Pseudogenes have been reported to play oncogenic or tumor-suppressive roles in cancer progression. However, the molecular mechanism of most pseudogenes in pancreatic ductal adenocarcinoma (PDAC) remains unknown. Herein, we characterized a novel pseudogene-miRNA-mRNA network associated with PDAC progression using bioinformatics analysis. After screening by dreamBase and GEPIA, 12 up-regulated and 7 down-regulated differentially expressed pseudogenes (DEPs) were identified. According to survival analysis, only elevated AK4P1 indicated a poor prognosis for PDAC patients. Moreover, we found that AK4 acts as a cognate gene of AK4P1 and also predicts worse survival for PDAC patients. Furthermore, 32 miRNAs were predicted to bind to AK4P1 by starBase, among which miR-375 was identified as the most potential binding miRNA of AK4P1. A total of 477 potential target genes of miR-375 were obtained by miRNet, in which 49 hub genes with node degree ≥ 20 were identified by STRING. Subsequent analysis for hub genes demonstrated that YAP1 may be a functional downstream target of AK4P1. To confirmed the above findings, microarray, and qRT-PCR assay revealed that YAP1 was dramatically upregulated in both PDAC cells and tissues. Functional experiments showed that knockdown of YAP1 significantly suppressed PDAC cells growth, increased apoptosis, and decreased the ability of invasion. In conclusion, amplification of AK4P1 may fuel the onset and development of PDAC by targeting YAP1 through competitively binding to miR-375, and serve as a promising biomarker and therapeutic target for PDAC.
[Background and Aim] Although Trastuzumab has been used for HER2(+) breast cancer, the treatment response of Trastuzumab therapy depends on unknown mechanisms among individual cases. In order to avoid unnecessary adverse events and to lighten financial burden, pre-treatment prediction of trastuzumab treatment response would be beneficial for patients. Thus, in this study, we develop a prediction algorithm using microRNA expression profile using formalin-fixed paraffin-embedded (FFPE) specimens of HER2(+) breast cancer. [Materials and Methods] Eighty-three breast cancer patients who underwent trastuzumab-chemo combined therapy before operations were enrolled with written informed consent. FFPE specimens of pre-treatment core needle biopsy samples were collected, and regions containing cancer and adjacent stromal cells were laser-microdissected. Total RNA samples extracted from the microdissected specimens were subjected for microRNA microarray (3D-Gene®, Toray, Japan) analysis. Among these 83 patients, 39 cases had pCR (definition: complete response in IDC regions regardless presence of DCIS without lymph node metastasis), and the other 44 cases did not. According to the pCR/non-pCR information, we develop a prediction model using 35 signature microRNAs by a SVM technique. Prediction accuracy assessed by Leave-one-out validation was AUROC=0.889. The 35 signature microRNAs for trastuzumab treatment response included 7 out of 8 let-7 family members and miR-125a-5p/b-5p, which were downregulated in pCR specimens. [Conclusion] microRNA profile could predict treatment response of trastuzumab-chemo combined therapy for HER2(+) breast cancer, and the developed prediction algorithm might be a useful tool for clinical decision making. Prediction Accuracy Outcome: non-pCR Outcome: pCR total Prediction: non-sensitive 35 6 41 Prediction: sensitive 9 33 42 total 44 39 83 Citation Format: Fumiaki Sato, Zhipeng Wang, Takayuki Ueno, Akira Myomoto, Satoko Takizawa, Feng Ling Pu, Norikazu Masuda, Yoshiki Mikami, Yoshiki Mikami, Kazuharu Shimizu, Shigehira Saji, Masakazu Toi. Development of microRNA-based prediction model of Trastuzumab treatment response for HER2-positive breast cancer using FFPE specimens. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1937. doi:10.1158/1538-7445.AM2013-1937
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