Background: Cervical cancer (CC) remains a significant global health challenge despite advancements in screening, HPV vaccination, and therapeutic strategies. Tumor heterogeneity, driven by epigenetic modifications, affects immune evasion, metastasis, and treatment response. Cancer-associated fibroblasts (CAFs) play a crucial role in CC progression and therapy resistance. Single-cell sequencing offers new insights but remains underutilized in CC research. This study integrates single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and deconvolution analysis to identify key genes and immunotherapy targets. By constructing a prognostic model and exploring the immune microenvironment, we aim to provide novel insights into CC pathogenesis and potential therapeutic strategies. Methods: We utilized scRNA-seq, spatial transcriptomics, deconvolution analysis, and pseudotime trajectory mapping to delineate fibroblast subtypes within the tumor immune microenvironment (TIME) of CC. Functional annotations, differential gene expression profiling, cell–cell communication pathways, and transcription factor networks were systematically analyzed. A prognostic model based on bulk RNA-seq data was constructed and validated through survival analysis, with correlations to immune microenvironment characteristics. Functional experiments investigated the role of SDC1, a critical mediator of fibroblast-tumor crosstalk. Additionally, Fibroblast–tumor cell co-culture systems and functional assays were employed to investigate the paracrine role of SDC1. The CAF MYH11⁺ subpopulation was isolated via fluorescence-activated cell sorting (FACS). Multiplex immunofluorescence and immunohistochemical analyses were performed on both cultured cells and human cervical cancer tissue samples to characterize the spatial distribution and dynamic remodeling of MYH11 during stromal reorganization. Results: Six distinct fibroblast subtypes were identified, including the C0 MYH11 + fibroblasts, which exhibited unique roles in stemness maintenance, metabolic activity, and immune regulation. Spatial and functional analyses revealed that the C0 subtype is central to tumor-fibroblast interactions, particularly through the MDK-SDC1 signaling axis. The prognostic model incorporating fibroblast-specific markers demonstrated robust predictive power for patient survival outcomes. Additionally, in vitro SDC1 knockdown significantly inhibited CC cell proliferation, migration, and invasion. Fibroblasts show spatially regulated heterogeneity, with activation markers enriched in the tumor zone and MYH11 highest in normal zones, indicating dynamic stromal remodeling. C0 MYH11 + CAF Promotes Tumor Cell Proliferation, Migration, and Inhibits Apoptosis via Soluble SDC1. Conclusion: Our results illustrate, in some ways, the possible immunomodulatory and tumor supporting roles of CAFs in CC TIME and highlight the possibility that the MDK-SDC1 pathway is a promising therapeutic target. This study not only promotes a partially new understanding of temporal heterogeneity in CC, but also provides a possible reference base for the development of new biomarkers and immunotherapy approaches to improve clinical outcomes.

Background. Cardiomyopathy encompasses a broad spectrum of diseases affecting myocardial tissue, characterized clinically by abnormalities in cardiac structure, heart failure, and/or arrhythmias. Clinically heterogeneous, major types include dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RM), ischemic cardiomyopathy (ICM), among which DCM is more prevalent, while ICM exhibits higher incidence and mortality rates. Myocardial injury during cardiomyopathy progression may lead to myocardial fibrosis. Failure to intervene early and inhibit the process of myocardial fibrosis may culminate in heart failure. Cardiac fibroblasts constitute crucial cellular components determining the extent and quality of myocardial fibrosis, with various subpopulations exerting diverse roles in cardiomyopathy progression. Despite this, understanding of the cellular plasticity and transcriptional regulatory networks of cardiac fibroblasts in cardiomyopathy remains limited. Therefore, in this study, we conducted comprehensive single-cell analysis of cardiac fibroblasts in cardiomyopathy to explore differences in cellular plasticity and transcriptional regulatory networks among fibroblast subpopulations, with the aim of providing as many useful references as possible for the diagnosis, prognosis, and treatment of cardiomyopathy. Materials and Methods. Cells with mitochondrial gene expression comprising >20 of total expressed genes were excluded. Differential expression genes (DEGs) and stemness genes within cardiac fibroblast subpopulations were subjected to Gene Ontology (GO) analysis of biological processes (BP) and AUCell analysis. Monocle software was employed to analyze the pseudo-temporal trajectory of cardiac fibroblasts in cardiomyopathy. Additionally, the Python package SCENIC was utilized to assess enrichment of transcription factors and activity of regulators within cardiac fibroblast subpopulations in cardiomyopathy. Results. Following batch effect correction, 179,927 cells were clustered into 32 clusters, designated as T_NK cells, endothelial cells, myeloid cells, fibroblasts, pericytes, SMCs, CMs, proliferating cells, EndoCs, and EPCs. Among them, 8148 fibroblasts were further subdivided into 4 subpopulations, namely C0 THBS4+ Fibroblasts, C1 LINC01133+ Fibroblasts, C2 FGF7+ Fibroblasts, and C3 AGT+Fibroblasts. Results from GO_BP and AUCell analyses suggest that C3 AGT+Fibroblasts may be associated with immune response activation, protein transport, and myocardial contractile function, correlating with disease progression in cardiomyopathy. Transcription factor enrichment analysis indicates that FOS is the most significant TF in C3 AGT+Fibroblasts, also associated with the M1 module, possibly implicated in protein hydrolysis, intracellular DNA replication, and cell proliferation. Moreover, correlation analysis of transcriptional regulatory activity between fibroblast subpopulations reveals a more pronounced heterogeneity within C3 AGT+Fibroblasts in cardiomyopathy. Conclusion. C3 AGT+Fibroblasts exhibit increased sensitivity towards adverse outcomes in cardiomyopathy, such as myocardial fibrosis and impaired cardiac contractile function, compared to other cardiac fibroblast subpopulations. The differential cellular plasticity and transcriptional regulatory activity between C3 AGT+Fibroblasts and other subgroups offer new perspectives for targeting fibroblast subpopulation activity to treat cardiomyopathy. Additionally, stemness genes EPAS1 and MYC, along with the regulator FOS, may play roles in modulating the biological processes of cardiac fibroblasts in cardiomyopathy.

Rationale: Synergic reprogramming of metabolic dominates neuroblastoma (NB) progression. It is of great clinical implications to develop an individualized risk prognostication approach with stratification-guided therapeutic options for NB based on elucidating molecular mechanisms of metabolic reprogramming.

Traumatic brain injury (TBI) is a major cause of morbidity and mortality, both in adult and pediatric populations. However, the dynamic changes of gene expression profiles following TBI have not been fully understood. In this study, we identified the differentially expressed genes (DEGs) following TBI. Remarkably, Serpina3n, Asf1b, Folr1, LOC100366216, Clec12a, Olr1, Timp1, Hspb1, Lcn2, and Spp1 were identified as the top 10 with the highest statistical significance. The weighted gene coexpression analysis (WGCNA) identified 12 functional modules from the DEGs, which showed specific expression patterns over time and were characterized by enrichment analysis. Specifically, the black and turquoise modules were mainly involved in energy metabolism and protein translation. The green yellow and yellow modules including Hmox1, Mif, Anxa2, Timp1, Gfap, Cd9, Gja1, Pdpn, and Gpx1 were related to response to wounding, indicating that expression of these genes such as Hmox1, Anxa2, and Timp1 could protect the brains from brain injury. The green yellow module highlighted genes involved in microglial cell activation such as Tyrobp, Cx3cr1, Grn, Trem2, C1qa, and Aif1, suggesting that these genes were responsible for the inflammatory response caused by TBI. The upregulation of these genes has been validated in an independent dataset. These results indicated that the key genes in microglia cell activation may serve as a promising therapeutic target for TBI. In summary, the present study provided a full view of the dynamic gene expression changes following TBI.

Multiple sclerosis (MS) is a neurodegenerative disorder characterized by periodic neuronal demyelination, which leads to a range of symptoms and eventually to disability. The goal of this research was to use UPLC‐Orbitrap/MS to identify validated biomarkers and explore the metabolic mechanisms of MS in mice. Thirty‐two C57BL/6 male mice were randomized into two groups that were fed either normal food or 0.2% CPZ for 11 weeks. The mouse demyelination model was assessed by LFB and the expression of MBP by immunofluorescence and immunohistochemistry. The metabolites of the corpus callosum were quantified using UPLC‐Orbitrap/MS. The mouse pole climbing experiment was used to assess coordination ability. Multivariate statistical analysis was adopted for screening differential metabolites, and the ingenuity pathway analysis (IPA) was used to reveal the metabolite interaction network. We successfully established the demyelination model. The CPZ group slowly lost weight and showed an increased pole climbing time during feeding compared to the CON group. A total of 81 metabolites (VIP > 1 and P < 0.05) were determined to be enriched in 24 metabolic pathways; 41 metabolites were markedly increased, while 40 metabolites were markedly decreased in the CPZ group. The IPA results revealed that these 81 biomarker metabolites were associated with neuregulin signaling, PI3K‐AKT signaling, mTOR signaling, and ERK/MAPK signaling. KEGG pathway analysis showed that two significantly different metabolic pathways were enriched, namely, the glycerophospholipid and sphingolipid metabolic pathways, comprising a total of nine biomarkers. Receiver operating characteristic analysis showed that the metabolites (e.g., PE (16 : 0/22 : 6(4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), PC (18 : 0/22 : 4(7Z, 10Z, 13Z, 16Z)), cytidine 5 ^′‐diphosphocholine, PS (18 : 0/22 : 6(4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), glycerol 3‐phosphate, SM (d18 : 0/16 : 1(9Z)), Cer (d18:1/18 : 0), galabiosylceramide (d18:1/18 : 0), and GlcCer (d18:1/18 : 0)) have good discrimination ability for the CPZ group. In conclusion, the differential metabolites have great potential to serve as biomarkers of demyelinating diseases. In addition, we identified metabolic pathways associated with CPZ‐induced demyelination pathogenesis, which provided a new perspective for understanding the relationship between metabolites and CNS demyelination pathogenesis.