Dataset Information

Effects of hub genes on the clinicopathological and prognostic features of lung adenocarcinoma.

ABSTRACT: Lung adenocarcinoma (LUAD) is a common malignancy; however, the majority of its underlying molecular mechanisms remain unknown. In the present study, weighted gene co-expression network analysis was applied to construct gene co-expression networks for the GSE19804 dataset, in order to screen hub genes associated with the pathogenesis of LUAD. In addition, with the aid of the Database for Annotation, Visualization and Integrated Discovery, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes, pathway enrichment analyses were performed on the genes in the selected module. Using the GSE40791 dataset and The Cancer Genome Atlas database, the hub genes were identified. It was discovered that the turquoise module was the most significant module associated with the tumor stage of LUAD. After performing functional enrichment analyses, it was indicated that the turquoise module was mainly enriched in signal transduction. Additionally, at the transcriptional and translational level, nine hub genes were identified and validated: Carbonic anhydrase 4 (CA4), platelet and endothelial cell adhesion molecule 1 (PECAM1), DnaJ member B4 (DNAJB4), advanced glycosylation end-product specific receptor (AGER), GTPase, IMAP family member 6 (GIMAP6), chromosome 10 open reading frame 54 (C10orf54), dedicator of cytokinesis 4 (DOCK4), Golgi membrane protein 1 (GOLM1) and platelet activating factor acetylhydrolase 1b catalytic subunit 3 (PAFAH1B3). CA4, PECAM1, DNAJB4, AGER, GIMAP6, C10orf54 and DOCK4 were expressed at lower levels in the tumor samples, whereas GOLM1 and PAFAH1B3 were highly expressed in tumor samples. In addition, all hub genes were associated with prognosis. In conclusion, one module and nine genes were recognized to be associated with the tumor stage of LUAD. These findings may enhance the understanding of the progression and prognosis of LUAD.

SUBMITTER: Yu DH

PROVIDER: S-EPMC6956410 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Effects of hub genes on the clinicopathological and prognostic features of lung adenocarcinoma.

Yu Dong-Hu DH Huang Jing-Yu JY Liu Xiao-Ping XP Ruan Xiao-Lan XL Chen Chen C Hu Wei-Dong WD Li Sheng S

Oncology letters 20191209 2

Lung adenocarcinoma (LUAD) is a common malignancy; however, the majority of its underlying molecular mechanisms remain unknown. In the present study, weighted gene co-expression network analysis was applied to construct gene co-expression networks for the GSE19804 dataset, in order to screen hub genes associated with the pathogenesis of LUAD. In addition, with the aid of the Database for Annotation, Visualization and Integrated Discovery, Gene Ontology, and Kyoto Encyclopedia of Genes and Genome ...[more]

PMID: 31966050

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Project description:Objective: This study aimed to develop and validate predictive models using clinical parameters, radiomic features, and a combination of both for invasive mucinous adenocarcinoma (IMA) of the lung in patients with lung adenocarcinoma. Method: A total of 173 and 391 patients with IMA and non-IMA, respectively, were retrospectively analyzed from January 2017 to September 2022 in our hospital. Propensity Score Matching was used to match the 2 groups of patients. A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into training and test groups at a ratio of 7:3. The least absolute shrinkage and selection operator algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (logistic), support vector machine (SVM), and decision tree. The best-performing model was adopted, and the radiomics score (Radscore) was then computed. A clinical model was developed using logistic regression. Finally, a combined model was established based on a clinical model and a radiomics model. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis were used to evaluate the predictive value of the developed models. Results: Both clinical and radiomics models established using the logistic method showed the best performance. The Delong test revealed that the combined model was superior to the clinical and radiomics models (P = .018 and .020, respectively). The ROC-AUC (also decision curve analysis) of the combined model was 0.840 and 0.850 in the training and testing groups, respectively, which showed good predictive performance for IMA. The Brier scores for the combined model were 0.161 and 0.154 in the training and testing groups, respectively. Conclusion: The combined model incorporating radiomic CT features and clinical predictors may have the potential to predict IMA in patients with lung cancer.

Project description:BackgroundMorule-like component (MLC) was a rare structure in primary lung adenocarcinoma. We aimed to reveal the clinicopathological, radiological, immunohistochemical, and molecular features of lung adenocarcinoma with MLCs.MethodsTwenty lung adenocarcinomas with MLCs were collected, and computed tomographic and histological documents were reviewed. Immunohistochemistry, targeted next-generation sequencing, and Sanger sequencing for β-catenin gene were performed.ResultsThere were 9 lepidic adenocarcinomas, 8 acinar adenocarcinomas, 2 papillary adenocarcinomas, and 1 minimally invasive adenocarcinoma. Most patients (16/17) were shown a pure solid nodule, and 1 patient was shown a partly solid nodule on chest computed tomography (CT). Nine cases were accompanied with micropapillary components, and 3 were with cribriform components in which 2 suffered a worse prognosis. No significant association was found between the MCLs and the overall survival of lung adenocarcinoma (P = 0.109). The MLCs were often arranged in whorled or streaming patterns. The cells in MLCs showed syncytial and mild appearance. The MLCs were positive for E-cadherin, CK7, TTF-1, napsin-A, vimentin, and β-catenin (membrane), and negative for CK5/6, p40, p63, Synaptophysin, chromogranin A, and Cdx-2. EGFR mutation, ALK-EML4 fusion, HER2 amplification, and PIK3CA mutation were detected in 16 cases, 2 cases, 1 case, and 1 case, respectively. EGFR mutation was more frequent in adenocarcinomas with MLCs than those without MLCs (P = 0.040). β-catenin gene mutation was not detected in any patients.ConclusionsMLC is often observed in the background of acinar, lepidic, and papillary adenocarcinomas. Lung adenocarcinomas with MLCs tend to appear as a solid mass on CT and harbor EGFR gene mutations. The micropapillary components and cribriform components may cause poor prognosis of lung adenocarcinomas with MLCs. Vimentin is always positive in MLCs, and it is a useful marker for the identification of MLCs.

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Dataset Information

Effects of hub genes on the clinicopathological and prognostic features of lung adenocarcinoma.

Publications

Effects of hub genes on the clinicopathological and prognostic features of lung adenocarcinoma.

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