Inhibition of PTPN21 has antitumor effects in glioma by restraining the EGFR/PI3K/AKT pathway

Li, Xiaoqiang; Liu, Bo-Chuan; Jiang, Xueshi; Gao, Wenwen; Ren, Bo; Yao, Wei; Wang, Feng-lu; Zhao, Haikang; Zhang, Liang

doi:10.1016/j.taap.2022.116180

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…In this study, we developed a genomics model consisting of 9 features with good performance in predicting LNM. These genes included PTPN21 (28), ST6GALNAC3 (29), FAM13A (30), and CHRNA7 (31), which were correlated with metastasis. These genes were also enriched in the PI3K/Akt, Notch1/Hes1 or Akt1/ mTOR signaling pathways and were reported to be correlated with LNM in breast cancer (32)(33)(34)(35).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study

Chen

Xiao

et al. 2022

Front. Oncol.

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IntroductionTo develop and validate a radiogenomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer compared to a genomics and radiomics model.MethodsThis retrospective study integrated transcriptomic data from The Cancer Genome Atlas with matched MRI data from The Cancer Imaging Archive for the same set of 111 patients with breast cancer, which were used as the training and testing groups. Fifteen patients from one hospital were enrolled as the external validation group. Radiomics features were extracted from dynamic contrast-enhanced (DCE)-MRI of breast cancer, and genomics features were derived from differentially expressed gene analysis of transcriptome data. Boruta was used for genomics and radiomics data dimension reduction and feature selection. Logistic regression was applied to develop genomics, radiomics, and radiogenomics models to predict ALNM. The performance of the three models was assessed by receiver operating characteristic curves and compared by the Delong test.ResultsThe genomics model was established by nine genomics features, and the radiomics model was established by three radiomics features. The two models showed good discrimination performance in predicting ALNM in breast cancer, with areas under the curves (AUCs) of 0.80, 0.67, and 0.52 for the genomics model and 0.72, 0.68, and 0.71 for the radiomics model in the training, testing and external validation groups, respectively. The radiogenomics model integrated with five genomics features and three radiomics features had a better performance, with AUCs of 0.84, 0.75, and 0.82 in the three groups, respectively, which was higher than the AUC of the radiomics model in the training group and the genomics model in the external validation group (both P < 0.05).ConclusionThe radiogenomics model combining radiomics features and genomics features improved the performance to predict ALNM in breast cancer.

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