Dataset Information

Construction of a 26?feature gene support vector machine classifier for smoking and non?smoking lung adenocarcinoma sample classification.

ABSTRACT: The present study aimed to identify the feature genes associated with smoking in lung adenocarcinoma (LAC) samples and explore the underlying mechanism. Three gene expression datasets of LAC samples were downloaded from the Gene Expression Omnibus database through pre?set criteria and the expression data were processed using meta?analysis. Differentially expressed genes (DEGs) between LAC samples of smokers and non?smokers were identified using limma package in R. The classification accuracy of selected DEGs were visualized using hierarchical clustering analysis in R language. A protein?protein interaction (PPI) network was constructed using gene interaction data from the Human Protein Reference Database for the DEGs. Betweenness centrality was calculated for each node in the network and genes with the greatest BC values were utilized for the construction of the support vector machine (SVM) classifier. The dataset GSE43458 was used as the training dataset for the construction and the other datasets (GSE12667 and GSE10072) were used as the validation datasets. The classification accuracy of the classifier was tested using sensitivity, specificity, positive predictive value, negative predictive value and area under curve parameters with the pROC package in R language. The feature genes in the SVM classifier were subjected to pathway enrichment analysis using Fisher's exact test. A total of 347 genes were identified to be differentially expressed between samples of smokers and non?smokers. The PPI network of DEGs were comprised of 202 nodes and 300 edges. An SVM classifier comprised of 26 feature genes was constructed to distinguish between different LAC samples, with prediction accuracies for the GSE43458, GSE12667 and GSE10072 datasets of 100, 100 and 94.83%, respectively. Furthermore, the 26 feature genes that were significantly enriched in 9 overrepresented biological pathways, including extracellular matrix?receptor interaction, proteoglycans in cancer, cell adhesion molecules, p53 signaling pathway, microRNAs in cancer and apoptosis, were identified to be smoking?related genes in LAC. In conclusion, an SVM classifier with a high prediction accuracy for smoking and non?smoking samples was obtained. The genes in the classifier may likely be the potential feature genes associated with the development of patients with LAC who smoke.

SUBMITTER: Yang L

PROVIDER: S-EPMC5783520 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.

Yang Lei L Sun Lu L Wang Wei W Xu Hao H Li Yi Y Zhao Jia-Ying JY Liu Da-Zhong DZ Wang Fei F Zhang Lin-You LY

Molecular medicine reports 20171207 2

The present study aimed to identify the feature genes associated with smoking in lung adenocarcinoma (LAC) samples and explore the underlying mechanism. Three gene expression datasets of LAC samples were downloaded from the Gene Expression Omnibus database through pre‑set criteria and the expression data were processed using meta‑analysis. Differentially expressed genes (DEGs) between LAC samples of smokers and non‑smokers were identified using limma package in R. The classification accuracy of ...[more]

PMID: 29257283

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Project description:The aim of the present study was to identify risk genes in myocardial infarction. Microarray data GSE34198, containing data from the peripheral blood of 49 myocardial infarction samples and 48 corresponding control samples, were downloaded from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). The DEGs were used to construct a protein‑protein interaction (PPI) network of patient samples, from which the feature genes were identified using the neighboring score method. The recursive feature elimination (RFE) algorithm was employed to select the risk genes among feature genes, which were subsequently applied to perform a support vector machine (SVM) classifier to identify the specific signature in myocardial infarction samples. Another dataset, GSE61144, was also downloaded to verify the efficacy of the classifier. A total of 724 downregulated and 483 upregulated DEGs were screened in patient samples compared with control samples in the GSE34198 dataset. The PPI network of myocardial infarction was comprised of 1,083 nodes (genes) and 46,363 lines (connections). Using the neighborhood scoring method, the top 100 feature genes in myocardial infarction samples were identified as the disease feature genes, which distinguish the myocardial infarction samples from the control samples. The RFE algorithm screened 15 risk genes, which were employed to construct a SVM classifier with an average precision of 88% to the patient sample following visualization by a confusion matrix. The predictive precision of the classifier on another microarray dataset, GSE61144, was 0.92, with an average true positive of 0.9278 and an average false positive of 0.2361. A‑kinase‑anchoring protein 12 (AKAP12) and glycine receptor α2 (GLRA2) were two risk genes in the SVM classifier. Therefore, AKAP12 and GLRA2 exert potential roles in the development of myocardial infarction, potentially by influencing cardiac contractility and protecting against ischemia‑reperfusion injury, which may provide clues in developing potential diagnostic biomarkers or therapeutic targets for myocardial infarction.

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Dataset Information

Construction of a 26?feature gene support vector machine classifier for smoking and non?smoking lung adenocarcinoma sample classification.

Publications

Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.

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