LRP1 Diagnostic Model for Endometriosis: Multi-Omics and Single-Cell Evidence

1,404 differentially expressed genes were identified from three merged GEO datasets (GSE7305, GSE11691, GSE25628) to study endometriosis pathogenesis. The dataset contains results from a machine learning algorithm that identified 30 hub genes, with LRP1 showing the highest diagnostic performance, supported by immune profiling and Mendelian randomization analysis. It was authored by Chengmao Xie and last updated on April 15, 2026.

Use Cases

• Developing diagnostic models for endometriosis based on the 30 identified hub genes.
• Analyzing the role of LRP1 in M2 macrophage infiltration based on the reported correlation (r=0.62).
• Investigating cell-cell communication in endometriosis lesions via the MIF signaling pathway, as suggested by single-cell RNA sequencing results.
• Validating causal relationships in disease progression using the provided Mendelian randomization odds ratio (OR = 1.35).

Strengths

• Integrates results from three distinct GEO datasets, providing a multi-source foundation.
• Includes specific statistical results, such as the correlation between LRP1 and M2 macrophages (r=0.62, p<0.001) and a causal odds ratio (OR = 1.35).
• Combines multiple analytical methods: WGCNA, machine learning, immune profiling, Mendelian randomization, and single-cell RNA sequencing.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is small (93.5 KB), indicating limited scope, likely containing summary results rather than raw sequencing data.

Provenance

Source

figshare, author Chengmao Xie.

Collection Method

Integrated analysis of three public GEO datasets (GSE7305, GSE11691, GSE25628) using WGCNA, machine learning, and single-cell RNA sequencing.

Time Range

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Freshness

Last updated 2026-04-15 04:22:10; freshness should be verified.

Geography

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