Multi-Omics and MR Analysis of PARP16–UPR Axis in Gastric Cancer Chemoresistance

827 differentially expressed genes were identified from transcriptome datasets GSE14210 and GSE31811, focusing on gastric cancer chemoresistance. The dataset, authored by Shuchang Tao and last updated in May 2026, integrates machine learning, Mendelian randomization, and single-cell data to highlight the role of PARP16 and the unfolded protein response. In vitro experiments showed a 3.1-fold increase in PARP16 expression in resistant cells and a reduction in cisplatin IC50 from 11.82 μg/mL to 4.67 μg/mL upon pathway inhibition.

Use Cases

• Identify key genes associated with cisplatin resistance in gastric cancer based on the 827 differentially expressed genes.
• Validate causal relationships between candidate genes and chemoresistance using Mendelian randomization analysis based on eQTL and GWAS data.
• Examine cell-type-specific expression patterns of resistance markers using the single-cell dataset GSE183904.
• Test machine learning models for feature selection in chemoresistance studies based on the 113 model combinations applied.
• Investigate the therapeutic potential of targeting the PARP16–UPR axis to reverse chemoresistance based on the in vitro experimental results.

Strengths

• Integrates multiple data types, including transcriptomic data from 827 differentially expressed genes, machine learning results from 113 model combinations, and single-cell expression data.
• Includes in vitro validation with specific quantitative results, such as a 3.1-fold PARP16 expression increase and IC50 values of 11.82 μg/mL and 4.67 μg/mL.
• Applies a structured analytical framework combining differential expression, enrichment analysis, Mendelian randomization, and cellular assays.

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 a 4.4 MB PDF file, which may require extraction and parsing to access underlying tabular data.

Provenance

Source

Transcriptome datasets GSE14210 and GSE31811 from the Gene Expression Omnibus (GEO) database, and single-cell dataset GSE183904.

Collection Method

Data obtained from public GEO repositories, analyzed using differential expression, machine learning, Mendelian randomization, and validated with in vitro cellular assays.

Freshness

Last updated 2026-05-01 04:18:54; freshness should be verified.