To evaluate the differential expression profiles of the lncRNAs, miRNAs, mRNAs and ceRNAs, and their implication in the prognosis in clear cell renal cell carcinoma (CCRCC), the large sample genomics analysis technologies were used in this study. The RNA and miRNA sequencing data of CCRCC were obtained from The Cancer Genome Atlas (TCGA) database, and R software was used for gene expression analysis and survival analysis. Cytoscape software was used to construct the ceRNA network. The results showed that a total of 1 570 lncRNAs, 54 miRNAs, and 17 mRNAs were differentially expressed in CCRCC, and most of their expression levels were up-regulated (false discovery rate < 0.01 and absolute log fold change > 2). The ceRNA regulatory network showed the interaction between 89 differentially expressed lncRNAs and 9 differentially expressed miRNAs. Further survival analysis revealed that 38 lncRNAs (including COL18A1-AS1, TCL6, LINC00475, UCA1, WT1-AS, HOTTIP, PVT1, etc.) and 2 miRNAs (including miR-21 and miR-155) were correlated with the overall survival time of CCRCC (P < 0.05). Together, this study provided us several new evidences for the targeted therapy and prognosis assessment of CCRCC.
Metastatic renal cell carcinoma accounts for 20%-30% of newly diagnosed renal cell carcinoma and its prognosis is poor. It is not sensitive to radiotherapy or chemotherapy, and traditional cytokine therapy has limited efficacy in patient with metastatic renal cell carcinoma. In recent years, with the emergence of targeted drugs and immune checkpoint inhibitors, the survival of patients with metastatic renal cancer has been greatly improved. This article reviews treatment and research progress of metastatic renal cell carcinoma. It mainly introduces the medical treatment, including cytokine therapy, targeted therapy and emerging immunotherapy, and further analyzes the value of cytoreductive nephrectomy in the context of targeted therapy. The purpose of this article is to provide evidence for reasonable choices of treatment regimens in order to better guide clinical treatment.
With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system in recent years, and have obtained many valuable research results. The article sorted the research status of artificial intelligence technology in the fields of renal tumors, bladder tumors and prostate tumors from three aspects: the number of papers, image data, and clinical tasks. The purpose is to summarize and analyze the research status and find new valuable research ideas in the future. The results show that the artificial intelligence model based on medical data such as digital imaging and pathological images is effective in completing basic diagnosis of urinary system tumors, image segmentation of tumor infiltration areas or specific organs, gene mutation prediction and prognostic effect prediction, but most of the models for the requirement of clinical application still need to be improved. On the one hand, it is necessary to further improve the detection, classification, segmentation and other performance of the core algorithm. On the other hand, it is necessary to integrate more standardized medical databases to effectively improve the diagnostic accuracy of artificial intelligence models and make it play greater clinical value.
Carney triad is a rare tumor syndrome with few reports. This case showed the enhanced CT and MRI images of a rare young woman patient with Carney triad (containing gastric stromal tumor, renal cell carcinoma, adrenal pheochromocytoma, and pulmonary chondrosarcoma), which is intended to provide a reference for clinical diagnosis and differential diagnosis. This case reminds the radiologists and clinicians that the patients with a history of primary gastrointestinal stromal tumor and neoplastic lesions occurring at specific sites (pulmonary chondrosarcoma, adrenal pheochromocytoma, renal cell carcinoma, etc.) need to be alerted to the possibility of combining with Carney triad.