N7-methylguanosine (m7G) modification signature has recently emerged as a crucial regulator of tumor progression and treatment in cancer. However, there is limited information available on the genomic profile of lower-grade gliomas (LGGs) related to m7G methylation modification genes’ function in tumorigenesis and progression. In this study, we employed bioinformatics methods to characterize m7G modifications in individuals with LGG from The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). We used gene set enrichment analysis (GSEA), single sample GSEA (ssGSEA), CIBERSORT algorithm, ESTIMATE algorithm, and TIDE to evaluate the association between m7G modification patterns, tumor microenvironment (TME) cell infiltration properties, and immune infiltration markers. The m7G scoring scheme using principal component analysis (PCA) was employed to investigate the m7G modification patterns quantitatively. We examined the m7G modification hub genes' expression levels in normal samples, refractory epilepsy samples, and LGG samples using immunohistochemistry, western-blotting, and qRT-PCR. Our findings revealed that individuals with LGG could be categorized into two groups based on m7G scores (high and low) according to the properties of m7G. Moreover, we observed that high m7G score was associated with significant clinical benefit and prolonged survival duration in the anti-PD-1 cohort, while low m7G score was associated with improved prognostic outcomes and increased likelihood of complete or partial response in the anti-PD-L1 cohort. Different m7G subtypes also showed varying Tumor Mutational Burden (TMB) and immune profiles and might have distinct responses to immunotherapy. Furthermore, we identified five potential genetic markers that were highly correlated with the m7G score signature index. These findings provide insight into the features and classification associated with m7G methylation modifications and may aid in improving the clinical outcome of LGG.

Background: N7-methylguanosine (m7G) modification signature has been found recently in cancer and is now known as an essential regulator of tumor progression and treatment. As a result of the function of m7G methylation modification genes in tumorigenesis and progression, there isn't much information related to the genomic profile of lower-grade gliomas(LGGs). Methods: In this research, bioinformatics analysis methods were used to characterize m7G modifications in individuals with LGG from The Chinese Glioma Genome Atlas(CGGA) and The Cancer Genome Atlas(TCGA). Afterward, gene set enrichment analysis(GSEA), single sample GSEA(ssGSEA), CIBERSORT algorithm, ESTIMATE algorithm, R package "GSVA," and TIDE were used to evaluate the association between m7G modification patterns, TME cell infiltration properties as well as the correlation regarding immune infiltration markers. The m7G scoring scheme using principal component analysis(PCA) was employed to investigate the m7G modification patterns of individual tumors quantitatively. We examined the m7G modification hub genes' expression levels in normal samples, refractory epilepsy samples, and lower-grade glioma samples using immunohistochemistry, western-blotting, and QRT-PCR. Result: It was discovered that individuals with LGG were categorized into two groups in terms of m7G scores (high and low) as per the properties of m7G. After observing the anti-PD-1 cohort, it has been noted that individuals having a high m7G score had significant clinical benefit, along with considerably prolonged survival duration. Opposing this, individuals in the anti-PD-L1 cohort having low m7G scores had improved prognostic outcomes and were more likely to have CR(Complete Response) / PR(Partial Response). Different m7G subtypes have different TMB(Tumor Mutational Burden) and immune profiles and might have varied responses to immunotherapy. Moreover, five potential genetic markers were found that were highly correlated with the index of the m7G score signature. Conclusion: Conclusively, a thorough investigation of the features and classification associated with m7G methylation modifications may improve the clinical outcome of LGG.

Lower WHO grade II and III gliomas (LGGs) are characterized by widespread genetic and transcriptional heterogeneity, yet little is known about the heterogeneity of the DNA Damage Repair (DDR), its function in tumor biology, coupling with the transcriptome and tumor microenvironment (TM) and its possible impact for tumor development. In this study, the DNA damage repair (DDR) alterations among LGG have been studied via multi-omics data integration. For this, LGG patients have been grouped into distinct subtypes (DDR-activated and the DDR-suppressed subtype) based on different clinicalparameters and molecular characteristics. The differences in gene mutation, immune spectrum, and immune cell infiltration between the two DDR subtypes were compared. We generated DDR subtype features (DDR scores) to implement DDR classification in LGG and confirmed the results using a multi-layer data cohort.The DDR activation subgroup was found to be associated with poorer overall survival based on molecular subtypes, and clinicopathological features of advanced-age and higher-grade were more common in the DDR-activated subgroup. DDR-suppressed subtypes also exhibited more frequent mutations in IDH1. We also found significant upregulation of activated immune cells in the DDR-activated subgroup, which indicates significant influence posed by infiltration of immune cells on tumor progression as well as immunotherapeutic responses. In addition, six DDR genes were selected to construct a DDR signature of LGG, dividing patients into low- and high-risk groups. The QRT-PCR results showed that the expression levels of CDK1, CDK2, TYMS, SMC4, and WEE1 were found to be considerably higher for LGG samples compared to normal brain tissue samples (p < 0.05).In conclusion, our work sheds light on the DDR heterogeneity of LGG and advances our knowledge of the molecular pathways in DDR that lead to LGG.

After online publication [1], the authors identified that Fig. 10 contains a mistake in the graphic combination, which has resulted in two Western blot prints being repeated in the same figure. For the sake of aesthetics, the authors had opened the AI with the same WB strip name for the two groups. When the PS file was re-imported, the PS file with the same name was placed into the AI image, resulting in the coincidence of the two groups. The corrected Fig. 10 is given in this Correction article. The legend of Fig. 8 was incompletely published in the original version. The corrected legend of Fig. 8 to avoid confusion about "pockets" was given below. Figure 8 legend: A m7G score-hub gene network for the top 5 most highly regulated genes. B Combination pattern diagram of Bleomycin and EIF4E. Yellow represents hydrogen bonding, and Amino acid residue includes ALA229, HIS228, ASN72, SER85, ARG87, ILE89, ASP71, ASP116, LYS183, and LYS138. C Combination pattern diagram of Etoposide and EIF4E2. Yellow represents hydrogen bonding, Amino acid residue includes SER24, THR22, SER64, and THR99. D Combination pattern diagram of Bleomycin and EIF4E3. Yellow represents hydrogen bonding, Amino acid residue includes ARG152, LEU83, ALA49, GLU93, ARG95, HIS194, and LYS192. E Combination pattern diagram of Bleomycin and NCBP1. Yellow represents hydrogen bonding; Amino acid residue includes LYS650, ARG610, ARG646, GLN753, ASP369, LYS455, ARG458, and GLN599. F Combination pattern diagram of Etoposide and NCBP2. Yellow represents hydrogen bonding,