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Background: Lung cancer is the leading cause of cancer deaths globally, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being major subtypes. Immunotherapy has emerged as a promising approach for the treatment of lung cancer, but understanding the underlying mechanisms of immune dysregulation is crucial for the development of effective therapies. This study aimed to investigate the distinctive cellular features of LUAD and LUSC and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Methods: We used digital cytometry techniques to analyze the RNA-Seq data of 1128 lung cancer patients from The Cancer Genome Atlas (TCGA) database. The abundance of cell subtypes and ecotypes in LUAD and LUSC patients was quantified. Univariate survival analysis was used to investigate their associations with patient overall survival (OS). Differential gene expression analysis and gene co-expression network construction were carried out to explore the gene expression patterns of LUSC patients with distinct survival outcomes. Scratch wound-healing assay, colony formation assay, and transwell assay were used to validate the candidate drugs for LUSC treatment. Results: We found differential expression of cell subtypes between LUAD and LUSC, with certain cell subtypes being prognostic for survival in both subtypes. We also identified differential gene expression and gene co-expression modules associated with macrophages.3/PCs.2 ratio in LUSC patients with distinct survival outcomes. Furthermore, ecotype ratios were found to be prognostic in both subtypes and machine learning models showed that certain cell subtypes, such as epithelial.cells.1, epithelial.cells.5, and endothelial.cells.2 are important for predicting LUSC. Ginkgolide B and triamterene can inhibit the proliferation, invasion, and migration of LUSC cell lines. Conclusion: We provide insight into the distinctive cellular features of LUAD and LUSC, and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Ginkgolide B and triamterene could be promising drugs for LUSC treatment.
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Objectives: Azvudine has been recommended as a potential treatment for the recently discovered Coronavirus disease (COVID-19) in 2019. However, the effectiveness of Azvudine in individuals who have both COVID-19 and pre-existing cancer remains uncertain. Consequently, we undertook a retrospective analysis to evaluate the clinical efficacy of Azvudine therapy in hospitalized patients with COVID-19 and pre-existing cancer. Methods: This is a single-center retrospective analysis of patients diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, selected from patients admitted to a specialist oncology hospital between June 1, 2022 to June 31, 2023 with positive RT-PCR and pre-existing cancer. After exclusion and propensity score matching, patients in the test group treated with Azvudine and control patients treated with standard antiviral therapy were included. The primary outcome is the interval time from the first dose of Azvudine to the attainment of the first negative result for nucleic acid. Secondary outcomes included the rate of nucleic acid conversion, the duration of hospitalization, and the admission to the intensive care unit (ICU). Cox proportional hazards models were used to analyze the hazard ratio (HR) of event outcomes and to assess whether cancer types and Azvudine treatment will affect the course of COVID-19, specifically the time it takes for primary symptoms to alleviate. Results: In this study, a total of 84 patients were included for analysis. Among them, 42 patients received Azvudine treatment after hospitalization, and the rest were treated with standard antiviral therapy. The results expressed that the time taken for the first negative nucleic acid test was significantly shorter in the Azvudine group compared to the control group [5 (IQR3-7) d vs 12 (IQR9-15) d], p<0.0001. This difference was statistically significant. Furthermore, a multivariate COX analysis indicated that Azvudine treatment could effectively reduce the time required for nucleic acid conversion in cancer patients (HR 1.994, 95% CI 1.064-3.736, p=0.031). And the type of cancer also had an impact on the course of COVID-19 in patients. (HR 3.442, 95%CI 1.214-9.756, p=0.020; HR 3.246, 95% CI 1.925-7.209, p=0.036). Conclusion: Azvudine was correlated with a reduced duration for achieving nucleic acid conversion in individuals diagnosed with cancer. And different types of cancer have a certain impact on the course of COVID-19 for patients.
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Introduction: Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC. METHODS: A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA). RESULTS: The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds. CONCLUSION: The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.