Targeted massively parallel sequencing of candidate regions on chromosome 22q predisposing to multiple schwannomas: An analysis of 51 individuals in a single-center experience

Screening of potential novel candidate genes in schwannomatosis patients

Schwannomatosis comprises a group of hereditary tumor predisposition syndromes characterized by, usually benign, multiple nerve sheath tumors, which frequently cause severe pain that does not typically respond to drug treatments. The most common schwannomatosis‐associated gene is NF2, but SMARCB1 and LZTR1 are also associated. There are still many cases in which no pathogenic variants (PVs) have been identified, suggesting the existence of as yet unidentified genetic risk factors. In this study, we performed extended genetic screening of 75 unrelated schwannomatosis patients without identified germline PVs in NF2, LZTR1, or SMARCB1. Screening of the coding region of DGCR8, COQ6, CDKN2A, and CDKN2B was carried out, based on previous reports that point to these genes as potential candidate genes for schwannomatosis. Deletions or duplications in CDKN2A, CDKN2B, and adjacent chromosome 9 region were assessed by multiplex ligation‐dependent probe amplification analysis. Sequencing analysis of a patient with multiple schwannomas and melanomas identified a novel duplication in the coding region of CDKN2A, disrupting both p14ARF and p16INK4a. Our results suggest that none of these genes are major contributors to schwannomatosis risk but the possibility remains that they may have a role in more complex mechanisms for tumor predisposition.

Updated diagnostic criteria and nomenclature for neurofibromatosis type 2 and schwannomatosis: An international consensus recommendation

PURPOSE

Neurofibromatosis type 2 (NF2) and schwannomatosis (SWN) are genetically distinct tumor predisposition syndromes with overlapping phenotypes. We sought to update the diagnostic criteria for NF2 and SWN by incorporating recent advances in genetics, ophthalmology, neuropathology, and neuroimaging.

METHODS

We used a multistep process, beginning with a Delphi method involving global disease experts and subsequently involving non-neurofibromatosis clinical experts, patients, and foundations/patient advocacy groups.

RESULTS

We reached consensus on the minimal clinical and genetic criteria for diagnosing NF2 and SWN. These criteria incorporate mosaic forms of these conditions. In addition, we recommend updated nomenclature for these disorders to emphasize their phenotypic overlap and to minimize misdiagnosis with neurofibromatosis type 1.

CONCLUSION

The updated criteria for NF2 and SWN incorporate clinical features and genetic testing, with a focus on using molecular data to differentiate the 2 conditions. It is likely that continued refinement of these new criteria will be necessary as investigators study the diagnostic properties of the revised criteria and identify new genes associated with SWN. In the revised nomenclature, the term "neurofibromatosis 2" has been retired to improve diagnostic specificity.

Prognostic biomarker SGSM1 and its correlation with immune infiltration in gliomas

Objective

Glioma was the most common type of intracranial malignant tumor. Even after standard treatment, the recurrence and malignant progression of lower-grade gliomas (LGGs) were almost inevitable. The overall survival (OS) of patients with LGG varied widely, making it critical for prognostic prediction. Small G Protein Signaling Modulator 1 (SGSM1) has hardly been studied in gliomas. Therefore, we aimed to investigate the prognostic role of SGSM1 and its relationship with immune infiltration in LGGs.

Methods

We obtained RNA sequencing data from The Cancer Genome Atlas (TCGA) to analyze SGSM1 expression. Functional enrichment analyses, immune infiltration analyses, immune checkpoint analyses, and clinicopathology analyses were performed. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. And nomogram model has been developed. Kaplan–Meier survival analysis and log-rank test were used to estimate the relationship between OS and SGSM1 expression. The survival analyses and Cox regression were validated in datasets from the Chinese Glioma Genome Atlas (CGGA).

Results

SGSM1 was significantly down-regulated in LGGs. Functional enrichment analyses revealed SGSM1 was correlated with immune response. Most immune cells and immune checkpoints were negatively correlated with SGSM1 expression. The Kaplan–Meier analyses showed that low SGSM1 expression was associated with a poor outcome in LGG and its subtypes. The Cox regression showed SGSM1 was an independent prognostic factor in patients with LGG (HR = 0.494, 95%CI = 0.311–0.784, P = 0.003).

Conclusion

SGSM1 was considered to be a new prognostic biomarker for patients with LGG. And our study provided a potential therapeutic target for LGG treatment.