The investigation used two publicly available gene-expression datasets from the Gene Expression Omnibus. GSE26051 served as the training cohort, while differential-expression results from GSE180836 contributed to candidate-gene discovery and screening. Because GSE180836 informed candidate selection, it was not used as an independent validation cohort. The selected biomarkers were instead assessed by reverse-transcription quantitative PCR, or RT-qPCR, in a separate clinical cohort containing 30 participants. This design meant that the principal laboratory confirmation came from clinical samples rather than from a second independent analysis of the public transcriptomic datasets.

To identify differentially expressed genes associated with the macrophage polarization-related transcriptional signature, the researchers combined differential-expression analysis with weighted gene co-expression network analysis, known as WGCNA. Three machine-learning algorithms were then applied to screen candidate biomarkers: least absolute shrinkage and selection operator, support vector machine-based recursive feature elimination, and the Boruta algorithm. The analysis selected PTCHD1, NEURL1, and ST8SIA5, all of which were significantly downregulated in the training cohort GSE26051.

Clinical testing supported two of the three candidates more clearly than the third. PTCHD1 and NEURL1 were each significantly downregulated in tendinopathy samples relative to controls in the cohort of 30, with P values below 0.01 for both comparisons. ST8SIA5 also exhibited a downward trend, but the difference did not reach statistical significance, with P = 0.074. On this evidence, PTCHD1 and NEURL1 were the more strongly supported markers associated with tendinopathy, while ST8SIA5 remained a candidate showing a consistent but inconclusive expression pattern.

The researchers also constructed a diagnostic nomogram from the selected biomarkers and evaluated it using receiver operating characteristic and calibration analyses. The model achieved an area under the ROC curve above 0.7 and showed good calibration. These results indicate that the model discriminated between the groups represented in the study, but the reported evidence does not establish its diagnostic performance in larger or more diverse populations. The distinction is particularly important because the public dataset GSE180836 contributed to candidate discovery rather than serving as an independent validation set, while the clinical cohort used for RT-qPCR confirmation included only 30 participants.

Functional enrichment and gene-set analyses associated the biomarker-related network with pathways including PI3K–Akt signaling and extracellular matrix–receptor interaction. These are computational associations within the studied expression data, not evidence that PTCHD1 or NEURL1 directly controls macrophage polarization or that either gene is a therapeutic target. Similarly, a drug–gene interaction analysis identified 16 potential therapeutic compounds, but the source reports network-level candidates rather than experimental tests of treatment effects. The study’s strongest supported finding is therefore the clinical association of reduced PTCHD1 and NEURL1 expression with tendinopathy, alongside a gene-based model that showed discrimination above 0.7 AUC in the available validation material.

Subject of Research: Macrophage-associated transcriptional biomarkers for tendinopathy

Article Title: Identification of genes associated with a macrophage polarization-related transcriptional signature as biomarkers for tendinopathy based on bioinformatics analysis

Article References: Ren, L., He, C., & Qin, Y. (2026). Identification of genes associated with a macrophage polarization-related transcriptional signature as biomarkers for tendinopathy based on bioinformatics analysis. Scientific Reports. https://doi.org/10.1038/s41598-026-75576-z

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75576-z

Keywords: tendinopathy, macrophage polarization, PTCHD1, NEURL1, ST8SIA5, gene biomarkers, RT-qPCR, WGCNA, machine learning, diagnostic nomogram, Identification, genes