Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer–Analysis of TCGA database View Full Text


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Article Info

DATE

2019-12

AUTHORS

Joon-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

ABSTRACT

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 < 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... »

PAGES

1803

References to SciGraph publications

  • 2013-03. Netrin-1 protects hypoxia-induced mitochondrial apoptosis through HSP27 expression via DCC- and integrin α6β4-dependent Akt, GSK-3β, and HSF-1 in mesenchymal stem cells in CELL DEATH & DISEASE
  • 2017. AJCC Cancer Staging Manual in NONE
  • 2011-08. Gene expression and hypoxia in breast cancer in GENOME MEDICINE
  • 2014-01. Clinical significance of microsatellite instability in colorectal cancer in LANGENBECK'S ARCHIVES OF SURGERY
  • 2017-12. Refining Long-Term Prediction of Cardiovascular Risk in Diabetes – The VILDIA Score in SCIENTIFIC REPORTS
  • 2016-11. A prognostic signature based on three-genes expression in triple-negative breast tumours with residual disease in NPJ GENOMIC MEDICINE
  • 2002-01. Hypoxia — a key regulatory factor in tumour growth in NATURE REVIEWS CANCER
  • 2018-02-08. Extracellular matrix protein 1 promotes cell metastasis and glucose metabolism by inducing integrin β4/FAK/SOX2/HIF-1α signaling pathway in gastric cancer in ONCOGENE
  • 2018-12. Clinical value of miR-182-5p in lung squamous cell carcinoma: a study combining data from TCGA, GEO, and RT-qPCR validation in WORLD JOURNAL OF SURGICAL ONCOLOGY
  • 2014-12. The prognostic value of preoperative NLR, d-NLR, PLR and LMR for predicting clinical outcome in surgical colorectal cancer patients in MEDICAL ONCOLOGY
  • 2016-04. Discovery of candidate tumor biomarkers for treatment with intraperitoneal chemotherapy for ovarian cancer in SCIENTIFIC REPORTS
  • 1987-06. Alcohol, physical activity and other risk factors for colorectal cancer: A prospective study in BRITISH JOURNAL OF CANCER
  • 2017-11-23. Role of HSPA1L as a cellular prion protein stabilizer in tumor progression via HIF-1α/GP78 axis in ONCOGENE
  • 2010-10. Beclin 1 over- and underexpression in colorectal cancer: distinct patterns relate to prognosis and tumour hypoxia in BRITISH JOURNAL OF CANCER
  • 2018-12. A RNA-Sequencing approach for the identification of novel long non-coding RNA biomarkers in colorectal cancer in SCIENTIFIC REPORTS
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    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-38116-y

    DOI

    http://dx.doi.org/10.1038/s41598-018-38116-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112063880

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30755640


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    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'


     

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