Background Head and neck squamous cell carcinoma (HNSCC) is one of the most common and highly heterogeneous malignancies worldwide. Increasing studies have proven that hypoxia and related long non‐coding RNA (lncRNA) are involved in the occurrence and prognosis of HNSCC. The goal of this work is to construct a risk assessment model using hypoxia‐related lncRNAs (hrlncRNAs) for HNSCC prognosis prediction and personalized treatment. Methods Transcriptome expression matrix, clinical follow‐up data, and somatic mutation data of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA). We used co‐expression analysis to identify hrlncRNAs, then screened for differentially expressed lncRNAs (DEhrlncRNAs), and paired these DEhrlncRNAs. The risk model was established through univariate, least absolute shrinkage and selection operator (LASSO), and stepwise multivariate Cox regression. Finally, we assessed the model from multiple perspectives of tumor mutation burden (TMB), tumor immune infiltration, chemotherapeutic sensitivity, immune checkpoint inhibitor (ICI), and functional enrichment. Results The risk assessment model included 14 hrlncRNA pairs. The risk score was observed to be a reliable prognostic factor. The high‐risk patients had an unfavorable prognosis and significant differences from the low‐risk group in TMB and tumor immune infiltration. In the high‐risk patients, the common immune checkpoints were down‐regulated, including CTLA4 and PDCD1, and the sensibility to paclitaxel and docetaxel was higher. The functional enrichment analysis suggested that the low‐risk group was accompanied by activated immune function. Conclusions The risk assessment model of 14‐hrlncRNA‐pairs demonstrated a promising prognostic prediction for HNSCC patients and can guide personalized clinical treatment.
Non-coding RNAs (ncRNAs) are transcribed from the genomes of mammals and other complex organisms, and many of them are alternately spliced and processed into smaller products. Types of ncRNAs include microRNAs (miRNAs), circular RNAs, and long ncRNAs. miRNAs are about 21 nucleotides long and form a broad class of post-transcriptional regulators of gene expression that affect numerous developmental and physiological processes in eukaryotes. They usually act as negative regulators of mRNA expression through complementary binding sequences in the 3'-UTR of the target mRNA, leading to translation inhibition and target degradation. In recent years, the importance of ncRNA in oral lichen planus (OLP), particularly miRNA, has attracted extensive attention. However, the biological functions of miRNAs and their mechanisms in OLP are still unclear. In this review, we discuss the role and function of miRNAs in OLP, and we also describe their potential functional roles as biomarkers for the diagnosis of OLP. MiRNAs are promising new therapeutic targets, but more work is needed to understand their biological functions.
Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN).Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis.Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model’s accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function.Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.
Background: Circular RNA (circRNA) has an important influence on oral squamous cell carcinoma (OSCC) progression as competing endogenous RNAs (ceRNAs). However, the link between ceRNAs and the OSCC immune microenvironment is unknown. The research aimed to find circRNAs implicated in OSCC carcinogenesis and progression and build a circRNA-based ceRNA network to create a reliable OSCC risk prediction model.Methods: The expression profiles of circRNA in OSCC tumors and normal tissues were assessed through RNA sequencing. From the TCGA database, clinicopathological data and expression patterns of microRNAs (miRNAs) and mRNAs were obtained. A network of circRNA-miRNA-mRNA ceRNA was prepared according to these differentially expressed RNAs and was analyzed through functional enrichment. Subsequently, based on the mRNA in the ceRNA network, the influence of the model on prognosis was then evaluated using a risk prediction model. Finally, considering survival, tumor-infiltrating immune cells (TICs), clinicopathological features, immunosuppressive molecules, and chemotherapy efficacy were analyzed.Results: Eleven differentially expressed circRNAs were found in cancer tissues relative to healthy tissues. We established a network of circRNA-miRNA-mRNA ceRNA, and the ceRNA network includes 123 mRNAs, six miRNAs, and four circRNAs. By the assessment of Genomes pathway and Kyoto Encyclopedia of Genes, it is found that in the cellular senescence, PI3K-AKT and mTOR signaling pathway mRNAs were mainly enrichment. An immune-related signature was created utilizing seven immune-related genes in the ceRNA network after univariate and multivariate analysis. The receiver operating characteristic of the nomogram exhibited satisfactory accuracy and predictive potential. According to a Kaplan-Meier analysis, the high-risk group’s survival rate was signally lower than the group with low-risk. In addition, risk models were linked to clinicopathological characteristics, TICs, immune checkpoints, and antitumor drug susceptibility.Conclusion: The profiles of circRNAs expression of OSCC tissues differ significantly from normal tissues. Our study established a circRNA-associated ceRNA network associated with OSCC and identified essential prognostic genes. Furthermore, our proposed immune-based signature aims to help research OSCC etiology, prognostic marker screening, and immune response evaluation.
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