Omics and Multiomics-Based Diagnostics for Invasive Candidiasis: Toward Precision Medicine

Area of Science:

• * Mycology and Infectious Diseases
• * Molecular Diagnostics and Bioinformatics
• * Precision Medicine

Background:

• * Invasive candidiasis (IC) is a severe, life-threatening, and costly healthcare-associated infection caused by Candida species.
• * Current diagnostic methods for IC are limited by nonspecific symptoms and a lack of early, accurate detection, leading to delayed treatment and high mortality rates.
• * The emergence of multidrug-resistant Candida strains further complicates treatment, underscoring the need for advanced diagnostic solutions.

Purpose of the Study:

• * To review the role of current and emerging omics technologies in developing innovative diagnostic biomarkers for invasive candidiasis.
• * To explore how integrating multi-omic data can lead to multidimensional biomarker signatures and computational algorithms for improved IC diagnosis.
• * To discuss the future challenges and prospects for the clinical implementation of these next-generation diagnostics.

Main Methods:

• * Comprehensive review of omics technologies including genomics, transcriptomics, proteomics, metabolomics, glycomics, immunomics, and microbiomics.
• * Analysis of biomarker development for early diagnosis, antifungal susceptibility, prognosis, follow-up, and therapeutic monitoring in IC.
• * Exploration of data integration strategies like integromics, multiomics, and panomics, coupled with systems biology and artificial intelligence.

Main Results:

• * Omics technologies provide a powerful platform for discovering novel biomarkers for various aspects of IC management.
• * Integration of multi-omic data holds significant potential for creating robust diagnostic signatures and predictive algorithms.
• * Next-generation diagnostics based on omics approaches promise to revolutionize IC management through personalized medicine.

Conclusions:

• * Advanced omics technologies are crucial for developing urgently needed innovative diagnostic tools for invasive candidiasis.
• * The integration of multi-omic data and AI can significantly enhance the accuracy and timeliness of IC diagnosis and treatment.
• * Future clinical implementation of these technologies will enable more precise, personalized treatment decisions, improving patient outcomes and reducing healthcare burdens.