Integrative Multi-Omics Profiling Identifies Candidate Biomarkers in Triple-Negative Breast Cancer 2258118

Comprehensive Analysis of m6A Regulators Identifies YTHDF3 as a Promising Prognostic Biomarker for Breast Cancer.

Collectively, m6A-related genes are dysregulated in breast cancer and correlate with patient outcomes, highlighting their biomarker potential, with YTHDF3 warranting in-depth investigation.

Integrative analysis identifies candidate biomarkers for bladder cancer: evidence from genomic and clinical validation

Integrative Molecular Profiling of miR-548f-3p in Triple-Negative Breast Cancer Highlights ANP32E as a Candidate Downstream Effector

Triple-negative breast cancer (TNBC) remains a major therapeutic challenge due to pronounced molecular heterogeneity, transcriptional plasticity, and frequent treatment resistance. MicroRNA (miRNA)-based strategies have emerged as potential approaches for modulating dysregulated gene expression networks in TNBC; however, the contribution of understudied miRNA families to TNBC-associated regulatory programs remains incompletely understood. This study aimed to investigate the tumor-suppressive role of miR-548f-3p in TNBC and to identify candidate downstream effectors, with particular focus on ANP32E. An integrative analysis combining public transcriptomic datasets, clinical expression profiling, computational target prediction, network-based prioritization, pathway analysis, and single-cell transcriptomic assessment identified miR-548f-3p as consistently downregulated in TNBC. Among candidate downstream targets, ANP32E, a chromatin-associated regulator involved in H2A.Z histone variant dynamics, was identified as a potential effector exhibiting increased expression in TNBC and enrichment within malignant epithelial cell populations. An inverse association between miR-548f-3p and ANP32E expression was observed in patient-derived samples. In breast cancer cell models, miR-548f-3p mimic restoration increased apoptosis, promoted G0/G1 accumulation, and reduced migration- and invasion-associated readouts, although measurable effects were also observed in non-tumorigenic MCF-10A cells. These phenotypic changes were accompanied by reduced ANP32E expression at the protein level, indicating that ANP32E expression is responsive to miR-548f-3p restoration. This study supports miR-548f-3p as a candidate tumor-suppressive miRNA in TNBC. The reduction in ANP32E protein expression following miR-548f-3p restoration, together with computational, single-cell, and clinical expression evidence, supports ANP32E as an expression-responsive candidate downstream effector of miR-548f-3p. Further reporter-based and rescue experiments are required to confirm direct 3′UTR-mediated targeting and to define the mechanistic contribution of ANP32E within the broader miR-548f-3p regulatory network.

MS4A3 as a potential prognostic biomarker for colon cancer: integrated analysis of expression patterns and immune cell infiltration

Background Membrane Spanning 4-Domains A3 (MS4A3) has been confirmed to possess significant tumor-suppressive potential in various malignancies. However, its expression characteristics and clinical prognostic value in colon cancer (CC) still lack systematic and in-depth investigation. This study aimed to systematically investigate the expression pattern, prognostic value, immune microenvironment association, and biological function of MS4A3 in CC through integrated bioinformatics analyses and experimental validation. Methods This study utilized The Cancer Genome Atlas-Colon Adenocarcinoma (TCGA-COAD) cohort to screen for genes significantly associated with CC and combined multiple independent Gene Expression Omnibus (GEO) datasets to validate the expression patterns and prognostic significance of MS4A3. Key biological pathways were identified through gene set enrichment analysis (GSEA), and tumor immune infiltration characteristics were evaluated using the CIBERSORT algorithm. Additionally, the expression of MS4A3 and its impacts on cellular functions were validated at the cellular level through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, Cell Counting Kit-8 (CCK-8), EdU, Transwell, and TUNEL assays. Results Analysis of public datasets revealed that MS4A3 is significantly downregulated in CC tissues, and its low expression is an independent risk factor for shortened overall survival (OS). GSEA indicated that MS4A3 downregulation is closely associated with the aberrant activation of the pentose phosphate pathway. Immune infiltration analysis showed that low MS4A3 expression is closely linked to the enrichment of M2 macrophages and neutrophils, as well as the upregulation of multiple immune checkpoint genes. In vitro experiments further confirmed that MS4A3 was lowly expressed in CC cell lines. Its overexpression significantly inhibited CC cell viability, proliferation, migration, and invasion, while simultaneously promoting cell apoptosis. Conclusions MS4A3 expression is significantly decreased in CC tissues and is significantly correlated with poor prognosis, suggesting that this gene may serve as a potential prognostic biomarker.

Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma

SIRPG is identified as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.

Integrated transcriptomic and immune-associated network analysis of breast cancer patient-derived organoids reveals candidate inflammatory biomarkers

Despite advances in targeted therapies, breast cancer remains one of the major challenges to global health. Although patient-derived organoids (PDOs) are physiologically relevant models, existing transcriptomic studies are limited by the poor integration of immune signals and the absence of shared biomarkers across subtypes. In this study, we hypothesised that an integrated transcriptomic and network-based analysis of PDOs could identify conserved transcriptional signatures and pathway interactions across breast cancer subtypes. PDOs representative of multiple breast cancer subtypes were analysed and compared with non-tumour organoids. Differential gene expression analysis was performed to identify transcriptional alterations, followed by pathway enrichment analysis and protein-protein interaction network analysis to investigate functional pathways and molecular interactions. Candidate biomarkers identified through computational analyses were subsequently validated experimentally by assessing gene expression and cytokine secretion profiles in tumour PDOs. A total of 646 differentially expressed genes were identified. Pathway enrichment analysis revealed translational machinery and ribosome biogenesis as the dominant statistically significant processes, consistent with hyperactivated protein synthesis in cancer cells. Although neurotrophin signaling pathways were not enriched at the transcriptome-wide level, network analysis identified a computationally predicted functional association between IL1RAP and NTRK3 in the STRING database. SLITRK3, IL1RAP and IRF2BP2 were consistently overexpressed in tumour PDOs and associated with activation of inflammatory pathways and increased secretion of IL-1 and IFN-γ. NTRK3 was identified as a direct network interactor of IL1RAP bridging inflammatory and neurotrophin signalling, though its transcriptional direction in BC PDOs could not be unambiguously resolved from the available data. Experimental validation confirmed the upregulation of SLITRK3, IL1RAP and IRF2BP2 together with elevated secretion of IL-1β and IFN-γ. Our findings suggest that IL1RAP and IRF2BP2 may be important in immune-related and candidated biomarker identification in breast cancer PDOs. We integrated transcriptomic, enrichment and network-based analyses for a robust strategy for biomarker discovery. This methodologically transparent framework highlights potential targets in breast cancer. Rather than only a list of candidate genes, it provides an organoid-derived computational framework for risk stratification.