Identification of high-risk genes in triple-negative breast cancer by bioinformatics Article Swipe
Xiang Lu
,
Caiping Chen
,
Guihong Ni
,
Min Tao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-136134/v2
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-136134/v2
Background Current research has failed to find a target gene for triple-negative breast cancer (TNBC), which has resulted in the treatment for TNBC being less effective than that for other types of breast cancer. Finding high-risk genes for TNBC by bioinformatics may help to identify target genes for TNBC. Methods The gene expression data of 4 chips (GSE7904, GSE31448, GSE45827, GSE65194) which contains of normal breast tissue and TNBC tissue were obtained from the Gene Expression Omnibus. The differentially expressed genes (DEGs) between normal breast tissue and TNBC tissue were identified. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed by the DAVID website. Protein-protein interaction network analysis of DEGs was carried out by the STRING website, and the results were imported into Cytoscape. Then, module analysis was carried out by using the MCODE app. The online tool of the Kaplan-Meier Plotter website was used to analyse associations between relapse-free survival (RFS) and the expression of genes obtained by MCODE, and the metastasis-free survival (MFS) data from GSE58812 were used for survival verification. The difference in the expression of the identified genes was verified by the online tool of the UALCAN website. Results There were 127 upregulated and 293 downregulated genes in the DEGs. The GO and KEGG analysis showed that the DEGs were particularly enriched in mitotic nuclear division, extracellular space, heparin binding, and ECM-receptor interaction. MCODE obtained a total of 47 genes in 4 gene clusters, 29 of which were related to RFS. Survival verification indicated that 14 out of 29 genes were related to MFS, namely, CCNB1, AURKB, KIF20A, BUB1B, DLGAP5, CXCL11, CXCL9, CXCL10, CXCL12, IGF1, FN1, CFD, SGO2 and CDCA5. Conclusions We identified 14 genes as the high-risk genes for TNBC. Further research on these genes may identify the target genes of TNBC.
Related Topics
Concepts
Triple-negative breast cancer
Identification (biology)
Breast cancer
Gene
Bioinformatics
Computational biology
Cancer
Medicine
Biology
Genetics
Internal medicine
Botany
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-136134/v2
- https://www.researchsquare.com/article/rs-136134/latest.pdf
- OA Status
- gold
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4255175998
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4255175998Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-136134/v2Digital Object Identifier
- Title
-
Identification of high-risk genes in triple-negative breast cancer by bioinformaticsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-07-22Full publication date if available
- Authors
-
Xiang Lu, Caiping Chen, Guihong Ni, Min TaoList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-136134/v2Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-136134/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-136134/latest.pdfDirect OA link when available
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Triple-negative breast cancer, Identification (biology), Breast cancer, Gene, Bioinformatics, Computational biology, Cancer, Medicine, Biology, Genetics, Internal medicine, BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.CCNB1, | 272 |
| abstract_inverted_index.CDCA5. | 286 |
| abstract_inverted_index.CXCL9, | 278 |
| abstract_inverted_index.MCODE, | 172 |
| abstract_inverted_index.STRING | 128 |
| abstract_inverted_index.UALCAN | 203 |
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| abstract_inverted_index.CXCL11, | 277 |
| abstract_inverted_index.CXCL12, | 280 |
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