This preprint is under consideration at BMC Medical Genomics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Identification of High-risk Genes in Triple-negative Breast Cancer by Bioinformatics
Min Tao
Department of Oncology, The First Affliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou 215006, Jiangsu Province, People’s Republic of China.
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7221-6084
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Keywords
Triple-negative breast cancer, High-risk gene, Target gene, Differentially expressed genes, Prognostic, Survival
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.docx
Additional file 1: Table S1. The list of normal tissue samples and TNBC tissue samples selected from GSE7904, GSE31448, GSE45827, and GSE65194.
- Additionalfile2.docx
Additional file 2: Table S2. The intersection DEGs were detected from the 4 chips (GSE7904, GSE31448, GSE45827 and GSE65194), including 127 upregulated genes and 293 downregulated genes in normal breast tissues compared to TNBC tissues.
- Additionalfile3.docx
Additional file 3: Fig. S1. Kaplan–Meier curves for 14 genes in MCODE 1 that were related to RFS. Fig. S2. Kaplan–Meier curves for 15 genes in MCODE 2 - 4 that were related to RFS.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 31 Dec, 2020
No community comments so far
Reviewers invited
Invitations sent on 11 Jan, 2021
Editor assigned
On 05 Jan, 2021
Submission checks complete
On 25 Dec, 2020
Editor invited
On 16 Dec, 2020
First submitted
On 09 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
Learn more about our company.
This preprint is under consideration at BMC Medical Genomics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Identification of High-risk Genes in Triple-negative Breast Cancer by Bioinformatics
Min Tao
Department of Oncology, The First Affliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou 215006, Jiangsu Province, People’s Republic of China.
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7221-6084
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 31 Dec, 2020
No community comments so far
Reviewers invited
Invitations sent on 11 Jan, 2021
Editor assigned
On 05 Jan, 2021
Submission checks complete
On 25 Dec, 2020
Editor invited
On 16 Dec, 2020
First submitted
On 09 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6