Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer

Soh, Jihee; Cho, Hyejin; Choi, Chan-Hun; Lee, Hyunju

doi:10.3390/cancers10120475

Cited by 6 publications

(5 citation statements)

References 52 publications

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“…The genes affected in the R337H+ group were located in the cytobands, which were mainly affected by CNAs in these cases, such as 1q21.2, 1q44, 2q13, 2q31.1, 2q32.2, 2q35, 8q21.3, 8q22.3, 8q23.1, 16q23.2, and 17q25.3 (in the R337H− group, only genes mapped at 8q were observed in this integration analysis). Although the impact of such alterations in gene and miRNA expression has to be confirmed in experimental expression assays, the observations support the finding that CNAs can affect genes that are also potentially regulated by miRNAs 33,34,[41][42][43][44] . Several of these genes were previously identified as members of the main signaling pathways observed and, interestingly, displayed direct protein interactions with p53 (data not shown-String Network v. 11.0 (https ://strin g-db.org/).…”

Section: Discussionsupporting

confidence: 56%

Frequency of the TP53 R337H variant in sporadic breast cancer and its impact on genomic instability

Mathias

Bortoletto²,

Centa³

et al. 2020

Sci Rep

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The R337H is a TP53 germline pathogenic variant that has been associated with several types of cancers, including breast cancer. Our main objective was to determine the frequency of the R337H variant in sporadic breast cancer patients from Paraná state, South Brazil, its association with prognosis and its impact in genomic instability. The genotyping of 805 breast cancer tissues revealed a genotypic and allelic frequency of the R337H variant of 2.36% and 1.18%, respectively. In these R337H+ cases a lower mean age at diagnosis was observed when compared to the R337H-cases. Array-CGH analysis showed that R337H+ patients presented a higher number of copy number alterations (CNAs), compared to the R337H−. These CNAs affected genes and miRNAs that regulate critical cancer signaling pathways; a number of these genes were associated with survival after querying the KMplot database. Furthermore, homozygous (R337H+/R337H+) fibroblasts presented increased levels of copy number variants when compared to heterozygous or R337H− cells. In conclusion, the R337H variant may contribute to 2.36% of the breast cancer cases without family cancer history in Paraná. Among other mechanisms, R337H increases the level of genomic instability, as evidenced by a higher number of CNAs in the R337H+ cases compared to the R337H−.

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Section: Discussionsupporting

confidence: 56%

Frequency of the TP53 R337H variant in sporadic breast cancer and its impact on genomic instability

Mathias

Bortoletto²,

Centa³

et al. 2020

Sci Rep

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“…Validation studies for candidate genes obtained from transcriptome data are typically conducted using several methods, such as in vitro and in vivo experiments, quantitative real-time polymerase chain reaction (real time-qPCR), and replication studies using an independent dataset. Through statistical analyses, Soh et al [ 34 ] identified cancer-related miRNAs associated with somatic copy number alterations and validated them in vitro by measuring the viability and proliferation rates of cells transfected with their inhibitors. Oh et al [ 35 ] identified aging-related genes in the kidney tissue using a regression method and validated them in vivo using mouse and zebrafish models.…”

Section: Methodsmentioning

confidence: 99%

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Lee

Hwang

Seo

et al. 2022

Cells

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Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

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“…miRNA expression analysis was performed using NanoString nCounter Human v3a miRNA Expression Assay (NanoString, Seattle, WA, USA) as previously described [35,36]. The nCounter assay contained human probes derived from miRBase version 22 (http:// www.mirbase.org, accessed on 13 August 2020) targeting 827 human miRNAs, six positive controls, eight negative controls, three ligation positive controls, three ligation negative controls, five internal reference genes (ACTB, B2M, GAPDH, RPL19, and RPL0), and five spike-in controls (ath-miR-159a, cel-miR-248, cel-miR-254, osa-miR-414, and osa-miR-442).…”

Section: Mirna Expression Analysismentioning

confidence: 99%

“…Most differentially expressed miRNAs between TNBC and QNBC samples were integrated with array-CGH data from the same tissue sample by two distinct steps [35,36]. The first step entailed mapping miRNAs to cytobands with CNAs and filtering based on their concordance level (i.e., cytobands with gains/amplifications or losses/deletions and upregulated or downregulated miRNAs, respectively).…”

Section: Integrated Analysis Of Array-cgh and Mirna Datamentioning

confidence: 99%

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QNBC Is Associated with High Genomic Instability Characterized by Copy Number Alterations and miRNA Deregulation

Bhattarai

Sugita²,

Bortoletto³

et al. 2021

IJMS

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Triple-negative breast cancer (TNBC) can be further classified into androgen receptor (AR)-positive TNBC and AR-negative TNBC or quadruple-negative breast cancer (QNBC). Here, we investigated genomic instability in 53 clinical cases by array-CGH and miRNA expression profiling. Immunohistochemical analysis revealed that 64% of TNBC samples lacked AR expression. This group of tumors exhibited a higher level of copy number alterations (CNAs) and a higher frequency of cases affected by CNAs than TNBCs. CNAs in genes of the chromosome instability 25 (CIN25) and centrosome amplification (CA) signatures were more frequent in the QNBCs and were similar between the groups, respectively. However, expression levels of CIN25 and CA20 genes were higher in QNBCs. miRNA profiling revealed 184 differentially expressed miRNAs between the groups. Fifteen of these miRNAs were mapped at cytobands with CNAs, of which eight (miR-1204, miR-1265, miR-1267, miR-23c, miR-548ai, miR-567, miR-613, and miR-943), and presented concordance of expression and copy number levels. Pathway enrichment analysis of these miRNAs/mRNAs pairings showed association with genomic instability, cell cycle, and DNA damage response. Furthermore, the combined expression of these eight miRNAs robustly discriminated TNBCs from QNBCs (AUC = 0.946). Altogether, our results suggest a significant loss of AR in TNBC and a profound impact in genomic instability characterized by CNAs and deregulation of miRNA expression.

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Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer

Cited by 6 publications

References 52 publications

Frequency of the TP53 R337H variant in sporadic breast cancer and its impact on genomic instability

Frequency of the TP53 R337H variant in sporadic breast cancer and its impact on genomic instability

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

QNBC Is Associated with High Genomic Instability Characterized by Copy Number Alterations and miRNA Deregulation

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