Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma

Role of epigenetics in paediatric cancer pathogenesis & drug resistance

Paediatric oncogenesis is tightly intertwined with errors in developmental processes involving cell specification and differentiation, which are governed by intricate temporal epigenetic signals. As paediatric cancers are characterised by a low number of somatic mutations, dysregulated chromatin landscapes are believed to be key drivers of oncogenesis. Epigenetic dysregulation is induced by mutations and aberrant expression of histones and epigenetic regulatory genes, to altered DNA methylation patterns and dysregulated noncoding RNA expression. In this review, we discuss epigenetic alterations in paediatric cancer oncogenesis and recurrence, and their potential as diagnostic biomarkers. We also discuss various epigenetic drugs that have entered clinical trials for aggressive paediatric cancers. Targeting paediatric-specific epigenetic vulnerabilities may improve recurrence-free survival in high-risk cancers.

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma

Aging is an inevitable process that biological changes accumulate with time and results in increased susceptibility to different tumors. But currently, aging-related genes (ARGs) in osteosarcoma were not clear. We investigated the potential prognostic role of ARGs and established an ARG-based prognostic signature for osteosarcoma. The transcriptome data and corresponding clinicopathological information of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Molecular subtypes were generated based on prognosis-related ARGs obtained from univariate Cox analysis. With ARGs, a risk signature was built by univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Differences in clinicopathological features, immune infiltration, immune checkpoints, responsiveness to immunotherapy and chemotherapy, and biological pathways were assessed according to molecular subtypes and the risk signature. Based on risk signature and clinicopathological variables, a nomogram was established and validated. Three molecular subtypes with distinct clinical outcomes were classified based on 36 prognostic ARGs for osteosarcoma. A nine-ARG-based signature in the TCGA cohort, including BMP8A, CORT, SLC17A9, VEGFA, GAL, SSX1, RASGRP2, SDC3, and EVI2B, has been created and developed and could well perform patient stratification into the high- and low-risk groups. There were significant differences in clinicopathological features, immune checkpoints and infiltration, responsiveness to immunotherapy and chemotherapy, cancer stem cell, and biological pathways among the molecular subtypes. The risk signature and metastatic status were identified as independent prognostic factors for osteosarcoma. A nomogram combining ARG-based risk signature and metastatic status was established, showing great prediction accuracy and clinical benefit for osteosarcoma OS. We characterized three ARG-based molecular subtypes with distinct characteristics and built an ARG-based risk signature for osteosarcoma prognosis, which could facilitate prognosis prediction and making personalized treatment in osteosarcoma.

Hippo signaling pathway: A comprehensive gene expression profile analysis in breast cancer

Breast cancer (BC) is the most frequently diagnosed malignancy in women and a major public health concern. The Hippo pathway is an evolutionarily conserved signaling pathway that serves as a key regulator for a wide variety of biological processes. Hippo signaling has been shown to have both oncogenic and tumor-suppressive functions in various cancers. Core components of the Hippo pathway consist of various kinases and downstream effectors such as YAP/TAZ. In the current report, differential expression of Hippo pathway elements as well as the correlation of Hippo pathway mRNAs with various clinicopathologic characteristics, including molecular subtypes, receptor status, and methylation status, has been investigated in BC using METABRIC and TCGA datasets. In this review, we note deregulation of several Hippo signaling elements in BC patients. Moreover, we see examples of negative correlations between methylation of Hippo genes and mRNA expression. The expression of Hippo genes significantly varies between different receptor subgroups. Because of the clear associations between mRNA expression and methylation status, DNA methylation may be one of the mechanisms that regulate the Hippo pathway in BC cells. Differential expression of Hippo genes among various BC molecular subtypes suggests that Hippo signaling may function differently in different subtypes of BC. Our data also highlights an interesting link between Hippo components' transcription and ER negativity in BC. In conclusion, substantial deregulation of Hippo signaling components suggests an important role of these genes in breast cancer.