A series of studies have demonstrated that plays a critical role in the development and progression of several cancers. However, the association between genetic variants in the gene and the clinical outcome of patients with non-small-cell lung cancer (NSCLC) has not been investigated. Six functional SNPs in were selected and genotyped using the Sequenom iPLEX genotyping system in a cohort of 484 Chinese NSCLC patients undergoing surgery. Multivariate Cox proportional hazards model were used for the prognosis analysis. We found that SNP rs2305158 exhibited a significant association with overall survival of NSCLC patients in the dominant model (hazard ratio [HR]: 0.68; 95% CI: 0.49-0.95; p = 0.02). Lymph node metastasis was significantly associated with increased death risk (HR: 1.73; 95% CI: 1.24-2.40; p = 0.001) in patients with the homozygous wildtype (WW) genotype of rs2305158. However, no significant association was observed between them in patients carrying a heterozygous variant (WV) or homozygous variant (VV) genotype of rs2305158. Finally, in the joint and interaction analysis, the patients carrying homozygous wildtype (WW) genotype and lymph node metastasis from N1 to N3 conferred a significant increased effect on death (HR: 2.29; 95% CI: 1.40-3.76; p = 0.001). Our results suggest that polymorphisms may serve as an independent prognostic marker for NSCLC patients.
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http://dx.doi.org/10.2217/fon-2020-0211 | DOI Listing |
BMC Cancer
January 2025
Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Objective: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of ROSE. Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value.
View Article and Find Full Text PDFChin Med
January 2025
Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China.
Background: With extended gefitinib treatment, the therapeutic effect in some non-small cell lung cancer (NSCLC) patients declined with the development of drug resistance. Aidi injection (ADI) is utilized in various cancers as a traditional Chinese medicine prescription. This study explores the molecular mechanism by which ADI, when combined with gefitinib, attenuates gefitinib resistance in PC9GR NSCLC cells.
View Article and Find Full Text PDFJ Food Drug Anal
December 2024
School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
Non-small cell lung cancer (NSCLC) is commonly treated with tyrosine kinase inhibitors (TKIs). However, adverse events from such treatment can lead to treatment discontinuation and additional medical expenditures. Ambulatory care from oncology pharmacists in patient education and symptom management can benefit patients with NSCLC.
View Article and Find Full Text PDFJ Food Drug Anal
December 2024
Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan.
This study was aimed to evaluate the cost-effectiveness of pembrolizumab with chemotherapy (pembrolizumab combination therapy) and compare it with standard-of-care platinum-based chemotherapy (chemotherapy alone) as a first-line treatment for metastatic nonsquamous NSCLC from the perspective of Taiwan's third-party-payer public health-care system. We used a partitioned survival model with an estimated time horizon of 10 years. The partitioned survival model uses Kaplan-Meier estimates of progression-free and overall survival from the KEYNOTE-189 clinical trial.
View Article and Find Full Text PDFLung Cancer
December 2024
Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explainability issues in this specific setting. Response to standard first-line immunotherapy (ICI) in metastatic Non-Small-Cell Lung Cancer (NSCLC) is an interesting population for machine learning (ML), since up to 30% of patients do not benefit.
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