Clinical Outcomes of Chemotherapeutic Molecules as Single and Multiple Agents in Advanced Non-Small-Cell Lung Carcinoma (NSCLC) Patients

Construction of a mortality risk prediction model for elderly people at risk of lobectomy for NSCLC

Background

An increasing number of lung cancer patients are opting for lobectomy for oncological treatment. However, due to the unique organismal condition of elderly patients, their short-term postoperative mortality is significantly higher than that of non-elderly patients. Therefore, there is a need to develop a personalised predictive tool to assess the risk of postoperative mortality in elderly patients.

Methods

Information on the diagnosis and survival of 35,411 older patients with confirmed lobectomy NSCLC from 2009 to 2019 was screened from the SEER database. The surgical group was divided into a high-risk mortality population group (≤90 days) and a non-high-risk mortality population group using a 90-day criterion. Survival curves were plotted using the Kaplan-Meier method to compare the differences in overall survival (OS) and lung cancer-specific survival (LCSS) between the two groups. The data set was split into modelling and validation groups in a ratio of 7.5:2.5, and model risk predictors of postoperative death in elderly patients with NSCLC were screened using univariate and multifactorial logistic regression. Columnar plots were constructed for model visualisation, and the area under the subject operating characteristic curve (AUC), DCA decision curve and clinical impact curve were used to assess model predictiveness and clinical utility.

Results

Multi-factor logistic regression results showed that sex, age, race, histology and grade were independent predictors of the risk of postoperative death in elderly patients with NSCLC. The above factors were imported into R software to construct a line graph model for predicting the risk of postoperative death in elderly patients with NSCLC. The AUCs of the modelling and validation groups were 0.711 and 0.713 respectively, indicating that the model performed well in terms of predictive performance. The DCA decision curve and clinical impact curve showed that the model had a high net clinical benefit and was of clinical application.

Conclusion

The construction and validation of a predictive model for death within 90 days of lobectomy in elderly patients with lung cancer will help the clinic to identify high-risk groups and give timely intervention or adjust treatment decisions.

Transcriptomic and immunologic implications of the epithelial-mesenchymal transition model reveal a novel role of SFTA2 in prognosis of non-small-cell lung carcinoma

Non-small-cell lung cancer (NSCLC) is the second most common cancer worldwide, and most deaths are associated with epithelial-mesenchymal transition (EMT). Therefore, this study aimed to explore the role of EMT-related transcriptomic profiles in NSCLC and the effect of EMT-based signatures on clinical diagnosis, prognosis, and treatment responses for patients with NSCLC. After integrating the transcriptomics and clinicopathological data, we first constructed EMT clusters (C1 and C2) using machine learning algorithms, found the significant relationship between EMT clusters and survival outcomes, and then explored the impact of EMT clusters on the tumor heterogeneity, drug efficiency, and immune microenvironment of NSCLC. Prominently, differential-enriched tumor-infiltrated lymphocytes were found between EMT clusters, especially the macrophages and monocyte. Next, we identified the most significantly down-regulated gene SFTA2 in the EMT clusters C2 with poor prognosis. Using RT-qPCR and RNA-seq data from the public database, we found prominently elevated SFTA2 expression in NSCLC tissues compared with normal lung tissues, and the tumor suppressor role of SFTA2 in 82 Chinese patients with NSCLC. After Cox regression and survival analysis, we demonstrated that higher SFTA2 expression in tumor samples significantly predicts favorable prognosis of NSCLC based on multiple independent cohorts. In addition, the prognostic value of SFTA2 expression differs for patients with lung adenocarcinoma and squamous cell carcinoma. In conclusion, this study demonstrated that the EMT process is involved in the malignant progression and the constructed EMT clusters exerted significant predictive drug resistance and prognostic value for NSCLC patients. In addition, we first identified the high tumoral expression of SFTA2 correlated with better prognosis and could serve as a predictive biomarker for outcomes and treatment response of NSCLC patients.

Increase in Anticancer Drug-Induced Toxicity by Fisetin in Lung Adenocarcinoma A549 Spheroid Cells Mediated by the Reduction of Claudin-2 Expression

Claudin-2 (CLDN2), a component of tight junction, is involved in the reduction of anticancer drug-induced toxicity in spheroids of A549 cells derived from human lung adenocarcinoma. Fisetin, a dietary flavonoid, inhibits cancer cell growth, but its effect on chemosensitivity in spheroids is unknown. Here, we found that fisetin (20 μM) decreases the protein level of CLDN2 to 22.3%. Therefore, the expression mechanisms were investigated by real-time polymerase chain reaction and Western blotting. Spheroids were formed in round-bottom plates, and anticancer drug-induced toxicity was measured by ATP content. Fisetin decreased the phosphorylated-Akt level, and CLDN2 expression was decreased by a phosphatidylinositol 3-kinase (PI3K) inhibitor, suggesting the inhibition of PI3K/Akt signal is involved in the reduction of CLDN2 expression. Hypoxia level, one of the hallmarks of tumor microenvironment, was reduced by fisetin. Although fisetin did not change hypoxia inducible factor-1α level, it decreased the protein level of nuclear factor erythroid 2-related factor 2, a stress response factor, by 25.4% in the spheroids. The toxicity of doxorubicin (20 μM) was enhanced by fisetin from 62.8% to 40.9%, which was rescued by CLDN2 overexpression (51.7%). These results suggest that fisetin can enhance anticancer drug toxicity in A549 spheroids mediated by the reduction of CLDN2 expression.