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

Expression and clinical significance of RBBP4 gene in lower-grade glioma: An integrative analysis

This study investigated the expression pattern of retinoblastoma binding protein 4 (RBBP4) gene in glioma and explored its associations with clinicopathologic characteristics and prognosis of patients. Data retrieved from the GEPIA, CGGA, HPA and TIMER databases were processed to analyze RBBP4 expression in glioma and investigate its relationship with clinicopathologic characteristics, tumor immune infiltration and prognosis in glioma patients. Immunohistochemistry was applied to determine the expression of RBBP4 protein in glioma tissue. Additionally, the Coexpedia database was visited to identify co-expressed genes for RBBP4 gene, while the Cytoscape software was run to visualize the enriched GO entries and KEGG pathways of these co-expressed genes. The expression levels of RBBP4 in lower-grade glioma (LGG) and glioblastoma (GBM) tissues were markedly elevated when compared to normal tissues (both p < 0.05). The up-regulation of RBBP4 expression was associated with an increase in WHO grade (II-IV), wild-type IDH, and 1p/19q non-codeletion (all p < 0.05). Multi-variate Cox regression analysis showed that both increased abundance of infiltrating macrophages and up-regulated RBBP4 expression independently predicted poor survival outcomes in LGG patients (both p < 0.05). Furthermore, RBBP4 expression exhibited significant positive correlations with the abundance of infiltrating B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell in LGG (all p < 0.05). Functional enrichment analyses indicated that the co-expressed genes associated with RBBP4 were highly involved in pathways such as the p53 signaling pathway, cell cycle, DNA replication, glutathione metabolism, as well as biological processes including cell cycle process, DNA replication, and DNA repair. High levels of RBBP4 are predictive for the poor survival outcome of LGG patients. RBBP4 gene, therefore, is expected to be a potential biomarker for prognosis of LGG and a target for immunotherapy.

The Oncogenic Role of Cyclin-Dependent Kinase Inhibitor 2C in Lower-Grade Glioma

Lower-grade gliomas (LGGs) are slow-growing, indolent tumors that usually affect younger patients and present a therapeutic challenge due to the heterogeneity of their clinical presentation. Dysregulation of cell cycle regulatory factors is implicated in the progression of many tumors, and drugs that target cell cycle machinery have shown efficacy as promising therapeutic approaches. To date, however, no comprehensive study has examined how cell cycle-related genes affect LGG outcomes. The cancer genome atlas (TCGA) data were used as the training set for differential analysis of gene expression and patient outcomes; the Chinese glioma genome atlas (CGGA) was used for validation. Levels of one candidate protein, cyclin-dependent kinase inhibitor 2C (CDKN2C), and its relationship to clinical prognosis were determined using a tissue microarray containing 34 LGG tumors. A nomogram was constructed to model the putative role of candidate factors in LGG. Cell type proportion analysis was performed to evaluate immune cell infiltration in LGG. Various genes encoding cell cycle regulatory factors showed increased expression in LGG and were significantly related to isocitrate dehydrogenase and chromosome arms 1p and 19q mutation status. CDKN2C expression independently predicted the outcome of LGG patients. High M2 macrophage values along with elevated CDKN2C expression were associated with poorer prognosis in LGG patients. CDKN2C plays an oncogenic role in LGG, which is associated with M2 macrophages.

Promoting Prognostic Model Application: A Review Based on Gliomas

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.

Promoting Prognostic Model Application: A Review Based on Gliomas

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.

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

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.

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

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We identified ten hub genes which were driving IDH status in GBM and LGG.

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We constructed a prognostic model for IDH-mutant patients.

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Our findings have important clinical implications for accurate treatment in glioma.

Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.

A novel DNA methylation 10-CpG prognostic signature of disease-free survival reveal that MYBL2 is associated with high risk in prostate cancer

BACKGROUND

Prostate cancer (PC) is the most common non-cutaneous malignancy among men in the western world. However, heterogeneity remains a pressing clinical problem.

RESEARCH DESIGN AND METHODS

The least absolute shrinkage and selection operator (LASSO) was used to screen the prognostic signature. Weighted correlation network analysis (WGCNA) was used to identify the target genes associated with high-risk characteristics. Gene set enrichment analysis was used to suggest the molecular mechanism of MYBL2 in PC. In addition, in vitro experiments were carried out to validate the role of MYBL2 in PC.

RESULTS

Ten DNA methylation sites were selected as the prognostic signature. A high expression of MYBL2 was associated with a poor prognosis in PC patients. The effect of MYBL2 in PC was related to KRAS, AKT, IL21, and ATM. MYBL2 facilitates the proliferation, migration, invasion, and metastasis of PC cells.

CONCLUSIONS

We developed a DNA methylation 10-CpG prognostic signature to predict the prognosis of PC patients. And the high expression of MYBL2 in PC may be related to poor prognosis.

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

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.