Dataset Information

Comprehensive Characterization of Prognostic Long Noncoding RNAs in Osteosarcoma.

ABSTRACT: The molecular mechanism of osteosarcoma (OS) based on protein-coding genes has largely been studied in the past decades. However, much remains to be explored when it comes to the role that long noncoding RNAs (lncRNAs) play in the pathogenesis and progression of OS and how they are associated with OS metastasis. In the present study, we collected RNA-seq-based gene expression data of 82 OS samples from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) database, along with their clinical information. We found that 50 lncRNAs were significantly associated with patients' survival by univariable Cox regression model. Moreover, we built multivariable Cox regression model based on 7 lncRNAs and successfully stratified patients into two risk groups, which exhibited significantly different prognostic outcomes. Significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways detected by differential expression analysis on DEGs between the two groups with different prognostic outcomes were both immune-related, indicating that such GO terms and pathways are critical for OS survival. Among the seven lncRNA signatures, AC011442.1 was predicted to act as an oncogenic driver in OS by correlation analysis of copy number alteration (CNA) and lncRNA expression, and it was predicted to regulate AMPK and hedgehog signaling pathways. In summary, the identification of novel prognostic lncRNAs in OS could not only improved our understanding of the lncRNAs involved in OS tumorigenesis or progression but also assist the diagnosis and development of molecularly targeted therapies for OS, which in turn benefit patients' survival.

SUBMITTER: Gao H

PROVIDER: S-EPMC7468628 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Comprehensive Characterization of Prognostic Long Noncoding RNAs in Osteosarcoma.

Gao Hua H Guo Yuanyuan Y Zhang Miaomiao M Yi Zuqiang Z

BioMed research international 20200824

The molecular mechanism of osteosarcoma (OS) based on protein-coding genes has largely been studied in the past decades. However, much remains to be explored when it comes to the role that long noncoding RNAs (lncRNAs) play in the pathogenesis and progression of OS and how they are associated with OS metastasis. In the present study, we collected RNA-seq-based gene expression data of 82 OS samples from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) database, al ...[more]

PMID: 32908905

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Project description:Osteosarcoma is a malignant bone cancer that originates from the bone with the strongest invasiveness. Tumor formation strongly correlates with immune cell infiltration into the tumor immune microenvironment (TIME). Therefore, we aimed to identify TIME-related biomarkers as potential prognostic markers of osteosarcoma. The mRNA and long noncoding RNA (lncRNA) transcriptome data of 88 patients with osteosarcoma and the expression profile of GSE99671 were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Immune infiltration scores and types were evaluated using ESTIMATE and CIBERSORT. A linear model was established to identify the differentially expressed genes (DEGs) and lncRNAs (DElncRNAs). Functional enrichment analysis of DEGs was conducted by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and gene set variation analysis. DElncRNAs were analyzed using a weighted gene co-expression network. Least absolute shrinkage and selection operator regression was applied to screen for prognostic markers. Patient survival was predicted by the risk score and analyzed by receiver operating characteristic curve. Clinical features affecting patient survival were assessed. Immune infiltration positively correlated with osteosarcoma patient survival. Different immune cell infiltrates in patients with osteosarcma may serve as prognostic indicators and targets for immunotherapy. In total, 1125 DEGs, 80 DElncRNAs, and 11 pairs of co-expressed lncRNA-mRNAs were identified. DEGs in the three modules were associated with immune infiltration into the TIME. Four DElncRNAs, namely AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, showed good prognostic ability for osteosarcoma and were positively correlated with the immune score. Tumor metastasis and risk scores alone were good prognostic indicators, and a combination of the two variables can better predict the prognosis of osteosarcoma. We identified four lncRNAs, AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, as potential biomarkers for osteosarcoma prognosis.

Project description:Ovarian cancer (OC) is a highly malignant gynecologic tumor with few treatments available and poor prognosis with the currently available diagnostic markers and interventions. More effective methods for diagnosis and treatment are urgently needed. Although the current evidence implicates ferroptosis in the development and therapeutic responses of various types of tumors, it is unclear to what extent ferroptosis affects OC. To explore the potential of ferroptosis-related genes as biomarkers and molecular targets for OC diagnosis and intervention, this study collected several datasets from The Cancer Genome Atlas-OC (TCGA-OC), analyzed and identified the coexpression profiles of 60 ferroptosis-related genes and two subtypes of OC with respect to ferroptosis and further examined and analyzed the differentially expressed genes between the two subtypes. The results indicated that the expression levels of ferroptosis genes were significantly correlated with prognosis in patients with OC. Single-factor Cox and LASSO analysis identified eight lncRNAs from the screened ferroptosis-related genes, including lncRNAs RP11-443B7.3, RP5-1028K7.2, TRAM2-AS1, AC073283.4, RP11-486G15.2, RP11-95H3.1, RP11-958F21.1, and AC006129.1. A risk scoring model was constructed from the ferroptosis-related lncRNAs and showed good performance in the evaluation of OC patient prognosis. The high- and low-risk groups based on tumor scores presented obvious differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration, indicating that the risk score has a good ability to predict the benefit of immunotherapy and may provide data to support the implementation of precise immunotherapy for OC. Although in vivo tests and research are needed in the future, our bioinformatics analysis powerfully supported the effectiveness of the risk signature of ferroptosis-related lncRNAs for prognosis prediction in OC. The findings suggest that these eight identified lncRNAs have great potential for development as diagnostic markers and intervention targets for OC and that patients with high ferroptosis-related lncRNA expression will receive greater benefits from conventional chemotherapy or treatment with ferroptosis inducers.

Dataset Information

Comprehensive Characterization of Prognostic Long Noncoding RNAs in Osteosarcoma.

Publications

Comprehensive Characterization of Prognostic Long Noncoding RNAs in Osteosarcoma.

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