Association mining of mutated cancer genes in different clinical stages across 11 cancer types
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Wangxiong Hu1,*, Xiaofen Li1,*, Tingzhang Wang2, Shu Zheng1
1Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China
2Zhejiang Institute of Microbiology, Hangzhou, Zhejiang 310012, China
*These authors have contributed equally to this work
Shu Zheng, email: [email protected]
Keywords: Apriori algorithm, association analysis, frequent mutation gene sets, mutation, Pan-Cancer
Received: November 26, 2015 Accepted: August 08, 2016 Published: August 19, 2016
Many studies have demonstrated that some genes (e.g. APC, BRAF, KRAS, PTEN, TP53) are frequently mutated in cancer, however, underlying mechanism that contributes to their high mutation frequency remains unclear. Here we used Apriori algorithm to find the frequent mutational gene sets (FMGSs) from 4,904 tumors across 11 cancer types as part of the TCGA Pan-Cancer effort and then mined the hidden association rules (ARs) within these FMGSs. Intriguingly, we found that well-known cancer driver genes such as BRAF, KRAS, PTEN, and TP53 were often co-occurred with other driver genes and FMGSs size peaked at an itemset size of 3~4 genes. Besides, the number and constitution of FMGS and ARs differed greatly among different cancers and stages. In addition, FMGS and ARs were rare in endocrine-related cancers such as breast carcinoma, ovarian cystadenocarcinoma, and thyroid carcinoma, but abundant in cancers contact directly with external environments such as skin melanoma and stomach adenocarcinoma. Furthermore, we observed more rules in stage IV than in other stages, indicating that distant metastasis needed more sophisticated gene regulatory network.
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