ABSTRACT
BACKGROUND: Lung cancer is a leading public health concern worldwide. Previous evidence suggests that chronic obstructive pulmonary disease (COPD) and asthma may contribute to its development. However, whether these common chronic pulmonary diseases are causal factors of lung cancer remained unclear. METHODS: Summary statistics from genome-wide association studies (GWAS) were used for Mendelian randomization (MR) analysis. Genetic data for COPD were obtained from the Global Biobank Meta-Analysis Initiative, and asthma data were retrieved from the UK Biobank cohort. Suitable instrumental variables were selected based on quality control measures. GWAS summary data for lung cancer were obtained from a large study involved 85,716 participants. MR analysis was performed using various methods, and sensitivity analyses were conducted. Multivariable MR (MVMR) analysis was employed to account for potential confounding factors. RESULTS: Our MR analysis revealed a significant causal association between COPD and lung cancer, including its subtypes such as lung squamous cell carcinoma, lung adenocarcinoma, and small cell lung carcinoma. Genetically predicted COPD was associated with a 64% increased risk of lung cancer and a 2.3 to 2.8-fold increased risk of the different subtypes. However, in the MVMR analysis adjusting for smoking, alcohol drinking, and body mass index, the association between COPD and lung cancer became non-significant. No significant association was observed between asthma (childhood-onset and adult-onset) and lung cancer and its histological subtypes. CONCLUSIONS: Our study suggests a potential causal association between COPD and lung cancer. However, this association became non-significant after adjusting for smoking in the multivariable analysis.
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Background: Lung adenocarcinoma (LUAD) remains heterogeneous in the prognosis of patients; oxidative stress (OS) has been widely linked to cancer progression. Therefore, it is necessary to explore the prognostic value of the OS-associated genes in LUAD. Methods: An OS-associated prognostic signature was developed using the Cox regression and random forest model in The Cancer Genome Atlas-LUAD dataset. Kaplan-Meier (K-M) survival curve and time-dependent receiver operating characteristic (tROC) curves were applied to evaluate and validate the predictive accuracy of this signature among the training and testing cohorts. A nomogram was constructed and also verified by the concordance index (C-index), calibration curves, and tROC curves, respectively. ESTIMATE algorithm and CIBERSORT algorithms were conducted to explore the signature's immune characteristics. Core target genes of the prognostic signature were identified in the protein-protein interaction network. Results: A six OS-associated prognostic gene signature (CDC25C, ERO1A, GRIA1, TERT, CAV1, BDNF) was developed. The tROC and K-M survival curves in the training and testing cohorts revealed that the signature had good and robust predictive capability to predict the overall survival of LUAD patients. Meanwhile, the risk score was an independent prognostic factor influencing patients' overall survival. The results of the C-index (0.714), calibration curves, and the 1-, 2-, and 3-year tROC curves (area under the curve = 0.703, 0.737, and 0.723, respectively) suggested that the nomogram had good predictive efficacy and prognostic value for LUAD. Then, the authors found that the high-risk group may be depletion or loss of antitumor function of immune cells. Finally, 10 core genes of the signature were predicted. Conclusion: Their study may provide a novel understanding for the identification of prognostic stratification in LUAD patients, as well as the regulation of OS-associated genes in LUAD progression.
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INTRODUCTION: Hepatocellular carcinoma (HCC) is a common and fatal cancer, and its molecular mechanisms are still not fully understood. This study aimed to explore the potential molecular mechanisms and immune infiltration characteristics of celecoxib combined with sorafenib in the treatment of HCC by analyzing the differentially expressed genes (DEGs) from the GSE45340 dataset in the GEO database and identifying key genes. METHODS: The GSE45340 dataset was downloaded from the GEO database, and DEGs were screened using GEO2R, and visualization and statistical analysis were performed. Metascape was used to perform functional annotation and protein-protein interaction network analysis of DEGs. The immune infiltration was analyzed using the TIMER database, and the expression of key genes and their relationship with patient survival were analyzed and verified using the UALCAN database. RESULTS: A total of 2181 DEGs were screened through GEO2R analysis, and heat maps were drawn for the 50 genes with the highest expression. Metascape was used for enrichment analysis, and the enrichment results of KEGG and GO and the PPI network were obtained, and 44 core genes were screened. Analysis of the TIMER database found that 12 genes were closely related to tumor immune infiltration. UALCAN analysis further verified the differential expression of these genes in HCC and was closely related to the overall survival of patients. CONCLUSIONS: Through comprehensive bioinformatics analysis, this study identified a group of key genes related to the treatment of HCC with celecoxib combined with sorafenib. These genes play an important role in tumor immune infiltration and patient survival, providing important clues for further studying the molecular mechanism of HCC and developing potential therapeutic targets.
ABSTRACT
BACKGROUND: Hepatocellular Carcinoma (HCC) is recognized as the second leading cause of cancer-associated deaths globally. Hypoxia-inducible factor 1alpha (HIF1A) has been documented to promote HCC cell migration, invasion and cell cycle. Dual specificity phosphatase 18 (DUSP18) has been predicted to be up-regulated in hypoxia and its expression is positively linked to HIF1A expression in HCC cells. However, their function and molecular mechanism have not been investigated in HCC in depth. PURPOSE: This study aimed to uncover the functional roles of HIF1A and DUSP18, as well as relevant mechanisms underlying their regulation in HCC cells. METHODS: RT-qPCR and western blot were performed to examine gene expression. Functional assays were implemented to reveal the regulatory impact of target genes on HCC cells. Mechanism experiments were conducted to analyze gene interaction. RESULTS: DUSP18 was found to have significantly high expression in hypoxia-induced HCC cells. HIF1A promoted HCC cell migration, invasion and cell cycle by transcriptionally activating DUSP18. DUSP18 mediated MAPK14 dephosphorylation to weaken MAPK14 activity, which further inhibited MAPK14-mediated TP53 phosphorylation, consequently promoting multiple biological behaviors of HCC cells. CONCLUSION: Hypoxia-induced HIF1A activates DUSP18 transcription to further promote MAPK14 dephosphorylation, thereby suppressing TP53 phosphorylation and functionally promoting malignant behaviors of HCC cells.