Редактирование: ERBB2

Receptor tyrosine-protein kinase erbB-2 precursor (EC 2.7.10.1) (Metastatic lymph node gene 19 protein) (MLN 19) (Proto-oncogene Neu) (Proto-oncogene c-ErbB-2) (Tyrosine kinase-type cell surface receptor HER2) (p185erbB2) (CD340 antigen) [HER2] [MLN19] [NEU] [NGL]

==Publications==

{{medline-entry
|title=The biological age linked to oxidative stress modifies breast cancer aggressiveness.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/29550329
|abstract=The incidence of breast cancer increases with age until menopause, and breast cancer is more aggressive in younger women. The existence of epidemiological links between breast cancer and aging indicates that both processes share some common mechanisms of development. Oxidative stress is associated with both cancer susceptibility and aging. Here we observed that [[ERBB2]]-positive breast cancer, which developed in genetically heterogeneous [[ERBB2]]-positive transgenic mice generated by a backcross, is more aggressive in chronologically younger than in older mice (differentiated by the median survival of the cohort that was 79 weeks), similar to what occurs in humans. In this cohort, we estimated the oxidative biological age using a mathematical model that integrated several subphenotypes directly or indirectly related to oxidative stress. The model selected the serum levels of HDL-cholesterol and magnesium and total [[AKT1]] and glutathione concentrations in the liver. The grade of aging was calculated as the difference between the predicted biological age and the chronological age. This comparison permitted the identification of biologically younger and older mice compared with their chronological age. Interestingly, biologically older mice developed more aggressive breast cancer than the biologically younger mice. Genomic regions on chromosomes 2 and 15 linked to the grade of oxidative aging were identified. The levels of expression of Zbp1 located on chromosome 2, a gene related to necroptosis and inflammation, positively correlated with the grade of aging and tumour aggressiveness. Moreover, the pattern of gene expression of genes linked to the inflammation and the response to infection pathways was enriched in the livers of biologically old mice. This study shows part of the complex interactions between breast cancer and aging.
|mesh-terms=* Aging
* Animals
* Breast Neoplasms
* Female
* Genes, erbB-2
* Glutathione
* Inflammation
* Liver
* Mice
* Mice, Inbred C57BL
* Mice, Transgenic
* Models, Theoretical
* Oxidative Stress
* Proto-Oncogene Proteins c-akt
* Quantitative Trait Loci
* Receptor, ErbB-2
* Transcriptome
|keywords=* Aging
* Biological age
* Breast cancer
* Mouse genetics
* Oxidative stress
* Subphenotypes
|full-text-url=https://sci-hub.do/10.1016/j.freeradbiomed.2018.03.012
}}
{{medline-entry
|title=Identification of human age-associated gene co-expressions in functional modules using liquid association.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/29416677
|abstract=Aging is a major risk factor for age-related diseases such as certain cancers. In this study, we developed Age Associated Gene Co-expression Identifier (AAGCI), a liquid association based method to infer age-associated gene co-expressions at thousands of biological processes and pathways across 9 human tissues. Several hundred to thousands of gene pairs were inferred to be age co-expressed across different tissues, the genes involved in which are significantly enriched in functions like immunity, ATP binding, DNA damage, and many cancer pathways. The age co-expressed genes are significantly overlapped with aging genes curated in the GenAge database across all 9 tissues, suggesting a tissue-wide correlation between age-associated genes and co-expressions. Interestingly, age-associated gene co-expressions are significantly different from gene co-expressions identified through correlation analysis, indicating that aging might only contribute to a small portion of gene co-expressions. Moreover, the key driver analysis identified biologically meaningful genes in important function modules. For example, [i]IGF1, [[ERBB2]], [[TP53]] and STAT5A[/i] were inferred to be key genes driving age co-expressed genes in the network module associated with function "T cell proliferation". Finally, we prioritized a few anti-aging drugs such as metformin based on an enrichment analysis between age co-expressed genes and drug signatures from a recent study. The predicted drugs were partially validated by literature mining and can be readily used to generate hypothesis for further experimental validations.

|keywords=* GTEx
* aging
* anti-aging drug prediction
* gene co-expression
* liquid association
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5787419
}}