基于铁死亡基因的肺腺癌预后模型数据集

数据集获取（Figshare）

• 源站标题：DataSheet1_Development of a ferroptosis-based model to predict prognosis, tumor microenvironment, and drug response for lung adenocarcinoma with weighted genes co-expression network analysis.CSV
• DOI：10.3389/fphar.2022.1072589.s001
• 发布日期：2022-11-17
• 文件数：1 个
• 文件总体积：8.75 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Development_of_a_ferroptosis-based_model_to_predict_prognosis_tumor_microenvironment_and_drug_response_for_lung_adenocarcinoma_with_weighted_genes_co-expression_network_analysis_CSV/21569727

命令行下载

curl -L -o "DataSheet1_Development of a ferroptosis-based model to predict prognosis, tumor microenvironment, and drug response for lung adenocarcinoma with weigh.CSV" "https://ndownloader.figshare.com/files/38232528"

文件清单

• DataSheet1_Development of a ferroptosis-based model to predict prognosis, tumor microenvironment, and drug response for lung adenocarcinoma with weigh.CSV（8.75 KB）

文件总体积 8.75 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

Objective: The goal of this study was to create a risk model based on the ferroptosis gene set that affects lung adenocarcinoma (LUAD) patients’ prognosis and to investigate the potential underlying mechanisms.
Material and Methods: A cohort of 482 LUAD patients from the TCGA database was used to develop the prognostic model. We picked the module genes from the ferroptosis gene set using weighted genes co-expression network analysis (WGCNA). The least absolute shrinkage and selection operator (LASSO) and univariate cox regression were used to screen the hub genes. Finally, the multivariate Cox analysis constructed a risk prediction score model. Three other cohorts of LUAD patients from the GEO database were included to validate the prediction ability of our model. Furthermore, the differentially expressed genes (DEG), immune infiltration, and drug sensitivity were analyzed.
Results: An eight-gene-based prognostic model, including PIR, PEBP1, PPP1R13L, CA9, GLS2, DECR1, OTUB1, and YWHAE, was built. The patients from the TCGA database were classified into the high-RS and low-RS groups. The high-RS group was characterized by poor overall survival (OS) and less immune infiltration. Based on clinical traits, we separated the patients into various subgroups, and RS had remarkable prediction performance in each subgroup. The RS distribution analysis demonstrated that the RS was significantly associated with the stage of the LUAD patients. According to the study of immune cell infiltration in both groups, patients in the high-RS group had a lower abundance of immune cells, and less infiltration was associated with worse survival. Besides, we discovered that the high-RS group might not respond well to immune checkpoint inhibitors when we analyzed the gene expression of immune checkpoints. However, drug sensitivity analysis suggested that high-RS groups were more sensitive to common LUAD agents such as Afatinib, Erlotinib, Gefitinib, and Osimertinib.
Conclusion: We constructed a novel and reliable ferroptosis-related model for LUAD patients, which was associated with prognosis, immune cell infiltration, and drug sensitivity, aiming to shed new light on the cancer biology

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphar.2022.1072589.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Development_of_a_ferroptosis-based_model_to_predict_prognosis_tumor_microenvironment_and_drug_response_for_lung_adenocarcinoma_with_weighted_genes_co-expression_network_analysis_CSV/21569727）。