Leveraging proteomics and machine learning for mechanism and biomarker discovery on glioma progression and transformation: from LGG to GBM.

BACKGROUND

Glioma was the most common malignant tumor of the central nervous system in adults. Low-grade gliomas (LGGs) have a potential of grade progression and histological transformation, and the relevant mechanisms of this malignant evolution were still unclear.

METHODS

In this study, we used 61 primary-recurrent paired glioma samples from 28 patients for proteomics analysis based upon DIA-NN approach.

RESULTS

Our results indicated that the Ras/p38-MAPK pathways were hub signaling pathways driving grade progression and histological transformation in LGG. The activation of non-MGMT dependent unspecific DNA damage repair system mediated by Replication Factor C (RFC) can be an important reason for the treatment resistance. Moreover, metabolic reprogramming was widely involved in the regulation of LGG grade progression and histological transformation. The enhanced synthesis of unsaturated fatty acids and the significantly activated peroxisomal fatty acid beta-oxidation pathways were metabolic characteristics of LGG after histological transformation. Last, we constructed a reliable LGG progression prediction model consisted of 2 proteins, which can be monitor biomarkers for LGG progression, with potential clinical translation value.

CONCLUSIONS

In summary, glioma grade progression and histological transformation were complex biological processes, which involved the abnormal up-regulation of Ras/p38-MAPK signalings, enhanced non-MGMT dependent unspecific DNA damage repair system regulated by RFC complex and metabolic reprogramming based on HK1-PFKP-ENO2 mediated glycolysis and peroxisomal FA beta oxidation. This study filled the gap in relevant research on the grade progression and histological transformation of LGG, providing novel and unique insights into the biological mechanisms of glioma progression.