APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

Chen, Jia-Jin Jason; Shen, Sipeng; Li, Yang; Fan, Juanjuan; Xiong, Shiyu; Xu, Jingtong; Zhu, Chenxu; Lin, Lijuan; Dong, Xuesi; Duan, Weiwei; Zhao, Yang; Xu, Qian; Z, Liu; Wei, Yongyue; Christiani, David C.; Zhang, Ruyang; Chen, Feng

doi:10.1016/j.ebiom.2022.104007

Cited by 11 publications

(17 citation statements)

References 59 publications

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“…LGG patients, with the better prognosis than GBM patients, account for about half of all glioma patients. But their survival time varies widely, ranging from 1 year to more than 10 years (Chen et al, 2022), notwithstanding LGG patients with the same WHO grade had same standardized sequential therapy including surgery, radiotherapy, and chemotherapy. The high heterogeneity of LGG, which results in inconsistent treatment effects and prognosis, is a clinical conundrum faced by most neurosurgeons.…”

Section: The Expression Levels Of Selected Serelated Genesmentioning

confidence: 99%

“…Low-grade gliomas (LGG), also known as WHO grade II and III tumors defined by the World Health Organization, are composed of diffuse low-grade and intermediate-grade gliomas (Brat et al, 2015). Compared with patients with glioblastoma (GBM), LGG patients have a relatively low degree of malignancy (Chen et al, 2022). However, even with comprehensive treatment including surgical resection, radiotherapy and chemotherapy, some LGG patients still have the characteristics of high recurrence and progression rates (Liu et al, 2018;Jiang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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The integrative analysis based on super-enhancer related genes for predicting different subtypes and prognosis of patient with lower-grade glioma

Yang

Cai

et al. 2023

Front. Genet.

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Objective: Emerging evidence revealed that super-enhancer plays a crucial role in the transcriptional reprogramming for many cancers. The purpose aimed to explored how the super-enhancer related genes affects the prognosis and tumor immune microenvironment (TIME) of patients with low-grade glioma (LGG).Methods: In this study, the differentially expressed genes (DEGs) between LGG cohorts and normal brain tissue cohort were identified by the comprehensive analysis of the super-enhancer (SE) related genes. Then non-negative matrix factorization was performed to seek the optimal classification based on the DEGs, while investigating prognostic and clinical differences between different subtypes. Subsequently, a prognostic related signature (SERS) was constructed for the comprehensive evaluation in term of individualized prognosis, clinical characteristics, cancer markers, genomic alterations, and immune microenvironment of patients with LGG.Results: Based on the expression profiles of 170 DEGs, we identified three SE subtypes, and the three subtypes showed significant differences in prognostic, clinicopathological features. Then, nine optimal SE-related genes were selected to construct the SERS through the least absolute shrinkage and selection operator Cox regression analysis. Survival analysis showed that SERS had strong and stable predictive ability for the prognosis of LGG patients in the The Cancer Genome Atlas, China Glioma Genome Atlas, and Remdrandt cohorts, respectively. We also found that SERS was highly correlated with clinicopathological features, tumor immune microenvironment, cancer hallmarks, and genomic alterations in LGG patients. In addition, the predictive power of SERS for immune checkpoint inhibitor treatment is also superior. The qRT-PCR results and immunohistochemical results also confirmed the difference in the expression of four key genes in normal cells and tumors, as well as in normal tissues and tumor tissues.Conclusion: The SERS could be suitable to utilize individualized prognosis prediction and immunotherapy options for LGG patients in clinical application.

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Section: The Expression Levels Of Selected Serelated Genesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The integrative analysis based on super-enhancer related genes for predicting different subtypes and prognosis of patient with lower-grade glioma

Yang

Cai

et al. 2023

Front. Genet.

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“…In this study, we developed a robust prognostic model for IDH wild-type GBM through incorporating a novel parameter, G×G interaction 19,20 , to effectively identify patients at high mortality risk. Interestingly, comprehensive bioinformatics analysis pinpointed a subset of NPC-like cells as an essential player in driving unfavorable outcome.…”

Section: Introductionmentioning

confidence: 99%

COVPRIG robustly predicts the overall survival of IDH wild-type glioblastoma and highlights METTL1+ neural-progenitor-like tumor cell in driving unfavorable outcome

Wang

Liu

et al. 2023

Preprint

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Background Accurately predicting the outcome of isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) remains hitherto challenging. This study aims to Construct and Validate a Robust Prognostic Model for IDH wild-type GBM (COVPRIG) for the prediction of overall survival using a novel metric, gene-gene (G×G) interaction, and explore molecular and cellular underpinnings. Methods Univariate and multivariate Cox regression of four independent trans-ethnic cohorts containing 800 samples. Prediction efficacy was comprehensively evaluated and compared with previous models by a systematic literature review. The molecular underpinnings of COVPRIG were elucidated by integrated analysis of several bulk-tumor and single-cell expression profiles. Results Using a Cox-ph model-based method, six of the 93,961 G×G interactions were screened to form an optimal combination which, together with age, comprised the COVPRIG model. COVPRIG was designed for RNA-seq and microarray, respectively, and effectively identified patients at high risk of mortality. The predictive performance of COVPRIG was satisfactory, with aera under the curve (AUC) ranging from 0.56 (CGGA693, RNA-seq, 6-month survival) to 0.79 (TCGA RNAseq, 18-month survival), which can be further validated by decision curves. Nomograms were constructed for individual risk prediction for RNA-seq and microarray-based cohorts, respectively. Besides, the prognostic significance of COVPRIG was also validated in GBM including the IDH mutant samples. Notably, COVPRIG was comprehensively evaluated and externally validated, and a systemic review disclosed that COVPRIG outperformed current validated models with an integrated discrimination improvement (IDI) of 6~16% when calibrating to the same condition. Moreover, integrative bioinformatics analysis predicted an essential role of METTL1+ neural-progenitor-like (NPC-like) malignant cell in driving unfavorable outcome. Conclusion This study provided a powerful tool for the outcome prediction for IDH wild-type GBM, and preliminary molecular underpinnings for future research.

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“…Although LGGs are inert precursors of GBMs and less aggressive, due to the heterogeneity of clinical manifestations, they still lead to a high incidence rate and poor prognosis, posing challenges to treatment (8,9). The tumor boundary of LGG surgery is difficult to define, complete surgical resection is considered impossible, and local recurrence and malignant progression are inevitable despite a combination of adjuvant radiotherapy and chemotherapy treatments (10)(11)(12). Therefore, it is necessary to further explore the exact pathogenesis of LGGs and the molecular mechanisms that affect the malignant biological behavior in order to develop new treatments.…”

Section: Introductionmentioning

confidence: 99%

Interfering with CALCRL expression inhibits glioma proliferation, promotes apoptosis, and predicts prognosis in low-grade gliomas

Gu¹,

Shu²,

Zhou³

et al. 2022

Ann Transl Med

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Background: CALCRL is involved in a variety of key biological processes, including cell proliferation, apoptosis, angiogenesis, and inflammation. However, the role of CALCRL in glioma remains unknown.The purpose of this study was to investigate the effect of differential CALCRL expression on the malignant progression of glioma and its value in glioma prognosis.Methods: Sequencing data from glioma and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, and the downloaded data were statistically analyzed using bioinformatics tools and the corresponding R package. The expression of CALCRL in normal brain tissue and different grades of glioma tissue was detected by pathological and immunohistochemical staining of clinical glioma specimens. The expression of CALCRL in different glioma cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR), and the U87 cell line with high expression was selected to construct the CALCRL knockdown model by transfection with short hairpin (shRNA). The cell proliferation ability was detected by Celigo assay and 3-(4,5-dimethylthiazol-2yl)-2,5-diphenyltetrazolium bromide (MTT) assay, the ability of cell clone formation was detected by clone formation assay, and the level of apoptosis was detected by flow cytometry. Results:The expression of CALCRL in glioma was significantly upregulated compared with that of normal tissue, especially in low-grade glioma (LGG) compared to glioblastoma, and the differential expression of CALCRL correlated significantly with the prognosis of LGG. Clinical pathology and immunohistochemistry showed that the expression of CALCRL was related to the pathological grade of glioma, and the highest expression was found in World Health Organization (WHO) grade Ⅲ glioma. The results of qRT-PCR showed that CALCRL expression was highest in the U87 cell line. After knockdown of CALCRL expression, the proliferation and clonogenic ability of U87 cells were significantly decreased, and the apoptosis rate was significantly increased.Conclusions: CALCRL is highly expressed in LGG. Interfering with CALCRL expression inhibits glioma cell proliferation and promotes apoptosis, and thus has potential as a biomarker and therapeutic target for the prognosis of those with LGGs.

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APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

Cited by 11 publications

References 59 publications

The integrative analysis based on super-enhancer related genes for predicting different subtypes and prognosis of patient with lower-grade glioma

The integrative analysis based on super-enhancer related genes for predicting different subtypes and prognosis of patient with lower-grade glioma

COVPRIG robustly predicts the overall survival of IDH wild-type glioblastoma and highlights METTL1+ neural-progenitor-like tumor cell in driving unfavorable outcome

Interfering with CALCRL expression inhibits glioma proliferation, promotes apoptosis, and predicts prognosis in low-grade gliomas

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