With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Flavonoids are a broad class of polyphenolic compounds with well-established biological and therapeutic relevance; however, their clinical application is often limited by unfavorable pharmacokinetic properties, including low solubility, poor bioavailability, and extensive metabolism. Enzymatic modification has emerged as a promising strategy to overcome these limitations, enabling regio- and stereoselective structural changes under mild and environmentally sustainable conditions. This study presents a systematic review conducted in accordance with the PRISMA 2020 guidelines, covering studies published between 2015 and 2025 on the enzymatic modification of flavonoids and their effects on physicochemical properties and bioavailability, with emphasis on biological activity and therapeutic potential. The literature search was performed in PubMed, Scopus, and Web of Science, complemented by citation searching and exploratory bioinformatics analysis. Evidence from in vitro, in vivo, and clinical studies indicates that enzymatic modifications such as glycosylation, deglycosylation, acylation, hydroxylation, methylation, and prenylation can improve stability, absorption, and biological activity. Despite promising results in experimental models, clinical evidence remains limited. Overall, enzymatic modification represents a promising strategy for improving flavonoid properties; however, most available evidence is derived from in vitro and in vivo studies, and further well-designed clinical trials are required to confirm their therapeutic potential in humans.

Understanding the molecular determinants of interindividual drug response variability remains a major challenge in pharmacogenomics. Very Important Pharmacogenes (VIPs), as defined by PharmGKB, represent genes with well-established roles in drug metabolism and efficacy. However, their activity occurs within complex molecular networks that extend beyond direct pharmacogenetic associations. We constructed a VIP-centered subnetwork and applied network topology analyses, including shortest path, signal propagation, and degree centrality, to identify key nodes mediating VIP interactions. Functional enrichment, transcription factor (TF) association, and drug–gene interaction analyses were subsequently performed to characterize the biological and pharmacological context of these networks. Our results revealed a dense VIP interactome enriched in metabolic, endocrine, and signaling pathways. Notably, we identified a subset of highly connected non-VIP genes that frequently bridge canonical VIPs, termed shadow VIPs. These genes, often encoding transcriptional regulators, such as NR1I2, NR1H4, and ESR2, and more frequent in the shortest paths connecting VIPs, such as POR, APP, and GIPC1, exhibited strong associations with approved drugs, particularly hormone-related and antineoplastic agents. This suggests that shadow VIPs may act as indirect regulators of pharmacogenomic phenotypes by influencing the expression or activity of canonical VIPs. Additionally, the analysis revealed that shadow VIPs, on average, exhibit lower RVIS values than VIPs, indicating a higher intolerance to functional mutations. This suggests that shadow VIPs are under stronger selective selection, underscoring their essential biological roles. Together, these findings expand the current pharmacogenomic framework, demonstrating that drug response mechanisms emerge from a wider network of regulatory and functional interactions. Introducing the concept of shadow VIPs highlights new molecular candidates for pharmacogenetic exploration and emphasizes the value of network-based approaches in advancing precision medicine.

Papillary thyroid carcinoma (PTC) is among the most common endocrine malignancies worldwide, and although generally associated with a favorable prognosis, a subset of patients develops aggressive disease with higher recurrence risk. This highlights the need for improved molecular characterization. Data integration approaches combined with computational methods offer new opportunities to refine diagnosis and uncover disease mechanisms. This study aims to integrate omics data and apply machine learning (ML) to identify clinically relevant biomarkers in papillary thyroid carcinoma. We selected 11 genes from the differentially expressed genes (DEGs)–LASSO intersection approach. Genes were validated using an independent external dataset (AUC = 91%, Sens. = 92%, Spec. = 97%, and Acc. = 95%). DEGs were integrated with metabolomics data from the literature, enabling the construction of a metabolite–gene interaction network, highlighting norepinephrine, arachidonic acid, and glutamic acid as representative metabolites, while the main genes were SLC6A14, ADK, ATIC, NT5E, and AR. We identified potential drug–gene interactions and performed survival analysis to assess the relevance of the possible biomarkers. This novel pipeline combining integration and machine learning provides new insights into thyroid cancer biology and identifies promising diagnostic markers, supporting advances in precision medicine.

Prostate cancer (PCa) exhibits marked biological heterogeneity, resulting in substantial differences in disease recurrence and clinical outcomes and highlighting the need for improved molecular risk stratification. In this study, we developed a machine learning–driven prognostic framework to identify transcriptomic features associated with disease-free survival (DFS) and stratify patients according to recurrence risk. Differentially expressed genes between prostate cancer and normal prostate tissue were initially screened, and their prognostic relevance was evaluated in the TCGA-PRAD cohort. A prognostic signature was subsequently developed using LASSO-Cox and multivariate Cox regression, generating a patient-level risk score for recurrence stratification. The final prognostic model comprised 33 genes and effectively separated patients into high- and low-risk groups, with significantly different DFS outcomes. The model demonstrated good prognostic discrimination, as indicated by the concordance index and time-dependent area under the receiver operating characteristic curve (AUC), and significantly separated patients into high- and low-risk groups. Random Survival Forest analysis further supported the prognostic relevance of the selected features, while SHAP analysis identified the relative contribution of individual genes to patient-level risk prediction. Drug–gene interaction analysis further identified a subset of the 33 signature genes targeted by FDA-approved drugs, providing hypothesis-generating information regarding potential therapeutic relevance. The results support the potential of the 33-gene signature for molecular risk stratification and recurrence prediction in PCa. However, the model was developed and evaluated within the TCGA-PRAD cohort and requires independent external and prospective validation before clinical translation.