Association between cigarette smoking history, metabolic phenotypes, and EGFR mutation status in patients with non-small cell lung cancer

Zhang, Xiaohui; Guo, Xiuyu; Gao, Qiaoling; Zhang, Jingfeng; Zheng, Jianjun; Zhao, Guofang; Okuda, Katsuhiro; Tartarone, Alfredo; Jiang, Maoqing

doi:10.21037/jtd-23-1371

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Zhang et al found that compared with NSCLC patients who smoked, non-smoking patients were more sensitive to EGFR tyrosine kinase inhibitors and had better prognosis. In addition, it was found that non-smoking patients had a higher maximum standardized uptake value of primary tumors and a lower incidence of EGFR mutations ( 27 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of prognostic biomarkers of smoking-related lung cancer

Liang,

Pan,

Zhou

et al. 2024

J Thorac Dis

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Background The early diagnosis and effective prognostic treatment measures for lung cancer are still limited, leading to a 5-year survival rate of less than 15% for these patients. Smoking is one of the causes of lung cancer, but it is not the initial carcinogenic factor. It is not clear what specific mechanism cigarette induces lung cancer, and there is a lack of research on the relationship between related genes and the prognosis of patients with smoking lung cancer. The objective of this study was to provide new theoretical evidence and potential therapeutic targets for the mechanisms of smoking-related lung cancer formation. Methods The gene expression profile data from the GSE12428 dataset which includes 63 lung cancer and normal tissue pairs were downloaded from the Gene Expression Omnibus (GEO) database, and data from smokers with lung cancer [both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)] from The Cancer Genome Atlas (TCGA) database were analyzed. The differential genes in smokers with lung cancer were screened using the linear model for microarray data via R software. The differential gene enrichment analysis was performed using the online analysis software Database for Annotation, Visualization and Integrated Discovery (DAVID). The expression levels of differential genes and their correlation with patient tumor clinical stage were analyzed using gene expression profiling interactive analysis (GEPIA). The overall survival rate was analyzed using Kaplan-Meier curves. Results In the GSE12428 dataset, 225 upregulated genes and 565 downregulated genes were identified in cancer tissues; based on smoking status, 1 upregulated gene and 4 downregulated genes were identified. Among smokers who also had lung cancer, 4 genes were downregulated, namely CSH1, BPIFA1, SLPI , and SCGB3A1 . Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that these genes were mainly associated with biological functions such as antibacterial response, humoral immune response, and response to external stimuli. Among them, BPIFA1, SLPI, and SCGB3A1 expression was decreased in lung cancer tissues, with SCGB3A1 showing significant differences. Additionally, high expression of SCGB3A1 was associated with favorable prognosis in patients with lung cancer. Conclusions Three genes BPIFA1, SLPI and SCGB3A1 , were identified as being associated with smokers with lung cancer, with SCGB3A1 showing a close correlation with patient prognosis. These findings provide potential new targets for the treatment of lung cancer. Certainly, this study needs to more investigate the mechanism of these genes regulation.

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Section: Discussionmentioning

confidence: 99%

Identification of prognostic biomarkers of smoking-related lung cancer

Liang,

Pan,

Zhou

et al. 2024

J Thorac Dis

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“…Smoking is identified as another significant risk factor for lung cancer (70,71). Prolonged cigarette consumption is known to contribute to airway remodeling and is implicated in the etiology of lung cancer and various pulmonary diseases (72).…”

Section: Discussionmentioning

confidence: 99%

The Contribution of Double-strand Break Repair Radiation Sensitive Protein 51 Genotypes to Lung Cancer in Taiwan

CHIU,

WANG,

et al. 2024

Anticancer Res

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Background/Aim: Numerous studies have reported the over-expression of the radiation-sensitive protein 51 (RAD51) in various types of cancer. However, the role of RAD51 genotypes in lung cancer remains largely unknown. This study aimed to assess the impact of the common variant RAD51 rs1801320 (G-135C) genotypes on the risk of lung cancer in Taiwan. Materials and Methods: The contribution of RAD51 rs1801320 genotypes to lung cancer risk was investigated in a cohort comprising 358 lung cancer patients and 716 age-and sex-matched healthy controls, utilizing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methodology. Results: The analysis revealed that among the control subjects, the percentages of GG, CG, and CC genotypes of RAD51 rs1801320 were 73.2%, 24.3%, and 2.5%, respectively. Among the lung cancer patients, these percentages were 71.0%, 25.1%, and 3.9%, respectively (p for trend=0.4075). Allelic frequency distributions showed no significant association between the C allele of RAD51 rs1801320 and lung cancer risk determination (p=0.2987). Specifically, the RAD51 rs1801320 CC genotypes were associated with an elevated risk of lung cancer among males [adjusted odds ratio (aOR)=2.28, 95% confidence interval (95%CI)=1. , but not among females and non-smokers. Conclusion: The RAD51 rs1801320 CC genotype was identified as a risk factor for elevated lung cancer risk in males and smokers. This genotype may serve as a molecular biomarker at the DNA level for early detection and prediction of lung cancer in Taiwan.

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Radiomics based on dual‐layer spectral detector CT for predicting EGFR mutation status in non‐small cell lung cancer

Jin,

Ni,

Tan

et al. 2024

J Applied Clin Med Phys

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ObjectiveTo explore the value of dual‐layer spectral computed tomography (DLCT)‐based radiomics for predicting epidermal growth factor receptor (EGFR) mutation status in patients with non‐small cell lung cancer (NSCLC).MethodsDLCT images and clinical information from 115 patients with NSCLC were collected retrospectively and randomly assigned to a training group (n = 81) and a validation group (n = 34). A radiomics model was constructed based on the DLCT radiomic features by least absolute shrinkage and selection operator (LASSO) dimensionality reduction. A clinical model based on clinical and CT features was established. A nomogram was built combining the radiomic scores (Radscores) and clinical factors. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used for the efficacy and clinical value of the models assessment.ResultsA total of six radiomic features and two clinical features were screened for modeling. The AUCs of the radiomic model, clinical model, and nomogram were 0.909, 0.797, and 0.922, respectively, in the training group and 0.874, 0.691, and 0.881, respectively, in the validation group. The AUCs of the nomogram and the radiomics model were significantly higher than that of the clinical model, but no significant difference was found between them. DCA revealed that nomogram had the greatest clinical benefit at most threshold intervals.ConclusionNomogram integrating clinical factors and pretreatment DLCT radiomic features can help evaluate the EGFR mutation status of patients with NSCLC in a noninvasive way.

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Association between cigarette smoking history, metabolic phenotypes, and EGFR mutation status in patients with non-small cell lung cancer

Cited by 4 publications

References 34 publications

Identification of prognostic biomarkers of smoking-related lung cancer

Identification of prognostic biomarkers of smoking-related lung cancer

The Contribution of Double-strand Break Repair Radiation Sensitive Protein 51 Genotypes to Lung Cancer in Taiwan

Radiomics based on dual‐layer spectral detector CT for predicting EGFR mutation status in non‐small cell lung cancer

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