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microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease. microRNA Profile in Stage I Clear Cell Renal Cell Carcinoma Predicts Progression to Metastatic Disease

NIAID Data Ecosystem2026-03-11 收录

癌症生物标志物

肾癌预后预测

数据链接：

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA545463 数据链接链接失效反馈

官方服务：

资源简介：

This SuperSeries is composed of the SubSeries listed below. Overall design: Refer to individual Series

应用场景：

创建时间：

2019-05-30

癌症生物标志物

肿瘤免疫治疗靶点

IntroductionProstate adenocarcinoma (PRAD) is a highly aggressive malignancy with high mortality and poor prognosis, and its potential mechanism remains unclear. Our study aimed to identify novel mark

NIAID Data Ecosystem20

DataSheet4_A systematic pan-cancer analysis reveals the clinical prognosis and immunotherapy value of C-X3-C motif ligand 1 (CX3CL1).ZIP

癌症生物标志物

肿瘤免疫治疗

It is now widely known that C-X3-C motif ligand 1 (CX3CL1) plays an essential part in the process of regulating pro-inflammatory cells migration across a wide range of inflammatory disorders, includin

NIAID Data Ecosystem00

Table1_Systematic pan-cancer analysis of the nicotinamide n-methyltransferase in human cancer.DOCX

癌症生物标志物

肿瘤免疫微环境

In several tumors, Nicotinamide N-Methyltransferase (NNMT) was identified as a bridge between methylation metabolism and tumorigenesis and was associated with a poor prognosis. This research aims is t

NIAID Data Ecosystem00

DataSheet_2_Identification of adenylate cyclase 2 methylation in bladder cancer with implications for prognosis and immunosuppressive microenvironment.zip

癌症生物标志物

甲基化分析

BackgroundThe incidence and mortality of bladder cancer (BCa) are increasing, while the existing diagnostic methods have limitations. Therefore, for early detection and response prediction, it is cruc

NIAID Data Ecosystem10

DataSheet1_Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma.ZIP

癌症生物标志物

弥漫性大B细胞淋巴瘤分析

Preventing, diagnosing, and treating diseases requires accurate clinical biomarkers, which remains challenging. Recently, advanced computational approaches have accelerated the discovery of promising

NIAID Data Ecosystem00