ArticleOncology letters2020
Identification and validation of seven prognostic long non-coding RNAs in oral squamous cell carcinoma.
Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- LncRNA EIF3J-DT promotes chemoresistance in oral squamous cell carcinoma.Oral diseases · 2024Article
- Immune-Related Long Non-Coding RNA Signatures for Tongue Squamous Cell Carcinoma.Current oncology (Toronto, Ont.) · 2023Article
- Article
- The role of long noncoding RNAs as regulators of the epithelial-Mesenchymal transition process in oral squamous cell carcinoma cells.Frontiers in molecular biosciences · 2022Review
- Identification and validation of seven RNA binding protein genes as a prognostic signature in oral cavity squamous cell carcinoma.Bioengineered · 2021Article
- Knockdown of lncRNA DLEU1 inhibits the tumorigenesis of oral squamous cell carcinoma via regulation of miR‑149‑5p/CDK6 axis.Molecular medicine reports · 2021Article
- Integrative Analysis of lncRNA-mRNA Coexpression in Human Lung Epithelial Cells Exposed to Dimethyl Selenide-Derived Secondary Organic Aerosols.Chemical research in toxicology · 2021Article
- Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Immunoautophagy-Related Genes and Tumor Microenvironment.International journal of general medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common malignancies worldwide, due to poor diagnosis and treatment. There is increasing evidence that demonstrates the involvement of long non-coding RNAs (lncRNAs) in carcinogenesis and cancer progression. Therefore, the aim of the present study was to explore potential lncRNA-associated features of patients with OSCC as a valuable and independent prognostic biomarker. A total of 268 lncRNA expression profiles and clinical patient information on OSCC were downloaded from The Cancer Genome Atlas database. The clinical information was exploited for prescreening, using Cox regression analysis, and differentially expressed lncRNAs (DElncRNAs) were identified using edgeR software. Using the 'caret' package, the datasets were categorized into test datasets and training datasets, respectively. Through bioinformatics, seven prognostic DElncRNAs were selected. Using the regression coefficients, a risk score based on the seven-DElncRNA signature was developed to assess the prognostic function of key DElncRNAs. According to the median risk score, patients were classified into high-risk and low-risk groups in the training and test datasets. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic DElncRNAs, and the optimal cut-off point was obtained from ROC analysis. Based on the optimal cut-off point, the patients were also categorized into high-risk and low-risk groups. Notably, the optimal cut-off point was more sensitive than the median risk score, particularly in the test dataset. The Kaplan-Meier survival and log rank test analysis results indicated that the P-value, based on the optimal cut-off, was less than the median risk cut-off. Additionally, stratified analysis results revealed that the seven-DElncRNAs signature was also independent of OSCC age. Furthermore, the findings of the present study suggested that the seven-DElncRNA signature can be used as a potential prognostic indicator and may have important clinical significance in OSCC.
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