Title |
Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features
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Published in |
BMC Genomics, July 2015
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DOI | 10.1186/s12864-015-1687-x |
Pubmed ID | |
Authors |
Zhaoqi Liu, Shihua Zhang |
Abstract |
Identification of tumor heterogeneity and genomic similarities across different cancer types is essential to the design of effective stratified treatments and for the discovery of treatments that can be extended to different types of tumors. However, systematic investigations on comprehensive molecular profiles have not been fully explored to achieve this goal. Here, we performed a network-based integrative pan-cancer genomic analysis on >3000 samples from 12 cancer types to uncover novel stratifications among tumors. Our study not only revealed recurrently reported cross-cancer similarities, but also identified novel ones. The macro-scale stratification demonstrates strong clinical relevance and reveals consistent risk tendency among cancer types. The micro-scale stratification shows essential pan-cancer heterogeneity with subgroup-specific gene network characteristics and biological functions. In summary, our comprehensive network-based pan-cancer stratification provides valuable information about inter- and intra- cancer stratification for patient clinical assessments and therapeutic strategies. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 20% |
Germany | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 60% |
Scientists | 1 | 20% |
Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Spain | 1 | 2% |
Netherlands | 1 | 2% |
Unknown | 50 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 13 | 25% |
Researcher | 9 | 17% |
Student > Master | 7 | 13% |
Student > Bachelor | 4 | 8% |
Student > Doctoral Student | 2 | 4% |
Other | 7 | 13% |
Unknown | 10 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 13 | 25% |
Biochemistry, Genetics and Molecular Biology | 11 | 21% |
Engineering | 5 | 10% |
Medicine and Dentistry | 4 | 8% |
Computer Science | 3 | 6% |
Other | 4 | 8% |
Unknown | 12 | 23% |