ArticleBioengineered2021
Identification and validation of seven RNA binding protein genes as a prognostic signature in oral cavity squamous cell carcinoma.
Article in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
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- EPYC functions as a novel prognostic biomarker for pancreatic cancer.Scientific reports · 2024Article
- A novel hypoxia-associated gene signature for prognosis prediction in head and neck squamous cell carcinoma.BMC oral health · 2023Article
- A risk score model based on endoplasmic reticulum stress related genes for predicting prognostic value of osteosarcoma.BMC musculoskeletal disorders · 2023Article
- Low-dose arecoline regulates distinct core signaling pathways in oral submucous fibrosis and oral squamous cell carcinoma.BMC oral health · 2023Article
- DNA damage repair-related gene signature predicts prognosis and indicates immune cell infiltration landscape in skin cutaneous melanoma.Frontiers in endocrinology · 2022Article
- Comprehensive analysis of a glycolysis and cholesterol synthesis-related genes signature for predicting prognosis and immune landscape in osteosarcoma.Frontiers in immunology · 2022Article
- An oxidative stress-related prognostic signature for indicating the immune status of oral squamous cell carcinoma and guiding clinical treatment.Frontiers in genetics · 2022Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA binding proteins (RBPs) play a pivotal role in various biological processes, and aberrant expression of RBPs is closely associated with tumorigenesis and progression. However, the role of RBPs in oral cavity squamous cell carcinoma (OCSCC) is yet unveiled. In this study, RNA sequences and clinical information of OCSCC samples were acquired from The Cancer Genome Atlas (TCGA) database. A total of 650 RBPs, with significantly different expression between healthy and OCSCC samples, were identified using the limma package. A prognostic model was constructed by Lasso-Cox analysis, resulting in the determination of 7 prognosis-related RBPs: ERMP1, RNASE3, ARL4D, CSRP2, ULK1, ZC3H12D, and RPS28. Based on the prognostic model, the risk scores of the OCSCC samples were calculated. The capability of the prognostic model was further evaluated using the receiver operating characteristic curve (ROC). The areas under ROC were 0.764, 0.771, and 0.809 at 1, 3 and 5-year respectively in the TCGA dataset. Internal and external validation showed satisfactory predictive capability for prognosis in OCSCC. In addition, a nomogram was created to graphically present the model. To further validate the analytical data, qRT-PCR was performed on normal and OCSCC cell lines. The mRNA expression of the 7 prognostic genes was in accordance with the analytical results. Functional analysis and gene connection networks were used to describe the biological functions and underlying interactions among the 7 prognostic genes Overall, 7 prognosis-related RBPs were identified, which could be used to predict clinical prognosis and to identify potential therapeutic targets for OCSCC.
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