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Objective: This study aimed to comprehensively evaluate perianal fistulas and their related complications using magnetic resonance imaging (MRI). Methods: We enrolled 115 eligible patients who underwent preoperative perianal MRI. Primary fistulas, internal and external openings, and related complications were evaluated using MRI. All fistulas were classified according to Park’s classification, Standard Practice Task Force classification, St. James’s grade, and the position of the internal opening. Results: In total, 169 primary fistulas were detected in 115 patients; 73 (63.5%) patients had a single primary tract and 42 (36.5%) patients had multiple primary tracts, and 198 internal and 129 external openings were identified. Based on Park’s classification, 150 (88.7%) primary fistulas were classified into the following types: intersphincteric (82, 54.7%), trans-sphincteric (58, 38.6%), suprasphincteric (8, 5.3%), extrasphincteric (1, 0.7%), and diffuse intersphincteric with trans-sphincteric (1, 0.7%) types. Based on St. James’s grade, 149 fistulas were classified into grade 1 (52, 34.9%), grade 2 (30, 20.1%), grade 3 (20, 13.4%), grade 4 (38, 25.5%), and grade 5 (9, 6.1%). We detected 92 (54.4%) simple and 77 (45.6%) complex perianal fistulas and 72 (42.6%) high and 97 (57.4%) low perianal fistulas. Furthermore, we detected 32 secondary tracts in 23 (20.0%) patients and 87 abscesses in 60 (52.2%) patients. Levator ani muscle involvement and extensive soft tissue edema were detected in 12 (10.4%) and 24 (20.9%) patients, respectively. Conclusion: MRI is a valuable and comprehensive tool that can not only be used to determine the general condition of perianal fistulas but also to classify them and identify related complications.

Xuechun Wang,1,* Jia Jia,2,* Xuehong Gu,3 Wei-wei Zhao,4 Caiping Chen,5 Wanxin Wu,6 Jiayuan Wang,1 Midie Xu7 1Department of Laboratory, Jiaxing First Hospital, Jiaxing, 314000, People’s Republic of China; 2Shanghai Center for Bioinformation Technology, Shanghai, 201202, People’s Republic of China; 3Department of Nursing, Jiaxing First Hospital, Jiaxing, 314000, People’s Republic of China; 4Department of Rehabilitation Medicine, Jiaxing First Hospital, Jiaxing, 314000, People’s Republic of China; 5Department of Breast Surgery, Jiaxing First Hospital, Jiaxing, 314000, People’s Republic of China; 6Department of Pathology, Jiaxing First Hospital, Jiaxing, 314000, People’s Republic of China; 7Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, 200032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xuehong GuDepartment of Nursing, Jiaxing First Hospital, No. 1882 of Central South Road, Jiaxing, 314000, People’s Republic of ChinaTel +86 13957368443Fax +86 573-83599079Email guxuehong145@163.comObjective: Breast cancer has become a fatal disease for women world-wide. Its incidence in China has been increasing yearly, and the identification of early-stage biomarkers is urgently required.Methods: ANOVA was carried out in the case of a primary tumor, adjacent normal tissue, and tumor metastasis of breast cancer, and on pan-cancer samples using the genome-wide methylation data of 31 solid tumor Illumina Methylation 450K chips downloaded from The Cancer Genome Atlas (TCGA) website in September 2018. Methylation sites showing a significant difference (P ≤ 0.05) were screened and compared with the whole-genome methylation data of 31 other solid tumor species in the TCGA database using t-tests in order to screen the methylation sites of breast cancer-specific expression. The expression of the screened methylation sites was confirmed through pyrosequencing in 45 cases of breast cancer, lung cancer, gastric cancer, and colorectal cancer.Results: A total of 10 specific breast cancer methylation sites (cg13683194, cg07996594, cg21646032, cg07671949, cg21185686, cg03625109, cg16429070, cg23601468, cg24818566, and cg01240931) were analyzed; nine genes (C9orf125, RARB, ESR1, RUNX3, PCDHGB7, DBC1, PDGFRB, TIMP3, and APC) were involved. The overall effect was excellent; a total of 4 methylation sites (2 in the DBC1 gene [cg03625109 and cg24818566], 1 in the C9orf125 gene [cg13683194], and 1 in the PDGFRB gene [cg16429070]) could effectively distinguish breast cancer from 31 other cancer species. The pyrosequencing results revealed that 7 screened methylation sites could significantly distinguish between breast cancer, lung cancer, gastric cancer, and colorectal cancer samples; these sites could also specifically distinguish between luminal A, luminal B, HER2, and Basal-like types of breast cancer.Conclusion: The 10 breast cancer methylation sites screened in the present study can effectively distinguish breast cancer from 31 other solid tumors, and they are expected to be used as biomarkers for early screening of breast cancer.Keywords: breast cancer, Basal-like breast cancer, methylation, biomarker, TCGA, ANOVA