Integrated bioinformatics analysis to identify 15 hub genes in breast cancer

Jin, Haoxuan; Huang, Xiaoyan; Shao, Kwang‐Tsao; Li, Guibo; Wang, Jian; Yang, Huanming; Hou, Yong

doi:10.3892/ol.2019.10411

Cited by 18 publications

(18 citation statements)

References 63 publications

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“…We independently related the expression of each CNAcor gene and each METcor gene to the OS of patients as described in a previous publication (Jin et al, 2019). To this end, for CNAcor or METcor genes, the median expression of each one was computed across the patients, then we received two groups of the patients: patients having the expression of genes was greater than the median value assigned to the first group "up-regulation"; meanwhile, the second group "down-regulation" dedicated to patients having the expression of genes was less than the median values.…”

Section: Expression Of Cnacor and Metcor Genes With Os In Brcamentioning

confidence: 99%

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Nguyen¹,

Nguyen

et al. 2020

Front. Genet.

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The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation)-to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

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Section: Expression Of Cnacor and Metcor Genes With Os In Brcamentioning

confidence: 99%

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Nguyen¹,

Nguyen

et al. 2020

Front. Genet.

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“…Currently, few relevant studies based on the same datasets that we used in the present study have been published. In one such study, Jin et al [52] analyzed the microarray dataset GSE10810 in a consortium with few other datasets and identi ed 15 different hub genes by constructing PPI network. They also analyzed the effect of hub genes expression on the survival duration of patients.…”

Section: Discussionmentioning

confidence: 99%

Up-regulation of FN1, Activation of Maturation Promoting Factor and Associated Signaling Pathway Facilitates Epithelial-Mesenchymal Transition, Inhibits Apoptosis and Elevates Proliferation Rate of Breast Cancer Cells: In Silico Analysis of Microarray Datasets

Hameed

Ejaz

2020

Preprint

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Background: Knowing that the molecular mechanisms underlying breast cancer (BC) are not yet fully understood it was considered worth to launch investigation for the detection of key molecular pathways and associated genes and proteins. Methods: In total two microarray based datasets (GSE10810 and GSE29431), consisting of 89 breast cancer samples and 31 controls, were retrieved from the Gene Expression Omnibus (GEO) database and processed to identify the differentially expressed genes (DEGs). The pathway and functional enrichment analyses of DEGs were performed using DAVID online tool. Protein-Protein interaction (PPI) network was constructed using an online tool STRING and visualized through Cytoscape software to identify the significant module and hub genes via MCODE and Cytohubba applications. The identified hub genes were then further analyzed to document their response in Kaplan-Meier (KM) survival curve analysis, investigate differential expression profile and its correlation with promoter’s methylation status, and finally for the construction of the gene-drug interaction network. Results: In total 449 DEGs were detected including 151 up-regulated and 298 down-regulated genes. The identified DEGs were enriched in various cancer related biological functions and pathways. Based on PPI network analysis of the DEGs, six hub genes, CDK1, FN1, AURKA, CCNB2, BIRC5, and TOP2A, were correlated with worse overall survival (OS) of the breast cancer patients. Conclusion: The extensive in silico analysis has been helpful to evaluate evidence highlighting the prominent role played by the identified hub genes in stimulating breast tumor growth through the activation of Maturation Promoting Factor (MPF), PI3K/AKT signaling, and associated pathways that could be targeted for devising effective treatment strategies.

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“…From that, Benjamini and Hochberg's false discovery rate (FDR) may be a better choice. Likewise, many previous works [49][50][51][52][53] related the expression levels of each identified driver gene to prognostic value (e.g., the overall survival of patients), and the genes when P-value ≤ 0.05 (Log-rank test) were considered to define significant association. Again, FDR control is crucial, so we developed the tool 'geneSA' (https ://githu b.com/huyng uyen2 50896 /geneS A) to automatically do the above task and only preserve the genes if Q-value ≤ 0.05 (Benjamini-Hochberg FDR).…”

Section: List Of Improvements Proposed In the Work Selection Of Drivmentioning

confidence: 99%

“…1, Stage 2-Step 4) and patient stratification ( Fig. 1, Stage 2-Step 5), which is a common selection from previous studies 10,11,14,49,51,52 . Furthermore, these previous works performed the hierarchical clustering method using complete linkage and Euclidean distance for the clustering task in co-expression network construction and patient stratification.…”

Section: Selection Of Tools For Unsupervised Identification Of Co-expmentioning

confidence: 99%

“…In this section, we analyzed associations between clinical features of interest and the identified driver genes. More specifically, gene expression data were used to examine the associations between individual drivers and several familiar clinical features, including survival rates 11,13,15,49 , numbers of positive lymph nodes 11,13 , Nottingham prognostic index, and pathologic stages 11,15 of 1,904 patients.…”

Section: Selection Of Tools For Unsupervised Identification Of Co-expmentioning

confidence: 99%

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Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

Nguyen¹,

2020

Sci Rep

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The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.

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Integrated bioinformatics analysis to identify 15 hub genes in breast cancer

Cited by 18 publications

References 63 publications

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Up-regulation of FN1, Activation of Maturation Promoting Factor and Associated Signaling Pathway Facilitates Epithelial-Mesenchymal Transition, Inhibits Apoptosis and Elevates Proliferation Rate of Breast Cancer Cells: In Silico Analysis of Microarray Datasets

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

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