SEPROGADIC – serum protein-based gastric cancer prediction model for prognosis and selection of proper adjuvant therapy View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-11-15

AUTHORS

Hee-Sung Ahn, Tae Sung Sohn, Mi Jeong Kim, Byoung Kyu Cho, Su Mi Kim, Seung Tae Kim, Eugene C. Yi, Cheolju Lee

ABSTRACT

Gastric cancer (GC) patients usually receive surgical treatment. Postoperative therapeutic options such as anticancer adjuvant therapies (AT) based on prognostic prediction models would provide patient-specific treatment to decrease postsurgical morbidity and mortality rates. Relevant prognostic factors in resected GC patient’s serum may improve therapeutic measures in a non-invasive manner. In order to develop a GC prognostic model, we designed a retrospective study. In this study, serum samples were collected from 227 patients at a 4-week recovery period after D2 lymph node dissection, and 103 cancer-related serum proteins were analyzed by multiple reaction monitoring mass spectrometry. Using the quantitative values of the serum proteins, we developed SEPROGADIC (SErum PROtein-based GAstric cancer preDICtor) prognostic model consisting of 6 to 14 serum proteins depending on detailed purposes of the model, prognosis prediction and proper AT selection. SEPROGADIC could clearly classify patients with good or bad prognosis at each TNM stage (1b, 2, 3 and 4) and identify a patient subgroup who would benefit from CCRT (combined chemoradiation therapy) rather than CTX (chemotherapy), or vice versa. Our study demonstrated that serum proteins could serve as prognostic factors along with clinical stage information in patients with resected gastric cancer, thus allowing patient-tailored postsurgical treatment. More... »

PAGES

16892

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-34858-x

DOI

http://dx.doi.org/10.1038/s41598-018-34858-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1109841500

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30442939


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1112", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oncology and Carcinogenesis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Blood Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Chemotherapy, Adjuvant", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Kaplan-Meier Estimate", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Biological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Prognosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proteome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stomach Neoplasms", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, 5 Hwarangro-14-gil, 02792, Seoul, Seongbuk-gu, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.412786.e", 
          "name": [
            "Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea", 
            "Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, 5 Hwarangro-14-gil, 02792, Seoul, Seongbuk-gu, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ahn", 
        "givenName": "Hee-Sung", 
        "id": "sg:person.01006052077.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01006052077.30"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.264381.a", 
          "name": [
            "Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sohn", 
        "givenName": "Tae Sung", 
        "id": "sg:person.016206171417.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016206171417.54"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.35541.36", 
          "name": [
            "Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Mi Jeong", 
        "id": "sg:person.013534600567.87", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013534600567.87"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Molecular Medicine and Biopharmaceutical Sciences, School of Convergence Science and Technology and College of Medicine, Seoul National University, 103 Daehak-ro, 03080, Jongno-gu, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "Department of Molecular Medicine and Biopharmaceutical Sciences, School of Convergence Science and Technology and College of Medicine, Seoul National University, 103 Daehak-ro, 03080, Jongno-gu, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cho", 
        "givenName": "Byoung Kyu", 
        "id": "sg:person.0726657204.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0726657204.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.264381.a", 
          "name": [
            "Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Su Mi", 
        "id": "sg:person.0705027632.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0705027632.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.264381.a", 
          "name": [
            "Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Seung Tae", 
        "id": "sg:person.01020412766.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01020412766.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Molecular Medicine and Biopharmaceutical Sciences, School of Convergence Science and Technology and College of Medicine, Seoul National University, 103 Daehak-ro, 03080, Jongno-gu, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "Department of Molecular Medicine and Biopharmaceutical Sciences, School of Convergence Science and Technology and College of Medicine, Seoul National University, 103 Daehak-ro, 03080, Jongno-gu, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yi", 
        "givenName": "Eugene C.", 
        "id": "sg:person.01064603366.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01064603366.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "KHU-KIST Department of Converging Science and Technology, Kyung Hee University, 26 Kyunghee-daero, 02447, Dongdaemun-gu, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.289247.2", 
          "name": [
            "Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea", 
            "Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, 5 Hwarangro-14-gil, 02792, Seoul, Seongbuk-gu, Republic of Korea", 
            "KHU-KIST Department of Converging Science and Technology, Kyung Hee University, 26 Kyunghee-daero, 02447, Dongdaemun-gu, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Cheolju", 
        "id": "sg:person.01122557123.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01122557123.15"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/ncomms12499", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041214834", 
          "https://doi.org/10.1038/ncomms12499"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep18189", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012674474", 
          "https://doi.org/10.1038/srep18189"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-60761-444-9_19", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035088424", 
          "https://doi.org/10.1007/978-1-60761-444-9_19"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4614-5915-6_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014348365", 
          "https://doi.org/10.1007/978-1-4614-5915-6_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00277-013-1941-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000212373", 
          "https://doi.org/10.1007/s00277-013-1941-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1234", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001413101", 
          "https://doi.org/10.1038/nbt1234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/bjc.1998.634", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009667228", 
          "https://doi.org/10.1038/bjc.1998.634"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm.3850", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002173905", 
          "https://doi.org/10.1038/nm.3850"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033684229", 
          "https://doi.org/10.1038/nbt1235"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-11-15", 
    "datePublishedReg": "2018-11-15", 
    "description": "Gastric cancer (GC) patients usually receive surgical treatment. Postoperative therapeutic options such as anticancer adjuvant therapies (AT) based on prognostic prediction models would provide patient-specific treatment to decrease postsurgical morbidity and mortality rates. Relevant prognostic factors in resected GC patient\u2019s serum may improve therapeutic measures in a non-invasive manner. In order to develop a GC prognostic model, we designed a retrospective study. In this study, serum samples were collected from 227 patients at a 4-week recovery period after D2 lymph node dissection, and 103 cancer-related serum proteins were analyzed by multiple reaction monitoring mass spectrometry. Using the quantitative values of the serum proteins, we developed SEPROGADIC (SErum PROtein-based GAstric cancer preDICtor) prognostic model consisting of 6 to 14 serum proteins depending on detailed purposes of the model, prognosis prediction and proper AT selection. SEPROGADIC could clearly classify patients with good or bad prognosis at each TNM stage (1b, 2, 3 and 4) and identify a patient subgroup who would benefit from CCRT (combined chemoradiation therapy) rather than CTX (chemotherapy), or vice versa. Our study demonstrated that serum proteins could serve as prognostic factors along with clinical stage information in patients with resected gastric cancer, thus allowing patient-tailored postsurgical treatment.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/s41598-018-34858-x", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "8"
      }
    ], 
    "keywords": [
      "adjuvant therapy", 
      "prognostic factors", 
      "prognostic model", 
      "D2 lymph node dissection", 
      "serum proteins", 
      "GC patients' serum", 
      "lymph node dissection", 
      "relevant prognostic factors", 
      "gastric cancer patients", 
      "proper adjuvant therapy", 
      "prognostic prediction model", 
      "node dissection", 
      "surgical treatment", 
      "therapeutic options", 
      "retrospective study", 
      "worse prognosis", 
      "postsurgical treatment", 
      "cancer patients", 
      "TNM stage", 
      "patient subgroups", 
      "postsurgical morbidity", 
      "cancer prediction model", 
      "patient sera", 
      "therapeutic measures", 
      "gastric cancer", 
      "mortality rate", 
      "prognosis prediction", 
      "patients", 
      "serum samples", 
      "patient-specific treatment", 
      "recovery period", 
      "prognosis", 
      "therapy", 
      "treatment", 
      "serum", 
      "non-invasive manner", 
      "stage information", 
      "morbidity", 
      "protein", 
      "CCRT", 
      "cancer", 
      "CTX", 
      "dissection", 
      "study", 
      "subgroups", 
      "factors", 
      "options", 
      "prediction model", 
      "multiple reaction", 
      "period", 
      "mass spectrometry", 
      "measures", 
      "rate", 
      "manner", 
      "quantitative values", 
      "stage", 
      "purpose", 
      "samples", 
      "model", 
      "selection", 
      "information", 
      "values", 
      "vice", 
      "detailed purposes", 
      "reaction", 
      "spectrometry", 
      "order", 
      "prediction"
    ], 
    "name": "SEPROGADIC \u2013 serum protein-based gastric cancer prediction model for prognosis and selection of proper adjuvant therapy", 
    "pagination": "16892", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1109841500"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-018-34858-x"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30442939"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-018-34858-x", 
      "https://app.dimensions.ai/details/publication/pub.1109841500"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T16:02", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_787.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/s41598-018-34858-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s41598-018-34858-x'

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/s41598-018-34858-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-34858-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-34858-x'


 

This table displays all metadata directly associated to this object as RDF triples.

274 TRIPLES      21 PREDICATES      113 URIs      96 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-018-34858-x schema:about N0966def152504adbaf9b0d986711346d
2 N10a406d9a8fc473094bd0f87a5562036
3 N172c05f841104d40931067c4c9cfdb66
4 N3249fdc190c5489191eab8c13e84ce2d
5 N3e71c2f805324856860ab8c52fd9e038
6 N72b225eedffe48be8b29af8d8a6cf75d
7 N7ac04c4b5ce640f49d5b7cffaad3c953
8 N977538ce5e4a408891db41b51a32b39e
9 Nbe048dc522f643daa85fa230731b8588
10 Ncb0bc54ccbee4c0189bc5314d8a4d917
11 Necfa6c0b88964d9fb2587a6a5366121c
12 anzsrc-for:11
13 anzsrc-for:1112
14 schema:author N2e8c55375bde42378cbbd32c40227224
15 schema:citation sg:pub.10.1007/978-1-4614-5915-6_11
16 sg:pub.10.1007/978-1-60761-444-9_19
17 sg:pub.10.1007/s00277-013-1941-8
18 sg:pub.10.1038/bjc.1998.634
19 sg:pub.10.1038/nbt1234
20 sg:pub.10.1038/nbt1235
21 sg:pub.10.1038/ncomms12499
22 sg:pub.10.1038/nm.3850
23 sg:pub.10.1038/srep18189
24 schema:datePublished 2018-11-15
25 schema:datePublishedReg 2018-11-15
26 schema:description Gastric cancer (GC) patients usually receive surgical treatment. Postoperative therapeutic options such as anticancer adjuvant therapies (AT) based on prognostic prediction models would provide patient-specific treatment to decrease postsurgical morbidity and mortality rates. Relevant prognostic factors in resected GC patient’s serum may improve therapeutic measures in a non-invasive manner. In order to develop a GC prognostic model, we designed a retrospective study. In this study, serum samples were collected from 227 patients at a 4-week recovery period after D2 lymph node dissection, and 103 cancer-related serum proteins were analyzed by multiple reaction monitoring mass spectrometry. Using the quantitative values of the serum proteins, we developed SEPROGADIC (SErum PROtein-based GAstric cancer preDICtor) prognostic model consisting of 6 to 14 serum proteins depending on detailed purposes of the model, prognosis prediction and proper AT selection. SEPROGADIC could clearly classify patients with good or bad prognosis at each TNM stage (1b, 2, 3 and 4) and identify a patient subgroup who would benefit from CCRT (combined chemoradiation therapy) rather than CTX (chemotherapy), or vice versa. Our study demonstrated that serum proteins could serve as prognostic factors along with clinical stage information in patients with resected gastric cancer, thus allowing patient-tailored postsurgical treatment.
27 schema:genre article
28 schema:isAccessibleForFree true
29 schema:isPartOf N7f7ef623b0454ca6bd2b341c9717a3d8
30 Ne1b125dbc30b4148b604e77cdf38fe90
31 sg:journal.1045337
32 schema:keywords CCRT
33 CTX
34 D2 lymph node dissection
35 GC patients' serum
36 TNM stage
37 adjuvant therapy
38 cancer
39 cancer patients
40 cancer prediction model
41 detailed purposes
42 dissection
43 factors
44 gastric cancer
45 gastric cancer patients
46 information
47 lymph node dissection
48 manner
49 mass spectrometry
50 measures
51 model
52 morbidity
53 mortality rate
54 multiple reaction
55 node dissection
56 non-invasive manner
57 options
58 order
59 patient sera
60 patient subgroups
61 patient-specific treatment
62 patients
63 period
64 postsurgical morbidity
65 postsurgical treatment
66 prediction
67 prediction model
68 prognosis
69 prognosis prediction
70 prognostic factors
71 prognostic model
72 prognostic prediction model
73 proper adjuvant therapy
74 protein
75 purpose
76 quantitative values
77 rate
78 reaction
79 recovery period
80 relevant prognostic factors
81 retrospective study
82 samples
83 selection
84 serum
85 serum proteins
86 serum samples
87 spectrometry
88 stage
89 stage information
90 study
91 subgroups
92 surgical treatment
93 therapeutic measures
94 therapeutic options
95 therapy
96 treatment
97 values
98 vice
99 worse prognosis
100 schema:name SEPROGADIC – serum protein-based gastric cancer prediction model for prognosis and selection of proper adjuvant therapy
101 schema:pagination 16892
102 schema:productId N063738ed6b024863a468e6491b598a13
103 N282be81354db4c4c8ea85aa9528f3a78
104 Nf5819f90a77d4c6f9390700996c188c4
105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109841500
106 https://doi.org/10.1038/s41598-018-34858-x
107 schema:sdDatePublished 2022-09-02T16:02
108 schema:sdLicense https://scigraph.springernature.com/explorer/license/
109 schema:sdPublisher N6bb5bbb8eac54dbebf91bf9474a431db
110 schema:url https://doi.org/10.1038/s41598-018-34858-x
111 sgo:license sg:explorer/license/
112 sgo:sdDataset articles
113 rdf:type schema:ScholarlyArticle
114 N063738ed6b024863a468e6491b598a13 schema:name pubmed_id
115 schema:value 30442939
116 rdf:type schema:PropertyValue
117 N0966def152504adbaf9b0d986711346d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Prognosis
119 rdf:type schema:DefinedTerm
120 N10a406d9a8fc473094bd0f87a5562036 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
121 schema:name Kaplan-Meier Estimate
122 rdf:type schema:DefinedTerm
123 N172c05f841104d40931067c4c9cfdb66 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Blood Proteins
125 rdf:type schema:DefinedTerm
126 N282be81354db4c4c8ea85aa9528f3a78 schema:name doi
127 schema:value 10.1038/s41598-018-34858-x
128 rdf:type schema:PropertyValue
129 N2e8c55375bde42378cbbd32c40227224 rdf:first sg:person.01006052077.30
130 rdf:rest N574d9473acaf43bda9665ac563a03336
131 N3249fdc190c5489191eab8c13e84ce2d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Middle Aged
133 rdf:type schema:DefinedTerm
134 N3e71c2f805324856860ab8c52fd9e038 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
135 schema:name Male
136 rdf:type schema:DefinedTerm
137 N574d9473acaf43bda9665ac563a03336 rdf:first sg:person.016206171417.54
138 rdf:rest Na5c2afff4c434f07b837c86bdc31f4e0
139 N6a52c367a7e54d3cbdf44605e824a39a rdf:first sg:person.01122557123.15
140 rdf:rest rdf:nil
141 N6bb5bbb8eac54dbebf91bf9474a431db schema:name Springer Nature - SN SciGraph project
142 rdf:type schema:Organization
143 N723892754d60475cb46dbef1b2d9390f rdf:first sg:person.01064603366.33
144 rdf:rest N6a52c367a7e54d3cbdf44605e824a39a
145 N72b225eedffe48be8b29af8d8a6cf75d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Female
147 rdf:type schema:DefinedTerm
148 N7ac04c4b5ce640f49d5b7cffaad3c953 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
149 schema:name Stomach Neoplasms
150 rdf:type schema:DefinedTerm
151 N7f7ef623b0454ca6bd2b341c9717a3d8 schema:volumeNumber 8
152 rdf:type schema:PublicationVolume
153 N933266dc4205478c805efed7197efc7e rdf:first sg:person.01020412766.91
154 rdf:rest N723892754d60475cb46dbef1b2d9390f
155 N977538ce5e4a408891db41b51a32b39e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name Proteome
157 rdf:type schema:DefinedTerm
158 Na5c2afff4c434f07b837c86bdc31f4e0 rdf:first sg:person.013534600567.87
159 rdf:rest Nd2d78e20f2d14f369aa2f7380c0f7c63
160 Nbe048dc522f643daa85fa230731b8588 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
161 schema:name Humans
162 rdf:type schema:DefinedTerm
163 Ncb0bc54ccbee4c0189bc5314d8a4d917 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
164 schema:name Chemotherapy, Adjuvant
165 rdf:type schema:DefinedTerm
166 Nd2d78e20f2d14f369aa2f7380c0f7c63 rdf:first sg:person.0726657204.24
167 rdf:rest Nf399348ab3744618881c6c679076de83
168 Ne1b125dbc30b4148b604e77cdf38fe90 schema:issueNumber 1
169 rdf:type schema:PublicationIssue
170 Necfa6c0b88964d9fb2587a6a5366121c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
171 schema:name Models, Biological
172 rdf:type schema:DefinedTerm
173 Nf399348ab3744618881c6c679076de83 rdf:first sg:person.0705027632.08
174 rdf:rest N933266dc4205478c805efed7197efc7e
175 Nf5819f90a77d4c6f9390700996c188c4 schema:name dimensions_id
176 schema:value pub.1109841500
177 rdf:type schema:PropertyValue
178 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
179 schema:name Medical and Health Sciences
180 rdf:type schema:DefinedTerm
181 anzsrc-for:1112 schema:inDefinedTermSet anzsrc-for:
182 schema:name Oncology and Carcinogenesis
183 rdf:type schema:DefinedTerm
184 sg:journal.1045337 schema:issn 2045-2322
185 schema:name Scientific Reports
186 schema:publisher Springer Nature
187 rdf:type schema:Periodical
188 sg:person.01006052077.30 schema:affiliation grid-institutes:grid.412786.e
189 schema:familyName Ahn
190 schema:givenName Hee-Sung
191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01006052077.30
192 rdf:type schema:Person
193 sg:person.01020412766.91 schema:affiliation grid-institutes:grid.264381.a
194 schema:familyName Kim
195 schema:givenName Seung Tae
196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01020412766.91
197 rdf:type schema:Person
198 sg:person.01064603366.33 schema:affiliation grid-institutes:grid.31501.36
199 schema:familyName Yi
200 schema:givenName Eugene C.
201 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01064603366.33
202 rdf:type schema:Person
203 sg:person.01122557123.15 schema:affiliation grid-institutes:grid.289247.2
204 schema:familyName Lee
205 schema:givenName Cheolju
206 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01122557123.15
207 rdf:type schema:Person
208 sg:person.013534600567.87 schema:affiliation grid-institutes:grid.35541.36
209 schema:familyName Kim
210 schema:givenName Mi Jeong
211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013534600567.87
212 rdf:type schema:Person
213 sg:person.016206171417.54 schema:affiliation grid-institutes:grid.264381.a
214 schema:familyName Sohn
215 schema:givenName Tae Sung
216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016206171417.54
217 rdf:type schema:Person
218 sg:person.0705027632.08 schema:affiliation grid-institutes:grid.264381.a
219 schema:familyName Kim
220 schema:givenName Su Mi
221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0705027632.08
222 rdf:type schema:Person
223 sg:person.0726657204.24 schema:affiliation grid-institutes:grid.31501.36
224 schema:familyName Cho
225 schema:givenName Byoung Kyu
226 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0726657204.24
227 rdf:type schema:Person
228 sg:pub.10.1007/978-1-4614-5915-6_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014348365
229 https://doi.org/10.1007/978-1-4614-5915-6_11
230 rdf:type schema:CreativeWork
231 sg:pub.10.1007/978-1-60761-444-9_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035088424
232 https://doi.org/10.1007/978-1-60761-444-9_19
233 rdf:type schema:CreativeWork
234 sg:pub.10.1007/s00277-013-1941-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000212373
235 https://doi.org/10.1007/s00277-013-1941-8
236 rdf:type schema:CreativeWork
237 sg:pub.10.1038/bjc.1998.634 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009667228
238 https://doi.org/10.1038/bjc.1998.634
239 rdf:type schema:CreativeWork
240 sg:pub.10.1038/nbt1234 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001413101
241 https://doi.org/10.1038/nbt1234
242 rdf:type schema:CreativeWork
243 sg:pub.10.1038/nbt1235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033684229
244 https://doi.org/10.1038/nbt1235
245 rdf:type schema:CreativeWork
246 sg:pub.10.1038/ncomms12499 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041214834
247 https://doi.org/10.1038/ncomms12499
248 rdf:type schema:CreativeWork
249 sg:pub.10.1038/nm.3850 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002173905
250 https://doi.org/10.1038/nm.3850
251 rdf:type schema:CreativeWork
252 sg:pub.10.1038/srep18189 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012674474
253 https://doi.org/10.1038/srep18189
254 rdf:type schema:CreativeWork
255 grid-institutes:grid.264381.a schema:alternateName Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea
256 Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea
257 schema:name Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea
258 Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, 06351, Gangnam-gu, Seoul, Republic of Korea
259 rdf:type schema:Organization
260 grid-institutes:grid.289247.2 schema:alternateName KHU-KIST Department of Converging Science and Technology, Kyung Hee University, 26 Kyunghee-daero, 02447, Dongdaemun-gu, Seoul, Republic of Korea
261 schema:name Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea
262 Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, 5 Hwarangro-14-gil, 02792, Seoul, Seongbuk-gu, Republic of Korea
263 KHU-KIST Department of Converging Science and Technology, Kyung Hee University, 26 Kyunghee-daero, 02447, Dongdaemun-gu, Seoul, Republic of Korea
264 rdf:type schema:Organization
265 grid-institutes:grid.31501.36 schema:alternateName Department of Molecular Medicine and Biopharmaceutical Sciences, School of Convergence Science and Technology and College of Medicine, Seoul National University, 103 Daehak-ro, 03080, Jongno-gu, Seoul, Republic of Korea
266 schema:name Department of Molecular Medicine and Biopharmaceutical Sciences, School of Convergence Science and Technology and College of Medicine, Seoul National University, 103 Daehak-ro, 03080, Jongno-gu, Seoul, Republic of Korea
267 rdf:type schema:Organization
268 grid-institutes:grid.35541.36 schema:alternateName Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea
269 schema:name Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea
270 rdf:type schema:Organization
271 grid-institutes:grid.412786.e schema:alternateName Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, 5 Hwarangro-14-gil, 02792, Seoul, Seongbuk-gu, Republic of Korea
272 schema:name Center for Theragnosis, Korea Institute of Science and Technology, 5 Hwarangro-14-gil, 02792, Seongbuk-gu, Seoul, Republic of Korea
273 Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, 5 Hwarangro-14-gil, 02792, Seoul, Seongbuk-gu, Republic of Korea
274 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...