基于基因表达和临床信息的肾透明细胞癌预后识别数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Identification of kidney renal clear cell carcinoma prognosis based on gene expression and clinical information.zip
• DOI：10.3389/fmolb.2025.1630250.s001
• 发布日期：2025-08-20
• 文件数：1 个
• 文件总体积：26.55 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Identification_of_kidney_renal_clear_cell_carcinoma_prognosis_based_on_gene_expression_and_clinical_information_zip/29947337

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curl -L -o "Data Sheet 1_Identification of kidney renal clear cell carcinoma prognosis based on gene expression and clinical information.zip" "https://ndownloader.figshare.com/files/57303467"

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• Data Sheet 1_Identification of kidney renal clear cell carcinoma prognosis based on gene expression and clinical information.zip（26.55 MB）

文件总体积 26.55 MB（源站 API 实测）。

数据说明

BackgroundKidney renal clear cell carcinoma (KIRC) prognosis exhibits substantial heterogeneity even among patients with identical clinicopathological staging, reflecting the limitations of current classification systems. Therefore, the development of reliable prognostic tools may improve clinical evaluation of KIRC outcomes and facilitate personalized therapy optimization.
MethodsThe KIRC data of GSE40435 and GSE46699 in the GEO database were immunologically grouped based on 29 immune gene sets through R language. At the same time, RNA sequencing data, clinical information and tumor mutation data of KIRC patients in the TCGA database were jointly processed to explore methods that facilitate clinicians to judge the prognosis of KIRC patients. Quantitative real-time PCR (qPCR) was performed to validate the expression of key prognostic related genes (PRGs) in KIRC and paired adjacent normal tissues.
ResultsThere were significant differences in the immune microenvironment and genetic composition of different immune subtypes of KIRC. A number of high-risk genes related to KIRC prognosis were screened out, and these genes were mainly involved in immune-related functions such as lymphocyte migration. At the same time, we combined TCGA and GEO to find four genes (BASP1, CCL8, FCGR1B, FKBP11) for determining the risk stratification of KIRC, and constructed a model for clinicians to assess KIRC prognosis based on gene expression and clinical information. qPCR confirmed that BASP1, FCGR1B, and FKBP11 were significantly upregulated in KIRC compared to adjacent normal tissues, whereas CCL8 showed no significant differential expression between KIRC and paracancerous tissues.
ConclusionOur study has the potential to assist clinicians assess KIRC prognosis and modify more appropriate personalized treatment for KIRC patients in a timely manner.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmolb.2025.1630250.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Identification_of_kidney_renal_clear_cell_carcinoma_prognosis_based_on_gene_expression_and_clinical_information_zip/29947337）。