Alpha Internexin: A Surrogate Marker for 1p/19q Codeletion and Prognostic Marker in Anaplastic (WHO grade III) Gliomas

Reliability of IDH1-R132H and ATRX and/or p53 immunohistochemistry for molecular subclassification of Grade 2/3 gliomas

Since the World Health Organization 2016 classification (2016 WHO), genetic status has been incorporated into the diagnosis of Grade 2/3 gliomas (lower-grade gliomas). Therefore, immunohistochemistry (IHC) of IDH1-R132H, ATRX, and p53 have been used in place of genetic status. We report the associations between histological findings, IHC, and genetic status. We performed IHC of IDH1-R132H, ATRX, and p53 in 76 lower-grade gliomas and discussed its validity based on the 2016 WHO and the upcoming 2021 WHO classification. The sensitivity and specificity of anti-ATRX, p53, and IDH1-R132H IHC were 40.9%/98.1%, 78.6%/85.4%, and 90.5%/84.6%, respectively. Among 21 IDH1-mutant gliomas without 1p/19q codeletion, two gliomas (9.5%) mimicked the so-called classic for oligodendroglioma (CFO) in their morphology. Of the 42 gliomas with 1p/19q codeletion, four cases were difficult to diagnose as oligodendroglioma through morphological examination. Moreover, there were three confusing cases with ATRX mutations but with retained ATRX-IHC positivity. The lessons learned from this study are as follows: (1) ATRX-IHC and p53-IHC should be supplementary to morphological diagnosis, (2) rare IDH mutations other than IDH1 R132H should be considered, and (3) there is no complete alternative test to detect molecular features of glioblastoma under the 2021 WHO classification.

Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso

Background and Purpose

Lower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model.

Methods

In this research, differentially expressed genes based on tumor microenvironment was obtained to further analysis. Log-rank test was used to identify genes in patients who received radiotherapy and patients who did not receive radiotherapy, respectively. Then, spike-and-slab lasso was performed to select genes in patients who received radiotherapy. Finally, three genes (INA, LEPREL1 and PTCRA) were included in the model. A radiosensitivity-related risk score model was established based on overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset that PFS as endpoint and two CGGA datasets that OS as endpoint. A novel nomogram integrated risk score with age and tumor grade was developed to predict the OS of LGG patients.

Results

We developed and verified a radiosensitivity-related risk score model. The radiosensitivity-related risk score is served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, the nomogram integrated risk score with age and tumor grade was established to perform better for predicting 1, 3, 5-year survival rate.

Conclusions

This model can be used by clinicians and researchers to predict patient’s survival rates and achieve personalized treatment of LGG.

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy

Background

The tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy.

Methods

The data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The Cox and LASSO regression analysis was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient’s response to immunotherapy and chemotherapy, respectively.

Results

The prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment.

Conclusion

An immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.