Lung cancer is currently the leading cause of cancer-related death worldwide and it is important to identify the predictive and/or prognostic markers for the cancer. , a proliferative and multipotent biomarker has been reported to be associated with prognosis in non-small cell lung cancer (NSCLC) in a few studies. In the present study, we retrospectively recruited 153 patients with NSCLC. protein expression in tumor samples was determined by immunohistochemistry staining. expression was related with tumor differentiation (P=0.036), lymphatic metastasis (N stage, P=0.011), and p-TNM stage (P=0.013), while there was no significant association between expression level and age, smoking habits, gender, histologic type, and T stage. was an independent prognostic factor for overall survival in NSCLC with an adjusted hazard ratio of 2.701 (95% CI, 1.616-4.513, P<0.001) after controlling the confounding factors. Then we determined the effects of on cell proliferation, colony formation, invasion, and apoptosis by knockout of with a new developed method, CRISPR/Cas9 mediated genome editing. It was observed that knockout of caused enhancement of cancer cell apoptosis and inhibition of cell proliferation, colony formation, and invasion in A549 and H1299 cell lines. Furthermore, we examined the expression of epithelial-mesenchymal transition (EMT) related biomarkers such as E-cadherin and Vimentin in -depleted lung cancer cells and knockout of was found to inhibit EMT, suggesting the involvement of mediated EMT signaling in lung cancer. The finding above demonstrated that might serve as a prognostic factor and therapeutic target in NSCLCs.
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Lancet Reg Health West Pac
January 2025
Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
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Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, 750 N. Lakeshore Dr. 10th Floor, Chicago, IL 60611, United States.
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Proceedings (IEEE Int Conf Bioinformatics Biomed)
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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).
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