Data Sheet 4_Unveiling prognostic genes and regulatory mechanisms of stress granules in ga

A 7.3 KB CSV file by Ruilong Kou, last updated March 2026, containing data from an integrated analysis of bulk transcriptomics and single-cell RNA sequencing for gastric cancer. The study identifies a four-gene signature (SERPINE1, CD36, MMRN1, GRP) linked to stress granules and patient prognosis, with a validated risk score model.

Use Cases

• Building prognostic risk models for gastric cancer based on the identified four-gene signature.
• Analyzing immune cell infiltration patterns, such as correlations with mast cells, based on gene expression data.
• Conducting functional enrichment analysis for pathways like neuroactive ligand-receptor interaction and ECM-receptor interaction.
• Investigating chemotherapy drug sensitivity differences between high-risk and low-risk patient groups.
• Identifying key cell types, such as endothelial cells, associated with prognostic gene expression using single-cell data.

Strengths

• Data is derived from an integrated analysis combining bulk transcriptomics and single-cell RNA sequencing.
• Identifies a specific four-gene prognostic signature (SERPINE1, CD36, MMRN1, GRP) with a validated risk model.
• Analysis includes specific correlation findings (e.g., genes correlated with mast cells, r > 0.3, P < 0.001).

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• The 7.3 KB file size suggests a very limited scope, likely containing summary or processed results rather than raw sequencing data.

Provenance

Source

figshare, author Ruilong Kou.

Collection Method

Data acquired from public databases and literature, analyzed via univariate Cox regression, risk modeling, GSEA, and scRNA-seq cell type identification.

Time Range

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Freshness

Last updated 2026-03 17 13:05:14.

Geography

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