Sepsis-Related Cell Senescence Genes and Potential Drugs Identified via Bioinformatics

Shuaijie Pei's dataset on figshare contains bioinformatics and machine learning analysis results for sepsis-related cellular senescence. It identifies eight differentially expressed senescence-related genes and potential targeted drugs from the DSigDB database. The dataset is 123.5 MB in size and was last updated on June 1, 2026.

Use Cases

• Validate diagnostic biomarkers for sepsis based on the eight identified hub genes.
• Investigate immune cell infiltration patterns in sepsis using the immunoinfiltration analysis results.
• Screen for potential drug candidates targeting sepsis-induced cellular senescence using the DSigDB-derived compound list.
• Model protein-ligand interactions for sepsis treatment based on the binding energy data for fenofibrate.

Strengths

• Identifies eight specific hub genes (TXN, CDKN1C, UTP6, BCL11B, SMAD3, ITPKB, PRPF19, BCL2) with diagnostic performance for sepsis.
• Includes validation of six hub genes across training, validation, and experimental sample sets.
• Links hub genes to specific immune cell types (e.g., eosinophils, macrophages M1/M2, NK cells, T cells CD8, Tregs).
• Provides a list of targeted compounds or drugs from the DSigDB database, with specific interactions noted for CDKN1C, BCL2, and SMAD3.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect methodological bias inherent to the specific bioinformatics and machine learning pipeline used.

Provenance

Source

Shuaijie Pei via figshare

Collection Method

Bioinformatics analysis and machine learning applied to unspecified source data.

Freshness

Last updated 2026-06-01 02:20:11; freshness should be verified.