Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes
Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-...
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| creator | Dong, Rui Chen, Shuran Lu, Fei Zheng, Ni Peng, Guisen Li, Yan Yang, Pan Wen, Hexin Qiu, Quanwei Wang, Yitong Wu, Huazhang Liu, Mulin |
| description | Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-modified DDR-related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan-Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C-index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR-related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low-risk group were significantly better than those in the high-risk group. Conclusion. We screened eight DDR-related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy. |
| doi_str_mv | 10.1155/2022/4909544 |
| format | Article |
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DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-modified DDR-related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan-Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C-index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR-related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low-risk group were significantly better than those in the high-risk group. Conclusion. We screened eight DDR-related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.</description><identifier>ISSN: 2314-6133</identifier><identifier>ISSN: 2314-6141</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2022/4909544</identifier><identifier>PMID: 36578802</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Algorithms ; Cancer ; Care and treatment ; Cell cycle ; Cell Line, Tumor ; Chemotherapy ; Complex systems ; Damage ; Deoxyribonucleic acid ; DNA ; DNA damage ; DNA Damage - genetics ; DNA methylation ; DNA repair ; DNA-Binding Proteins ; Drug therapy ; Gastric cancer ; Gene expression ; Gene set enrichment analysis ; Genes ; Genetic aspects ; Genomes ; Health aspects ; Humans ; Immune checkpoint ; Immunotherapy ; Medical prognosis ; Microenvironments ; Microsatellite instability ; Mutation ; N6-methyladenosine ; Nomograms ; Patients ; Prognosis ; Receptor activity modifying proteins ; Risk ; Risk groups ; Stomach cancer ; Stomach Neoplasms - genetics ; Stomach Neoplasms - therapy ; Survival analysis ; Transcription Factors ; Tumor cells ; Tumor Microenvironment ; Tumors</subject><ispartof>BioMed research international, 2022-12, Vol.2022, p.4909544-21</ispartof><rights>Copyright © 2022 Rui Dong et al.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Rui Dong 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 © 2022 Rui Dong et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-c6691840f03e8f5a0cfa6110b01e2a2edb8c6a472cac11adbf816f83de99d5743</citedby><cites>FETCH-LOGICAL-c476t-c6691840f03e8f5a0cfa6110b01e2a2edb8c6a472cac11adbf816f83de99d5743</cites><orcidid>0000-0002-8768-995X ; 0000-0001-6103-2573 ; 0000-0003-1645-6427 ; 0000-0002-9451-6678 ; 0000-0002-5482-3761 ; 0000-0002-6849-3775 ; 0000-0003-0824-7633 ; 0000-0002-1818-6021 ; 0000-0002-2872-6714 ; 0000-0002-3345-7141 ; 0000-0002-7774-2411 ; 0000-0002-6283-839X</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/PMC9792237/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792237/$$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/36578802$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Muddassir Ali, Muhammad</contributor><contributor>Muhammad Muddassir Ali</contributor><creatorcontrib>Dong, Rui</creatorcontrib><creatorcontrib>Chen, Shuran</creatorcontrib><creatorcontrib>Lu, Fei</creatorcontrib><creatorcontrib>Zheng, Ni</creatorcontrib><creatorcontrib>Peng, Guisen</creatorcontrib><creatorcontrib>Li, Yan</creatorcontrib><creatorcontrib>Yang, Pan</creatorcontrib><creatorcontrib>Wen, Hexin</creatorcontrib><creatorcontrib>Qiu, Quanwei</creatorcontrib><creatorcontrib>Wang, Yitong</creatorcontrib><creatorcontrib>Wu, Huazhang</creatorcontrib><creatorcontrib>Liu, Mulin</creatorcontrib><title>Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-modified DDR-related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan-Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C-index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR-related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low-risk group were significantly better than those in the high-risk group. Conclusion. We screened eight DDR-related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.</description><subject>Algorithms</subject><subject>Cancer</subject><subject>Care and treatment</subject><subject>Cell cycle</subject><subject>Cell Line, Tumor</subject><subject>Chemotherapy</subject><subject>Complex systems</subject><subject>Damage</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA damage</subject><subject>DNA Damage - genetics</subject><subject>DNA methylation</subject><subject>DNA repair</subject><subject>DNA-Binding Proteins</subject><subject>Drug therapy</subject><subject>Gastric cancer</subject><subject>Gene expression</subject><subject>Gene set enrichment analysis</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Immune checkpoint</subject><subject>Immunotherapy</subject><subject>Medical prognosis</subject><subject>Microenvironments</subject><subject>Microsatellite instability</subject><subject>Mutation</subject><subject>N6-methyladenosine</subject><subject>Nomograms</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Receptor activity modifying proteins</subject><subject>Risk</subject><subject>Risk groups</subject><subject>Stomach cancer</subject><subject>Stomach Neoplasms - genetics</subject><subject>Stomach Neoplasms - therapy</subject><subject>Survival analysis</subject><subject>Transcription Factors</subject><subject>Tumor cells</subject><subject>Tumor Microenvironment</subject><subject>Tumors</subject><issn>2314-6133</issn><issn>2314-6141</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</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>eNp9kc9rFDEYhgdRbKm9eZaAF0HX5ncmHoRlq2uhahE9h2zmm9mUmWSbzFj735t11616MJcEvocn38tbVU8Jfk2IEGcUU3rGNdaC8wfVMWWEzyTh5OHhzdhRdZrzNS6nJhJr-bg6YlKousb0uPrxMTbQZ9TGhK4SNN6NPnToC-RNDBnQGNHFMEwhjmtIdnOHbGgKGLsQs8_IB3RlRw9hzOjWj2u0tHlM3qGFDQ7SG3T-aY7O7WA7uHcuIUB-Uj1qbZ_hdH-fVN_ev_u6-DC7_Ly8WMwvZ44rOc6clJrUHLeYQd0Ki11rJSF4hQlQS6FZ1U5arqizjhDbrNoSsq1ZA1o3QnF2Ur3deTfTaoDGlVWT7c0m-cGmOxOtN39Pgl-bLn43WmlKmSqCF3tBijcT5NEMPjvoexsgTtlQJTSVQnNR0Of_oNdxSqHE-0XVCjOl7qnO9mB8aGP5122lZq4Y0QITvKVe7SiXYs4J2sPKBJtt92bbvdl3X_Bnf8Y8wL-bLsDLHbD2obG3_v-6n00QtjU</recordid><startdate>20221219</startdate><enddate>20221219</enddate><creator>Dong, Rui</creator><creator>Chen, Shuran</creator><creator>Lu, Fei</creator><creator>Zheng, Ni</creator><creator>Peng, Guisen</creator><creator>Li, Yan</creator><creator>Yang, Pan</creator><creator>Wen, Hexin</creator><creator>Qiu, Quanwei</creator><creator>Wang, Yitong</creator><creator>Wu, Huazhang</creator><creator>Liu, Mulin</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/0000-0002-8768-995X</orcidid><orcidid>https://orcid.org/0000-0001-6103-2573</orcidid><orcidid>https://orcid.org/0000-0003-1645-6427</orcidid><orcidid>https://orcid.org/0000-0002-9451-6678</orcidid><orcidid>https://orcid.org/0000-0002-5482-3761</orcidid><orcidid>https://orcid.org/0000-0002-6849-3775</orcidid><orcidid>https://orcid.org/0000-0003-0824-7633</orcidid><orcidid>https://orcid.org/0000-0002-1818-6021</orcidid><orcidid>https://orcid.org/0000-0002-2872-6714</orcidid><orcidid>https://orcid.org/0000-0002-3345-7141</orcidid><orcidid>https://orcid.org/0000-0002-7774-2411</orcidid><orcidid>https://orcid.org/0000-0002-6283-839X</orcidid></search><sort><creationdate>20221219</creationdate><title>Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes</title><author>Dong, Rui ; Chen, Shuran ; Lu, Fei ; Zheng, Ni ; Peng, Guisen ; Li, Yan ; Yang, Pan ; Wen, Hexin ; Qiu, Quanwei ; Wang, Yitong ; Wu, Huazhang ; Liu, Mulin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-c6691840f03e8f5a0cfa6110b01e2a2edb8c6a472cac11adbf816f83de99d5743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Cancer</topic><topic>Care and treatment</topic><topic>Cell cycle</topic><topic>Cell Line, Tumor</topic><topic>Chemotherapy</topic><topic>Complex systems</topic><topic>Damage</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA damage</topic><topic>DNA Damage - genetics</topic><topic>DNA methylation</topic><topic>DNA repair</topic><topic>DNA-Binding Proteins</topic><topic>Drug therapy</topic><topic>Gastric cancer</topic><topic>Gene expression</topic><topic>Gene set enrichment analysis</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genomes</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Immune checkpoint</topic><topic>Immunotherapy</topic><topic>Medical prognosis</topic><topic>Microenvironments</topic><topic>Microsatellite instability</topic><topic>Mutation</topic><topic>N6-methyladenosine</topic><topic>Nomograms</topic><topic>Patients</topic><topic>Prognosis</topic><topic>Receptor activity modifying proteins</topic><topic>Risk</topic><topic>Risk groups</topic><topic>Stomach cancer</topic><topic>Stomach Neoplasms - genetics</topic><topic>Stomach Neoplasms - therapy</topic><topic>Survival analysis</topic><topic>Transcription Factors</topic><topic>Tumor cells</topic><topic>Tumor Microenvironment</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dong, Rui</creatorcontrib><creatorcontrib>Chen, Shuran</creatorcontrib><creatorcontrib>Lu, Fei</creatorcontrib><creatorcontrib>Zheng, Ni</creatorcontrib><creatorcontrib>Peng, Guisen</creatorcontrib><creatorcontrib>Li, Yan</creatorcontrib><creatorcontrib>Yang, Pan</creatorcontrib><creatorcontrib>Wen, Hexin</creatorcontrib><creatorcontrib>Qiu, Quanwei</creatorcontrib><creatorcontrib>Wang, Yitong</creatorcontrib><creatorcontrib>Wu, Huazhang</creatorcontrib><creatorcontrib>Liu, Mulin</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>Dong, Rui</au><au>Chen, Shuran</au><au>Lu, Fei</au><au>Zheng, Ni</au><au>Peng, Guisen</au><au>Li, Yan</au><au>Yang, Pan</au><au>Wen, Hexin</au><au>Qiu, Quanwei</au><au>Wang, Yitong</au><au>Wu, Huazhang</au><au>Liu, Mulin</au><au>Muddassir Ali, Muhammad</au><au>Muhammad Muddassir Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2022-12-19</date><risdate>2022</risdate><volume>2022</volume><spage>4909544</spage><epage>21</epage><pages>4909544-21</pages><issn>2314-6133</issn><issn>2314-6141</issn><eissn>2314-6141</eissn><abstract>Objective. DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-modified DDR-related gene was established to evaluate its role in patients with gastric cancer. Methods. We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan-Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C-index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Results. In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR-related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low-risk group were significantly better than those in the high-risk group. Conclusion. We screened eight DDR-related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>36578802</pmid><doi>10.1155/2022/4909544</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-8768-995X</orcidid><orcidid>https://orcid.org/0000-0001-6103-2573</orcidid><orcidid>https://orcid.org/0000-0003-1645-6427</orcidid><orcidid>https://orcid.org/0000-0002-9451-6678</orcidid><orcidid>https://orcid.org/0000-0002-5482-3761</orcidid><orcidid>https://orcid.org/0000-0002-6849-3775</orcidid><orcidid>https://orcid.org/0000-0003-0824-7633</orcidid><orcidid>https://orcid.org/0000-0002-1818-6021</orcidid><orcidid>https://orcid.org/0000-0002-2872-6714</orcidid><orcidid>https://orcid.org/0000-0002-3345-7141</orcidid><orcidid>https://orcid.org/0000-0002-7774-2411</orcidid><orcidid>https://orcid.org/0000-0002-6283-839X</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; PubMed Central Open Access; Wiley Online Library Open Access; PubMed Central; Alma/SFX Local Collection |
| subjects | Algorithms Cancer Care and treatment Cell cycle Cell Line, Tumor Chemotherapy Complex systems Damage Deoxyribonucleic acid DNA DNA damage DNA Damage - genetics DNA methylation DNA repair DNA-Binding Proteins Drug therapy Gastric cancer Gene expression Gene set enrichment analysis Genes Genetic aspects Genomes Health aspects Humans Immune checkpoint Immunotherapy Medical prognosis Microenvironments Microsatellite instability Mutation N6-methyladenosine Nomograms Patients Prognosis Receptor activity modifying proteins Risk Risk groups Stomach cancer Stomach Neoplasms - genetics Stomach Neoplasms - therapy Survival analysis Transcription Factors Tumor cells Tumor Microenvironment Tumors |
| title | Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T00%3A11%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Models%20for%20Predicting%20Response%20to%20Immunotherapy%20and%20Prognosis%20in%20Patients%20with%20Gastric%20Cancer:%20DNA%20Damage%20Response%20Genes&rft.jtitle=BioMed%20research%20international&rft.au=Dong,%20Rui&rft.date=2022-12-19&rft.volume=2022&rft.spage=4909544&rft.epage=21&rft.pages=4909544-21&rft.issn=2314-6133&rft.eissn=2314-6141&rft_id=info:doi/10.1155/2022/4909544&rft_dat=%3Cgale_pubme%3EA731950107%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2759870377&rft_id=info:pmid/36578802&rft_galeid=A731950107&rfr_iscdi=true |