乙型肝炎病毒发病机制免疫信号及氨基酸利用风险因素数据集

数据集获取（Figshare）

• 源站标题：DataSheet1_Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine.CSV
• DOI：10.3389/fphar.2022.1079566.s001
• 发布日期：2022-12-09
• 文件数：1 个
• 文件总体积：1.39 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Hepatitis_B_virus_pathogenesis_relevant_immunosignals_uncovering_amino_acids_utilization_related_risk_factors_guide_artificial_intelligence-based_precision_medicine_CSV/21700001

命令行下载

curl -L -o "DataSheet1_Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence.CSV" "https://ndownloader.figshare.com/files/38489435"

文件清单

• DataSheet1_Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence.CSV（1.39 KB）

文件总体积 1.39 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

Background: Although immune microenvironment-related chemokines, extracellular matrix (ECM), and intrahepatic immune cells are reported to be highly involved in hepatitis B virus (HBV)-related diseases, their roles in diagnosis, prognosis, and drug sensitivity evaluation remain unclear. Here, we aimed to study their clinical use to provide a basis for precision medicine in hepatocellular carcinoma (HCC) via the amalgamation of artificial intelligence.
Methods: High-throughput liver transcriptomes from Gene Expression Omnibus (GEO), NODE (https://www.bio.sino.org/node), the Cancer Genome Atlas (TCGA), and our in-house hepatocellular carcinoma patients were collected in this study. Core immunosignals that participated in the entire diseases course of hepatitis B were explored using the “Gene set variation analysis” R package. Using ROC curve analysis, the impact of core immunosignals and amino acid utilization related gene on hepatocellular carcinoma patient’s clinical outcome were calculated. The utility of core immunosignals as a classifier for hepatocellular carcinoma tumor tissue was evaluated using explainable machine-learning methods. A novel deep residual neural network model based on immunosignals was constructed for the long-term overall survival (LS) analysis. In vivo drug sensitivity was calculated by the “oncoPredict” R package.
Results: We identified nine genes comprising chemokines and ECM related to hepatitis B virus-induced inflammation and fibrosis as CLST signals. Moreover, CLST was co-enriched with activated CD4+ T cells bearing harmful factors (aCD4) during all stages of hepatitis B virus pathogenesis, which was also verified by our hepatocellular carcinoma data. Unexpectedly, we found that hepatitis B virus-hepatocellular carcinoma patients in the CLSThighaCD4high subgroup had the shortest overall survival (OS) and were characterized by a risk gene signature associated with amino acids utilization. Importantly, characteristic genes specific to CLST/aCD4 showed promising clinical relevance in identifying patients with early-stage hepatocellular carcinoma via explainable machine learning. In addition, the 5-year long-term overall survival of hep

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphar.2022.1079566.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Hepatitis_B_virus_pathogenesis_relevant_immunosignals_uncovering_amino_acids_utilization_related_risk_factors_guide_artificial_intelligence-based_precision_medicine_CSV/21700001）。