ZEB1-AS1 mediates bone metastasis through targeting miR-320b/BMPR1A axis in lung cancer.

OBJECTIVE: This study aimed to explore the role and regulatory mechanism of lncRNA ZEB1-AS1 in lung cancer. METHODS: The expression of ZEB1-AS1 and miR-320b was determined by qRT-PCR. Cell viability, proliferation migration, and invasion were assessed using the CCK-8, colony-forming, and Transwell assay. EMT markers were quantified using western blot. The growth of subcutaneous tumor growth and metastatic bone tumors was evaluated in mouse model of lung cancer. Additionally, metastatic bone tumors were examined using H&E staining. RESULTS: ZEB1-AS1 expression was upregulated, while miR-320b levels were downregulated in lung cancer. Knockdown of ZEB1-AS1 resulted in a significant suppression of cell viability, proliferation, migration, invasion, and EMT in A549 cells. Furthermore, we confirmed the targeting relationship between ZEB1-AS1 and miR-320b, as well as between miR-320b and BMPR1A. Our findings suggested that ZEB1-AS1 regulated cell viability, proliferation, migration, and invasion, as well as EMT, in lung cancer cells by targeting the miR-320b/BMPR1A axis. Moreover, our in vivo experiments confirmed that ZEB1-AS1 mediated bone metastasis through targeting miR-320b/BMPR1A axis in mice with lung cancer. CONCLUSION: ZEB1-AS1 mediated bone metastasis through targeting miR-320b/BMPR1A axis in lung cancer.

Establishment and validation of a long-term prognosis prediction model for patients with non-small cell lung cancer.

Background: The incidence of non-small cell lung cancer ranks second among malignant tumors, while the mortality rate ranks first. We established a prediction model for the long-term prognosis of lung cancer patients to accurately identify patients with a high risk of postoperative death and provide a theoretical basis for improving the prognosis of patients with non-small cell lung cancer. Methods: The data of 277 non-small cell lung cancer patients who underwent radical lung cancer resection at Shanghai Fengxian District Central Hospital between January 2016 and December 2017 were retrospectively collected. The patients, who were followed up for 5 years, were divided into a deceased group (n=127) and survival group (n=150) according to whether the patients had died 5 years after surgery or not. The clinical characteristics of the two groups were observed, and the risk factors of death within 5 years of surgery in lung cancer patients were analyzed. A nomogram predictive model was then established to analyze the value of the model in predicting the death within 5 years of surgery in patients with non-small cell lung cancer. Results: Multivariate logistics regression analysis showed that carcinoembryonic antigen (CEA) >193.5 ng/mL, stage III lung cancer, peritumor invasion, and vascular tumor thrombus were independent risk factors of tumor-specific death after surgery in patients with non-small cell lung cancer (P<0.05). R 4.0.3 statistical software was used to randomly divide the dataset into a training set and validation set. The sample size of the training set was 194, and the sample size of the validation set was 83. The area under the receiver operating characteristic (ROC) curve was 0.850 [95% confidence interval (CI): 0.796-0.905] in the training set, and it was 0.779 (95% CI: 0.678-0.880) in the validation set. In the validation set, the model was assessed using the Hosmer-Lemeshow goodness-of-fit test, with a chi-square value of 9.270 and a P value of 0.320. Conclusions: Our model could accurately identify high risk of death within 5 years of surgery in non-small cell lung cancer patients. Strengthening the management of high-risk patients may help improve the prognosis of these patients.

A risk stratification and prognostic prediction model for lung adenocarcinoma based on aging-related lncRNA.

To create a risk model of aging-related long non-coding RNAs (arlncRNAs) and determine whether they might be useful as markers for risk stratification, prognosis prediction, and targeted therapy guidance for patients with lung adenocarcinoma (LUAD). Data on aging genes and lncRNAs from LUAD patients were obtained from Human Aging Genomic Resources 3 and The Cancer Genome Atlas, and differential co-expression analysis of established differentially expressed arlncRNAs (DEarlncRNAs) was performed. They were then paired with a matrix of 0 or 1 by cyclic single pairing. The risk coefficient for each sample of LUAD individuals was obtained, and a risk model was constructed by performing univariate regression, least absolute shrinkage and selection operator regression analysis, and univariate and multivariate Cox regression analysis. Areas under the curve were calculated for the 1-, 3-, and 5-year receiver operating characteristic curves to determine Akaike information criterion-based cutoffs to identify high- and low-risk groups. The survival rate, correlation of clinical characteristics, malignant-infiltrating immune-cell expression, ICI-related gene expression, and chemotherapeutic drug sensitivity were contrasted with the high- and low-risk groups. We found that 99 DEarlncRNAs were upregulated and 12 were downregulated. Twenty pairs of DEarlncRNA pairs were used to create a prognostic model. The 1-, 3-, and 5-year survival curve areas of LUAD individuals were 0.805, 0.793, and 0.855, respectively. The cutoff value to classify patients into two groups was 0.992. The mortality rate was higher in the high-risk group. We affirmed that the LUAD outcome-related independent predictor was the risk score (p < 0.001). Validation of tumor-infiltrating immune cells and ICI-related gene expression differed substantially between the groups. The high-risk group was highly sensitive to docetaxel, erlotinib, gefitinib, and paclitaxel. Risk models constructed from arlncRNAs can be used for risk stratification in patients with LUAD and serve as prognostic markers to identify patients who might benefit from targeted and chemotherapeutic agents.

Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma.

Model algorithms were used in constructing the risk coefficient model of necroptosis-related long non-coding RNA in identifying novel potential biomarkers in the prediction of the sensitivity to chemotherapeutic agents and prognosis of patients with lung adenocarcinoma (LUAD). Clinic and transcriptomic data of LUAD were obtained from The Cancer Genome Atlas. Differently expressed necroptosis-related long non-coding RNAs got identified by performing both the univariate and co-expression Cox regression analyses. Subsequently, the least absolute shrinkage and selection operator technique was adopted in constructing the nrlncRNA model. We made a comparison of the areas under the curve, did the count of the values of Akaike information criterion of 1-year, 2-year, as well as 3-year receiver operating characteristic curves, after which the cut-off value was determined for the construction of an optimal model to be used in identifying high risk and low risk patients. Genes, tumor-infiltrating immune cells, clinical correlation analysis, and chemotherapeutic agents data of both the high-risk and low-risk subgroups were also performed. We identified 26 DEnrlncRNA pairs, which were involved in the Cox regression model constructed. The curve areas under survival periods of 1 year, 2 years, and 3 years of patients with LUAD were 0.834, 0.790, and 0.821, respectively. The cut-off value set was 2.031, which was used in the identification of either the high-risk or low-risk patients. Poor outcomes were observed in patients belonging to the high-risk group. The risk score was the independent predictor of the LUAD outcome (p < 0.001). The expression levels of immune checkpoint and infiltration of specific immune cells were anticipated by the gene risk model. The high-risk group was found to be highly sensitive to docetaxel, erlotinib, cisplatin, and paclitaxel. The model established through nrlncRNA pairs irrespective of the levels of expression could give a prediction on the LUAD patients' prognosis and assist in identifying the patients who might gain more benefit from chemotherapeutic agents.

USP47 stabilizes BACH1 to promote the Warburg effect and non-small cell lung cancer development via stimulating Hk2 and Gapdh transcription.

Increasing studies demonstrated that ubiquitination plays a crucial part in the pathogenesis of non-small cell lung cancer (NSCLC), and targeted adjustment of the deubiquitination enzymes is a potential means for cancer treatment. However, the role of ubiquitin carboxyl-terminal hydrolase 47 (USP47) in NSCLC is still unclear. Here, we show that USP47 was upregulated in NSCLC clinical tissues and greatly related to advanced tumor stages and survival rate. Functional experimental results showed that USP47 promoted the cell proliferation in vitro and tumor growth in vivo. And the overexpression of USP47 promoted the glycolysis capacity of lung cancer cells. Mechanistic investigations showed that USP47 promoted NSCLC development, which depends a lot on directly binding to and deubiquitination of the basic leucine zipper transcription factor 1 (BACH1, BTB and CNC homology 1). BACH1 was also significantly overexpressed in primary NSCLC tissues and positively correlated with the expression of USP47. The promotion of USP47 on the Warburg effect and NSCLC progression was mediated by the deubiquitination of BACH1 and the downstream transcriptional regulation of hexokinase 2 (Hk2) and glyceraldehyde-phosphate dehydrogenase (Gapdh). Therefore, targeting USP47/BACH1 axis might offer a new way to inhibit the progression of NSCLC.