Yingru Xing

Silica exposure is associated with an increased risk of lung adenocarcinoma, but its molecular mechanism remains unclear. This study aims to establish a repeatable experimental protocol to explore how the nuclear factor erythroid 2-related factor 2 (NRF2) inhibitor ML385 inhibits silica-induced malignant progression of lung adenocarcinoma by regulating the ROS/NRF2-autophagy axis pathway. Firstly, a silicosis model was established by intranasal perfusion of silica suspension in C57BL/6 mice. The degree of pulmonary fibrosis was evaluated by Masson staining, the infiltration of immune cells was analyzed by immunofluorescence, and the expressions of NRF2, cyclin-dependent kinase 1 (CDK1), and voltage-dependent anion channel 1 (VDAC1) in lung tissue were detected by immunohistochemistry. Subsequently, in in vitro experiments, the RAW264.7 macrophage cell line was treated with silica. Autophagic flux and oxidative stress levels were evaluated by LC3-lysosome co-localization and ROS probes, and intervention was carried out with the NRF2 inhibitor ML385. Finally, the effects of ML38 on migration, invasion, and apoptosis of lung adenocarcinoma cells were analyzed by scratch assay, cells passing through pores of a specific size assay, and flow cytometry. The results showed that silica could induce pulmonary fibrosis, immune cell infiltration, and upregulation of NRF2, CDK1, and VDAC1 in mice (p < 0.001), and inhibit the autophagic flux of macrophages and reduce ROS levels. ML385 can reverse these effects (p < 0.05). This protocol provides a complete experimental process from in vivo model construction to in vitro mechanism research, offering an operational technical path for studying the molecular mechanism of silica-related lung adenocarcinoma and therapeutic strategies targeting NRF2.

Silica exposure is associated with an increased risk of lung adenocarcinoma, but its molecular mechanism remains unclear. This study aims to establish a repeatable experimental protocol to explore how the nuclear factor erythroid 2-related factor 2 (NRF2) inhibitor ML385 inhibits silica-induced malignant progression of lung adenocarcinoma by regulating the ROS/NRF2-autophagy axis pathway. Firstly, a silicosis model was established by intranasal perfusion of silica suspension in C57BL/6 mice. The degree of pulmonary fibrosis was evaluated by Masson staining, the infiltration of immune cells was analyzed by immunofluorescence, and the expressions of NRF2, cyclin-dependent kinase 1 (CDK1), and voltage-dependent anion channel 1 (VDAC1) in lung tissue were detected by immunohistochemistry. Subsequently, in in vitro experiments, the RAW264.7 macrophage cell line was treated with silica. Autophagic flux and oxidative stress levels were evaluated by LC3-lysosome co-localization and ROS probes, and intervention was carried out with the NRF2 inhibitor ML385. Finally, the effects of ML38 on migration, invasion, and apoptosis of lung adenocarcinoma cells were analyzed by scratch assay, cells passing through pores of a specific size assay, and flow cytometry. The results showed that silica could induce pulmonary fibrosis, immune cell infiltration, and upregulation of NRF2, CDK1, and VDAC1 in mice (p < 0.001), and inhibit the autophagic flux of macrophages and reduce ROS levels. ML385 can reverse these effects (p < 0.05). This protocol provides a complete experimental process from in vivo model construction to in vitro mechanism research, offering an operational technical path for studying the molecular mechanism of silica-related lung adenocarcinoma and therapeutic strategies targeting NRF2.

To screen key biomarkers of esophageal cancer (ESCA) by bioinformatics and analyze the correlation between key genes and immune infiltration. Expression profile data of ESCA was downloaded from TCGA database, and DEGs in ESCA were screened with R software. After the RNA binding proteins (RBPs) in DEGs were screened, the protein interaction network was constructed using tools such as STRING and Cytoscape and the key genes (HENMT1) were screened. Survival analysis of HENMT1 was performed by Kaplan–Meier method. Functional enrichment analysis of HENMT1 interacting proteins was performed using the DAVID website, and GSEA predicted the signal pathways involved by HENMT1. CIBERSORT algorithm was used to analyze the infiltration of immune cells in ESCA. The expression of HENMT1 in ESCA was detected by immunohistochemistry. A total of 105 RNA binding proteins (RBPs) were differentially expressed in ESCA, and a PPI network was constructed to screen the key gene HENMT1. The expression level of hemmt1 gene was closely related to the infiltration of B cells naive, T cells regulatory (Tregs), neutrophils, T cells CD4 memory activated, master cells resting and dendritic cells resting in ESCA tissues (P < .05). Immunohistochemical results showed that HENMT1 was highly expressed in ESCA tissues and was positively correlated with the expression of MKI67. HENMT1 is related to the occurrence and prognosis of ESCA, and is also related to the infiltration of immune cells in ESCA tissue, which may provide a new idea for the targeted treatment of ESCA.

Objective: The aim of the study is to assess whether occupational groups exposed to dust and noise increase their risk of developing hypertension and to identify associated risk factors. Methods: Logistic regression analysis was used to analyze the influence of exposure factors on the occurrence of hypertension, and confounding factors were adjusted to identify independent effects. Stratified analysis and smoothed curve fitting were used to explore the effects in different populations. Results: Combined dust + noise exposure significantly increased the risk of hypertension in workers (model 1: odds ratio [OR], 2.75; model 2: OR, 2.66; model 3: OR, 2.85). Further analysis showed that when exposed to dust and noise for more than 17 years, the risk of hypertension increased by 15%. Conclusions: The combined exposure of dust and noise significantly increases the risk of hypertension among occupational groups, especially among workers who have worked for more than 17 years.

Background: To determine the populations who suitable for surgical treatment in elderly patients (age ≥ 75 y) with IA stage. Methods: The clinical data of NSCLC patients diagnosed from 2010 to 2015 were collected from the SEER database and divided into surgery group (SG) and no-surgery groups (NSG). The confounders were balanced and differences in survival were compared between groups using PSM (Propensity score matching, PSM). Cox regression analysis was used to screen the independent factors that affect the Cancer-specific survival (CSS). The surgery group was defined as the patients who surgery-benefit and surgery-no benefit according to the median CSS of the no-surgery group, and then randomly divided into training and validation groups. A surgical benefit prediction model was constructed in the training and validation group. Finally, the model is evaluated using a variety of methods. Results: A total of 7297 patients were included. Before PSM (SG: n = 3630; NSG: n = 3665) and after PSM (SG: n = 1725, NSG: n = 1725) confirmed that the CSS of the surgery group was longer than the no-surgery group (before PSM: 82 vs. 31 months, P < .0001; after PSM: 55 vs. 39 months, P < .0001). Independent prognostic factors included age, gender, race, marrital, tumor grade, histology, and surgery. In the surgery cohort after PSM, 1005 patients (58.27%) who survived for more than 39 months were defined as surgery beneficiaries, and the 720 patients (41.73%) were defined surgery-no beneficiaries. The surgery group was divided into training group 1207 (70%) and validation group 518 (30%). Independent prognostic factors were used to construct a prediction model. In training group (AUC = .678) and validation group (AUC = .622). Calibration curve and decision curve prove that the model has better performance. Conclusions: This predictive model can well identify elderly patients with stage IA NSCLC who would benefit from surgery, thus providing a basis for clinical treatment decisions.

Objective: To develop and validate a generalized prediction model that can classify epidermal growth factor receptor (EGFR) mutation status in non–small cell lung cancer patients. Methods: A total of 346 patients (296 in the training cohort and 50 in the validation cohort) from four centers were included in this retrospective study. First, 1085 features were extracted using IBEX from the computed tomography images. The features were screened using the intraclass correlation coefficient, hypothesis tests and least absolute shrinkage and selection operator. Logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) were used to build a radiomics model for classification. The models were evaluated using the following metrics: area under the curve (AUC), calibration curve (CAL), decision curve analysis (DCA), concordance index (C-index), and Brier score. Results: Sixteen features were selected, and models were built using LR, DT, RF, and SVM. In the training cohort, the AUCs was .723, .842, .995, and .883; In the validation cohort, the AUCs were .658, 0567, .88, and .765. RF model with the best AUC, its CAL, C-index (training cohort=.998; validation cohort=.883), and Brier score (training cohort=.007; validation cohort=0.137) showed a satisfactory predictive accuracy; DCA indicated that the RF model has better clinical application value. Conclusion: Machine learning models based on computed tomography images can be used to evaluate EGFR status in patients with non–small cell lung cancer, and the RF model outperformed LR, DT, and SVM.

A number of miRNAs have been found to be abnormally expressed or mutated in numerous cancers and thus, are considered to act as oncogenes or tumor suppressor genes. The aim of the present study was to investigate the effect of miR-181 on cisplatin-resistant non-small cell lung cancer (NSCLC). In patients with cisplatin-resistant NSCLC, miR-181 expression was found to be markedly decreased. In addition, in the cisplatin-resistant human lung adenocarcinoma cell line A549/DDP, miR-181 downregulation promoted cell growth and metastasis and inhibited cell apoptosis, whereas miR-181 overexpression exerted the opposite effects. Furthermore, miR-181 downregulation suppressed LC3 and ATG5 protein expression in A549/DDP cells through suppression of the PTEN/PI3K/AKT/mTOR pathway, whereas miR-181 overexpression recovered LC3 and ATG5 protein expression by promoting PTEN/PI3K/AKT/mTOR signaling. In turn, PTEN inhibitors reduced the anticancer effects of miR-181 overexpression on A549/DDP cell growth via the regulation of autophagy through the PI3K/AKT/mTOR pathway. Therefore, miR-181 may be a novel and important regulator of cisplatin-resistant NSCLC by serving a role in the regulation of apoptosis, as an established rate-limiting miRNA target.