ETP-ALL转录组与相互作用组整合数据集

数据集获取（Figshare）

• 源站标题：Table5_In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Propensity.XLSX
• DOI：10.3389/fcell.2022.899752.s006
• 发布日期：2022-05-13
• 文件数：1 个
• 文件总体积：12.12 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table5_In_Silico_Integration_of_Transcriptome_and_Interactome_Predicts_an_ETP-ALL-Specific_Transcriptional_Footprint_that_Decodes_its_Developmental_Propensity_XLSX/19763335

命令行下载

curl -L -o "Table5_In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Pr.XLSX" "https://ndownloader.figshare.com/files/35113330"

文件清单

• Table5_In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Pr.XLSX（12.12 KB）

文件总体积 12.12 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

Early T precursor acute lymphoblastic leukemia (ETP-ALL) exhibits poor clinical outcomes and high relapse rates following conventional chemotherapeutic protocols. Extensive developmental flexibility of the multipotent ETP-ALL blasts with considerable intra-population heterogeneity in terms of immunophenotype and prognostic parameters might be a target for novel therapeutic interventions. Using a public gene expression dataset (GSE28703) from NCBI GEO DataSets with 12 ETP-ALL and 40 non-ETP-ALL samples, such heterogeneity was found to be reflected in their transcriptome as well. Hub genes were identified from the STRING-derived functional interaction network of genes showing differential expression between ETP-ALL and non-ETP-ALL as well as variable expression across ETP-ALL. Nine genes (KIT, HGF, NT5E, PROM1, CD33, ANPEP, CDH2, IL1B, and CXCL2) among the hubs were further validated as possible diagnostic ETP-ALL markers using another gene expression dataset (GSE78132) with 17 ETP-ALL and 27 non-ETP-ALL samples. Linear dimensionality reduction analysis with the expression levels of the hub genes in ETP-ALL revealed their divergent inclinations towards different hematopoietic lineages, proposing them as novel indicators of lineage specification in the incompletely differentiated ETP-ALL blasts. This further led to the formulation of a personalized lineage score calculation algorithm, which uncovered a considerable B-lineage-bias in a substantial fraction of ETP-ALL subjects from the GSE28703 and GSE78132 cohorts. In addition, STRING-derived physical interactome of the potential biomarkers displayed complete segregation of the B-lineage-skewed markers from other lineage-associated factors, highlighting their distinct functionality and possible druggability in ETP-ALL. A panel of these biomarkers might be useful in pinpointing the dominant lineage specification programmes in the ETP-ALL blasts on a personalized level, urging the development of novel lineage-directed precision therapies as well as repurposing of existing therapies against leukemia of different hematopoietic lineages; which might overcome the drawbacks of conventional chemotherapy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcell.2022.899752.s006（source: https://frontiersin.figshare.com/articles/dataset/Table5_In_Silico_Integration_of_Transcriptome_and_Interactome_Predicts_an_ETP-ALL-Specific_Transcriptional_Footprint_that_Decodes_its_Developmental_Propensity_XLSX/19763335）。