Gastric Cancer Prognostic Gene Signature from Integrated Transcriptomic Analysis

A study by Ruilong Kou, last updated March 2026, identifies a four-gene signature related to stress granules for predicting gastric cancer patient survival. The 19.5 KB dataset contains results from an integrated analysis of bulk transcriptomics and single-cell RNA sequencing data from public databases. It includes a validated risk score model and associated analyses of immune infiltration and drug sensitivity.

Use Cases

• Building prognostic risk models for gastric cancer based on the identified four-gene signature (SERPINE1, CD36, MMRN1, GRP).
• Analyzing immune cell infiltration patterns in gastric cancer based on correlations with prognostic genes.
• Investigating chemotherapy drug sensitivity in gastric cancer patient subgroups based on the described risk stratification.
• Identifying key cell populations, such as endothelial cells, associated with prognostic gene expression using single-cell data.

Strengths

• Dataset is based on an integrated analysis of both bulk and single-cell RNA sequencing data.
• Risk model was validated in an independent cohort, suggesting predictive performance.
• Includes analysis of multiple biological aspects: functional pathways, immune infiltration, and drug sensitivity.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small (19.5 KB), indicating it likely contains summary results rather than raw sequencing data.
• Description metadata is limited; actual data quality and completeness require manual inspection after download.

Provenance

Source

figshare, author Ruilong Kou.

Collection Method

Integrated analysis of public gastric cancer transcriptomic data and stress granule-related genes from literature.

Time Range

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Freshness

Last updated 2026-03-17 13:05:11; freshness should be verified.

Geography

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