Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome
Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrink...
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| Veröffentlicht in: | Journal of cellular and molecular medicine 2020-06, Vol.24 (11), p.6373-6384 |
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| creator | Hu, Fang Chen, Si‐liang Dai, Yu‐jun Wang, Yun Qin, Zhe‐yuan Li, Huan Shu, Ling‐ling Li, Jin‐yuan Huang, Han‐ying Liang, Yang |
| description | Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814‐7.630) and 2.047 (1.013‐4.138) in the training cohort and validation cohort, respectively. The AUC of 3‐year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high‐risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS. |
| doi_str_mv | 10.1111/jcmm.15283 |
| format | Article |
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At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814‐7.630) and 2.047 (1.013‐4.138) in the training cohort and validation cohort, respectively. The AUC of 3‐year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high‐risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.</description><identifier>ISSN: 1582-1838</identifier><identifier>EISSN: 1582-4934</identifier><identifier>DOI: 10.1111/jcmm.15283</identifier><identifier>PMID: 32337851</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>Acute myeloid leukemia ; Adult ; Aged ; Aged, 80 and over ; Blast cells ; Bone marrow ; Cohort Studies ; Databases, Genetic ; Datasets ; Female ; Gene expression ; gene set enrichment analyses ; Hemoglobin ; Humans ; Kaplan-Meier Estimate ; Leukemia ; Male ; Medical prognosis ; Metabolism ; Metabolites ; Middle Aged ; Multivariate Analysis ; Myelodysplastic syndrome ; Myelodysplastic syndromes ; Myelodysplastic Syndromes - diagnosis ; Myelodysplastic Syndromes - genetics ; Myelodysplastic Syndromes - metabolism ; Original ; Pathogenesis ; Patients ; Prognosis ; prognostic model ; Proportional Hazards Models ; Reproducibility of Results ; Risk Factors ; ROC Curve ; Software ; Statistical analysis ; Stem cells ; the least absolute shrinkage and selection operator ; Time Factors ; Young Adult</subject><ispartof>Journal of cellular and molecular medicine, 2020-06, Vol.24 (11), p.6373-6384</ispartof><rights>2020 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.</rights><rights>2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4483-6edcf89691db78f08c49e2b534a5ad2e1b7edc44c378ce6de3188be7efbaf5ad3</citedby><cites>FETCH-LOGICAL-c4483-6edcf89691db78f08c49e2b534a5ad2e1b7edc44c378ce6de3188be7efbaf5ad3</cites><orcidid>0000-0002-4095-5974</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/PMC7294120/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7294120/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1416,11560,27922,27923,45572,45573,46050,46474,53789,53791</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32337851$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hu, Fang</creatorcontrib><creatorcontrib>Chen, Si‐liang</creatorcontrib><creatorcontrib>Dai, Yu‐jun</creatorcontrib><creatorcontrib>Wang, Yun</creatorcontrib><creatorcontrib>Qin, Zhe‐yuan</creatorcontrib><creatorcontrib>Li, Huan</creatorcontrib><creatorcontrib>Shu, Ling‐ling</creatorcontrib><creatorcontrib>Li, Jin‐yuan</creatorcontrib><creatorcontrib>Huang, Han‐ying</creatorcontrib><creatorcontrib>Liang, Yang</creatorcontrib><title>Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome</title><title>Journal of cellular and molecular medicine</title><addtitle>J Cell Mol Med</addtitle><description>Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814‐7.630) and 2.047 (1.013‐4.138) in the training cohort and validation cohort, respectively. The AUC of 3‐year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high‐risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.</description><subject>Acute myeloid leukemia</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Blast cells</subject><subject>Bone marrow</subject><subject>Cohort Studies</subject><subject>Databases, Genetic</subject><subject>Datasets</subject><subject>Female</subject><subject>Gene expression</subject><subject>gene set enrichment analyses</subject><subject>Hemoglobin</subject><subject>Humans</subject><subject>Kaplan-Meier Estimate</subject><subject>Leukemia</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Middle Aged</subject><subject>Multivariate Analysis</subject><subject>Myelodysplastic syndrome</subject><subject>Myelodysplastic syndromes</subject><subject>Myelodysplastic Syndromes - diagnosis</subject><subject>Myelodysplastic Syndromes - genetics</subject><subject>Myelodysplastic Syndromes - metabolism</subject><subject>Original</subject><subject>Pathogenesis</subject><subject>Patients</subject><subject>Prognosis</subject><subject>prognostic model</subject><subject>Proportional Hazards Models</subject><subject>Reproducibility of Results</subject><subject>Risk Factors</subject><subject>ROC Curve</subject><subject>Software</subject><subject>Statistical analysis</subject><subject>Stem cells</subject><subject>the least absolute shrinkage and selection operator</subject><subject>Time Factors</subject><subject>Young Adult</subject><issn>1582-1838</issn><issn>1582-4934</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</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>eNp9kUtLxDAUhYMovjf-ACm4EWG0ebRNNoIMPnFwo0sJaXo7ZkibmnSU_nszzijqwmwSbr57OIeD0AFOT3E8ZzPdNKc4I5yuoW2ccTJigrL11RtzyrfQTgizNKU5pmITbVFCacEzvI2ebytoe1MbrXrj2sTViUoa6FXprNHJFFpIOtWCTXqXdB4qo_ukf4lD76atCyYsVpoBrKuG0FkV-rgWhrbyroE9tFErG2B_de-ip6vLx_HN6P7h-nZ8cT_SjHE6yqHSNRe5wFVZ8DrlmgkgZUaZylRFAJdFJBjT0bSGvAKKOS-hgLpUdSToLjpf6nbzsolojOSVlZ03jfKDdMrI3z-teZFT9yYLIhgmaRQ4Xgl49zqH0MvGBA3WxuhuHiShIiNZLlIW0aM_6MzNfRvjSRK1ipxnVETqZElp70LwUH-bwalctCYXrcnP1iJ8-NP-N_pVUwTwEng3FoZ_pOTdeDJZin4ARXaleQ</recordid><startdate>202006</startdate><enddate>202006</enddate><creator>Hu, Fang</creator><creator>Chen, Si‐liang</creator><creator>Dai, Yu‐jun</creator><creator>Wang, Yun</creator><creator>Qin, Zhe‐yuan</creator><creator>Li, Huan</creator><creator>Shu, Ling‐ling</creator><creator>Li, Jin‐yuan</creator><creator>Huang, Han‐ying</creator><creator>Liang, Yang</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</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>7QP</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>M2P</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4095-5974</orcidid></search><sort><creationdate>202006</creationdate><title>Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome</title><author>Hu, Fang ; Chen, Si‐liang ; Dai, Yu‐jun ; Wang, Yun ; Qin, Zhe‐yuan ; Li, Huan ; Shu, Ling‐ling ; Li, Jin‐yuan ; Huang, Han‐ying ; Liang, Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4483-6edcf89691db78f08c49e2b534a5ad2e1b7edc44c378ce6de3188be7efbaf5ad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Acute myeloid leukemia</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Blast cells</topic><topic>Bone marrow</topic><topic>Cohort Studies</topic><topic>Databases, Genetic</topic><topic>Datasets</topic><topic>Female</topic><topic>Gene expression</topic><topic>gene set enrichment analyses</topic><topic>Hemoglobin</topic><topic>Humans</topic><topic>Kaplan-Meier Estimate</topic><topic>Leukemia</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Middle Aged</topic><topic>Multivariate Analysis</topic><topic>Myelodysplastic syndrome</topic><topic>Myelodysplastic syndromes</topic><topic>Myelodysplastic Syndromes - diagnosis</topic><topic>Myelodysplastic Syndromes - genetics</topic><topic>Myelodysplastic Syndromes - metabolism</topic><topic>Original</topic><topic>Pathogenesis</topic><topic>Patients</topic><topic>Prognosis</topic><topic>prognostic model</topic><topic>Proportional Hazards Models</topic><topic>Reproducibility of Results</topic><topic>Risk Factors</topic><topic>ROC Curve</topic><topic>Software</topic><topic>Statistical analysis</topic><topic>Stem cells</topic><topic>the least absolute shrinkage and selection operator</topic><topic>Time Factors</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Fang</creatorcontrib><creatorcontrib>Chen, Si‐liang</creatorcontrib><creatorcontrib>Dai, Yu‐jun</creatorcontrib><creatorcontrib>Wang, Yun</creatorcontrib><creatorcontrib>Qin, Zhe‐yuan</creatorcontrib><creatorcontrib>Li, Huan</creatorcontrib><creatorcontrib>Shu, Ling‐ling</creatorcontrib><creatorcontrib>Li, Jin‐yuan</creatorcontrib><creatorcontrib>Huang, Han‐ying</creatorcontrib><creatorcontrib>Liang, Yang</creatorcontrib><collection>Wiley-Blackwell Open Access Collection</collection><collection>Wiley Online Library</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>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest Science Journals</collection><collection>Biological Science Database</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>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of cellular and molecular medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Fang</au><au>Chen, Si‐liang</au><au>Dai, Yu‐jun</au><au>Wang, Yun</au><au>Qin, Zhe‐yuan</au><au>Li, Huan</au><au>Shu, Ling‐ling</au><au>Li, Jin‐yuan</au><au>Huang, Han‐ying</au><au>Liang, Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome</atitle><jtitle>Journal of cellular and molecular medicine</jtitle><addtitle>J Cell Mol Med</addtitle><date>2020-06</date><risdate>2020</risdate><volume>24</volume><issue>11</issue><spage>6373</spage><epage>6384</epage><pages>6373-6384</pages><issn>1582-1838</issn><eissn>1582-4934</eissn><abstract>Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814‐7.630) and 2.047 (1.013‐4.138) in the training cohort and validation cohort, respectively. The AUC of 3‐year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high‐risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>32337851</pmid><doi>10.1111/jcmm.15283</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-4095-5974</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acute myeloid leukemia Adult Aged Aged, 80 and over Blast cells Bone marrow Cohort Studies Databases, Genetic Datasets Female Gene expression gene set enrichment analyses Hemoglobin Humans Kaplan-Meier Estimate Leukemia Male Medical prognosis Metabolism Metabolites Middle Aged Multivariate Analysis Myelodysplastic syndrome Myelodysplastic syndromes Myelodysplastic Syndromes - diagnosis Myelodysplastic Syndromes - genetics Myelodysplastic Syndromes - metabolism Original Pathogenesis Patients Prognosis prognostic model Proportional Hazards Models Reproducibility of Results Risk Factors ROC Curve Software Statistical analysis Stem cells the least absolute shrinkage and selection operator Time Factors Young Adult |
| title | Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome |
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