Dataset Information

Distinguishing Lung Adenocarcinoma from Lung Squamous Cell Carcinoma by Two Hypomethylated and Three Hypermethylated Genes: A Meta-Analysis.

ABSTRACT: Significant differences in the aberrant methylation of genes exist among various histological types of non-small cell lung cancer (NSCLC), which includes adenocarcinoma (AC) and squamous cell carcinoma (SCC). Different chemotherapeutic regimens should be administered to the two NSCLC subtypes due to their unique genetic and epigenetic profiles. The purpose of this meta-analysis was to generate a list of differentially methylated genes between AC and SCC. Our meta-analysis encompassed 151 studies on 108 genes among 12946 AC and 10243 SCC patients. Our results showed two hypomethylated genes (CDKN2A and MGMT) and three hypermethylated genes (CDH13, RUNX3 and APC) in ACs compared with SCCs. In addition, our results showed that the pooled specificity and sensitivity values of CDH13 and APC were higher than those of CDKN2A, MGMT and RUNX3. Our findings might provide an alternative method to distinguish between the two NSCLC subtypes.

SUBMITTER: Huang T

PROVIDER: S-EPMC4749211 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Distinguishing Lung Adenocarcinoma from Lung Squamous Cell Carcinoma by Two Hypomethylated and Three Hypermethylated Genes: A Meta-Analysis.

Huang Tao T Li Jinyun J Zhang Cheng C Hong Qingxiao Q Jiang Danjie D Ye Meng M Duan Shiwei S

PloS one 20160210 2

Significant differences in the aberrant methylation of genes exist among various histological types of non-small cell lung cancer (NSCLC), which includes adenocarcinoma (AC) and squamous cell carcinoma (SCC). Different chemotherapeutic regimens should be administered to the two NSCLC subtypes due to their unique genetic and epigenetic profiles. The purpose of this meta-analysis was to generate a list of differentially methylated genes between AC and SCC. Our meta-analysis encompassed 151 studies ...[more]

PMID: 26862903

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Project description:ObjectiveBased on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC).MethodsA total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility.ResultsA total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility.ConclusionThe combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.

Project description:Background: Esophageal cancer (EC) is one of the deadliest cancers in the world. However, the mechanism that drives the evolution of EC is still unclear. On this basis, we identified the key genes and molecular pathways that may be related to the progression of esophageal adenocarcinoma and squamous cell carcinoma to find potential markers or therapeutic targets. Methods: GSE26886 were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) among normal samples, EA, and squamous cell carcinoma were determined using R software. Then, potential functions of DEGs were determined using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The STRING software was used to identify the most important modules in the protein-protein interaction (PPI) network. The expression levels of hub genes were confirmed using UALCAN database. Kaplan-Meier plotters were used to confirm the correlation between hub genes and outcomes in EC. Results: In this study, we identified 1,098 genes induced in esophageal adenocarcinoma (EA) and esophageal squamous cell carcinoma (ESCC), and 669 genes were reduced in EA and ESCC, suggesting that these genes may play an important role in the occurrence and development of EC tumors. Bioinformatics analysis showed that these genes were involved in cell cycle regulation and p53 and phosphoinositide 3-kinase (PI3K)/Akt signaling pathway. In addition, we identified 147 induced genes and 130 reduced genes differentially expressed in EA and ESCC. The expression of ESCC in the EA group was different from that in the control group. By PPI network analysis, we identified 10 hub genes, including GNAQ, RGS5, MAPK1, ATP1B1, HADHA, HSDL2, SLC25A20, ACOX1, SCP2, and NLN. TCGA validation showed that these genes were present in the dysfunctional samples between EC and normal samples and between EA and ESCC. Kaplan-Meier analysis showed that MAPK1, ACOX1, SCP2, and NLN were associated with overall survival in patients with ESCC and EA. Conclusions: In this study, we identified a series of DEGs between EC and normal samples and between EA and ESCC samples. We also identified 10 key genes involved in the EC process. We believe that this study may provide a new biomarker for the prognosis of EA and ESCC.

Project description:BackgroundLung cancer is one of the most common malignant tumors in the world. Exportins are closely associated with the cellular activity and disease progression in a variety of different tumors. However, the expression level, genetic variation, immune infiltration, and biological function of different exportins in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), as well as their relationship with the prognosis of patients with LUAD and LUSC have not been fully clarified.MethodsTo analyze the differential expression, prognostic value, genetic variation, biological function, and immune cell infiltration of exportins in patients with LUAD and LUSC, the ONCOMINE; UALCAN; Human Protein Atlas (HPA); Kaplan-Meier plotter; cBioPortal; Search Tool for the Retrieval of Interacting Genes/Proteins (STRING); Database for Annotation, Visualization, and Integrated Discovery (DAVID); Tumor Immune Estimation Resource (TIMER); and LinkedOmics databases were used in this study.ResultsThe transcriptional and protein expression levels of CSE1L and XPO1/5/6/7 were increased in patients with LUAD and LUSC, and the increased transcriptional levels of CSE1L and XPO5/6/7 were related to worse prognosis. An increased transcriptional level of XPO1 was associated with a better prognosis. These results indicated that CSE1L and XPO1/5/6/7 may be potential prognostic biomarkers for the survival of patients with LUAD and LUSC. Moreover, the high mutation rate of exportins in non-small cell lung cancer was 50.48%, and the largest proportion of mutations included high messenger RNA expression. The expression of exportins was significantly correlated with the infiltration of various immune cells. Differentially expressed exportins could regulate the occurrence and development of LUAD and LUSC by involving a variety of microRNAs and transcription factor E2F1.ConclusionsOur study provides novel insights into the selection of prognostic biomarkers of exportins in LUAD and LUSC.

Dataset Information

Distinguishing Lung Adenocarcinoma from Lung Squamous Cell Carcinoma by Two Hypomethylated and Three Hypermethylated Genes: A Meta-Analysis.

Publications

Distinguishing Lung Adenocarcinoma from Lung Squamous Cell Carcinoma by Two Hypomethylated and Three Hypermethylated Genes: A Meta-Analysis.

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