Identification of a gene expression breast cancer nodal metastasis profile

Ellsworth, Rachel E.; Heckman, Caroline; Seebach, Jeff; Field, LA; Love, Brad; Hooke, Jeffrey A.; Shriver, CD

doi:10.1200/jco.2008.26.15_suppl.1022

Cited by 3 publications

(1 citation statement)

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“…Genomic location Significance PPM1D (Protein Phosphatase 1D) chr17q23.2 Amplification is associated with the overexpression of ErbB2, promotes cell proliferation and inhibits apoptosis (Rauta et al, 2006) PITPNC1 (Phosphatidylinositol Transfer Protein Cytoplasmic 1) chr17q24.2 Amplification is associated with breast cancer metastasis and metastatic progression (Halberg et al, 2016) CD79B chr17q23.3 Found to be highly expressed in metastatic lymph node tumors (Ellsworth et al, 2008) CACNG1 (Calcium Voltage-Gated Channel Auxiliary Subunit Gamma 1) chr17q24.2 Amplification alters cell motility and promotes metastasis (Kanwar et al, 2020) BPTF (Bromodomain PHD Finger Transcription Factor)…”

Section: Genementioning

confidence: 99%

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

et al. 2023

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Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45 years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner.Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification.Results: PAM50 subtype analysis revealed that young patients (≤45 years-old) had four-fold more basal tumors and worst progression-free survival (median of 101 months), compared with the 45–65 years group (median of 168 months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway.Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.

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Section: Genementioning

confidence: 99%

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

et al. 2023

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Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes

Shan

Qiao

Cheng

et al. 2020

Journal of Computational and Graphical Statistics

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Lemma 1: Let { Â * , Γ(m) βkk * , Î * kk , P (m) kk * } be a local minimizer of Q1, then there exists a local minimizer { Â * * , Γ(m) βkk * * , Î * * kk , P (m) kk * * } of Q2, such that α * kk Γ(m) βkk * = α * * kk Γ(m) βkk * * and ιkk i,j * ρkk(m) i,j * = ιkk i,j * * ρkk(m) i,j * * . Similarly, let { Â * * , Γ(m) βkk * * , Î * * kk , P (m) kk * * } be a local minimizer of Q2, then there exists a local minimizer { Â * , Γ(m) βkk * , Î * kk , P (m) kk * } of Q1, such that α * kk Γ(m) βkk * = α * * kk Γ(m) βkk * * and ιkk i,j * ρkk(m) i,j * = ιkk i,j * * ρkk(m) i,j * * . Proof: Suppose { Â * , Γ(m) βkk * , Î * kk , P (m) kk * } is a local minimizer of Q1. We would like to prove { Â * * = * * = Ω(m) kk * * / η 12 M m=1 | Ω(m) kk * * | 1 ,

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Identifying DNA Methylation Modules Associated with a Cancer by Probabilistic Evolutionary Learning

Rhee

Kim

Zhang

2018

IEEE Comput. Intell. Mag.

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Identification of a gene expression breast cancer nodal metastasis profile

Cited by 3 publications

References 0 publications

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes

Identifying DNA Methylation Modules Associated with a Cancer by Probabilistic Evolutionary Learning

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