Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics
Objectives. Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC. Materials and Methods. Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differ...
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| description | Objectives. Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC. Materials and Methods. Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate. Results. Of 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients. Conclusions. We successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC. |
| doi_str_mv | 10.1155/2021/6657767 |
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Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC. Materials and Methods. Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate. Results. Of 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients. Conclusions. We successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2021/6657767</identifier><identifier>PMID: 33869632</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Analysis ; Bioinformatics ; Biomarkers ; Cancer ; Carcinoma, Squamous Cell - genetics ; Carcinoma, Squamous Cell - pathology ; Care and treatment ; Cluster Analysis ; Computational Biology ; CTLA-4 protein ; Data analysis ; Diagnosis ; Encyclopedias ; Extracellular matrix ; Female ; Gene expression ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Ontology ; Gene sequencing ; Genes ; Genetic aspects ; Genetic Association Studies ; Genomes ; Genomics ; Head & neck cancer ; Humans ; Logistic Models ; Male ; Medical prognosis ; Methods ; Middle Aged ; Mouth cancer ; Mouth Neoplasms - genetics ; Mouth Neoplasms - pathology ; Multivariate Analysis ; Network analysis ; Oral cancer ; Oral squamous cell carcinoma ; Peptides ; Prognosis ; Protein interaction ; Protein Interaction Maps - genetics ; Protein-protein interactions ; Proteins ; Rank tests ; Regression analysis ; Ribonucleic acid ; Risk factors ; RNA ; RNA sequencing ; ROC Curve ; Sequence Analysis, RNA ; Software ; Squamous cell carcinoma ; Squamous Cell Carcinoma of Head and Neck - genetics ; Survival ; Survival Analysis ; Therapeutic targets ; Tongue ; Transcription, Genetic ; Tumors ; Values</subject><ispartof>BioMed research international, 2021-04, Vol.2021, p.6657767-14</ispartof><rights>Copyright © 2021 Yang-Yang Zhang et al.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>Copyright © 2021 Yang-Yang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Yang-Yang Zhang et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-a35c270c3243128f5bd6b4709cd16b8dc5919c450c4b8eb4c1aafc92489cf5193</citedby><cites>FETCH-LOGICAL-c476t-a35c270c3243128f5bd6b4709cd16b8dc5919c450c4b8eb4c1aafc92489cf5193</cites><orcidid>0000-0003-4379-9089 ; 0000-0002-3157-3413</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/PMC8032525/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032525/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33869632$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Xu, Yanming</contributor><contributor>Yanming Xu</contributor><creatorcontrib>Zhang, Yang-Yang</creatorcontrib><creatorcontrib>Mao, Ming-Hui</creatorcontrib><creatorcontrib>Han, Zheng-Xue</creatorcontrib><title>Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Objectives. Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC. Materials and Methods. Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate. Results. Of 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients. Conclusions. We successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC.</description><subject>Analysis</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Carcinoma, Squamous Cell - genetics</subject><subject>Carcinoma, Squamous Cell - pathology</subject><subject>Care and treatment</subject><subject>Cluster Analysis</subject><subject>Computational Biology</subject><subject>CTLA-4 protein</subject><subject>Data analysis</subject><subject>Diagnosis</subject><subject>Encyclopedias</subject><subject>Extracellular matrix</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Gene Ontology</subject><subject>Gene sequencing</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetic Association Studies</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Head & neck cancer</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Mouth cancer</subject><subject>Mouth Neoplasms - genetics</subject><subject>Mouth Neoplasms - pathology</subject><subject>Multivariate Analysis</subject><subject>Network analysis</subject><subject>Oral cancer</subject><subject>Oral squamous cell carcinoma</subject><subject>Peptides</subject><subject>Prognosis</subject><subject>Protein interaction</subject><subject>Protein Interaction Maps - genetics</subject><subject>Protein-protein interactions</subject><subject>Proteins</subject><subject>Rank tests</subject><subject>Regression analysis</subject><subject>Ribonucleic acid</subject><subject>Risk factors</subject><subject>RNA</subject><subject>RNA sequencing</subject><subject>ROC Curve</subject><subject>Sequence Analysis, RNA</subject><subject>Software</subject><subject>Squamous cell carcinoma</subject><subject>Squamous Cell Carcinoma of Head and Neck - genetics</subject><subject>Survival</subject><subject>Survival Analysis</subject><subject>Therapeutic targets</subject><subject>Tongue</subject><subject>Transcription, Genetic</subject><subject>Tumors</subject><subject>Values</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kc1v1DAQxS0EolXpjTOyxAUJlvo78QVpu4JSqaKIhbPlOE7qKrFbOwH1v2fCLivggC-27J_fzJuH0HNK3lIq5RkjjJ4pJatKVY_QMeNUrBQV9PHhzPkROi3llsCqqSJaPUVHnNdKK86OUblsfZxCF5ydQoo4ddjiCx89_pxTH1OZgsPb0Ec7zdnjLmV8ne2At_ezHdNc8MYPA97Y7EJMo8XNA_7yaY23_n72Ee56bGOLz0MKEf6OUMSVZ-hJZ4fiT_f7Cfr24f3XzcfV1fXF5WZ9tXKiUtPKculYRRxnglNWd7JpVSMqol1LVVO3TmqqnZDEiab2jXDU2s5pJmrtOkk1P0Hvdrp3czP61oFRaN3c5TDa_GCSDebvlxhuTJ--m5pwJpkEgVd7gZzAT5nMGIoDwzZ68G6YpJJURGgB6Mt_0Ns05wj2FgqmLsQvwT3V28GbZSRQ1y2iZg2BQO9M10C92VEup1Ky7w4tU2KW2M0Su9nHDviLP20e4N8hA_B6B9yE2Nof4f9yPwF40LPw</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Zhang, Yang-Yang</creator><creator>Mao, Ming-Hui</creator><creator>Han, Zheng-Xue</creator><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><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>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-4379-9089</orcidid><orcidid>https://orcid.org/0000-0002-3157-3413</orcidid></search><sort><creationdate>20210401</creationdate><title>Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics</title><author>Zhang, Yang-Yang ; Mao, Ming-Hui ; Han, Zheng-Xue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-a35c270c3243128f5bd6b4709cd16b8dc5919c450c4b8eb4c1aafc92489cf5193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Analysis</topic><topic>Bioinformatics</topic><topic>Biomarkers</topic><topic>Cancer</topic><topic>Carcinoma, Squamous Cell - genetics</topic><topic>Carcinoma, Squamous Cell - pathology</topic><topic>Care and treatment</topic><topic>Cluster Analysis</topic><topic>Computational Biology</topic><topic>CTLA-4 protein</topic><topic>Data analysis</topic><topic>Diagnosis</topic><topic>Encyclopedias</topic><topic>Extracellular matrix</topic><topic>Female</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Gene Ontology</topic><topic>Gene sequencing</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genetic Association Studies</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Head & neck cancer</topic><topic>Humans</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Mouth cancer</topic><topic>Mouth Neoplasms - genetics</topic><topic>Mouth Neoplasms - pathology</topic><topic>Multivariate Analysis</topic><topic>Network analysis</topic><topic>Oral cancer</topic><topic>Oral squamous cell carcinoma</topic><topic>Peptides</topic><topic>Prognosis</topic><topic>Protein interaction</topic><topic>Protein Interaction Maps - genetics</topic><topic>Protein-protein interactions</topic><topic>Proteins</topic><topic>Rank tests</topic><topic>Regression analysis</topic><topic>Ribonucleic acid</topic><topic>Risk factors</topic><topic>RNA</topic><topic>RNA sequencing</topic><topic>ROC Curve</topic><topic>Sequence Analysis, RNA</topic><topic>Software</topic><topic>Squamous cell carcinoma</topic><topic>Squamous Cell Carcinoma of Head and Neck - genetics</topic><topic>Survival</topic><topic>Survival Analysis</topic><topic>Therapeutic targets</topic><topic>Tongue</topic><topic>Transcription, Genetic</topic><topic>Tumors</topic><topic>Values</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Yang-Yang</creatorcontrib><creatorcontrib>Mao, Ming-Hui</creatorcontrib><creatorcontrib>Han, Zheng-Xue</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><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>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</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>AIDS and Cancer Research Abstracts</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>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content 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>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Yang-Yang</au><au>Mao, Ming-Hui</au><au>Han, Zheng-Xue</au><au>Xu, Yanming</au><au>Yanming Xu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2021-04-01</date><risdate>2021</risdate><volume>2021</volume><spage>6657767</spage><epage>14</epage><pages>6657767-14</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Objectives. Oral squamous cell carcinoma (OSCC) is the most common oral cancer and has a poor prognosis. We aimed to identify new biomarkers or potential therapeutic targets for OSCC. Materials and Methods. Four pairs of tumor and adjacent normal tissues were collected from OSCC patients, and differentially expressed genes (DEGs) were screened via high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze the DEGs. A protein-protein interaction (PPI) network was established with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape, and two significant clusters were found. Candidate genes were screened by analyzing head and neck squamous cell carcinoma (HNSCC) data from The Cancer Genome Atlas (TCGA). A DEG-based risk model was established to predict the overall survival (OS) of OSCC patients via Kaplan-Meier analysis and the log-rank test. Furthermore, univariate Cox regression analysis was applied to assess associations between potential biomarkers and the overall survival rate. Results. Of 720 total DEGs, fifty-two DEGs in the two subclusters of the PPI network analysis were selected. A risk model was established, and five candidate genes (SPRR2E, ICOS, CTLA4, HTR1D, and CCR4) were identified as biomarkers of OS in OSCC patients. Conclusions. We successfully constructed a prognostic signature to predict prognosis and identified five candidate genes associated with the OS of OSCC patients that are potential tumor biomarkers and targets in OSCC.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>33869632</pmid><doi>10.1155/2021/6657767</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4379-9089</orcidid><orcidid>https://orcid.org/0000-0002-3157-3413</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Bioinformatics Biomarkers Cancer Carcinoma, Squamous Cell - genetics Carcinoma, Squamous Cell - pathology Care and treatment Cluster Analysis Computational Biology CTLA-4 protein Data analysis Diagnosis Encyclopedias Extracellular matrix Female Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Ontology Gene sequencing Genes Genetic aspects Genetic Association Studies Genomes Genomics Head & neck cancer Humans Logistic Models Male Medical prognosis Methods Middle Aged Mouth cancer Mouth Neoplasms - genetics Mouth Neoplasms - pathology Multivariate Analysis Network analysis Oral cancer Oral squamous cell carcinoma Peptides Prognosis Protein interaction Protein Interaction Maps - genetics Protein-protein interactions Proteins Rank tests Regression analysis Ribonucleic acid Risk factors RNA RNA sequencing ROC Curve Sequence Analysis, RNA Software Squamous cell carcinoma Squamous Cell Carcinoma of Head and Neck - genetics Survival Survival Analysis Therapeutic targets Tongue Transcription, Genetic Tumors Values |
| title | Identification of a Gene Prognostic Signature for Oral Squamous Cell Carcinoma by RNA Sequencing and Bioinformatics |
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