Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype lacking ER, PR, and HER2 expression, with limited targeted therapies and poor outcomes. Epigenetic dysregulation, particularly aberrant DNA methylation, is a key driver. Decitabine, a DNA methyltransferase inhibitor (DNMTi), shows promise by reactivating silenced tumor suppressor genes and modulating immune responses. This systematic review evaluates preclinical and clinical evidence on decitabine’s efficacy, mechanisms, and translational potential in TNBC. Methods: A PRISMA-2020 compliant search of PubMed, EBSCO, Web of Science, and Semantic Scholar was conducted up to April 2025. Included studies assessed decitabine alone or in combination in TNBC preclinical or clinical settings. Risk of bias was assessed using QUIPS and RoB 2.0 tools. Results: Twenty-five studies were included. In vitro, decitabine-induced growth inhibition, apoptosis, and re-expression of silenced genes (such as BRCA1 and CDH1). In vivo, it reduced tumor burden and enhanced anti-tumor immunity through MHC-I, PD-L1, and STING pathway upregulation. Synergy was noted with anti-PD-1, HDAC inhibitors, and chemotherapy. Resistance mechanisms included persistent DNMT activity, low DCK, and miRNA-driven escape (miR-155–TSPAN5). Conclusions: Decitabine demonstrates strong preclinical and early clinical potential in TNBC via epigenetic reprogramming and immune activation. Future strategies should focus on biomarker-based selection and resistance mitigation.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with limited biomarkers available for early diagnosis and risk stratification. In this study, we performed an integrative analysis of tissue and circulating microRNA (miRNA) expression profiles to identify candidates linked to disease progression and clinical outcomes. Tumor miRNA data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) and serum miRNA data from the Gene Expression Omnibus (GSE113740) were analyzed using differential expression, survival analysis, functional enrichment, and clinical subgroup evaluation. We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect. Clinical analyses revealed stage-specific upregulation of hsa-miR-106b and downregulation of the hsa-miR-124 family in metastatic tumors. Functional enrichment highlighted pathways such as PI3K-Akt, MAPK signalling, and nucleocytoplasmic transport. Circulating miRNAs, including hsa-miR-3619-3p, hsa-miR-1290, and hsa-miR-1185-2-3p, correlated with AFP levels and disease stage, underscoring their value as non-invasive biomarkers. These findings demonstrate that integrated analysis of tissue and serum miRNAs can identify clinically relevant biomarkers and potential therapeutic targets in HCC.

The regulatory networks of miRNAs, lncRNAs, and their target genes play a crucial role in controlling various cellular processes, including cell growth, apoptosis, and immune responses. In this study, we performed an in silico analysis to explore the interactions between miRNAs, lncRNAs, and their target genes in the context of disease mechanisms. We utilized multiple computational approaches, including miRNA-target interaction prediction, lncRNA-target network analysis, differential expression analysis using RNA-seq data, and validation of interactions using miRNA target prediction tools. Our results highlight key genes involved in apoptosis, cell cycle regulation, and tumorigenesis, providing valuable insights into the molecular mechanisms underlying disease progression. Non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), have emerged as pivotal regulators of gene expression in human cells. Despite substantial research into their roles, there remains a critical gap in understanding how these molecules interact within complex regulatory networks. In this study, we employed a comprehensive bioinformatics approach to systematically identify and analyze ncRNA-mediated gene regulation in human cells. We utilized publicly available datasets from the ENCODE and GEO repositories, combined with computational tools such as miRBase, LNCipedia, TargetScan, and Cytoscape, to predict ncRNA-gene interactions and construct regulatory networks. Our analysis reveals several novel ncRNA regulators and their associated gene targets, which were further explored through pathway enrichment analysis. This study provides new insights into the regulatory networks of ncRNAs in human gene expression, offering a foundation for future functional studies and potential therapeutic applications.

In the Middle East and North Africa (MENA) region, the prevalence of Type 2 Diabetes (T2D) is 17–18%, substantially higher than the ~9–10% reported in Western populations, with some Gulf states approaching 25% in adults. Historically, Arab diets, characterized by high fiber intake from whole grains, legumes, and fermented dairy products, have contrasted markedly with the Western dietary pattern increasingly prevalent among urbanized Arab populations. These nutritional shifts have been associated with changes in gut microbial composition, including lower representation of short-chain fatty acid–producing bacteria and higher abundance of dysbiosis-associated taxa. Concurrently, diet-derived compounds and microbial metabolites have been associated with changes in DNA methylation, histone modifications, and non-coding RNA expression. Epigenome-wide association studies revealed both shared and population-specific methylation signatures in patients with T2D. However, integrated multi-omics studies remain limited in Arab populations, where the disease burden is highest. This review integrates emerging evidence on diet-linked epigenetic alterations, microbiome-associated metabolic pathways, and their intersection in potentially contributing to T2D risk and progression. Given the heterogeneity of T2D across populations, there is a pressing need for culturally contextualized precision medicine frameworks that integrate population-specific diet–microbiome–epigenome dynamics rather than extrapolating findings across populations. Additionally, this review synthesizes evidence that dietary patterns are associated with T2D-relevant pathways through the diet–microbiome–epigenome axis, with emphasis on Arab/MENA populations and Western comparator cohorts.

Background/Objectives: Gliomas arise from distinct subgroups driven by different genetic alterations. Gain-of-function mutations in isocitrate dehydrogenase 1/2 (IDH1/2) are frequent in a subset of gliomas and are associated with improved survival compared with wild-type tumors. IDH-mutant gliomas can be classified as having the CpG island methylator phenotype (G-CIMP), which is characterized by widespread epigenetic changes associated with IDH1 mutations. G-CIMP-based stratification provides an added improvement in glioma classification when considered alongside grade and histology. Despite the success of G-CIMP-based stratification in predicting outcome in some patients, it does not fully account for non-responders, largely due to heterogeneity within G-CIMP gliomas. Methods: Using a The Cancer Genome Atlas (TCGA) glioblastoma, IDH-wildtype discovery cohort, we identified gene-level DNA methylation and expression patterns associated with clinical outcomes, including epigenetically regulated genes such as TUBB2A and TM4SF1. Results: In an independent TCGA diffuse glioma validation cohort enriched for IDH-mutant tumors, canonical IDH-defined G-CIMP classification was applied, and promoter methylation of TUBB2A and TM4SF1 showed significant associations with overall survival. Conclusions: These findings highlight molecular heterogeneity within IDH-defined G-CIMP gliomas and demonstrate that gene-specific methylation events can refine prognostic stratification beyond IDH status alone.

Background: Gliomas are characterized by a high degree of molecular heterogeneity, which impairs the reproducibility of predictive biomarkers derived from bulk-based molecular profiling due to immune/stromal contamination of tumors and the high prevalence of the IDH mutation signature. Methods: In this study, we used MOFA+ to derive intrinsic molecular signatures from transcriptional, methylation, and genomic profiles of a cohort of 667 diffuse gliomas in the Cancer Genome Atlas database. Thereafter, factor scores were derived for two separate Chinese Glioma Genome Atlas batches (Batch 1, n = 325; Batch 2, n = 693) without any retraining on the model. The prognostic independence of identified molecular signatures was assessed using multivariable Cox regression adjusted for IDH mutation status and tumor purity; purity-residualized survival analyses; IDH-stratified Cox regression in each cohort; validation by concordance index against established molecular signatures; and survival extreme profiling. To characterize the biological significance of factor signatures, we projected gene set signatures corresponding to each factor signature onto a single-cell RNA-seq dataset of GBM (GSE131928). Results: MOFA+ identified 12 latent factors, of which a vascular–extracellular matrix (ECM) remodeling axis (Factor 1) explained the highest multi-omics variance (24.9%) and was the strongest independent prognostic factor. In multivariable Cox regression adjusting for IDH status and tumor purity, Factor 1 remained independently prognostic (HR = 1.67, 95% CI 1.27–2.20, p = 0.0002); in a fully-adjusted model additionally including age, WHO grade, MGMT methylation, and 1p/19q codeletion (plus radiotherapy and chemotherapy status in the CGGA cohorts), Factor 1 remained prognostic in both CGGA cohorts (CGGA1: HR = 1.50, p = 3.8 × 10⁻⁵; CGGA2: HR = 1.18, p = 0.003) but lost significance in TCGA (HR = 1.04, p = 0.83), consistent with the cohort-dependent magnitude reported in the IDH-stratified and meta-regression analyses below. Purity-residualized survival analysis showed negligible attenuation of the Factor 1 signal (raw HR = 3.57 vs. residualized HR = 3.72; concordance 96.5%). Within IDH-wildtype gliomas, Factor 1 was significant in both external validation cohorts (CGGA1: HR = 1.64, FDR = 4.6 × 10⁻⁶; CGGA2: HR = 1.20, FDR = 0.02), though the TCGA IDH-wildtype subgroup showed a trend that did not survive FDR correction (FDR = 0.060). All validation was performed without model retraining. Within IDH-mutant gliomas, Factor 1 was strongly prognostic in both CGGA cohorts but was not significant in TCGA (HR = 1.17, FDR = 0.33). These findings should therefore be interpreted as consistent in directionality across cohorts but not uniformly replicated at the FDR-adjusted significance threshold in the TCGA discovery dataset. Concordance index benchmarking on a matched subset (n = 503) showed Factor 1 achieved discrimination comparable to the Mesenchymal signature (C = 0.797 vs. 0.801; ΔC = −0.004) while outperforming four other established classifiers. Factor 1 consistently separated patients with extreme survival phenotypes (OS < 6 vs. >15 months) across all three cohorts (all log-rank p < 0.001). Projection onto a single-cell GBM atlas (GSE131928), supported by inferCNV-based malignant-cell classification, localized the Vascular–ECM program to malignant cells and the Immune–ECM axis to myeloid compartments. Conclusions: The Vascular–ECM axis is a consistent, prognostic program robust to purity adjustment for diffuse gliomas that remains relevant across IDH-defined subgroups in three independent datasets comprising 1685 patients. The Vascular–ECM axis is a reproducible, purity-robust prognostic program in diffuse glioma, with directionally consistent adverse effects across TCGA, CGGA Batch 1, and CGGA Batch 2 (pooled n = 1685). Given the strong co-loading of endothelial, ECM, and myeloid genes observed in the single-cell projection, Factor 1 is best interpreted as a vascular/ECM-associated tumor–microenvironment ecosystem program rather than a malignant-cell-autonomous signature. Its FDR-adjusted significance within IDH-stratified subgroups is cohort-dependent and robust in both CGGA cohorts but attenuated in the TCGA IDH-wildtype (FDR = 0.060) and TCGA IDH-mutant (FDR = 0.33) strata. The pooled signal should therefore be interpreted as evidence of a generalizable biological program rather than a uniformly replicated subgroup-specific biomarker. It is possible to calculate factor scores based on RNA sequencing alone using fixed loadings (Z = XWᵀ), which may have implications for future translational applications. All findings are correlative; a causal role for the Vascular–ECM program in glioma progression, invasion, or therapy resistance remains to be established through functional perturbation experiments.