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Data S1 from Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer

NIAID Data Ecosystem2026-05-02 收录

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空间转录组学

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https://figshare.com/articles/dataset/Data_S1_from_Combining_Spatial_Transcriptomics_Pseudotime_and_Machine_Learning_Enables_Discovery_of_Biomarkers_for_Prostate_Cancer/29457352 数据链接链接失效反馈

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The patient characteristics

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创建时间：

2025-07-02

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环状RNA-miRNA调控

Additional file 2: Table S1. The predicted miRNA targets for hsa_circ_0086414 in the CircBank database. Table S2. The miRNAs that were significantly differentially expressed between the EGFR mutated a

Figshare2019-02-19 更新20

Table_7_Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers.XLSX

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通路突变分析

DNA mutations play a crucial role in cancer development and progression. Mutation profiles vary dramatically in different cancer types and between individual tumors. Mutations of several individual ge

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

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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

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DataSheet_2_Integrated bioinformatics analysis of IFITM1 as a prognostic biomarker and investigation of its immunological role in prostate adenocarcinoma.zip

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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

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

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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

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Data S1 from Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer

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