Zhang L
Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features
Recurrence is the key factor affecting the prognosis of osteosarcoma. Currently, there is a lack of clinically useful tools to predict osteosarcoma recurrence. The application of pathological images for artificial intelligence-assisted accurate prediction of tumour outcomes is increasing.
ReadTRAF4 inhibits bladder cancer progression by promoting BMP/SMAD signalling pathway
Bladder cancer is a highly prevalent cancer with poor clinical outcomes, especially in advanced stages of progression when the cancer starts invading the bladder muscle (1). Epithelial-to-mesenchymal transition (EMT) has been implicated in bladder cancer progression and metastasis. EMT is a dynamic process in which epithelial cells lose their cell–cell contacts and apical–basal polarity and gain mesenchymal traits with increased migration and invasion abilities (2, 3). Cells lose the expression of epithelial markers such as E-cadherin and gain the expression of mesenchymal markers such as N-cadherin (4). This process is orchestrated by EMT-inducing transcription factors, including SNAIL and SLUG (5, 6). TGFβ signaling pathway is known to stimulate EMT (7).
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