Purpose: Lung cancer is the main cause of cancer-related mortality worldwide. We report here the biological role of nuclear paraspeckle assembly transcript 1 (NEAT1) in the pathogenesis of lung cancer and the underlying mechanisms. Methods: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blotting analysis were used to evaluate expression of mRNA and protein. RNA immunoprecipitation (RIP) assay, chromatin immunoprecipitation followed by qPCR analysis, and reporter assay were used to detect DNA/RNA and protein binding. Tumorinfiltrating lymphocytes were assessed with hematoxylin-eosin staining. Cytotoxic T cell infiltration was evaluated with flow cytometric analysis and immunohistochemistry (IHC) staining. The changes of cell viability and cell invasive and migratory ability were analyzed by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), colony formation, and Transwell assays, respectively. Syngeneic tumor model was set up to evaluate antitumor effect. Results: The results showed that NEAT1 was overexpressed in lung cancer tissues and cancer cell lines. This aberrant expression was closely related with tumor stage and lymph node metastasis. Tumor sample with high CD8 + showed lower NEAT1 expression. In vitro studies displayed that inhibition of NEAT1 with shRNA resulted in suppression of survival and migration/invasion of lung cancer cells. On the other side, NEAT1 was found to promote tumor growth via inhibiting cytotoxic T cell immunity in syngeneic models. Finally, NEAT1 was found to interact with DNMT1, which in turn inhibited P53 and cyclic GMP-AMP synthase stimulator of interferon genes (cGAS/STING) expression. Conclusion: Our findings demonstrated that NEAT1 interacted with DNMT1 to regulate cytotoxic T cell infiltration in lung cancer via inhibition of cGAS/STING pathway. The results provided the novel mechanistic insight into the pathogenesis of lung cancer.

Immune escape is an important mechanism in tumorigenesis. The aim of this study was to investigate roles of SKIL in tumorigenesis and immune escape of non-small-cell lung cancer (NSCLC). SKIL expression levels in NSCLC cell line, clinical sample, and adjacent normal tissue were measured by quantitative PCR, western blot, or immunohistochemistry. Lentivirus was used to overexpress/silence SKIL or TAZ expression. Malignant phenotypes of NSCLC cells were evaluated by colony formation, transwell, and MTT assays, and in xenograft mice model. Syngeneic mice model and flow cytometry were used to evaluate T cell infiltration. Quantitative PCR and western blot were applied to evaluate relevant mRNA and protein levels, respectively. Co-immunoprecipitation was applied to unveil the interaction between SKIL and TAZ. SKIL expression was higher in NSCLC tissue compared to adjacent normal tissue. Silencing of SKIL inhibited malignant phenotypes of NSCLC cells and promoted T cell infiltration. SKIL-knockdown inhibited autophagy and activated the STING pathway in NSCLC cells through down-regulation of TAZ. Silencing of TAZ cancelled the effects of SKIL overexpression on malignant phenotypes and autophagy of NSCLC cells. Inhibition of autophagy reversed the effects of SKIL/TAZ overexpression on the STING pathway. In conclusion, SKIL promoted tumorigenesis and immune escape of NSCLC cells through upregulation of TAZ/autophagy axis and inhibition on downstream STING pathway.

IntroductionThe treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects. We are currently unable to accurately predict therapeutic response. We aimed to develop a radiomics-based nomogram to predict a major pathological response (MPR) of potentially resectable NSCLC to neoadjuvant immunochemotherapy using pretreatment computed tomography (CT) images and clinical characteristics.MethodsA total of 89 eligible participants were included and randomly divided into training (N=64) and validation (N=25) sets. Radiomic features were extracted from tumor volumes of interest in pretreatment CT images. Following data dimension reduction, feature selection, and radiomic signature building, a radiomics-clinical combined nomogram was developed using logistic regression analysis.ResultsThe radiomics-clinical combined model achieved excellent discriminative performance, with AUCs of 0.84 (95% CI, 0.74-0.93) and 0.81(95% CI, 0.63-0.98) and accuracies of 80% and 80% in the training and validation sets, respectively. Decision curves analysis (DCA) indicated that the radiomics-clinical combined nomogram was clinically valuable.DiscussionThe constructed nomogram was able to predict MPR to neoadjuvant immunochemotherapy with a high degree of accuracy and robustness, suggesting that it is a convenient tool for assisting with the individualized management of patients with potentially resectable NSCLC.