scholarly journals Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer

2019 ◽  
Author(s):  
Dana Aisina ◽  
Raigul Niyazova ◽  
Shara Atambayeva ◽  
Anatoliy Ivashchenko
Keyword(s):  
Breast Cancer ◽  
Candidate Genes ◽  
Binding Sites ◽  
Target Genes ◽  
Untranslated Regions ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Nucleotide Binding Sites ◽  
Free Energy Of Binding ◽  
Triple Negative Subtype

Distinct sets of candidate genes control the development of breast cancer subtypes. The expression of many genes is regulated by the binding of their mRNAs with miRNAs. The prediction of miRNA associations and target genes is essential in studying of breast cancer. The MirTarget program defines the following features of binding miRNA to mRNA: the start of the initiation of miRNA binding to mRNA; the localization of miRNA binding sites in 5'-untranslated regions (5'UTR), coding domain sequences (CDS) and 3'-untranslated regions (3'UTR); the free energy of binding of all miRNA nucleotides with mRNA; the schemes of interactions of all miRNAs nucleotides with mRNAs. The mRNAs of many genes have clusters (miRNA binding sites with overlapping nucleotide sequences) located in 5'UTR, CDS, or 3'UTR. There are clusters in 5'UTR of mRNA EPOR, MAZ and NISCH candidate genes of HER2 subtype. There are four clusters in CDS of mRNA MAZ gene, and in 3'UTR of mRNA BRCA2 and CDK6 genes. Candidate genes of triple-negative subtype are targets for multiple miRNAs. In 5'UTR of mRNA СBL gene, there are 11 sites; the mRNA for MMP2 gene contains five sites; the mRNA of RAB5A gene contains two clusters each of three sites. In 3'UTR of mRNA SFN gene, there are two clusters, each of three sites, and one cluster of 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in 5'UTR of mRNA FOXA1 gene and mRNA HMGA2 gene contains 15 sites. There are clusters of five sites in CDS of mRNA ITGB1 gene and five sites in 3'UTR of mRNA SOX4 genes. Clusters of eight sites and ten sites are identified in 3'UTR of mRNA SMAD3 and TGFB1 genes, respectively. The organization of miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in 5'UTR, CDS and 3'UTRs. This overlapping of miRNA binding sites creates a competition among miRNAs for the binding site. From 6,272 studied miRNAs only 29 miRNAs from miRBase and 88 novel miRNAs have binding sites in clusters of mRNA target genes of breast cancer.

scholarly journals Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer

2019 ◽  
Author(s):  
Dana Aisina ◽  
Raigul Niyazova ◽  
Shara Atambayeva ◽  
Anatoliy Ivashchenko
Keyword(s):  
Breast Cancer ◽  
Candidate Genes ◽  
Binding Sites ◽  
Target Genes ◽  
Untranslated Regions ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Nucleotide Binding Sites ◽  
Free Energy Of Binding ◽  
Triple Negative Subtype

Distinct sets of candidate genes control the development of breast cancer subtypes. The expression of many genes is regulated by the binding of their mRNAs with miRNAs. The prediction of miRNA associations and target genes is essential in studying of breast cancer. The MirTarget program defines the following features of binding miRNA to mRNA: the start of the initiation of miRNA binding to mRNA; the localization of miRNA binding sites in 5'-untranslated regions (5'UTR), coding domain sequences (CDS) and 3'-untranslated regions (3'UTR); the free energy of binding of all miRNA nucleotides with mRNA; the schemes of interactions of all miRNAs nucleotides with mRNAs. The mRNAs of many genes have clusters (miRNA binding sites with overlapping nucleotide sequences) located in 5'UTR, CDS, or 3'UTR. There are clusters in 5'UTR of mRNA EPOR, MAZ and NISCH candidate genes of HER2 subtype. There are four clusters in CDS of mRNA MAZ gene, and in 3'UTR of mRNA BRCA2 and CDK6 genes. Candidate genes of triple-negative subtype are targets for multiple miRNAs. In 5'UTR of mRNA СBL gene, there are 11 sites; the mRNA for MMP2 gene contains five sites; the mRNA of RAB5A gene contains two clusters each of three sites. In 3'UTR of mRNA SFN gene, there are two clusters, each of three sites, and one cluster of 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in 5'UTR of mRNA FOXA1 gene and mRNA HMGA2 gene contains 15 sites. There are clusters of five sites in CDS of mRNA ITGB1 gene and five sites in 3'UTR of mRNA SOX4 genes. Clusters of eight sites and ten sites are identified in 3'UTR of mRNA SMAD3 and TGFB1 genes, respectively. The organization of miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in 5'UTR, CDS and 3'UTRs. This overlapping of miRNA binding sites creates a competition among miRNAs for the binding site. From 6,272 studied miRNAs only 29 miRNAs from miRBase and 88 novel miRNAs have binding sites in clusters of mRNA target genes of breast cancer.

scholarly journals Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8049 ◽  
Author(s):  
Dana Aisina ◽  
Raigul Niyazova ◽  
Shara Atambayeva ◽  
Anatoliy Ivashchenko
Keyword(s):  
Breast Cancer ◽  
Candidate Genes ◽  
Binding Sites ◽  
Target Genes ◽  
Breast Cancer Subtype ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Nucleotide Binding Sites ◽  
Cancer Subtype ◽  
Gene Mrna

The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ and NISCH candidate genes of the HER2 subtype have clusters, and there are four clusters in mRNAs of MAZ, BRCA2 and CDK6 genes. Candidate genes of the triple-negative subtype are targets for multiple miRNAs. There are 11 sites in CBL mRNA, five sites in MMP2 mRNA, and RAB5A mRNA contains two clusters in each of the three sites. In SFN mRNA, there are two clusters in three sites, and one cluster in 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in FOXA1 mRNA and 15 sites in HMGA2 mRNA. There are clusters of five sites in mRNAs of ITGB1 and SOX4 genes. Clusters of eight sites and 10 sites are identified in mRNAs of SMAD3 and TGFB1 genes, respectively. Organizing miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in mRNAs. This overlapping of miRNA binding sites creates a competition among miRNAs for a binding site. From 6,272 miRNAs studied, only 29 miRNAs from miRBase and 88 novel miRNAs had binding sites in clusters of target gene mRNA in breast cancer. We propose using associations of miRNAs and their target genes as markers in breast cancer subtype diagnosis.

scholarly journals Luminal A Breast Cancer Co-expression Network: Structural and Functional Alterations

2021 ◽  
Vol 12 ◽  
Author(s):  
Diana García-Cortés ◽  
Enrique Hernández-Lemus ◽  
Jesús Espinal-Enríquez
Keyword(s):  
Breast Cancer ◽  
Network Topology ◽  
Binding Sites ◽  
Target Genes ◽  
Molecular Subtype ◽  
Differential Expression Analysis ◽  
Ctcf Binding ◽  
Luminal A ◽  
Long Distance ◽  
Transcriptomic Level

Luminal A is the most common breast cancer molecular subtype in women worldwide. These tumors have characteristic yet heterogeneous alterations at the genomic and transcriptomic level. Gene co-expression networks (GCNs) have contributed to better characterize the cancerous phenotype. We have previously shown an imbalance in the proportion of intra-chromosomal (cis-) over inter-chromosomal (trans-) interactions when comparing cancer and healthy tissue GCNs. In particular, for breast cancer molecular subtypes (Luminal A included), the majority of high co-expression interactions connect gene-pairs in the same chromosome, a phenomenon that we have called loss of trans- co-expression. Despite this phenomenon has been described, the functional implication of this specific network topology has not been studied yet. To understand the biological role that communities of co-expressed genes may have, we constructed GCNs for healthy and Luminal A phenotypes. Network modules were obtained based on their connectivity patterns and they were classified according to their chromosomal homophily (proportion of cis-/trans- interactions). A functional overrepresentation analysis was performed on communities in both networks to observe the significantly enriched processes for each community. We also investigated possible mechanisms for which the loss of trans- co-expression emerges in cancer GCN. To this end we evaluated transcription factor binding sites, CTCF binding sites, differential gene expression and copy number alterations (CNAs) in the cancer GCN. We found that trans- communities in Luminal A present more significantly enriched categories than cis- ones. Processes, such as angiogenesis, cell proliferation, or cell adhesion were found in trans- modules. The differential expression analysis showed that FOXM1, CENPA, and CIITA transcription factors, exert a major regulatory role on their communities by regulating expression of their target genes in other chromosomes. Finally, identification of CNAs, displayed a high enrichment of deletion peaks in cis- communities. With this approach, we demonstrate that network topology determine, to at certain extent, the function in Luminal A breast cancer network. Furthermore, several mechanisms seem to be acting together to avoid trans- co-expression. Since this phenomenon has been observed in other cancer tissues, a remaining question is whether the loss of long distance co-expression is a novel hallmark of cancer.

scholarly journals Integrated transcriptomic meta-analysis provides novel insights into breast cancer molecular subtypes

2021 ◽  
Author(s):  
Akhilesh Kumar Bajpai ◽  
Sravanthi Davuluri ◽  
Kavitha Thirumurugan ◽  
Kshitish K Acharya
Keyword(s):  
Breast Cancer ◽  
Signaling Pathways ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Meta Analysis ◽  
Interaction Network ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

Breast cancer is the most common cancer in women worldwide. There are four major breast cancer subtypes (luminal A, luminal B, HER2-enriched and triple-negative/TNBC). TNBC is the most aggressive form with the worst prognosis. However, the differences among the subtypes have not been completely established at the molecular level, thereby limiting therapeutic and diagnostic options for TNBC. We performed a meta-analysis of microarray and RNA-sequencing data to identify candidate genes with an expression-based association in each molecular subtype of breast cancer. The protein interaction network of the candidate genes was analyzed to discover functionally significant gene clusters and hub genes. Potential therapeutic candidates for TNBC were explored through gene-miRNA interactions. We identified 316, 347, 382, and 442 candidate genes in luminal A, luminal B, HER2 and TNBC subtypes, respectively. A total of 135 (26 up- and 109 down-regulated) candidate genes were shared by all four subtypes. Functional analysis of the candidate genes indicated up-regulation of 'cell cycle' and 'p53 signaling' pathways and down-regulation of multiple signaling pathways. COL10A1 was found to be highly up-regulated in all subtypes. It may be a good target for research towards multiple types of applications, including therapeutics. KIF4A, a commonly up-regulated X-chromosome gene was significantly associated with the survival of breast cancer patients. Protein interaction network and centrality analysis revealed that low-moderately differentially regulated genes play an important role in functional cascades across proteins in breast cancer subtypes and may be potential candidates for therapeutics. Targeting FN1 (fibronectin 1), the key up-regulated hub gene by miR-1271 5p may be an important molecular event to be targeted for potential therapeutic application in TNBC.

AGE FEATURES OF MOLECULAR-BIOLOGICAL SUBTYPES OF BREAST CANCER

2020 ◽  
Vol 17 (2) ◽  
pp. 187-192
Author(s):  
E.A. Novikova ◽  
◽  
O.V. Kostromina ◽  
D.V. Mikhailov ◽  
S.L. Leontiev ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Hormone Receptors ◽  
Age Groups ◽  
Luminal A ◽  
Ki 67 ◽  
Luminal B ◽  
Age Related ◽  
Age Features ◽  
Immunohistochemical Studies ◽  
Triple Negative Subtype

Aim. The aim of the study was to determine the presence of peculiarities of the age structure in patients with various surrogate molecular biological subtypes of breast cancer. Materials and research methods. This work analyzes the age-related characteristics of the occurrence of molecular biological subtypes in 499 patients with invasive breast cancer. All cases were divided into 5 molecular biological subtypes based on immunohistochemical studies of hormone receptors, Her2, Ki-67. The average age of the patients was 53.4±0.39 years, the predominant group was patients from 50 to 60 years (37.2% of the total). Research results. In patients under 40 years old, the triple negative subtype prevailed (44.8%). Luminal A subtype prevailed in the groups 51-60 years old (more than 41.4%) and over 60 years old (39.7%). Luminal B (Her2-) subtype was equally found in all age groups.

scholarly journals Profiling the expression of pro-metastatic genes in association with the clinicopathological features of primary breast cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Seyed-Mohammad Mazloomi ◽  
Mitra Foroutan-Ghaznavi ◽  
Vahid Montazeri ◽  
Gholamreza Tavoosidana ◽  
Ashraf Fakhrjou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Target Genes ◽  
Case Series ◽  
Clinicopathological Features ◽  
Cell Junctions ◽  
Homologous Gene ◽  
Axillary Lymph ◽  
Luminal A ◽  
Transcriptional Profiles ◽  
First Time

Abstract Background Metastasis accounts for ninety percent of breast cancer (BrCa) mortality. Cortactin, Ras homologous gene family member A (RhoA), and Rho-associated kinase (ROCK) raise cellular motility in favor of metastasis. Claudins (CLDN) belong to tight junction integrity and are dysregulated in BrCa. Thus far, epidemiologic evidence regarding the association of different pro-metastatic genes with pathological phenotypes of BrCa is largely inconsistent. This study aimed to determine the possible transcriptional models of pro-metastatic genes incorporate in holding the integrity of epithelial cell–cell junctions (CTTN, RhoA, ROCK, CLDN-1, CLDN-2, and CLDN-4), for the first time, in association with clinicopathological features of primary BrCa. Methods In a consecutive case-series design, 206 newly diagnosed non-metastatic eligible BrCa patients with histopathological confirmation (30–65 years) were recruited in Tabriz, Iran (2015–2017). Real-time RT-PCR was used. Then fold changes in the expression of target genes were measured. Results ROCK amplification was associated with the involvement of axillary lymph node metastasis (ALNM; ORadj. = 3.05, 95%CI 1.01–9.18). Consistently, inter-correlations of CTTN-ROCK (β = 0.226, P < 0.05) and RhoA-ROCK (β = 0.311, P < 0.01) were determined among patients diagnosed with ALNM+ BrCa. In addition, the overexpression of CLDN-4 was frequently observed in tumors identified by ALNM+ or grade III (P < 0.05). The overexpression of CTTN, CLDN-1, and CLDN-4 genes was correlated positively with the extent of tumor size. CTTN overexpression was associated with the increased chance of luminal-A positivity vs. non-luminal-A (ORadj. = 1.96, 95%CI 1.02–3.77). ROCK was also expressed in luminal-B BrCa tumors (P < 0.05). The estrogen receptor-dependent transcriptions were extended to the inter-correlations of RhoA-ROCK (β = 0.280, P < 0.01), ROCK-CLDN-2 (β = 0.267, P < 0.05), and CLDN-1-CLDN-4 (β = 0.451, P < 0.001). Conclusions For the first time, our findings suggested that the inter-correlations of CTTN-ROCK and RhoA-ROCK were significant transcriptional profiles determined in association with ALNM involvement; therefore the overexpression of ROCK may serve as a potential molecular marker for lymphatic metastasis. The provided binary transcriptional profiles need more approvals in different clinical features of BrCa metastasis.

scholarly journals The association of women’s birth size with risk of molecular breast cancer subtypes: a cohort study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Marie S. Sandvei ◽  
Signe Opdahl ◽  
Marit Valla ◽  
Pagona Lagiou ◽  
Ellen Veronika Vesterfjell ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Birth Weight ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Head Circumference ◽  
Birth Length ◽  
Birth Size ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Luminal B

Abstract Background Because birth size appears to be positively associated with breast cancer risk, we have studied whether this risk may differ according to molecular breast cancer subtypes. Methods A cohort of 22,931 women born 1920–1966 were followed up for breast cancer occurrence from 1961 to 2012, and 870 were diagnosed during follow-up. Archival diagnostic material from 537 patients was available to determine molecular breast cancer subtype, specified as Luminal A, Luminal B (human epidermal growth factor receptor 2 (HER2)-), Luminal B (HER2+), HER2 type, and Triple negative (TN) breast cancer. Information on the women’s birth weight, birth length and head circumference at birth was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for each molecular subtype, applying Cox regression, and stratified by maternal height. Results Birth length (per 2 cm increments) was positively associated with Luminal A (HR = 1.2, 95% CI, 1.0–1.3), Luminal B (HER2+) (HR = 1.3, 95% CI, 1.0–1.7), and TN breast cancer (HR = 1.4, 95% CI, 1.0–1.9). No clear association was found for birth weight and head circumference. The positive associations of birth length were restricted to women whose mothers were relatively tall (above population median). Conclusion We found a positive association of birth length with risk of Luminal A, Luminal B (HER2+) and TN breast cancer that appears to be restricted to women whose mothers were relatively tall. This may support the hypothesis that breast cancer risk is influenced by determinants of longitudinal growth and that this finding deserves further scrutiny.

Comprehensive immunohistochemical analysis of RET, BCAR1, and BCAR3 expression in patients with Luminal A and B breast cancer subtypes

2022 ◽  
Author(s):  
Ana Carolina Pavanelli ◽  
Flavia Rotea Mangone ◽  
Piriya Yoganathan ◽  
Simone Aparecida Bessa ◽  
Suely Nonogaki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immunohistochemical Analysis ◽  
Breast Cancer Subtypes ◽  
Luminal A ◽  
Cancer Subtypes

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

2020 ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract 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.

scholarly journals FAK-Copy-Gain Is a Predictive Marker for Sensitivity to FAK Inhibition in Breast Cancer

Cancers ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1288 ◽  
Author(s):  
Young-Ho Kim ◽  
Hyun-Kyoung Kim ◽  
Hee Yeon Kim ◽  
HyeRan Gawk ◽  
Seung-Hyun Bae ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Candidate Genes ◽  
Breast Cancer Cells ◽  
Target Genes ◽  
Xenograft Model ◽  
Predictive Marker ◽  
Marker Genes ◽  
Mouse Xenograft Model ◽  
Apoptotic Effect

Background: Cancers with copy-gain drug-target genes are excellent candidates for targeted therapy. In order to search for new predictive marker genes, we investigated the correlation between sensitivity to targeted drugs and the copy gain of candidate target genes in NCI-60 cells. Methods: For eight candidate genes showing copy gains in NCI-60 cells identified in our previous study, sensitivity to corresponding target drugs was tested on cells showing copy gains of the candidate genes. Results: Breast cancer cells with Focal Adhesion Kinase (FAK)-copy-gain showed a significantly higher sensitivity to the target inhibitor, FAK inhibitor 14 (F14). In addition, treatment of F14 or FAK-knockdown showed a specific apoptotic effect only in breast cancer cells showing FAK-copy-gain. Expression-profiling analyses on inducible FAK shRNA-transfected cells showed that FAK/AKT signaling might be important to the apoptotic effect by target inhibition. An animal experiment employing a mouse xenograft model also showed a significant growth-inhibitory effect of F14 on breast cancer cells showing FAK-copy-gain, but not on those without FAK-copy-gain. Conclusion: FAK-copy-gain may be a predictive marker for FAK inhibition therapy in breast cancer.

Sign in / Sign up

Export Citation Format

Share Document