Sepsis is a systemic inflammatory response syndrome caused by an infection featuring high morbidity and mortality due to complex mechanisms underlying immune dysfunction. In this study, based on the sepsis transcriptome profiles from the GEO datasets (GSE65682, GSE28750, GSE95233, and GSE167363), we used the machine learning method and other computational algorithms, such as differential gene expression analysis, weighted gene coexpression network analyses (WGCNA), and the building of PPI networks to identify four hub genes (DDX24, GZMM, KCNA3, and NCL). The quantitative reverse transcription PCR performed preliminary validation that all four hub genes were significantly downregulated in patients with sepsis. DDX24 had the highest diagnostic performance (AUC > 0.8) for discriminating patients from normal subjects. GZMM was found to be significantly related to the prognoses of patients as well as APACHE II scores, and the downregulated expression pattern might represent T cell and NK cell exhaustion. Analysis based on single‐cell RNA sequencing showed that DDX24 and GZMM were mainly expressed in T cells and NK cells, and the expression trends strongly correlate with patient survival. Functional enrichment analysis suggested that the hub genes likely participate in regulation of immune responses, especially those pertaining to T cells. Drug prediction found 25 candidate drugs that will serve as new therapeutic targets for precision medicine to treat sepsis. Overall, the multifaceted study shed light on key roles played by these hub genes (especially DDX24 and GZMM) in the development of sepsis and will be useful references in diagnosing patients and estimating prognosis.

BackgroundIntracerebral hemorrhage (ICH) is a devastating stroke subtype with high mortality and limited therapeutic options. While neuroinflammation contributes to secondary brain injury, the role of peripheral CD8+ T cell dysfunction in ICH pathogenesis remains poorly characterized. This study is aimed at identifying disease‐associated CD8+ T cell subpopulations and potential therapeutic targets through integrative multiomics analysis.MethodsWe performed bulk RNA sequencing on peripheral blood from 130 patients (66 ICH and 64 hypertension controls) across two independent cohorts, combined with single‐cell RNA sequencing of 13 patients. The scPAS algorithm integrated bulk and single‐cell data to identify phenotype‐associated cells. Five machine learning algorithms (LASSO, random forest, XGBoost, SVM, and Boruta) were employed for biomarker discovery. The therapeutic efficacy of rutin was evaluated in murine hypertensive ICH models.ResultsWe identified a distinct SELL‐high CD8+ T cell subpopulation (scPAS+ cells) exhibiting comprehensive effector dysfunction, characterized by downregulation of cytotoxicity genes (GZMA, GZMB, GNLY, NKG7, and CCL5). Pseudotime trajectory analysis revealed progressive differentiation toward this dysfunctional phenotype. SELL emerged as a consensus diagnostic biomarker across all five algorithms, demonstrating excellent discriminative performance (AUC: 0.876–0.936). In vivo, rutin treatment reduced SELL expression, restored CD8+ T cell cytotoxicity, decreased hemorrhage incidence, and attenuated neuroinflammation and oxidative stress.ConclusionsThis study identifies SELL‐marked effector‐deficient CD8+ T cells as a hallmark of ICH and establishes SELL as a robust diagnostic biomarker. Rutin represents a promising therapeutic candidate targeting peripheral immune dysfunction in hypertensive ICH.

IntroductionHepatocellular carcinoma (HCC) remains a leading cause of cancer‐related mortality worldwide, with cellular senescence playing a context‐dependent role in tumor progression and the immunosuppressive microenvironment. This study is aimed at identifying senescence‐related gene signatures through integrated single‐cell and transcriptomic analyses to construct a robust prognostic model for predicting survival and immunotherapy response in HCC patients.MethodsWe obtained single‐cell RNA sequencing (scRNA‐seq) data from the Gene Expression Omnibus (GEO) database and transcriptomic data from The Cancer Genome Atlas (TCGA). The scRNA‐seq data were processed using the Seurat and Harmony packages for cell clustering and batch correction. Senescence scores were calculated via the AUCell package, and differentially expressed genes were identified using the limma package. Prognostic genes were selected through univariate and LASSO Cox regression (glmnet package) to construct a risk model, which was validated in multiple independent cohorts. Immune infiltration was assessed with single‐sample gene set enrichment analysis (ssGSEA), TIMER, and MCPCounter algorithms, and response to immune checkpoint blockade was predicted using the tumor immune dysfunction and exclusion (TIDE) platform. Experimental validation included qRT‐PCR, Cell Counting Kit‐8 (CCK‐8), wound healing, and Transwell assays in HCC cell lines.ResultsA total of 80,997 identified cells were allocated to eight clusters, with an evidently higher percentage of natural killer (NK) cells in HCC samples. A higher senescence score was also seen in HCC samples, and poor prognosis was noticed in the patients of high senescence score group. Further, the DEGs were intersected with the genes highly expressed in Population 4 of NK cells to reveal their enrichment in cell cycle and cell division. Further, eight genes (TMEM106C, BSG, COPE, CDCA8, KPNA2, LIG1, UQCRH, and CCT5) with differential expression in HCC were applied to construct the risk model, which could stratify HCC patients into different risks and predict the prognosis. Besides, the high immune infiltration and expression levels of immune checkpoint–relevant genes yet poor immunotherapy response were noticed in HCC patients of high risk. Further validation tests have suggested that the knockdown of CDCA8 repressed the malignant phenotypes of HCC cells.DiscussionThis integrated analysis establishes a senescence‐related gene signature as a robust tool for prognostic stratification and immunotherapy response prediction in HCC. The model highlights the complex interplay between cellular senescence and the immunosuppressive tumor microenvironment, offering insights for personalized treatment strategies. Furthermore, the identified biomarker CDCA8 represents a promising therapeutic target warranting further investigation.ConclusionThese discoveries provide novel evidence on senescence in HCC, which may tailor the pharmacological interventions to improve the clinical management.

BackgroundAs a common gynecological malignancy, cervical cancer has a rising incidence rate and mortality, which has brought huge pressure to global public health. Although immunotherapy has been applied in clinical practice, its therapeutic effect is still far from satisfactory.MethodsInferCNV was used to calculate the CNV score and the ssGSE, which is an algorithm to calculate the abundance of samples. CellChat analysis and pseudotime analysis were used to observe the evolution and interaction relationships between different clusters. Establish a prognostic model for CC patients using univariate, LASSO, and Cox analysis, and evaluate copy number variation and TME in low‐risk groups. Finally, ssGSEA was applied to calculate the relationship between the hallmark gene sets and immune cycle steps and to calculate drug sensitivity in different risk groups using “oncopredict.” A series of experiments including CCK‐8 assay, clone formation, EdU assay, and Transwell assay were performed to detect the role of COL4A1 in CC.ResultsThe epithelial cells were divided into nine clusters. Among them, Cluster 8 has a lower CNV score, a lower degree of variation, and a better prognosis. After that, Cluster 8 sends a signal to fibroblasts through the PTN signaling pathway. A cervical cancer–related model (CCM) was constructed based on the marker genes of Cluster 8, and it can effectively distinguish the prognosis. There is a great difference in standardized TMB, immune cell infiltration, and ESTIMATE scores between the groups. Nine drugs were identified which may achieve better therapeutic effects when applied to low‐risk patients. Finally, knockdown of COL4A1 inhibits the proliferation and metastatic ability of CC cells.ConclusionOur study revealed different interactions between subgroups in the tumor microenvironment of CC epithelial cells. We established an effective prognostic model. Ultimately, through a series of in vitro function experiments, COL4A1 was recognized as a new potential target for the therapeutic intervention of CC.

AimsThe recovery of cardiac function after acute myocardial infarction is crucial for the prognosis of patients with myocardial infarction. Proprotein convertase subtilisin/Kexin Type 9 (PCSK9) inhibitors are widely used in patients with acute myocardial infarction due to their potent low‐density lipoprotein‐lowering effects. Recent studies have shown that elevated levels of circulating PCSK9 are associated with increased platelet reactivity and thrombosis; however, the effect and mechanism of PCSK9 on cardiac repair after myocardial infarction through the induction of platelet activation remain unclear. Therefore, the objective of this study was to investigate and clarify the specific effect of PCSK9 on cardiac repair processes following myocardial infarction. The detailed molecular and cellular mechanisms through which PCSK9 regulates cardiac repair after myocardial infarction by inducing platelet activation were observed.Methods and ResultsHearts from wild‐type (WT) C57BL/6J mice and PCSK9 knockout (PCSK9−/−) mice were subjected to left coronary artery (LAD) ligation to establish a myocardial infarction model. Six weeks postoperation, echocardiographic analysis and Masson staining revealed that inhibiting the increase in PCSK9 expression after myocardial infarction significantly reduced myocardial fibrosis. Transcriptome sequencing of mouse myocardial tissue suggested that PCSK9 suppresses immune regulation and adhesion pathways and that the platelet marker integrin subunit alpha 2b (Itga2b) is a potential key molecule. Subsequent in vivo and in vitro experiments demonstrated that PCSK9 promotes platelet activation and induces the fibrogenic phenotypic transformation of fibroblasts by transforming growth factor‐β (TGF‐β). In further studies, coculture experiments of fibroblasts and platelets revealed that PCSK9 promotes the conversion of fibroblasts to myofibroblasts by inducing platelet‐derived TGF‐β secretion.ConclusionPCSK9 promotes platelet activation, induces the secretion of platelet‐derived TGF‐β, and thereby accelerates myocardial fibrosis after myocardial infarction.

Ovarian cancer (OC) poses a significant threat to women’s health, with current treatment strategies remaining suboptimal, necessitating the exploration of novel therapeutic targets and immune microenvironment dynamics. This study integrates multiomics data from TCGA, GEO, and IEU‐Open‐GWAS, employing scRNA‐seq, scPagwas, BayesPrism, and WGCNA to identify key cell subpopulations and genes, followed by functional validation through EdU, colony formation, Transwell assays, and ferroptosis markers (MDA, ROS, and ferrous ions). Results reveal MALAT1+ epithelial cells as a core cell subpopulation in OC, with higher abundance correlating with shorter overall survival, suppressed immune microenvironments, and potential immunotherapy resistance, while their infiltration levels are closely associated with OC immune dynamics and somatic mutations. Further analysis identifies IPO9 as a core gene upregulated in OC, promoting tumor progression by inhibiting HMOX1‐dependent ferroptosis. These findings highlight MALAT1+ epithelial cells as drivers of immune suppression in OC and propose IPO9 as a promising therapeutic target, offering new avenues for immunotherapy development.

BackgroundMigrasomes, a newly identified subtype of extracellular vesicles generated during cell migration, play crucial roles in tumor microenvironment modulation. However, their systematic characterization in lung adenocarcinoma (LUAD) remains unexplored. This study is aimed at deciphering migrasome‐related molecular features and their clinical significance through multiomics integration.MethodsWe integrated bulk transcriptomes (541 LUAD samples from TCGA/GEO) with single‐cell RNA‐seq (GSE156632). Migrasome‐related genes (MIGgenes) were identified through WGCNA and differential expression analysis. A machine learning framework incorporating 10 algorithms generated 101 combinatorial models, with the optimal prognostic signature (MIGsig) selected via 10‐fold cross‐validation. Biological mechanisms were investigated through ssGSEA, TME analysis, and in vitro validation.ResultsOur analysis revealed significant migrasome activity enrichment in endothelial cells and fibroblasts, with 115 cross‐omics MIGgenes identified including 31 prognostic markers. The Lasso–Cox‐derived 3‐gene signature (GSTM5/DNASE1L3/PDGFB) demonstrated robust predictive performance (training set C index = 0.703; validation set GSE50081 AUC = 0.678). The low‐MIGsig group exhibited characteristic “hot tumor” features, including elevated immune infiltration and higher tumor mutational burden, and significantly improved immunotherapy response rates in the IMvigor210 cohort. Finally, MIGsig‐related genes were further validated by in vitro experiments and public database.ConclusionsThis study establishes the first migrasome‐based prognostic model for LUAD, demonstrating both independent survival prediction capability and clinical utility for identifying immunotherapy beneficiaries. The MIGsig signature provides novel biological insights into migrasome‐mediated tumor–immune interactions and represents a promising tool for precision oncology applications in LUAD management.

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.

The 2015 guidelines recommend using a large, diverse, and race-matched reference database. However, defining expectations in this context is subjective due to factors like genetic diversity and penetrance. ClinGen forms VCEPs to provide gene-specific interpretations of ACMG/AMP guidelines, including population information. Our study evaluates VCEP guidelines on allele frequency information. We analyzed genetic codes in databases to determine the frequency and potential pathogenicity of variants among 39 VCEPs, considering factors like allele frequency thresholds and disease prevalence. Our analysis found a variety of approved cutoffs among VCEPs, showing diverse disease mechanisms. We also noted variability in methods used to establish cutoffs and inconsistencies in parameters deemed necessary for approved thresholds. Understanding thresholds requires knowledge of genetics and diseases. VCEP guidelines on allele frequency evidence can help curators identify recommended thresholds. However, more guidance is needed for consistency in population evidence utilization.

Bladder cancer (BC) is a prevalent malignant tumor worldwide, posing a significant public health burden and challenge to human society. Current therapeutic modalities for BC include surgical treatment, radiotherapy, chemotherapy, targeted therapy, and immunosuppressive therapy. However, almost all patients experience disease progression and ultimately succumb to BC. Our study demonstrated that elevated expression of Heat Shock Protein Beta-6 (HSPB6) correlated with higher clinical grades and stages, establishing it as an independent prognostic risk factor for BC. Enrichment analysis indicated that HSPB6 is associated with the extracellular matrix in BC. Experimental validation revealed that HSPB6 overexpression inhibits the proliferation of BC cell line T24. This effect may be achieved by inhibiting the PI3K/Akt signaling pathway, which in turn leads to inhibition of epithelial-mesenchymal transition (EMT). Furthermore, we developed a prognostic risk model that incorporated DDR2, DPYSL3, MFAP5, PDGFRB, and SPOCD1, allowing accurate prediction of patient outcomes based on immunological status. In conclusion, this study highlights that HSPB6 overexpression can restrain the proliferation of BC cells and inhibit EMT, underscoring its potential as a diagnostic marker and therapeutic target in BC.