Dataset Information

Identification of pyroptosis-related genes and long non-coding RNAs signatures in osteosarcoma.

ABSTRACT: Osteosarcoma is a highly malignant tumor, with very high disability and fatality rates. However, the overall prognosis is not optimistic. Pyroptosis is a newly discovered cell death modality accompanied by inflammation, which is closely related to varieties of cancers. In this study, the RNA-seq data were downloaded from public databases, the differences in the expression of the pyroptosis-related genes (PRGs) were identified, and the six PRGs signature was established through the univariate and LASSO Cox analysis. The patients were grouped according to the PRGs signature, and the prognosis between the two groups was further compared. In addition, a ten pyroptosis-related lncRNAs (PRLs) prognostic signature was also constructed. Through functional analysis of the differentially expressed genes (DEGs), the immune-related pathways were found to be enriched. The Pearson correlation analysis showed a strong correlation between the pyroptosis-related biomarkers. Finally, we identified a promising biomarker, CHMP4C, which is highly expressed in osteosarcoma. Overexpression of CHMP4C promoted the proliferation, migration and invasion of the osteosarcoma cell. Our results thus provide new evidence for exploring prognostic biomarkers and therapeutic targets of osteosarcoma.

SUBMITTER: Zhang J

PROVIDER: S-EPMC9575257 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of pyroptosis-related genes and long non-coding RNAs signatures in osteosarcoma.

Zhang Jian J Deng Jianjian J Ding Rui R Yuan Jinghong J Liu Jiahao J Zhao Xiaokun X Wu Tianlong T Jia Jingyu J Cheng Xigao X

Cancer cell international 20221016 1

Osteosarcoma is a highly malignant tumor, with very high disability and fatality rates. However, the overall prognosis is not optimistic. Pyroptosis is a newly discovered cell death modality accompanied by inflammation, which is closely related to varieties of cancers. In this study, the RNA-seq data were downloaded from public databases, the differences in the expression of the pyroptosis-related genes (PRGs) were identified, and the six PRGs signature was established through the univariate and ...[more]

PMID: 36244998

Similar Datasets

Project description:In the past decade, hundreds of long noncoding RNAs (lncRNAs) have been identified as significant players in diverse types of cancer; however, the functions and mechanisms of most lncRNAs in cancer remain unclear. Several computational methods have been developed to detect associations between cancer and lncRNAs, yet those approaches have limitations in both sensitivity and specificity. With the goal of improving the prediction accuracy for associations of lncRNA with cancer, we upgraded our previously developed cancer-related lncRNA classifier, CRlncRC, to generate CRlncRC2. CRlncRC2 is an eXtreme Gradient Boosting (XGBoost) machine learning framework, including Synthetic Minority Over-sampling Technique (SMOTE)-based over-sampling, along with Laplacian Score-based feature selection. Ten-fold cross-validation showed that the AUC value of CRlncRC2 for identification of cancer-related lncRNAs is much higher than previously reported by CRlncRC and others. Compared with CRlncRC, the number of features used by CRlncRC2 dropped from 85 to 51. Finally, we identified 439 cancer-related lncRNA candidates using CRlncRC2. To evaluate the accuracy of the predictions, we first consulted the cancer-related long non-coding RNA database Lnc2Cancer v2.0 and relevant literature for supporting information, then conducted statistical analysis of somatic mutations, distance from cancer genes, and differential expression in tumor tissues, using various data sets. The results showed that our approach was highly reliable for identifying cancer-related lncRNA candidates. Notably, the highest ranked candidate, lncRNA AC074117.1, has not been reported previously; however, integrated multi-omics analyses demonstrate that it is the target of multiple cancer-related miRNAs and interacts with adjacent protein-coding genes, suggesting that it may act as a cancer-related competing endogenous RNA, which warrants further investigation. In conclusion, CRlncRC2 is an effective and accurate method for identification of cancer-related lncRNAs, and has potential to contribute to the functional annotation of lncRNAs and guide cancer therapy.

Project description:Background and objectiveOral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck, and its morbidity and mortality are increasing year by year. Changes in key genes are thought to be closely related to the occurrence and development of OSCC. Pyroptosis is an inflammatory form of programmed cell death that has been implicated in malignancies and inflammatory diseases. Changes in the expression of long noncoding RNAs may also affect tumorigenesis and progression. In this study, our main objective was to evaluate the association between pyroptosis-related lncRNAs and prognosis in patients with OSCC.MethodsThe RNA-seq data and clinicopathological data of OSCC patients are from The Cancer Genome Atlas database. The pyroptosis gene set is obtained from Gene Set Enrichment Analysis database. Univariate COX, Lasso and multivariate COX regression analyses were used for the construction of risk prognostic models of OSCC, eight lncRNAs were incorporated into prognostic models. The Kaplan-Meier method and log-rank test were used to evaluate the differences of overall survival between patients in high-risk and low-risk groups. The reliability of predictions across the dataset was analyzed by receiver operating characteristic (ROC) curves. The immune signature score was calculated using the single-sample gene set enrichment analysis.ResultsEight pyroptosis-related lncRNAs were used to construct prognostic signature of OSCC, including AC136475.2, AC024075.2, JPX, ZFAS1, TNFRSF10A-AS1, LINC00847, AC099850.3 and IER3-AS1. According to this prognostic signature, patients with OSCC were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis showed that the survival rate of the high-risk group was significantly lower than the low-risk group. ROC area for risk score was 0.716, and ROC area of the 8 lncRNAs are all between 0.5 and 1, implied that these lncRNAs had high accuracy in predicting the prognosis of OSCC patients. Immune Infiltration findings suggested that these lncRNAs affected immune responses in the microenvironment of OSCC.ConclusionThe prognostic signature based on pyroptosis-related lncRNAs potentially serves as an independent prognostic indicator for OSCC patients. And this signature facilitates research on targeted diagnosis and treatment of patients diagnosed with OSCC.

Dataset Information

Identification of pyroptosis-related genes and long non-coding RNAs signatures in osteosarcoma.

Publications

Identification of pyroptosis-related genes and long non-coding RNAs signatures in osteosarcoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets