Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer
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. The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gen...
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| creator | Han, Hong-Yan Mou, Jiang-Tao Jiang, Wen-Ping Zhai, Xiu-Ming Deng, Kun |
| description | 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.
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.
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.
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. |
| doi_str_mv | 10.1042/BSR20204394 |
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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.
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.
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.</description><identifier>ISSN: 0144-8463</identifier><identifier>EISSN: 1573-4935</identifier><identifier>DOI: 10.1042/BSR20204394</identifier><identifier>PMID: 33616161</identifier><language>eng</language><publisher>England: Portland Press Ltd The Biochemical Society</publisher><subject>Accuracy ; Assembly ; Bioinformatics ; Biomarkers ; Biomarkers, Tumor - genetics ; Biomarkers, Tumor - metabolism ; Cancer ; Cancer therapies ; Cell cycle ; Cell Cycle Proteins - genetics ; Cell Cycle Proteins - metabolism ; Cervical cancer ; Chemotherapy ; Chromatin Assembly Factor-1 - genetics ; Chromatin Assembly Factor-1 - metabolism ; Chromatin remodeling ; Computational Biology ; Datasets ; Decision trees ; Diagnosis ; DNA (Cytosine-5-)-Methyltransferase 1 - genetics ; DNA (Cytosine-5-)-Methyltransferase 1 - metabolism ; DNA methylation ; DNA methyltransferase ; DNA microarrays ; DNMT1 protein ; Encyclopedias ; Female ; Function analysis ; Gene expression ; Gene sequencing ; Genes ; Genomes ; Humans ; Identification ; Machine learning ; Malignancy ; Medical prognosis ; Microtubule-Associated Proteins - genetics ; Microtubule-Associated Proteins - metabolism ; Minichromosome Maintenance Complex Component 2 - genetics ; Minichromosome Maintenance Complex Component 2 - metabolism ; Morbidity ; Polymerase chain reaction ; Prognosis ; Proteins ; Survival analysis ; Transcriptome ; Tumors ; Uterine Cervical Neoplasms - genetics ; Uterine Cervical Neoplasms - metabolism ; Uterine Cervical Neoplasms - pathology</subject><ispartof>Bioscience reports, 2021-03, Vol.41 (3)</ispartof><rights>2021 The Author(s).</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 The Author(s). 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-d7705131fd87ad4300a17aa8f8661825e743bb6e615686cb94a04f047aa94e893</citedby><cites>FETCH-LOGICAL-c409t-d7705131fd87ad4300a17aa8f8661825e743bb6e615686cb94a04f047aa94e893</cites><orcidid>0000-0002-5655-8412</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955105/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2958254412?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,12725,12754,21368,21369,21370,21371,21372,23236,27903,27904,33431,33432,33509,33510,33682,33683,33723,33724,33984,33985,34293,34294,34313,34314,36244,36245,43595,43638,43766,43784,43932,44046,44052,44383,53770,53772,64362,64364,64366,72216</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33616161$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Han, Hong-Yan</creatorcontrib><creatorcontrib>Mou, Jiang-Tao</creatorcontrib><creatorcontrib>Jiang, Wen-Ping</creatorcontrib><creatorcontrib>Zhai, Xiu-Ming</creatorcontrib><creatorcontrib>Deng, Kun</creatorcontrib><title>Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer</title><title>Bioscience reports</title><addtitle>Biosci Rep</addtitle><description>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.
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.
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.
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.</description><subject>Accuracy</subject><subject>Assembly</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Cell cycle</subject><subject>Cell Cycle Proteins - genetics</subject><subject>Cell Cycle Proteins - metabolism</subject><subject>Cervical cancer</subject><subject>Chemotherapy</subject><subject>Chromatin Assembly Factor-1 - genetics</subject><subject>Chromatin Assembly Factor-1 - metabolism</subject><subject>Chromatin remodeling</subject><subject>Computational Biology</subject><subject>Datasets</subject><subject>Decision trees</subject><subject>Diagnosis</subject><subject>DNA (Cytosine-5-)-Methyltransferase 1 - genetics</subject><subject>DNA (Cytosine-5-)-Methyltransferase 1 - metabolism</subject><subject>DNA methylation</subject><subject>DNA methyltransferase</subject><subject>DNA microarrays</subject><subject>DNMT1 protein</subject><subject>Encyclopedias</subject><subject>Female</subject><subject>Function analysis</subject><subject>Gene expression</subject><subject>Gene sequencing</subject><subject>Genes</subject><subject>Genomes</subject><subject>Humans</subject><subject>Identification</subject><subject>Machine learning</subject><subject>Malignancy</subject><subject>Medical prognosis</subject><subject>Microtubule-Associated Proteins - genetics</subject><subject>Microtubule-Associated Proteins - metabolism</subject><subject>Minichromosome Maintenance Complex Component 2 - genetics</subject><subject>Minichromosome Maintenance Complex Component 2 - metabolism</subject><subject>Morbidity</subject><subject>Polymerase chain reaction</subject><subject>Prognosis</subject><subject>Proteins</subject><subject>Survival analysis</subject><subject>Transcriptome</subject><subject>Tumors</subject><subject>Uterine Cervical Neoplasms - genetics</subject><subject>Uterine Cervical Neoplasms - metabolism</subject><subject>Uterine Cervical Neoplasms - pathology</subject><issn>0144-8463</issn><issn>1573-4935</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkVFLHDEUhYMourV98l0GfCmU0ZvkZmbyIuhS24IgWIW-hUyScaOzkzWZXfHfN4urqOQhuTkfh3M5hBxQOKaA7OT87zUDBsglbpEJFTUvUXKxTSZAEcsGK75HvqR0DwBZwF2yx3lF12dC_l34lSuMHqy3enRF68NcxwcXU6FTCsbnT1s8-XFWjDNXWK_vhpB8VgdbLGLYTKErjIsrb3S_Nsvvr2Sn031y3zb3Prm9-Hkz_V1eXv36Mz27LA2CHEtb1yAop51tam2RA2haa910TVXRhglXI2_bylVUVE1lWokasAPMjETXSL5PTl98F8t27qxxwxh1rxbR5z2eVdBefVQGP1N3YaVqKQQFkQ2-bwxieFy6NKq5T8b1vR5cWCbFUDImgVKa0aNP6H1YxiGvp5gUOS0iZZn68UKZGFKKrnsLQ0GtG1PvGsv04fv8b-xrRfw_NXeQ2g</recordid><startdate>20210326</startdate><enddate>20210326</enddate><creator>Han, Hong-Yan</creator><creator>Mou, Jiang-Tao</creator><creator>Jiang, Wen-Ping</creator><creator>Zhai, Xiu-Ming</creator><creator>Deng, Kun</creator><general>Portland Press Ltd The Biochemical Society</general><general>Portland Press Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M2P</scope><scope>M7N</scope><scope>M7P</scope><scope>MBDVC</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-5655-8412</orcidid></search><sort><creationdate>20210326</creationdate><title>Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer</title><author>Han, Hong-Yan ; Mou, Jiang-Tao ; Jiang, Wen-Ping ; Zhai, Xiu-Ming ; Deng, Kun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-d7705131fd87ad4300a17aa8f8661825e743bb6e615686cb94a04f047aa94e893</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Assembly</topic><topic>Bioinformatics</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - 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genetics</topic><topic>Microtubule-Associated Proteins - metabolism</topic><topic>Minichromosome Maintenance Complex Component 2 - genetics</topic><topic>Minichromosome Maintenance Complex Component 2 - metabolism</topic><topic>Morbidity</topic><topic>Polymerase chain reaction</topic><topic>Prognosis</topic><topic>Proteins</topic><topic>Survival analysis</topic><topic>Transcriptome</topic><topic>Tumors</topic><topic>Uterine Cervical Neoplasms - genetics</topic><topic>Uterine Cervical Neoplasms - metabolism</topic><topic>Uterine Cervical Neoplasms - pathology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Han, Hong-Yan</creatorcontrib><creatorcontrib>Mou, Jiang-Tao</creatorcontrib><creatorcontrib>Jiang, Wen-Ping</creatorcontrib><creatorcontrib>Zhai, Xiu-Ming</creatorcontrib><creatorcontrib>Deng, Kun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>ProQuest Biological Science Journals</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioscience reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Han, Hong-Yan</au><au>Mou, Jiang-Tao</au><au>Jiang, Wen-Ping</au><au>Zhai, Xiu-Ming</au><au>Deng, Kun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer</atitle><jtitle>Bioscience reports</jtitle><addtitle>Biosci Rep</addtitle><date>2021-03-26</date><risdate>2021</risdate><volume>41</volume><issue>3</issue><issn>0144-8463</issn><eissn>1573-4935</eissn><abstract>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.
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.
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.
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.</abstract><cop>England</cop><pub>Portland Press Ltd The Biochemical Society</pub><pmid>33616161</pmid><doi>10.1042/BSR20204394</doi><orcidid>https://orcid.org/0000-0002-5655-8412</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Assembly Bioinformatics Biomarkers Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Cancer Cancer therapies Cell cycle Cell Cycle Proteins - genetics Cell Cycle Proteins - metabolism Cervical cancer Chemotherapy Chromatin Assembly Factor-1 - genetics Chromatin Assembly Factor-1 - metabolism Chromatin remodeling Computational Biology Datasets Decision trees Diagnosis DNA (Cytosine-5-)-Methyltransferase 1 - genetics DNA (Cytosine-5-)-Methyltransferase 1 - metabolism DNA methylation DNA methyltransferase DNA microarrays DNMT1 protein Encyclopedias Female Function analysis Gene expression Gene sequencing Genes Genomes Humans Identification Machine learning Malignancy Medical prognosis Microtubule-Associated Proteins - genetics Microtubule-Associated Proteins - metabolism Minichromosome Maintenance Complex Component 2 - genetics Minichromosome Maintenance Complex Component 2 - metabolism Morbidity Polymerase chain reaction Prognosis Proteins Survival analysis Transcriptome Tumors Uterine Cervical Neoplasms - genetics Uterine Cervical Neoplasms - metabolism Uterine Cervical Neoplasms - pathology |
| title | Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer |
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