scholarly journals Comprehensive analysis of metabolism-related lncRNAs related to the progression and prognosis in osteosarcoma from TCGA

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Xingyin Chen ◽  
Zhengyun Ye ◽  
Pan Lou ◽  
Wei Liu ◽  
Ying Liu
Keyword(s):  
Prognostic Factors ◽  
Molecular Mechanisms ◽  
Clinical Decision Making ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Clinical Decision ◽  
Gene Set Enrichment Analysis ◽  
Malignant Neoplasms ◽  
Prognostic Signature ◽  
Prognostic Analysis

Abstract Background Osteosarcoma is one of the most common malignant neoplasms in children and adolescents. Studies have shown that metabolism-related pathways are vital for the development and metastasis of osteosarcoma. Long non-coding RNA (lncRNA) plays a key role in the occurrence and progression of cancer in a variety of ways. However, the detailed molecular mechanisms of metabolism-related lncRNA in osteosarcoma remain to be deeply elucidated. Methods In this study, all metabolism-related mRNAs and lncRNAs in osteosarcoma were extracted and identified based on transcriptomic data from the TCGA database. Usingsurvival analysis, univariate and multivariate independent prognostic analysis, gene set enrichment analysis, and nomogram, a prognostic signature with metabolic lncRNAs as prognostic factors was constructed. Results Nine prognostic factors included lncRNA AC009779.2, lncRNA AL591895.1, lncRNA AC026271.3, lncRNA LPP-AS2, lncRNA LINC01857, lncRNA AP005264.1, lncRNA LINC02454, lncRNA AL133338.1, and lncRNA AC135178.5, respectively. Survival analysis indicated that alterations of specific lncRNA expression were strongly correlated with poor prognosis in osteosarcoma. Univariate and multivariate independent prognostic analysis showed that the prognostic signature had a good independent predictive ability for patient survival. The results of GSEA suggested that these predictors may be involved in the metabolism of certain substances or energy in cancer. The nomogram was further drawn for clinical guidance and assistance in clinical decision-making. Conclusions This study identified multiple metabolism-related lncRNAs, which may be novel therapeutic targets for osteosarcoma, and contributed to better explore the specific metabolic regulatory mechanisms of lncRNA in osteosarcoma.

Comprehensive Analysis of Metabolism-Related lncRNA Related To The Progression And Prognosis In Osteosarcoma From TCGA

2021 ◽  
Author(s):  
Xingyin Chen ◽  
Zhengyun Ye ◽  
Pan Lou ◽  
Wei Liu ◽  
Ying Liu
Keyword(s):  
Survival Analysis ◽  
Molecular Mechanisms ◽  
Clinical Decision Making ◽  
Malignant Neoplasm ◽  
Enrichment Analysis ◽  
Clinical Decision ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Prognostic Analysis

Abstract Background: Osteosarcoma is one of the most common malignant neoplasm among children and adolescents. Studies have shown that metabolism-related pathways are more important for the development and metastasis of osteosarcoma. Long non-coding RNA (LncRNA) plays a key role in the occurrence and progression of cancer in a variety of ways, Metabolism-related lncRNA-mediated molecular mechanisms in the regulation of osteosarcoma have not been fully elucidated.Methods: In this study, all metabolic-related mRNAs and metabolic-related LncRNA in osteosarcoma were extracted and identified based on transcriptomic data from the TCGA database. The survival analysis, The univariable and multivariable independent prognostic analysis, The results of gene set enrichment analysis (GSEA) and the nomogram were used to construct a prognosis signature with metabolic LncRNA as prognostic factor.Results: 9 prognostic factors including that LncRNA AC009779.2, LncRNA AL591895.1, LncRNA AC026271.3, LncRNA LPP-AS2, LncRNA LINC01857, LncRNA AP005264.1, LncRNA LINC02454, LncRNA AL133338.1 and LncRNA AC135178.5, respectively. The survival analysis showed that the difference in expression of an individual LncRNA was closely related to poor prognosis in osteosarcoma. The univariable and multivariable independent prognostic analysis showed that the signature had good independent predictive ability for patient survival. The results of gene set enrichment analysis (GSEA) suggest that these predictors may be involved in the metabolism of certain substances or energy in cancer. The nomogram is further drawn for clinical guidance and assistance in clinical decision-making.Conclusions: This study identified multiple metabolic-related lncRNA that can be considered as novel therapeutic targets for osteosarcoma and contribute to better exploring the specific regulatory mechanisms of lncRNA in the metabolism of osteosarcoma.

scholarly journals Prognostic Value of BTNL9 Combined with Genes of Proteasome and Ubiquitin System in Pancreatic Cancer

2021 ◽  
Author(s):  
Ke Xu ◽  
Qingfan Mo ◽  
Bo Liu ◽  
Rongfei Huang ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factors ◽  
Prognostic Value ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Immune Checkpoints ◽  
Prognostic Prediction ◽  
The Relationship

Abstract Background: An accurate prognostic prediction can improve the individualized management of patients with pancreatic cancer (PC), and the exploration of biomarkers with prognostic value for clinical practice is the prerequisite of the work. Butyrophilin-Like 9 (BTNL9) has recently been found to function as a tumor suppressor gene in a variety of malignancies and has the potency to serve as a prognostic biomarker. Our aim was to explore the relationship between BTNL9 expression and the prognosis of PC, and to unearth its upstream and downstream molecular mechanisms. Methods: The RNA expression of BTNL9 was analyzed in 5 datasets from Gene Expression Omnibus (GEO) database. The protein expression of BTNL9 was detected by immunohistochemistry in a cohort including 42 PC patients. The relationship between BTNL9 expression and prognosis was analyzed by survival and prognostic factors analysis. Online database and Gene Set Enrichment Analysis (GSEA) were used to explore the upstream and downstream molecular mechanisms of BTNL9. Correlation analysis and CIBERSORT were applied to investigate the relationship between BTNL9 and tumor immunology.Results: In multiple datasets and our cohort, BTNL9 expression was decreased in PC tissues. Patients with high expression of BTNL9 had a better prognosis. BTNL9, age and N stage were identified as the independent prognostic factors of PC. BTNL9 was predicted to be down-regulated by hsa-miR-1910-5p, and it may be involved in the proteasome and PC signaling pathway. Interestingly, genes of proteasome (PSMD2, PSMD7 and PMSD14) and deubiquitin system (USP20, USP27X and USP30) combined BTNL9 could improve the prognostic prediction of PC. In addition, the expression of BTNL9 correlates with the expression of immune checkpoints and influences the infiltration of tumor immune cells. Conclusions: BTNL9 can serve as a prognostic marker of PC, and high expression of BTNL9 was generally associated with better prognosis. Combined the expression of BTNL9 and the expression of PSMD2, PSMD7, PMSD14, USP20, USP27X and USP30 can more accurately analyze the prognosis of patients with PC.

scholarly journals S100A2 Is a Prognostic Biomarker Involved in Immune Infiltration and Predict Immunotherapy Response in Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Chen ◽  
Chengcheng Wang ◽  
Jianlu Song ◽  
Ruiyuan Xu ◽  
Rexiati Ruze ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Calcium Binding ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Tumor Development ◽  
Enrichment Analysis ◽  
Clinical Decision ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Incidence And Mortality

Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. It is of great significance to construct an effective prognostic signature of PC and find the novel biomarker for the optimization of the clinical decision-making. Due to the crucial role of immunity in tumor development, a prognostic model based on nine immune-related genes was constructed, which was proved to be effective in The Cancer Genome Atlas (TCGA) training set, TCGA testing set, TCGA entire set, GSE78229 set, and GSE62452 set. Furthermore, S100A2 (S100 Calcium Binding Protein A2) was identified as the gene occupying the most paramount position in risk model. Gene set enrichment analysis (GSEA), ESTIMATE and CIBERSORT algorithm revealed that S100A2 was closely associated with the immune status in PC microenvironment, mainly related to lower proportion of CD8+T cells and activated NK cells and higher proportion of M0 macrophages. Meanwhile, patients with high S100A2 expression might get more benefit from immunotherapy according to immunophenoscore algorithm. Afterwards, our independent cohort was also used to demonstrate S100A2 was an unfavorable marker of PC, as well as its remarkably positive correlation with the expression of PD-L1. In conclusion, our results demonstrate S100A2 might be responsible for the preservation of immune-suppressive status in PC microenvironment, which was identified with significant potentiality in predicting prognosis and immunotherapy response in PC patients.

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

2021 ◽  
Vol 8 ◽  
Author(s):  
Enchong Zhang ◽  
Fujisawa Shiori ◽  
Mo Zhang ◽  
Peng Wang ◽  
Jieqian He ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Model ◽  
Clinical Decision Making ◽  
Immune Cell ◽  
Prostate Cancer Risk ◽  
Enrichment Analysis ◽  
Clinical Decision ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering

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.

scholarly journals DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

2021 ◽  
Vol 17 (11) ◽  
pp. e1009562
Author(s):  
Maxim Barenboim ◽  
Michal Kovac ◽  
Baptiste Ameline ◽  
David T. W. Jones ◽  
Olaf Witt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Random Forest ◽  
Homologous Recombination ◽  
Clinical Decision Making ◽  
Genome Instability ◽  
Enrichment Analysis ◽  
Clinical Decision ◽  
Gene Set Enrichment Analysis ◽  
Molecular Features

Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

scholarly journals Development of an immune-related gene pairs index for the prognosis analysis of metastatic melanoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rong-zhi Huang ◽  
Min Mao ◽  
Jie Zheng ◽  
Hai-qi Liang ◽  
Feng-ling Liu ◽  
...  
Keyword(s):  
Decision Making ◽  
Skin Cancer ◽  
Metastatic Melanoma ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Gene Set Enrichment Analysis ◽  
Comprehensive Treatment ◽  
Related Gene ◽  
Gene Pairs ◽  
Immune Related Gene

AbstractMelanoma is a skin cancer with great metastatic potential, which is responsible for the major deaths in skin cancer. Although the prognosis of melanoma patients has been improved with the comprehensive treatment, for patients with metastasis, the complexity and heterogeneity of diffuse diseases make prognosis prediction and systematic treatment difficult and ineffective. Therefore, we established a novel personalized immune-related gene pairs index (IRGPI) to predict the prognosis of patients with metastatic melanoma, which was conducive to provide new insights into clinical decision-making and prognostic monitoring for metastatic melanoma. Through complex analysis and filtering, we identified 24 immune-related gene pairs to build the model and obtained the optimal cut-off value from receiver operating characteristic curves, which divided the patients into high and low immune-risk groups. Meantime, the Kaplan–Meier analysis, Cox regression analysis and subgroup analysis showed that IRGPI had excellent prognostic value. Furthermore, IRGPI was shown that was closely associated with immune system in the subsequent tumor microenvironment analysis and gene set enrichment analysis. In addition, we broken through the data processing limitations of traditional researches in different platforms through the application of gene pairs, which would provide great credibility for our model. We believe that our research would provide a new perspective for clinical decision-making and prognostic monitoring in metastatic melanoma.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

scholarly journals In Silico Identification of Contradictory Role of ADAMTS5 in Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382098682
Author(s):  
Zhipeng Zhu ◽  
Jiuhua Xu ◽  
Xiaofang Wu ◽  
Sihao Lin ◽  
Lulu Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Subgroup Analysis ◽  
Prognostic Value ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Clinicopathological Parameters ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
The Impact

Background: ADAMTS5 has different roles in multiple types of cancers and participates in various molecular mechanisms. However, the prognostic value of ADAMTS5 in patients with hepatocellular carcinoma (HCC) still remains unclear. We carried the study to evaluate the prognostic value and identified underlying molecular mechanisms in HCC. Methods: Firstly, the association of ADAMTS5 expression and clinicopathological parameters was evaluated by in GSE14520. Next, ADAMTS5 expression in HCC was performed using GSE14520, GSE36376, GSE76427 and The Cancer Genome Atlas (TCGA) profile. Furthermore, Kaplan-Meier analysis, Univariate and Multivariate Cox regression analysis, subgroup analysis was performed to evaluate the prognostic value of ADAMTS5 in HCC. Finally, GO enrichment analysis, gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA) were performed to revealed underlying molecular mechanisms. Result: The expression of ADAMTS5 was positively correlated with the development of HCC. Next, high ADAMTS5 expression was significantly associated with poorer survival (all P < 0.05) and the impact of ADAMTS5 on all overall survival (OS), disease-free survival (DFS), relapse-free survival (RFS), disease specific survival (DSS) and progression free interval (PFI) was specific for HCC among other 29 cancer types. Subgroup analysis showed that ADAMTS5 overexpression was significantly associated with poorer OS in patients with HCC. Finally, ADAMTS5 might participate in the status conversion from metabolic-dominant to extracellular matrix-dominant, and the activation of ECM-related biological process might contribute to high higher mortality risk for patients with HCC. Conclusion: ADAMTS5 may play an important role in the progression of HCC, and may be considered as a novel and effective biomarker for predicting prognosis for patients with HCC.

Prognostic Factors for Persistent Symptoms in Adults With Mild Traumatic Brain Injury: an Overview of Systematic Reviews

2021 ◽  
Author(s):  
Julien Déry ◽  
Béatrice Ouellet ◽  
Élaine de Guise ◽  
Ève-Line Bussières ◽  
Marie-Eve Lamontagne
Keyword(s):  
Traumatic Brain Injury ◽  
Prognostic Factors ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Systematic Reviews ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Persistent Symptoms ◽  
Wide Range ◽  
Overview Of Systematic Reviews

Abstract Background: Mild traumatic brain injury (mTBI) is an increasing public health problem, because of its persistent symptoms and several functional consequences. Understanding the prognosis of a condition is an important component of clinical decision-making and can help to guide prevention of persistent symptoms following mTBI. Prognosis of mTBI has stimulated several empirical primary research papers and many systematic reviews leading to the identification of a wide range of factors. We aim to synthesize these factors to get a better understanding of their breadth and scope.Methods: We conducted an overview of systematic reviews. We searched in databases systematic reviews synthesizing evidence about prognosis of persistent symptoms after mTBI in the adult population. Two reviewers independently screened all references and selected eligible reviews based on eligibility criteria. They extracted relevant information using an extraction grid. They also rated independently the risk of bias using the ROBIS tool. We synthesized evidence into a comprehensive conceptual map to facilitate the understanding of prognostic factors that have an impact on persistent post-concussion symptoms.Results: From the 3857 references retrieved in database search, we included 25 systematic reviews integrating the results of 312 primary articles published between 1957 and 2019. We examined 35 prognostic factors from the systematics reviews. No single prognostic factor demonstrated convincing and conclusive results. However, age, sex and multiple concussions showed an affirmatory association with persistent post-concussion outcomes in systematic reviews.Conclusion: We highlighted the need of a comprehensive picture of prognostic factors related to persistent post-concussion symptoms. We believe that these prognostic factors would guide clinical decision and research related to prevention and intervention regarding persistent post-concussion symptoms.Systematic review registration: PROSPERO CRD42020176676

A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

2021 ◽  
Author(s):  
Yanjia Hu ◽  
Jing Zhang ◽  
Jing Chen
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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