Journal of Advanced Biological Sciences
ISSN Print: N/A
ISSN Online: 3134-8823
About: Journal of Advanced Biological Sciences (JABS) is a peer-reviewed, open-access journal that aims to publish cutting-edge research and advancements in all areas of biological sciences. The journal serves as a platform for researchers, academicians, and professionals to contribute their scientific knowledge and foster innovation in biology-related fields.
Publisher Responsibility
Lumina Quest Publishing is responsible for providing the journal with the necessary publishing infrastructure, editorial support, production services, digital preservation, and dissemination of published content. The publisher supports transparent and ethical publishing practices and ensures that the journal’s publishing operations are conducted in accordance with applicable scholarly publishing standards.
The publisher is also responsible for maintaining the journal’s website, publication systems, archiving arrangements, and administrative support while respecting the independence of editorial decision-making.
Editorial Responsibility
The Editor-in-Chief and Editorial Board are responsible for maintaining the academic and scientific quality of the journal. The editorial team oversees the evaluation of submitted manuscripts, selection and coordination of peer reviewers, editorial assessment and decisions, and adherence to the journal’s scope and ethical standards.
Editorial decisions are made independently on the basis of academic quality, originality, methodological soundness, relevance, and ethical compliance, without inappropriate influence from the publisher, authors, reviewers, or commercial interests.
Editorial Independence
The editorial team maintains editorial independence in all decisions concerning manuscript evaluation, peer review, acceptance, revision, and rejection. The publisher does not interfere with the academic judgment of editors or reviewers.
Journal of Advanced Biological Sciences | Year 2025 | Volume 2 | Issue 2 | Pages 20-21
Multi-Omics Approaches in Diagnostic Pathology: An Updated Review of Current Evidence
Aarav Mehta1, Nisha Verma2 and Mohammed Faisal 3*1Department of Pathology, Central Institute of Medical Sciences, New Delhi, India
2Department of Molecular Pathology, National Medical Research Centre, Mumbai, India
3Department of Translational Medicine, Eastern Medical University, Kolkata, India
View PDF Download XML Download DOI XML DOI: 10.66590/jabs2025020204
Abstract
Diagnostic pathology has undergone a remarkable transformation from a predominantly morphology-based discipline to an integrated field that combines histopathology, immunohistochemistry, molecular pathology, and computational biology. The emergence of multi-omics approaches—including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and spatial omics—has enabled comprehensive characterization of disease biology by providing complementary molecular insights. These technologies have significantly advanced precision medicine by improving disease classification, facilitating early and accurate diagnosis, identifying clinically actionable biomarkers, predicting therapeutic response, and uncovering mechanisms of treatment resistance. Furthermore, the integration of multi-omics data through advanced computational and artificial intelligence–driven analytical methods has enhanced diagnostic accuracy and personalized clinical decision-making. This review provides a comprehensive overview of current multi-omics technologies, their applications in diagnostic pathology, computational integration strategies, clinical significance, existing challenges, and future directions for their implementation in precision diagnostics and personalized medicine.
INTRODUCTION
Traditional pathology has relied primarily on microscopic examination of tissue morphology. While morphology remains fundamental, advances in molecular technologies have transformed pathology into a multidisciplinary field capable of characterizing diseases at multiple biological levels. Multi-omics integrates diverse molecular datasets to provide a comprehensive understanding of disease biology that cannot be achieved using a single analytical platform [1].
Major Multi-Omics Technologies
- Genomics: DNA mutations, copy number alterations, structural variants, and inherited susceptibility
- Transcriptomics: Gene-expression profiling, RNA sequencing, alternative splicing, and non-coding RNA analysis
- Proteomics: Protein expression, post-translational modifications, and signaling pathways
- Metabolomics: Small-molecule metabolites reflecting cellular metabolism
- Epigenomics: DNA methylation, histone modifications, and chromatin accessibility
- Spatial Omics: Molecular information preserved within tissue architecture [2]
Applications in Diagnostic Pathology
Multi-omics contributes to:
- Improved tumor classification
- Identification of diagnostic biomarkers
- Prognostic risk stratification
- Prediction of therapeutic response
- Detection of minimal residual disease
- Characterization of rare tumors
- Precision oncology and personalized treatment
Examples include molecular classification of brain tumors, integrated diagnosis of hematologic malignancies, molecular profiling of breast carcinoma, colorectal cancer, lung cancer, thyroid carcinoma, and soft tissue sarcomas [3-5].
Role of Artificial Intelligence
Artificial intelligence assists in [6]:
- Integrating heterogeneous omics datasets
- Predicting molecular alterations from histopathology
- Biomarker discovery
- Survival prediction
- Automated patient stratification
- Clinical decision support
Advantages
|
Advantage |
Clinical Impact |
|
Comprehensive disease characterization |
Improved diagnostic accuracy |
|
Biomarker discovery |
Earlier diagnosis |
|
Precision medicine |
Personalized therapy |
|
Molecular subclassification |
Better prognostic assessment |
|
Treatment prediction |
Optimized patient management |
Challenges
Major limitations include:
- High analytical cost
- Large data storage requirements
- Complex bioinformatic analysis
- Lack of standardized workflows
- Limited access in resource-constrained laboratories
- Data privacy and ethical considerations
- Need for multidisciplinary expertise
Future Perspectives
Emerging directions include:
- Single-cell multi-omics
- Spatial transcriptomics integrated with digital pathology
- AI-driven multi-modal diagnostics
- Cloud-based bioinformatics platforms
- Routine clinical implementation of integrated molecular pathology
- Real-time precision diagnostics using combined histological and molecular data
CONCLUSIONS
Multi-omics approaches are redefining diagnostic pathology by integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic information with conventional histopathology. These technologies are enhancing diagnostic precision, improving prognostic evaluation, and supporting individualized treatment decisions. Continued advances in artificial intelligence, computational pathology, and standardized clinical workflows are expected to make multi-omics an increasingly important component of routine pathology practice.
REFERENCES
- Hasin, Yehudit, Marcus Seldin, and Aldons Lusis. "Multi-Omics Approaches to Disease." Genome Biology, vol. 18, no. 1, 2017, p. 83. https://doi.org/10.1186/s13059-017-1215-1.
- The Cancer Genome Atlas Research Network. "Comprehensive Molecular Characterization of Human Cancers." Nature, vol. 487, no. 7407, 2012, pp. 330–337. https://doi.org/10.1038/nature11247.
- World Health Organization Classification of Tumours Editorial Board. WHO Classification of Tumours. 5th ed., International Agency for Research on Cancer (IARC), 2019–present.
- Aebersold, Ruedi, and Matthias Mann. "Mass-Spectrometric Exploration of Proteome Structure and Function." Nature, vol. 537, no. 7620, 2016, pp. 347–355. https://doi.org/10.1038/nature19949.
- Stuart, Tim, and Rahul Satija. "Integrative Single-Cell Analysis." Nature Reviews Genetics, vol. 20, no. 5, 2019, pp. 257–272. https://doi.org/10.1038/s41576-019-0093-7.
- Marx, Vivien. "Method of the Year: Spatially Resolved Transcriptomics." Nature Methods, vol. 18, no. 1, 2021, pp. 9–14. https://doi.org/10.1038/s41592-020-01033-y.