Background The posttranscriptional modifications of transfer RNA (tRNA) are critical for all aspects of the tRNA function and have been implicated in the tumourigenesis and progression of many human cancers. By contrast, the biological functions of methyltransferase‐like 1 (METTL1)‐regulated m 7 G tRNA modification in bladder cancer (BC) remain obscure. Results In this research, we show that METTL1 was highly expressed in BC, and its level was correlated with poor patient prognosis. Silencing METTL1 suppresses the proliferation, migration and invasion of BC cells in vitro and in vivo. Multi‐omics analysis reveals that METTL1‐mediated m 7 G tRNA modification altered expression of certain target genes, including EGFR/EFEMP1. Mechanistically, METTL1 regulates the translation of EGFR/EFEMP1 via modifying certain tRNAs. Furthermore, forced expression of EGFR/EFEMP1 partially rescues the effect of METTL1 deletion on BC cells. Conclusions Our findings demonstrate the oncogenic role of METTL1 and the pathological significance of the METTL1‐m 7 G‐EGFR/EFEMP1 axis in the BC development, thus providing potential therapeutic targets for the BC treatment.

Renal cell carcinoma (RCC) is one of the most common tumours of the urinary system, and is insidious and not susceptible to chemoradiotherapy. As the most common subtype of RCC (70-80% of cases), clear cell renal cell carcinoma (ccRCC) is characterized by the loss of von Hippel-Lindau and the accumulation of robust lipid and glycogen. For advanced RCC, molecular-targeted drugs, tyrosine kinase inhibitors (TKIs) and the immune checkpoint inhibitors (ICIs) have been increasingly recommended and investigated. Due to the existence of a highly dynamic, adaptive and heterogeneous tumour microenvironment (TME), and due to the glucose and lipid metabolism in RCC, this cancer may be accompanied by various types of resistance to TKIs and ICIs. With the increased production of lactate, nitric oxide, and other new by-products of metabolism, novel findings of the TME and key metabolic enzymes drived by HIF and other factors have been increasingly clarified in RCC carcinogenesis and therapy. However, there are few summaries of the TME and tumour metabolism for RCC progression and therapy. Here, we summarize and discuss the relationship of the important implicated characteristics of the TME as well as metabolic molecules and RCC carcinogenesis to provide prospects for future treatment strategies to overcome TME-related resistance in RCC.

Obstruction is an independent indicator for the survival and postoperative recurrence for patients with colorectal cancer. Patients in the COC group have worse overall survival with high postoperative recurrence rate compared to those in the NOC group.

Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.

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