Table 4_细胞死亡相关特征基因：重症脓毒症的风险预测生物标志物和潜在治疗靶点.xls

数据集获取（Figshare）

• 源站标题：Table 4_Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.xls
• DOI：10.3389/fmed.2025.1577203.s004
• 发布日期：2025-05-30
• 文件数：1 个
• 文件总体积：3.72 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_Cell_death-related_signature_genes_risk-predictive_biomarkers_and_potential_therapeutic_targets_in_severe_sepsis_xls/29194838

命令行下载

curl -L -o "Table 4_Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.xls" "https://ndownloader.figshare.com/files/54958313"

文件清单

• Table 4_Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.xls（3.72 KB）

文件总体积 3.72 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

Sepsis is a systemic inflammatory response syndrome that predisposes to severe lung infections (SeALAR) such as sepsis-associated acute lung injury (Se/ALI) or sepsis-associated acute respiratory distress syndrome (Se/ARDS). Through a systematic bioinformatics approach, this study aimed to unravel the pathogenesis of SeALAR and explore potential biomarkers and individualized therapeutic targets. We analyzed differential genes in the peripheral blood of SeALAR patients based on the GSE10474 and GSE32707 datasets, and identified 352 significantly differentially expressed genes. Various signaling pathways related to immune regulation were found to be significantly altered via GO and KEGG enrichment analysis. Further combining cell death-related gene screening and four machine learning algorithms (including LASSO-logistic, Gradient Boosting Machine, Random Forest and xGBoost), nine SeALAR-characterized cell death genes (SeDGs) were screened and a risk prediction model based on SeDGs was constructed that demonstrated good prediction performance. In immunoassays, ssGSEA showed that Activated.CD8.T.cell, CD56bright.natural.killer.cell, MDSC, Natural.killer.T.cell, T.follicular.helper. cell and TType.1.T.helper.cell had significantly lower infiltration abundance than lower infiltration levels compared to the Se group. GSEA analysis revealed key immune pathways in which SeDGs may be involved. In addition, unsupervised clustering analysis revealed that SeALAR patients could be classified into two molecular subtypes, providing a new direction for the development of individualized immunotherapy strategies. In conclusion, this study systematically analyzed the molecular features and immune disorder mechanism of SeALAR from a multidimensional perspective, and thus provides a theoretical basis and potential targets for precision medicine intervention and targeted drug development.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1577203.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_4_Cell_death-related_signature_genes_risk-predictive_biomarkers_and_potential_therapeutic_targets_in_severe_sepsis_xls/29194838）。