Evaluating the potential of circulating hTERT levels in glioma: can plasma levels serve as an independent prognostic marker?

Association of human telomerase reverse transcriptase promoter mutation with unfavorable prognosis in glioma: A systematic review and meta-analysis

Background

Glioma is one of the most malignant and aggressive tumors, with an extremely poor prognosis. Human telomerase reverse transcriptase (hTERT) promoter mutation is regarded as a risk factor in tumor growth. Although the prevalence of hTERT promoter (pTERT) mutation in gliomas has been investigated, the results are inconsistent. This meta-analysis aims to investigate the prognostic value of hTERT in glioma patients and its interaction with other biomarkers.

Materials and Methods

We searched 244 citations from four databases: PubMed (2000-2021), Web of Science (2000-2021), Embase (2010-2021), and Cochrane Library (2000-2021) with 28 articles included.

Results

We calculated hazard ratios (HRs) using the random effect model and the pooled result suggested that TERT promoter mutation predicted poorer overall survival (HR: 1.53, 95% confidence interval [CI]: 1.34-1.75, P < 0.001, I2: 49.9%, pheterogeneity:0.002) and progression-free survival (HR: 1.55, 95% CI: 1.27-1.88, P < 0.001, I2: 0.0%, pheterogeneity: 0.473). For subgroup analysis, we analyzed multiple factors including iso-citrate dehydrogenase (IDH) genotype, age, diagnosis, pTERT region, so as to locate the sources of heterogeneity. Interestingly, in IDH mutant subgroup, pTERT mutation became a beneficial prognostic factor (HR: 0.73, 95% CI: 0.57-0.93, I2: 22.3%, pheterogeneity: 0.277), which is contrary to the results in pooled analysis.

Conclusion

In general, pTERT mutation may result in shorter survival time in glioma patients, but longer survival time when glioma patients are combined with IDH mutation.

The road-map for establishment of a prognostic molecular marker panel in glioma using liquid biopsy: current status and future directions

Gliomas are primary intracranial tumors with defined molecular markers available for precise diagnosis. The prognosis of glioma is bleak as there is an overlook of the dynamic crosstalk between tumor cells and components of the microenvironment. Herein, different phases of gliomagenesis are presented with reference to the role and involvement of secreted proteomic markers at various stages of tumor initiation and development. The secreted markers of inflammatory response, namely interleukin-6, tumor necrosis factor-α, interferon-ϒ, and kynurenine, proliferation markers human telomerase reverse transcriptase and microtubule-associated-protein-Tau, and stemness marker human-mobility-group-AThook-1 are involved in glial tumor initiation and growth. Further, hypoxia and angiogenic factors, heat-shock-protein-70, endothelial-growth-factor-receptor-1 and vascular endothelial growth factor play a major role in promoting vascularization and tumor volume expansion. Eventually, molecules such as matrix-metalloprotease-7 and intercellular adhesion molecule-1 contribute to the degradation and remodeling of the extracellular matrix, ultimately leading to glioma progression. Our study delineates the roadmap to develop and evaluate a non-invasive panel of secreted biomarkers using liquid biopsy for precisely evaluating disease progression, to accomplish a clinical translation.

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.

Commentary: Evaluating potential glioma serum biomarkers, with future applications

Systemic inflammation within malignant glioma is a topic of ongoing significance. In this commentary, we highlight recent findings from Gandhi et al and discuss alternative approaches. We present a counter argument with findings that IL-6 markers are controversial. We highlight the potential benefit of looking at microRNAs and other biomarkers. Finally, we present ideas for future application involving differentiation between radiation necrosis and recurrence. The commentary is intended to serve as a catalyst for further scientific discovery.

Novel molecular panel for evaluating systemic inflammation and survival in therapy naïve glioma patients

BACKGROUND

Inflammation is crucial to tumor progression. A traumatic event at a specific site in the brain activates the signaling molecules, which triggers inflammation as the initial response within the tumor and its surroundings. The educated immune cells and secreted proteins then initiate the inflammatory cascade leading to persistent chronic inflammation. Therefore, estimation of the circulating inflammatory indicators kynurenine (KYN), interleukin-6 (IL-6), tissue-inhibitor of matrix-metalloproteinase-1 and human telomerase reverse transcriptase (hTERT) along with neutrophil-lymphocyte ratio (NLR) has prognostic value.

AIM

To assess the utility of chosen inflammatory marker panel in estimating systemic inflammation.

METHODS

The chosen markers were quantitatively evaluated in 90 naive, molecularly sub-typed plasma samples of glioma. A correlation between the markers and confounders was assessed to establish their prognostication power. Follow-up on the levels of the indicators was done 3-mo post-surgery. To establish the validity of circulating KYN, it was also screened qualitatively by dot-immune-assay and by immunofluorescence-immunohistochemistry in tumor tissues.

RESULTS

Median values of circulating KYN, IL-6, hTERT, tissue-inhibitor of matrix-metalloproteinase-1 and NLR in isocitrate-dehydrogenase-mutant/wildtype and within the astrocytic sub-groups were estimated, which differed from controls, reaching statistical significance (P < 0.0001). All markers negatively correlated with mortality (P < 0.0001). Applying combination-statistics, the panel of KYN, IL-6, hTERT and NLR achieved higher sensitivity and specificity (> 90%) than stand-alone markers, to define survival. The inflammatory panel could discriminate between WHO grades, and isocitrate-dehydrogenase-mutant/wildtype and define differential survival between astrocytic isocitrate-dehydrogenase-mutant/wildtype. Therefore, its assessment for precise disease prognosis is indicated. Association of KYN with NLR, IL-6 and hTERT was significant. Cox-regression described KYN, IL-6, NLR, and hTERT as good prognostic markers, independent of confounders. Multivariate linear-regression analysis confirmed the association of KYN and hTERT with inflammation marker IL-6.There was a concomitant significant decrease in their levels in a 3-mo follow-up.

CONCLUSION

The first evidence-based study of circulating-KYN in molecularly defined gliomas, wherein the tissue expression was found to be concomitant with plasma levels. A non-invasive model for assessing indicators of chronic systemic inflammation is proposed.

Ensemble based machine learning approach for prediction of glioma and multi-grade classification

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.