Multi-Omics Data on Gastric Cancer Chemoresistance via PARP16-UPR Axis

A multi-omics dataset from a study investigating cisplatin resistance in gastric cancer. The data underpins an analysis integrating transcriptomics from GEO datasets GSE14210 and GSE31811, machine learning feature selection, and Mendelian randomization. It was authored by Shuchang Tao and last updated on May 1, 2026.

Use Cases

• Identifying differentially expressed genes associated with chemoresistance based on transcriptomic data from GEO.
• Validating machine learning feature selection models for predicting drug resistance based on the 113 model combinations tested.
• Investigating causal relationships between genes and chemoresistance using Mendelian randomization based on eQTL and GWAS data.
• Analyzing cell-type-specific expression patterns in cisplatin-resistant cells based on single-cell dataset GSE183904.

Strengths

• Integrates multiple data types and analytical methods, including transcriptomics, machine learning, and Mendelian randomization.
• Includes results from in vitro cellular validation, such as a 3.1-fold PARP16 expression increase and measured cisplatin IC50 values.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data file is a 4.4 MB PDF, which may require extraction of underlying data.

Provenance

Source

Gene Expression Omnibus (GEO) databases GSE14210 and GSE31811.

Collection Method

Transcriptomic analysis, machine learning modeling, Mendelian randomization, and in vitro cellular assays.

Freshness

Last updated 2026-05-01 04:18:55; freshness should be verified.