GARNL3 identified as a crucial target for overcoming temozolomide resistance in EGFRvIII-positive glioblastoma.

OBJECT: Amplification of the epidermal growth factor receptor (EGFR) and its active mutant type III (EGFRvIII), frequently occurr in glioblastoma (GBM), contributing to chemotherapy and radiation resistance in GBM. Elucidating the underlying molecular mechanism of temozolomide (TMZ) resistance in EGFRvIII GBM could offer valuable insights for cancer treatment. METHODS: To elucidate the molecular mechanisms underlying EGFRvIII-mediated resistance to TMZ in GBM, we conducted a comprehensive analysis using Gene Expression Omnibus and The cancer genome atlas (TCGA) databases. Initially, we identified common significantly differentially expressed genes (DEGs) and prioritized those correlating significantly with patient prognosis as potential downstream targets of EGFRvIII and candidates for drug resistance. Additionally, we analyzed transcription factor expression changes and their correlation with candidate genes to elucidate transcriptional regulatory mechanisms. Using estimate method and databases such as Tumor IMmune Estimation Resource (TIMER) and CellMarker, we assessed immune cell infiltration in TMZ-resistant GBM and its relationship with candidate gene expression. In this study, we examined the expression differences of candidate genes in GBM cell lines following EGFRvIII intervention and in TMZ-resistant GBM cell lines. This preliminary investigation aimed to verify the regulatory impact of EGFRvIII on candidate targets and its potential involvement in TMZ resistance in GBM. RESULTS: Notably, GTPase Activating Rap/RanGAP Domain Like 3 (GARNL3) emerged as a key DEG associated with TMZ resistance and poor prognosis, with reduced expression correlating with altered immune cell profiles. Transcription factor analysis suggested Epiregulin (EREG) as a putative upstream regulator of GARNL3, linking it to EGFRvIII-mediated TMZ resistance. In vitro experiments confirmed EGFRvIII-mediated downregulation of GARNL3 and decreased TMZ sensitivity in GBM cell lines, further supported by reduced GARNL3 levels in TMZ-resistant GBM cells. CONCLUSION: GARNL3 downregulation in EGFRvIII-positive and TMZ-resistant GBM implicates its role in TMZ resistance, suggesting modulation of EREG/GARNL3 signaling as a potential therapeutic strategy.

An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis.

Background and objective: Symptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine learning model based on high-resolution vessel wall imaging (HR-VWI) to predict the risk of stroke recurrence in SICAS. Methods: This study retrospectively collected data from 180 SICAS stroke patients treated at the hospital between 2020.01 and 2022.01. Relevant imaging and clinical data were collected, and follow-up was conducted. The dataset was divided into a training set and a validation set in a ratio of 7:3. We employed the least absolute shrinkage and selection operator (LASSO) regression to perform a selection on the baseline data, laboratory tests, and neuroimaging data generated by HR-VWI scans collected from the training set. Finally, five machine learning techniques, including logistic regression model (LR), support vector machine (SVM), Gaussian naive Bayes (GNB), Complement naive Bayes (CNB), and k-nearest neighbors algorithm (kNN), were employed to develop a predictive model for stroke recurrence. Shapley Additive Explanation (SHAP) was used to provide visualization and interpretation for each patient. The model's effectiveness was evaluated using average accuracy, sensitivity, specificity, precision, f1 score, PR curve, calibration curve, and decision curve analysis. Results: LASSO analysis revealed that "history of hypertension," "homocysteine level," "NWI value," "stenosis rate," "intracranial hemorrhage," "positive remodeling," and "enhancement grade" were independent risk factors for stroke recurrence in SICAS patients. In 10-fold cross-validation, the area under the curve (AUC) ranged from 0.813 to 0.912 in ROC curve analysis. The area under the precision-recall curve (AUPRC) ranged from 0.655 to 0.833, with the Gaussian Naive Bayes (GNB) model exhibiting the best ability to predict stroke recurrence in SICAS. SHAP analysis provided interpretability for the machine learning model and revealed essential factors related to the risk of stroke recurrence in SICAS. Conclusion: A precise machine learning-based prediction model for stroke recurrence in SICAS has been established to assist clinical practitioners in making clinical decisions and implementing personalized treatment measures.

Characteristics of SARS-CoV-2 transmission in a medium-sized city with traditional communities during the early COVID-19 epidemic in China.

The nationwide COVID-19 epidemic ended in 2020, a few months after its outbreak in Wuhan, China at the end of 2019. Most COVID-19 cases occurred in Hubei Province, with a few local outbreaks in other provinces of China. A few studies have reported the early SARS-CoV-2 epidemics in several large cities or provinces of China. However, information regarding the early epidemics in small and medium-sized cities, where there are still traditionally large families and community culture is more strongly maintained and thus, transmission profiles may differ, is limited. In this study, we characterized 60 newly sequenced SARS-CoV-2 genomes from Anyang as a representative of small and medium-sized Chinese cities, compared them with more than 400 reference genomes from the early outbreak, and studied the SARS-CoV-2 transmission profiles. Genomic epidemiology revealed multiple SARS-CoV-2 introductions in Anyang and a large-scale expansion of the epidemic because of the large family size. Moreover, our study revealed two transmission patterns in a single outbreak, which were attributed to different social activities. We observed the complete dynamic process of single-nucleotide polymorphism development during community transmission and found that intrahost variant analysis was an effective approach to studying cluster infections. In summary, our study provided new SARS-CoV-2 transmission profiles representative of small and medium-sized Chinese cities as well as information on the evolution of SARS-CoV-2 strains during the early COVID-19 epidemic in China.