Histone Chaperone and Mendelian Randomization Analysis for Esophageal Cancer Biomarkers

An analysis dataset from a study integrating transcriptomic stratification and two-sample Mendelian randomization to identify prognostic biomarkers in esophageal cancer. The data likely contains results from RNA-seq and clinical data for 184 tumors from TCGA-ESCA and 119 tumors from an external cohort, identifying ADORA2B and SAPCD2 as candidate genes. The dataset was authored by Bihan Xia and last updated on April 13, 2026.

Use Cases

• Validate prognostic biomarker candidates like ADORA2B and SAPCD2 based on the described Mendelian randomization and expression analysis.
• Investigate associations between histone chaperone-related gene expression and patient survival outcomes in esophageal cancer.
• Model predicted drug sensitivity (e.g., to vinorelbine, etoposide, paclitaxel) based on gene expression groups described in the study.
• Perform immune deconvolution analysis based on the CIBERSORT results mentioned in the description.
• Build or validate a clinicogenomic prognostic nomogram combining gene expression with stage and nodal status.

Strengths

• Analysis integrates data from two cohorts: TCGA-ESCA (184 tumors) and an external validation cohort (GSE53624, 119 tumors).
• Methodology includes two-sample Mendelian randomization using an EC GWAS with 998 cases and 475,308 controls.
• Candidate biomarkers ADORA2B and SAPCD2 were validated via RT-qPCR in paired tissues as mentioned in the description.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 234.9 KB, indicating limited raw data scope.

Provenance

Source

figshare, authored by Bihan Xia.

Collection Method

Analysis of RNA-seq and clinical data from TCGA-ESCA and GSE53624, followed by Mendelian randomization and validation steps.

Time Range

Temporal coverage of the source data is not specified.

Freshness

Last updated 2026-04-13 04:31:02; freshness should be verified.

Geography

The GWAS data used is described as from European ancestry populations; geographic coverage of the tumor cohorts is not specified.