A molecular signature for the prediction of recurrence in colorectal cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-02-03

AUTHORS

Lisha Wang, Xiaohan Shen, Zhimin Wang, Xiuying Xiao, Ping Wei, Qifeng Wang, Fei Ren, Yiqin Wang, Zebing Liu, Weiqi Sheng, Wei Huang, Xiaoyan Zhou, Xiang Du

ABSTRACT

BACKGROUND: Several clinical and pathological factors have an impact on the prognosis of colorectal cancer (CRC), but they are not yet adequate for risk assessment. We aimed to identify a molecular signature that can reliably identify CRC patients at high risk for recurrence. RESULTS: Two hundred eighty-one CRC samples (stage II/III) were included in this study. A two-step gene expression profiling study was conducted. First, gene expression measurements from 81 fresh frozen CRC samples were obtained using Affymetrix Human Genome U133 Plus 2.0 Arrays. Second, a focused gene expression assay, including prognostic genes and genes of interest from literature reviews, was performed using 200 fresh frozen samples and a Taqman low-density array (TLDA) analysis. An optimal 31-gene expression classifier for the prediction of recurrence among patients with stage II/III CRC was developed using logistic regression analysis. This gene expression signature classified 58.5% of patients as low-risk and 41.5% as high-risk (P < 0.001). The signature was the strongest independent prognostic factor in the multivariate analysis. The five-year relapse-free survival (RFS) rates for the low-risk patients and the high-risk patients were 88.5% and 41.3% (P < 0.001), respectively. CONCLUSION: We identified a 31-gene expression signature that is closely associated with the clinical outcome of stage II/III CRC patients. More... »

PAGES

22

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12943-015-0296-2

DOI

http://dx.doi.org/10.1186/s12943-015-0296-2

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https://app.dimensions.ai/details/publication/pub.1014868044

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25645394


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29 schema:description BACKGROUND: Several clinical and pathological factors have an impact on the prognosis of colorectal cancer (CRC), but they are not yet adequate for risk assessment. We aimed to identify a molecular signature that can reliably identify CRC patients at high risk for recurrence. RESULTS: Two hundred eighty-one CRC samples (stage II/III) were included in this study. A two-step gene expression profiling study was conducted. First, gene expression measurements from 81 fresh frozen CRC samples were obtained using Affymetrix Human Genome U133 Plus 2.0 Arrays. Second, a focused gene expression assay, including prognostic genes and genes of interest from literature reviews, was performed using 200 fresh frozen samples and a Taqman low-density array (TLDA) analysis. An optimal 31-gene expression classifier for the prediction of recurrence among patients with stage II/III CRC was developed using logistic regression analysis. This gene expression signature classified 58.5% of patients as low-risk and 41.5% as high-risk (P < 0.001). The signature was the strongest independent prognostic factor in the multivariate analysis. The five-year relapse-free survival (RFS) rates for the low-risk patients and the high-risk patients were 88.5% and 41.3% (P < 0.001), respectively. CONCLUSION: We identified a 31-gene expression signature that is closely associated with the clinical outcome of stage II/III CRC patients.
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37 CRC patients
38 CRC samples
39 Five-year relapse-free survival rates
40 Genome U133
41 Human Genome U133
42 II/III CRC
43 II/III CRC patients
44 III CRC
45 III CRC patients
46 TaqMan low-density array analysis
47 U133
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49 array
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52 cancer
53 classifier
54 clinical outcomes
55 colorectal cancer
56 expression
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58 expression measurements
59 expression profiling study
60 expression signatures
61 factors
62 focused gene expression
63 fresh frozen CRC samples
64 fresh frozen samples
65 frozen CRC samples
66 frozen samples
67 gene expression
68 gene expression measurements
69 gene expression profiling study
70 gene expression signatures
71 gene of interest
72 genes
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76 independent prognostic factor
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78 literature review
79 logistic regression analysis
80 low-density array analysis
81 low-risk patients
82 measurements
83 molecular signatures
84 multivariate analysis
85 outcomes
86 pathological factors
87 patients
88 prediction
89 prediction of recurrence
90 profiling studies
91 prognosis
92 prognostic factors
93 prognostic genes
94 rate
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96 regression analysis
97 relapse-free survival rate
98 review
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