As a type of non-coding RNA, circular RNA (circRNA) figures prominently in human cancer progression. Nonetheless, the expression, function, and regulatory mechanism of circ_0001287 in non-small cell lung cancer (NSCLC) remain obscure. In this work, quantitative real-time polymerase chain reaction (qRT-PCR) was implemented to quantify circ_0001287 and miR-21 expressions in NSCLC tissues and cells. The relationship between circ_0001287 expression and the clinicopathological parameters of NSCLC patients was examined. Cell counting kit-8 (CCK-8), 5-bromo-2©-deoxyuridine (BrdU), and Transwell experiments were conducted to detect the multiplication, migration, and invasion of NSCLC cells after circ_0001287 was overexpressed or knocked down. The survival of NSCLC cells was studied using colony formation experiment under different doses of radiation. RNA immunoprecipitation (RIP) experiment and luciferase reporter gene experiment verified the binding relationship between circ_0001287 and miR-21. Western blot was employed to examine the regulatory effects of circ_0001287 and miR-21 on phosphatase and tensin homolog (PTEN) expression. We reported that circ_0001287 expression was down-modulated in NSCLC tissues and cell lines. Besides, circ_0001287 low expression was associated with low differentiation and positive lymph node invasion of NSCLC. Circ_0001287 overexpression suppressed the multiplication, migration, invasion, and radioresistance of NSCLC cells, whereas circ_0001287 knockdown promoted the above phenotypes. Circ_0001287 could adsorb miR-21 and repress its expression, and indirectly up-modulate PTEN expression in NSCLC cells. Taken together, circ_0001287/miR-21/PTEN axis is probably involved in regulating NSCLC cell multiplication, metastasis, and radioresistance.

Background: The COVID-19 (2019 novel coronavirus disease) pandemic is deeply concerning because of its massive mortality and morbidity, creating adverse perceptions among patients likely to impact on their overall medical care. Thus, we evaluated the impact of the COVID-19 pandemic on the pattern of primary care consultations within a Shanghai health district.Methods: A retrospective observational cohort study was performed, with data analyzed concerning the pattern of patient visits to general practitioners within the Tongren Hospital network (the sole provider of general practice to the population of 700,000). Data from all general practice consultations for adults were collected for the first 6 months of 2020, which included a 60-day lockdown period (January 24–March 24, 2020) and compared to corresponding data from the first 6 months of 2019. We evaluated changes to the numbers and patterns of primary care consultations, including subgroup analysis based on age, sex, and primary diagnosis.Results: A substantial reduction in patient visits, associated with increased median age, was observed during the first wave of the pandemic in the first 6 months of 2020, compared to the same interval during 2019. Additionally, reduced reappointments and waiting times, but increased costs per visit were observed. When analyzed by primary disease diagnosis, patient visits were reduced for all the major systems. The most striking visit reductions were in cardiovascular, respiratory, endocrine, and gastrointestinal diseases. However, psychological disorders were increased following lockdown, but there was also a dramatic fall in consultations for depression. Reduced monthly patient numbers correlated with both rate of reappointment and average waiting time during the first 6 months of both 2019 and 2020, but an inverse correlation was observed between cost per visit and monthly patient numbers. Specifically during the lockdown period, there was ~50% reduced patient visits.Conclusions: The lockdown has had a serious impact on patients' physical and psychological health. Our analysis provides objective health-related data that may inform the current controversy concerning the balance between the detrimental effects of the use of lockdown vs. the use of a more targeted approach to eliminate viral transmission. These data may improve decision-making in medical practice, policy, and education.

BackgroundGastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.MethodsBased on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes.ResultsWe identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well.ConclusionThis study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.

Background. With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC). Methods. Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis. Results. A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes ( P < 0.01 ). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC. Conclusion. The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.