A prognostic risk model for glioma patients by systematic evaluation of genomic variations.
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ABSTRACT:
SUBMITTER: Zhang B
PROVIDER: S-EPMC9758519 | biostudies-literature | 2022 Dec
REPOSITORIES: biostudies-literature
Publications
A prognostic risk model for glioma patients by systematic evaluation of genomic variations.
Zhang Baifeng B Wan Weiqing W Li Zibo Z Gao Zhixian Z Ji Nan N Xie Jian J Wang Junmei J Wang Bin B Lai-Wan Kwong Dora D Guan Xinyuan X Gao Shengjie S Zhao Yuanli Y Lu Youyong Y Zhang Liwei L Rodland Karin D KD Tsang Shirley X SX
iScience 20221128 12
The overall survival rate of gliomas has not significantly improved despite new effective treatments, mainly due to tumor heterogeneity and drug delivery. Here, we perform an integrated clinic-genomic analysis of 1, 477 glioma patients from a Chinese cohort and a TCGA cohort and propose a potential prognostic model for gliomas. We identify that SBS11 and SBS23 mutational signatures are associated with glioma recurrence and indicate worse prognosis only in low-grade type of gliomas and IDH-Mut su ...[more]
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Project description:BackgroundMolecular characteristics are essential for the classification and grading of gliomas. However, diagnostic classification of midline glioma is still debatable and substantial molecular and clinical heterogeneity within each subgroup suggested that they should be further stratified. Here, we studied the mutation landscape of Chinese midline glioma patients in hope to provide new insights for glioma prognosis and treatment.MethodsTissue samples from 112 midline glioma patients underwent next-generation sequencing targeting 425 cancer-relevant genes. Gene mutations and copy number variations were investigated for their somatic interactions and prognostic effect using overall survival data. Pathway-based survival analysis was performed for ten canonical oncogenic pathways.ResultsWe identified several currently established diagnostic and prognostic biomarkers of glioma, including TP53 (33%), EGFR (26%), TERT (24%), PTEN (21%), PIK3CA (14%), ATRX (14%), BRAF (13%), and IDH1/2 (6%). Among all genetic aberrations with more than 5% occurrence rate, six mutations and three copy number gains were greatly associated with poor overall survival (univariate, P < 0.1). Of these, TERT mutations (hazard ratio [HR], 3.00; 95% confidence interval [CI], 1.37-6.61; P = 0.01) and PIK3CA mutations (HR, 2.04; 95% CI, 1.08-3.84; P = 0.02) remained significant in multivariate analyses. Additionally, we have also identified a novel MCL1 amplification (found in 31% patients) as a potential independent biomarker for glioma (multivariate HR, 2.78; 95% CI, 1.53-5.08; P < 0.001), which was seldom reported in public databases. Pathway analyses revealed significantly worse prognosis with abnormal PI3K (HR, 1.81; 95% CI, 1.12-2.95; P = 0.01) and cell cycle pathways (HR, 1.97; 95% CI, 1.15-3.37; P = 0.01), both of which stayed meaningful after multivariate adjustment.ConclusionsIn this study, we discovered shorter survival in midline glioma patients with PIK3CA and TERT mutations and with abnormal PI3K and cell cycle pathways. We also revealed a novel prognostic marker, MCL1 amplification that collectively provided new insights and opportunities in understanding and treating midline gliomas.
| S-EPMC7968371 | biostudies-literature
Project description:BackgroundTo explore the specific prognosis related microRNAs (miRNAs) of glioma.MethodsThe miRNA-Seq data and clinical information of glioma patients were downloaded from the TCGA (510 cases) and GEO (GSE112009, 25 cases) database. LASSO & COX regression was used to develop a miRNA-based model for predicting patient survival in the training set (n=255), to carry out glioma prognostic related miRNAs screening, and to construct a linear risk model based on the expression profiles of seven miRNAs. COX regression analysis was used to determine whether the miRNAs risk model was an independent prognostic factor.ResultsSeven survival-related miRNAs (miR-140-5p, miR-145-5p, miR-148a-3p, miR-183-5p, miR-222-3p, miR-223-3p, and miR-374a-5p) were identified in the training set. This showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group in the training set, prediction set, and validation set (P<0.05). Further analysis revealed that age and Karnofsky score both affected the risk of glioma. By crossing seven potential target genes of microRNAs, 620 effective target genes were obtained and GO analysis showed that these were related to the positive regulation of cell migration, neuron migration, and the response of transforming growth factor, and KEGG analysis showed they were related to the TGF-beta signaling pathway, MAPK signaling, and AGE-RAGE signaling pathway in diabetic complications.ConclusionsSeven miRNAs which regulate target genes to participate in related signaling pathways and lead to a poor prognosis were identified as biomarkers of glioma.
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Project description:An independent cohort study was conducted to validate a mathematical genomic model for survival of glioma patients that was introduced previously. Of the 102 new subjects that were employed in this study, 40 were long-term survivors (survival ≥ 3 years), and 62 were short-term survivors (survival ≤ 1 year). Utilizing the gene expression of 5 genes as captured by mRNA sequencing of primary tumor tissue, obtained from the initial biopsy during the diagnosis, and prior to the administration of any treatment, the model classified correctly all but three of the 102 subjects. More specifically, of the 62 STS (short-term survivors), 61 were classified correctly (sensitivity = 98.4%); and of the 40 LTS (long-term survivors), 38 were classified correctly (specificity = 95.0%). The 5 gene expression input variables to the model were: FAM120AOS, MXI1, OCIAD2, PCDH15, and PDLIM4. Of the top 29 most significantly differentially expressed genes between STS and LTS subjects, as identified in the original study, all but one were highly significant. Furthermore, with respect to survival, the model - designed to operate at the molecular level (gene expression of tumor cells) - was also able to statistically differentiate between the two subgroups of the STS group, namely, the STS subjects with lower grade glioma and the STS subjects with glioblastoma; whereas variables either at the tissue level or at the organismal level were not able to do so. Based on these results, and taking into account that accurate clinical prognosis for short-term vs. long-term survival for glioma patients is currently nonexistent, this study provides further, independent evidence for the accuracy and the clinical utility of the model.
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Project description:BackgroundsGlioma 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.MethodsA 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.ResultsOur 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.ConclusionWe 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.
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Project description:ObjectiveTo screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.MethodsThe relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model.Results339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage.ConclusionThe prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.
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Project description:BackgroundThe potential link between Prognostic Nutritional Index (PNI) and prognosis in patients with glioma remains uncertain. This meta-analysis was conducted to assess the clinical value of PNI in glioma patients by integrating all available evidence to enhance statistical power.MethodA systematic search of databases including Medline, EMBASE, Google Scholar, and Cochrane Library was conducted from inception to January 8, 2023 to retrieve all pertinent peer-reviewed articles. The primary outcome of the study was to examine the association between a high PNI value and overall survival, while secondary outcome included the relationship between a high PNI and progression-free survival.ResultsIn this meta-analysis, we included 13 retrospective studies published from 2016 to 2022, which analyzed a total of 2,712 patients. Across all studies, surgery was the primary treatment modality, with or without chemotherapy and radiotherapy as adjunct therapies. A high PNI was linked to improved overall survival (Hazard Ratio (HR) = 0.61, 95% CI: 0.52 to 0.72, p < 0.00001, I2 = 25%), and this finding remained consistent even after conducting sensitivity analysis. Subgroup analyses based on ethnicity (Asian vs. non-Asian), sample size (<200 vs. >200), and source of hazard ratio (univariate vs. multivariate) yielded consistent outcomes. Furthermore, patients with a high PNI had better progression-free survival than those with a low PNI (HR=0.71, 95% CI: 0.58 to 0.88, p=0.001, I2 = 0%).ConclusionOur meta-analysis suggested that a high PNI was associated with better overall survival and progression-free survival in patients with glioma. These findings may have important implications in the treatment of patients with glioma. Additional studies on a larger scale are necessary to investigate if integrating the index into the treatment protocol leads to improved clinical outcomes in individuals with glioma.Systematic review registration[https://www.crd.york.ac.uk/prospero/], identifier [CRD42023389951].
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Project description:BackgroundCurrent risk models for renal cell carcinoma (RCC) based on clinicopathological factors are sub-optimal in accurately identifying high-risk patients. Here, we perform a head-to-head comparison of previously published DNA methylation markers and propose a potential prognostic model for clear cell RCC (ccRCC).Patients and methodsPromoter methylation of PCDH8, BNC1, SCUBE3, GREM1, LAD1, NEFH, RASSF1A, GATA5, SFRP1, CDO1, and NEURL was determined by nested methylation-specific PCR. To identify clinically relevant methylated regions, The Cancer Genome Atlas (TCGA) was used to guide primer design. Formalin-fixed paraffin-embedded (FFPE) tissue samples from 336 non-metastatic ccRCC patients from the prospective Netherlands Cohort Study (NLCS) were used to develop a Cox proportional hazards model using stepwise backward elimination and bootstrapping to correct for optimism. For validation purposes, FFPE ccRCC tissue of 64 patients from the University Hospitals Leuven and a series of 232 cases from The Cancer Genome Atlas (TCGA) were used.ResultsMethylation of GREM1, GATA5, LAD1, NEFH, NEURL, and SFRP1 was associated with poor ccRCC-specific survival, independent of age, sex, tumor size, TNM stage or tumor grade. Moreover, the association between GREM1, NEFH, and NEURL methylation and outcome was shown to be dependent on the genomic region. A prognostic biomarker model containing GREM1, GATA5, LAD1, NEFH and NEURL methylation in combination with clinicopathological characteristics, performed better compared to the model with clinicopathological characteristics only (clinical model), in both the NLCS and the validation population with a c-statistic of 0.71 versus 0.65 and a c-statistic of 0.95 versus 0.86 consecutively. However, the biomarker model had limited added prognostic value in the TCGA series with a c-statistic of 0.76 versus 0.75 for the clinical model.ConclusionIn this study we performed a head-to-head comparison of potential prognostic methylation markers for ccRCC using a novel approach to guide primers design which utilizes the optimal location for measuring DNA methylation. Using this approach, we identified five methylation markers that potentially show prognostic value in addition to currently known clinicopathological factors.
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Project description:Background: Immunotherapy has shown promising therapeutic efficacy in various cancers but not gliomas. Circulating lymphocytes play critical roles in cancer control and responses to immune checkpoint inhibitors. Treatment-related lymphopenia has been associated with poor survival in patients with various tumors. This meta-analysis evaluated the risk and impact of lymphopenia in patients with glioma. Methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were comprehensively searched. Eligible studies were included if they reported the incidence and risk factors of lymphopenia and the impact of lymphopenia on survival. Stata 16.0 was used for this meta-analysis. Results: A total of 21 studies were included in the final systematic review and 20 were included in the quantitative analysis. The overall incidence of grade III/IV lymphopenia was 31.6% [95% confidence interval (CI), 22.3-40.8%]. Pooled results based on pathology of glioma revealed that the incidence in astrocytoma and astrocytoma oligodendroglioma patients was 20.2% (95% CI:5.9-34.4%), and the incidence in glioblastoma patients was 27.6% (95% CI:16.2-38.9%). Lymphopenia was associated with poor overall survival (hazard ratio, 1.99; 95% CI, 1.74-2.27; P< 0.001) compared to no lymphopenia. Brain receiving radiation dose of 20 or 25 Gy, female sex, older age, lower baseline lymphocyte count, and dexamethasone dose > 2 mg instead of baseline use were risk factors for lymphopenia. Conclusions: Treatment-related lymphopenia was associated with decreased survival in patients with glioma. Optimization of chemoradiation regimens, particularly in patients with concurrent risk factors, can reduce lymphopenia and potentially improve survival in the era of immunotherapy.
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Project description:Fatigue is the most prevalent and disabling symptom in cancer patients. Yet, scientific literature on this topic is scarce and reports disparate results. This study systematically reviews how fatigue is assessed in patients with low-grade glioma and evaluates its prevalence in LGG patients. A systematic literature search was performed in PubMed, Embase and PsychINFO for articles reporting on fatigue in patients with LGG. Two reviewers independently extracted data from selected articles. Inclusion criteria were: (1) patients with suspected or confirmed LGG; (2) fatigue was assessed as primary or secondary outcome measure; (3) age??18 years; (4) full-length article written in English or Dutch. In total, 19 articles were selected, including 971 patients. Seven self-assessment instruments were identified. Prevalence rates ranged from 39 to 77%. Fatigue was found to be a common side effect of treatment. The prevalence rates ranged from 20 to 76% when fatigue was reported as a mild or moderate side effect and fatigue was prevalent in 4% when reported as a severe side effect. Fatigue is a common problem in LGG patients that warrants more therapeutic and scientific attention. Gaining deeper insight in the underlying mechanisms of fatigue is essential in targeting therapy to individual patients.
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Project description:Predictive markers and prognostic models are useful for the individualization of cancer treatment. In this study, we sought to identify clinical and molecular factors to predict overall survival in recurrent glioma patients receiving bevacizumab-containing regimens. A cohort of 102 patients was retrospectively collected from June 2011 to January 2022 at our institution. A nomogram was generated by Cox regression and feature selection algorithms based on 19 clinicopathological and 60 molecular variables. The model's performance was internally evaluated by bootstrapping in terms of discrimination and calibration. The median overall survival from the initiation of bevacizumab administration to death or last follow-up was 11.6 months (95% CI: 9.2-13.8 months) for all 102 patients, 10.2 months (95% CI: 6.4-13.3 months) for 66 patients with grade 4 tumors, and 13.8 months (lower limit of 95% CI: 11.5 months) for 36 patients with tumors of grade lower or not available. In the final model, a lower WHO 2021 grade (Grade lower or not available vs. Grade 4, HR: 0.398, 95% CI: 0.223-0.708, p = 0.00172), having received adjuvant radiochemotherapy (Yes vs. No, HR: 0.488, 95% CI: 0.268-0.888, p = 0.0189), and wildtype EGFR (Wildtype vs. Altered, HR: 0.193, 95% CI: 0.0506-0.733, p = 0.0157; Not available vs. Altered, HR: 0.386, 95% CI: 0.184-0.810, p = 0.0118) were significantly associated with longer overall survival in multivariate Cox regression. The overall concordance index was 0.652 (95% CI: 0.566-0.714), and the areas under the time-dependent curves for 6-, 12-, and 18-month overall survival were 0.677 (95% CI: 0.516-0.816), 0.654 (95% CI: 0.470-0.823), and 0.675 (95% CI: 0.491-0.860), respectively. A prognostic model for overall survival in recurrent glioma patients treated with bevacizumab-based therapy was established and internally validated. It could serve as a reference tool for clinicians to assess the extent the patients may benefit from bevacizumab and stratify their treatment response.
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