Multi-Omics in Precision Medicine

Topic Information

Dear Colleagues,

Precision medicine embraces the power to predict the most suitable treatment strategy for patients affected by complex diseases, thus improving medical and public health management. This ap-proach moves through the definition of multi-omics profiles in combination with patients’ clinical data with the final goal of determining disease susceptibility or discovering diagnostic, prognostic, and predictive biomarkers. Starting from single patients, it is possible to develop predictive models to identify early risk or identify therapeutic strategies and knowledge bases for predictive and per-sonalized healthcare in diverse populations. These aims can be easily fulfilled owing to the high fea-sibility of collecting omics data, including genomics, proteomics, and metabolomics. Furthermore, artificial intelligence and machine-learning algorithms improve precision medicine by leveraging and extending the value of original data. In fact, through integration procedures, pa-tient-specific multi-omics profiles can be modelled against public data repositories and annotation databases to obtain new insights or shape existing knowledge into disease mechanisms. This Topic will include emerging and significant advances in multi-omics strategies for implement-ing precision medicine, welcoming research papers, reviews, and communications based on exper-imental in addition to in silico characterization of multi-omics datasets and the management of big data.

Dr. Michele Costanzo
Dr. Armando Cevenini
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomedicines biomedicines	3.9	6.8	2013	21 Days	CHF 2600	Submit
Metabolites metabolites	3.7	6.9	2011	16.7 Days	CHF 2700	Submit
Proteomes proteomes	3.6	7.2	2013	28.6 Days	CHF 1800	Submit
Genes genes	2.8	5.5	2010	14.6 Days	CHF 2600	Submit
J J	-	-	2018	24.1 Days	CHF 1200	Submit

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Published Papers (3 papers)

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All Biomedicines Metabolites Proteomes Genes J

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Open AccessArticle

Multi-Modal Metabolomics Deciphers Pan-Cancer Metabolic Landscapes and Spatial-Niche-Specific Alternations

by Tingze Feng, Hai-Long Piao and Di Chen

Metabolites 2026, 16(2), 129; https://doi.org/10.3390/metabo16020129 - 13 Feb 2026

Viewed by 188

Abstract

Background: Metabolic reprogramming is a hallmark of cancer and supports tumor growth and adaptation within the tumor microenvironment (TME). The complexity of this reprogramming manifests as both distinct variations across cancer types and spatial heterogeneity within individual tumors. The specificity of these metabolic [...] Read more.

Background: Metabolic reprogramming is a hallmark of cancer and supports tumor growth and adaptation within the tumor microenvironment (TME). The complexity of this reprogramming manifests as both distinct variations across cancer types and spatial heterogeneity within individual tumors. The specificity of these metabolic alterations, whether to cancer type, spatial niche, or as shared features, remains unclear, highlighting a critical gap in our systematic, pan-cancer understanding of metabolic reprogramming. Methods: We integrated bulk metabolomics and spatial metabolomics to investigate pan-cancer metabolic features and used blood-based metabolomics and spatial transcriptomics data to validate key findings. Metabolic differences were compared between tumor and normal tissues across multiple cancer types at the bulk level to identify metabolic modules shared across cancers or specific to individual cancer types. A two-step clustering framework was applied to identify both local and global TME-associated spatial metabolic modules of spatial metabolomics data from various tumor tissue slices. Results: We have identified a spectrum of metabolic features, including those specific to individual cancer types or spatial architectures and others shared across cancers, with some features emerging only at bulk-level and others uniquely discernible through spatial metabolomics. Integrative analyses also identified 19 metabolites consistently altered in both bulk and spatial data, especially carnitine species, which also showed concordant changes in blood samples and spatial associations with genes involved in fatty acid metabolism. Conclusions: This pan-cancer, multi-scale integrative analysis highlights substantial metabolic heterogeneity within the TME and across cancer types and identifies metabolites with consistent alterations across analytical layers, providing candidate features for future studies of tumor metabolism and potential metabolic biomarkers. Full article

(This article belongs to the Topic Multi-Omics in Precision Medicine)

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14 pages, 1843 KB  

Open AccessArticle

Transcriptome Profiling of the Anterior Cingulate Cortex in a CFA-Induced Inflammatory Pain Model Identifies ECM-Related Genes in a Model of Rheumatoid Arthritis

by Guang-Xin Xie, Jian-Mei Li, Bai-Tong Liu, Jiang-Tao Wang, Lu-Shuang Xie, Xiao-Yi Xiong, Qiao-Feng Wu and Shu-Guang Yu

Genes 2026, 17(1), 15; https://doi.org/10.3390/genes17010015 - 25 Dec 2025

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Abstract

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent joint inflammation and progressive bone destruction. However, its complex pathogenesis remains poorly understood, and effective therapeutic targets are still lacking. Objective: This study aimed to identify key genes associated with RA [...] Read more.

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent joint inflammation and progressive bone destruction. However, its complex pathogenesis remains poorly understood, and effective therapeutic targets are still lacking. Objective: This study aimed to identify key genes associated with RA and elucidate their biological significance by integrating bioinformatic analysis with experimental validation. Methods: Whole-transcriptome data from the anterior cingulate cortex (ACC) of Complete Freund’s Adjuvant (CFA)-induced inflammatory pain and control mice (GSE147216 dataset, GEO database) were collected from NCBI (National Center for Biotechnology Information). Differentially expressed genes (DEGs) were first identified. Subsequent analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, construction of a protein–protein interaction (PPI) network, and identification of hub genes using a Random Forest machine learning algorithm. Quantitative PCR (qPCR) was performed to validate gene expression levels. Results: A total of 76 DEGs were identified, including 64 upregulated and 12 downregulated genes. Among them, Fn1 (fibronectin 1), Bgn (biglycan), and Lum (lumican) were identified as hub genes. Functional enrichment analysis revealed inflammatory responses, extracellular matrix (ECM) remodeling, and the TGF-β signaling pathway. qPCR validation confirmed significant upregulation of Fn1, Bgn, and Lum mRNA in the CFA group. Conclusions: This study highlights the potential roles of Fn1, Bgn, and Lum in the central sensitization associated with inflammatory pain, offering insights relevant to RA. Full article

(This article belongs to the Topic Multi-Omics in Precision Medicine)

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30 pages, 1280 KB  

Open AccessReview

Extracellular Vesicle (EV) Proteomics in Corneal Regenerative Medicine

by Zohreh Arabpour, Hanieh Niktinat, Firouze Hatami, Amal Yaghmour, Zarife Jale Yucel, Seyyedehfatemeh Ghalibafan, Hamed Massoumi, Zahra Bibak Bejandi, Majid Salehi, Elmira Jalilian, Mahmood Ghassemi, Victor H. Guaiquil, Mark Rosenblatt and Ali R. Djalilian

Proteomes 2025, 13(4), 49; https://doi.org/10.3390/proteomes13040049 - 3 Oct 2025

Cited by 2 | Viewed by 1880

Abstract

Corneal regeneration has gained growing interest in recent years, largely due to the limitations of conventional treatments and the persistent shortage of donor tissue. Among the emerging strategies, extracellular vehicles (EVs), especially those derived from mesenchymal stromal cells (MSCs), have shown great promise [...] Read more.

Corneal regeneration has gained growing interest in recent years, largely due to the limitations of conventional treatments and the persistent shortage of donor tissue. Among the emerging strategies, extracellular vehicles (EVs), especially those derived from mesenchymal stromal cells (MSCs), have shown great promise as a cell-free therapeutic approach. These nanoscale vesicles contribute to corneal healing by modulating inflammation, supporting epithelial and stromal regeneration, and promoting nerve repair. Their therapeutic potential is largely attributed to the diverse and bioactive proteomic cargo they carry, including growth factors, cytokines, and proteins involved in extracellular matrix remodeling. This review presents a comprehensive examination of the proteomic landscape of EVs in the context of corneal regenerative medicine. We explore the biological functions of EVs in corneal epithelial repair, stromal remodeling, and neurodegeneration. In addition, we discuss advanced proteomic profiling techniques such as mass spectrometry (MS) and liquid chromatography–mass spectrometry (LC-MS/MS), which have been used to identify and characterize the protein contents of EVs. This review also compares the proteomic profiles of EVs derived from various MSC sources, including adipose tissue, bone marrow, and umbilical cord, and considers how environmental cues, such as hypoxia and inflammation, influence their protein composition. By consolidating current findings, this article aims to provide valuable insights for advancing the next generation of cell-free therapies for corneal repair and regeneration. Full article

(This article belongs to the Topic Multi-Omics in Precision Medicine)

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