Aim: The main of this study was to investigate the role of IGF2BP2 in oral squamous cell carcinoma (OSCC) with the overarching of providing new biomarkers or potential therapeutic targets for OSCC. Methods: We combined datasets downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and samples collected from the clinic to evaluate the expression of IGF2BP2 in OSCC. IGF2BP2 survival analysis was respectively performed based on TCGA, GEO, and clinical samples. Correlations between IGF2BP2 expression and clinicopathological parameters were then analyzed, and signaling pathways associated with IGF2BP2 expression were identified using gene set enrichment analysis (GSEA 4.1.0). Moreover, an IGF2BP2 co-expressed gene network was constructed, followed by gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on IGF2BP2 co-expressed genes. Finally, TIMER and CIBERSORT were used to analyze the correlations among IGF2BP2, IGF2BP2-coexpressed genes, and tumor-infiltrating immune cells (TICs). Results: IGF2BP2 was highly expressed in OSCC and significantly correlated with overall survival of OSCC patients (p<0.01). High IGF2BP2 expression correlated with poor overall survival. The GSEA results showed that cell apoptosis-, tumor-, and immune-related pathways were significantly enriched in samples with high IGF2BP2 expression. Furthermore, GO and KEGG enrichment analysis results of IGF2BP2 co-expressed genes indicated that these genes are mainly associated with immunity/inflammation and tumorigenesis. In addition, IGF2BP2 and its co-expressed genes are associated with tumor-infiltrating immune cells (p<0.01). Conclusion: IGF2BP2 may be a potential prognostic biomarker in OSCC and correlates with immune infiltrates.
Background: Currently, no systematic analysis has been conducted to assess the potential of multiple autophagy-related long non-coding RNAs (lncRNA) to predict the prognosis of head and neck squamous cell carcinoma (HNSCC). we investigated the prognostic potential of autophagy-related long non-coding RNAs (lncRNA) in HNSCC patients. Methods: Patient information and Autophagy-associated genes were obtained from The Cancer Genome Atlas (TCGA) and Human Autophagy data resource. Autophagy-related lncRNAs were determined through Lasso and Cox regression analyses. Then, on the basis of autophagy- related lncRNAs, a risk score and a nomogram were constructed for estimation of prognostic outcomes for HNSCC patients. These models were verified internally using the TCGA and. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene functional analyses. Results: Three autophagy-related lncRNAs (AC002401.4, AC245041.2 and TMEM44-AS1) that are associated with HNSCC were identified. Univariate and multivariate Cox regression analyses revealed that the risk score is an independent prognostic indicator (p ≤ 0.001), with its ability to predict prognosis being higher than that of other clinicopathological indicators (AUC=0.732). Concordance index of the nomogram was 0.712, and AUC values for one-year, three-year and five-year survival rates were 0.730, 0.745 and 0.728, respectively. Internal verifications revealed that this nomogram had a good ability to predict prognosis. Functional analysis showed that the genes were mostly enriched in autophagy and tumor-related cascades. Conclusion: The autophagy-related lncRNAs model can predict the prognosis of patients with HNSCC.Trial registration: Prospective, Observational, Real-world Oral Malignant Tumors Study (POROMS), NCT02395367. Registered 23 March 2015, https://clinicaltrials.gov/ct2/show/NCT02395367
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