DOI: 10.1097/js9.0000000000005601 ISSN: 1743-9159

Genomic profiles of anatomically distinct single-lobe and multi-lobe gliomas

Ying Wang, Zhansheng Fang, Kang Xie, Jingxing Leng, Kang Yang, Tao Song, Tiangxiang Huang, Jun Wu, Longbo Zhang

Background:

A critical aspect of glioma biology is their presentation as either single-lobe gliomas (SLGs) or multi-lobe gliomas (MLGs). Although MLGs are often presumed to represent more advanced disease stages, emerging evidence suggests that they may arise as distinct biological entities with unique genetic and molecular characteristics.

Methods:

In this multi-center retrospective study, we conducted a comprehensive genomic and molecular analysis of 174 glioma samples collected from 2018 to 2021, focusing on the mutational landscapes associated with SLGs and MLGs. Tumor samples were collected during operation and stored at –80 ° C. All DNA sequencing was performed using Illumina HiSeq. Tumor sites were defined based on pre-operative magnetic resonance imaging scans. R package “Maftools” was used for visualization of genomic mutations and statistical analyses.

Materials and results:

Our findings revealed that MLGs are associated with significantly shorter progression-free survival compared to SLGs. While canonical glioma-associated mutations, such as those in TERT, TP53, IDH, ATRX , and PTEN , were present in both groups, SLGs exhibited higher frequencies of mutations in ASXL1, CIC , and APC . This mutational enrichment was also conserved within the GBM subgroup, where single-lobe glioblastomas predominantly exhibited mutations in ASXL1, PIK3R1, EPHA2, IRS2, KDM5C, NOTCH4, TET2 , and TSC2 . Furthermore, pathway enrichment analysis revealed that SLGs showed enhanced activation of receptor tyrosine kinase–RAS (RTK-RAS), PI3K, and cell cycle signaling pathways, whereas MLGs demonstrated greater genomic instability and higher mutant allele frequencies.

Conclusion:

This study delineates the genetic and molecular distinctions between SLGs and MLGs, offering new insights into glioma heterogeneity and disease progression. These findings highlight the need to account for anatomical dispersion when interpreting glioma genomic profiles and could inform future stratified treatment approaches.

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