Deconvolution and network analysis of IDH-mutant lower grade glioma predict recurrence and indicate therapeutic targets

Li, Guangqi; Jiang, Yufei; Lyu, Xintong; Cai, Yiru; Zhang, Miao; Wang, Zuoyuan; Qiao, Qiao

doi:10.2217/epi-2019-0137

Cited by 9 publications

(7 citation statements)

References 44 publications

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“…Hsu et al [ 31 ] and Cheng et al [ 30 ] values for AUC are extremely high (above 0.98 and 0.93, respectively). For deficiencies in this group, the seven models [ 30 – 32 , 35 – 38 ] lack performance estimation in external or internal validation sets, and Ni et al did not validate their 25-gene model [ 37 ].…”

Section: Rna Modelsmentioning

confidence: 99%

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Promoting Prognostic Model Application: A Review Based on Gliomas

Liang

Wang

Dai

et al. 2021

Journal of Oncology

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Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

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Section: Rna Modelsmentioning

confidence: 99%

“…For the medium-quality group, five models exhibit high accuracy [30][31][32][33][34], three are acceptable [35][36][37], and one is lowly accurate [38]. Song et al constructed a 21- [33] and a 20gene model [34] both internally and externally, and the former was validated via four independent datasets.…”

Section: 11mentioning

confidence: 99%

Promoting Prognostic Model Application: A Review Based on Gliomas

Liang

Wang

Dai

et al. 2021

Journal of Oncology

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“…We determined 7 as the soft power threshold, which generated a high connectivity network with scale‐free topology. The network was constructed with the same parameters mentioned in our previous study . Five modules were detected and then related to the Rscore and Cscore through an eigengene‐based Pearson correlation analysis.…”

Section: Methodsmentioning

confidence: 99%

Gene signatures based on therapy responsiveness provide guidance for combined radiotherapy and chemotherapy for lower grade glioma

Jiang

Lyu

et al. 2020

J Cellular Molecular Medi

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For a long time, the guidance for adjuvant chemoradiotherapy for lower grade glioma (LGG) lacks instructions on the application timing and order of radiotherapy (RT) and chemotherapy. We, therefore, aimed to develop indicators to distinguish between the different beneficiaries of RT and chemotherapy, which would provide more accurate guidance for combined chemoradiotherapy. By analysing 942 primary LGG samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases, we trained and validated two gene signatures (Rscore and Cscore) that independently predicted the responsiveness to RT and chemotherapy (Rscore AUC = 0.84, Cscore AUC = 0.79) and performed better than a previous signature. When the two scores were combined, we divided patients into four groups with different prognosis after adjuvant chemoradiotherapy: RSCS (RT‐sensitive and chemotherapy‐sensitive), RSCR (RT‐sensitive and chemotherapy‐resistant), RRCS (RT‐resistant and chemotherapy‐sensitive) and RRCR (RT‐resistant and chemotherapy‐resistant). The order and dose of RT and chemotherapy can be adjusted more precisely based on this patient stratification. We further found that the RRCR group exhibited a microenvironment with significantly increased T cell inflammation. In silico analyses predicted that patients in the RRCR group would show a stronger response to checkpoint blockade immunotherapy than other patients.

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“…A weighted correlation network analysis (WGCNA) can be used find phenotype-associated gene modules (Langfelder and Horvath, 2008;Li et al, 2019). RNA-seq data in TPM format were used as the input for a WGCNA.…”

Section: Weighted Correlation Network Analysismentioning

confidence: 99%

Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model

Zhang

Shiori

Zhang

et al. 2021

Front. Mol. Biosci.

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Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis (i.e., consensus clustering). We identified three subtypes with differences in prognosis (Risk_H, Risk_M, and Risk_L). Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making.

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Deconvolution and network analysis of IDH-mutant lower grade glioma predict recurrence and indicate therapeutic targets

Cited by 9 publications

References 44 publications

Promoting Prognostic Model Application: A Review Based on Gliomas

Promoting Prognostic Model Application: A Review Based on Gliomas

Gene signatures based on therapy responsiveness provide guidance for combined radiotherapy and chemotherapy for lower grade glioma

Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model

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