&lt;p&gt;Identification of key pathways and hub genes in basal-like breast cancer using bioinformatics analysis&lt;/p&gt;

Yang, Kai‐Di; Gao, Junying; Luo, Mao

doi:10.2147/ott.s158619

Cited by 46 publications

(50 citation statements)

References 36 publications

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“…Such pathways included signaling pathways like EIF2 and AHR, as well as pathways involved in mitosis and other molecular mechanisms of cancer. Both sets of genes also contained many gene ontology (GO) biological processes pertaining to the cell cycle, consistent with previous studies that found such enrichment in basal-like subtypes of breast cancer Yang, Gao and Luo, 2019). We further validated our results by testing for overlaps with known gene sets via GSEA analysis (Mootha et al, 2003;Subramanian et al, 2005).…”

Section: Numerical Examplessupporting

confidence: 88%

Model-Based Feature Selection and Clustering of Rna-Seq Data for Unsupervised Subtype Discovery

Lim

Rashid

Ibrahim

2020

Preprint

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Clustering is a form of unsupervised learning that aims to uncover latent groups within data based on similarity across a set of features. A common application of this in biomedical research is in delineating novel cancer subtypes from patient gene expression data, given a set of informative genes. However, it is typically unknown a priori what genes may be informative in discriminating between clusters, and what the optimal number of clusters are. Few methods exist for performing unsupervised clustering of RNA-seq samples, and none currently adjust for between-sample global normalization factors, select cluster-discriminatory genes, or account for potential confounding variables during clustering. To address these issues, we propose the Feature Selection and Clustering of RNA-seq (FSCseq): a model-based clustering algorithm that utilizes a finite mixture of regression (FMR) model and utilized the quadratic penalty method with a SCAD penalty. The maximization is done by a penalized Classification EM algorithm, allowing us to include normalization factors and confounders in our modeling framework. Given the fitted model, our framework allows for subtype prediction in new patients via posterior probabilities of cluster membership. Based on simulations and real data analysis, we show the advantages of our method relative to competing approaches.

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Section: Numerical Examplessupporting

confidence: 88%

Model-Based Feature Selection and Clustering of Rna-Seq Data for Unsupervised Subtype Discovery

Lim

Rashid

Ibrahim

2020

Preprint

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“…4,5 Maternal embryonic leucine zipper kinase (MELK) is a member of the AMP protein kinase (AMPK) family of serine/threonine kinases, and MELK activates multiple cellular pathways that drive oncogenic growth. [8][9][10][11][12][13] It has been shown that MELK is overexpressed in multiple human tumours, including the following: melanoma, 8 diffuse intrinsic pontine glioma (DIPG), 14 breast cancer, 6,15 gastric cancer, 16 high-grade prostate cancer, 17 hepatocellular carcinoma, 18 kidney cancer, 19 small lung cancer, 20 myeloma, 21 acute myeloid leukaemia (AML) 22 and chronic lymphocytic leukaemia (CLL). [8][9][10][11][12][13] It has been shown that MELK is overexpressed in multiple human tumours, including the following: melanoma, 8 diffuse intrinsic pontine glioma (DIPG), 14 breast cancer, 6,15 gastric cancer, 16 high-grade prostate cancer, 17 hepatocellular carcinoma, 18 kidney cancer, 19 small lung cancer, 20 myeloma, 21 acute myeloid leukaemia (AML) 22 and chronic lymphocytic leukaemia (CLL).…”

Section: Introductionmentioning

confidence: 99%

“…The ATM-CHK2 pathway is activated when DSBs occur. MELK was proven to be associated with mitotic progression and DNA damage [8][9][10][11][12][13]. Kig et al confirmed that MELK was required for the repair of DNA damage (including dou-…”

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confidence: 99%

Inhibition of MELK produces potential anti‐tumour effects in bladder cancer by inducing G1/S cell cycle arrest via the ATM/CHK2/p53 pathway

Chen

Zhou

Guo

et al. 2019

J Cellular Molecular Medi

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We aimed to investigate the biological function of MELK and the therapeutic potential of OTSSP167 in human bladder cancer (BCa). First, we observed overexpression of MELK in BCa cell lines and tissues and found that it was associated with higher tumour stage and tumour grade, which was consistent with transcriptome analysis.High expression of MELK was significantly correlated with poor prognosis in BCa patients, and MELK was found to have a role in the cell cycle, the G1/S transition in mitosis, and DNA repair and replication. Furthermore, BCa cells presented significantly decreased proliferation capacity following silencing of MELK or treatment with OTSSP167 in vitro and in vivo. Functionally, reduction in MELK or treatment of cells with OTSSP167 could induce cell cycle arrest and could suppress migration. In addition, these treatments could activate phosphorylation of ATM and CHK2, which would be accompanied by down-regulated MDMX, cyclin D1, CDK2 and E2F1; however, p53 and p21 would be activated. Opposite results were observed when MELK expression was induced. Overall, MELK was found to be a novel oncogene in BCa that induces cell cycle arrest via the ATM/CHK2/p53 pathway. OTSSP167 displays potent anti-tumour activities, which may provide a new molecule-based strategy for BCa treatment. K E Y W O R D S bladder cancer, cell cycle, MELK, OTSSP167, p53 | 1805 CHEN Et al.

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“…Of the genes associated with poor prognosis in patients with basal-like breast cancer in this study, CDC7 [13], KIF18A [9], and CKS2 [22] have previously been shown to be associated with breast cancer. STIL, also known as STIL centriolar assembly protein, encodes a cytoplasmic protein involved in the regulation of mitotic spindle checkpoints.…”

Section: Discussionmentioning

confidence: 92%

Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

Jia

Liang

et al. 2020

Preprint

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Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7 , KIF18A , STIL , and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

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<p>Identification of key pathways and hub genes in basal-like breast cancer using bioinformatics analysis</p>

Cited by 46 publications

References 36 publications

Model-Based Feature Selection and Clustering of Rna-Seq Data for Unsupervised Subtype Discovery

Model-Based Feature Selection and Clustering of Rna-Seq Data for Unsupervised Subtype Discovery

Inhibition of MELK produces potential anti‐tumour effects in bladder cancer by inducing G1/S cell cycle arrest via the ATM/CHK2/p53 pathway

Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

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