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Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis
Pengfei Ning
Inner Mongolia Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4017-0744
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at World Journal of Surgical Oncology.
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.
Keywords
luminal A breast cancer, basal-like breast cancer, neoplasm genes, bioinformatics
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CURRENT STATUS: Published
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Posted 22 Sep, 2020
Published
On 16 Oct, 2020
No community comments so far
Editorial decision: Accept
On 28 Sep, 2020
Review #1 received
Received 27 Sep, 2020
Review #2 received
Received 27 Sep, 2020
Reviewers invited
Invitations sent on 26 Sep, 2020
Reviewer #1 agreed
On 26 Sep, 2020
Reviewer #2 agreed
On 26 Sep, 2020
Editor assigned
On 22 Sep, 2020
Submission checks complete
On 21 Sep, 2020
Editor invited
On 21 Sep, 2020
No comments provided
Editorial decision: Major Revision
On 14 Sep, 2020
Reviewer #3 agreed
On 31 Aug, 2020
Review #3 received
Received 31 Aug, 2020
Review #2 received
Received 21 Jul, 2020
Review #1 received
Received 21 Jul, 2020
Reviewer #2 agreed
On 13 Jul, 2020
Reviewer #1 agreed
On 12 Jul, 2020
Reviewers invited
Invitations sent on 13 May, 2020
Editor assigned
On 10 May, 2020
Submission checks complete
On 09 May, 2020
Editor invited
On 09 May, 2020
First submitted
On 08 May, 2020
Metrics
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Subject Areas
Oncology
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This preprint is under consideration at World Journal of Surgical Oncology. 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
Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis
Pengfei Ning
Inner Mongolia Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4017-0744
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: Published
Version 2
Posted 22 Sep, 2020
Published
On 16 Oct, 2020
No community comments so far
Editorial decision: Accept
On 28 Sep, 2020
Review #1 received
Received 27 Sep, 2020
Review #2 received
Received 27 Sep, 2020
Reviewers invited
Invitations sent on 26 Sep, 2020
Reviewer #1 agreed
On 26 Sep, 2020
Reviewer #2 agreed
On 26 Sep, 2020
Editor assigned
On 22 Sep, 2020
Submission checks complete
On 21 Sep, 2020
Editor invited
On 21 Sep, 2020
No comments provided
Editorial decision: Major Revision
On 14 Sep, 2020
Reviewer #3 agreed
On 31 Aug, 2020
Review #3 received
Received 31 Aug, 2020
Review #2 received
Received 21 Jul, 2020
Review #1 received
Received 21 Jul, 2020
Reviewer #2 agreed
On 13 Jul, 2020
Reviewer #1 agreed
On 12 Jul, 2020
Reviewers invited
Invitations sent on 13 May, 2020
Editor assigned
On 10 May, 2020
Submission checks complete
On 09 May, 2020
Editor invited
On 09 May, 2020
First submitted
On 08 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Oncology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at World Journal of Surgical Oncology.
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10