肺腺癌单细胞数据集GSE189357复现系列之十：联合bulk数据进行细胞亚群预后验证
Ranking
No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - This tutorial integrates lung adenocarcinoma single-cell dataset GSE189357 with TCGA-LUAD bulk RNA-seq and survival data to assess the prognostic relevance of tumor epithelial subpopulations. It provides a reproducible GSVA and Kaplan–Meier workflow for translating cell-level signatures into clinically testable biomarkers.
- Single-cell epithelial-subcluster marker genes are converted into gene sets and scored across TCGA-LUAD tumors using GSVA.
- Clinical validation uses Kaplan–Meier analyses with both median-based and optimized cutoffs after excluding samples with missing survival data or follow-up under 30 days.
- The workflow separately evaluates UBE2C, associated with a proliferative tumor subpopulation, as a candidate overall-survival marker.
- The article presents methodology and code but does not provide enough reported statistics here to establish prognostic significance.
Sources (1)
肺腺癌单细胞数据集GSE189357复现系列之十：联合bulk数据进行细胞亚群预后验证
TL;DR - This tutorial integrates lung adenocarcinoma single-cell dataset GSE189357 with TCGA-LUAD bulk RNA-seq and survival data to assess the prognostic relevance of tumor epithelial subpopulations. It provides a reproducible GSVA and Kaplan–Meier workflow for translating cell-level signatures into clinically testable biomarkers.
- Single-cell epithelial-subcluster marker genes are converted into gene sets and scored across TCGA-LUAD tumors using GSVA.
- Clinical validation uses Kaplan–Meier analyses with both median-based and optimized cutoffs after excluding samples with missing survival data or follow-up under 30 days.
- The workflow separately evaluates UBE2C, associated with a proliferative tumor subpopulation, as a candidate overall-survival marker.
- The article presents methodology and code but does not provide enough reported statistics here to establish prognostic significance.