A five metastasis-related long noncoding RNA risk signature for osteosarcoma survival prediction

Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept

The major prevalent primary bone cancer is osteosarcoma. Preoperative chemotherapy is accompanied by resection as part of the normal course of treatment. The diagnosis and treatment of patients are based on the chemotherapy reaction. Contrarily, chemotherapy without operation results in persistent cancer and an osteosarcoma regrowth. Thus, osteosarcoma patients should receive comprehensive therapy, which includes tumor-free surgery and global chemotherapy, to improve their survival. Hence, early diagnosis and individualized care of osteosarcoma are essential since they may lead to more effective therapies and higher survival rates. Here, the main goal of the recommended research is to use a unique deep learning approach to predict the osteosarcoma on histology images. Initially, the data is collected from the navigation confluence mobile osteosarcoma data of UT Southwestern/UT Dallas dataset. Next, the pre-processing of the collected images is accomplished by the Weiner filter technique. Further, the segmentation for the pre-processed images is done by the 2D Otsu's method. From the segmented images, the features are extracted by the linear discriminant analysis (LDA) approach. These extracted features undergo the final prediction phase that is accomplished by the novel improved recurrent gated recurrent unit (IGRU), in which the parameter tuning of GRU is accomplished by the osprey optimization algorithm (OOA) with the consideration of error minimization as the major objective function. On contrast with various conventional methods, the simulation findings demonstrate the effectiveness of the developed model in terms of numerous analysis.

Epigenetic Changes Associated with Osteosarcoma: A Comprehensive Review

Osteosarcoma is the most common malignant primary bone tumor in children and adolescents. While clinical outcomes have improved, the 5-year survival rate is only around 60% if discovered early and can require debilitating treatments, such as amputations. A better understanding of the disease could lead to better clinical outcomes for patients with osteosarcoma. One promising avenue of osteosarcoma research is in the field of epigenetics. This research investigates changes in genetic expression that occur above the genome rather than in the genetic code itself. The epigenetics of osteosarcoma is an active area of research that is still not fully understood. In a narrative review, we examine recent advances in the epigenetics of osteosarcoma by reporting biomarkers of DNA methylation, histone modifications, and non-coding RNA associated with disease progression. We also show how cancer tumor epigenetic profiles are being used to predict and improve patient outcomes. The papers in this review cover a large range of epigenetic target genes and pathways that modulate many aspects of osteosarcoma, including but not limited to metastases and chemotherapy resistance. Ultimately, this review will shed light on the recent advances in the epigenetics of osteosarcoma and illustrate the clinical benefits of this field of research.

A four-lncRNA risk signature for prognostic prediction of osteosarcoma

Aim: Osteosarcoma is the most common primary malignant tumor of bone. However, our understanding of the prognostic indicators and the genetic mechanisms of the disease progression are still incomplete. The aim of this study was to identify a long noncoding RNA (lncRNA) risk signature for osteosarcoma survival prediction. Methods: RNA sequencing data and relevant clinical information of osteosarcoma patients were downloaded from the database of Therapeutically Applicable Research to Generate Effective Treatments (TARGET). We analyzed the differentially expressed lncRNAs between deceased and living patients by univariate and multivariate Cox regression analysis to identify a risk signature. We calculated a prognostic risk score for each sample according to this prognosis signature, and divided patients into high-risk and low-risk groups according to the median value of the risk score (0.975). Kaplan-Meier analysis and receiver operating characteristic (ROC) curve statistics were used to evaluate the performance of the signature. Next, we analyzed the signature's potential function through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene-set enrichment analysis (GSEA). Lastly, qRT-PCR was used to validate the expression levels of the four lncRNAs in clinical samples. Results: Twenty-six differentially expressed lncRNAs were identified between deceased and living patients. Four of these lncRNAs (CTB-4E7.1, RP11-553A10.1, RP11-24N18.1, and PVRL3-AS1) were identified as independent prognostic factors, and a risk signature of these four lncRNAs for osteosarcoma survival prediction was constructed. Kaplan-Meier analysis showed that the five-year survival time in high-risk and low-risk groups was 33.1% and 82.5%, and the area under the curve (AUC) of the ROC was 0.784, which demonstrated that the prognostic signature was reliable and had the potential to predict the survival of patients with osteosarcoma. The expression level of the four lncRNAs in osteosarcoma tissues and cells was determined by qRT-PCR. Functional enrichment analysis suggested that the signature might be related to osteosarcoma through regulation of the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the extracellular matrix and also provided new insights into the study of osteosarcoma, including the role of papillomavirus infection, olfactory receptor activity, and olfactory transduction in osteosarcoma. Conclusion: We constructed a novel lncRNA risk signature that served as an independent biomarker for predicting the prognosis of osteosarcoma patients.

Roles of lncRNAs in childhood cancer: Current landscape and future perspectives

According to World Health Organization (WHO), cancer is the leading cause of death for children and adolescents. Leukemias, brain cancers, lymphomas and solid tumors, such as neuroblastoma, ostesarcoma and Wilms tumors are the most common types of childhood cancers. Approximately 400,000 children and adolescents between the ages of 0 and 19 are diagnosed with cancer each year worldwide. The cancer incidence rates have been rising for the past few decades. Generally, the prognosis of childhood cancers is favorable, but the survival rate for many unresectable or recurring cancers is substantially worse. Although random genetic mutations, persistent infections, and environmental factors may serve as contributing factors for many pediatric malignancies, the underlying mechanisms are yet unknown. Long non-coding RNAs (lncRNAs) are a group of transcripts with longer than 200 nucleotides that lack the coding capacity. However, increasing evidence indicates that lncRNAs play vital regulatory roles in cancer initiation and development in both adults and children. In particular, many lncRNAs are stable in cancer patients' body fluids such as blood and urine, suggesting that they could be used as novel biomarkers. In support of this notion, lncRNAs have been identified in liquid biopsy samples from pediatric cancer patients. In this review, we look at the regulatory functions and underlying processes of lncRNAs in the initiation and progression of children cancer and discuss the potential of lncRNAs as biomarkers for early detection. We hope that this article will help researchers explore lncRNA functions and clinical applications in pediatric cancers.

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan–Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature’s predictive ability was further examined by the Kaplan–Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells’ roles in the gene signature’s prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature’s prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.