Integrative multi-omics based prediction of biomarkers across mood disorders (schizophrenia, major depressive disorder, and bipolar disorder)

1. Overview

Provide a brief overview of: (1) scientific rationale and importance, (2) confirmation of funding/facilities/ethics approvals, (3) estimated timeline, and (4) data sharing commitment.

Mood disorders such as schizophrenia, major depressive disorder (MDD), and bipolar disorder are leading contributors to global disability but lack the objective, blood-based biomarkers for accurate, clinical diagnosis. Current diagnostic criteria rely almost exclusively on clinical evaluation, leading to frequent misdiagnosis and delayed intervention.

This project proposes comprehensive bioinformatics and systems biology framework to identify transcriptomic and proteomic biomarkers detectable in blood that are unique to each disorder as well as common across them. Data will be integrated from curated disease-gene repositories, proteomic databases and transcriptomic datasets, followed by network analysis, pathway enrichment, and miRNA prediction to construct a probable panel for robust diagnostic signatures.

The ultimate outcome will be a set of prioritized candidate biomarkers that can be rapidly transitioned toward clinical validation and psychiatry applications, directly benefitting diagnostic development.

The timeline of the project is distinguished as:

a. Months 1-3: Data collection from different databases, filtering, data cleaning, and classification

b. Months 4-6: Integration across disorder, overlap analysis and network analysis, pathway enrichment, centrality ranking

c. Months 6-7: Top dysregulated panel selection, validation across datasets and manuscript preparation

2. Introduction

Summarize theoretical background, key literature, research questions, and numbered hypotheses (H1, H2, etc.). Explain the study's importance with an impact statement, regardless of potential outcomes.

Background and Key Literature

Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are among the leading global causes of disability and premature mortality (1). In addition to schizophrenia, these disorders collectively affect hundreds of millions worldwide and account for nearly 5% of the global burden of disability-adjusted life years (DALYs) and approximately 15% of years lived with disability (YLDs) (2). Despite decades of research, no clinically validated blood biomarkers exist for their diagnosis (3). This gap perpetuates misdiagnosis, suboptimal treatment and poor long-term outcomes.

Evidence suggests that these disorders share overlapping biological mechanisms, including immune dysregulation, neuroinflammation, oxidative stress and synaptic signaling abnormalities, yet also maintain distinct pathophysiological profiles (4–7). Identifying both shared and unique disorder-specific biomarkers could transform diagnostic practice by enabling objective, minimally invasive and rapid assays for clinical use.

Previous studies have explored biomarker discovery within single disorders or associated comorbidities, but few have undertaken a systematic cross-disorder analysis integrating genetic and protein markers specifically detectable in blood. Leveraging multi-omics data and advanced bioinformatics can address the existing gap and establish a foundation for precision psychiatry diagnosis.

This project directly addresses a critical unmet need in psychiatry— the absence of objective diagnostic biomarkers. By integrating genetic and proteomic data into a blood-based biomarker discovery framework, it provides a practical pathway toward precision psychiatry. The discovery of shared markers may illuminate universal mechanisms of mood disorders, while unique markers will enable improved diagnosis. The study will systematically leverage curated biomedical repositories such as OMIM, OTP, TTD, DisGeNET, GEO, GeneCards and KEGG disease pathway (8–15). Integrated data from these resources will collectively enhance biomarker discovery by providing validated, clinically relevant gene-disease associations and drug target predictions making the findings translationally robust and directly applicable for future clinical research. This work is clinically feasible, methodologically robust and translationally relevant- offering immediate value for future diagnostic development and potential stratification of patients in clinical trials.

Research Questions

a. Which transcriptomic and proteomic biomarkers detectable in blood are unique to schizophrenia, MDD, and bipolar disorder?

b. What common biomarkers exist across these disorders, and which pathways they enrich into?

c. What are the differential profiles of biomarkers unique and common to central nervous system and peripheral circulation?

d. Which biomarkers detectable in blood holds the strongest potential for clinical translation as diagnostic tool?

Hypothesis

a. H1: Integrated multi-database mining will identify reproducible sets of detectable biomarkers in blood which are unique to each disorder

b. H2: Integrated multi-database mining will identify reproducible sets of detectable biomarkers in blood which are common across disorders.

c. H3: Shared probable biomarkers and predicted miRNAs will converge on common biological interpretations related to mood disorders.

d. H4: The selected biomarker panel with the most prominent expression will provide a feasible basis for translation into minimally invasive diagnostic assay suitable for clinical trials.

Expected Outcomes

a. A curated list of unique and common blood biomarkers across schizophrenia, MDD and bipolar disorder.

b. Identification of probable high-expression biomarker hubs in neurogenesis, immune, neuronal and synaptic signaling pathways.

c. Prediction of a biomarker panel for future validation based on translational results for future diagnostic reference.

3. Methods

Participants/Sample

Define target sample, inclusion/exclusion criteria, recruitment strategy, and sample size justification (including power analysis).

Materials and Procedures

Step 1: Data Collection (Months: 1-2)

a. Genes associated with disorders, Schizophrenia, MDD, bipolar disorder will be retrieved from GeneCards, GEO, KEGG, OTP, TTD, OMIM and DisGeNet.

b. Extract protein biomarkers detectable in blood from Human Protein Atlas, ProteomicsDB and UniProt and literature mining.

Step 2: GEO Analysis (Months: 2-4)

a. Significant and non-significant genes will be retrieved for downstream study.

b. Retrieved data will be curated in disorder-specific manner.

Step 3: Network and pathway Analysis (Months: 4-5)

a. The biomarkers will be mapped and centrality scores will be calculated on the basis of protein-protein interaction (PPI) network analysis using STRING and Cytoscape modules.

b. Centrality metrics (degree, betweenness, closeness) will be applied to identify hubs.

c. Pathway enrichment will be done by KEGG and Reactome to determine critical cascades which will get affected.

Step 4: Panel Selection and Validation (Months: 5-7)

a. Top probable dysregulated biomarkers predicted from the study will be selected as candidates for future diagnostic panel development.

b. The panel will be cross-validated across multiple GEO datasets.

c. miRNA prediction will be carried out to observe the regulation of predicted biomarkers.

Planned Analyses

Outline analysis pipeline, preprocessing steps, statistical tests, and hypothesis-specific predictions. Include any contingent analysis decisions as IF-THEN statements.

Ethics and Data Management

a. Ethics approval is not required for bioinformatics analysis.

b. All data will be retrieved from public biomedical repositories.

c. Predicted biomarker panels, workflows and codes will be openly shared via GitHub and/or Zenodo.

4. Pilot Data (Optional)

Summarize any pilot data or simulations that demonstrate method feasibility and inform power analysis. Clearly distinguish from main study.

5. Budget

*Item*	*Estimated Cost*
Open Access Publication Fees	$4000
Data sharing & hosting	$500
Miscellaneous/Contingency	$500

6. References

1. Byrne P. Premature mortality of people with severe mental illness: a renewed focus for a new era. Ir j psychol Med. 2023 Mar;40(1):74–83.

2. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry. 2022 Feb;9(2):137–50.

3. Yatham LN. Biomarkers for clinical use in psychiatry: where are we and will we ever get there? World Psychiatry. 2023 June;22(2):263–4.

4. Bauer ME, Teixeira AL. Neuroinflammation in Mood Disorders: Role of Regulatory Immune Cells. Neuroimmunomodulation. 2021;28(3):99–107.

5. Ortega MA, Fraile-Martinez O, García-Montero C, Diaz-Pedrero R, Lopez-Gonzalez L, Monserrat J, et al. Understanding immune system dysfunction and its context in mood disorders: psychoneuroimmunoendocrinology and clinical interventions. Military Med Res. 2024 Dec 17;11(1):80.

6. Lu Z, Pu C, Zhang Y, Sun Y, Liao Y, Kang Z, et al. Oxidative Stress and Psychiatric Disorders: Evidence from the Bidirectional Mendelian Randomization Study. Antioxidants. 2022 July 18;11(7):1386.

7. Leung E, Lau EW, Liang A, De Dios C, Suchting R, Östlundh L, et al. Alterations in brain synaptic proteins and mRNAs in mood disorders: a systematic review and meta-analysis of postmortem brain studies. Mol Psychiatry. 2022 Mar;27(3):1362–72.

8. Savojardo C, Babbi G, Martelli PL, Casadio R. Mapping OMIM Disease–Related Variations on Protein Domains Reveals an Association Among Variation Type, Pfam Models, and Disease Classes. Front Mol Biosci. 2021 May 7;8:617016.

9. Buniello A, Suveges D, Cruz-Castillo C, Llinares MB, Cornu H, Lopez I, et al. Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research. 2025 Jan 6;53(D1):D1467–75.

10. Lin Y, Hu Z. Bioinformatics analysis of candidate genes involved in ethanol-induced microtia pathogenesis based on a human genome database: GeneCards. International Journal of Pediatric Otorhinolaryngology. 2021 Mar;142:110595.

11. Kanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M. KEGG: biological systems database as a model of the real world. Nucleic Acids Research. 2025 Jan 6;53(D1):D672–7.

12. Barshir R, Fishilevich S, Iny-Stein T, Zelig O, Mazor Y, Guan-Golan Y, et al. GeneCaRNA: A Comprehensive Gene-centric Database of Human Non-coding RNAs in the GeneCards Suite. Journal of Molecular Biology. 2021 May;433(11):166913.

13. Hu Y, Guo X, Yun Y, Lu L, Huang X, Jia S. DisGeNet: a disease-centric interaction database among diseases and various associated genes. Database. 2025 Jan 11;2025:baae122.

14. Barrett T. NCBI GEO: mining millions of expression profiles--database and tools. Nucleic Acids Research. 2004 Dec 17;33(Database issue):D562–6.

15. Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, et al. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Research. 2024 Jan 5;52(D1):D1465–77.

Integrative multi-omics based prediction of biomarkers across mood disorders (schizophrenia, major depressive disorder, and bipolar disorder)

Integrative multi-omics based prediction of biomarkers across mood disorders (schizophrenia, major depressive disorder, and bipolar disorder)

Funding Progress

Related Funding

Request for Proposals - Bioinformatics & Genomics Research

Nonprofit Recipient

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