Non‑small-cell lung cancer pathological subtype‑related gene selection and bioinformatics analysis based on gene expression profiles

Chen, Jiangpeng; Dong, Xiaoqi; Lei, Xun; Xia, Yinyin; Zeng, Qing; Que, Ping; Wen, Xin; Huang, Shan; Peng, Bin

doi:10.3892/mco.2017.1516

Cited by 6 publications

(6 citation statements)

References 30 publications

(25 reference statements)

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“…Piao et al identified 16 hub genes, the expression of 14 of which were associated with prognosis of NSCLC patients by a bioinformatics approach incorporating functional and pathway enrichment analysis, PPI network and OS analysis based on gene and miRNA expression profiles from the GEO database ( Piao et al, 2018 ). Chen et al identified 8 disease genes from one GEO database by using Naïve Bayesian Classifier based on the Maximum Relevance Minimum Redundancy feature selection method following preprocessing, shortest path analysis and function and pathway enrichment analysis ( Chen et al, 2018 ). Tian et al identified 7 important genes from one GEO database by using data preprocessing and screening of DEGs, functional enrichment analysis and construction of transcriptional regulatory network ( Tian et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis

Liu

et al. 2018

Front. Genet.

109

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Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80–85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC.Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein–protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset.Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

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Section: Discussionmentioning

confidence: 99%

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis

Liu

et al. 2018

Front. Genet.

109

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“…The data used in this study were downloaded from the Gene Expression Omnibus (GEO) [ 23 ]. GSE59684 was obtained as the noncoding RNA data.…”

Section: Methodsmentioning

confidence: 99%

“…The human protein interaction pairs were downloaded from the STRING database [ 23 ]. Cytoscape software was used to visualize the PPI network based on the output files of the STRING database.…”

Section: Methodsmentioning

confidence: 99%

Characterization of DNA methylation as well as mico-RNA expression and screening of epigenetic markers in adipogenesis

et al. 2022

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This study aimed to use bioinformatics methods to characterize epigenetic changes in terms of micro-RNA(miRNA) expression and DNA methylation during adipogenesis. The mRNA and miRNA expression microarray and DNA methylation dataset were obtained from the GEO database. Differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs) and differentially methylated probes (DMPs) were filtered using the limma package. The R language cluster profile package was used for functional and enrichment analysis. A protein–protein interaction (PPI) network was constructed using STRING and visualized in Cytoscape. The Connection map (CMap) website tool was used to screen potential therapeutic drugs for adipogenesis. When comparing the early and late stages of adipogenesis, 111 low miRNA targeted upregulated genes and 64 high miRNA targeted downregulated genes were obtained, as well as 663 low-methylated high-expressed genes and 237 high-methylated low-expressed genes. In addition, 41 genes (24 upregulated and 17 downregulated) were simultaneously regulated by abnormal miRNA changes and DNA methylation. Ten chemicals were identified as putative therapeutics for adipogenesis. In addition, among the dual-regulated genes identified, CANX, HNRNPA1, MCL1, and PPIF may play key roles in the epigenetic regulation of adipogenesis and may serve as aberrant methylation or miRNA targeting biomarkers.

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“…Furthermore, potential regulated biological processes and signaling pathways of AD16 treatment were predicted by KEGG analysis, and anti-NSCLC-related signal pathways were summarized (Figure 8). In addition to the PI3K-Akt signaling pathway and proteoglycans in cancers, pathways in cancer, viral carcinogenesis, and oocyte meiosis were the other main patterns for AD16 to achieve its anti-NSCLC effects (Zhang et al, 2017;Chen et al, 2018;Kim et al, 2018).…”

Section: Network Pharmacology Analysismentioning

confidence: 99%

Dissection of the Functional Mechanism of Human Gut Bacterial Strain AD16 by Secondary Metabolites’ Identification, Network Pharmacology, and Experimental Validation

Wang

et al. 2021

Front. Pharmacol.

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Gut microbiota plays important roles in several metabolic processes, such as appetite and food intake and absorption of nutrients from the gut. It is also of great importance in the maintenance of the health of the host. However, much remains unknown about the functional mechanisms of human gut microbiota itself. Here, we report the identification of one anticancer gut bacterial strain AD16, which exhibited potent suppressive effects on a broad range of solid and blood malignancies. The secondary metabolites of the strain were isolated and characterized by a bioactivity-guided isolation strategy. Five new compounds, streptonaphthalenes A and B (1-2), pestaloficins F and G (3-4), and eudesmanetetraiol A (5), together with nine previously known compounds, were isolated from the effective fractions of AD16. Structures of the new compounds were established by 1D and 2D NMR and MS analysis, and the absolute configurations were determined by the CD method. The analysis of network pharmacology suggested that 3, 2, and 13 could be the key components for the anti-NSCLC activity of AD16. In addition to the PI3K–Akt signaling pathway, the proteoglycans in cancer pathway could be involved in the anti-NSCLC action of AD16.

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Non‑small-cell lung cancer pathological subtype‑related gene selection and bioinformatics analysis based on gene expression profiles

Cited by 6 publications

References 30 publications

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis

Characterization of DNA methylation as well as mico-RNA expression and screening of epigenetic markers in adipogenesis

Dissection of the Functional Mechanism of Human Gut Bacterial Strain AD16 by Secondary Metabolites’ Identification, Network Pharmacology, and Experimental Validation

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