Association of strong risk of hTERT gene polymorphic variants to malignant glioma and its prognostic implications with respect to different histological types and survival of glioma cases

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci

Backgrounds

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Methods

A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes.

Results

Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable.

Conclusion

We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Telomere length and TERT polymorphisms as biomarkers in asbestos-related diseases

BACKGROUND

Asbestos exposure has been proposed as a risk factor for shorter telomere length. The aim of our study was to investigate whether telomere length in leukocytes and hTERT genetic polymorphisms may serve as potential biomarkers for the risk of developing asbestos-related diseases and as biomarkers of progression and chemotherapy response rate in malignant mesothelioma (MM).

SUBJECTS AND METHODS

We conducted two retrospective studies. In the first study, a case-control study, telomere length and hTERT polymorphisms were determined in patients with MM, subjects with pleural plaques and controls without the asbestos related disease, who were occupationally exposed to asbestos. In the second study, a longitudinal observational study, telomere length was also determined in samples from MM patients before and after chemotherapy. Telomere length was determined by monochromatic multiplex quantitative polymerase chain reaction (PCR), while competitive allele-specific PCR was used to genotype hTERT rs10069690, rs2736100 and rs2736098. Logistic regression and survival analysis were used in statistical analysis.

RESULTS

Patients with MM had shorter telomere length than subjects with pleural plaques (p < 0.001). After adjustment for age, rs2736098 CT, and rs10069690 TT and CT+TT genotypes were significantly associated with a higher risk of MM (padj = 0.023; padj = 0.026 and padj = 0.017), while rs2736100 AA and CA+AA genotypes conferred to a lower risk for MM compared to all other subjects (padj = 0.017, and padj = 0.026). Telomere length was not associated with a response to chemotherapy (p > 0.05) or time to disease progression (p > 0.05). Carriers of one or two polymorphic rs10069690 T alleles had a good response to chemotherapy (p = 0.039, and p = 0.048), these associations remained statistically significant after adjustment for age (padj = 0.019; padj = 0.017). Carriers of two polymorphic rs2736100 A alleles had a longer time to disease progression (p = 0.038).

CONCLUSIONS

Shorter telomere length and hTERT polymorphisms may serve as a biomarker for the risk of developing MM. Additionally, rs10069690 and rs2736100 polymorphisms, but not telomere length, were associated with a chemotherapy response or MM progression.

Pertinence of glioma and single nucleotide polymorphism of TERT, CCDC26, CDKN2A/B and RTEL1 genes in glioma: a meta-analysis

Background

Previous genetic-epidemiological studies considered TERT (rs2736100), CCDC26 (rs4295627), CDKN2A/B (rs4977756) and RTEL1 (rs6010620) gene polymorphisms as the risk factors specific to glioma. However, the data samples of previous genetic-epidemiological studies are modest to determine whether they have definite association with glioma.

Method

The study paid attention to systematically searching databases of PubMed, Embase, Web of Science (WoS), Scopus, Cochrane Library and Google Scholars. Meta-analysis under 5 genetic models, namely recessive model (RM), over-dominant model (O-DM), allele model (AM), co-dominant model (C-DM) and dominant model (DM) was conducted for generating odds ratios (ORs) and 95% confidence intervals (CIs). That was accompanied by subgroup analyses according to various racial groups. The software STATA 17.0 MP was implemented in the study.

Result

21 articles were collected. According to data analysis results, in four genetic models (AM, RM, DM and C-DM) TERT gene rs2736100 polymorphism, CCDC26 gene rs4295627 polymorphism, CDKN2A/B gene rs4977756 polymorphism and RTEL1 gene rs6010620 polymorphisms increased the risk of glioma in Caucasians to different degrees. In Asian populations, the CCDC26 gene rs4295627 polymorphism and CDKN2A/B gene rs4977756 polymorphism did not exhibit a relevance to the risk of glioma. It is suggested to cautiously explain these results as the sample size is small.

Conclusion

The current meta-analysis suggested that the SNP of TERT (rs2736100), CCDC26 (rs4295627), CDKN2A/B (rs4977756) and RTEL1 (rs6010620) genes in glioma might increase risk of glioma, but there are ethnic differences. Further studies evaluating these polymorphisms and glioma risk are warranted.

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.