Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer. Issue 3 (26th March 2021)
- Record Type:
- Journal Article
- Title:
- Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer. Issue 3 (26th March 2021)
- Main Title:
- Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer
- Authors:
- Han, Hong-Yan
Mou, Jiang-Tao
Jiang, Wen-Ping
Zhai, Xiu-Ming
Deng, Kun - Abstract:
- Abstract: Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC. Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes. Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues. Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 andAbstract: Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC. Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes. Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues. Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC. … (more)
- Is Part Of:
- Bioscience reports. Volume 41:Issue 3(2021)
- Journal:
- Bioscience reports
- Issue:
- Volume 41:Issue 3(2021)
- Issue Display:
- Volume 41, Issue 3 (2021)
- Year:
- 2021
- Volume:
- 41
- Issue:
- 3
- Issue Sort Value:
- 2021-0041-0003-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-03-26
- Subjects:
- biomarkers -- cervical cancer -- differentially expressed genes -- machine learning
Molecular biology -- Periodicals
Cytology -- Periodicals
572.8 - Journal URLs:
- http://www.bioscirep.org/ ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1042/BSR20204394 ↗
- Languages:
- English
- ISSNs:
- 0144-8463
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2089.611600
British Library HMNTS - ELD Digital store - Ingest File:
- 16172.xml