Search Results (104)

Background/Objectives: The escalating antimicrobial resistance crisis underscores the urgent need to explore underexplored ecological niches as reservoirs of novel bioactive compounds. The Brazilian Caatinga, a unique semi-arid biome, represents a promising reservoir for microbial discovery. Methods: In this study, we report the polyphasic characterization of Streptomyces galbus I339, a strain isolated from Caatinga soil. Whole-genome sequencing and phylogenomic analysis confirmed its taxonomic identity. In silico mining of the genome was conducted to assess biosynthetic potential. This genetic promise was experimentally validated through an integrated metabolomic approach, including liquid chromatography-tandem mass spectrometry (LC-MS/MS), nuclear magnetic resonance (NMR) spectroscopy, and gas chromatography-mass spectrometry (GC-MS) profiling. The anti-mycobacterial activity of the crude extract was evaluated against Mycobacterium tuberculosis. Results: The strain S. galbus I339 possesses a 7.55 Mbp genome with a high GC content (73.17%). Genome mining uncovered a remarkable biosynthetic potential, with 45 biosynthetic gene clusters (BGCs) predicted, including those for known antibiotics like actinomycins, as well as numerous orphan clusters. Genome mining uncovered a remarkable biosynthetic potential, with 45 biosynthetic gene clusters (BGCs) predicted, including those for known antibiotics like actinomycins, as well as numerous orphan clusters. Metabolomic analyses confirmed the production of actinomycins and identified abundant diketopiperazines. Furthermore, the crude extract exhibited antimycobacterial activity, with a potent MIC of 0.625 µg/mL. Conclusions: The convergence of genomic and metabolomic data not only validates the expression of a fraction of this strain’s biosynthetic arsenal but also highlights a significant untapped potential, with the majority of BGCs remaining silent under the tested conditions. Our work establishes S. galbus I339 as a compelling candidate for biodiscovery and underscores the value of integrating genomics and metabolomics to unlock the chemical diversity of microbes from extreme environments. Full article

Multiple sclerosis (MS), the most prevalent chronic inflammatory, demyelinating and neurodegenerative disease of the central nervous system in young adults, exhibits marked sexual dimorphism, with a 3:1 female-to-male ratio, but more severe symptoms and greater neurological damage in males. Increasing attention has focused on identifying circulating molecules that reflect inflammatory activity within the central nervous system and could clarify the mechanisms underlying MS. Pleiotrophin (PTN), a cytokine implicated in autoimmune and neurological diseases, is significantly elevated in patients with relapsing-remitting MS (RRMS). To explore the potential contribution of PTN and its receptors to neuroinflammatory signaling, we quantified the mRNA expression of PTN receptors in peripheral blood mononuclear cells from RRMS patients compared to untreated RRMS patients and healthy control subjects. We further performed an in silico molecular docking and molecular dynamics analysis to assess the possible functional significance of PTN-receptor interactions. Our results show a significant overexpression of integrin subunit beta-3 (ITGB3) mRNA in peripheral blood mononuclear cells from RRMS patients compared to healthy control subjects. Molecular docking shows that PTN could binds to the metal ion-dependent adhesion site domain of ITGB3 via Mg²⁺/Ca²⁺-mediated stabilization and has a higher binding affinity than fibrinogen, the canonical endogenous ligand. These findings suggest that ITGB3 could be a dynamically regulated integrin receptor in RRMS that may participate in PTN-driven neuroinflammatory pathways in peripheral blood immune cells, influenced by disease stage, sex, and immunotherapy. While our results support the biological plausibility of PTN–ITGB3 engagement, they remain hypothesis-generating and require functional validation. The integration of molecular expression data and computational modeling underscores the potential involvement of ITGB3 as a possible participant in MS and warrants further investigation of its clinical and mechanistic role. Full article

Background/Objectives: Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease strongly influenced by genetic factors. Genome-wide association studies (GWASs) have identified numerous non-coding susceptibility loci, but their functional roles remain poorly understood. The single-nucleotide variant (SNV) rs2836882, located in an enhancer near the ETS2 proto-oncogene, has been implicated in immune regulation, though its contribution to SLE is unclear. Methods: We analyzed rs2836882 in 246 Italian patients with SLE and 216 matched controls using TaqMan genotyping. A weighted genetic risk score (wGRS) combining rs2836882 with other known SLE variants was calculated. ETS2 mRNA expression was quantified by RT-qPCR in PBMCs from 60 individuals, and in silico analyses assessed the variant’s functional context. Results: The rs2836882 risk allele was significantly associated with SLE (OR = 1.54, p = 0.02). Patients showed a markedly higher wGRS than controls (p < 0.00001), confirming an additive genetic burden. In silico data indicated that rs2836882 lies within an active enhancer region (H3K4me1/H3K27ac+) containing PU.1 binding motifs and functions as an expression quantitative trait locus (eQTL) for ETS2. Expression analysis demonstrated that carriers of the risk allele exhibited significantly increased ETS2 expression compared to non-carriers (p = 0.002) in both groups. Conclusions: In conclusion, rs2836882 is a functional regulatory variant that enhances ETS2 transcription and contributes to increased SLE susceptibility. These findings establish a mechanistic link between a non-coding GWAS locus and disease risk, emphasizing the role of regulatory variants in autoimmune pathogenesis and supporting the integration of functional non-coding variants into genetic risk models for improved patient stratification. Full article

Marinesco–Sjögren syndrome (MSS) is a rare autosomal recessive disorder characterized by cerebellar ataxia, congenital cataracts, developmental delay, hypotonia, and progressive myopathy. Most reported cases are linked to pathogenic variants in SIL1, a gene encoding a co-chaperone essential for protein folding in the endoplasmic reticulum. Here, we present a comprehensive case study of a Turkish pediatric patient diagnosed with MSS, supported by genetic, bioinformatic, and structural modeling analyses. Whole-exome sequencing revealed a homozygous splice-site variant (SIL1 c.453+1G>T), confirmed by Sanger sequencing and segregation analysis. In silico annotation using Genomize, InterVar, Franklin, VarSome, ClinVar, OMIM, and PubMed classified the variant as pathogenic according to ACMG guidelines. Structural modeling by Phyre2 and I-TASSER demonstrated that the variant abolishes the intron 5 donor site, leading to truncation of the wild-type 461-amino-acid protein into a shortened ~189-amino-acid polypeptide. This truncation results in the loss of critical Armadillo (ARM) repeats required for HSPA5 interaction, explaining the observed instability and impaired chaperone function. Clinically, the patient presented with congenital cataracts, ataxia, developmental delay, and progressive muscle weakness, consistent with previously reported MSS cases. Comparison with the literature confirmed that splice-site variants frequently correlate with severe phenotypes, including early-onset ataxia and cataracts. This report highlights the importance of integrating genomic, structural, and clinical data to better understand genotype–phenotype correlations in MSS. Our findings expand the mutational spectrum of SIL1, reinforce the role of splicing defects in disease pathogenesis, and emphasize the necessity of comprehensive molecular diagnostics for rare neurogenetic syndromes. Full article

Background/Objectives: Fused deposition modeling (FDM) three-dimensional (3D) printing represents an emerging manufacturing platform for personalized oral dosage forms. Its success relies on developing robust drug-loaded filaments with consistent mechanical, thermal, and dissolution properties. This work aims to (i) develop and characterize ketoprofen-loaded filaments using hot-melt extrusion (HME) and (ii) utilize them to fabricate both immediate-release (IR) and sustained-release (SR) tablets via FDM 3D printing. Methods: Filaments were prepared using Kollicoat^® IR and hydroxypropyl methylcellulose (HPMC, 2600–5600 cP) as functional polymers. Sorbitol and sodium lauryl sulfate (SLS) were incorporated as plasticizer and surfactant, respectively. Filaments were evaluated for quality attributes, drug content, tensile strength, and physicochemical and surface characteristics using Scanning Electron Microscopy (SEM), Attenuated Total Reflection Fourier-transform infrared (ATR-FTIR), X-ray Diffraction (XRD), Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA). Optimized filaments were fed into an FDM 3D printer to fabricate ketoprofen tablets with varied geometries, shell numbers, and infill densities. Tablets were subjected to USP tests (weight variation, friability, hardness, disintegration, assay, content uniformity), dissolution profiling, and release kinetics modeling. Comparative dissolution studies with market Profenid^® and Bi-Profenid^® tablets were conducted. GastroPlus^® simulations were used for in vitro–in silico correlation. Results: Among the tested formulations, Kollicoat^® IR-based filaments with sorbitol and SLS (F6) demonstrated superior printability, characterized by consistent feeding, stable extrusion, and reliable formation of uniform structures for immediate-release applications. In contrast, HPMC-based filaments with sorbitol (F13) offered the most robust performance for SR formulations. Both exhibited uniform diameter, drug loading, and mechanical strength. IR tablets achieved >80% release within 30 min, while SR tablets prolonged release up to 12 h, following Higuchi and Korsmeyer–Peppas kinetics. All quality attributes complied with USP limits. Market products showed comparable dissolution, validating the approach. GastroPlus^® simulations predicted pharmacokinetic profiles consistent with reported data, supporting IVIVC. Conclusions: This integrated workflow establishes a robust strategy for producing IR and SR ketoprofen tablets from a single FDM platform. The results highlight the feasibility of point-of-care, personalized medicine using 3D printing technologies. Full article

Background. Hypophosphatasia (HPP) is a rare genetic disorder caused by impaired tissue non-specific alkaline phosphatase (ALPL/TNSALP) activity that impacts the musculoskeletal and neurological systems. It is extremely variable, with up to six forms of increasing severity. The large phenotypic variability and the still remaining high number of variants of uncertain significance (VUS) in the ALPL gene represent a conundrum for clinicians dealing with people suspected to be suffering from HPP. Methods. We applied a multi-faceted bench-based and high-throughput bioinformatics analysis to investigate the effect of 21 ALPL variants (18 deleterious—pathogenic or likely pathogenic—and 3 VUS) on the structure and function of the mutated encoded protein. The results were compared with available clinical and biochemical data. Results. Most variants were downregulated or not expressed by Western blot analysis. Impairment of the enzymatic activity was confirmed in vitro for all variants by a specific colorimetric enzymatic assay. In silico prediction was in line with functional data and allowed for preliminary categorization of variants based on their impact on both the overall stability of the protein complex and local structural alterations. Coherence among bioinformatics, experimental and clinical data was documented for more than 70% of the variants. Conclusions. Functional and in silico characterizations of ALPL variants in people with a suspicion of HPP offer integrative strategies to genotyping in assisting clinicians for diagnosis confirmation in doubtful cases. Full article

Therapeutic strategies targeting “tumor-educated platelets” (TEPs) and platelet–tumor interactions by key signaling pathways (ITAM, P2Y12) may reduce metastasis and cancer. Using a TEP gene expression dataset originally created to study swarm intelligence-enhanced detection of lung cancer cells (GSE89843), we did perform extensive transcriptome analysis to integrate these data with directed protein–protein interactions and build a TEP-specific signaling network. We analyze network topology and controllability and identify critical and indispensable nodes, as well as high-weight, usually high-score nodes. We reconstruct (pharmacological) controllable subnetworks of TEP signaling, which we then explore for drugs targets. We found 111 upregulated and 108 downregulated genes compared to control platelets, enriched in pathways related to extracellular matrix interactions, cytoskeleton organization, immune signaling, and platelet activation. Ribosomal function, apoptosis, and immune signaling were among the downregulated processes, highlighting unique TEP profiles in non-small-cell lung cancer (NSCLC). Our integrative analysis of TEPs in NSCLC reveals key transcriptional and network-based alterations harmful for the cancer patient. Using four complementary strategies, we identified five high-confidence genes (Gene symbols always given throughout the paper), ITGA2B, FLNA, GRB2, FCGR2A, and APP, as central to TEP signaling. These can be targeted by FDA-approved drugs. Fostamatinib, an SYK inhibitor, emerged as the top candidate drug to disrupt ITAM-mediated platelet activation selectively; metastasis-promoting metalloprotease and cytoskeletal targets influencing adhesion were also identified. A low-dose combination therapy of fostamatinib, Aducanumab, and acetylsalicylic acid (aspirin) may control TEP effects. In conclusion, our preclinical in silico approach revealed FDA-approved drugs that allow therapeutic targeting of metastasis-promoting TEPs and target NSCLC at the same time. Full article

Objectives: This in silico study sought to identify specific biomarkers for mild traumatic brain injury (mTBI) through the analysis of publicly available gene and miRNA databases, hypothesizing their influence on neuronal structure, axonal integrity, and regeneration. Methods: This study implemented a three-step process: (1) data searching for mTBI-related genes in Gene and MalaCard databases and literature review, (2) data analysis involved performing functional annotation through GO and KEGG, identifying hub genes using Cytoscape, mapping protein–protein interactions via DAVID and STRING, and predicting miRNA targets using miRSystem, miRWalk2.0, and mirDIP, and (3) RNA-sequencing analysis applied to the mTBI dataset GSE123336. Results: Eleven candidate hub genes associated with mTBI outcome were identified: APOE, S100B, GFAP, BDNF, AQP4, COMT, MBP, UCHL1, DRD2, ASIC1, and CACNA1A. Enrichment analysis linked these genes to neuron projection regeneration and synaptic plasticity. miRNAs linked to the mTBI candidate genes were hsa-miR-9-5p, hsa-miR-204-5p, hsa-miR-1908-5p, hsa-miR-16-5p, hsa-miR-10a-5p, has-miR-218-5p, has-miR-34a-5p, and has-miR-199b-5p. The RNA sequencing revealed 2664 differentially expressed miRNAs post-mTBI, with 17 showing significant changes at the time of injury and 48 h post-injury. Two miRNAs were positively correlated with direct head hits. Conclusions: Our bioinformatic analysis suggests that specific genes and miRNAs, particularly hsa-miR-10a-5p, may be involved in molecular pathways influencing mTBI outcomes. Our research may guide future mTBI diagnostics, emphasizing the need to measure and track these specific genes and miRNAs in diverse cohorts. Full article

Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. This study investigates multi-omics lung cancer data obtained from The Cancer Genome Atlas (TCGA) repository. Differentially expressed genes were identified using four statistical approaches: LIMMA, T-test, Canonical Correlation Analysis (CCA), and the Wilcoxon test applied across gene expression (GE), DNA methylation, and microRNA (miRNA) datasets. Kernel Machine Regression (KMR) was subsequently employed to perform data fusion across the multi-modal datasets. The empirical results highlight notable interactions among GE, miRNA expression, and DNA methylation in lung cancer. Our analysis identified 38 genes that show significant associations with lung cancer. Among these, 8 genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, and ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that may offer promising therapeutic potential against lung cancer. The effectiveness of these candidate drugs is further reinforced by evidence from independent research studies, which emphasize their potential in lung cancer treatment. Full article

Human induced pluripotent cells (hiPSCs), generated in vitro, represent a groundbreaking tool for tissue regeneration and repair. Understanding the metabolic intricacies governing hiPSCs is crucial for optimizing their performance across diverse environmental conditions and improving production strategies. To this end, in this work, we introduce hiPSCGEM01, the first genome-scale, context-specific metabolic model (GEM) uniquely tailored to fibroblast-derived hiPSCs, marking a clear distinction from existing models of embryonic and cancer stem cells. hiPSCGEM01 was developed using relevant genome expression data carefully selected from the Gene Expression Omnibus (GEO), and integrated with the RECON 3D framework, a comprehensive genome-scale metabolic model of human metabolism. Redundant and unused reactions and genes were identified and removed from the model. Key reactions, including those facilitating the exchange and transport of metabolites between extracellular and intracellular environments, along with all metabolites required to simulate the growth medium, were integrated into hiPSCGEM01. Finally, blocked reactions and dead-end metabolites were identified and adequately solved. Knockout simulations combined with flux balance analysis (FBA) were employed to identify essential genes and metabolites within the metabolic network, providing a comprehensive systems-level view of fibroblast-derived hiPSC metabolism. Notably, the model uncovered the unexpected involvement of nitrate and xenobiotic metabolism—pathways not previously associated with hiPSCs—highlighting potential novel mechanisms of cellular adaptation that merit further investigation. hiPSCGEM01 establishes a robust platform for in silico analysis and the rational optimization of in vitro experiments. Future applications include the evaluation and refinement of culture media, the design of new formulations, and the prediction of hiPSC responses under varying growth conditions, ultimately advancing both experimental and clinical outcomes. Full article

Neopestalotiopsis zimbabwana is an emerging phytopathogen with multiple hosts. Considering the environmental, toxicological, and resistance issues linked to synthetic fungicides, Origanum vulgare L. essential oil (OEO) was evaluated through in vitro, in vivo, and in silico approaches. The pathogen, isolated from Watsonia borbonica L., was molecularly identified. Gas chromatography–mass spectrometry (GC–MS) analysis showed hexadecanoic acid (15.98%), dodecanoic acid (15.74%), terpinen-4-ol (11.61%), and thymol (7.65%) as the main components. In vitro assays determined a minimum inhibitory concentration (MIC) of 30% OEO and a minimal fungicidal concentration (MFC) of 60% OEO. Growth chamber trials demonstrated that preventive sprays maintained 0% foliar damage—similar to Captan^®—while controls reached ≈98%; suspending applications after week 4 resulted in ≈45% damage by week 8. These results confirm that OEO lacks systemic residual activity, acting only as a protectant within preventive integrated pest management (IPM) schemes. Docking to cytochrome b (protein data bank, PDB: 5TL8) indicated strong binding of α-farnesene (−7.638 kcal·mol⁻¹), isoterpinolene (−6.944), and α-terpineol (−6.918), suggesting disruption of mitochondrial respiration via Complex III. OEO represents a promising eco-friendly alternative for managing N. zimbabwana under controlled conditions and reducing reliance on synthetic fungicides. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies for which there are few effective biomarkers for diagnosis, prognosis, and treatment monitoring. Given the paucity of data in the literature, this study aimed to evaluate the biomarker potential of selected miRNAs (miR-222-3p, miR-3154, miR-3945, miR-4534, and miR-4742) and their protein targets in the context of PDAC. Methods: The expression levels of miRNA candidates were quantified by real-time quantitative PCR in lymphocyte samples from 46 PDAC patients and 50 healthy controls. In silico analyses were performed to identify potential target genes and proteins. ELISA was used to measure protein expression in both groups. Statistical analyses included ROC curve analysis, linear regression, and correlation analyses. In addition, correlations between miRNA/protein expression and clinicopathologic characteristics, including survival, were investigated. Results: miR-222-3p and miR-3154 were significantly downregulated in PDAC patients compared to controls (p < 0.001). Among the dual miRNA combinations, miR-222-3p and miR-4534 showed the highest discriminatory power (AUC = 0.629, p = 0.022). The miR-222-3p expression was significantly increased in patients with a history of alcohol consumption (p = 0.02). Significant correlations were observed between miR-3154 expression and T-stage (p = 0.01) and between perineural invasion and miR-222-3p levels (p = 0.02). Survival analysis showed that high miR-3945 expression was significantly associated with shorter overall survival (p = 0.001). Elevated levels of ESR1, HCFC1, and EPC1 were significantly associated with lymphatic invasion (p < 0.05), while high KCNA1 expression correlated with shorter survival (p = 0.006), indicating its potential as a negative prognostic biomarker. Linear regression analysis revealed a significant positive correlation between miR-3945 and KCNA1 expression (β = 0.259, p = 0.038), indicating a possible regulatory interaction. A borderline correlation was also found between miR-4742 and EPC1 expression (p = 0.055). Conclusions: This study identifies several miRNAs and associated proteins with diagnostic and prognostic significance in PDAC. The results emphasize the clinical relevance of integrating multi-layered analyses of miRNA–protein interactions. The observed associations highlight the role of these molecular markers in tumor progression and patient survival and offer promising opportunities for future research and clinical application in precision oncology. Full article

Amorphous solid dispersions (ASDs) represent a promising formulation strategy for improving the solubility and bioavailability of poorly water-soluble drugs, a major challenge in pharmaceutical development. This review provides a comprehensive analysis of the physicochemical principles underlying ASD stability, with a focus on drug–polymer miscibility, molecular mobility, and thermodynamic properties. The main manufacturing techniques including hot-melt extrusion, spray drying, and KinetiSol^® dispersing are discussed for their impact on formulation homogeneity and scalability. Recent advances in excipient selection, molecular modeling, and in silico predictive approaches have transformed ASD design, reducing dependence on traditional trial-and-error methods. Furthermore, machine learning and artificial intelligence (AI)-based computational platforms are reshaping formulation strategies by enabling accurate predictions of drug–polymer interactions and physical stability. Advanced characterization methods such as solid-state NMR, IR, and dielectric spectroscopy provide valuable insights into phase separation and recrystallization. Despite these technological innovations, ensuring long-term stability and maintaining supersaturation remain significant challenges for ASDs. Integrated formulation design frameworks, including PBPK modeling and accelerated stability testing, offer potential solutions to address these issues. Future research should emphasize interdisciplinary collaboration, leveraging computational advancements together with experimental validation to refine formulation strategies and accelerate clinical translation. The scientists can unlock the full therapeutic potential with emerging technologies and a data-driven approach. Full article

Viral hepatitis B and C (HBV and HCV) and metabolic dysfunction-associated steatotic liver disease (MASLD) are major public health concerns in Mexico, driving liver cirrhosis and hepatocellular carcinoma. The Genome-based Mexican (GENOMEX) diet, rich in bioactive compounds, may provide a nutritional strategy for preventing and managing liver disease. This study combines a literature review with integrative bioinformatic analyses to map the antiviral and hepatoprotective mechanisms activated by GENOMEX-derived bioactives and assess their therapeutic potential for preventing and managing liver disease. A literature-based review integrated with bioinformatics to identify the pathways activated by nutrients and bioactive compounds of the GENOMEX diet against HBV, HCV, and MASLD, incorporating data from in silico, in vitro, in vivo, and clinical studies, was conducted. An integrative bioinformatic approach, incorporating the Comparative Toxicogenomic Database and Functional Enrichment Analysis (STRING, DAVID, and Enrichr), was used to identify links between genes, nutrients, and bioactive compounds, with a subset of Mexican food staples included in the GENOMEX diet. The GENOMEX diet includes bioactive nutrients that may modulate molecular pathways related to immune response, oxidative stress, nutrient metabolism, and inflammation. Through integrative analysis, we identified key molecular targets—including TNF, PPARA, TP53, and IL6—that are implicated in viral replication, MASLD progression, and hepatocarcinogenesis. Functional enrichment revealed that these traditional Mexican foods and their nutrients are associated with genes and pathways involved in viral infection, metabolic dysfunction, fibrosis, and liver cancer. These findings highlight that the gene–nutrient interactions of the Mexican staple food in the GENOMEX diet can be integrated into nutritional strategies to prevent and manage HBV, HCV, and MASLD, while reducing fibrosis and HCC progression. These strategies are especially relevant in regions where antiviral treatments are limited due to high costs, antiviral resistance, and an escalating mismatch between the population’s evolutionary genetics and modern environment. Full article

Background: Despite clinical and pathological risk tools, predicting outcomes in non-muscle-invasive bladder cancer (NMIBC), particularly high-grade (HG) cases, remains challenging due to its unpredictable recurrence and progression. There is an urgent need for molecular biomarkers to enhance risk stratification and guide treatment. Methods: We assessed the prognostic potential of eight miRNAs (miR-9, miR-143, miR-182, miR-205, miR-27a, miR-369, let-7c, and let-7g) in a cohort of ninety patients with primary bladder cancer. Expression data were retrieved from our previously published studies. Kaplan–Meier’s and Cox’s regression analyses were used to evaluate the associations with overall survival (OS), metastasis-free survival (MFS), and clinical outcomes. Principal component analysis (PCA) was performed to identify informative miRNA combinations. Target gene prediction, pathway enrichment (DAVID), and drug–gene interaction mapping (DGIdb) were conducted in silico. Results: A high expression of let-7g and miR-9 was significantly associated with better OS in HG NMIBC and MIBC, respectively (p = 0.013 and p = 0.000). MiR-9 downregulation correlated with metastasis in MIBC (p = 0.018). Among all combinations, miR-205 and miR-27a best predicted intermediate-risk NMIBC progression and recurrence (r² = 0.982, p = 0.000). A functional analysis revealed that these miRNAs regulate key cancer-related pathways (MAPK, mTOR, and p53) through genes such as TP53, PTEN, and CDKN1A. Drug interaction mapping identified nine target genes (e.g., DAPK1, ATR, and MTR) associated with eight FDA-approved bladder cancer therapies, including cisplatin and gemcitabine. Conclusions: Let-7g, miR-9, miR-143, miR-182, and miR-205 emerged as promising biomarkers for outcome prediction in NMIBC. Their integration into liquid biopsy platforms could support non-invasive monitoring and personalized treatment strategies. These findings warrant validation in larger, prospective studies and through functional assays. Full article