Background: To determine the role of miRNA in the progression and outcome of renal clear cell carcinoma (ccRCC), establish a model for predicting outcome in patients with ccRCC and verify it using a Cox regression model. The miRNA target genes were predicted to understand their biological functions.Methods: The microRNAs of 71 normal tissues and 545 tumor tissues were downloaded from TCGA (https://tcga-data.nci.nih.gov/tcga/). We also downloaded 537 clinical materials from this website. The miRNA difference analysis was carried out. A prognostic model was constructed using differential miRNA.The model was verified using Cox survival analysis, receiver operator characteristic (ROC), and independent predictive analysis.Results: MiR-130b-3p, miR-365b-3p, miR-149-5p, miR-155-5p, and miR-144-5p can be used as independent prognostic indicators. We also analyzed the related functions of the target gene and found that target genes of miRNAs are involved in the signal pathways of some tumors, including cholesterol metabolism, HIF-1 signal pathway, focus adhesion, the Rap1 signal pathway, and hepatitis C. Conclusions:The prognostic model constructed using five miRNAs is an independent and accurate factor. These miRNAs target genes are involved in regulating a variety of tumorigenesis and signal pathways.Therefore, we have reason to believe that the regulation of signal pathways by miRNA may play a critical role in the occurrence, development, and outcome of ccRCC, provide a new therapeutic target for ccRCC, and improve outcomes.

Background: To establish a preoperative prediction model of myometrial invasion of bladder cancer (BC) based on the radiomics characteristics of multi-parameter thin-slice enhanced computed tomography (CT) imaging.Methods: Data from 100 patients with BC were analyzed retrospectively. The patients were divided into two groups: muscular invasive BC and non-muscular invasive BC. The tumor region was segmented from enhanced CT images (arterial-and venous-phase calibration maps) of all patients using Slicer-3D software. We extracted 1,223 texture features from tumor image data based on the shape and gray-level co-occurrence matrix, gray size region matrix, gray run-length matrix, adjacent gray difference matrix, and gray correlation matrix. The patients were randomly divided into a training group (n=70) and a verification group (n=30) in a 7:3 ratio. Interclass correlation coefficients >0.75, least absolute shrinkage, and selection operator regression were used for feature selection. The prediction model was established by combining Rad-score, independent clinical factors, and support vector machine (SVM), and a radiomics nomogram was constructed. The nomogram was tested using the consistency index, calibration curve, time-dependent receiver operating characteristic curve, and clinical decision curve to predict the myometrial invasion of the bladder preoperatively.Results: Six radiomics features that were significantly related to myometrial invasion of BC were selected to construct a predictive model. The area under the curve (AUC) values of training group and verification group based on SVM were 0.898 (95% CI: 0.820-0.976) and 0.702 (95% CI: 0.495-0.909), respectively.Single factor and multiple factor analysis showed that albuminuria (95% CI: 0.243-2.206, P=0.0014) and metabolic syndrome (95% CI: 0.850-2.935, P<0.001) were independent influencing factors of BC myometrial invasion. Clinical factors and 11 radiomics features were used to construct a comprehensive model for predicting the pathological grade of BC (radiomics + clinical). After a comprehensive comparison, we found that the overall effectiveness of the model (radiomics + clinical) was the highest (AUC =0.8457). Conclusions:Based on the multi-parameter thin-layer enhanced CT radiomics feature can be used as a potential independent predictor of BC myometrial invasion, the model based on parameters can initially quantitatively characterize the risk of myometrial invasion, and has excellent potential for predicting myometrial invasion of BC.

Background: Immunotherapy is a new and powerful weapon against tumors, represented by inhibitors of programmed death-1 (PD-1) and cytotoxic T lymphocyte-associated protein-4 (CTLA-4). This study aimed to determine the similarities and differences between PD-1 and CTLA-4 in 33 cancers in The Cancer Genome Atlas (TCGA) and the impact of subtypes of the immune environment on tumor production and treatment.Methods: From the Xena browser, we downloaded TNM stage, immune subtypes, and tumor microenvironment scores for 33 tumors from TCGA. Expression of CTLA-4 and PD-1 in normal and tumor samples were compared for various tumors with normal tissue sample sizes greater than five. The relationship between expression and overall survival was investigated using one-way Cox analysis. The immune scores of 33 tumors were assessed using ESTIMATE prediction software to predict the degree of immune cell infiltration across tumors and calculate the correlation between PD-1 and CTLA-4 expression with the tumor microenvironment and tumor stem cells. We also examined the correlation between genes and drug sensitivity.Results: PD-1 and CTLA-4 were highly expressed in breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), and kidney renal clear cell carcinoma (KIRC) (P<0.05), highly correlated with immune subtypes C2 (IFN-γ-dominant) and C6 (TGF-β-dominant), and positively correlated with tumor microenvironmental immune scores (P<0.05). In renal clear cell carcinoma, PD-1 and CTLA-4 expression was positively correlated with clinical stage and microenvironmental score (r>0.7, P<0.05). Conclusions:The finding that PD1 and CTLA-4 are associated with the prognosis of most tumour patients and are closely related to the tumour microenvironment is of great value and provides a research direction for the screening of populations benefiting from immunotherapy.

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