In the recent outbreak of novel coronavirus infection worldwide, the risk of thrombosis and bleeding should be concerned. We aimed to observe the dynamic changes of D-dimer levels during disease progression to evaluate their value for thrombosis. In this study, we report the clinical and laboratory results of 57 patients with confirmed COVID-19 pneumonia and 46 patients with confirmed community-acquired bacterial pneumonia (CAP). And their concentrations of D-dimer, infection-related biomarkers, and conventional coagulation were retrospectively analyzed. The Padua prediction score is used to identify patients at high risk for venous thromboembolism (VTE). The results found that, on admission, both in COVID-19 patients and CAP patients, D-dimer levels were significantly increased, and compared with CAP patients, D-dimer levels were higher in COVID-19 patients (P < 0.05). Besides, we found that in COVID-19 patients, D-dimer were related with markers of inflammation, especially with hsCRP (R = 0.426, P < 0.05). However, there was low correlation between VTE score and D-dimer levels (Spearman’s R = 0.264, P > 0.05) weakened the role of D-dimer in the prediction of thrombosis. After treatments, D-dimer levels decreased which was synchronous with hsCRP levels in patients with good clinical prognosis, but there were still some patients with anomalous increasing D-dimer levels after therapy. In conclusion, elevated baseline D-dimer levels are associated with inflammation but not with VTE score in COVID-19 patients, suggesting that it is unreasonable to judge whether anticoagulation is needed only according to D-dimer levels. However, the abnormal changes of D-dimer and inflammatory factors suggest that anticoagulant therapy might be needed.

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. Tumor-infiltrating 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.

Introduction: The 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.Methods: A 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.Results: The 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.Discussion: The 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.

Background: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC).Methods: Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples.Results: Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905–0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml⁻¹) from high risk population (AUC=0.93; 95% CI 0.892–0.969).Conclusions: We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort.Trial registration: The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(#NCT04383353).

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.