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Related Concept Videos

Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Decoding preterm birth: Non-Invasive biomarkers and personalized multi-omics strategies.

Neda Farzizadeh1, Zahra Najmi2, Alan J Rosenbaum3

  • 1Department of Midwifery, School of Nursing and Midwifery, Ardabil University of Medical Sciences, Ardabil, Iran.

Developmental Biology
|October 10, 2025
PubMed
Summary

Multi-omics approaches reveal novel liquid biomarkers for predicting preterm birth (PTB). Integrating proteomic, metabolomic, and genomic data enhances early risk assessment and personalized prenatal care strategies.

Keywords:
BiomarkersEpigenomicsGenomicsMetabolomicsMulti-omicsPredictionPreterm birthProteomicsTranscriptomicsVaginal microbiome

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Area of Science:

  • Biomarkers
  • Genomics
  • Proteomics
  • Metabolomics
  • Transcriptomics
  • Epigenomics

Background:

  • Preterm birth (PTB) before 37 weeks of gestation is a global health issue causing significant neonatal morbidity and mortality.
  • Multi-omics technologies offer advanced tools for understanding PTB's complex molecular mechanisms.
  • Identifying reliable biomarkers is crucial for early prediction and risk stratification of PTB.

Purpose of the Study:

  • To review emerging liquid biomarkers from multi-omics studies for PTB prediction.
  • To highlight the integration of various omics data for enhanced understanding of PTB pathogenesis.
  • To discuss the potential of multi-omics for personalized prenatal care and PTB prevention.

Main Methods:

  • Comprehensive review of proteomic, metabolomic, genomic, transcriptomic, and epigenomic studies on PTB.
  • Analysis of liquid biomarkers in maternal and fetal compartments.
  • Integration of multi-omics data using machine learning models for predictive accuracy assessment.

Main Results:

  • Proteomics identified proteins linked to inflammation and extracellular matrix pathways.
  • Metabolomics revealed lipid and metabolite profiles associated with energy metabolism.
  • Genomics, epigenomics, and transcriptomics uncovered genetic variations, microRNAs, and ncRNAs involved in PTB.
  • Multi-omics integration with machine learning showed superior predictive performance for PTB.

Conclusions:

  • Multi-omics approaches provide deep insights into PTB's molecular underpinnings.
  • Integrated multi-omics data and machine learning significantly improve PTB prediction.
  • Future research requires longitudinal studies and diverse cohorts for clinical translation.
  • Developing accessible biomarker panels and standardized guidelines is essential for clinical implementation and reducing the global PTB burden.