食管鳞状细胞癌中基于乳酸代谢相关LncRNAs的风险特征识别表1数据集

数据集获取（Figshare）

• 源站标题：Table1_Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma.DOCX
• DOI：10.3389/fcell.2022.845293.s003
• 发布日期：2022-05-12
• 文件数：1 个
• 文件总体积：13.29 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Identification_of_A_Risk_Signature_Based_on_Lactic_Acid_Metabolism-Related_LncRNAs_in_Patients_With_Esophageal_Squamous_Cell_Carcinoma_DOCX/19752718

命令行下载

curl -L -o "Table1_Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma.DOCX" "https://ndownloader.figshare.com/files/35096644"

文件清单

• Table1_Identification of A Risk Signature Based on Lactic Acid Metabolism-Related LncRNAs in Patients With Esophageal Squamous Cell Carcinoma.DOCX（13.29 KB）

文件总体积 13.29 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

Lactic acid, formerly thought of as a byproduct of glycolysis or a metabolic waste produced, has now been identified as a key regulator of cancer growth, maintenance, and progression. However, the results of investigations on lactic acid metabolism-related long non-coding RNAs (LRLs) in esophageal squamous cell carcinoma (ESCC) remain inconclusive. In this study, univariate Cox regression analysis was carried out in the TCGA cohort, and 9 lncRNAs were shown to be significantly associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were then used in the GEO cohort. 6 LRLs were identified as independent prognostic factors for ESCC patients used to construct a prognostic risk-related signature subsequently. Two groups were formed based on the middle value of risk scores: a low-risk group and a high-risk group. Following that, we conducted Kaplan-Meier survival analysis, which revealed that the high-risk group had a lower survival probability than the low-risk group in both GEO and TCGA cohorts. On multivariate Cox regression analysis, the prognostic signature was shown to be independent prognostic factor, and it was found to be a better predictor of the prognosis of ESCC patients than the currently widely used grading and staging approaches. The established nomogram can be conveniently applied in the clinic to predict the 1-, 3-, and 5- year survival rates of patients. There was a significant link found between the 6 LRLs-based prognostic signature and immune-cell infiltration, tumor microenvironment (TME), tumor somatic mutational status, and chemotherapeutic treatment sensitivity in the study population. Finally, we used GTEx RNA-seq data and qRT-PCR experiments to verify the expression levels of 6 LRLs. In conclusion, we constructed a prognostic signature which could predict the prognosis and immunotherapy response of ESCC patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcell.2022.845293.s003（source: https://frontiersin.figshare.com/articles/dataset/Table1_Identification_of_A_Risk_Signature_Based_on_Lactic_Acid_Metabolism-Related_LncRNAs_in_Patients_With_Esophageal_Squamous_Cell_Carcinoma_DOCX/19752718）。