Accurate identification of the genetic determinants of rare diseases is essential for effective recurrence‐risk management and informed reproductive decision‐making. Although whole‐exome sequencing (WES) and whole‐genome sequencing (WGS) have significantly improved diagnostic capabilities, a subset of affected families still receives no definitive molecular diagnosis. RNA sequencing (RNA‐seq) has emerged as a promising complementary diagnostic tool, yet its clinical implementation in the context of preconception genetic counseling remains underexplored. We used phytohemagglutinin‐activated peripheral blood cells (PHACs) as a robust RNA source and enhanced conventional RNA‐seq through the integration of three analytical innovations: (1) transcript isoform distribution (TID) analysis, (2) realignment against the MANE (Matched Annotation from NCBI and EMBL‐EBI) reference transcriptome, and (3) pharmacological induction–based cryptic splicing detection. This optimized pipeline was applied to 55 rare‐disease families with negative WES/WGS results who were undergoing preconception genetic counseling. Based on prior evaluations, families were grouped as VUS (n = 7), suspected‐gene/variant‐negative (n = 10), and unsolved/no‐candidate (n = 38). PHACs showed reduced interindividual variability and higher RNA integrity than fresh PBMCs (median RIN: 9.77 vs. 8.97; p < 0.0001). The optimized workflow improved diagnostic yield by 2.2‐fold (20% vs. 9%). Stratified analysis revealed positive rates of 71% (VUS), 40% (suspected‐gene/variant‐negative), and 5.2% (unsolved/no‐candidate). Among the 11 positive cases, 10 received definitive diagnoses, leading to diverse reproductive decisions. This enhanced RNA‐seq workflow provides a clinically applicable and scalable strategy for improving molecular diagnostics in reproductive and preconception settings, offering a valuable model for future clinical transcriptomics.

Genetic variants are the leading cause of congenital cataract (CC). To date, numerous genes have been implicated in the development of CC. The objective of the present study was to report two previously unrecognized gene variants associated with CC in two unrelated Chinese families, identified through whole exome sequencing (WES). Two unrelated Chinese families affected by CC were recruited. Cytogenetic and molecular genetic analyses were performed using karyotyping, chromosomal microarray analysis (CMA), and WES. In addition, RNA sequencing was conducted to assess differentially expressed genes in affected individuals compared with healthy controls. Karyotype and CMA elicited none of chromosome abnormalities in both of the families. However, WES revealed a novel missense variant NM_006891.4:c.154 T > C(p.S52P) in the CRYGD gene in the proband of Family 1, which was inherited from her mother with CC. In Family 2, a novel frameshift variant NM_000276.4:c.1046dup(p.M349Ifs∗36) in the OCRL gene was identified in the fetus via WES, which was inherited from the mother who had CC. RNA sequencing further demonstrated significantly reduced OCRL mRNA expression in the fetus compared with age-matched controls. The present study reports, for the first time, two novel variants in CRYGD and OCRL that were identified in the Chinese families with CC. These findings may expand the mutational spectrum of CC and highlight the utility of WES for the genetic diagnosis of patients with CC.

Glioblastoma (GBM) remains a lethal brain tumor with limited prognostic tools. Metabolic reprogramming, particularly in understudied pathways like propionate metabolism, may offer new biomarkers. Here, we identified a novel prognostic signature based on seven propionate metabolism‐related genes (SLC9A1, ELANE, ACADS, SOAT2, MYD88, ADSL, and BMP2) from the TCGA‐GBM cohort. A risk scoring model was constructed via LASSO Cox regression effectively stratified patients into high‐ and low‐risk groups with significant survival differences, which was also validated in independent GEO datasets. Multiomics analysis revealed that the high‐risk group was associated with an immunosuppressive microenvironment, characterized by increased immune checkpoint expression and distinct immune cell infiltration. Mutational profiling showed a strong association with key driver alterations, including enrichment of RB1 mutations in high‐risk and IDH1 mutations in low‐risk patients. Single‐cell RNA‐seq (scRNA‐seq) analysis confirmed the specific enrichment of signature genes within malignant cells, and coexpression network analysis (hdWGCNA) further linked the high‐risk phenotype to transcriptional modules. In conclusion, we established and validated a robust metabolic gene signature that not only predicts prognosis but also delineates a high‐risk GBM subtype defined by integrated metabolic, immunogenomic, and transcriptional features, providing new insights into the determinants of GBM aggressiveness.

Background Hepatocellular carcinoma (HCC) is still an important risk to public health, and its pathogenesis is still unclear. Therefore, it is important to probe the HCC pathogenesis and constantly find new diagnostic and therapeutic targets in HCC markers. Materials and Methods TCGA database, western blot, and qPCR were used to identify the role of NARS1 in tumor tissues from HCC tumor tissues. Co‐immunoprecipitation was used to investigate the direct interaction between NARS1 and HuR. Pulldown assays, co‐expression plasmid, and dual‐luciferase reporter gene assays were identified to investigate the role of NARS1 in NF‐ κ B signaling pathway. Results In the present study, we demonstrated that NARS1 promotes HCC proliferation and the potential target for diagnosis and treatment of HCC. Through multicohort analysis, the expression of NARS1 in HCC was significantly upregulated, and NARS1 expression in tumor tissues was positively correlated with the poor prognosis of patients. NARS1 can activate the NF‐ κ B signaling pathway by forming heterodimers with HuR, which further enhanced HCC proliferation. Conclusions Collectively, these studies reveal the intrinsic mechanism by which NARS1 promotes HCC proliferation and impart a potential rationale for the subsequent NARS1 clinical translational application.

Tumor-associated macrophages (TAMs) are key regulators of immune homeostasis within the tumor microenvironment (TME) and play critical roles in malignant progression. However, the molecular mechanisms linking macrophage metabolic remodeling to immune regulation remain incompletely understood. Glycine cleavage system H protein (GCSH), a core regulator of copper-dependent cell death, has been implicated in metabolic regulation in triple-negative breast cancer (TNBC), suggesting a potential role in macrophage-mediated TME remodeling. We integrated single-cell RNA sequencing and spatial transcriptomic data from TNBC tissues to systematically characterize macrophage subpopulations with high GCSH expression. Pseudotime trajectory analysis, cuproptosis-related scoring, cell-cell communication inference, metabolic pathway enrichment, and spatial localization analyses were performed to delineate their functional heterogeneity and microenvironmental context. In addition, mutation profiling, immunogenomic analysis, drug sensitivity prediction, and in vitro and in vivo functional experiments were conducted to comprehensively evaluate the biological and therapeutic relevance of GCSH. GCSH expression was predominantly enriched in macrophages, particularly in early activated subsets, and was associated with enhanced amino acid and lipid metabolic activity. GCSH + macrophages exhibited extensive interactions with T cells via pathways such as MIF-CD74-CXCR4 and LGALS9-CD45, contributing to an immunosuppressive, tumor-promoting microenvironment. Spatial analysis revealed their preferential localization at the tumor core-stroma interface. Notably, GCSH missense mutations were associated with increased M1 macrophage infiltration and enrichment of immune and inflammatory pathways. Clinically, high GCSH expression correlated with poor survival, genomic instability, and chemotherapy resistance. Functional experiments demonstrated that GCSH silencing suppressed tumor cell proliferation, migration, and clonogenicity, induced apoptosis, enhanced proinflammatory cytokine secretion, and significantly inhibited tumor growth in vivo. GCSH acts as a central molecular link between macrophage metabolic reprogramming, immune suppression, and TNBC progression, highlighting its potential as both a prognostic biomarker and therapeutic target.

Cardiovascular mortality remains predominantly driven by acute myocardial infarction (AMI), necessitating comprehensive elucidation of mechanisms governing cardiomyocyte reprogramming for therapeutic advancement. Characterizing molecular dynamics throughout cardiac repair processes presents substantial methodological challenges. We employed a comprehensive analytical framework combining bulk and single-cell RNA sequencing datasets from AMI patients, utilizing 26 distinct machine learning algorithms to delineate critical regulatory gene signatures associated with cardiac repair. Gene prioritization was achieved through differential expression profiling coupled with consensus clustering methodologies. Functional validation experiments in H9c2 cardiomyoblast cellular models demonstrated that enhanced SPP1 expression is associated with augmented cellular viability and stimulated cardioprotective factor release, though these findings require validation in more physiologically relevant systems. Machine learning architectures successfully identified robust cardiomyocyte reprogramming signatures correlating with cardiac functional restoration. Consensus clustering analysis of 276 genes revealed two phenotypically distinct repair subtypes demonstrating divergent recovery trajectories (p < 0.001). Enhanced-recovery clusters exhibited upregulated proliferation markers alongside intensified angiogenic signaling cascades. SPP1 demonstrated exceptional predictive capacity (AUC = 0.896), with experimental validation suggesting its potential functional role in promoting cellular survival, proliferation, and secretion of cardioprotective mediators (VEGF, IGF-1; all p < 0.05). This machine learning-driven approach successfully identified novel candidate prognostic biomarkers and potential therapeutic targets for cardiac repair interventions, substantially advancing mechanistic understanding of postinfarction cardiac remodeling processes. These findings generate testable hypotheses requiring validation in independent clinical cohorts and more physiologically relevant experimental systems.

Lung adenocarcinoma is a very aggressive cancer with poor clinical results. New molecular indicators are desperately needed to improve treatment decision‐making. This study looks at the relationship between the immunological microenvironment and genes linked to pyrimidine metabolism, particularly those that undergo acetylation, and the prognostic significance of these genes. Using publicly accessible genomic and clinical data, we used Gene Set Variation Analysis (GSVA) to identify acetylated pyrimidine pathway components that are highly correlated with survival outcomes. Three potential genes—TK1, RRM2B, and NME4—were identified for their prognostic relevance by using sophisticated predictive modeling approaches including CoxBoost and a random forest survival analysis. Using CIBERSORT deconvolution and single‐sample gene set enrichment, immunological landscape disparities were identified, and it was discovered that varied gene expression‐acetylation patterns were linked to varying immune cell infiltration. Gene activity and acetylation status‐based low‐risk patients showed positive survival patterns and higher levels of antitumor immune populations, indicating possible receptivity to immune‐based treatments. Functional validation experiments targeting TK1, including RNA interference followed by proliferation (CCK‐8, EdU), migration (Transwell), and wound healing assays, substantiated its role in promoting tumor aggressiveness. Collectively, our findings suggest that integrating metabolic gene signatures with immunological context offers a promising framework for precision oncology in lung adenocarcinoma.

Nonsynonymous single nucleotide polymorphisms (nsSNPs) in angiotensin Type II receptor (AGTR2) have been identified as a potential cause of cardiovascular illness in humans. Identifying structurally and functionally relevant alterations in AGTR2 is critical to investigate possible therapeutic targets. A comprehensive computational pipeline was employed to evaluate deleterious nsSNPs using multiple prediction algorithms, including SIFT, PolyPhen-2, CADD, REVEL, Mutation Assessor, MetaLR, I-Mutant, MutPred, and Phylo3D. Molecular docking and molecular dynamic simulation strategies were further utilized to thoroughly validate these nsSNPs. Additionally, gene-gene interaction networks were constructed to explore AGTR2's functional associations. Our findings indicated that four nsSNPs, including rs200599388, rs1556673810, rs3729979, and rs1556673736, potentially have the most deleterious effect on the AGTR2 gene. MD simulations revealed that these variants induced increased structural fluctuations and conformational instability compared with the wild-type protein. Gene-gene interaction analysis indicated that AGTR2 participates in several key regulatory pathways relevant to cardiovascular physiology. These findings will form the basis to design precision medicines for cardiovascular diseases in the future and welcome further preclinical and clinical investigations.

The extensive use of next‐generation sequencing (NGS) multi‐gene panels and advanced analysis algorithms have led to the identification of numerous genetic variants associated with breast, ovarian, and pancreatic cancer. Copynumber variations (CNVs), defined as deletions and duplications of specific DNA regions, account for up to 10% of pathogenic variants and can affect any of the cancer‐predisposing genes. Despite this, CNVs’ contribution beyond BRCA1 and BRCA2 remains underexplored. This observational study analyzed data from 2949 patients, primarily affected by breast or ovarian cancer, who underwent NGS testing with a 22‐gene hereditary cancer panel between 2018 and 2023, with a focus on CNV results. In line with comparison studies, a total diagnostic yield of 14.8% was observed with pathogenic variants in BRCA1, BRCA2, CHEK2, ATM, and PALB2 accounting for most of positive findings. In contrast, CNVs were found in 1.4% of patients, displaying a peculiar distribution pattern. PALB2 exhibited the highest frequency of pathogenic CNVs (66.7%), representing 62.2% of all PALB2 pathogenic variants. Notably, 24 out of 28 PALB2 CNV carriers shared the deletion of Exon 11. Further investigations revealed identical breakpoints and common geographical origins, and moreover, the same haplotype for some of the families suggests a relatively recent founder effect. Simultaneous sequence and copy number analyses resulted in likely higher positive predictive value of the test and, more interestingly, disclosed an unforeseen single contribution of CNVs in PALB2 gene, confirming geography as a key factor in shaping human genetic variations.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by a complex tumor microenvironment. Ubiquitination regulates key oncogenic processes and microenvironmental remodeling; however, its cell type-specific activity and spatial organization within the PDAC microenvironment remain poorly understood. Single-cell RNA sequencing, spatial transcriptomics, and bulk RNA-seq datasets of PDAC were integrated for multiomics analysis. Ubiquitination activity was quantified using gene-set scoring algorithms, followed by cell-communication and spatial interaction analyses. Prognostic models were constructed using bulk cohorts, and key findings were validated through gene-silencing functional assays. Ubiquitination activity was significantly increased in PDAC tissues compared with adjacent normal tissues, with ductal epithelial cells and fibroblasts showing the most prominent elevation. High ubiquitination states were associated with enhanced ductal-fibroblast interactions and distinct spatial patterns linked to invasion-associated tumor regions. Integration of ubiquitination-associated gene signatures identified a robust prognostic model, highlighting an extracellular matrix-related factor consistently overexpressed in tumors and associated with poor survival. Functional assays demonstrated that suppression of this factor inhibited proliferation, migration, invasion, and survival of pancreatic cancer cells. Ubiquitination organizes ductal-fibroblast crosstalk within the PDAC microenvironment and links spatial tumor ecology with disease aggressiveness and patient prognosis. Targeting ubiquitination-associated microenvironmental programs may offer new strategies for prognostic stratification and therapeutic intervention.