Jiangjin Central Hospital

To identify key RNA-binding proteins (RBPs) associated with breast cancer (BRCA) prognosis and to construct an RBP-based prognostic scoring model and clinical nomogram. Additionally, to explore the functional role of RPL9 in BRCA cell proliferation and apoptosis. Breast cancer is a molecularly heterogeneous malignancy in which posttranscriptional dysregulation contributes significantly to tumor progression. RNAbinding proteins regulate multiple steps of RNA metabolism and have emerged as promising prognostic biomarkers and therapeutic targets in BRCA. RNA-sequencing data and clinical information of BRCA patients were obtained from TCGA. Differential expressions, Cox, and LASSO regression analyses were performed to identify prognosis-related RBPs and establish a risk scoring model. A clinical nomogram integrating the model with clinicopathological features was generated. Tumor mutational burden (TMB), tumor immune microenvironment (TIME), and immune checkpoint expression were analyzed between risk groups. Functional assays, including CCK-8 proliferation and flow cytometry-based apoptosis analyses, were conducted in BRCA cell lines to determine the role of RPL9. Nine prognosis-related RBPs were identified and used to construct the RBPbased prognostic scoring model, which effectively predicted 3-, 5-, and 9-year survival outcomes. The model remained robust in an external validation cohort. A clinical nomogram based on the model showed strong predictive performance. High-risk patients displayed higher TMB, lower immune scores, reduced CD8⁺ T-cell infiltration, and decreased immune checkpoint expression. RPL9 was markedly downregulated in BRCA tissues and cells, and RPL9 overexpression inhibited cell proliferation and significantly increased apoptosis, supporting its tumor-suppressive role. We identified and constructed a 9-RBP based scoring model and a clinical prognostic nomogram for accurately predicting the survival probability and assessing the tumor microenvironment of patients with BRCA. Moreover, our findings firstly demonstrated that RPL9 downregulation correlated with poor clinical outcomes and functionally drives tumor cell proliferation, establishing it as a potential therapeutic target and prognostic biomarker for BRCA.

ObjectiveTo investigate blood-brain barrier (BBB) permeability abnormalities in cerebral white matter regions of non-brain metastatic lung cancer (LC) patients and their association with histopathological subtypes.Methods221 subjects (74 Healthy Controls [HC], 78 LC, 69 LC with Brain Metastasis [LCBM]) were enrolled. LC was subdivided into Adenocarcinoma (ADC), Squamous Cell Carcinoma (SCC), Small Cell Lung Cancer (SCLC). Dynamic Contrast-Enhanced MRI (DCE-MRI) assessed Ktrans, Kep, Ve, Vp in deep (frontal/parietal/temporal/occipital lobes) and periventricular (anterior/posterior horns) white matter. Non-parametric tests were used (p<0.05 significant).ResultsLC group had Ktrans comparable to LCBM but higher than HC (p<0.05), mainly in deep white matter and posterior periventricular regions (anterior periventricular: p>0.05). ADC showed higher Ktrans, Kep, Vp than SCC (all p<0.001) and SCLC (Ktrans/Kep p<0.001; Vp p=0.71); Ve was lower than SCLC (p=0.003) but higher than SCC (p=0.001). SCLC had higher Ve than SCC (p<0.0001), with higher Ktrans/Vp than SCC (p<0.01). Vp correlated positively with Ktrans in ADC/SCC (r=0.433/0.359, p<0.001), not in SCLC.ConclusionLC impairs BBB integrity via paraneoplastic effects, with subtype heterogeneity: ADC (high perfusion/leakage) involves global white matter; SCLC (prominent interstitial disruption) affects mainly deep regions. Findings aid LC brain metastasis risk stratification and neuroprotective strategies.

Pancreatic ductal adenocarcinoma (PDAC) poses a major challenge due to the lack of effective treatment options and its extremely poor prognosis. Nanodrug delivery systems can improve drug solubility and enable efficient targeted delivery, offering new possibilities for PDAC therapy. The oncogenic role of KCa3.1 in PDAC was validated through analyses of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases combined with functional assays. To overcome the limitations of conventional therapies, we developed a targeted nanodrug delivery system, TRAM@PPF, based on PLGA nanoparticles modified with polyethylene glycol-folate (PEG2000-FA). This system was prepared by the emulsion-solvent evaporation method to specifically deliver the KCa3.1 channel inhibitor TRAM-34 to PDAC cells. We characterized the nanosystem's physical properties and release profile and evaluated its antitumor efficacy in vitro and in vivo. The synthesized TRAM@PPF nanoparticles demonstrated uniform size (~142 nm) and excellent stability, with superior cellular uptake compared to non-folate-modified nanoparticles. In vitro, TRAM@PPF showed potent antitumor activity by markedly inhibiting cell proliferation and enhancing apoptosis. Following intravenous administration in pancreatic cancer mouse models, TRAM@PPF significantly inhibited tumor growth, reduced tumor weight, and prolonged survival. Moreover, TRAM@PPF showed excellent biosafety in animal models, suggesting strong potential for further clinical translation in PDAC therapy. TRAM@PPF preserves folate-mediated tumor-targeting capability while significantly enhancing antitumor activity, offering a promising strategy for targeted therapy of pancreatic cancer.

Background Tazemetostat is an oral inhibitor of enhancer of zeste homolog 2 (EZH2) and is used for the treatment of epithelioid sarcoma (ES) and follicular lymphoma (FL). In this study, we investigated the adverse events (AEs) of tazemetostat based on real data from the U.S Food and Drug Administration(FDA) Adverse Event Reporting System (FAERS). Methods To quantify the signals of AEs associated with tazemetostat, we employed disproportionality analyze, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms. Results A total of 7,133,678 reports of AEs were collected from the FAERS database, of which 1,110 reports were identified with tazemetostat as the “primary suspect (PS)”. A total of 32 significant disproportionality preferred terms(PTs) conforming to the four algorithms were simultaneously retained. There were multiple unexpected AEs caused by tazemetostat that were not mentioned in the label yet, including pleural effusion (ROR = 3.84, PRR = 3.83, IC = 3.83, IBGM = 1.94), lymphadenopathy (ROR = 3.88, PRR = 3.88, IC = 3.87, IBGM = 1.95) and taste disorder(ROR = 20.48, PRR = 20.23, IC = 20.16, IBGM = 4.33). Additionally, the majority of tazemetostat-associated AEs occurred within the first month of treatment initiation (n = 126, 49.8%). Conclusion Clinicians need to monitor safety for tazemetostat during the first 30 days and pay more attention to new AE signals.

BackgroundFailure to extubate successfully from mechanical ventilation is a critical event associated with poor prognosis in ICU patients, significantly prolonging hospital stays and increasing mortality rates. It is widely accepted in academic circles that developing prediction models for extubation failure can facilitate precise extubation decisions. Despite the rapid proliferation of relevant prediction models, their methodological quality and bedside applicability remain ambiguous.ObjectiveThis study aims to outline the predictive factors associated with the risk of extubation failure in patients undergoing mechanical ventilation in the Intensive Care Unit (ICU) and to summarize the existing predictive models.MethodsWe searched the China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Database, China Biomedical Database, PubMed, Embase, Web of Science, and Cochrane Library. We included both prospective and retrospective studies that developed or validated risk prediction models for extubation failure in patients undergoing mechanical ventilation in the ICU. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the bias and applicability of the models.ResultsThis analysis includes 14 studies. Frequency analysis of the predictors revealed that there are 15 predictors that appeared at least twice, among which mechanical ventilation duration, GCS score, APACHE II score, age, and hemoglobin were the most common predictors. From the perspective of the models, only 2 studies conducted both internal and external validation, 3 studies ultimately employed machine learning, while 11 studies utilized traditional modeling methods. However, we found that many studies faced issues such as insufficient sample sizes, missing crucial methodological information, and all models being rated as having a high risk of bias.ConclusionMost published predictive models lack methodological rigor, leading to a heightened risk of bias. Future research should prioritize the enhancement of methodological rigor and the external validation of risk prediction models for extubation failure in ICU patients receiving mechanical ventilation. Additionally, it is essential to emphasize adherence to scientific methods and transparent reporting to improve the accuracy and generalizability of research findings.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/recorddashboard, Registration number:CRD420251124371.

Background and ObjectivesTo investigate the associations of C-peptide-based insulin resistance index (HOMA2 IR-CP) and triglyceride-glucose-body mass index (TyG-BMI) with metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with type 2 diabetes mellitus (T2DM), identify independent risk factors for MASLD, construct a clinical predictive model, and evaluate its predictive performance.MethodsA total of 311 T2DM patients were enrolled and randomly assigned to a derivation cohort (218 cases) and a validation cohort (93 cases) at a 7:3 ratio. Predictive factors were screened via LASSO regression, with results integrated with those from logistic regression analysis. A nomogram was developed, and model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Restricted cubic spline (RCS) regression was used to explore dose-response relationships, and stratified analyses and interaction analyses were performed.ResultsHOMA2 IR-CP and TyG-BMI as independent risk factors for MASLD in T2DM patients. The area under the curve (AUC) was 0.805 in the derivation cohort and 0.730 in the validation cohort. Calibration curves showed good agreement between predicted probabilities and actual outcomes, and DCA verified the model’s substantial clinical net benefit. RCS analysis revealed a non-linear association between HOMA2 IR-CP and MASLD (P for non-linearity < 0.05), whereas TyG-BMI exhibited a linear association with MASLD (P for non-linearity = 0.139). No significant interaction effects were observed across subgroups stratified by gender, age, BMI, or HbA1c level; however, the associations were more prominent in patients with BMI < 28 kg/m2 and those with HbA1c ≥ 7%.ConclusionHOMA2 IR-CP and TyG-BMI are independent risk factors for MASLD in T2DM patients. The nomogram model constructed based on these two factors exhibits good predictive performance and can provide a reference for MASLD risk assessment in T2DM patients.

BackgroundManagement of the immunosuppressive tumor microenvironment (TME) is crucial for microsatellite stability (MSS) colorectal cancer (CRC), which responds poorly to immunotherapy. PIN1, a peptidyl-prolyl cis-trans isomerase that is overexpressed in human malignancies, regulates TME immunosuppression. However, its role in MSS CRC remains insufficiently explored.MethodsWe divided 411 CRC patients from the TCGA-COAD database into MSS or microsatellite instability-high (MSI-H) groups and analyzed their gene expression profiles. Using smoothed t-statistic SVM, weighted correlation network analysis, and sample clustering, we identified PIN1 as a key biomarker. We assessed Pin1 expression in CRC cell lines and tissues, and conducted functional assays, in vitro co-cultures, and in vivo studies (using a Pin1 inhibitor and anti-PD-1 in CT26 subcutaneous tumor and liver metastasis mouse models) to evaluate its effects and mechanisms.ResultsPIN1 was overexpressed in MSS CRC and negatively correlated with CD4+ T and CD8+ T cell infiltration. Knockdown of PIN1 in MSS CRC cells significantly reduced cell proliferation (assessed by CCK-8 assay), impaired migratory capacity (assessed via wound-healing assay), and increased the apoptotic rate (detected by flow cytometry). In CT26 mouse models, combining Pin1 inhibition with PD-1 blockade enhanced immunotherapy efficacy by reducing Treg infiltration, suppressing cancer-associated fibroblast (CAF) activity, and promoting CD8+ T cell recruitment. Mechanistically, PIN1 activated the NF-κB pathway and modulated CCL3-CCR5 signaling, which are critical for Treg migration and CAF activation.ConclusionOur findings suggest that Pin1 reshapes the immunosuppressive TME in MSS CRC through the NF-κB-CCL3-CCR5 axis, driving CRC progression and immunotherapy resistance. This pathway presents a potential target for overcoming immunotherapy resistance in MSS CRC.

BackgroundLysosomes play an important role in the pathological processes of cancer development. However, its effects on the prognosis and tumor microenvironment of hepatocellular carcinoma (HCC) remain unclear. Therefore, we aim to explore the novel molecular subtypes of HCC via lysosome-related genes (LRGs) for prognosis and therapy prediction in this study.MethodsUsing the data of TCGA, differential expression and survival analyses were performed. Consequently, 109 key LRGs were obtained and 374 HCC samples were clustered into two groups: C1 and C2. A three-gene prognostic prediction nomogram was constructed using WCGNA and Cox regression analyses. Furthermore, pathway enrichment conditions, immune infiltration, immune checkpoint expression, and drug sensitivity were analyzed for the two subtypes. RT-qPCR was also used to validate the expression of the selected key LRGs.ResultsKey LRGs were highly expressed in the C1 subtype, and their prognosis was worse. The degree of immune cell infiltration and pathway enrichment results were also significantly different between the two subtypes. Furthermore, the three-gene prognostic prediction nomogram including LAPTM4B, PRKCD and LPCAT1, had a relatively high prognostic prediction ability. Meanwhile, the expression of immune checkpoints, human leukocyte antigen, and TIDE score were higher in the C1 subtype, suggesting that immune evasion was more likely to occur in this subtype. Drug sensitivity analysis showed that several drugs were more sensitive to C1 subtypes and might serve as drug candidates for these patients.ConclusionWe identified two novel molecular subtypes of HCC based on LRGs, and found that the LRGs related subtypes demonstrated significant efficacy in predicting the prognosis and therapeutic outcomes for patients with HCC. Moreover, a novel prognostic prediction nomogram was also developed, which possessed excellent prognostic prediction capabilities. We hope the novel LRG-related subtypes and nomogram of HCC would provide new insights and guidelines for clinical practice in the future.

NEDD4L, an E3 ubiquitin ligase, has served a pivotal function in the malignant progression of different cancers. However, research focusing on its involvement in hepatocellular carcinoma (HCC) remains relatively scarce. This investigation examined NEDD4L's expression, survival implications, and regulatory mechanisms of NEDD4L in HCC using RNA-seq and microarray data across multiple databases. Additionally, we investigated the impact of NEDD4L expression on malignant biological behaviors of HCC by in vitro functional assays including Edu, CCK-8, Transwell, and wound healing assay. Finally, single-cell sequencing data from HCC patients were employed to further investigate and validate NEDD4L expression patterns across various stages of HCC and its immune functional states. NEDD4L was identified as one of the most markedly differentially expressed genes linked to ubiquitination in HCC and was noted to be an independent prognostic marker for overall survival, with higher expression levels correlating with poorer outcomes. Knockdown of NEDD4L significantly inhibited cell proliferation, migration, and scratch healing ability in HCC. Moreover, NEDD4L expression was closely linked to the regulation of the cell cycle and DNA damage repair, potentially driving abnormal cell cycle progression and HCC development by mediating the degradation of inhibitory cell cycle checkpoints or their upstream transcription factors via ubiquitination. Single-cell sequencing analysis revealed that NEDD4L was notably enriched in cancer stem cell populations across different HCC developmental stages and immune states, with subgroups exhibiting high NEDD4L expression sharing substantial co-expressed genes with stem cell subpopulations. Furthermore, an analysis of NEDD4L's relationships with immune infiltration indicated that NEDD4L could facilitate immune evasion in HCC by downregulating stimulatory immune checkpoints and immune cell infiltration, a phenomenon also observed in other types of tumors. These findings indicate that NEDD4L is upregulated in HCC and strongly associated with unfavorable patient outcomes, pointing to its prospective utility as a biomarker for HCC. Furthermore, silencing NEDD4L could effectively inhibit the malignant behaviors of HCC. Given its dual role in regulating both cell cycle and immune checkpoints, NEDD4L represents a promising therapeutic target for HCC. Targeting NEDD4L could potentially enhance the efficacy of cyclin-dependent kinase inhibitors and immune checkpoint inhibitors.

Cisplatin (DDP) remains the commonly used chemotherapeutic drug for ovarian cancer (OV); however, DDP resistance poses a great challenge to the outcomes of patients. This work investigated the biological function and mechanism of Spartin in DDP resistance of OV. The growth and apoptosis of DDP-resistant OV cells were assessed by CCK-8, colony formation, and flow cytometry, respectively. Autolysosome fusion was observed by immunofluorescent staining of LC3 and LAMP2. The interaction between E3 ligase Smurf1 and YWHAZ or Spartin protein, and the ubiquitination level of YWHAZ were determined by Co-IP assay. Expression levels of autophagy or apoptosis-related markers were measured by RT-qPCR, western blotting, and immunohistochemistry. DDP resistance was assessed by xenograft tumor experiments invivo. We found that Spartin expression was lower, while YWHAZ expression was higher in DDP-resistant OV samples and cells. Lower expression of Spartin indicated a poorer survival rate of OV patients. In addition, overexpression of Spartin sensitized OV cells to DDP and repressed autophagy. Moreover, Spartin bound to Smurf1 to promote Smurf1-mediated ubiquitination and degradation of YWHAZ, restraining autophagy and DDP resistance. Overexpression of YWHAZ counteracted the effects of Spartin against DDP resistance by promoting autophagy. In conclusion, Spartin-induced Smurf1-mediated ubiquitination modification of YWHAZ to inactivate autophagy, thereby increasing the sensitivity of OV cells to DDP. Our findings suggest that Spartin-combined therapy might act as an effective approach to fight against DDP resistance in OV.