The epithelial-mesenchymal transition (EMT) is a genetic reprogramming that tumor cells utilize for metastasis. Epsin-3 (EPN3) is an endocytic adapter protein involved in clathrin-mediated endocytosis and had been previously linked to EMT in breast cancer and glioma metastasis. In this study, identified the role of epsin-3 in lung adenocarcinoma and metastasis and epsin-3 levels identified using an expression profile analysis of patient data indicated the protein was abnormally overexpressed in lung adenocarcinoma patients and this was directly linked to disease severity. Gene knockdowns of EPN3 in human adenocarcinoma cell line A549 and the non-small cell lung carcinoma cell line H1299 decreased the levels of mesenchymal markers, including vimentin (VIM), N-cadherin (NCAD) and embryonic transcription factors like zinc finger E-box binding homeobox 1(ZEB1), snail, and the key molecules of Wnt pathway such as β-catenin and resulted in increased expression of the epithelial marker E-cadherin (ECAD). Our data links EPN3 to the EMT process in lung cancer and inhibition of its expression reduced the metastatic and invasive ability of lung adenocarcinoma cells by inhibiting the EMT process.
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http://dx.doi.org/10.1038/s41598-024-68193-1 | DOI Listing |
Anticancer Drugs
January 2025
Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning.
Uncommon atypical mutations account for 10-15% of all epidermal growth factor receptor (EGFR) activating mutations in nonsmall-cell lung cancer (NSCLC). Tumors harboring rare EGFR mutations show highly heterogeneous responses to EGFR tyrosine kinase inhibitors (TKIs). There is insufficient clinical evidence for uncommon types of EGFR mutations, especially those with compound EGFR mutations.
View Article and Find Full Text PDFProceedings (IEEE Int Conf Bioinformatics Biomed)
December 2024
Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, USA.
Lung cancer remains a predominant cause of cancer-related deaths, with notable disparities in incidence and outcomes across racial and gender groups. This study addresses these disparities by developing a computational framework leveraging explainable artificial intelligence (XAI) to identify both patient- and cohort-specific biomarker genes in lung cancer. Specifically, we focus on two lung cancer subtypes, Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), examining distinct racial and sex-specific cohorts: African American males (AAMs) and European American males (EAMs).
View Article and Find Full Text PDFFront Public Health
January 2025
School of Public Health, Gansu University of Chinese Medicine, Lanzhou, China.
Objective: To investigate the role of PCBP1 in the inhibition of lung adenocarcinoma proliferation by carbon irradiation.
Methods: A549 cells were irradiated with different doses of carbon ions to observe clonal survival and detect changes in cell proliferation. Whole transcriptome sequencing and the Illumina platform were used to analyze the differentially expressed genes in A549 cells after carbon ion irradiation.
Front Oncol
January 2025
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Objective: To develop and validate a deep learning signature for noninvasive prediction of spread through air spaces (STAS) in clinical stage I lung adenocarcinoma and compare its predictive performance with conventional clinical-semantic model.
Methods: A total of 513 patients with pathologically-confirmed stage I lung adenocarcinoma were retrospectively enrolled and were divided into training cohort (n = 386) and independent validation cohort (n = 127) according to different center. Clinicopathological data were collected and CT semantic features were evaluated.
Front Oncol
January 2025
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Purpose: To develop and validate a radiomics nomogram model for predicting the micropapillary pattern (MPP) in lung adenocarcinoma (LUAD) tumors of ≤2 cm in size.
Methods: In this study, 300 LUAD patients from our institution were randomly divided into the training cohort (n = 210) and an internal validation cohort (n = 90) at a ratio of 7:3, besides, we selected 65 patients from another hospital as the external validation cohort. The region of interest of the tumor was delineated on the computed tomography (CT) images, and radiomics features were extracted.
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