Integrated genomic and machine learning analyses identify SPOP as a candidate biomarker associated with tumor-immune features in nasopharyngeal carcinoma.
TL;DR
SPOP was positively correlated with estimated CD8⁺ T-cell infiltration and was associated with altered immune-related features and was associated with altered tumor-immune interactions in NPC.
Abstract
Background
The efficacy of immunotherapy in nasopharyngeal carcinoma (NPC) is limited by heterogeneous mechanisms of immune evasion. Identifying robust molecular biomarkers that predict this phenotype is crucial for patient stratification. This study aimed to identify candidate genes derived from a predefined methylation-related gene set and to evaluate their association with tumor-immune features in NPC using an integrative computational and experimental approach.
Methods
We performed a comprehensive analysis of NPC transcriptomic datasets combined with a curated methylation-related gene set. Differentially expressed genes were intersected with the predefined methylation-related gene set, followed by machine-learning-based candidate prioritization. Candidate genes were prioritized through a machine learning pipeline, prioritizing genes with both diagnostic power and biological relevance to the tumor immune microenvironment. Among the four retained genes, SPOP was prioritized as the lead candidate for downstream validation and was further evaluated for its diagnostic performance, immune relevance, and functional role in vitro.
Results
The machine learning-driven analysis prioritized SPOP, ZNF667, TADA2B, and ABLIM1 as candidate genes with diagnostic and prognostic relevance, with SPOP selected for subsequent functional validation. SPOP was positively correlated with estimated CD8⁺ T-cell infiltration and was associated with altered immune-related features. Experimental validation showed that SPOP knockdown inhibited NPC cell proliferation and increased apoptosis. In tumor cell-T-cell co-culture, SPOP knockdown was associated with greater tumor-cell killing and higher IFN-γ and IL-2 levels. Molecular analyses further showed that SPOP modulation was accompanied by changes in SOCS3 expression and STAT3 phosphorylation, whereas CHX chase assays suggested altered SOCS3 protein stability.
Conclusion
This integrative study identifies SPOP as a candidate regulator associated with NPC cell phenotypes and altered tumor-immune interactions. SPOP modulation was accompanied by changes in SOCS3 abundance and STAT3 phosphorylation, while SPOP knockdown altered tumor-cell survival and cytokine-related readouts in the T-cell co-culture system. Further biochemical, rescue, and in vivo studies are required.