Purpose Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer in the world, accounting for more than 90% of head and neck malignant tumors. However, its molecular mechanism is largely unknown. To help elucidate the potential mechanism of HNSCC tumorigenesis, we investigated the gene interaction patterns associated with tumorigenesis. Methods Weighted gene co-expression network analysis (WGCNA) can help us to predict the intrinsic relationship or correlation between gene expression. Additionally, we further explored the combination of clinical information and module construction. Results Sixteen modules were constructed, among which the key module most closely associated with clinical information was identified. By analyzing the genes in this module, we found that the latter may be related to the immune response, inflammatory response and formation of the tumor microenvironment. Sixteen hub genes were identified— ARHGAP9, SASH3, CORO1A, ITGAL, PPP1R16B, TBC1D10C, IL10RA, ITK, AKNA, PRKCB, TRAF3IP3, GIMAP4, CCR7, P2RY8, GIMAP7 , and SP140 . We further validated these genes at the transcriptional and translation levels. Conclusion The innovative use of a weighted network to analyze HNSCC samples provides new insights into the molecular mechanism and prognosis of HNSCC. Additionally, the hub genes we identified can be used as biomarkers and therapeutic targets of HNSCC, laying the foundation for the accurate diagnosis and treatment of HNSCC in clinical and research in the future.
To help provide evidence for prognosis prediction and personalized targeted therapy for patients with head and neck squamous cell carcinoma (HNSCC), we investigated prognosis-specific methylation-driven genes in HNSCC. Survival time data, RNA sequencing data, and methylation data for HNSCC patients were downloaded from The Cancer Genome Atlas. The MethylMix R package based on the β mixture model was utilized to screen genes with different methylation statuses in tumor tissues and adjacent normal tissues, and a total of 182 HNSCC-related methylation-driven genes were then identified. A survival prediction scoring model based on multivariate Cox analysis was developed to screen the genes related to the prognosis of HNSCC, and a linear risk model of the methylation status of six genes (INA, LINC01354, TSPYL4, MAGEB2, EPHX3, and ZNF134) was constructed. The prognostic values of the six genes were further independently explored by survival analysis combined with methylation and gene expression analyses. The 5-year survival rate in the highrisk group of patients in the test set was 30.4% (95% CI: 22.7%-40.8%) and that in the low-risk group of patients was 65.5% (95% CI: 56.1%-76.5%). The area under the receiver operating characteristic curve for the model was 0.723, which further verified the specificity and sensitivity of the model. In addition, subsequent combined survival analysis revealed that all six genes could be used as independent prognostic markers and thus might be potential drug targets. The innovative method provides new insight into the molecular mechanism and prognosis of HNSCC. K E Y W O R D SDNA methylation-driven genes, epigenetics, HNSCC, prognostic risk model, survival analysis
Backround Tongue squamous cell carcinoma (TSCC) is the most common malignant tumor in the oral cavity. An increasing number of studies have suggested that long noncoding RNA (lncRNA) plays an important role in the biological process of disease and is closely related to the occurrence and development of disease, including TSCC. Although many lncRNAs have been discovered, there remains a lack of research on the function and mechanism of lncRNAs. To better understand the clinical role and biological function of lncRNAs in TSCC, we conducted this study. Methods In this study, 162 tongue samples, including 147 TSCC samples and 15 normal control samples, were investigated and downloaded from The Cancer Genome Atlas (TCGA). We constructed a competitive endogenous RNA (ceRNA) regulatory network. Then, we investigated two lncRNAs as key lncRNAs using Kaplan–Meier curve analysis and constructed a key lncRNA-miRNA-mRNA subnetwork. Furthermore, gene set enrichment analysis (GSEA) was carried out on mRNAs in the subnetwork after multivariate survival analysis of the Cox proportional hazards regression model. Results The ceRNA regulatory network consists of six differentially expressed miRNAs (DEmiRNAs), 29 differentially expressed lncRNAs (DElncRNAs) and six differentially expressed mRNAs (DEmRNAs). Kaplan-Meier curve analysis of lncRNAs in the TSCC ceRNA regulatory network showed that only two lncRNAs, including LINC00261 and PART1, are correlated with the total survival time of TSCC patients. After we constructed the key lncRNA-miRNA -RNA sub network, the GSEA results showed that key lncRNA are mainly related to cytokines and the immune system. High expression levels of LINC00261 indicate a poor prognosis, while a high expression level of PART1 indicates a better prognosis.
Objectives Adipose-derived stem cells are frequently used for bone regeneration both in vitro and in vivo. N6-methyladenosine (m6A) is the most abundant post-transcriptional modification on eukaryotic RNAs and plays multifaceted roles in development and diseases. However, the regulatory mechanisms of m6A in osteogenic differentiation of human adipose-derived stem cells (hASCs) remain elusive. The present study aimed to build the transcriptome-wide m6A methylome during the osteogenic differentiation of hASCs. Materials and methods hASCs were harvested after being cultured in a basic or osteogenic medium for 7 days, and the osteogenic differentiation was validated by alkaline phosphatase (ALP) and Alizarin Red S staining, ALP activity assay, and qRT-PCR analysis of ALP, RUNX2, BGLAP, SPP1, SP7, and COL1A1 genes. The m6A level was colorimetrically measured, and the expression of m6A regulators was confirmed by qRT-PCR and western blot. Moreover, m6A MeRIP-seq and RNA-seq were performed to build the transcriptome and m6A methylome. Furthermore, bioinformatic analyses including volcano plots, Venn plots, clustering analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, gene sets enrichment analysis, and protein-protein interaction analysis were conducted. Results In total, 1145 differentially methylated peaks, 2261 differentially expressed genes, and 671 differentially methylated and expressed genes (DMEGs) were identified. GO and KEGG pathway analyses conducted for these DMEGs revealed extensive and osteogenic biological functions. The “PI3K-Akt signaling pathway”; “MAPK signaling pathway”; “parathyroid hormone synthesis, secretion, and action”; and “p53 signaling pathway” were significantly enriched, and the DMEGs in these pathways were identified as m6A-specific key genes. A protein-protein interaction network based on DMEGs was built, and VEGFA, CD44, MMP2, HGF, and SPARC were speculated as the hub DMEGs. Conclusions The total m6A level was reduced with osteogenic differentiation of hASCs. The transcriptome-wide m6A methylome built in the present study indicated quite a few signaling pathways, and hub genes were influenced by m6A modification. Future studies based on these epigenetic clues could promote understanding of the mechanisms of osteogenic differentiation of hASCs.
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