Decoding lactylation in neuropathic pain: Immune cell infiltration patterns and machine learning-identified candidate...

Area of Science:

• Biochemistry and Molecular Biology
• Immunology
• Genetics

Background:

• Neuropathic pain (NP) pathogenesis remains incompletely understood.
• Lactylation, a post-translational modification, is implicated in various biological processes.
• Identifying novel biomarkers for NP is crucial for improved diagnosis and treatment.

Purpose of the Study:

• To identify lactylation-associated genes linked to immune infiltration in neuropathic pain.
• To explore the diagnostic potential of these genes using bioinformatic and machine learning approaches.
• To investigate the correlation between candidate biomarkers and immune cell distribution in NP.

Main Methods:

• Integrated analysis of microarray datasets (GSE124272, GSE150408, GSE95849) from NP patients and healthy controls.
• Application of machine learning algorithms (LASSO, SVM-RFE, Random Forest) for diagnostic gene signature identification.
• Functional enrichment analyses (GO, KEGG) and immune infiltration analysis.

Main Results:

• Five potential diagnostic genes (CYP27A1, ELAC2, TMEM126B, LYRM7, PHKB) were identified.
• CYP27A1 and PHKB were validated as robust diagnostic biomarkers in an independent dataset.
• Significant alterations in 19 immune cell types were observed, with CYP27A1 and PHKB correlating with immune cell distribution.

Conclusions:

• CYP27A1 and PHKB are identified as potential lactylation-associated biomarkers for neuropathic pain.
• These biomarkers offer insights into NP pathogenesis and immune system involvement.
• The findings provide a theoretical basis for enhanced diagnostic strategies for NP.