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pmid:37373065

In Silico Identification of a BRCA1:miR-29:DNMT3 Axis Involved in the Control of Hormone Receptors in BRCA1-Associated Breast Cancers.

Manuela Santarosa · Davide Baldazzi · Michela Armellin · Roberta Maestro

International journal of molecular sciences · 2023 doi:10.3390/ijms24129916

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title
In Silico Identification of a BRCA1:miR-29:DNMT3 Axis Involved in the Control of Hormone Receptors in BRCA1-Associated Breast Cancers.
doi
10.3390/ijms24129916
pmid
37373065
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Manuela Santarosa
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Davide Baldazzi
[2]
Michela Armellin
[3]
Roberta Maestro
journal
International journal of molecular sciences
year
2023
abstract
Germline inactivating mutations in the BRCA1 gene lead to an increased lifetime risk of ovarian and breast cancer (BC). Most BRCA1-associated BC are triple-negative tumors (TNBC), aggressive forms of BC characterized by a lack of expression of estrogen and progesterone hormone receptors (HR) and HER2. How BRCA1 inactivation may favor the development of such a specific BC phenotype remains to be elucidated. To address this question, we focused on the role of miRNAs and their networks in mediating BRCA1 functions. miRNA, mRNA, and methylation data were retrieved from the BRCA cohort of the TCGA project. The cohort was divided into a discovery set (Hi-TCGA) and a validation set (GA-TCGA) based on the platform used for miRNA analyses. The METABRIC, GSE81002, and GSE59248 studies were used as additional validation data sets. BCs were differentiated into BRCA1-like and non-BRCA1-like based on an established signature of BRCA1 pathway inactivation. Differential expression of miRNAs, gene enrichment analysis, functional annotation, and methylation correlation analyses were performed. The miRNAs downregulated in BRCA1-associated BC were identified by comparing the miRNome of BRCA1-like with non-BRCA1-like tumors from the Hi-TCGA discovery cohort. miRNAs:gene-target anticorrelation analyses were then performed. The target genes of miRNAs downregulated in the Hi-TCGA series were enriched in the BRCA1-like tumors from the GA-TCGA and METABRIC validation data sets. Functional annotation of these genes revealed an over-representation of several biological processes ascribable to BRCA1 activity. The enrichment of genes related to DNA methylation was particularly intriguing, as this is an aspect of BRCA1 functions that has been poorly explored. We then focused on the miR-29:DNA methyltransferase network and showed that the miR-29 family, which was downregulated in BRCA1-like tumors, was associated with poor prognosis in these BCs and inversely correlated with the expression of the DNA methyltransferases DNMT3A and DNMT3B. This, in turn, correlated with the methylation extent of the promoter of HR genes. These results suggest that BRCA1 may control the expression of HR via a miR-29:DNMT3:HR axis and that disruption of this network may contribute to the receptor negative phenotype of tumors with dysfunctional BRCA1.
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Spearman rank order correlation coefficients were computed between the normalized miRNA expression levels from the Hi-TCGA data set and the corresponding log2 RSEM-normalized counts of the putative targets. Statistical differences between groups were evaluated using the Welch t-test or a Mann–Whitney rank sum test, depending on the normal distribution of the variables considered. Survival was estimated using the Kaplan–Meier method, and differences between groups were evaluated with the log-rank test using the Survival and Survminer R packages [78,79].
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sections[14] "4.4. Disease-Free Survival and Statistical Analysis" paragraph 2
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Spearman rank order correlation coefficients were computed between the normalized miRNA expression levels from the Hi-TCGA data set and the corresponding log2 RSEM-normalized counts of the putative targets.
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Statistical differences between groups were evaluated using the Welch t-test or a Mann–Whitney rank sum test, depending on the normal distribution of the variables considered.
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sections[14] "4.4. Disease-Free Survival and Statistical Analysis" paragraph 2
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Survival was estimated using the Kaplan–Meier method, and differences between groups were evaluated with the log-rank test using the Survival and Survminer R packages [78,79].
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The data sets analyzed in the current study are publicly available from TCGA Research Network: TCGA-BRCA project, https://www.cancer.gov/tcga; from the METABRIC project (information at https://ega-archive.org/studies/EGAS00000000098); and from Gene Expression Omnibus database (GEO; https://www.ncbi.nlm.nih.gov/gds) for the GSE81002 and GSE59248 series.
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The data sets analyzed in the current study are publicly available from TCGA Research Network: TCGA-BRCA project, https://www.cancer.gov/tcga; from the METABRIC project (information at https://ega-archive.org/studies/EGAS00000000098); and from Gene Expression Omnibus database (GEO; https://www.ncbi.nlm.nih.gov/gds) for the GSE81002 and GSE59248 series.
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[248]
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[249]
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[250]
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[251]
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[252]
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[254]
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[255]
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[256]
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[257]
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[260]
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[263]
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[264]
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[265]
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[266]
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[267]
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[268]
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[269]
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[270]
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[271]
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[272]
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[274]
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[275]
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[278]
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[279]
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[280]
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[281]
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[283]
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[284]
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[285]
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[286]
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[287]
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[288]
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[289]
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[290]
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[291]
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[292]
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[293]
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[294]
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[295]
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[296]
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[297]
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[298]
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[299]
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[300]
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[316]
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[319]
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[320]
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[322]
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[323]
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[324]
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[325]
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[326]
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[327]
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[328]
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[329]
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[330]
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[332]
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[333]
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[334]
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[335]
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[336]
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[337]
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[338]
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[339]
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[340]
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[341]
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[342]
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[343]
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[344]
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[345]
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[346]
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[347]
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[348]
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[349]
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[350]
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[351]
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[352]
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[353]
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[354]
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[355]
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[356]
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[357]
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[358]
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[359]
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[360]
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[361]
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[362]
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[363]
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[364]
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[365]
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[366]
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[367]
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[368]
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[369]
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[370]
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[371]
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[372]
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[373]
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[374]
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[375]
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[376]
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[377]
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[378]
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[379]
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[380]
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[381]
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[382]
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[383]
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[384]
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[385]
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[386]
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[387]
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[388]
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[389]
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[390]
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[391]
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[392]
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[393]
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[394]
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[395]
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[396]
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[397]
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[398]
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[399]
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[400]
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[401]
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[402]
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[403]
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[404]
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[405]
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[406]
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[407]
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[408]
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[409]
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[410]
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[411]
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[412]
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[413]
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[414]
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[415]
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[416]
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[417]
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[418]
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[419]
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[420]
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[421]
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[422]
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[423]
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[424]
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[425]
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[426]
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[427]
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[428]
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[429]
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[430]
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[431]
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[432]
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[433]
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[434]
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[435]
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[436]
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[437]
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[438]
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[439]
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[440]
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[441]
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[442]
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[443]
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[444]
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[445]
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[446]
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[447]
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[448]
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[449]
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[450]
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[451]
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[452]
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[453]
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[454]
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[455]
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[456]
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[457]
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[458]
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[459]
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[460]
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[461]
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[462]
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[463]
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[464]
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[465]
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[466]
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[467]
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[468]
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[469]
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[470]
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[471]
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[472]
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[473]
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[474]
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[475]
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[476]
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[477]
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[478]
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[479]
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[480]
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[481]
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[482]
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[483]
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[484]
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[485]
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[486]
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[487]
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[488]
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[489]
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[490]
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[491]
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[492]
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[493]
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[494]
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[495]
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[496]
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[497]
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[498]
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[499]
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[500]
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[501]
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[502]
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[503]
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[504]
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[505]
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[506]
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[507]
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[508]
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[509]
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[510]
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[511]
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[512]
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[513]
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[514]
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[515]
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[516]
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[517]
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[518]
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[519]
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[520]
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[521]
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[522]
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[523]
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[524]
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[525]
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[526]
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[527]
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[528]
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[529]
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[530]
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[531]
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[532]
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[533]
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[534]
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[535]
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[536]
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[537]
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[538]
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[539]
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[540]
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[541]
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[543]
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[544]
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[545]
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[546]
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[547]
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[548]
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[549]
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[550]
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[551]
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[552]
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[553]
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[554]
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[555]
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[556]
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[557]
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[560]
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[561]
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[564]
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[565]
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[566]
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[567]
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[568]
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[569]
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[570]
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[571]
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[572]
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[573]
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[574]
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[575]
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[576]
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[577]
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[578]
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[579]
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[580]
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[581]
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[582]
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[583]
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[584]
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[585]
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[586]
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[587]
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[588]
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[589]
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[590]
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[591]
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[592]
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[593]
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[594]
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[595]
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[596]
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[597]
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[598]
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[599]
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[600]
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[601]
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[602]
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[603]
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[604]
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[605]
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[606]
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[607]
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[610]
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[611]
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[612]
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[613]
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[614]
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[615]
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[616]
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[617]
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[618]
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[619]
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[620]
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[621]
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[622]
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[623]
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[624]
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[625]
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[626]
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[627]
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[628]
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[629]
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[631]
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[632]
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[633]
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[634]
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[635]
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[636]
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[637]
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[638]
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[639]
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[640]
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[641]
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[642]
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[643]
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[644]
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[645]
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[646]
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[647]
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[648]
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[649]
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[650]
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[652]
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[653]
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[655]
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[656]
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[657]
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[658]
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[659]
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[660]
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[661]
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[662]
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[663]
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[664]
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[665]
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[666]
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[667]
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[668]
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[669]
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[671]
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[672]
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[673]
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[674]
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[675]
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[676]
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[677]
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[678]
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[679]
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[680]
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[681]
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[682]
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[683]
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[687]
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[690]
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[692]
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[693]
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[695]
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[696]
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[697]
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[698]
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[699]
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[700]
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[701]
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[702]
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[703]
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[704]
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[705]
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[706]
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[707]
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[708]
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[709]
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[710]
GSM2140460
[711]
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[712]
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[713]
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[714]
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[715]
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[716]
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[717]
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[718]
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[719]
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[720]
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[721]
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[722]
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[723]
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[724]
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[725]
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[726]
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[727]
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[728]
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[733]
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[734]
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[738]
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[747]
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GSM2140555
sample_count
806
group_sizes
null
group_sizes_basis
null
platform
10656;14550
taxon
Homo sapiens
title
Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 4 items
[0]
asset:data:geo:GSE59248 not downloaded
[1]
asset:data:geo:GSE59248 licence not read
[2]
asset:data:geo:GSE81002 not downloaded
[3]
asset:data:geo:GSE81002 licence not read
analysis object · 4 fields
state
complete
methods array · 2 items
[0] object · 10 fields
id
method-1
family
group_comparison
specific
welch_t_test
label
Group comparison
dsl_operations array · 1 item
[0]
welch_t
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
1
band
reordered
quote
Spearman rank order correlation coefficients were computed between the normalized miRNA expression levels from the Hi-TCGA data set and the corresponding log2 RSEM-normalized counts of the putative targets. Statistical differences between groups were evaluated using the Welch t-test or a Mann–Whitney rank sum test, depending on the normal distribution of the variables considered. Survival was estimated using the Kaplan–Meier method, and differences between groups were evaluated with the log-rank test using the Survival and Survminer R packages [78,79].
[1] object · 10 fields
id
method-2
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
log-rank
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
welch_t
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: welch_t_test.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 2 items
[0]
GSE81002
[1]
GSE59248
resolved_accessions array · 2 items
[0]
GSE81002
[1]
GSE59248
statements array · 1 item
[0]
The data sets analyzed in the current study are publicly available from TCGA Research Network: TCGA-BRCA project, https://www.cancer.gov/tcga; from the METABRIC project (information at https://ega-archive.org/studies/EGAS00000000098); and from Gene Expression Omnibus database (GEO; https://www.ncbi.nlm.nih.gov/gds) for the GSE81002 and GSE59248 series.
basis
The paper names GSE81002, GSE59248 in Gene Expression Omnibus database (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
welch_t
assets array · 2 items
[0]
source:pmid:37373065
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Spearman rank order correlation coefficients were computed between the normalized miRNA expression levels from the Hi-TCGA data set and the corresponding log2 RSEM-normalized counts of the putative targets. Statistical differences between groups were evaluated using the Welch t-test or a Mann–Whitney rank sum test, depending on the normal distribution of the variables considered. Survival was estimated using the Kaplan–Meier method, and differences between groups were evaluated with the log-rank test using the Survival and Survminer R packages [78,79].
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Complete file
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Most BRCA1-associated BC are triple-negative tumors (TNBC), aggressive forms of BC characterized by a lack of expression of estrogen and progesterone hormone receptors (HR) and HER2. How BRCA1 inactivation may favor the development of such a specific BC phenotype remains to be elucidated. To address this question, we focused on the role of miRNAs and their networks in mediating BRCA1 functions. miRNA, mRNA, and methylation data were retrieved from the BRCA cohort of the TCGA project. The cohort was divided into a discovery set (Hi-TCGA) and a validation set (GA-TCGA) based on the platform used for miRNA analyses. The METABRIC, GSE81002, and GSE59248 studies were used as additional validation data sets. BCs were differentiated into BRCA1-like and non-BRCA1-like based on an established signature of BRCA1 pathway inactivation. Differential expression of miRNAs, gene enrichment analysis, functional annotation, and methylation correlation analyses were performed. The miRNAs downregulated in BRCA1-associated BC were identified by comparing the miRNome of BRCA1-like with non-BRCA1-like tumors from the Hi-TCGA discovery cohort. miRNAs:gene-target anticorrelation analyses were then performed. The target genes of miRNAs downregulated in the Hi-TCGA series were enriched in the BRCA1-like tumors from the GA-TCGA and METABRIC validation data sets. Functional annotation of these genes revealed an over-representation of several biological processes ascribable to BRCA1 activity. The enrichment of genes related to DNA methylation was particularly intriguing, as this is an aspect of BRCA1 functions that has been poorly explored. We then focused on the miR-29:DNA methyltransferase network and showed that the miR-29 family, which was downregulated in BRCA1-like tumors, was associated with poor prognosis in these BCs and inversely correlated with the expression of the DNA methyltransferases DNMT3A and DNMT3B. This, in turn, correlated with the methylation extent of the promoter of HR genes. These results suggest that BRCA1 may control the expression of HR via a miR-29:DNMT3:HR axis and that disruption of this network may contribute to the receptor negative phenotype of tumors with dysfunctional BRCA1.", "claims": [{"id": "claim-method", "statement": "Spearman rank order correlation coefficients were computed between the normalized miRNA expression levels from the Hi-TCGA data set and the corresponding log2 RSEM-normalized counts of the putative targets. Statistical differences between groups were evaluated using the Welch t-test or a Mann–Whitney rank sum test, depending on the normal distribution of the variables considered. 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806, "group_sizes": null, "group_sizes_basis": null, "platform": "10656;14550", "taxon": "Homo sapiens", "title": "Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE59248 not downloaded", "asset:data:geo:GSE59248 licence not read", "asset:data:geo:GSE81002 not downloaded", "asset:data:geo:GSE81002 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "welch_t_test", "label": "Group comparison", "dsl_operations": ["welch_t"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "reordered", "quote": "Spearman rank order correlation coefficients were computed between the normalized miRNA expression levels from the Hi-TCGA data set and the corresponding log2 RSEM-normalized counts of the putative targets. Statistical differences between groups were evaluated using the Welch t-test or a Mann–Whitney rank sum test, depending on the normal distribution of the variables considered. Survival was estimated using the Kaplan–Meier method, and differences between groups were evaluated with the log-rank test using the Survival and Survminer R packages [78,79]."}}, {"id": "method-2", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["welch_t"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: welch_t_test."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE81002", "GSE59248"], "resolved_accessions": ["GSE81002", "GSE59248"], "statements": ["The data sets analyzed in the current study are publicly available from TCGA Research Network: TCGA-BRCA project, https://www.cancer.gov/tcga; from the METABRIC project (information at https://ega-archive.org/studies/EGAS00000000098); and from Gene Expression Omnibus database (GEO; https://www.ncbi.nlm.nih.gov/gds) for the GSE81002 and GSE59248 series."], "basis": "The paper names GSE81002, GSE59248 in Gene Expression Omnibus database (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["welch_t"], "assets": ["source:pmid:37373065", "data:geo:GSE59248"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE59248"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:45:07Z", "independence": "single_reader"}, "evidence_check": {"method": "reordered", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9685, "completion_tokens": 2680, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37373065", "type": "paper", "label": "pmid:37373065"}, {"id": "source:pmid:37373065", "type": "manuscript", "label": "PMID 37373065 full text"}, {"id": "data:geo:GSE59248", "type": "dataset", "label": "GEO GSE59248"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37373065", "type": "paper", "label": "pmid:37373065"}, {"id": "source:pmid:37373065", "type": "manuscript", "label": "PMID 37373065 full text"}, {"id": "data:geo:GSE59248", "type": "dataset", "label": "GEO GSE59248"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37373065", "type": "extraction", "used": ["source:pmid:37373065"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE59248", "type": "accession_resolution", "used": ["data:geo:GSE59248"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:37373065", "parent_hash": "sha256:55e8cfc491c45577b1e198ecbe7f7f0434e38067ac1518cfff4c266864f6ef0f", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE59248 licence not read", "asset:data:geo:GSE59248 not downloaded", "asset:data:geo:GSE81002 licence not read", "asset:data:geo:GSE81002 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["welch_t"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Spearman rank order correlation coefficients were computed between the normalized miRNA expression levels from the Hi-TCGA data set and the corresponding log2 RSEM-normalized counts of the putative targets. Statistical differences between groups were evaluated using the Welch t-test or a Mann–Whitney rank sum test, depending on the normal distribution of the variables considered. Survival was estimated using the Kaplan–Meier method, and differences between groups were evaluated with the log-rank test using the Survival and Survminer R packages [78,79]."], "order_basis": "analysis_only", "id": "assay:1"}]}