Breast cancer (BC) is one of the most common and deadly cancers affecting women worldwide. This study is aimed at investigating the association between BC risk and two single nucleotide polymorphisms (SNPs): ESR1 (rs2234693) and PIK3CA (rs6443624) in a Bangladeshi population. A case-control study was conducted with 112 BC patients and 124 healthy controls (HCs). Genomic DNA was extracted from peripheral blood samples, and genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Genotype and allele frequencies were analyzed to assess their association with BC risk. Genotype distributions for both ESR1 and PIK3CA conformed to Hardy-Weinberg equilibrium. The CT genotype of ESR1 was associated with a reduced risk of BC, whereas the CA genotype of PIK3CA was linked to an increased risk. The dominant model for ESR1 (CT + TT vs. CC) demonstrated a significant protective effect (aOR = 0.288, 95% CI: 0.160-0.516), whereas the dominant model for PIK3CA (CA + CC vs. AA) showed a higher risk (aOR = 4.166, 95% CI: 2.363-7.347). Over-dominant models supported these findings, while recessive models for both SNPs showed no significant associations. The findings suggest that ESR1 (rs2234693) may have a protective role and PIK3CA (rs6443624) may increase susceptibility to BC in Bangladeshi women. These SNPs may provide preliminary evidence for potential use as genetic susceptibility markers, but larger multicenter studies are needed before any clinical application can be considered.

Single-cell transcriptomics and machine learning methods are increasingly used to identify immune-related biomarkers in solid tumors, yet their combined application to microenvironment-related drivers of therapeutic resistance in clear cell renal cell carcinoma (ccRCC) is still limited. Here, we investigated the biological and clinical significance of myeloid-derived growth factor (MYDGF) through an integrative strategy spanning single-cell profiling, bulk multiomics, and functional validation. Analysis of scRNA-seq data (GSE156632) revealed that MYDGF is preferentially detected in malignant epithelial subpopulations and associated with the composition of myeloid and lymphoid compartments. Integration with TCGA-KIRC transcriptomic and clinical datasets demonstrated strong associations between MYDGF expression and immune-checkpoint activation, immune dysfunction signatures, and PI3K/AKT-MAPK pathway activity. Tumors with high MYDGF expression exhibited an immune-infiltrated yet functionally impaired microenvironment and were predicted to show reduced responsiveness to immune checkpoint blockade. Differential expression and enrichment analyses further highlighted MYDGF-associated genes involved in inflammatory, extracellular, and receptor-binding functions. A machine learning pipeline using LASSO Cox regression identified a preliminary 19-gene MYDGF-related prognostic gene set that requires further validation. Functional experiments confirmed that MYDGF knockdown suppressed proliferation, migration, and invasion in ccRCC cells. Overall, our analyses characterize MYDGF as a microenvironment-related biomarker linked to immune-associated features, signaling-associated alterations, and adverse prognosis in ccRCC. These results nominate MYDGF as a candidate prognostic biomarker and show the value of pairing single-cell resolution with computational modeling for biomarker discovery in renal cancer.

[This corrects the article DOI: 10.1155/humu/1463685.].

Genetic neuromuscular diseases are highly heterogeneous disorders characterized by diagnostic challenges and limited therapeutic options, underscoring an urgent need for precise biomarkers. The rapid advancement of multi-omics technologies has broadened biomarker discovery from single genomics to multidimensional integrative analyses encompassing transcriptomics, proteomics, and metabolomics. This progression offers opportunities to improve disease diagnosis, subtyping, prognosis assessment, and treatment monitoring. However, translational gaps persist between multi-omics discoveries and clinically applicable biomarkers. This review systematically examines the current application of multi-omics biomarkers in genetic neuromuscular diseases. It provides an in-depth analysis of the multifaceted barriers encountered during the translation process, including technical hurdles, clinical validation complexities, data interpretation challenges, and health system-level obstacles. Furthermore, the review explores emerging solutions including artificial intelligence-assisted decision-making, ethical governance, and policy preparedness. The review aims to offer a framework for constructing a potentially responsible and efficient multi-omics translation in genetic neuromuscular diseases.

Cancer-associated fibroblasts (CAFs) orchestrate immune-excluded tumor microenvironment (TME), but the CAF heterogeneity remains incompletely understood in gastric cancer (GC). In this study, we integrated multicohort single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk transcriptomic data to construct a comprehensive atlas of the GC TME. Unsupervised clustering identified eight transcriptionally distinct CAF subpopulations, among which CTHRC1+ CAFs were selectively enriched in tumors and showed the strongest association with T cell exclusion. Pseudotemporal trajectory analysis, gene regulatory network inference, and cell-cell communication analysis revealed that basic helix-loop-helix family member e41 (BHLHE41) serves as a key transcription factor driving CTHRC1+ CAF differentiation, whereas spatial analyses demonstrated these fibroblasts contribute to fibrotic niches at the tumor-stroma interface through macrophage migration inhibitory factor (MIF)-mediated signaling. Finally, we developed and validated a CTHRC1+ cancer-associated fibroblast-related risk signature (CRS) that accurately predicts immunotherapy response across independent cohorts. These findings establish CTHRC1+ CAFs as a critical stromal determinant of immune exclusion in GC, suggesting that targeting the CTHRC1+ CAF-MIF axis or applying CRS-guided patient stratification may enhance immunotherapy efficacy.

CTNNA3 encodes αT-catenin, an intercalated disc (ICD) protein essential for cardiomyocyte coupling. Human omics studies have shown reduced CTNNA3 expression, ICD ultrastructural disruption, and dilated cardiomyopathy (DCM)-associated hyperphosphorylation of αT-catenin. Direct RNA-level evidence linking biallelic CTNNA3 variants to human cardiomyopathy has been lacking. Clinical exome sequencing was performed in a 21-year-old man with DCM, severe left ventricular systolic dysfunction (LVEF 20%), and atrial fibrillation (AF). The identified homozygous variant (NM_013266.4 : c.1733 - 1G > C) was assessed with multiple splicing prediction tools and functionally validated via a minigene hybrid assay in HEK293 cells. Splicing predictors indicated loss of the canonical acceptor site. The minigene assay confirmed three aberrant transcripts: out-of-frame exon 13 skipping with a premature stop codon, a 24-nucleotide in-frame deletion, and partial intron retention, resulting in a possibly nonfunctional protein. Under guideline-directed medical therapy, LVEF normalized, but a persistent arrhythmic phenotype remained, with recurrent AF, frequent ventricular ectopic beats, and nonsustained ventricular tachycardia. We report a recessive form of DCM associated with a homozygous canonical splice-site variant in CTNNA3, encoding the ICD protein αT-catenin. Our results are consistent with human omics studies. Altogether, these data provide evidence that biallelic CTNNA3 splice-disrupting variants can cause human cardiomyopathy driven by ICD dysfunction. The dissociation between ventricular recovery and persistent arrhythmia highlights the complex phenotypic spectrum of CTNNA3-related disease.

Traditional medicine is now moving from the "one-size-fits-all" model toward personalized medicine, where diagnostic and therapeutic decisions are guided by the patient's unique genetic profile. Recent advances in genomics and pharmacogenomics have facilitated the identification of genetic variants linked to disease susceptibility and progression, as well as variability in drug response. However, translating these findings into clinical practice remains challenging, primarily due to the high cost and sophisticated genetic analysis infrastructure, which is only available in centralized genetic laboratories. A newly developed Portable Genetic Analyzer (PortaGen) was designed for point-of-care molecular genetics and pharmacogenomics analysis and evaluated in this study. PortaGen integrates 3D-printed parts and laptop-based centralized software control, along with digital recording and storage of results to support decentralized genetic testing. The prototype portable device was validated in comparison with an established portable polymerase chain reaction (PCR) workstation that complies with current operational standards and a reference laboratory-based method. Genotyping analysis was performed using ARMS-PCR (Amplification Refractory Mutation System Polymerase Chain Reaction) to detect and analyze CYP2C19 genetic variants (CYP2C19 ∗ 2; rs4244285, and CYP2C19 ∗ 17; rs12248560), relevant to pharmacogenomics, as well as the HBB: c.93-21(G>A) genetic variant, the most common variant leading to β-thalassemia. Concordance in genotyping calls between the new device, the established portable workstation, and the reference method was assessed using percentage agreement and Cohen's kappa coefficient, demonstrating consistently high concordance with statistically significant results (p < 0.05). These findings demonstrate that the new portable genotyping analyzer has improved throughput, visualization, and workflow efficiency into a suitcase-sized, portable point-of-care molecular genetic analysis device, which holds promise to advance personalized medicine interventions in a scalable and affordable fashion.

Diffuse large B-cell lymphoma (DLBCL) is characterized by profound heterogeneity that underpins varied clinical outcomes. To decipher this complexity, we performed an integrated single-cell and genomic analysis. Using scRNA-seq data (GSE182434), we identified six distinct malignant B-cell subclusters (MB1-MB6) within the DLBCL ecosystem. Cell-cell communication analysis revealed intricate interaction networks, particularly involving the MIF and Complement pathways. Prognostic analysis of bulk transcriptomic data (GSE32918) identified the MB5-related gene signature as the most critical factor associated with poor overall survival. This MB5 subgroup was associated with enhanced proliferative processes, a higher tumor mutational burden, and specific comutations. Leveraging MB5 marker genes, we developed and validated a robust CoxBoost-RSF machine-learning model that effectively stratified patient risk in independent cohorts. Our study defines the MB5 malignant B-cell subgroup as a key driver of DLBCL aggressiveness and provides both a novel prognostic biomarker and a framework for personalized therapeutic targeting.