Sensitive Glioma Detection and Recurrence Monitoring Using a Machine Learning Model Based on Circulating Monocytes
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Background
Glioma induces profound systemic immune alterations despite its anatomical confinement to the central nervous system. Circulating immune cells, particularly monocytes, are key mediators of tumor–host crosstalk and may retain tumor-induced transcriptional imprints. However, their potential clinical utility as blood-based biomarkers for detection and monitoring, remain largely unexplored.
Methods and findings
In this study, we performed integrated single-cell RNA sequencing of blood immune cells and demonstrated that circulating CD14⁺ monocytes are significantly expanded in glioma patients, exhibiting features of differentiation arrest and increased transcriptional plasticity. These cells harbor glioma-specific molecular signatures distinct from those observed in healthy controls and patients with other tumors. Leveraging these findings, we developed an ensemble machine learning diagnostic model based on transcriptomic profiles of circulating CD14⁺ monocytes (training cohort, n=107), which achieved a mean area under the receiver operating characteristic curve (AUC) of 0.971 during cross-validation. In an independent cohort of 567 participants, the model maintained high diagnostic accuracy, yielding an AUC of 0.877 for distinguishing glioma from controls and other tumors. And it achieved a recurrence detection AUC of 0.969 in 51 postoperative samples. Moreover, in a follow-up study involving 30 glioma patients, lower model-derived scores of postoperation were significantly associated with prolonged progression-free survival (log-rank test, P=0.043), supporting its prognostic utility.
Conclusion
We demonstrate circulating CD14⁺ monocytes undergo glioma-specific transcriptional reprogramming, generating systemic tumor-associated signal captured via transcriptomic profiling. This blood-based diagnostic model provides non-invasive, scalable approach for glioma detection, recurrence surveillance, outcome prediction.
Author summary
Why was this study done?
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Diagnosis and recurrence monitoring for glioma remain challenging with current MRI and biopsy.
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Gliomas alter systemic immunity, but whether circulating monocytes carry tumor-specific signals remains unclear.
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We aimed to develop a blood-based test using circulating monocyte transcriptomes for glioma detection and monitoring.
What did the researchers do and find?
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Single-cell RNA-seq revealed that glioma patients have more CD14⁺ monocytes with abnormal differentiation.
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We built a machine learning model based on monocyte gene expression. It achieved a diagnostic AUC of 0.877 in 567 independent samples and a recurrence detection AUC of 0.969 in 51 postoperative samples.
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In a follow-up study of 30 patients, lower postoperative model-derived scores predicted longer progression-free survival ( P = 0.043).
What do these findings mean?
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Circulating monocytes capture glioma-specific transcriptional reprogramming features, enabling non-invasive liquid biopsy.
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The model may help distinguish recurrence from pseudo-progression and guide postoperative risk stratification.
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Larger prospective multicenter validation studies are needed to further confirm clinical generalizability.
