Ontology type: schema:ScholarlyArticle Open Access: True
2019-12
AUTHORSJoon-Hyop Lee, Sohee Jung, Won Seo Park, Eun Kyung Choe, Eunyoung Kim, Rumi Shin, Seung Chul Heo, Jae Hyun Lee, Kwangsoo Kim, Young Jun Chai
ABSTRACTHypoxia-related gene (HRG) expression is associated with survival outcomes of colorectal cancer (CRC). Our aim was developing a nomogram predicting CRC overall survival (OS) with HRGs and clinicopathological factors. The Cancer Genome Atlas (TCGA) database was used as discovery cohort and two Gene Expression Omnibus databases (GSE39582 and GSE41258) served as validation cohorts. A genetic risk score model prognosticating OS was developed using mRNA expression level of HRGs. Nomogram predicting OS was developed using genetic risk score model and clinicopathological variables. The genetic risk score model included four HRGs (HSPA1L, PUM1, UBE2D2, and HSP27) and successfully prognosticated OS of discovery and two validation cohorts (p < 0.001 for TCGA discovery set, p < 0.003 for the GSE39582 and p = 0.042 for the GSE41258 datasets). Nomogram included genetic risk score, age, and TNM stage. Harrell's concordance indexes of the nomogram were higher than those of TNM stage alone in the discovery set (0.77 vs. 0.69, p < 0.001), GSE39582 (0.65 vs. 0.63, p < 0.001), and GSE41258 datasets (0.78 vs. 0.77, p < 0.001). Our nomogram successfully predicted OS of CRC patients. The mRNA expression level of the HRGs might be useful as an ancillary marker for prognosticating CRC outcome. More... »
PAGES1803
http://scigraph.springernature.com/pub.10.1038/s41598-018-38116-y
DOIhttp://dx.doi.org/10.1038/s41598-018-38116-y
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1112063880
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/30755640
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Genetics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Biological Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Gachon University Gil Medical Center",
"id": "https://www.grid.ac/institutes/grid.411653.4",
"name": [
"Department of Surgery, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Lee",
"givenName": "Joon-Hyop",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Seoul National University Hospital",
"id": "https://www.grid.ac/institutes/grid.412484.f",
"name": [
"Division of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Jung",
"givenName": "Sohee",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Kyung Hee University Medical Center",
"id": "https://www.grid.ac/institutes/grid.411231.4",
"name": [
"Department of Surgery, Kyung Hee University Hospital, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Park",
"givenName": "Won Seo",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Seoul National University Hospital",
"id": "https://www.grid.ac/institutes/grid.412484.f",
"name": [
"Department of Surgery, Seoul National University Hospital Healthcare System, Gangnam Center, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Choe",
"givenName": "Eun Kyung",
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Medical Center",
"id": "https://www.grid.ac/institutes/grid.415619.e",
"name": [
"Department of Surgery, National Medical Center, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Kim",
"givenName": "Eunyoung",
"type": "Person"
},
{
"affiliation": {
"name": [
"Department of Surgery, Seoul Metropolitan Government, Seoul National University Boramae Medical Center, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Shin",
"givenName": "Rumi",
"type": "Person"
},
{
"affiliation": {
"name": [
"Department of Surgery, Seoul Metropolitan Government, Seoul National University Boramae Medical Center, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Heo",
"givenName": "Seung Chul",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Korea University",
"id": "https://www.grid.ac/institutes/grid.222754.4",
"name": [
"Department of Statistics, Korea University, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Lee",
"givenName": "Jae Hyun",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Seoul National University Hospital",
"id": "https://www.grid.ac/institutes/grid.412484.f",
"name": [
"Division of Clinical Bioinformatics, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Kim",
"givenName": "Kwangsoo",
"type": "Person"
},
{
"affiliation": {
"name": [
"Department of Surgery, Seoul Metropolitan Government, Seoul National University Boramae Medical Center, Seoul, Republic of Korea"
],
"type": "Organization"
},
"familyName": "Chai",
"givenName": "Young Jun",
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1002/sim.4154",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003725111"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1172/jci30269",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007788289"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pmed.1001453",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009812382"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/npjgenmed.2015.15",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010848023",
"https://doi.org/10.1038/npjgenmed.2015.15"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/(sici)1097-0215(19980812)77:4<549::aid-ijc13>3.0.co;2-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013876634"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/srep21591",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014819176",
"https://doi.org/10.1038/srep21591"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0113513",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022277571"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.humpath.2010.04.008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1022320141"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1158/1078-0432.ccr-13-2750",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023759754"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.humpath.2008.02.013",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026051135"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5009/gnl14302",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028296692"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrc704",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028740665",
"https://doi.org/10.1038/nrc704"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrc704",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028740665",
"https://doi.org/10.1038/nrc704"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/bjc.1987.140",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031116464",
"https://doi.org/10.1038/bjc.1987.140"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/bjc.1987.140",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031116464",
"https://doi.org/10.1038/bjc.1987.140"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3109/10428194.2013.858812",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032187306"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1155/2010/537531",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033480815"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0080337",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033683683"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1200/jco.2010.28.7045",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033931920"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gm271",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035267092",
"https://doi.org/10.1186/gm271"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/cddis.2013.94",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036331898",
"https://doi.org/10.1038/cddis.2013.94"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12032-014-0305-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038415360",
"https://doi.org/10.1007/s12032-014-0305-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.0902232106",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043372355"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1091/mbc.e10-04-0312",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046615898"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1200/jco.2007.12.9791",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049544341"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1541-0420.2006.00726.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049734522"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/sj.bjc.6605904",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051380082",
"https://doi.org/10.1038/sj.bjc.6605904"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00423-013-1112-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052949694",
"https://doi.org/10.1007/s00423-013-1112-3"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1200/jco.2011.36.5080",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053418776"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1158/0008-5472.can-09-2110",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053677764"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3748/wjg.v21.i41.11877",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1071373142"
],
"type": "CreativeWork"
},
{
"id": "https://app.dimensions.ai/details/publication/pub.1079030687",
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.critrevonc.2017.02.025",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1083917469"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pone.0175960",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085035553"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/biomedicines5020021",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085216491"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.21873/anticanres.11582",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085374888"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41598-017-04935-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090352710",
"https://doi.org/10.1038/s41598-017-04935-8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/onc.2017.263",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1090929012",
"https://doi.org/10.1038/onc.2017.263"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/onc.2017.363",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092351829",
"https://doi.org/10.1038/onc.2017.363"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/onc.2017.363",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092351829",
"https://doi.org/10.1038/onc.2017.363"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3892/ol.2017.7414",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1092724608"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3892/mmr.2017.8283",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1099849122"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41598-017-18407-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100249030",
"https://doi.org/10.1038/s41598-017-18407-6"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.bbrc.2018.02.078",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100919919"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s12957-018-1378-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1103149141",
"https://doi.org/10.1186/s12957-018-1378-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/s12957-018-1378-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1103149141",
"https://doi.org/10.1186/s12957-018-1378-6"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/genes9070361",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1105709615"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-319-40618-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1108910915",
"https://doi.org/10.1007/978-3-319-40618-3"
],
"type": "CreativeWork"
}
],
"datePublished": "2019-12",
"datePublishedReg": "2019-12-01",
"description": "Hypoxia-related gene (HRG) expression is associated with survival outcomes of colorectal cancer (CRC). Our aim was developing a nomogram predicting CRC overall survival (OS) with HRGs and clinicopathological factors. The Cancer Genome Atlas (TCGA) database was used as discovery cohort and two Gene Expression Omnibus databases (GSE39582 and GSE41258) served as validation cohorts. A genetic risk score model prognosticating OS was developed using mRNA expression level of HRGs. Nomogram predicting OS was developed using genetic risk score model and clinicopathological variables. The genetic risk score model included four HRGs (HSPA1L, PUM1, UBE2D2, and HSP27) and successfully prognosticated OS of discovery and two validation cohorts (p\u2009<\u20090.001 for TCGA discovery set, p\u2009<\u20090.003 for the GSE39582 and p\u2009=\u20090.042 for the GSE41258 datasets). Nomogram included genetic risk score, age, and TNM stage. Harrell's concordance indexes of the nomogram were higher than those of TNM stage alone in the discovery set (0.77 vs. 0.69, p\u2009<\u20090.001), GSE39582 (0.65 vs. 0.63, p\u2009<\u20090.001), and GSE41258 datasets (0.78 vs. 0.77, p\u2009<\u20090.001). Our nomogram successfully predicted OS of CRC patients. The mRNA expression level of the HRGs might be useful as an ancillary marker for prognosticating CRC outcome.",
"genre": "research_article",
"id": "sg:pub.10.1038/s41598-018-38116-y",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1045337",
"issn": [
"2045-2322"
],
"name": "Scientific Reports",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "9"
}
],
"name": "Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer\u2013Analysis of TCGA database",
"pagination": "1803",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"1b754dd379d6caa8584599d12b2724ce60e27745660e02ee0d0a13c37a369e29"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"30755640"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"101563288"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/s41598-018-38116-y"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1112063880"
]
}
],
"sameAs": [
"https://doi.org/10.1038/s41598-018-38116-y",
"https://app.dimensions.ai/details/publication/pub.1112063880"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T09:14",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000338_0000000338/records_47997_00000002.jsonl",
"type": "ScholarlyArticle",
"url": "https://www.nature.com/articles/s41598-018-38116-y"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38116-y'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38116-y'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38116-y'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-38116-y'
This table displays all metadata directly associated to this object as RDF triples.
286 TRIPLES
21 PREDICATES
73 URIs
21 LITERALS
9 BLANK NODES