COL8A1 Promotes NSCLC Progression Through IFIT1/IFIT3-Mediated EGFR Activation.

Activation of EGFR is a major risk factor for non-small cell lung cancer (NSCLC). Understanding the molecular events promoting EGFR activation can help us gain more insights into the progression of NSCLC. In this study, we demonstrate that collagen type VIII alpha 1 chain (COL8A1), an extracellular matrix component, was overexpressed in NSCLC. In NSCLC cells, knockdown of COL8A1 suppressed cell growth, cycle progression, and migration, and induced cell apoptosis. While COL8A1 overexpression promoted cell proliferation and inhibited cell apoptosis. In addition, we found that COL8A1 depletion reduced interferon response signaling and downregulated (IFIT1) and interferon-induced proteins with tetratricopeptide repeats 3 (IFIT3). Moreover, we indicated that COL8A1 could upregulate IFIT1 and IFIT3 mediated EGFR activation in vitro and in vivo. Lastly, there was a positive correlation among COL8A1, IFIT1, and IFIT3 expression, and EGFR activity in patients with NSCLC. Overall, our data demonstrate that COL8A1 contributes to NSCLC proliferation and invasion through EGFR activation, dependent on IFIT1 and IFIT3 expression.

The Association of Trefoil Factors with Gastric Cancer and Premalignant Lesions: A Cross-Sectional Population-Based Cohort Study.

BACKGROUND: A lack of research on the association of trefoil factors (TFF) with gastric cancer and premalignant lesions (PML) in the general population is an important obstacle to the application of TFFs for gastric cancer screening. We aimed to analyze the association of TFFs with gastric cancer and PMLs in a general population. METHODS: We evaluated 3,986 adults residing in Wuwei, China. We collected baseline characteristics and gastric cancer risk factors, including TFFs, endoscopic diagnosis, and pathologic information. Three logistic regression models were generated to analyze the association between TFFs and gastric cancer, as well as PMLs. Adjusted odds ratio (OR) and 95% confidence intervals (95% CI) were calculated to determine the strength of association. RESULTS: Compared with pepsinogen (PG) and anti-Helicobacter pylori immunoglobulin G antibody (Hp-IgG), TFFs had significant association with gastric cancer and PMLs after adjusting for biomarkers and risk factors (P < 0.05). The ORs (95% CI) for TFF1 (1.67; 1.27-2.20), TFF2 (2.66; 2.01-3.51), and TFF3 (1.32; 1.00-1.74) were larger than the ORs for PGI (0.79; 0.61-1.03), PGI/II (1.00; 0.76-1.31), and Hp-IgG (0.99; 0.73-1.35) in the gastric cancer group. In the intestinal metaplasia (IM) group, not only the TFF3 serum level was the highest, but also the OR (1.92; 1.64-2.25) was the highest. CONCLUSIONS: TFFs were associated with risk of gastric cancer and PMLs. IMPACT: Serum TFFs can improve the screening of high-risk populations for gastric cancer.

Biomarkers for detecting malignant pleural mesothelioma: Protocol for a reanalysis of published data based on systematic reviews of diagnostic test accuracy.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a highly invasive tumor caused primarily by asbestos exposure. In recent decades, the incidence of MPM has shown an increasing trend, posing a great threat to human health. Although there is currently no effective way to treat MPM, patients can survive for more than 5 years if the tumor is removed early. Several systematic reviews (SRs) have evaluated the diagnostic value of biomarkers for diagnosing MPM. However, no studies have been conducted to analyze the quality of these SRs and it remains unclear which biomarker is the excellent diagnostic test. This study aims to assess the methodological quality of the SRs and reanalyze the published data based on SRs to find the optimal biomarker for the early diagnosis of MPM. METHODS: A systematic search will be performed in PubMed, Embase.com, the Cochrane Library of Systematic Reviews, and Web of Science to identify SRs reporting value of biomarkers for detecting MPM. We will evaluate the risk of bias of the included SRs according to the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) instrument. Standard pairwise meta-analysis and adjusted indirect comparison will be used to compare the diagnostic value of different biomarkers. RESULTS: The results of this study will be submitted to a peer-reviewed journal for publication. CONCLUSION: This study will reanalyze the published data based on SRs to find a biomarker with the superior diagnostic performance for the diagnosis of MPM. ETHICS AND DISSEMINATION: Ethics approval and patient consent are not required as this study is an overview based on published systematic reviews. PROSPERO REGISTRATION NUMBER: CRD42019125880.

A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study.

BACKGROUND: Liquid biopsies based on blood samples have been widely accepted as a diagnostic and monitoring tool for cancers, but extremely high sensitivity is frequently needed due to the very low levels of the specially selected DNA, RNA, or protein biomarkers that are released into blood. However, routine blood indices tests are frequently ordered by physicians, as they are easy to perform and are cost effective. In addition, machine learning is broadly accepted for its ability to decipher complicated connections between multiple sets of test data and diseases. OBJECTIVE: The aim of this study is to discover the potential association between lung cancer and routine blood indices and thereby help clinicians and patients to identify lung cancer based on these routine tests. METHODS: The machine learning method known as Random Forest was adopted to build an identification model between routine blood indices and lung cancer that would determine if they were potentially linked. Ten-fold cross-validation and further tests were utilized to evaluate the reliability of the identification model. RESULTS: In total, 277 patients with 49 types of routine blood indices were included in this study, including 183 patients with lung cancer and 94 patients without lung cancer. Throughout the course of the study, there was correlation found between the combination of 19 types of routine blood indices and lung cancer. Lung cancer patients could be identified from other patients, especially those with tuberculosis (which usually has similar clinical symptoms to lung cancer), with a sensitivity, specificity and total accuracy of 96.3%, 94.97% and 95.7% for the cross-validation results, respectively. This identification method is called the routine blood indices model for lung cancer, and it promises to be of help as a tool for both clinicians and patients for the identification of lung cancer based on routine blood indices. CONCLUSIONS: Lung cancer can be identified based on the combination of 19 types of routine blood indices, which implies that artificial intelligence can find the connections between a disease and the fundamental indices of blood, which could reduce the necessity of costly, elaborate blood test techniques for this purpose. It may also be possible that the combination of multiple indices obtained from routine blood tests may be connected to other diseases as well.

Male Smokers' and Non-Smokers' Response Inhibition in Go/No-Go Tasks: Effect of Three Task Parameters.

Impaired response inhibition plays a major role in many addictive behaviors. However, in studies using go/no-go tasks, findings regarding the presence of response inhibition deficits in nicotine-dependent individuals are mixed. This might be due to differences between studies on a number of task parameters. Here we aimed to identify task conditions under which go/no-go task performance deficits can be observed in smokers and to characterize the nature of such deficits. Sixty-one male students (30 smokers, 31 non-smokers) performed a go/no-go task while independently manipulating three task parameters: (1) percentage no-go trials (50% or 25%), (2) stimulus presentation time (600 ms or 200 ms), and (3) nature of no-go stimuli (cigarette related or cigarette unrelated). Three measures, reaction time on go trials and percentage correct responses on go and no-go trials, served as performance indicators. Under 200-ms but not 600-ms stimulus presentation conditions, the smokers responded faster on go trials and made more errors on both go and no-go trials than the non-smokers did. These differences occurred irrespective of the percentage of no-go trials and nature of no-go stimuli. The accuracy differences disappeared after controlling for the response time differences, suggesting a strong speed-accuracy trade-off. This study contributes to unraveling the conditions under which smokers display impaired inhibition performance and helps to characterize the nature of this impairment. Under task conditions prompting fast responding, smokers are more prone to increase response speed and to make more errors than non-smokers.