Ovarian Cancer Chemoresistance Network Pharmacology Analysis

26 overlapping genes were identified as shared targets of hydroxychloroquine and sulfasalazine in ovarian cancer models. The study classifies patients into chemoresistant and chemosensitive subgroups and establishes a seven-hub-gene predictive signature. It integrates transcriptomics, network pharmacology, and clinical cytokine data to investigate microenvironment remodeling.

Use Cases

• Train a classifier to predict chemoresistant (Subtype 1) vs. chemosensitive (Subtype 2) patient subgroups using the identified gene expression profiles.
• Validate the seven-hub-gene predictive signature against independent clinical outcome data for prognostic modeling.
• Analyze cytokine signaling data to characterize the localized immunosuppressive niche in the tumor microenvironment.
• Perform molecular docking simulations using predicted binding affinity scores (e.g., -6.99 kcal/mol for SSZ–TNF) to prioritize drug-target interactions.

Strengths

• Analysis identifies 26 overlapping genes as key shared therapeutic targets.
• Establishes a seven-hub-gene signature for predicting chemoresistance.
• Integrates multiomics data including transcriptomics, network pharmacology, and clinical validation.

Limitations

• The dataset is very small at 474.5 KB, indicating limited raw data or processed summary results.
• Underlying sample sizes for transcriptomic models and clinical cytokine profiling are not specified.
• Data represents a specific research analysis; direct application may require expert biological domain knowledge.

Provenance

Source

figshare, author Xin Hong.

Collection Method

Multiomics strategy integrating transcriptomics from resistant and sensitive ovarian cancer models with network pharmacology, consensus clustering, machine learning, and molecular docking.

Time Range

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Freshness

Last updated March 2026.

Geography

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