control group and the MDD patient group. The specificity and sensitivity of miRNA-221-3p, miRNA-451a, miRNA-34a-5p, and let-7d-3p showed high accuracy in the diagnosis of MDD. Discuss – The unknown etiology of MDD makes some patients have no results or significant progress of their depression in clinical settings which is contributed by gene regulatory pathway that shows high significance associated with neural function brain in depressive disorders such as apoptotic pathway, P13K-Akt signaling, axon guidance, neurotrophin signaling pathway, mTOR signaling pathway. Conclusion- The diagnostic effectiveness of miRNA in major depressive disorder can be significantly enhanced by using miRNA expression coding and has been proven by various tests. Therefore, this research is expected to be an innovation in diagnosing MDD for further research. Keywords: MDD, depression, microRNA, diagnostic

MicroRNAs as Diagnostic Biomarkers and Predictors of Antidepressant Response in Major Depressive Disorder: A Systematic Review.

Despite the hardships of major depressive disorder (MDD), biomarkers for the diagnosis and pharmacological management of this condition are lacking. MicroRNAs are epigenetic mechanisms that could provide promising MDD biomarkers. Our aim was to summarize the findings and provide validation for the selection and use of specific microRNAs as biomarkers in the diagnosis and treatment of MDD. A systematic review was conducted using the PubMed/Medline, Cochrane, PsycINFO, Embase, and LILACS databases from March 2022 to November 2023, with clusters of terms based on "microRNA" and "antidepressant". Studies involving human subjects, animal models, and cell cultures were included, whereas those that evaluated herbal medicines, non-pharmacological therapies, or epigenetic mechanisms other than miRNA were excluded. The review revealed differences in the expression of various microRNAs when considering the time of assessment (before or after antidepressant treatment) and the population studied. However, due to the heterogeneity of the microRNAs investigated, the limited size of the samples, and the wide variety of antidepressants used, few conclusions could be made. Despite the observed heterogeneity, the following microRNAs were determined to be important factors in MDD and the antidepressant response: mir-1202, mir-135, mir-124, and mir-16. The findings indicate the potential for the use of microRNAs as biomarkers for the diagnosis and treatment of MDD; however, more homogeneous studies are needed.

Peripheral Upregulation of Parkinson's Disease-Associated Genes Encoding α-Synuclein, β-Glucocerebrosidase, and Ceramide Glucosyltransferase in Major Depression.

Due to the high comorbidity of Parkinson's disease (PD) with major depressive disorder (MDD) and the involvement of sphingolipids in both conditions, we investigated the peripheral expression levels of three primarily PD-associated genes: α-synuclein (SNCA), lysosomal enzyme β-glucocerebrosidase (GBA1), and UDP-glucose ceramide glucosyltransferase (UGCG) in a sex-balanced MDD cohort. Normalized gene expression was determined by quantitative PCR in patients suffering from MDD (unmedicated n = 63, medicated n = 66) and controls (remitted MDD n = 39, healthy subjects n = 61). We observed that expression levels of SNCA (p = 0.036), GBA1 (p = 0.014), and UGCG (p = 0.0002) were higher in currently depressed patients compared to controls and remitted patients, and expression of GBA1 and UGCG decreased in medicated patients during three weeks of therapy. Additionally, in subgroups, expression was positively correlated with the severity of depression and anxiety. Furthermore, we identified correlations between the gene expression levels and PD-related laboratory parameters. Our findings suggest that SNCA, GBA1, and UGCG analysis could be instrumental in the search for biomarkers of MDD and in understanding the overlapping pathological mechanisms underlying neuro-psychiatric diseases.

TiRNA-Gly-GCC-001 in major depressive disorder: Promising diagnostic and therapeutic biomarker.

In major depressive disorder (MDD), exploration of biomarkers will be helpful in diagnosing the disorder as well as in choosing a treatment and predicting the treatment response. Currently, tRNA-derived small ribonucleic acids (tsRNAs) have been established as promising non-invasive biomarker candidates that may enable a more reliable diagnosis or monitoring of various diseases. Herein, we aimed to explore tsRNA expression together with functional activities in MDD development. Serum samples were obtained from patients with MDD and healthy controls, and small RNA sequencing (RNA-Seq) was used to profile tsRNA expression. Dysregulated tsRNAs in MDD were validated by quantitative real-time polymerase chain reaction (qRT-PCR). The diagnostic utility of specific tsRNAs and the expression of these tsRNAs after antidepressant treatment were analysed. In total, 38 tsRNAs were significantly differentially expressed in MDD samples relative to healthy individuals (34 up-regulated and 4 down-regulated). qRT-PCR was used to validate the expression of six tsRNAs that were up-regulated in MDD (tiRNA-1:20-chrM.Ser-GCT, tiRNA-1:33-Gly-GCC-1, tRF-1:22-chrM.Ser-GCT, tRF-1:31-Ala-AGC-4-M6, tRF-1:31-Pro-TGG-2 and tRF-1:32-chrM.Gln-TTG). Interestingly, serum tiRNA-Gly-GCC-001 levels exhibited an area under the ROC curve of 0.844. Moreover, tiRNA-Gly-GCC-001 is predicted to suppress brain-derived neurotrophic factor (BDNF) expression. Furthermore, significant tiRNA-Gly-GCC-001 down-regulation was evident following an 8-week treatment course and served as a promising baseline predictor of patient response to antidepressant therapy. Our current work reports for the first time that tiRNA-Gly-GCC-001 is a promising MDD biomarker candidate that can predict patient responses to antidepressant therapy.

Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder.

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models. Methods: In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. Serum amino acid profiling was conducted by means of chromatography-mass spectrometry. A total of 21 metabolites were analysed, with 17 from a preset amino acid panel and the remaining 4 from a preset kynurenine panel. Logistic regression was applied to differentiate MDD patients from HCs. Results: The best-performing model utilised both feature selection and hyperparameter optimisation and yielded a moderate area under the receiver operating curve (AUC) classification value of 0.76 on the testing data. The top five metabolites identified as potential biomarkers for MDD were 3-hydroxy-kynurenine, valine, kynurenine, glutamic acid, and xanthurenic acid. Conclusions: Our study highlights the potential of using an interpretable machine learning analysis model based on amino acids to aid and increase the diagnostic accuracy of MDD in clinical practice.

Quantification of Plasma Dopamine in Depressed Patients Using Silver-Enriched Silicon Nanowires as SERS-Active Substrates.

A decrease in the levels of dopamine (DA)─a key catecholamine biomarker for major depressive disorder─highlights the need for quantitative analysis of biological fluids to aid in the early diagnosis of diverse neuropsychiatric disorders. This study developed silicon nanowires enriched with silver nanoparticles to serve as a surface-enhanced Raman scattering (SERS) substrate to enable precise and sensitive quantification of blood plasma DA levels in humans. The silver-enriched silicon nanowires (SiNWs@Ag) yielded flower-like assemblies with densely populated SERS "hot spots," allowing sensitive DA detection. By correlating DA concentration with Raman intensity at 1156 cm-1, the plasma DA levels in treatment-naïve patients with major depression (n = 18) were 2 orders of magnitude lower than those in healthy controls (n = 18) (6.56 × 10-10 M vs 1.43 × 10-8 M). The plasma DA concentrations differed significantly between the two groups (two-tailed p = 5.77×10-7), highlighting a distinct demarcation between depression patients and healthy controls. Furthermore, the SiNWs@Ag substrate effectively differentiated between DA and norepinephrine (NE) in mixtures at nanomolar levels, demonstrating its selective detection capability. This study represents the first report on the quantitative detection of DA levels in human blood samples from individuals with major depression using an SERS technique, emphasizing its potential clinical utility in the evaluation and diagnosis of neuropsychiatric disorders.

Gray matter biomarkers for major depressive disorder and manic disorder using logistic regression

The study investigates morphometric changes using surface-based measures and logistic regression in Major depressive-disorder (MDD) and Manic-disorder patients as compared to controls.MDD (n = 21) and manic (n = 20) subjects were recruited from psychiatric clinics, along with 19 healthy-controls from local population, after structured and semi-structured clinical interview (DSM-IV, brief Psychotic-Rating Scale (BPRS), Young Mania Rating Scale (YMRS), Hamilton depression rating scale (HDRS), cognitive function by postgraduate Institute Battery of Brain Dysfunction (PGIBBD)).Using 3D T1-weighted images, gray matter (GM) cortical thickness and GM-based morphometric signatures (using logistic regression) were compared among MDD, manic disorder and controls using analysis of covariance (ANCOVA). No significant difference was found between the MDD and manic disorder patients. When compared to controls, cortical thinning was observed in bilateral rostral middle frontal gyrus and parsopercularis, right lateral occipital cortex, right lingual gyrus in MDD; and bilateral rostral middle frontal and superior frontal gyrus, right middle temporal gyrus, left supramarginal and left precentral gyrus in Manic disorders.Logistic regression analysis exhibited GM cortical thinning in the bilateral parsopercularis, right lateral occipital cortex and lingual gyrus in MDD; and bilateral rostral middle, superior frontal gyri, right middle temporal gyrus in Manic with a sensitivity and specificity of 85.7 % and 94.7 % and 90.0 % and 94.7 %, respectively in comparison with controls. Both groups exhibited GM loss in bilateral rostral middle frontal gyrus brain regions compared to controls. Multivariate analysis revealed common changes in GM in MDD and manic disorders associated with mood temperament, but differences when compared to controls.

Classification accuracy of structural and functional connectomes across different depressive phenotypes

Abstract Phenotyping of major depressive disorder (MDD) can vary from study to study, which, together with heterogeneity of the disorder, may contribute to the inconsistent associations with neuroimaging features and underlie previous problems with machine-learning methods for MDD diagnostic applications. In this study, we examined the classification accuracy of structural and functional connectomes across different depressive phenotypes, including separating MDD subgroups into those with and without self-reported exposure to childhood trauma (CT) (one of the largest risk factors for MDD associated with brain development). We applied logistic ridge regression to classify control and MDD participants defined by six different MDD definitions in a large community-based sample (N=14,507). We used brain connectomic data based on six structural and two functional network weightings and conducted a comprehensive analysis to (i) explore how well different connectome modalities predict different MDD phenotypes commonly used in research, (ii) whether stratification of MDD based on self-reported exposure to childhood trauma (measured with the childhood trauma questionnaire (CTQ)) may improve the accuracies, and (iii) identify important predictive features across different MDD phenotypes. We found that functional connectomes outperformed structural connectomes as features for MDD classification across phenotypes. The highest accuracy of 64.8% (chance level 50.0%) was achieved in the Currently Depressed (defined by the presence of more than five symptoms of depression in the past 2 weeks) sample with additional CTQ criterion using partial correlation functional connectomes. The predictive feature overlap, measured using Jaccard index, indicated that there were neurobiological differences between MDD patients with and without childhood adversity. Further analysis of predictive features for different MDD phenotypes with hypergeometric tests revealed sensorimotor and visual subnetworks as important predictors of MDD. Our results suggest that differences in sensorimotor and visual subnetworks may serve as potential biomarkers of MDD.

A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder

Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Patients with MDD and healthy controls. Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

Abnormal functional connectivity within the prefrontal cortex is associated with multiple plasma lipid species in major depressive disorder

BackgroundAbnormalities in functional connectivity (FC) in major depressive disorder (MDD) have been widely reported. Analysis of the relationship between FC and plasma lipid profiles would be meaningful in the exploration of pathophysiological mechanisms and helpful for the identification of biomarkers for MDD. MethodsPatients with MDD (n = 49) and healthy controls (HC, n = 87) were recruited. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected for FC construction. The plasma lipid profiles were acquired using ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS) analysis and clustered as co-expression modules. The differential FC and lipid modules between HCs and patients with MDD were identified, and then the association between FC and lipid co-expression modules was analyzed using correlation analysis. The modules associated molecular function was explored using metabolite set enrichment analysis (MSEA). ResultsMDD-associated FC and lipid co-expression modules were identified. One module was associated with FC values between the right orbital part of the middle frontal gyrus and the opercular part of the left inferior frontal gyrus, which was enriched in lipid sets of diacylglycerols and fatty alcohols; another module was associated with FC values between the right middle frontal gyrus and the right anterior cingulate and paracingulate gyri, which was enriched in lipid sets of glycerophosphocholines and glycerophosphoethanolamines. ConclusionOur results indicated that abnormal FC in the prefrontal cortex is associated with multiple plasma lipid species, which may provide novel clues for exploring the pathophysiology of MDD.

Monomeric C-reactive protein as a biomarker for major depressive disorder.

Neuroinflammation has been postulated to be a key factor in the pathogenesis of major depressive disorder (MDD). With this is mind, there has been a wave of research looking into pro-inflammatory mediators as potential biomarkers for MDD. One such mediator is the acute phase protein, C-reactive protein (CRP). While several studies have investigated the potential of CRP as a biomarker for MDD, the results have been inconsistent. One explanation for the lack of consistent findings may be that the high-sensitivity CRP tests utilized in these studies only measure the pentameric isoform of CRP (pCRP). Recent research, however, has indicated that the monomeric isoform of CRP (mCRP) is responsible for the pro-inflammatory function of CRP, while pCRP is weakly anti-inflammatory. The objective of this minireview is to re-examine the evidence of CRP involvement in MDD with a view of mCRP as a potential biomarker.

MicroRNA-451a is a candidate biomarker and therapeutic target for major depressive disorder

BackgroundIncreasing evidence supports the role of microRNAs (miRNAs) in major depressive disorder (MDD), but the pathophysiological mechanism remains elusive.AimsTo explore the mechanism of microRNA-451a (miR-451a) in the pathology and behaviours...

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC’s full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Adenylate cyclase 3: a potential genetic link between obesity and major depressive disorder.

Obesity and major depressive disorder (MDD) are both significant health issues that have been increasing in prevalence and are associated with multiple comorbidities. Obesity and MDD have been shown to be bidirectionally associated, and they are both influenced by genetics and environmental factors. However, the molecular mechanisms that link these two diseases are not yet fully understood. It is possible that these diseases are connected through the actions of the cAMP/protein kinase A (PKA) pathway. Within this pathway, adenylate cyclase 3 (Adcy3) has emerged as a key player in both obesity and MDD. Numerous genetic variants in Adcy3 have been identified in humans in association with obesity. Rodent knockout studies have also validated the importance of this gene for energy homeostasis. Furthermore, Adcy3 has been identified as a top candidate gene and even a potential blood biomarker for MDD. Adcy3 and the cAMP/PKA pathway may therefore serve as an important genetic and functional link between these two diseases. In this mini-review, we discuss the role of both Adcy3 and the cAMP/PKA pathway, including specific genetic mutations, in both diseases. Understanding the role that Adcy3 mutations play in obesity and MDD could open the door for precision medicine approaches and treatments for both diseases that target this gene.

TCF4 and RBFOX1 as peripheral biomarkers for the differential diagnosis and treatment of major depressive disorder

BackgroundRecent genome-wide association studies on major depressive disorder (MDD) have indicated the involvement of LRFN5 and OLFM4; however, the expression levels and roles of these molecules in MDD remain unclear. The present study aimed to determine the serum levels of TCF4 and RBFOX1 in patients with MDD and to investigate whether these molecules could be used as biomarkers for MDD diagnosis. MethodsThe study included 99 drug-naïve MDD patients, 90 drug-treated MDD patients, and 81 healthy controls (HCs). Serum TCF4 and RBFOX1 levels were measured by ELISA. Pearson's correlation analysis was conducted to determine the association between TCF4/RBFOX1 and clinical variables. Linear support vector machine classifier was used to evaluate the diagnostic capabilities of TCF4 and RBFOX1. ResultsSerum TCF4 and RBFOX1 levels were substantially higher in MDD patients than in HCs and significantly lower in drug-treated MDD patients than in drug-naïve MDD patients. Moreover, serum TCF4 and RBFOX1 levels were associated with the Hamilton Depression Scale score, duration of illness, serum lipids levels, and hepatic function. Thus, both these molecules showed potential as biomarkers for MDD. TCF4 and RBFOX1 combination exhibited a higher diagnostic performance, with the mean area under the curve values of 0.9861 and 0.9936 in the training and testing sets, respectively. LimitationsSmall sample size and investigation of only the peripheral nervous system. ConclusionsTCF4 and RBFOX1 may be involved in the pathogenesis of MDD, and their combination may serve as a diagnostic biomarker panel for MDD.

Identification and Analyses of Crucial Genes Associated with Pathogenesis of Major Depressive Disorder.

Major depressive disorder is a debilitating mental condition that causes severe disability leading to a high fatality rate. No valid blood-based biomarkers for major depressive disorder are currently available. The purpose of this research is to investigate gene biomarkers and pathways that may be linked to major depressive disorder pathogenesis. Two microarray databases were retrieved from Gene Expression Omnibus for screening of candidate differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed followed by protein-protein interaction network of differentially expressed genes. About 1181 differentially expressed genes were identified from the microarray databases. Gene Ontology analyses indicated that these differentially expressed genes were significantly enriched in mRNA splicing via spliceosome, neutrophil degranulation, peptide antigen assembly with MHC class II protein complex, and immunoglobulin production-mediated immune response. The most enriched Kyoto Encyclopedia of Genes and Genomes pathway terms of the 10 significant were Hematopoietic cell lineage. About 20 genes were identified as hub genes after pathway analyses, mostly involved in colorectal cancer and the composition of ribosomes and protein processing, including KRAS, CD86, RPL9, RPL3, and RPL18. New candidate genes have been identified using bioinformatic approaches that suggest their involvement in the pathogenesis of major depressive disorder and serve as potential genetic diagnostic markers as well as new therapeutic targets.