Hotspots and frontiers of autophagy and chemotherapy in lung cancer: a bibliometric and visualization analysis from 2003 to 2023.

Autophagy was considered to induce resistance in chemotherapy, which was significantly associated with proliferation of cancer; however, few bibliometric studies on the relation between autophagy and chemotherapy in lung cancer are available. The aim of the present study was to provide a comprehensive overview of the knowledge structure and research hotspots of autophagy and chemotherapy in lung cancer by bibliometric analysis. Publications related to autophagy and chemotherapy in lung cancer from 2003 to 2023 were searched on the Web of Science Core Collection (WoSCC) database. The bibliometric analysis was conducted by using VOSviewers, CiteSpace, and the R package "bibliometrix." A total of 675 articles from 70 countries, led by China and the United States, were included in the analysis. The number of publications related to autophagy and chemotherapy in lung cancer is increasing year by year. Nanjing Medical University, Zhejiang University, China Medical University, and Sichuan University are among the main research institutions contributing to this field. The journal Cancers is the most popular publication in this area, with Autophagy being the most co-cited journal. These publications involve 4481 authors, with Chiu Chien-chih and Gewirtz David having published the most papers, and Noboru Mizushima being the most frequently co-cited author. Studying the relation between autophagy and chemotherapy in the occurrence and development of lung cancer, and exploring therapeutic strategies involving autophagy and chemotherapy in lung cancer, are the primary topics in this research field. "Tumor stem cells," "microRNA," and "EGFR" emerge as the primary keywords in the emerging research hotspots. Indeed, this bibliometric study provides valuable insights into the research trends and developments concerning autophagy and chemotherapy in lung cancer. By identifying recent research frontiers and highlighting hot directions, this study serves as a valuable reference for scholars interested in understanding the relationship between autophagy and chemotherapy in lung cancer. The comprehensive summary of findings offers a foundation for further exploration and advancement in this critical area of cancer research.

Network pharmacology in combination with bibliometrics analysis on the mechanism of compound Kushen injection in the treatment of radiation pneumonia and lung cancer.

Radiation pneumonia is a common adverse reaction during radiotherapy in lung cancer patients, which negatively impacts the quality of life and survival of patients. Recent studies have shown that compound Kushen injection (CKI), a traditional Chinese medicine (TCM), has great anti-inflammatory and anticancer potential, but the mechanism is still unclear. We used CiteSpace, the R package "bibliometrix," and VOSviewers to perform a bibliometrics analysis of 162 articles included from the Web of Science core collection. A network pharmacology-based approach was used to screen effective compounds, screen and predict target genes, analyze biological functions and pathways, and construct regulatory networks and protein interaction networks. Molecular docking experiments were used to identify the affinity of key compounds and core target. The literature metrology analysis revealed that over 90% of the CKI-related studies were conducted by Chinese scholars and institutions, with a predominant focus on tumors, while research on radiation pneumonia remained limited. Our investigation identified 60 active ingredients of CKI, 292 genes associated with radiation pneumonia, 533 genes linked to lung cancer, and 37 common targets of CKI in the treatment of both radiation pneumonia and lung cancer. These core potential targets were found to be significantly associated with the OS of lung cancer patients, and the key compounds exhibited a good docking affinity with these targets. Additionally, GO and KEGG enrichment analysis highlighted that the bioinformatics annotation of these common genes mainly involved ubiquitin protein ligase binding, cytokine receptor binding, and the PI3K/Akt signaling pathway. Our study revealed that the main active components of CKI, primarily quercetin, luteolin, and naringin, might act on major core targets, including AKT1, PTGS2, and PPARG, and further regulated key signaling pathways such as the PI3K/Akt pathway, thereby playing a crucial role in the treatment of radiation pneumonia and lung cancer. Moreover, this study had a certain promotional effect on further clinical application and provided a theoretical basis for subsequent experimental research.

CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules.

Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer (CRC) and compare the efficacy of single-slice and whole-lesion histogram analysis. Methods: Our study enrolled 196 CRC patients with pulmonary nodules (136 in the training dataset and 60 in the validation dataset). Twenty morphological features of contrast-enhanced chest CT were evaluated. The regions of interests were delineated in single-slice and whole-tumor lesions, and 22 histogram parameters were extracted. Stepwise logistic regression analyses were applied to choose the independent factors of lung metastasis in the morphological features model, the single-slice histogram model and whole-lesion histogram model. The areas under the curve (AUC) was applied to quantify the predictive accuracy of each model. Finally, we built a morphological-histogram nomogram for pulmonary metastasis prediction. Results: The whole-lesion histogram analysis (AUC of 0.888 and 0.865 in the training and validation datasets, respectively) outperformed the single-slice histogram analysis (AUC of 0.872 and 0.819 in the training and validation datasets, respectively) and the CT morphological features model (AUC of 0.869 and 0.845 in the training and validation datasets, respectively). The morphological-histogram model, developed with significant morphological features and whole-lesion histogram parameters, achieved favorable discrimination in both the training dataset (AUC = 0.919) and validation dataset (AUC = 0.895), and good calibration. Conclusions: CT morphological features in combination with whole-lesion histogram parameters can be used to prognosticate pulmonary metastasis for patients with colorectal cancer.