Diagnostic accuracy of 1p/19q codeletion tests in oligodendroglioma: A comprehensive meta-analysis based on a Cochrane systematic review

Precision Medicine in Cytopathology

Over the last decade, cancer diagnostics has undergone a notable transformation with increasing complexity. Minimally invasive diagnostic tests, driven by advanced imaging and early detection protocols, are redefining patient care and reducing the need for more invasive procedures. Modern cytopathologists now safeguard patient samples for vital biomarker and molecular testing. In this article, we explore ancillary testing modalities and the role of biomarkers in organ-specific contexts, underscoring the transformative impact of precision medicine. Finally, the advent of more than 80 Food and Drug Administration-approved predictive biomarkers signals a new era, guiding cancer care toward personalized and targeted strategies.

Comprehensive analysis of the prognostic and immunological signature of TNFAIP8 family genes in human glioma

TNFAIP8 family molecules have been recognized for their involvement in the progression of tumors across a range of cancer types. Emerging experimental data suggests a role for certain TNFAIP8 family molecules in the development of glioma. Nonetheless, the comprehensive understanding of the genomic alterations, prognostic significance, and immunological profiles of TNFAIP8 family molecules in glioma remains incomplete. In the study, using the comprehensive bioinformatics tools, we explored the unique functions of 4 TNFAIP8 members including TNFAIP8, TNFAIP8L1, TNFAIP8L2 and TNFAIP8L3 in glioma. The expressions of TNFAIP8, TNFAIP8L1, TNFAIP8L2, and TNFAIP8L3 were notably upregulated in glioma tissues compared to normal tissues. Furthermore, survival analysis indicated that elevated expression levels of TNFAIP8, TNFAIP8L1 and TNFAIP8L2 were correlated with unfavorable outcomes in terms of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) among glioma patients. Genetic modifications, such as mutations and copy number alterations, within the TNFAIP8 family exhibited a significant association with extended OS, DSS and PFS in individuals diagnosed with glioma. The findings suggest a noteworthy correlation between TNFAIP8 family members and the age and 1p/19q codeletion status of glioma patients. We also found that there were significant relationships between TNFAIP8 family expression and tumor immunity in glioma. Furthermore, functional annotation of TNFAIP8 family members and their co-expressed genes in gliomas was carried out using GO and KEGG pathway analysis. The GO analysis revealed that the primary biological processes influenced by the TNFAIP8 family co-expressed genes included cell chemotaxis, temperature homeostasis, and endocytic vesicle formation. Additionally, the KEGG analysis demonstrated that TNFAIP8 family co-expressed genes are involved in regulating various pathways such as inflammatory mediator regulation of TRP channels, pathways in cancer, prolactin signaling pathway, and Fc gamma R-mediated phagocytosis. Overall, the findings suggest that TNFAIP8 family members may play a significant role in the development of glioma and have the potential to serve as prognostic indicators and therapeutic targets for individuals with glioma.

T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas

AIM

To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs).

MATERIALS AND METHODS

One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models.

RESULTS

The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort.

CONCLUSION

The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice.

Predictive potentials of glycosylation-related genes in glioma prognosis and their correlation with immune infiltration

Glycosylation is currently considered to be an important hallmark of cancer. However, the characterization of glycosylation-related gene sets has not been comprehensively analyzed in glioma, and the relationship between glycosylation-related genes and glioma prognosis has not been elucidated. Here, we firstly found that the glycosylation-related differentially expressed genes in glioma patients were engaged in biological functions related to glioma progression revealed by enrichment analysis. Then seven glycosylation genes (BGN, C1GALT1C1L, GALNT13, SDC1, SERPINA1, SPTBN5 and TUBA1C) associated with glioma prognosis were screened out by consensus clustering, principal component analysis, Lasso regression, and univariate and multivariate Cox regression analysis using the TCGA-GTEx database. A glycosylation-related prognostic signature was developed and validated using CGGA database data with significantly accurate prediction on glioma prognosis, which showed better capacity to predict the prognosis of glioma patients than clinicopathological factors do. GSEA enrichment analysis based on the risk score further revealed that patients in the high-risk group were involved in immune-related pathways such as cytokine signaling, inflammatory responses, and immune regulation, as well as glycan synthesis and metabolic function. Immuno-correlation analysis revealed that a variety of immune cell infiltrations, such as Macrophage, activated dendritic cell, Regulatory T cell (Treg), and Natural killer cell, were increased in the high-risk group. Moreover, functional experiments were performed to evaluate the roles of risk genes in the cell viability and cell number of glioma U87 and U251 cells, which demonstrated that silencing BGN, SDC1, SERPINA1, TUBA1C, C1GALT1C1L and SPTBN5 could inhibit the growth and viability of glioma cells. These findings strengthened the prognostic potentials of our predictive signature in glioma. In conclusion, this prognostic model composed of 7 glycosylation-related genes distinguishes well the high-risk glioma patients, which might potentially serve as caner biomarkers for disease diagnosis and treatment.

Molecular neuropathology: an essential and evolving toolbox for the diagnosis and clinical management of central nervous system tumors

Molecular profiling has transformed the diagnostic workflow of CNS tumors during the last years. The latest WHO classification of CNS tumors (5th edition), published in 2021, pushed forward the integration between histopathological features and molecular hallmarks to achieve reproducible and clinically relevant diagnoses. To address these demands, pathologists have to appropriately deal with multiple molecular assays mainly including DNA methylation profiling and DNA/RNA next generation sequencing. Tumor classification by DNA methylation profiling is now a critical tool for many diagnostic tasks in neuropathology including the assessment of complex cases, to evaluate novel tumor types and to perform tumor subgrouping in hetereogenous entities like medulloblastoma or ependymoma. DNA/RNA NGS allow the detection of multiple molecular alterations including single nucleotide variations, small insertions/deletions (InDel), and gene fusions. These molecular markers can provide key insights for diagnosis, for example, if a tumor-specific mutation is detected, but also for treatment since targeted therapies are progressively entering the clinical practice. In the present review, a brief, but comprehensive overview of these tools will be provided, discussing their technical specifications, diagnostic value, and potential limitations. Moreover, the importance of molecular profiling will be shown in a representative series of CNS neoplasms including both the most frequent tumor types and other selected entities for which molecular characterization plays a critical role.

Radiogenomics Provides Insights into Gliomas Demonstrating Single-Arm 1p or 19q Deletion

BACKGROUND AND PURPOSE

IDH-mutant gliomas are further divided on the basis of 1p/19q status: oligodendroglioma, IDH-mutant and 1p/19q-codeleted, and astrocytoma, IDH-mutant (without codeletion). Occasionally, testing may reveal single-arm 1p or 19q deletion (unideletion), which remains within the diagnosis of astrocytoma. Molecular assessment has some limitations, however, raising the possibility that some unideleted tumors could actually be codeleted. This study assessed whether unideleted tumors had MR imaging features and survival more consistent with astrocytomas or oligodendrogliomas.

MATERIALS AND METHODS

One hundred twenty-one IDH-mutant grade 2-3 gliomas with 1p/19q results were identified. Two neuroradiologists assessed the T2-FLAIR mismatch sign and calcifications, as differentiators of astrocytomas and oligodendrogliomas. MR imaging features and survival were compared among the unideleted tumors, codeleted tumors, and those without 1p or 19q deletion.

RESULTS

The cohort comprised 65 tumors without 1p or 19q deletion, 12 unideleted tumors, and 44 codeleted. The proportion of unideleted tumors demonstrating the T2-FLAIR mismatch sign (33%) was similar to that in tumors without deletion (49%; P = .39), but significantly higher than codeleted tumors (0%; P = .001). Calcifications were less frequent in unideleted tumors (0%) than in codeleted tumors (25%), but this difference did not reach statistical significance (P = .097). The median survival of patients with unideleted tumors was 7.8 years, which was similar to that in tumors without deletion (8.5 years; P = .72) but significantly shorter than that in codeleted tumors (not reaching median survival after 12 years; P = .013).

CONCLUSIONS

IDH-mutant gliomas with single-arm 1p or 19q deletion have MR imaging appearance and survival that are similar to those of astrocytomas without 1p or 19q deletion and significantly different from those of 1p/19q-codeleted oligodendrogliomas.

Comprehensive clinical assays for molecular diagnostics of gliomas: the current state and future prospects

Glioma is one of the most intractable types of cancer, due to delayed diagnosis at advanced stages. The clinical symptoms of glioma are unclear and due to a variety of glioma subtypes, available low-invasive testing is not effective enough to be introduced into routine medical laboratory practice. Therefore, recent advances in the clinical diagnosis of glioma have focused on liquid biopsy approaches that utilize a wide range of techniques such as next-generation sequencing (NGS), droplet-digital polymerase chain reaction (ddPCR), and quantitative PCR (qPCR). Among all techniques, NGS is the most advantageous diagnostic method. Despite the rapid cheapening of NGS experiments, the cost of such diagnostics remains high. Moreover, high-throughput diagnostics are not appropriate for molecular profiling of gliomas since patients with gliomas exhibit only a few diagnostic markers. In this review, we highlighted all available assays for glioma diagnosing for main pathogenic glioma DNA sequence alterations. In the present study, we reviewed the possibility of integrating routine molecular methods into the diagnosis of gliomas. We state that the development of an affordable assay covering all glioma genetic aberrations could enable early detection and improve patient outcomes. Moreover, the development of such molecular diagnostic kits could potentially be a good alternative to expensive NGS-based approaches.

Identification of a cellular senescence-related-lncRNA (SRlncRNA) signature to predict the overall survival of glioma patients and the tumor immune microenvironment

Background: Gliomas are brain tumors that arise from glial cells, and they are the most common primary intracranial tumors with a poor prognosis. Cellular senescence plays a critical role in cancer, especially in glioma. In this study, we constructed a senescence-related lncRNA (SRlncRNA) signature to assess the prognosis of glioma. Methods: The Cancer Genome Atlas was used to collect SRlncRNA transcriptome profiles and clinical data about glioma. Patients were randomized to training, testing, and whole cohorts. LASSO and Cox regression analyses were employed to construct the SRlncRNA signature, and Kaplan-Meier (K-M) analysis was performed to determine each cohort's survival. Receiver operating characteristic (ROC) curves were applied to verify the accuracy of this signature. Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. RT-qPCR and IHC were used to identify the consistency of the signature in tumor tissue. Results: An SRlncRNA signature consisting of six long non-coding RNAs (lncRNAs) was constructed, and patients were divided into high-risk and low-risk groups by the median of their riskscore. The KM analysis showed that the high-risk group had worse overall survival, and the ROC curve confirmed that the riskscore had more accurate predictive power. A multivariate Cox analysis and its scatter plot with clinical characteristics confirmed the riskscore as an independent risk factor for overall survival. GSEA showed that the GO and KEGG pathways were mainly enriched in the immune response to tumor cells, p53 signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. Further validation also yielded significant differences in the risk signature in terms of immune cell infiltration, which may be closely related to prognostic differences, and qRT-PCR and IHC confirmed the consistency of the expression differences in the major lncRNAs with those in the prediction model. Conclusion Our findings indicated that the SRlncRNA signature might be used as a predictive biomarker and that there is a link between it and immune infiltration. This discovery is consistent with the present categorization system and may open new avenues for research and personalized therapy.