Computational and mathematical methods in medicine · 2022
The Cuproptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Tumour Immune Analysis in Osteosarcoma.
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Jiang Jinxia, Chu Dejun, Lai Xingming, Liu Li, Tao Jun
Аннотация
BACKGROUND: Cuprotopsis is a type of programmed cell death discovered in recent years. Long noncoding RNAs (lncRNAs) play an important regulatory role in programmed cell death. The effect of cuproptosis-related lncRNAs on osteosarcoma is unknown. Our work, based on cuproptosis-related lncRNAs, proposes a gene signature to assess the prognosis of patients with osteosarcoma.
METHODS: Osteosarcoma gene expression data from The Cancer Genome Atlas (TCGA), clinical features of osteosarcoma and RNA sequencing data of normal adipose tissue were obtained from the UCSC Xena database. A cuproptosis-related lncRNA risk model was established to calculate the risk score. At the same time, cluster analysis, clinicopathological analysis, functional enrichment analysis, and prediction of compounds with potential therapeutic value were evaluated. We analyzed whether there was a correlation between the risk score and tumour immunity. RT-qPCR was used to verify the expression level of lncRNA.
RESULTS: Nine lncRNAs (AC124798.1, AC006033.2, AL450344.2, AL512625.2, LINC01060, LINC00837, AC004943.2, AC064836.3, and AC100821.2) were identified to create a risk model and indicate the prognosis of patients with osteosarcoma. The high-risk group had a worse prognosis than the low-risk group. Analysis of clinicopathological features, principal component analysis, receiver operating characteristic curve, c-index curve, and comparative analysis of models proved that the model is reliable. Functional enrichment analysis suggests that the risk score may correlate with cell energy metabolism and tumour-related biological function. Three potentially therapeutic compounds have been predicted. These analyses may be beneficial to the treatment of osteosarcoma in the future. RT-qPCR verified the expression level of three lncRNA (LINC01060, NKILA, and SNHG8).
CONCLUSIONS: Cuproptosis-related lncRNAs have a strong relationship with osteosarcoma patients. Nine lncRNA models can effectively forecast the prognosis of osteosarcoma and may play a significant role in the individualized treatment of osteosarcoma patients in the future.
METHODS: Osteosarcoma gene expression data from The Cancer Genome Atlas (TCGA), clinical features of osteosarcoma and RNA sequencing data of normal adipose tissue were obtained from the UCSC Xena database. A cuproptosis-related lncRNA risk model was established to calculate the risk score. At the same time, cluster analysis, clinicopathological analysis, functional enrichment analysis, and prediction of compounds with potential therapeutic value were evaluated. We analyzed whether there was a correlation between the risk score and tumour immunity. RT-qPCR was used to verify the expression level of lncRNA.
RESULTS: Nine lncRNAs (AC124798.1, AC006033.2, AL450344.2, AL512625.2, LINC01060, LINC00837, AC004943.2, AC064836.3, and AC100821.2) were identified to create a risk model and indicate the prognosis of patients with osteosarcoma. The high-risk group had a worse prognosis than the low-risk group. Analysis of clinicopathological features, principal component analysis, receiver operating characteristic curve, c-index curve, and comparative analysis of models proved that the model is reliable. Functional enrichment analysis suggests that the risk score may correlate with cell energy metabolism and tumour-related biological function. Three potentially therapeutic compounds have been predicted. These analyses may be beneficial to the treatment of osteosarcoma in the future. RT-qPCR verified the expression level of three lncRNA (LINC01060, NKILA, and SNHG8).
CONCLUSIONS: Cuproptosis-related lncRNAs have a strong relationship with osteosarcoma patients. Nine lncRNA models can effectively forecast the prognosis of osteosarcoma and may play a significant role in the individualized treatment of osteosarcoma patients in the future.