BACKGROUND: The genetic architecture of bone and soft tissue sarcomas susceptibility is yet to be elucidated. We aimed to comprehensively collect and meta-analyze the current knowledge on genetic susceptibility in these rare tumors. METHODS: We conducted a systematic review and meta-analysis of the evidence on the association between DNA variation and risk of developing sarcomas through searching PubMed, The Cochrane Library, Scopus and Web of Science databases. To evaluate result credibility, summary evidence was graded according to the Venice criteria and false positive report probability (FPRP) was calculated to further validate result noteworthiness. Integrative analysis of genetic and eQTL (expression quantitative trait locus) data was coupled with network and pathway analysis to explore the hypothesis that specific cell functions are involved in sarcoma predisposition. RESULTS: We retrieved 90 eligible studies comprising 47,796 subjects (cases: 14,358, 30%) and investigating 1,126 polymorphisms involving 320 distinct genes. Meta-analysis identified 55 single nucleotide polymorphisms (SNPs) significantly associated with disease risk with a high (N=9), moderate (N=38) and low (N=8) level of evidence, findings being classified as noteworthy basically only when the level of evidence was high. The estimated joint population attributable risk for three independent SNPs (rs11599754 of ZNF365/EGR2, rs231775 of CTLA4, and rs454006 of PRKCG) was 37.2%. We also identified 53 SNPs significantly associated with sarcoma risk based on single studies.Pathway analysis enabled us to propose that sarcoma predisposition might be linked especially to germline variation of genes whose products are involved in the function of the DNA repair machinery. CONCLUSIONS: We built the first knowledgebase on the evidence linking DNA variation to sarcomas susceptibility, which can be used to generate mechanistic hypotheses and inform future studies in this field of oncology.
Genetic susceptibility to bone and soft tissue sarcomas: A field synopsis and meta-analysis
Benna, Clara
;Pasquali, Sandro;Rajendran, Senthilkumar;Spiro, Giovanna;Virgone, Calogero;Rossi, Carlo Riccardo;Mocellin, Simone
2018
Abstract
BACKGROUND: The genetic architecture of bone and soft tissue sarcomas susceptibility is yet to be elucidated. We aimed to comprehensively collect and meta-analyze the current knowledge on genetic susceptibility in these rare tumors. METHODS: We conducted a systematic review and meta-analysis of the evidence on the association between DNA variation and risk of developing sarcomas through searching PubMed, The Cochrane Library, Scopus and Web of Science databases. To evaluate result credibility, summary evidence was graded according to the Venice criteria and false positive report probability (FPRP) was calculated to further validate result noteworthiness. Integrative analysis of genetic and eQTL (expression quantitative trait locus) data was coupled with network and pathway analysis to explore the hypothesis that specific cell functions are involved in sarcoma predisposition. RESULTS: We retrieved 90 eligible studies comprising 47,796 subjects (cases: 14,358, 30%) and investigating 1,126 polymorphisms involving 320 distinct genes. Meta-analysis identified 55 single nucleotide polymorphisms (SNPs) significantly associated with disease risk with a high (N=9), moderate (N=38) and low (N=8) level of evidence, findings being classified as noteworthy basically only when the level of evidence was high. The estimated joint population attributable risk for three independent SNPs (rs11599754 of ZNF365/EGR2, rs231775 of CTLA4, and rs454006 of PRKCG) was 37.2%. We also identified 53 SNPs significantly associated with sarcoma risk based on single studies.Pathway analysis enabled us to propose that sarcoma predisposition might be linked especially to germline variation of genes whose products are involved in the function of the DNA repair machinery. CONCLUSIONS: We built the first knowledgebase on the evidence linking DNA variation to sarcomas susceptibility, which can be used to generate mechanistic hypotheses and inform future studies in this field of oncology.
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