血浆外泌体IRAK1预测IgA肾病患者RASi治疗反应的潜在生物标志物数据集

数据集获取（Figshare）

• 源站标题：Table_1_Plasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response to renin-angiotensin system inhibitors in patients with IgA nephropathy.docx
• DOI：10.3389/fimmu.2022.978315.s002
• 发布日期：2022-11-07
• 文件数：1 个
• 文件总体积：11.08 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Plasma_exosomal_IRAK1_can_be_a_potential_biomarker_for_predicting_the_treatment_response_to_renin-angiotensin_system_inhibitors_in_patients_with_IgA_nephropathy_docx/21511494

命令行下载

curl -L -o "Table_1_Plasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response to renin-angiotensin system inhibitors in patients wi.docx" "https://ndownloader.figshare.com/files/38126964"

文件清单

• Table_1_Plasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response to renin-angiotensin system inhibitors in patients wi.docx（11.08 KB）

文件总体积 11.08 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundRenin-angiotensin system inhibitors (RASi) are the first choice and basic therapy for the treatment of IgA nephropathy (IgAN) with proteinuria. However, approximately 40% of patients have no response to RASi treatment. The aim of this study was to screen potential biomarkers for predicting the treatment response of RASi in patients with IgAN.
MethodsWe included IgAN patients who were treatment-naive. They received supportive treatment, including a maximum tolerant dose of RASi for 3 months. According to the degree of decrease in proteinuria after 3 months of follow-up, these patients were divided into a sensitive group and a resistant group. The plasma of the two groups of patients was collected, and the exosomes were extracted for high-throughput sequencing. The screening of hub genes was performed using a weighted gene co-expression network (WGCNA) and filtering differentially expressed genes (DEGs). We randomly selected 20 patients in the sensitive group and 20 patients in the resistant group for hub gene validation by real-time quantitative polymerase chain reaction (qRT−PCR). A receiver operating characteristic (ROC) curve was used to evaluate hub genes that predicted the efficacy of the RASi response among the 40 validation patients.
ResultsAfter screening 370 IgAN patients according to the inclusion and exclusion criteria and the RASi treatment response evaluation, there were 38 patients in the sensitive group and 32 patients in the resistant group. IRAK1, ABCD1 and PLXNB3 were identified as hub genes by analyzing the high-throughput sequencing of the plasma exosomes of the two groups through WGCNA and DEGs screening. The sequencing data were consistent with the validation data showing that these three hub genes were upregulated in the resistant group compared with the sensitive group. The ROC curve indicated that IRAK1 was a good biomarker to predict the therapeutic response of RASi in patients with IgAN.
ConclusionsPlasma exosomal IRAK1 can be a potential biomarker for predicting the treatment response of RASi in patients with IgAN.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2022.978315.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Plasma_exosomal_IRAK1_can_be_a_potential_biomarker_for_predicting_the_treatment_response_to_renin-angiotensin_system_inhibitors_in_patients_with_IgA_nephropathy_docx/21511494）。