An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes

An, Hasan; Mw, Ahmad; Madar, Inamul Hasan; Bl, Grace; Tn, Hasan

doi:10.6026/97320630011229

Cited by 17 publications

(14 citation statements)

References 49 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the survival curve analysis of 10 upregulated genes using TCGA data indicated that high www.nature.com/scientificreports www.nature.com/scientificreports/ expression of HSP90AA1, RAC1 and CDKN1A induced by GaAs was clearly related to the lower survival probability in LUAD or LUSC patients. HSP90AA1 was found to be directly associated with lung cancer 34 . The overexpression of Rac1 was linked to aggressive growth and other malignant characteristics of tumours, and a high level of Rac1 could predict a poor prognosis in different types of cancer 35 .…”

Section: Discussionmentioning

confidence: 97%

RNA Sequencing Analyses Reveal the Potential Mechanism of Pulmonary Injury Induced by Gallium Arsenide Particles in Human Bronchial Epithelioid Cells

Ouyang

Liu²,

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Extensive use of gallium arsenide (GaAs) has led to increased exposure to humans working in the semiconductor industry. this study employed physicochemical characterization of GaAs obtained from a workplace, cytotoxicity analysis of damage induced by GaAs in 16HBE cells, RNA-seq and related bioinformatic analysis, qRT-PCR verification and survival analysis to comprehensively understand the potential mechanism leading to lung toxicity induced by GaAs. We found that GaAs-induced abnormal gene expression was mainly related to the cellular response to chemical stimuli, the regulation of signalling, cell differentiation and the cell cycle, which are involved in transcriptional misregulation in cancer, the MAPK signalling pathway, the TGF-β signalling pathway and pulmonary disease-related pathways. Ten upregulated genes (FOS, JUN, HSP90AA1, CDKN1A, ESR1, MYC, RAC1, CTNNB1, MAPK8 and FOXO1) and 7 downregulated genes (TP53, AKT1, NFKB1, SMAD3, CDK1, E2F1 and PLK1) related to GaAs-induced pulmonary toxicity were identified. High expression of HSP90AA1, RAC1 and CDKN1A was significantly associated with a lower rate of overall survival in lung cancers. The results of this study indicate that GaAs-associated toxicities affected the misregulation of oncogenes and tumour suppressing genes, activation of the TGF-β/MAPK pathway, and regulation of cell differentiation and the cell cycle. These results help to elucidate the molecular mechanism underlying GaAs-induced pulmonary injury.

show abstract

Section: Discussionmentioning

confidence: 97%

RNA Sequencing Analyses Reveal the Potential Mechanism of Pulmonary Injury Induced by Gallium Arsenide Particles in Human Bronchial Epithelioid Cells

Ouyang

Liu²,

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The first data set GDS3837 provides insight into potential prognostic biomarkers and therapeutic targets for non-small cell lung carcinoma, where it has 54674 genes, 60 normal and 60 tumor samples that are taken from nonsmoking females [23, 24]. The second data set GDS3257 provides insight into the molecular basis of lung carcinogenesis induced by smoking, where it has a total of 22283 genes, and contains 107 samples that are taken from former, current and never smokers [23, 24], where GDS3837 and GDS3257 can be downloaded from NCBI’s GEO Database.…”

Section: Methodsmentioning

confidence: 99%

“…To evaluate the reliability of the MG-PCC and t-SNE-SSP, we applied them to gene expression data sets of lung cancers [23, 24]. Results showed that MG-PCC algorithm was able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP maps gave these mini-groups clear boundaries also, which helped us to mine the subtypes of cancers.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

2018

View full text Add to dashboard Cite

BackgroundFor analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure.ResultsHere, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value.ConclusionsFrom the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2495-5) contains supplementary material, which is available to authorized users.

show abstract

“…While the expression of GPM6A is never above 5 among early-stage cancer patients, among 28.1% of late-stage patients its expression is above 5. GPM6A has been suggested to be a biomarker in colon and lung cancers (Camps et al, 2009;Hasan et al, 2015).…”

Section: S2 Application Using Colon Cancer Microarray Datamentioning

confidence: 99%

Theory of Optimal Bayesian Feature Filtering

pour¹,

Dalton²

2020

Bayesian Anal.

View full text Add to dashboard Cite

Optimal Bayesian feature filtering (OBF) is a supervised screening method designed for biomarker discovery. In this article, we prove two major theoretical properties of OBF. First, optimal Bayesian feature selection under a general family of Bayesian models reduces to filtering if and only if the underlying Bayesian model assumes all features are mutually independent. Therefore, OBF is optimal if and only if one assumes all features are mutually independent, and OBF is the only filter method that is optimal under at least one model in the general Bayesian framework. Second, OBF under independent Gaussian models is consistent under very mild conditions, including cases where the data is non-Gaussian with correlated features. This result provides conditions where OBF is guaranteed to identify the correct feature set given enough data, and it justifies the use of OBF in non-design settings where its assumptions are invalid.

show abstract

An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes

Cited by 17 publications

References 49 publications

RNA Sequencing Analyses Reveal the Potential Mechanism of Pulmonary Injury Induced by Gallium Arsenide Particles in Human Bronchial Epithelioid Cells

RNA Sequencing Analyses Reveal the Potential Mechanism of Pulmonary Injury Induced by Gallium Arsenide Particles in Human Bronchial Epithelioid Cells

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

Theory of Optimal Bayesian Feature Filtering

Contact Info

Product

Resources

About