Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock
Background. Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We dow...
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| description | Background. Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model. Results. A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls. Conclusion. In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock. |
| doi_str_mv | 10.1155/2020/7170464 |
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Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model. Results. A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls. Conclusion. In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2020/7170464</identifier><identifier>PMID: 33344646</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Adaptor Proteins, Signal Transducing - genetics ; Biomarkers ; Blood ; Cell Line, Tumor ; Cell Movement ; Cell Proliferation ; Child ; Clustering ; Computational Biology ; Databases, Factual ; Diagnosis ; Diagnostic systems ; Disease Progression ; Gene expression ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Gene Silencing ; Genetic aspects ; Health aspects ; HT29 Cells ; Humans ; Immune response ; Infections ; Inflammatory diseases ; MicroRNAs ; Minor Histocompatibility Antigens - genetics ; Model accuracy ; Mortality ; mRNA ; Neoplasm Invasiveness ; Pediatric research ; Pediatrics ; Prediction models ; Principal components analysis ; Protein interaction ; Protein Interaction Maps ; Proteins ; Regression analysis ; Regression models ; Repressor Proteins - genetics ; Risk factors ; RNA ; RNA polymerase ; RNA, Long Noncoding - genetics ; RNA, Messenger - genetics ; Sepsis ; Septic shock ; Shock, Septic - genetics ; Software ; Statistical models ; T cell receptors ; Tripartite Motif Proteins - genetics</subject><ispartof>BioMed research international, 2020, Vol.2020 (2020), p.1-9</ispartof><rights>Copyright © 2020 Yong Wu et al.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>Copyright © 2020 Yong Wu 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 © 2020 Yong Wu et al. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-c69f864ddc703849b94afaba63efa9c83aa651e7b3b7aadcdaff5c8f98d4843a3</citedby><cites>FETCH-LOGICAL-c499t-c69f864ddc703849b94afaba63efa9c83aa651e7b3b7aadcdaff5c8f98d4843a3</cites><orcidid>0000-0001-5217-0966</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/PMC7725549/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725549/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4010,27900,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33344646$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Bottillo, Irene</contributor><contributor>Irene Bottillo</contributor><creatorcontrib>Luan, Hengfei</creatorcontrib><creatorcontrib>Zhu, Pin</creatorcontrib><creatorcontrib>Zhang, Xiaobao</creatorcontrib><creatorcontrib>Yin, Qigai</creatorcontrib><creatorcontrib>Wu, Yong</creatorcontrib><creatorcontrib>Chen, Ying</creatorcontrib><title>Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Background. Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model. Results. A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls. Conclusion. In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.</description><subject>Adaptor Proteins, Signal Transducing - genetics</subject><subject>Biomarkers</subject><subject>Blood</subject><subject>Cell Line, Tumor</subject><subject>Cell Movement</subject><subject>Cell Proliferation</subject><subject>Child</subject><subject>Clustering</subject><subject>Computational Biology</subject><subject>Databases, Factual</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Disease Progression</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Gene Regulatory Networks</subject><subject>Gene Silencing</subject><subject>Genetic aspects</subject><subject>Health aspects</subject><subject>HT29 Cells</subject><subject>Humans</subject><subject>Immune response</subject><subject>Infections</subject><subject>Inflammatory diseases</subject><subject>MicroRNAs</subject><subject>Minor Histocompatibility Antigens - genetics</subject><subject>Model accuracy</subject><subject>Mortality</subject><subject>mRNA</subject><subject>Neoplasm Invasiveness</subject><subject>Pediatric research</subject><subject>Pediatrics</subject><subject>Prediction models</subject><subject>Principal components analysis</subject><subject>Protein interaction</subject><subject>Protein Interaction Maps</subject><subject>Proteins</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Repressor Proteins - genetics</subject><subject>Risk factors</subject><subject>RNA</subject><subject>RNA polymerase</subject><subject>RNA, Long Noncoding - genetics</subject><subject>RNA, Messenger - genetics</subject><subject>Sepsis</subject><subject>Septic shock</subject><subject>Shock, Septic - genetics</subject><subject>Software</subject><subject>Statistical models</subject><subject>T cell receptors</subject><subject>Tripartite Motif Proteins - genetics</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkc9v0zAUxy0EYlPZjTOKxAWJhdnxj8QXpGgMhlRtES1n68V2Wo_ULk4yxH-Pu5YOOOGLn_X9-Ov3_EXoJcHvCOH8osAFvihJiZlgT9BpQQnLBWHk6bGm9ASdDcMdTqsiAkvxHJ1QSlm6IU5RmAe_ym6C18G4VH25qbPldd3IvF6QDLzJlovmtm7Iw7mJ4d4ZmzVhtH500GcfHKx8GEans4VbeRinaIesCzFrrHEwxp1gtw_6OuhvL9CzDvrBnh32Gfr68Wp5eZ3Pbz99vqznuWZSjrkWsqsEM0aXmFZMtpJBBy0IajuQuqIAghNbtrQtAYw20HVcV52sDKsYBTpD7_e-26ndWKNTuxF6tY1uA_GnCuDU34p3a7UK96osC86ZTAZvDgYxfJ_sMKqNG7Tte_A2TIMqWEk4pViyhL7-B70LU_RpvEQJKSpcVPyRWkFvlfNdSO_qnamqhSwJ5jz5zdD5ntIxDEO03bFlgtUucrWLXB0iT_irP8c8wr8DTsDbPbB23sAP9592NjHppx9pQoVI8_4C5vS7Vw</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Luan, Hengfei</creator><creator>Zhu, Pin</creator><creator>Zhang, Xiaobao</creator><creator>Yin, Qigai</creator><creator>Wu, Yong</creator><creator>Chen, Ying</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><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/0000-0001-5217-0966</orcidid></search><sort><creationdate>2020</creationdate><title>Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock</title><author>Luan, Hengfei ; Zhu, Pin ; Zhang, Xiaobao ; Yin, Qigai ; Wu, Yong ; Chen, Ying</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c499t-c69f864ddc703849b94afaba63efa9c83aa651e7b3b7aadcdaff5c8f98d4843a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptor Proteins, Signal Transducing - genetics</topic><topic>Biomarkers</topic><topic>Blood</topic><topic>Cell Line, Tumor</topic><topic>Cell Movement</topic><topic>Cell Proliferation</topic><topic>Child</topic><topic>Clustering</topic><topic>Computational Biology</topic><topic>Databases, Factual</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Disease Progression</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Gene Regulatory Networks</topic><topic>Gene Silencing</topic><topic>Genetic aspects</topic><topic>Health aspects</topic><topic>HT29 Cells</topic><topic>Humans</topic><topic>Immune response</topic><topic>Infections</topic><topic>Inflammatory diseases</topic><topic>MicroRNAs</topic><topic>Minor Histocompatibility Antigens - genetics</topic><topic>Model accuracy</topic><topic>Mortality</topic><topic>mRNA</topic><topic>Neoplasm Invasiveness</topic><topic>Pediatric research</topic><topic>Pediatrics</topic><topic>Prediction models</topic><topic>Principal components analysis</topic><topic>Protein interaction</topic><topic>Protein Interaction Maps</topic><topic>Proteins</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Repressor Proteins - genetics</topic><topic>Risk factors</topic><topic>RNA</topic><topic>RNA polymerase</topic><topic>RNA, Long Noncoding - genetics</topic><topic>RNA, Messenger - genetics</topic><topic>Sepsis</topic><topic>Septic shock</topic><topic>Shock, Septic - genetics</topic><topic>Software</topic><topic>Statistical models</topic><topic>T cell receptors</topic><topic>Tripartite Motif Proteins - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luan, Hengfei</creatorcontrib><creatorcontrib>Zhu, Pin</creatorcontrib><creatorcontrib>Zhang, Xiaobao</creatorcontrib><creatorcontrib>Yin, Qigai</creatorcontrib><creatorcontrib>Wu, Yong</creatorcontrib><creatorcontrib>Chen, Ying</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><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>Luan, Hengfei</au><au>Zhu, Pin</au><au>Zhang, Xiaobao</au><au>Yin, Qigai</au><au>Wu, Yong</au><au>Chen, Ying</au><au>Bottillo, Irene</au><au>Irene Bottillo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Background. Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model. Results. A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls. Conclusion. In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>33344646</pmid><doi>10.1155/2020/7170464</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-5217-0966</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adaptor Proteins, Signal Transducing - genetics Biomarkers Blood Cell Line, Tumor Cell Movement Cell Proliferation Child Clustering Computational Biology Databases, Factual Diagnosis Diagnostic systems Disease Progression Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Gene Silencing Genetic aspects Health aspects HT29 Cells Humans Immune response Infections Inflammatory diseases MicroRNAs Minor Histocompatibility Antigens - genetics Model accuracy Mortality mRNA Neoplasm Invasiveness Pediatric research Pediatrics Prediction models Principal components analysis Protein interaction Protein Interaction Maps Proteins Regression analysis Regression models Repressor Proteins - genetics Risk factors RNA RNA polymerase RNA, Long Noncoding - genetics RNA, Messenger - genetics Sepsis Septic shock Shock, Septic - genetics Software Statistical models T cell receptors Tripartite Motif Proteins - genetics |
| title | Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock |
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