基于SARS-CoV-2相关基因预测肺腺癌患者预后、免疫微环境和治疗策略的新型特征的计算识别与实验验证表格数据

数据集获取（Figshare）

• 源站标题：Table_1_Computational identification and experimental verification of a novel signature based on SARS-CoV-2–related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.xlsx
• DOI：10.3389/fimmu.2024.1366928.s002
• 发布日期：2024-03-27
• 文件数：1 个
• 文件总体积：79.94 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Computational_identification_and_experimental_verification_of_a_novel_signature_based_on_SARS-CoV-2_related_genes_for_predicting_prognosis_immune_microenvironment_and_therapeutic_strategies_in_lung_adenocarcinoma_patients_xlsx/25486957

命令行下载

curl -L -o "Table_1_Computational identification and experimental verification of a novel signature based on SARS-CoV-2–related genes for predicting prognosis, im.xlsx" "https://ndownloader.figshare.com/files/45298591"

文件清单

• Table_1_Computational identification and experimental verification of a novel signature based on SARS-CoV-2–related genes for predicting prognosis, im.xlsx（79.94 KB）

文件总体积 79.94 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

BackgroundEarly research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined.
MethodsWe acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD.
ResultsOut of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion.
ConclusionOur research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis,

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fimmu.2024.1366928.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Computational_identification_and_experimental_verification_of_a_novel_signature_based_on_SARS-CoV-2_related_genes_for_predicting_prognosis_immune_microenvironment_and_therapeutic_strategies_in_lung_adenocarcinoma_patients_xlsx/25486957）。