透明细胞肾细胞癌预后及免疫治疗反应预测的吞噬依赖性基因签名数据集

数据集获取（Figshare）

• 源站标题：Table_2_Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma.xlsx
• DOI：10.3389/fimmu.2022.853088.s002
• 发布日期：2023-01-19
• 文件数：1 个
• 文件总体积：20.84 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Development_of_a_Phagocytosis-Dependent_Gene_Signature_to_Predict_Prognosis_and_Response_to_Checkpoint_Inhibition_in_Clear-Cell_Renal_Cell_Carcinoma_xlsx/21921648

命令行下载

curl -L -o "Table_2_Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Car.xlsx" "https://ndownloader.figshare.com/files/38886462"

文件清单

• Table_2_Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Car.xlsx（20.84 KB）

文件总体积 20.84 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

AimThe action of immune checkpoint inhibition (ICI) largely depends on antibody-dependent cellular phagocytosis (ADCP). We thus aim to develop ADCP-based ccRCC risk stratification as both prognostic and therapeutic markers of ICI.
MethodGenomic data from multiple public datasets (TCGA, etc.) were integrated. A cancer-intrinsic ADCP gene set for ccRCC tailored from a recent report was constructed based on the association with prognosis, immune infiltrates, and response to ICI. Therapeutic potential was profiled using genome-drug sensitivity datasets.
ResultsADCP genes were selected from a recent CRISPR/Cas9 screen report. Following a four-module panel based on clinical traits, we generated a six-gene signature (ARPC3, PHF19, FKBP11, MS4A14, KDELR3, and CD1C), which showed a strong correlation with advanced grade and stage and worsened prognosis, with a nomogram showing predictive efficacies of 0.911, 0.845, and 0.867 (AUC) at 1, 3, and 5 years, respectively. Signatures were further dichotomized, and groups with a higher risk score showed a positive correlation with tumor mutation burden, higher expressions of inhibitory checkpoint molecules, and increased antitumor immune infiltrates and were enriched for antitumor immune pathways. The high risk-score group showed better response to ICI and could benefit from TKIs of axitinib, tivozanib, or sorafenib, preferentially in combination, whereas sunitinib and pazopanib would better fit the low risk-score group.
ConclusionHere we showed a six-gene ADCP signature that correlated with prognosis and immune modulation in ccRCC. The signature-based risk stratification was associated with response to both ICI and tyrosine kinase inhibition in ccRCC.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2022.853088.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Development_of_a_Phagocytosis-Dependent_Gene_Signature_to_Predict_Prognosis_and_Response_to_Checkpoint_Inhibition_in_Clear-Cell_Renal_Cell_Carcinoma_xlsx/21921648）。