scholarly journals Association Between Triglyceride Glucose Index and Non-Small Cell Lung Cancer Risk in Chinese Population

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Yan ◽  
Yujuan Gao ◽  
Jingzhi Tong ◽  
Mi Tian ◽  
Jinghong Dai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cancer Risk ◽  
Small Cell Lung Cancer ◽  
Roc Curve ◽  
Small Cell ◽  
Small Cell Lung ◽  
Tyg Index ◽  
The Relationship

BackgroundNumerous studies showed that insulin resistance (IR) was associated with cancer risk. However, few studies investigated the relationship between IR and non-small cell lung cancer (NSCLC). The aim of this study is to explore the association of triglyceride glucose (TyG) index, a simple surrogate marker of IR, with NSCLC risk.Methods791 histologically confirmed NSCLC cases and 787 controls were enrolled in the present study. Fasting blood glucose and triglyceride were measured. The TyG index was calculated as ln [fasting triglycerides (mg/dl) ×fasting glucose (mg/dl)/2]. Logistic regression analysis was performed to estimate the relationship between NSCLC risk and the TyG index.ResultsThe TyG index was significantly higher in patients with NSCLC than that in controls (8.42 ± 0.55 vs 8.00 ± 0.45, P < 0.01). Logistic regression analysis showed that the TyG index (OR = 3.651, 95%CI 2.461–5.417, P < 0.001) was independently associated with NSCLC risk after adjusting for conventional risk factors. In addition, a continuous rise in the incidence of NSCLC was observed along the tertiles of the TyG index (29.4 vs 53.8 vs 67.2%, P < 0.001). However, there were no differences of the TyG index in different pathological or TNM stages. In receiver operating characteristic (ROC) curve analysis, the optimal cut-off level for the TyG index to predict incident NSCLC was 8.18, and the area under the ROC curve (AUROC) was 0.713(95% CI 0.688–0.738).ConclusionsThe TyG index is significantly correlated with NSCLC risk, and it may be suitable as a predictor for NSCLC.

scholarly journals Racial and Ethnic Differences in the Relationship between Aspirin Use and Non–Small Cell Lung Cancer Risk and Survival

2018 ◽  
Vol 27 (12) ◽  
pp. 1518-1526 ◽  
Author(s):  
Patricia Erickson ◽  
Lisa D. Gardner ◽  
Christopher A. Loffredo ◽  
Diane Marie St. George ◽  
Elise D. Bowman ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Small Cell Lung Cancer ◽  
Ethnic Differences ◽  
Cell Lung Cancer ◽  
Lung Cancer Risk ◽  
Small Cell ◽  
Small Cell Lung ◽  
Racial And Ethnic Differences ◽  
The Relationship

scholarly journals Identified GNGT1 and NMU as Combined Diagnosis Biomarker of Non-Small-Cell Lung Cancer Utilizing Bioinformatics and Logistic Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jia-Jia Zhang ◽  
Jiang Hong ◽  
Yu-Shui Ma ◽  
Yi Shi ◽  
Dan-Dan Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Small Cell Lung Cancer ◽  
Logistic Regression Analysis ◽  
Cell Lung Cancer ◽  
Genetic Alterations ◽  
Small Cell ◽  
Small Cell Lung ◽  
Hub Genes

Non-small-cell lung cancer (NSCLC) is one of the most devastating diseases worldwide. The study is aimed at identifying reliable prognostic biomarkers and to improve understanding of cancer initiation and progression mechanisms. RNA-Seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Subsequently, comprehensive bioinformatics analysis incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the protein-protein interaction (PPI) network was conducted to identify differentially expressed genes (DEGs) closely associated with NSCLC. Eight hub genes were screened out using Molecular Complex Detection (MCODE) and cytoHubba. The prognostic and diagnostic values of the hub genes were further confirmed by survival analysis and receiver operating characteristic (ROC) curve analysis. Hub genes were validated by other datasets, such as the Oncomine, Human Protein Atlas, and cBioPortal databases. Ultimately, logistic regression analysis was conducted to evaluate the diagnostic potential of the two identified biomarkers. Screening removed 1,411 DEGs, including 1,362 upregulated and 49 downregulated genes. Pathway enrichment analysis of the DEGs examined the Ras signaling pathway, alcoholism, and other factors. Ultimately, eight prioritized genes (GNGT1, GNG4, NMU, GCG, TAC1, GAST, GCGR1, and NPSR1) were identified as hub genes. High hub gene expression was significantly associated with worse overall survival in patients with NSCLC. The ROC curves showed that these hub genes had diagnostic value. The mRNA expressions of GNGT1 and NMU were low in the Oncomine database. Their protein expressions and genetic alterations were also revealed. Finally, logistic regression analysis indicated that combining the two biomarkers substantially improved the ability to discriminate NSCLC. GNGT1 and NMU identified in the current study may empower further discovery of the molecular mechanisms underlying NSCLC’s initiation and progression.

scholarly journals Association of p73 G4C14-to-A4T14 polymorphism with non-small cell lung cancer risk

2015 ◽  
Vol 10 (2) ◽  
pp. 995-999
Author(s):  
SHUANG SHUANG WANG ◽  
XIANG QIN ZHU ◽  
SHAO DI YANG ◽  
LIN LI DONG ◽  
WEN LI ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Lung Cancer Risk ◽  
Small Cell ◽  
Small Cell Lung

Association Between Polymorphisms in COMT, PLCH1, and CYP17A1, and Non–Small-Cell Lung Cancer Risk in Chinese Nonsmokers

2013 ◽  
Vol 14 (1) ◽  
pp. 45-49 ◽  
Author(s):  
Yongjun Zhang ◽  
Shi Hua ◽  
Aiqin Zhang ◽  
Xiangming Kong ◽  
Chuming Jiang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Lung Cancer Risk ◽  
Small Cell ◽  
Small Cell Lung

Cigarette Smoke-Induced Reduction of 14-3-3σ Expression Promotes The Progression of Non-Small Cell Lung Cancer

2021 ◽  
Author(s):  
Longxia Dai ◽  
Quanwen Deng ◽  
Aibin Liu ◽  
Shuya He ◽  
Qiong Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Protein Expression ◽  
Small Cell Lung Cancer ◽  
Cigarette Smoke ◽  
Cell Lung Cancer ◽  
A549 Cells ◽  
Small Cell ◽  
Small Cell Lung ◽  
Smoking Group ◽  
The Relationship

Abstract Background Lung cancer is a common malignant tumour and the leading cause of cancer death. Smoking is closely related to lung cancer, which can not only induce the occurrence of lung cancer but also affect its progress and prognosis. Objectives To investigated the relationship between smoking and 14-3-3σ protein expression in non-small-cell lung cancer (NSCLC), investigated the relationship between 14-3-3σ expression and cell migration in A549 cells induced by cigarette smoke extract (CSE) and explored whether DNA methylation plays a role in the decreased expression of 14-3-3σ induced by CSE. Methods 14-3-3σ protein expression was examined by immunohistochemistry in 152 NSCLC tissue samples. In vitro experiments were divided into three groups: The current smoking group (CS), the ex-smoking group (ES) and the normal control group (NC). Cell transfection was used for 14-3-3σ protein overexpression. The mRNA and protein expression levels of 14-3-3σ were detected by RT-PCR and Western blotting, respectively. Cell migration was detected by Transwell and wound-healing assays, and the methylation of 14-3-3σ was detected by methylation-specific PCR. Results 14-3-3σ protein expression was decreased in NSCLC patients with a history of smoking. The expression of 14-3-3σ was decreased in A549 cells treated with CSE. The migration capacity of A549 cells treated with CSE was enhanced. DNA methylation in the cigarette smoke-treated A549 cells was higher than that in the untreated cells. Conclusion Cigarette smoke induced reduction of 14-3-3σ expression can promote the progression of non-small cell lung cancer.

Non–Small-Cell Lung Cancer: Real-World Cost Consequence Analysis

2021 ◽  
pp. OP.20.00863
Author(s):  
Alessandra Buja ◽  
Giulia Pasello ◽  
Giuseppe De Luca ◽  
Alberto Bortolami ◽  
Manuel Zorzi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Real World ◽  
Average Cost ◽  
Small Cell ◽  
Tobit Regression ◽  
Small Cell Lung ◽  
Tobit Regression Analysis

PURPOSE: The present work aimed at conducting a real-world data analysis on the management costs and survival analysis comparing data from non–small-cell lung cancer (NSCLC) cases diagnosed in the Veneto region before (2015) and after (2017) the implementation of a regional diagnostic and therapeutic pathway including all new diagnostic and therapeutic strategies. METHOD: This study considered 254 incidental cases of NSCLC in 2015 and 228 in 2017 within the territory of the Padua province (Italy), as recorded by the Veneto Cancer Registry. Tobit regression analysis was performed to verify if total and each item costs (2 years after NSCLC diagnosis) are associated with index year, adjusting by year of diagnosis, sex, age, and stage at diagnosis. Logistic regression models were run to study overall mortality at 2 years, adjusting by the same covariates. RESULTS: The 2017 cohort had a lower mortality odd (odds ratio, 0.93; P = .02) and a significant increase in the average overall costs ( P = .009) than the 2015 cohort. The Tobit regression analysis by cost item showed a very significant increase in the average cost of drugs (coefficient = 5,953, P = .008) for the 2017 cohort, as well as a decrease in the average cost of hospice care (coefficient = –1,822.6, P = .022). CONCLUSION: Our study showed a survival improvement for patients with NSCLC as well as an economic burden growth. Physicians should therefore be encouraged to follow new clinical care pathways, while the steadily rising related costs underscore the need for policymakers and health professionals to pursue.

Sign in / Sign up

Export Citation Format

Share Document