sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers

MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group o...
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Autor*in:	Jiangcheng Shi [verfasserIn] Qinghua Cui [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	miRNA set biomarker single-sample enrichment analysis gene expression

Übergeordnetes Werk:	In: Molecular Therapy: Nucleic Acids - Elsevier, 2013, 21(2020), Seite 670-675
Übergeordnetes Werk:	volume:21 ; year:2020 ; pages:670-675

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.omtn.2020.07.004

Katalog-ID:	DOAJ035111968

Internformat


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author_variant	j s js q c qc
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callnumber-subject-code	RM
publishDate	2020
allfields	10.1016/j.omtn.2020.07.004 doi (DE-627)DOAJ035111968 (DE-599)DOAJ613abe62eb834f0483f2a78064b74bcb DE-627 ger DE-627 rakwb eng RM1-950 Jiangcheng Shi verfasserin aut sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level. miRNA set biomarker single-sample enrichment analysis gene expression Therapeutics. Pharmacology Qinghua Cui verfasserin aut In Molecular Therapy: Nucleic Acids Elsevier, 2013 21(2020), Seite 670-675 (DE-627)718632702 (DE-600)2662631-7 21622531 nnns volume:21 year:2020 pages:670-675 https://doi.org/10.1016/j.omtn.2020.07.004 kostenfrei https://doaj.org/article/613abe62eb834f0483f2a78064b74bcb kostenfrei http://www.sciencedirect.com/science/article/pii/S2162253120301931 kostenfrei https://doaj.org/toc/2162-2531 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2020 670-675
spelling	10.1016/j.omtn.2020.07.004 doi (DE-627)DOAJ035111968 (DE-599)DOAJ613abe62eb834f0483f2a78064b74bcb DE-627 ger DE-627 rakwb eng RM1-950 Jiangcheng Shi verfasserin aut sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level. miRNA set biomarker single-sample enrichment analysis gene expression Therapeutics. Pharmacology Qinghua Cui verfasserin aut In Molecular Therapy: Nucleic Acids Elsevier, 2013 21(2020), Seite 670-675 (DE-627)718632702 (DE-600)2662631-7 21622531 nnns volume:21 year:2020 pages:670-675 https://doi.org/10.1016/j.omtn.2020.07.004 kostenfrei https://doaj.org/article/613abe62eb834f0483f2a78064b74bcb kostenfrei http://www.sciencedirect.com/science/article/pii/S2162253120301931 kostenfrei https://doaj.org/toc/2162-2531 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2020 670-675
allfields_unstemmed	10.1016/j.omtn.2020.07.004 doi (DE-627)DOAJ035111968 (DE-599)DOAJ613abe62eb834f0483f2a78064b74bcb DE-627 ger DE-627 rakwb eng RM1-950 Jiangcheng Shi verfasserin aut sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level. miRNA set biomarker single-sample enrichment analysis gene expression Therapeutics. Pharmacology Qinghua Cui verfasserin aut In Molecular Therapy: Nucleic Acids Elsevier, 2013 21(2020), Seite 670-675 (DE-627)718632702 (DE-600)2662631-7 21622531 nnns volume:21 year:2020 pages:670-675 https://doi.org/10.1016/j.omtn.2020.07.004 kostenfrei https://doaj.org/article/613abe62eb834f0483f2a78064b74bcb kostenfrei http://www.sciencedirect.com/science/article/pii/S2162253120301931 kostenfrei https://doaj.org/toc/2162-2531 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2020 670-675
allfieldsGer	10.1016/j.omtn.2020.07.004 doi (DE-627)DOAJ035111968 (DE-599)DOAJ613abe62eb834f0483f2a78064b74bcb DE-627 ger DE-627 rakwb eng RM1-950 Jiangcheng Shi verfasserin aut sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level. miRNA set biomarker single-sample enrichment analysis gene expression Therapeutics. Pharmacology Qinghua Cui verfasserin aut In Molecular Therapy: Nucleic Acids Elsevier, 2013 21(2020), Seite 670-675 (DE-627)718632702 (DE-600)2662631-7 21622531 nnns volume:21 year:2020 pages:670-675 https://doi.org/10.1016/j.omtn.2020.07.004 kostenfrei https://doaj.org/article/613abe62eb834f0483f2a78064b74bcb kostenfrei http://www.sciencedirect.com/science/article/pii/S2162253120301931 kostenfrei https://doaj.org/toc/2162-2531 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2020 670-675
allfieldsSound	10.1016/j.omtn.2020.07.004 doi (DE-627)DOAJ035111968 (DE-599)DOAJ613abe62eb834f0483f2a78064b74bcb DE-627 ger DE-627 rakwb eng RM1-950 Jiangcheng Shi verfasserin aut sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level. miRNA set biomarker single-sample enrichment analysis gene expression Therapeutics. Pharmacology Qinghua Cui verfasserin aut In Molecular Therapy: Nucleic Acids Elsevier, 2013 21(2020), Seite 670-675 (DE-627)718632702 (DE-600)2662631-7 21622531 nnns volume:21 year:2020 pages:670-675 https://doi.org/10.1016/j.omtn.2020.07.004 kostenfrei https://doaj.org/article/613abe62eb834f0483f2a78064b74bcb kostenfrei http://www.sciencedirect.com/science/article/pii/S2162253120301931 kostenfrei https://doaj.org/toc/2162-2531 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 21 2020 670-675
language	English
source	In Molecular Therapy: Nucleic Acids 21(2020), Seite 670-675 volume:21 year:2020 pages:670-675
sourceStr	In Molecular Therapy: Nucleic Acids 21(2020), Seite 670-675 volume:21 year:2020 pages:670-675
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	miRNA set biomarker single-sample enrichment analysis gene expression Therapeutics. Pharmacology
isfreeaccess_bool	true
container_title	Molecular Therapy: Nucleic Acids
authorswithroles_txt_mv	Jiangcheng Shi @@aut@@ Qinghua Cui @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	718632702
id	DOAJ035111968
language_de	englisch
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callnumber-first	R - Medicine
author	Jiangcheng Shi
spellingShingle	Jiangcheng Shi misc RM1-950 misc miRNA set misc biomarker misc single-sample enrichment analysis misc gene expression misc Therapeutics. Pharmacology sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers
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topic_title	RM1-950 sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers miRNA set biomarker single-sample enrichment analysis gene expression
topic	misc RM1-950 misc miRNA set misc biomarker misc single-sample enrichment analysis misc gene expression misc Therapeutics. Pharmacology
topic_unstemmed	misc RM1-950 misc miRNA set misc biomarker misc single-sample enrichment analysis misc gene expression misc Therapeutics. Pharmacology
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title	sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers
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title_full	sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers
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title_sort	stam: an online tool for the discovery of mirna-set level disease biomarkers
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title_auth	sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers
abstract	MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level.
abstractGer	MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level.
abstract_unstemmed	MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the “tumor suppressor miRNA set” on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the “brain development miRNA set” can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level.
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title_short	sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers
url	https://doi.org/10.1016/j.omtn.2020.07.004 https://doaj.org/article/613abe62eb834f0483f2a78064b74bcb http://www.sciencedirect.com/science/article/pii/S2162253120301931 https://doaj.org/toc/2162-2531
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up_date	2024-07-04T01:48:35.369Z
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