Clinical and Biological Significances of a Methyltransferase-Related Signature in Diffuse Glioma

Comprehensive Analysis of GDF10 Methylation Site-Associated Genes as Prognostic Markers for Endometrial Cancer

Growth differentiation factor-10 (GDF10) with its methylation trait has recently been found to play a crucial regulatory and communication role in cancers. This investigation aims to identify GDF10 methylation site-associated genes that are closely associated with endometrial cancer (EC) patients' survival based on normal and UCEC samples from the UCSC Xena database. Our study revealed for the first time that EC exhibited significantly higher levels of GDF10 promoter methylation in comparison with normal tissues. Multiple differentiated methylation sites, which have prognostic value due to their apparent survival differences, were found in the GDF10 promoter region. We performed weighted gene coexpression network analysis (WGCNA) on EC tissues and paraneoplastic tissues while using these differentially methylated sites as phenotypes for selecting the most correlated key modules and their internal genes. To obtain a gene set, the key module genes and differentially expressed genes (DEGs) of EC were intersected. The least absolute shrinkage and selection operator (LASSO) regression along with multivariate Cox regression were performed from the gene set and we screened out the key genes B4GALNT3, DNAJC22, and GREB1. Finally, a prognostic model was validated for effectiveness based on these genes. Additionally, Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC) were applied to assess and verify the model, and they showed good prognosis prediction. Moreover, the differences in risk scores were statistically significant with age, tumor stage, and grade. They may be related to the immune infiltration of tumors as well. In conclusion, based on the methylation-related genes associated with GDF10, we developed a prognosis model for EC patients. It might provide a fresh view for further research and treatment of EC.

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.