MicroRNAs as Potential Biomarkers for Diagnosing Cancers of Central Nervous System: a Meta-analysis
Recent studies have shown abnormal microRNA (miRNA) expression levels in the central nervous system (CNS) of cancer patients, suggesting that miRNAs may serve as promising biomarkers for cancers of CNS. However, other studies have arrived at conflicting results. Therefore, this meta-analysis aims to...
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Veröffentlicht in: | Molecular neurobiology 2015-06, Vol.51 (3), p.1452-1461 |
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description | Recent studies have shown abnormal microRNA (miRNA) expression levels in the central nervous system (CNS) of cancer patients, suggesting that miRNAs may serve as promising biomarkers for cancers of CNS. However, other studies have arrived at conflicting results. Therefore, this meta-analysis aims to systematically measure the potential diagnostic value of miRNAs for CNS cancers. Electronic databases as well as other sources were searched until to April 12, 2014 for relevant articles. Data from different studies were pooled using the random-effects model. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), together with the summary receiver operator characteristic (SROC) curve, and area under the SROC curve (AUC) value were used to estimate overall diagnostic performance. Twenty-three studies from 6 articles were included in the current meta-analysis with a total of 299 CNS cancer patients and 418 controls. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.85 (95 % CI, 0.80–0.89), 0.83 (95 % CI, 0.76–0.88), 5.1 (95 % CI, 3.4–7.5), 0.18 (95 % CI, 0.12–0.26), 28 (95 % CI, 14–58), and 0.91 (95 % CI, 0.88–0.93), respectively. Subgroup analyses showed that cerebrospinal fluid (CSF)-based miRNAs assays yielded more accurate results and seemed to be more sensitive in diagnosing of primary central nervous system lymphoma (PCNSL). In conclusion, miRNAs may be suitable for serving as noninvasive biomarkers for CNS cancers detection. However, further validation based on a larger sample of patients and controls is still required. |
doi_str_mv | 10.1007/s12035-014-8822-6 |
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However, other studies have arrived at conflicting results. Therefore, this meta-analysis aims to systematically measure the potential diagnostic value of miRNAs for CNS cancers. Electronic databases as well as other sources were searched until to April 12, 2014 for relevant articles. Data from different studies were pooled using the random-effects model. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), together with the summary receiver operator characteristic (SROC) curve, and area under the SROC curve (AUC) value were used to estimate overall diagnostic performance. Twenty-three studies from 6 articles were included in the current meta-analysis with a total of 299 CNS cancer patients and 418 controls. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.85 (95 % CI, 0.80–0.89), 0.83 (95 % CI, 0.76–0.88), 5.1 (95 % CI, 3.4–7.5), 0.18 (95 % CI, 0.12–0.26), 28 (95 % CI, 14–58), and 0.91 (95 % CI, 0.88–0.93), respectively. Subgroup analyses showed that cerebrospinal fluid (CSF)-based miRNAs assays yielded more accurate results and seemed to be more sensitive in diagnosing of primary central nervous system lymphoma (PCNSL). In conclusion, miRNAs may be suitable for serving as noninvasive biomarkers for CNS cancers detection. However, further validation based on a larger sample of patients and controls is still required.</description><identifier>ISSN: 0893-7648</identifier><identifier>EISSN: 1559-1182</identifier><identifier>DOI: 10.1007/s12035-014-8822-6</identifier><identifier>PMID: 25081587</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Biomarkers ; Biomarkers, Tumor - genetics ; Biomedical and Life Sciences ; Biomedicine ; Cancer ; Cell Biology ; Central Nervous System Neoplasms - diagnosis ; Central Nervous System Neoplasms - genetics ; Humans ; Medical diagnosis ; Meta-analysis ; MicroRNAs ; MicroRNAs - genetics ; Nervous system ; Neurobiology ; Neurology ; Neurosciences ; ROC Curve ; Sensitivity and Specificity ; Systematic review</subject><ispartof>Molecular neurobiology, 2015-06, Vol.51 (3), p.1452-1461</ispartof><rights>Springer Science+Business Media New York 2014</rights><rights>Springer Science+Business Media New York 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c508t-e432cda8420bcb36741fc04bd3ef784debf29762ec45991f9edf30eeca35afa03</citedby><cites>FETCH-LOGICAL-c508t-e432cda8420bcb36741fc04bd3ef784debf29762ec45991f9edf30eeca35afa03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12035-014-8822-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12035-014-8822-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25081587$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wei, Dong</creatorcontrib><creatorcontrib>Wan, Qun</creatorcontrib><creatorcontrib>Li, Li</creatorcontrib><creatorcontrib>Jin, Haifeng</creatorcontrib><creatorcontrib>Liu, Yonghong</creatorcontrib><creatorcontrib>Wang, Yangang</creatorcontrib><creatorcontrib>Zhang, Guangyun</creatorcontrib><title>MicroRNAs as Potential Biomarkers for Diagnosing Cancers of Central Nervous System: a Meta-analysis</title><title>Molecular neurobiology</title><addtitle>Mol Neurobiol</addtitle><addtitle>Mol Neurobiol</addtitle><description>Recent studies have shown abnormal microRNA (miRNA) expression levels in the central nervous system (CNS) of cancer patients, suggesting that miRNAs may serve as promising biomarkers for cancers of CNS. However, other studies have arrived at conflicting results. Therefore, this meta-analysis aims to systematically measure the potential diagnostic value of miRNAs for CNS cancers. Electronic databases as well as other sources were searched until to April 12, 2014 for relevant articles. Data from different studies were pooled using the random-effects model. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), together with the summary receiver operator characteristic (SROC) curve, and area under the SROC curve (AUC) value were used to estimate overall diagnostic performance. Twenty-three studies from 6 articles were included in the current meta-analysis with a total of 299 CNS cancer patients and 418 controls. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.85 (95 % CI, 0.80–0.89), 0.83 (95 % CI, 0.76–0.88), 5.1 (95 % CI, 3.4–7.5), 0.18 (95 % CI, 0.12–0.26), 28 (95 % CI, 14–58), and 0.91 (95 % CI, 0.88–0.93), respectively. Subgroup analyses showed that cerebrospinal fluid (CSF)-based miRNAs assays yielded more accurate results and seemed to be more sensitive in diagnosing of primary central nervous system lymphoma (PCNSL). In conclusion, miRNAs may be suitable for serving as noninvasive biomarkers for CNS cancers detection. However, further validation based on a larger sample of patients and controls is still required.</description><subject>Biomarkers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer</subject><subject>Cell Biology</subject><subject>Central Nervous System Neoplasms - diagnosis</subject><subject>Central Nervous System Neoplasms - genetics</subject><subject>Humans</subject><subject>Medical diagnosis</subject><subject>Meta-analysis</subject><subject>MicroRNAs</subject><subject>MicroRNAs - genetics</subject><subject>Nervous system</subject><subject>Neurobiology</subject><subject>Neurology</subject><subject>Neurosciences</subject><subject>ROC Curve</subject><subject>Sensitivity and Specificity</subject><subject>Systematic review</subject><issn>0893-7648</issn><issn>1559-1182</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><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>eNp1kd1LHDEUxUNRdGv9A3yRgC--pOZjPpK-6WpbQW2p9jlkMjfL2NnJmjsr7H9vlrGlCD4Fbn733MM5hBwJ_llwXp-hkFyVjIuCaS0lqz6QmShLw4TQcofMuDaK1VWh98lHxEfOpRS83iP7suRalLqeEX_b-RR_3Z0jdUh_xhGGsXM9veji0qU_kJCGmOhl5xZDxG5Y0Lkb_HYcA51nOGX4DtJzXCO93-AIyy_U0VsYHXOD6zfY4SeyG1yPcPj6HpDfX68e5t_ZzY9v1_PzG-aznZFBoaRvnS4kb3yjqroQwfOiaRWEWhctNEGaupLgi9IYEQy0QXEA71TpguPqgJxOuqsUn9aAo1126KHv3QDZnhWVFkaUlTEZPXmDPsZ1yn4nSnFVKZ0pMVE5IsQEwa5Sl2PZWMHttgE7NWBzA3bbgK3yzvGr8rpZQvtv42_kGZATgPlrWED67_S7qi8Bc5EL</recordid><startdate>20150601</startdate><enddate>20150601</enddate><creator>Wei, Dong</creator><creator>Wan, Qun</creator><creator>Li, Li</creator><creator>Jin, Haifeng</creator><creator>Liu, Yonghong</creator><creator>Wang, Yangang</creator><creator>Zhang, Guangyun</creator><general>Springer US</general><general>Springer Nature B.V</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>7QR</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88G</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M2P</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20150601</creationdate><title>MicroRNAs as Potential Biomarkers for Diagnosing Cancers of Central Nervous System: a Meta-analysis</title><author>Wei, Dong ; Wan, Qun ; Li, Li ; Jin, Haifeng ; Liu, Yonghong ; Wang, Yangang ; Zhang, Guangyun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c508t-e432cda8420bcb36741fc04bd3ef784debf29762ec45991f9edf30eeca35afa03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Biomarkers</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Cancer</topic><topic>Cell Biology</topic><topic>Central Nervous System Neoplasms - diagnosis</topic><topic>Central Nervous System Neoplasms - genetics</topic><topic>Humans</topic><topic>Medical diagnosis</topic><topic>Meta-analysis</topic><topic>MicroRNAs</topic><topic>MicroRNAs - genetics</topic><topic>Nervous system</topic><topic>Neurobiology</topic><topic>Neurology</topic><topic>Neurosciences</topic><topic>ROC Curve</topic><topic>Sensitivity and Specificity</topic><topic>Systematic review</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei, Dong</creatorcontrib><creatorcontrib>Wan, Qun</creatorcontrib><creatorcontrib>Li, Li</creatorcontrib><creatorcontrib>Jin, Haifeng</creatorcontrib><creatorcontrib>Liu, Yonghong</creatorcontrib><creatorcontrib>Wang, Yangang</creatorcontrib><creatorcontrib>Zhang, Guangyun</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>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</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>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>Psychology Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</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>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Molecular neurobiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wei, Dong</au><au>Wan, Qun</au><au>Li, Li</au><au>Jin, Haifeng</au><au>Liu, Yonghong</au><au>Wang, Yangang</au><au>Zhang, Guangyun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MicroRNAs as Potential Biomarkers for Diagnosing Cancers of Central Nervous System: a Meta-analysis</atitle><jtitle>Molecular neurobiology</jtitle><stitle>Mol Neurobiol</stitle><addtitle>Mol Neurobiol</addtitle><date>2015-06-01</date><risdate>2015</risdate><volume>51</volume><issue>3</issue><spage>1452</spage><epage>1461</epage><pages>1452-1461</pages><issn>0893-7648</issn><eissn>1559-1182</eissn><abstract>Recent studies have shown abnormal microRNA (miRNA) expression levels in the central nervous system (CNS) of cancer patients, suggesting that miRNAs may serve as promising biomarkers for cancers of CNS. However, other studies have arrived at conflicting results. Therefore, this meta-analysis aims to systematically measure the potential diagnostic value of miRNAs for CNS cancers. Electronic databases as well as other sources were searched until to April 12, 2014 for relevant articles. Data from different studies were pooled using the random-effects model. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), together with the summary receiver operator characteristic (SROC) curve, and area under the SROC curve (AUC) value were used to estimate overall diagnostic performance. Twenty-three studies from 6 articles were included in the current meta-analysis with a total of 299 CNS cancer patients and 418 controls. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.85 (95 % CI, 0.80–0.89), 0.83 (95 % CI, 0.76–0.88), 5.1 (95 % CI, 3.4–7.5), 0.18 (95 % CI, 0.12–0.26), 28 (95 % CI, 14–58), and 0.91 (95 % CI, 0.88–0.93), respectively. Subgroup analyses showed that cerebrospinal fluid (CSF)-based miRNAs assays yielded more accurate results and seemed to be more sensitive in diagnosing of primary central nervous system lymphoma (PCNSL). In conclusion, miRNAs may be suitable for serving as noninvasive biomarkers for CNS cancers detection. However, further validation based on a larger sample of patients and controls is still required.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>25081587</pmid><doi>10.1007/s12035-014-8822-6</doi><tpages>10</tpages></addata></record> |
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subjects | Biomarkers Biomarkers, Tumor - genetics Biomedical and Life Sciences Biomedicine Cancer Cell Biology Central Nervous System Neoplasms - diagnosis Central Nervous System Neoplasms - genetics Humans Medical diagnosis Meta-analysis MicroRNAs MicroRNAs - genetics Nervous system Neurobiology Neurology Neurosciences ROC Curve Sensitivity and Specificity Systematic review |
title | MicroRNAs as Potential Biomarkers for Diagnosing Cancers of Central Nervous System: a Meta-analysis |
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