A T-Cell Inspired Sonoporation System Enhances Low-Dose X-Ray-Mediated Pyroptosis and Radioimmunotherapy Efficacy by Restoring Gasdermin-E Expression.

Genome editing has the potential to improve the unsatisfactory therapeutic effect of antitumor immunotherapy. However, the cell plasma membrane prevents the entry of almost all free genome-manipulation agents. Therefore, a system can be spatiotemporally controlled and can instantly open the cellular membrane to allow the entry of genome-editing agents into target cells is needed. Here, inspired by the ability of T cells to deliver cytotoxins to cancer cells by perforation, an ultrasound (US)-controlled perforation system (UPS) is established to enhance the delivery of free genome-manipulating agents. The UPS can perforate the tumor cell membrane while maintaining cell viability via a controllable lipid peroxidation reaction. In vitro, transmembrane-incapable plasmids can enter cells and perform genome editing with the assistance of UPS, achieving an efficiency of up to 90%. In vivo, the UPS is biodegradable, nonimmunogenic, and tumor-targeting, enabling the puncturing of tumor cells under US. With the application of UPS-assisted genome editing, gasdermin-E expression in 4T1 tumor-bearing mice is successfully restored, which leads to pyroptosis-mediated antitumor immunotherapy via low-dose X-ray irradiation. This study provides new insights for designing a sonoporation system for genome editing. Moreover, the results demonstrate that restoring gasdermin expression by genome editing significantly improves the efficacy of radioimmunotherapy.

A Bioinspired Immunostimulatory System for Inducing Powerful Antitumor Immune Function by Directly Causing Plasma Membrane Rupture.

The Gasdermin protein is a membrane disruptor that can mediate immunogenic pyroptosis and elicit anti-tumor immune function. However, cancer cells downregulate Gasdermin and develop membrane repair mechanisms to resist pyroptosis. Therefore, an artificial membrane disruptor (AMD) that can directly mediate membrane rupture in pyroptosis-deficient cells and induce antitumor immune responses in a controllable manner will be valuable in preclinical and clinical research. A micron-scale Ce6-based AMD that can directly induce plasma membrane rupture (PMR) in gasdermin-deficient tumor cells is established. Micron-scale AMDs localize Ce6 specifically to the plasma membrane without labeling other organelles. Compared to free Ce6 molecules, the use of AMDs results in a higher degree of specificity for the plasma membrane. Due to this specificity, AMDs mediate fast and irreversible PMR under 660 nm red light. Furthermore, the AMDs are capable of inducing programmed cell death and lytic cell death in a catalytic manner, demonstrating that the amount of Ce6 used by AMDs is only one-fifth of that used by Ce6 alone when inducing 80% of cancer cell death. In vivo, the AMDs show specificity for tumor targeting and penetration, suggesting that light-driven programmed cell death is specific to tumors. AMDs are applied to antitumor therapy in gasdermin-deficient tumors, resulting in efficient tumor elimination with minimal damage to major organs when combined with anti-PD-1 therapy. Tumor regression is correlated with PMR-mediated inflammation and T-cell-based immune responses. This study provides new insights for designing bioinspired membrane disruptors for PMR and mediating anti-tumor immunotherapy. Additionally, AMD is a dependable tool for examining the immunogenicity of PMR both in vitro and in vivo.

GNPNAT1 is a Biomarker That Predicts a Poor Prognosis of Breast Cancer.

Background: Breast cancer (BC) is increasingly becoming the primary reason for death in women, which sounded the alarm. Thus, finding a novel management target for BC is imminent. Materials and Methods: The data on gene expression and clinicopathological characteristics were downloaded from The Cancer Genome Atlas (TCGA). The expression of GNPNAT1 in 40 paired breast cancer and adjacent tissues was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Univariate and Multivariate logistic regression methodology was applied to analyze the prognostic factors for lymph node metastasis (LNM). Based on the status of breast cancer-relative receptors, patients were distributed into six groups, and then the Kaplan-Meier survival analysis with a Log rank test was applied to investigate the involvement among the expression of GNPNAT1 and overall survival (OS). Results: We found higher expression of GNPNAT1 was connected with poor survival in breast cancer by COX regulation analysis. GO, KEGG, and GSEA analysis prompted that GNPNAT1 was connected with the defense mechanism of cells, cell proliferation, and division. Immunization infiltration analysis showed that high GNPNAT1 was negatively connected with 16 immunization infiltration cell types and positively connected with four immunization infiltration cell types. Conclusion: As a whole, our results indicated that GNPNAT1 might be a probable biomarker for diagnosis and prognosis in breast cancer.

Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction.

BACKGROUND: Persistent efforts are required to further reduce the in-hospital mortality of patients suffering from acute myocardial infarction (AMI), even in the face of a global trend of declining AMI-related fatalities. We investigated deep machine learning models for in-hospital death prediction in patients on their first AMI. METHOD: In this 2-center retrospective analysis, first AMI patients from Hospital I and Hospital II were included; 4783 patients from Hospital 1 were split in a 7:3 ratio between the training and testing sets. Data from 1053 AMI patients in Hospital II was used for further validation. 70 clinical information and laboratory examination parameters as predictive indicators were included. Logistic Regression Classifier (LR), Random Forests Classifier (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine Classifier (SVM), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Bootstrapped Aggregation (Bagging) models with AMI patients were developed. The importance of selected variables was obtained through the Shapley Additive exPlanations (SHAP) method. The performance of each model was shown using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (Average Precision; AP). RESULT: The in-hospital mortality for AMI in the training, testing, and validation sets were 5.7 %, 5.6 %, and 6.0 %, respectively. The top 8 predictors were D-dimer, brain natriuretic peptide, cardiogenic shock, neutrophil, prothrombin time, blood urea nitrogen, cardiac arrest, and phosphorus. In the testing cohort, the models of LR, RF, XGB, SVM, MLP, GBM, and Bagging yielded AUROC values of 0.929, 0.931, 0.907, 0.868, 0.907, 0.923, and 0.932, respectively. Bagging has good predictive value and certain clinical value in external validation with AUROC 0.893. CONCLUSION: In order to improve the forecasting accuracy of the risk of AMI patients, guide clinical nursing practice, and lower AMI inpatient mortality, this study looked into significant indicators and the optimal models for predicting AMI inpatient mortality.

The construction and analysis of a prognostic assessment model based on P53-related multi-genes in breast carcinoma.

BACKGROUND: Breast cancer ranks second in female tumor mortality, with an estimation of 2 million new cases diagnosed each year worldwide. METHODS: In our current study, we screened 13 genes highly distributed on the P53 phenotype which were significantly expressed and had a strong correlation with survival in the Cancer Genome Atlas breast cancer dataset. Least absolute shrinkage and selection operator Cox regression was conducted to construct the risk assessment model. Based on bioinformatics and statistical methods, we confirmed the credibility and validity of the model by training set and testing set. RESULTS: The result of comparing the other two previous hypoxia models was also satisfying. We also verified the model on one of the Gene Expression Omnibus datasets-GSE20685. Using clinical data from patients in the Cancer Genome Atlas, we acknowledged the risk score as an independent influence on breast cancer survival prognosis, and strong relevance was suggested between risk signature and age, lymphatic metastasis, tumor size and clinical stage by performing univariate and multivariate analysis. Immunology analysis demonstrated that the macrophages subset was positively associated with a risk score and other immune cell types had a negative effect with the risk score increases. The risk score was also emerging as a valuable prognostic factor for the prediction of chemotherapy drug curative effect because Gemcitabine, vinorelbine, paclitaxel and cisplatin known as a generic drug for breast cancer had more pleasing sensitivity in high-scored patients than low-scored patients. CONCLUSION: The P53-related risk assessment model is promising to be a potential predictor for the prognosis of patients with breast cancer and a powerful guide for the selection of therapeutic strategies.

Construction and Analysis of a Colorectal Cancer Prognostic Model Based on N6-Methyladenosine-Related lncRNAs.

Given the relatively poor understanding of the expression and functional effects of the N6-methyladenosine (m6A) RNA methylation on colorectal cancer (CRC), we attempted to measure its prognostic value and clinical significance. We comprehensively screened 37 m6A-related prognostic long non-coding RNAs (lncRNAs) with significant differences in expression based on 21 acknowledged regulators of m6A modification and data on 473 colorectal cancer tissues and 41 para-cancer tissues obtained from the TCGA database. Accordingly, we classified 473 CRC patients into two clusters by consensus clustering on the basis of significantly different survival outcomes. We also found a potential correlation between m6A-related prognostic lncRNAs and BRAF-KRAS expression, as well as immune cell infiltration. Then, we established a prognostic model by selecting 16 m6A-related prognostic lncRNAs via LASSO Cox analysis and grouped the CRC patients into low- and high-risk groups to calculate risk scores. Then, we performed stratified sampling to validate and confirm our model by categorising the 473 samples into a training group (N = 208) and a testing group (N = 205) in a 1:1 ratio. The survival curve showed a distinct clinical outcome in the low- and high-risk subgroups. We reconfirmed the reliability and independence of the prognostic model through various measures: risk curve, heat map and univariate and multivariate Cox analyses. To ensure that the outcomes were applicable to clinical settings, we performed stratified analyses on different clinical features, such as age, lymph node status and clinical stage. CRC patients with downregulated m6A-related gene expression, lower immune score, distant metastasis, lymph node metastasis or more advanced clinical staging had higher risk scores, indicating less-desirable outcomes. Moreover, we explored the immunology of colorectal cancer cells. The risk score showed positive correlations with eosinophils, M2 macrophages and neutrophils. In summary, our effort revealed the significance of m6A RNA methylation regulators in colorectal cancer, and the prognostic model we constructed may be used as an essential reference for predicting the outcome of CRC patients.

Effects of Tamoxifen vs. Toremifene on fatty liver development and lipid profiles in breast Cancer.

BACKGROUND: Tamoxifen (TAM) and Toremifene (TOR), two kinds of selective estrogen receptor modulators (SERMs), have equal efficacy in breast cancer patients. However, TAM has been proved to affect serum lipid profiles and cause fatty liver disease. The study aimed to compare the effects of TAM and TOR on fatty liver development and lipid profiles. METHODS: This study performed a retrospective analysis of 308 SERMs-treated early breast cancer patients who were matched 1:1 based on propensity scores. The follow-up period was 3 years. The primary outcomes were fatty liver detected by ultrasonography or computed tomography (CT), variation in fibrosis indexes, and serum lipid profiles change. RESULTS: The cumulative incidence rate of new-onset fatty liver was higher in the TAM group than in the TOR group (113.2 vs. 67.2 per 1000 person-years, p < 0.001), and more severe fatty livers occurred in the TAM group (25.5 vs. 7.5 per 1000 person-years, p = 0.003). According to the Kaplan-Meier curves, TAM significantly increased the risk of new-onset fatty liver (25.97% vs. 17.53%, p = 0.0243) and the severe fatty liver (5.84% vs. 1.95%, p = 0.0429). TOR decreased the risk of new-onset fatty liver by 45% (hazard ratio = 0.55, p = 0.020) and showed lower fibrotic burden, independent of obesity, lipid, and liver enzyme levels. TOR increased triglycerides less than TAM, and TOR increased high-density lipoprotein cholesterol, while TAM did the opposite. No significant differences in total cholesterol and low-density lipoprotein cholesterol are observed between the two groups. CONCLUSIONS: TAM treatment is significantly associated with more severe fatty liver disease and liver fibrosis, while TOR is associated with an overall improvement in lipid profiles, which supports continuous monitoring of liver imaging and serum lipid levels during SERM treatment.

Aberrant expression of WDR4 affects the clinical significance of cancer immunity in pan-cancer.

Recent publications have presented research showing that WD repeat domain 4 (WDR4) plays a significant role in various kinds of malignant tumours. However, the expression profile of WDR4 is still unspecified, as is its significance in the analysis of human pan-cancer. We conducted an in-depth analysis of three aspects of WDR4 expression patterns from 33 types of cancer and determined the value of WDR4 for prognostic prediction and carcinoma drug resistance prediction. WDR4 was expressed in different cancer cell lines at inconsistent levels. Aberrant expression of WDR4 has been observed in various malignant cancers and is significantly implicated in overall survival outcomes. The expression level of WDR4 is also strongly associated with tumour immunity, such as immune scores and tumour-infiltrating immune cells. The level of WDR4 is related to microsatellite instability and tumour mutation burden in several types of malignancy, and validation studies implied that WDR4-associated terms and pathways are involved in malignancy. We explored the expression level of WDR4 across 33 types of cancer and showed that WDR4 plays a significant role during cancer development. More crucially, WDR4 is associated with immune infiltration, which suggests that WDR4 could be an immunotherapy target in cancers. In summary, our research showed that WDR4 plays a vital role in tumorigenesis and has the potential for to be targeted with treatments.

Pan-cancer investigation of CENPK gene: clinical significance and oncogenic immunology.

Many studies have confirmed that the CENPK gene regulates the progression of cancers, but its specific molecular mechanism remains unidentified, as does its significance in the analysis of human cancers. We specify a comprehensive genomic architecture of the CENPK gene associated with the tumor immune microenvironment and its clinical relevance across a broad spectrum of solid tumors. Statistics from The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) of over 30 solid tumors were examined. CENPK was expressed differentially in several cancers and is significantly associated in survival outcomes, with higher CENPK signifying a worse prognosis for ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and SARC. We further examined its clinical relevance with tumor immunogenic features. The expression level of CENPK was not only strongly linked to the tumor infiltration, such as tumor-infiltrating immune cells and immune scores but also linked to microsatellite instability and tumor mutation burden in diverse cancers (P<0.05). I mmune markers such as TNFRSF14 and VSIR were highly expressed on over 20 kinds of human cancer and mismatch repair genes like MLH1, MSH2, MSH6, and PMS2 were positively related with CENPK expression. Moreover, the methyltransferases and functional pathways also seem to have a relationship with the CENPK. CENPK is expected to be a guiding marker gene for clinical prognosis and tumor personalized immunotherapy.

Long Non-Coding RNA NLIPMT as a Tumor Suppressor and Inhibitor of Cell Proliferation and Metastasis in Papillary Thyroid Carcinoma.

INTRODUCTION: Gene expression association studies of tumor samples have uncovered several long non-coding RNAs (lncRNAs) closely related to various types of cancer. Several lncRNAs have been reported to play essential roles in the progression of papillary thyroid carcinoma (PTC). Novel lncRNA inhibiting proliferation and metastasis (lnc-NLIPMT) is a known regulator of mammary cell proliferation and motility, but its involvement in PTC is unclear. MATERIALS AND METHODS: We investigated the role of lnc-NLIPMT in PTC by quantitative real-time polymerase chain reaction (qRT-PCR) on various PTC tissue samples and cell lines. We assessed the effects of overexpression or knockdown of lnc-NLIPMT on the proliferation, migration, and invasion of PTC cells using CCK-8, cell clone formation, and transwell assays. Changes in the expression of N-cadherin and vimentin were detected by immunoblotting. RESULTS: Our results revealed a downregulation of the expression of lnc-NLIPMT in PTC and a negative correlation between lnc-NLIPMT expression and tumor size (P=0.006). Overexpression of lnc-NLIPMT in TPC-1 and B-CPAP cells significantly suppressed cell proliferation, migration, and invasion, while lnc-NLIPMT knockdown had the opposite effect. In addition, lnc-NLIPMT played an important role in the regulation of the expression of N-cadherin and vimentin. CONCLUSION: lnc-NLIPMT inhibits cell proliferation and metastasis of PTC cells and is a potential diagnostic and prognostic biomarker in PTC.