CCL2 and PAK6 as Candidate Biomarkers of Neuroinflammation in Parkinson’s Disease: An Integrated Machine Learning and Single-Nucleus Transcriptomic Study

Background: Neuroinflammation is recognized as a key contributor to Parkinson’s disease (PD), but the relationships between inflammatory signaling, immune-state alterations, and cell-type-specific transcriptional programs remain unclear. Methods: Public transcriptomic datasets, including GSE20141 (discovery cohort) and the substantia nigra subset of GSE114517 (external validation cohort), were analyzed. Genes identified by exploratory differential-expression screening in the discovery cohort were intersected with predefined inflammation- and chemokine-related gene sets to define a candidate space for downstream prioritization. Protein–protein interaction, Gene Ontology, KEGG, and immune-signature analyses were performed, followed by machine learning-based feature prioritization using Elastic Net, support vector machine-recursive feature elimination, and random forest. Prioritized candidates were further evaluated by cross-platform validation, single-nucleus transcriptomic mapping, and a hypothesis-generating in silico perturbation analysis in PD astrocytes. Results: Seventeen genes were retained at the intersection of PD-related differentially expressed genes and inflammation-/chemokine-associated gene sets. These candidates formed a response module enriched in mitochondrial organization, oxidative phosphorylation, and mitophagy pathways. Immune-signature analysis suggested an altered transcriptome-derived immune landscape in PD, with changes in NK cell-related signatures and significant correlations between immune-state scores and the candidate genes. Machine learning-based prioritization yielded five shared candidates, of which only CCL2 and PAK6 showed same-direction support with nominal significance in the external validation cohort. Single-nucleus transcriptomic analysis localized CCL2 predominantly to astrocytes, whereas PAK6 was more strongly associated with neuronal populations, particularly OTX2-positive ventral midbrain neurons. In silico perturbation analysis further predicted that CCL2 suppression in PD astrocytes may be associated with translational- and ribosome-related regulatory programs. Conclusions: CCL2 and PAK6 emerged as prioritized candidate biomarkers associated with PD-related inflammatory and chemokine-linked transcriptional alterations in the substantia nigra. More broadly, this study provides a multi-layered framework for candidate prioritization, cross-platform validation, and cell-type-level contextualization in PD neuroinflammation. Because the study is computational and the perturbation analysis is predictive, orthogonal experimental validation will be required to determine whether CCL2 and PAK6 are biomarkers of disease-associated transcriptional states, functional contributors to PD pathogenesis, or both.