Frontiers in Molecular Biosciences最新文献

Background: Cancer-associated fibroblasts (CAFs) are key drivers of tumor progression in bladder cancer (BLCA), yet their molecular heterogeneity and prognostic utility remain incompletely characterized. Single-cell studies have revealed distinct CAF subpopulations with divergent clinical impacts, necessitating refined prognostic frameworks that capture CAF-mediated progression.

Methods: We analyzed single-cell RNA sequencing data (GSE267718) from 8 BLCA patients to identify CAF populations and define progression-associated gene signatures. Using 359 TCGA-BLCA samples as the training cohort, we performed non-negative matrix factorization (NMF) consensus clustering on 85 prognostically significant CAF genes, identifying two molecular clusters with distinct survival outcomes. Through LASSO-Cox regression and stepwise selection, we constructed a four-gene Tumor-Progressing Fibroblast Riskscore model comprising FOXA1, TBX3, LRIG1, and RNF11. Model performance was validated in the E-MTAB-4321 cohort (n = 476). Functional validation of RNF11 was performed using shRNA-mediated knockdown in T24 and 5637 bladder cancer cell lines, followed by proliferation, migration, invasion assays, and transcriptomic profiling.

Results: Single-cell analysis identified 557 differentially expressed genes between non-muscle-invasive bladder cancer and muscle-invasive bladder cancer CAFs. NMF clustering stratified TCGA patients into 2 clusters with significantly different overall survival. The TPFR model showed consistent prognostic performance in both training and validation cohorts, with high-risk patients showing significantly worse survival. Functional enrichment analysis revealed that TPFR scores correlated with ECM-receptor interaction, focal adhesion, and cytoskeletal regulation pathways. Stratified analysis revealed superior model performance in elderly (>60 years), male, and early-stage patients. In particular, RNF11 knockdown significantly reduced proliferation, migration, and invasion in 5637 and T24 cells, while transcriptomic analysis revealed alterations in tumors after RNF11 knockdown including TNF and MAPK signaling pathway, indicating a potential mechanism by which RNF11 regulates bladder cancer progression.

Conclusion: We established a CAF-based prognostic model that integrates single-cell insights with bulk transcriptomics for robust risk stratification in BLCA. The TPFR model shows clinical utility particularly in elderly and early-stage patients. Functional characterization showed that RNF11 regulates proliferation and migration of bladder cancer. These findings highlight the prognostic value of CAF signatures and provide a framework for precision medicine approaches in bladder cancer management.

Acute ischemic stroke (AIS) is an acute neurological deficit that results from focal cerebral ischemia associated with permanent brain infarction, and is a leading cause of death and disability worldwide. Considerable attention has been paid to reducing mortality and improving the prognosis of patients with AIS. Targeted temperature management (TTM), including hypothermia therapy, normothermia control, and febrile intervention, has been widely investigated in laboratory and preclinical studies and has provided substantial protection for neurological function. The effect of TTM on neurological function prognosis in patients with AIS has attracted significant attention. This review summarizes the related mechanisms of action, clinical applications, and short- and long-term effects of TTM on neurological function in AIS, providing a clinical reference for the application and prognosis of TTM in patients with AIS.

The cold shock domain (CSD)-containing protein, CSDE1, interacts with AGO2 and regulates miRNA function in post-transcriptional gene silencing. While the individual roles of CSDE1 and AGO2 in regulating gene expression underlying stem cell pluripotency and differentiation are well known, the effects of their interaction remain unclear. Here, we demonstrate that CSDE1 stabilizes AGO2 and key pluripotent proteins, NANOG, SOX2, and Oct4, in mouse embryonic stem cells. CSDE1 stabilizes AGO2 and the stem cell markers, preventing their ubiquitination. Further, the N-terminal domain, CSD1, which is necessary for CSDE1 interaction with AGO2, is crucial for maintaining AGO2 levels and the pluripotent proteins, thereby revealing an additional layer of control over AGO2 function and gene expression associated with stem cell fate at the post-translational level.

We examine the collective behavior of single cells in microbial systems to provide insights into the origin of the biological clock. Microfluidics has opened a window onto how single cells can synchronize their behavior. Four hypotheses are proposed to explain the origin of the clock from the synchronized behavior of single cells. These hypotheses depend on the presence or absence of a communication mechanism between the clocks in single cells and the presence or absence of a stochastic component in the clock mechanism. To test these models, we integrate physical models for the behavior of the clocks in single cells or filaments with new approaches to measuring clocks in single cells. As an example, we provide evidence for a quorum-sensing signal both with microfluidics experiments on single cells and with continuous in vivo metabolism NMR (CIVM-NMR). We also provide evidence for the stochastic component in clocks of single cells. Throughout this study, ensemble methods from statistical physics are used to characterize the clock at both the single-cell level and the macroscopic scale of 10⁶ cells.

Introduction: In this study, we introduce the design and implementation of PDBMine, a large-scale, queryable platform for mining sequence-structure statistics from the Protein Data Bank (PDB). PDBMine enables rapid analysis of local conformational trends across proteins by extracting dihedral angles and sequence patterns at scale. In addition to the design and implementation of PDBMine, we also present results validating its ability to return structurally meaningful information.

Methods: We first assess the accuracy of its dihedral angle distributions by comparing them to established Ramachandran space and verifying expected behaviors of residues such as glycine and proline. We then use PDBMine to analyze the statistical properties of amino acid subsequences of length $k$ = 1 to 5.

Results: Our findings reveal that longer $k$ -mers exhibit significant departures from statistical independence, suggesting context-dependent constraints on amino acid co-occurrence. We also show that increasing local sequence context restricts dihedral angle variability, with longer $k$ -mers producing distributions that more closely align with experimentally observed backbone geometries. Finally, we present a high-dimensional clustering method for grouping full-sequence dihedral conformations, enabling identification of dominant local structural motifs.

Discussion: These results highlight PDBMine's potential as a versatile tool for structure validation, statistical modeling, and probing the principles that govern sequence-structure compatibility in proteins.

Background: Tumor staging is critical for guiding therapeutic decisions and determining prognosis in liver hepatocellular carcinoma (LIHC). This study aimed to identify potential tissue biomarkers intrinsically linked to disease stage to enhance our understanding of LIHC biology.

Methods: Transcriptome and clinical data from LIHC patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression analysis was conducted using the "limma" package. Weighted gene co-expression network analysis (WGCNA) was used to identify the gene module most strongly associated with LIHC and to extract hub genes. The hub genes then underwent differential expression, prognostic, and clinical staging analyses, immunohistochemical validation, and multivariable Cox regression analysis.

Results: This analysis included data from 373 LIHC tumors and 50 solid tissue normal samples obtained from the TCGA database. Differential expression analysis identified 319 upregulated and 853 downregulated genes in LIHC tumors compared to these normal samples. An enrichment analysis highlighted key pathways, including cell cycle, DNA replication, and base excision repair. Three independent validation datasets confirmed 18 downregulated and 7 upregulated genes. Among them, DNASE1L3, APOF, and FCN3 were consistently identified as core genes within the WGCNA-derived purple module. A further analysis using the UCSC database revealed that DNASE1L3 and APOF were significantly associated with LIHC prognosis. A MEXPRESS analysis showed strong correlations between these genes and clinical stage, which was further supported by a SangerBox-based staging analysis, indicating significant differences in gene expression between early and advanced disease stages. Immunohistochemical data demonstrated that DNASE1L3 levels decreased from stage I to stage III in LIHC. Multivariable Cox regression confirmed that low DNASE1L3 expression is an independent predictor of poor prognosis in LIHC.

Conclusion: Our results identified DNASE1L3 as a promising tissue biomarker. Loss of DNASE1L3 is indicative of advanced and aggressive LIHC, and therefore its expression may offer complementary information to current staging systems to improve prognostic assessment.

Circular RNAs (circRNAs) are emerging as pivotal regulators within the tumor immune microenvironment (TIME) of hepatocellular carcinoma (HCC). Despite the transformative role of immunotherapy, its efficacy remains limited by primary and acquired resistance, a challenge not fully addressed by current research. This review uniquely synthesizes the latest evidence to delineate how specific circRNAs orchestrate immunosuppression in HCC through two interconnected axes: (1) by directly modulating the function and polarization of key immune cells (e.g., T cells, NK cells, macrophages), and (2) by interfering with core immune-related signaling pathways (e.g., NF-κB, MAPK, Wnt/β-catenin). We critically examine how these mechanisms collectively fuel immune evasion and confer resistance to immune checkpoint inhibitors. Moving beyond mechanism, we further explore the dual translational potential of circRNAs: as stable, minimally invasive diagnostic/prognostic biomarkers and as novel therapeutic targets via RNA interference or circRNA-based vaccine strategies. By connecting fundamental molecular insights to clinical challenges, this review provides a cohesive framework for understanding circRNA-driven immunomodulation in HCC and highlights promising avenues for overcoming immunotherapy resistance.

Alopecia areata (AA) and androgenetic alopecia (AGA) both present with hair loss but require different therapies, and reliable biomarkers to guide treatment remain lacking. We integrated bulk and single-cell RNA-seq to compare JAK-STAT signaling in AA versus AGA. In AA, 257 immune-enriched differentially expressed genes (DEGs) were identified; WGCNA and consensus machine learning (LASSO, SVM-RFE, random forest) yielded six candidate hub genes, and external validation narrowed these to four key genes-granzyme A (GZMA), interleukin-2 receptor $\beta$ (IL2RB) and $\gamma$ (IL2RG) chains, and eomesodermin (EOMES). Building on these biology-anchored features, we introduced an interpretable few-shot deep learning classifier as an explainable AI alternative to a nomogram: bulk expression profiles are projected onto pathway/cell-type-aligned MultiPLIER latent variables (a frozen prior), the latent channels are re-weighted via element-wise multiplication with the expression levels of the key hub genes, and a Relation-style set-to-set comparator then aggregates support-query similarities (Hadamard mapping + permutation-invariant aggregation) before a shallow head predicts AA versus control. This prior-informed approach enables robust discrimination under limited sample conditions while retaining mechanistic interpretability, thereby exemplifying a next-generation XAI solution for small-cohort genomic diagnosis. Cross-database functional annotation and wet-lab validation (RT-qPCR and Western blot) in independent AA/AGA/healthy scalp samples confirmed that the IL2RB/IL2RG-EOMES-GZMA axis is selectively activated at both mRNA and protein levels in AA. Single-cell analysis localized GZMA to cytotoxic T cells and IL2RG to proliferating lymphocytes, outlining an ${\text{EOMES}}^{+}$ ${\text{CD}8}^{+}$ T-cell GZMA-IL2RB/IL2RG cytotoxic loop driving JAK-STAT hyperactivation in AA. Drug-gene network analysis linked these targets to JAK inhibitors and cyclosporine. AGA showed no comparable JAK-STAT perturbation, consistent with its androgen-centric biology. In summary, this four-gene loop provides a non-invasive AA biomarker and a tractable target for precision JAK blockade, while the proposed few-shot framework offers a general, prior-driven alternative to nomograms for transcriptomic diagnosis in small cohorts, illustrating an XAI-driven diagnostic approach for precision medicine.

DNA methylation plays a critical role in gene expression regulation and has emerged as a robust biomarker of biological age. This modification will become heavier or site drift along with aging. Recently, it is termed epigenetic clocks-such as Horvath, Hannum, PhenoAge, and GrimAge-leverage specific methylation patterns to accurately predict age-related decline, disease risk, and mortality. These tools are now widely applied across diverse tissues, populations, and disease contexts. Beyond age-related loss of methylation control, accelerated DNA methylation age has been linked to environmental exposures, lifestyle factors, and chronic diseases, further reinforcing its value as a dynamic and clinically relevant marker of biological aging. DNA methylation is reshaping our understanding of aging and disease risk, with promising implications for preventive medicine and interventions aimed at promoting healthy longevity. However, it must be admitted that some challenges remain, including limited generalizability across populations, an unclear mechanism, and inconsistent longitudinal performance. In this review, we examine the biological foundations of DNA methylation, major advances in epigenetic clock development, and their expanding applications in aging research, disease prediction and health monitoring.

Introduction: Although flavonoids are natural compounds with anti-cancer potential, their clinical application is limited due to the low bioavailability. Structural modification, such as halogenation, has been identified as a strategy to enhance drug-like properties. The rationale behind this is that halogen substituents can increase lipophilicity, alter electronic distribution, and improve interactions with cellular targets. Here, we investigated the cytotoxic mechanisms of three halogenated flavones - 4'-chloroflavone (Cl-F), 6,8-dichloroflavone (DiCl-F), and 8-bromo-6-chloroflavone (BrCl-F) - in two canine B-cell models, CLB70 (leukemia) and CLBL-1 (lymphoma), chosen for their translational relevance to human hematological cancers.

Methods: Cytotoxicity was assessed by MTT assay, apoptosis by annexin V/PI staining, Bcl-2 and Bcl-XL expression by Western blotting, cell cycle distribution by flow cytometry, and DNA damage by changes in H2AX phosphorylation.

Results: BrCl-F demonstrated the strongest cytotoxic activity, significantly reducing metabolic activity and increasing the proportion of apoptotic cells in both cell lines. In CLB70 cells, BrCl-F treatment was accompanied by decreased expression of Bcl-2 and Bcl-XL. DiCl-F showed moderate cytotoxicity but induced a marked increase in γH2AX levels and accumulation of cells in the G2/M phase. Cl-F exhibited weaker effects and reduced cell viability primarily at higher concentrations.

Conclusion: Halogenated flavones display distinct cytotoxic profiles in canine B-cell leukemia and lymphoma models, with BrCl-F showing the highest anticancer activity. These findings support further investigation of halogenated flavones as potential anticancer agents in comparative oncology.