Efficient expansion of bone marrow-derived mesenchymal stem cells (BMMSCs) is critical for research and clinical applications, yet limited proliferative capacity and culture-associated cell loss remain major challenges. Here, we show that phytohemagglutinin (PHA), a plant lectin commonly used as a lymphocyte mitogen, enhances BMMSCs expansion under serum-containing culture conditions. PHA increased BMMSCs cell numbers and culture density without obvious morphological changes, and this effect was reproduced in both DMEM/F-12 and BGJb media. Time-course CCK-8 analyses demonstrated that the PHA effect peaked at 24 h and persisted at 48 h and 72 h. Consistently, Ki67 staining revealed an increased fraction of Ki67+ cells, and TUNEL assay demonstrated reduced apoptosis in PHA-treated cultures. Together, these results identify PHA as a convenient supplement that enhances BMMSCs expansion in vitro and support its potential utility as a practical strategy for large-scale BMMSCs production for regenerative applications.

ETHNOPHARMACOLOGICAL RELEVANCE:Danshen-Chuanxiong (DS-CX), a classical herb pair composed of Salviae Miltiorrhizae Radix et Rhizoma (the dried roots and rhizomes of Salvia miltiorrhiza Bunge) and Chuanxiong Rhizoma (the dried rhizomes of Ligusticum chuanxiong Hort.), has been widely used in traditional Chinese medicine (TCM) for promoting blood circulation and resolving blood stasis. DS-CX has been developed into Guanxinning preparations, which are widely used to treat cardiovascular disorders. However, the potential role of DS-CX in deep vein thrombosis (DVT) and its underlying mechanisms remain poorly understood. OBJECTIVE:This study aimed to investigate the protective effects of DS-CX against DVT and elucidate the molecular mechanisms, with a focus on the regulation of neutrophil extracellular traps (NETs). METHODS:A mouse model of DVT induced by inferior vena cava stenosis was established to evaluate the effects of DS-CX pretreatment in vivo. Antithrombotic outcomes were assessed via ultrasonography (thrombus area and blood flow velocity), morphological observation (thrombus weight and length), coagulation-related parameters (PT, APTT, FIB, TT, D-Dimer, and TAT), histopathological examination, and the tail bleeding test. To determine the role of NETs, mice were co-treated with recombinant DNase I (rDNase I) or the PAD4 inhibitor Cl-amidine. NET formation was analyzed using ELISA detection, PicoGreen assays, and immunofluorescence staining. In vitro, mouse bone marrow-derived neutrophils (BMDNs) were stimulated with PMA to establish a reproducible NETosis model for mechanistic investigation. BMDNs were pre-incubated with DS-CX, rDNase I, or ROS/MAPK modulators, followed by PMA stimulation. Subsequently, NET-related markers and signaling pathway were determined through immunofluorescence, flow cytometry, SYTOX Green staining, and Western blotting. Furthermore, UPLC-MS/MS profiling combined with functional validation was performed to identify candidate active constituents responsible for the anti-NET effects of DS-CX. RESULTS:DS-CX significantly reduced thrombus area, improved venous blood flow velocity, reduced thrombus weight and length, and ameliorated thrombus-associated pathological changes. Concurrently, coagulation function was modulated, with decreased D-Dimer, prolonged APTT, and reduced TAT. Importantly, DS-CX treatment did not significantly prolong tail bleeding time or alter hemoglobin levels. Both in vivo and in vitro results demonstrated that DS-CX suppressed NET formation, as evidenced by marked reductions in NETosis markers, including citrullinated histone H3 (citH3), myeloperoxidase (MPO), MPO-DNA complexes, neutrophil elastase (NE), and extracellular DNA. Additionally, the anti-NET efficacy of DS-CX was comparable to that of rDNase I, and co-administration with Cl-amidine yielded no additive benefits, suggesting a shared inhibitory pathway. Mechanistically, DS-CX suppressed NET formation by inhibiting ROS generation and subsequent MAPK signaling activation, as confirmed by pathway-specific inhibitors and activators. Furthermore, UPLC-MS/MS analysis and functional validation indicated that salvianolic acid B and tetramethylpyrazine are potential contributors to the anti-NET effects of DS-CX, with the combination showing enhanced inhibitory effects. CONCLUSION:DS-CX has a distinctive antithrombotic effect against DVT without increasing the risk of bleeding, and its mechanism of action may be closely related to ROS-MAPK-mediated NET formation. Salvianolic acid B and tetramethylpyrazine may represent important contributors to these protective effects. These findings provide mechanistic insights into the traditional use of DS-CX and support its potential as a safer immunothrombosis-targeting approach for thrombotic disorders.

RNA processing and modification are critical for virus replication and pathogenesis, yet how the host exploits viral RNA features, particularly the poly(A) tail-for antiviral defense remains unclear. Through multi-omics integration and systematic functional screening, we identify the poly(A)-binding protein PABPC1 as a broad-spectrum restriction factor against multiple coronaviruses. We demonstrate that PABPC1 preferentially binds viral RNAs bearing short poly(A) tails-distinct from the longer and more heterogeneous host poly(A) tails and, in a poly(A)-length-dependent manner, recruits the mitochondrial exonuclease EXD2 to assemble a degradative RNA-protein complex. Both the recognition and subsequent degradation of viral RNA strictly depend on poly(A) tail length, enabling selective decay of short-tailed viral transcripts while sparing host mRNAs. Importantly, inflammatory signaling enhances the expression of PABPC1 and EXD2, suggesting its role as an inducible host defense pathway activated during viral infection. Capitalizing on this discovery, we engineered a synthetic fusion protein mimicking the PABPC1-EXD2 complex and achieved efficient delivery using lipid nanoparticles (LNPs). This rationally designed therapeutic exhibits robust suppression of coronavirus replication in both cellular and murine models. Collectively, our findings uncover a novel host antiviral strategy that targets a conserved viral RNA structural element and provide a conceptual framework for developing host-derived and broad-spectrum anti-coronavirus therapeutics. ### Competing Interest Statement The authors have declared no competing interest. National Key R&D Program of China, 2021YFA1300800 China NSFC projects, 82341061, 82502690 Fundamental Research Funds for the Central Universities, 2042022dx0003

RNA splicing is a fundamental driver of eukaryotic transcriptomic and proteomic diversity. Constrained by compact genomes, diverse DNA and RNA viruses, including adenovirus, HIV-1, and influenza virus, have evolved to hijack the host splicing machinery. This exploitation not only maximizes viral coding capacity but also ensures the precise spatiotemporal regulation of viral infection. In this review, we summarize current advances in the molecular mechanisms of viral RNA splicing, illustrating how viruses co-opt the host spliceosome and reprogram global alternative splicing landscapes to support their infection cycle. Through representative viral models, we detail the convergent strategies of alternative splice site selection and the dynamic interplay between viral RNA elements and host trans-acting factors. Furthermore, we spotlight the emerging frontier of viral circular RNAs (vcircRNAs), highlighting their biogenesis via non-canonical back-splicing and their versatile roles in immune evasion. Finally, we summarize recent methodological breakthroughs, particularly long-read sequencing and single-cell analyses, that are rapidly charting the complex splicing landscape. Together, this review provides an integrated perspective on the virus-host splicing interface, exposing critical vulnerabilities that offer promising avenues for next-generation, broad-spectrum antiviral interventions.

Background: As the SARS-CoV-2 virus continues to mutate, there is a critical need for precision risk assessment. We developed and validated machine learning (ML) algorithms to assess COVID-19 reinfection risk by integrating clinical and multi-dimensional immunological data. Methods: We conducted a two-center prospective longitudinal cohort study involving 1,186 participants recruited from Wuxi and Nantong, China, between May 2024 and February 2025. Participants from the Wuxi site (n = 586) were allocated into a training cohort (n = 411) and an internal validation cohort (n = 175) using a 7:3 stratified random split. The Nantong cohort (n = 600) was designated as an independent external validation cohort. The Boruta algorithm was applied to select the most significant predictors from a pool of 21 clinical and immunological parameters. Six ML models were subsequently developed: adaptive boosting (ADB), gaussian naive bayes (GNB), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). Predictive performance and clinical utility were rigorously evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Finally, SHAP values were utilized for model interpretation and transparency. Findings: During a median follow-up of 10 months, 128 (10.8%) reinfection cases were identified. The Boruta algorithm identified spike-specific nasal secretory immunoglobulin A (sIgA), spike-specific CD4⁺ central memory T cells (TCM), and spike-specific CD4⁺ naive T cells (TN) as the most critical predictors. Among six models, XGB demonstrated superior performance, achieving an AUC of 0.880 (95% CI: 0.782–0.977) in the internal validation and 1.000 (95% CI: 0.999–1.000) in the external validation. SHAP dependence plots further revealed that spike-specific nasal sIgA demonstrated a protective effect, spike-specific CD4+ TCM cells exhibited an optimal beneficial range, whereas elevated levels of spike-specific CD4+ TN cells were associated with an increased risk. Interpretation: We developed and validated an explainable ML model for predicting SARS-CoV-2 reinfection risk using large-sample, multicenter data. This framework supports individualized clinical decision-making and offers a scalable intelligent platform for managing future emerging infectious diseases.

Objective: To predict the pharmacokinetic (ADMET) properties of cinnamaldehyde using molecular modeling and provide a theoretical foundation for its development as a drug candidate. Methods: The ADMET module in Discovery Studio 2.5 was employed to evaluate six key parameters: passive intestinal absorption, aqueous solubility, blood–brain barrier (BBB) penetration, cytochrome P450 2D6 (CYP2D6) inhibition, hepatotoxicity, and plasma protein binding. While acknowledging the emergence of machine learning (ML) approaches such as Support Vector Regression (SVR) and Random Forest (RF) in Computer-Aided Drug Design (CADD) , this study utilizes rule-based simulation for rapid profiling. However, we recognize that this approach has limitations in analyzing the relationship between molecular descriptors and activity compared to advanced QSAR models . Recent studies on anti-breast cancer candidate drugs have demonstrated that multi-objective optimization models combining ML algorithms (such as LightGBM, Random Forest, and XGBoost) with Particle Swarm Optimization (PSO) can achieve significant improvements in both biological activity prediction (R² = 0.743) and ADMET property optimization , highlighting the potential of integrated ML approaches in drug development. Additionally, the predicted low systemic bioavailability of cinnamaldehyde due to rapid hepatic metabolism warrants further investigation. Results: Predicted values for cinnamaldehyde generally fell within acceptable ranges for drug-likeness. However, the analysis highlighted potential limitations in systemic bioavailability due to rapid hepatic metabolism. Both cis- and trans-isomers showed similar ADMET profiles, suggesting configurational changes have minimal impact on these specific pharmacokinetic parameters within the resolution of this model.

Background::The prerequisite for achieving the goal of the World Health Organization to completely eliminate viral hepatitis by 2030 is China’s accurate understanding of the current disease burden, thereby providing a basis for formulating and optimizing intervention measures.Methods::Based on the Global Burden of Disease (GBD) 2023 dataset, we extracted data on acute hepatitis A, B, C, and E in China, including incidence, mortality, disability-adjusted life years (DALYs), and their corresponding age-standardized rates (ASRs). To account for differences in age and sex distributions, we calculated ASRs based on the age-specific data extracted from the GBD 2023 dataset. To further assess temporal patterns across different age groups, we estimated the average annual percent change (AAPC). We applied the autoregressive integrated moving average model to project acute viral hepatitis (AVH) disease burden for 2024-2030.Results::In 2023, there were an estimated 48.7 million incidence cases of AVH in China, including approximately 19.0 million cases of acute hepatitis A (AHA), 19.2 million cases of acute hepatitis B (AHB), 1.35 million cases of acute hepatitis C (AHC), and 9.2 million cases of acute hepatitis E (AHE). From 1990 to 2023, the overall age-standardized incidence rates (ASIR) of the four major AVH types in China declined (AAPC = -0.90%, 95% confidence interval [CI]: -0.93% to -0.87%). In the previous decade, divergent trends were observed. The ASIR of AHC increased (AAPC = 1.42%, 95% CI: 1.39-1.46%), AHE remained stable (AAPC = -0.04%, 95% CI: -0.39-0.32%), whereas AHA and AHB decreased. Moreover, the age-standardized mortality and DALYs rates for AVH significantly decreased. Age-specific analysis further revealed an upward trend in standardized incidence among individuals aged 25-29 years (AAPC = 0.14%, 95% CI: 0.10-0.19%). A continued decline in the ASIR of AVH is projected for 2024-2030.Conclusions::In China, the overall burden of AVH has decreased between 1990 and 2023. AHB vaccination among younger populations should be strengthened, and targeted prevention and control strategies should be implemented for those at high risk of AHC, to curb the spread of the disease and reduce the overall burden.

IntroductionThe emergence of new SARS-CoV-2 variants with immune evasion capabilities underscores the importance of developing a broad-spectrum and effective vaccine. The receptor binding domain (RBD) of the Spike protein has been widely utilized in vaccine due to its high immunogenicity. However, the Spike protein, particularly the RBD region, exhibits significant variability in the evolution of SARS-CoV-2, leading to viral immune evasion and reduced vaccine effectiveness.MethodsA broad-spectrum antigen (M5-RBD) was developed via mutation patching, incorporating key high-impact mutation sites (K417T, L452R, T478K, E484K, N501Y). Additionally, extra mutations (N440K or G446S) were introduced into M5-RBD to evaluate their impact on immune response. M5-RBD was further combined with a novel CpG adjuvant HP007 for immunization.ResultsM5-RBD elicited high titers of broad-spectrum neutralizing antibodies against SARS-CoV-2 wild-type and various variants (Delta, Omicron BA.1, BA.2, BA.2.75, BA.5, BF.7, BQ.1.1, XBB, EG.5, JN.1, KP.3 strains). Introduction of N440K or G446S significantly diminished the immune response to viral strains. When combined with HP007 adjuvant, M5-RBD induced efficient and durable T cell responses, providing protection to K18-hACE2 KI mice against lethal infections with both wild-type and Omicron BA.2 strains.DiscussionRationally designed with key high-impact mutation sites, M5-RBD effectively overcomes SARS-CoV-2 variant immune evasion and elicits broad-spectrum neutralizing antibodies. The combination with HP007 adjuvant enhances immune protection, providing a promising strategy for the development of next-generation COVID-19 vaccines.

The incidence of acute-onset autoimmune hepatitis (A-AIH) is increasing, yet diagnosis remains challenging, especially in patients with recent hepatotoxic drug exposure. The clinical presentations of A-AIH and drug-induced autoimmune-like hepatitis (DI-ALH) at onset are often indistinguishable, complicating timely diagnosis. We conducted a three-center retrospective study in China, screening patients with acute liver injury, hepatotoxic drug exposure, and autoimmune features. Patients were ultimately classified as DI-ALH if they achieved sustained remission after culprit drug cessation, or as A-AIH if they relapsed. We compared baseline demographics, laboratory indices, immunological profiles, liver histology, and established AIH diagnostic criteria. We developed and independently validated a multivariable logistic regression model to improve discrimination between A-AIH and DI-ALH. Of 458 patients screened, 238 met inclusion criteria and constituted the final cohort (94 A-AIH and 144 DI-ALH). Compared to DI-ALH, A-AIH showed significantly higher IgG levels, lower platelet counts, and higher autoantibody titers. Histologically, A-AIH exhibited more severe portal inflammation, interface hepatitis, fibrosis, and rosette formation. The discriminatory capacity of the 2022 and 2008 histological criteria was limited and comparable (AUC 0.62 vs. 0.59, p = 0.616). The Dx-AID score, incorporating platelet count, IgG, autoantibodies, and histological features, achieved high diagnostic accuracy in both the derivation cohort (AUC 0.84, 95

Abstract Background and Aims Acute-on-chronic liver failure (ACLF) is associated with high short-term mortality, but substantial heterogeneity among existing diagnostic and prognostic models results in inconsistent patient identification and risk assessment. We conducted a systematic head-to-head comparison of major ACLF diagnostic and prognostic models to evaluate concordance, short-term mortality prediction and clinical utility, with the goal of informing harmonization of ACLF assessment. Methods We analysed 3,370 patients with acute decompensation of cirrhosis in the COSSH cohort, with external validation in an independent Ambi-Spective cohort from India (n=2,055). Five ACLF diagnostic models were evaluated for identification of patients at risk of 28-day mortality. Reclassification was assessed using net reclassification improvement. Prognostic scores were compared using concordance index, integrated discrimination improvement, calibration, and decision-curve analysis. Results Diagnostic frameworks identified markedly different proportions of ACLF. A-TANGO and COSSH-ACLF classified the largest high-risk populations while maintaining substantial short-term mortality and balanced sensitivity–specificity profiles. Compared with COSSH-ACLF, A-TANGO improved net reclassification by 7.7%, with further gains versus EASL-CLIF (11.8%), APASL-ACLF (36.4%), and NACSELD-ACLF (45.9%). In the external cohort, A-TANGO and COSSH-ACLF showed similar discrimination and identified comparable proportions of patients. Combined application of the two models delineated three clinically meaningful strata, identifying a discordant intermediate-risk group with approximately 11% 28–day mortality. Among prognostic scores, COSSH-ACLF II and A-TANGO OF scores demonstrated strong and complementary performance across cohorts. Conclusions Outcome-anchored ACLF definitions converge in identifying patients at highest short-term risk across diverse populations. Alignment between A-TANGO and COSSH-ACLF, together with identification of an intermediate-risk phenotype, supports a data-driven framework for improving consistency and advancing global harmonization of ACLF diagnosis and risk stratification.

Cytoplasmic RNA serves as a typical damage-associated molecular pattern (DAMP) signal; yet the mechanisms governing its release and role in inflammatory tissue damage remain poorly understood. In our study, we demonstrated that mimicking bacterial infection by lipopolysaccharide (LPS) combined with Nigericin (Ng) effectively activates Gasdermin D (GSDMD). Conversely, Vesicular Stomatitis Virus (VSV) selectively activates Gasdermin E (GSDME). Both GSDMD and GSDME form pores in the mitochondrial membrane, facilitating the release of mitochondrial RNA (mtRNA) into the cytosol. This released mtRNA is recognized by the RNA sensor Viral Interferon Stimulated Gene Activator (VISA), which subsequently induces a robust secondary inflammatory response. Importantly, the inhibition of GSDMD and GSDME prevents mitochondrial dysfunction and mtRNA release, thereby attenuating secondary inflammatory response mediated by the VISA pathway. Utilizing an experimental mice model, we found that LPS-induced lung tissue inflammation was restored by VISA knockout (VISA-/-) mice. Our findings highlight the potential targeting of GSDMD, GSDME, or VISA pathway signaling as a therapeutic strategy to modulate mtRNA-mediated inflammatory responses in microbial infectious diseases.

Background and Aims:Early risk stratification of severe acute liver injury (SLI) that may progress to acute liver failure (ALF), is vital for timely intervention, but no universal prognostic assessment tool covers both conditions. This study aimed to develop a simplified prognostic model for early risk assessment in SLI/ALF patients. Methods:A retrospective cohort study consecutively enrolled SLI patients (including those progressing to ALF) from July 1, 2020 to May 31, 2025. Baseline clinical and laboratory data on admission were collected, with 90-day transplant-free survival as the primary outcome. Independent prognostic factors were screened via Cox regression to build a simplified scoring model, whose performance was compared with the Model for End-Stage Liver Disease (MELD), King's College Criteria (KCC), and the Acute Liver Failure Study Group Prognostic Index (ALFSG-PI). Results:Of 302 patients, 190 (62.9%) achieved 90-day transplant-free survival. Multivariate Cox regression identified international normalized ratio (hazard ratio [HR]: 1.118, 95% confidence interval [CI]: 1.050-1.191), platelet count (HR: 0.995, 95% CI: 0.993-0.998), and hepatic encephalopathy grade ≥ 2 (HR: 5.187, 95% CI: 3.403-7.907) as independent predictors, forming the HIP (derived from the above-mentioned three predictors) model. It showed good discrimination (area under the receiver operating characteristic curve [AUC]: 0.82), outperforming MELD (AUC: 0.76, P = 0.019) and KCC (AUC: 0.72, P = 0.002), and performing comparably to ALFSG-PI (AUC: 0.80, P = 0.429). The model also performed robustly in ALF subgroups defined by the American College of Gastroenterology and the 2024 Chinese Medical Association guidelines (AUCs: 0.80 and 0.76, respectively) and achieved an AUC of 0.85 in the validation set. Conclusions:The HIP model is a simple and effective tool for prognostic risk stratification in SLI/ALF patients, suitable for emergency and primary care to facilitate timely intervention.

Soil microarthropods are known to harbor diverse microbial communities that play crucial roles in host physiology and ecological functions. Due to the minute size of oribatid mites, microbial studies have traditionally relied on whole body DNA extraction after surface sterilization, potentially confounding gut microbiota with microorganisms from other tissues. This study employed high-throughput Illumina sequencing to comprehensively compare bacterial and fungal communities between the whole body and dissected gut samples of the oribatid mite Eremobelba eharai. Our results revealed significantly higher bacterial α-diversity (Shannon–Wiener and Pielou evenness indices) in gut samples compared to whole body samples, while fungal richness was significantly lower in gut samples. Non-metric multidimensional scaling demonstrated significant divergence in bacterial community composition between sample types (PERMANOVA: R2 = 0.560, P = 0.010), but not for fungal communities (R2 = 0.119, P = 0.362). Taxonomic analysis identified the bacteria families Yersiniaceae and Rhizobiaceae as enriched in gut samples, potentially involved in nitrogen metabolism and plant polysaccharide degradation, whereas Mycobacteriaceae dominated whole body samples, potentially involved in lipid metabolism and antimicrobial defense. Fungal communities were predominantly pathotrophic, with minimal symbiotrophic representation. Our findings demonstrate that the choice of sample processing method (dissected gut vs. whole body) significantly influences microbial community characterization in soil microarthropods. This empirical validation highlights a critical methodological consideration for future studies of gut microbiota in soil microarthropods.

IntroductionAcute-on-chronic liver failure (ACLF) is a highly lethal clinical syndrome with limited effective therapeutic options. Urine-derived stem cells (USCs) represent a non-invasive and readily accessible cell source, but whether USCs obtained from patients with severe liver dysfunction retain therapeutic and immunomodulatory potential remains unclear.MethodsTo address this question, USCs derived from ACLF patients (LF-USCs) were evaluated in a Concanavalin A (Con A)-induced immune-mediated acute liver injury mouse model. Hydrogel-encapsulated LF-USCs were transplanted, and therapeutic efficacy was assessed by survival analysis, serum biochemical parameters, histological examination, and inflammatory cytokine profiling.ResultsTransplantation of hydrogel-encapsulated LF-USCs significantly improved mouse survival, reduced serum transaminase levels, and alleviated hepatocellular necrosis (p < 0.05). At the mechanistic level, LF-USC treatment was associated with decreased systemic inflammatory cytokine levels, attenuation of intrahepatic inflammatory injury, and dynamic modulation of macrophage-associated inflammatory signatures.DiscussionThese findings demonstrate that functionally competent USCs can be successfully obtained from ACLF patients and highlight their potential as a readily accessible autologous cell source for immune modulation and liver tissue repair in immune-mediated acute liver injury.

Compared to traditional sonication-based DNA fragmentation, enzyme-based DNA fragmentation has many advantages: no upfront equipment investment, fast and efficient processing, easy for automation, etc. However, enzyme-fragmentation can introduce artifacts into DNA sequences and complicates somatic variant calling from formalin-fixed paraffin-embedded (FFPE) samples. Existing bioinformatics algorithms struggle to identify and remove these artifacts, leading to false somatic variants. This study introduces a novel filtering algorithm, Stem-Loop Artifact Identifier (SLAI), to address this problem. By understanding the molecular mechanism that generates the artifact reads in the enzymatic fragmentation process, SLAI identifies and eliminates artifact-associated reads, a common byproduct of enzymatic fragmentation, thereby reducing false positives. We validated SLAI in two batches of samples, one normal-quality batch and the other low-quality batch, using a 769-gene cancer panel, demonstrating its ability to improve variant accuracy in enzyme-fragmented samples, esp. for low variant allele frequency (VAF) variants, on par with that of sonication-fragmented samples. SLAI effectively reduces false positive somatic variants introduced by enzyme-based DNA fragmentation, improving the accuracy of variant calling, particularly in low-quality FFPE samples. This enhances the reliability of genomic studies utilizing enzyme-based fragmentation methods.

RNA 5-methylcytosine (m5C) plays a critical role in cancer, yet its functional mechanisms and therapeutic relevance in cervical cancer remain unclear. Here, we generate the first base-resolution m5C transcriptome maps in cervical cancer, revealing globally elevated m5C levels in tumors. By integrating spatial transcriptomics and single-cell RNA-seq, we identify SERPINB5 as a novel m5C-regulated oncogenic effector. m5C modification enhances SERPINB5 mRNA stability and protein expression, promoting tumor growth, metastasis, and resistance to microtubule-targeting chemotherapeutics. Mechanistically, SERPINB5 upregulates mitotic regulators and microtubule motor proteins, including CENPE, enhancing mitotic progression and counteracting drug-induced mitotic arrest. Loss-of-function experiments demonstrate that SERPINB5 depletion sensitizes cervical cancer cells to paclitaxel and vincristine, while its reintroduction restores chemoresistance even in m5C-deficient cells. Our study uncovers a previously unrecognized m5C-SERPINB5 axis as a central driver of cervical cancer malignancy and chemoresistance, highlighting SERPINB5 as a clinically actionable target to improve outcomes for patients receiving microtubule-targeting chemotherapy.

The immune system is crucial in the development and advancement of cancerous tumors, particularly in head and neck squamous cell carcinoma (HNSC). This study aimed to identify immune-related gene signatures (IRGs) for predicting the prognosis of HNSC. Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to immune gene data from ImmPort, were examined. Using Cox-LASSO screening, nine IRGs were identified, and patients were classified into high- and low-risk cohorts based on risk scores. Differential expression, survival analysis, Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), single-sample GSEA (ssGSEA), CIBERSORT, and drug sensitivity analyses were performed between the cohorts. The high-risk cohorts exhibited lower immune scores and survival rates, while the low-risk cohorts exhibited higher immune scores and better outcomes. Cox regression identified CD19, CD79A, CTLA4, ICOS, and LAT as protective genes and CHGB, DKK1, PDGFA, and PTX3 as risk genes. Based on these nine genes, we established a nomogram prediction model to further assess patient prognosis. These findings highlight the prognostic value of IRGs in HNSC, offering the potential for personalized treatment strategies based on immune risk profiles.