Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer
Background Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and p...
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PeerJ Inc.
2020-09-01
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| Online Access: | https://peerj.com/articles/9975.pdf |
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| author | Xiuming Zhai Zhaowei Yang Xiji Liu Zihe Dong Dandan Zhou |
| author_facet | Xiuming Zhai Zhaowei Yang Xiji Liu Zihe Dong Dandan Zhou |
| author_sort | Xiuming Zhai |
| collection | DOAJ |
| description | Background Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and prognosis. Material/Methods Three independent data sets (GSE45827, GSE38959, GSE65194) were downloaded from the Gene Expression Omnibus (GEO). The R software packages were used to integrate the gene profiles and identify differentially expressed genes (DEGs). A variety of bioinformatics tools were used to explore the hub genes, including the DAVID database, STRING database and Cytoscape software. Reverse transcription quantitative PCR (RT-qPCR) was used to verify the hub genes in 14 pairs of TNBC paired tissues. Results In this study, we screened out 161 DEGs between 222 non-TNBC and 126 TNBC samples, of which 105 genes were up-regulated and 56 were down-regulated. These DEGs were enriched for 27 GO terms and two pathways. GO analysis enriched mainly in “cell division”, “chromosome, centromeric region” and “microtubule motor activity”. KEGG pathway analysis enriched mostly in “Cell cycle” and “Oocyte meiosis”. PPI network was constructed and then 10 top hub genes were screened. According to the analysis results of the Kaplan-Meier survival curve, the expression levels of only NUF2, FAM83D and CENPH were associated with the recurrence-free survival in TNBC samples (P < 0.05). RT-qPCR confirmed that the expression levels of NUF2 and FAM83D in TNBC tissues were indeed up-regulated significantly. Conclusions The comprehensive analysis showed that NUF2 and FAM83D could be used as potential biomarkers for diagnosis and prognosis of TNBC. |
| first_indexed | 2024-03-09T06:52:43Z |
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| language | English |
| last_indexed | 2024-03-09T06:52:43Z |
| publishDate | 2020-09-01 |
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| spelling | doaj.art-f6683ba3678040fca222ebe7a1ff019a2023-12-03T10:25:10ZengPeerJ Inc.PeerJ2167-83592020-09-018e997510.7717/peerj.9975Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancerXiuming Zhai0Zhaowei Yang1Xiji Liu2Zihe Dong3Dandan Zhou4Department of Laboratory Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Breast and Thyroid, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, ChinaDepartment of Laboratory Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, ChinaDepartment of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, ChinaBackground Breast cancer is a heterogeneous disease. Compared with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is easy to metastasize and has a short survival time, less choice of treatment options. Here, we aimed to identify the potential biomarkers to TNBC diagnosis and prognosis. Material/Methods Three independent data sets (GSE45827, GSE38959, GSE65194) were downloaded from the Gene Expression Omnibus (GEO). The R software packages were used to integrate the gene profiles and identify differentially expressed genes (DEGs). A variety of bioinformatics tools were used to explore the hub genes, including the DAVID database, STRING database and Cytoscape software. Reverse transcription quantitative PCR (RT-qPCR) was used to verify the hub genes in 14 pairs of TNBC paired tissues. Results In this study, we screened out 161 DEGs between 222 non-TNBC and 126 TNBC samples, of which 105 genes were up-regulated and 56 were down-regulated. These DEGs were enriched for 27 GO terms and two pathways. GO analysis enriched mainly in “cell division”, “chromosome, centromeric region” and “microtubule motor activity”. KEGG pathway analysis enriched mostly in “Cell cycle” and “Oocyte meiosis”. PPI network was constructed and then 10 top hub genes were screened. According to the analysis results of the Kaplan-Meier survival curve, the expression levels of only NUF2, FAM83D and CENPH were associated with the recurrence-free survival in TNBC samples (P < 0.05). RT-qPCR confirmed that the expression levels of NUF2 and FAM83D in TNBC tissues were indeed up-regulated significantly. Conclusions The comprehensive analysis showed that NUF2 and FAM83D could be used as potential biomarkers for diagnosis and prognosis of TNBC.https://peerj.com/articles/9975.pdfBiomarkerTriple-negative breast cancerBioinformaticsRT-qPCR |
| spellingShingle | Xiuming Zhai Zhaowei Yang Xiji Liu Zihe Dong Dandan Zhou Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer PeerJ Biomarker Triple-negative breast cancer Bioinformatics RT-qPCR |
| title | Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer |
| title_full | Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer |
| title_fullStr | Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer |
| title_full_unstemmed | Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer |
| title_short | Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer |
| title_sort | identification of nuf2 and fam83d as potential biomarkers in triple negative breast cancer |
| topic | Biomarker Triple-negative breast cancer Bioinformatics RT-qPCR |
| url | https://peerj.com/articles/9975.pdf |
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