Integrative Multi-Omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma

Inhibin A contributes to the tumorigenesis of oral squamous cell carcinoma by KIAA1429-mediated m6A modification

BACKGROUND

Inhibin A and N6-methyladenosine methylation modifications participate in oral squamous cell carcinoma development. However, the N6-methyladenosine modification of Inhibin A in oral squamous cell carcinoma has not been revealed. This study reveals a key gene "Inhibin A" that may affect the tumorigenesis of oral squamous cell carcinoma and its molecular mechanisms on N6-methyladenosine methyltransferase KIAA1429-mediated N6-methyladenosine methylation modification.

METHODS

Bioinformatics analysis and quantitative real-time polymerase chain reaction identified the potential regulatory genes in oral squamous cell carcinoma. We examined the changes in the proliferation (Cell Counting Kit-8 assay), migration (transwell migration assay), and invasion (transwell invasion assays) of oral squamous cell carcinoma cells. We performed a xenograft tumor experiment to validate the role of Inhibin A in oral squamous cell carcinoma in vivo. The interactions between Inhibin A and KIAA1429 were analyzed using bioinformatics, methylated RNA immunoprecipitation-qPCR, quantitative real-time polymerase chain reaction, and Western blotting experiments.

RESULTS

Inhibin A had the highest expression in patients with oral squamous cell carcinoma. Inhibin A silencing impaired the ability of oral squamous cell carcinoma cells to proliferate, migrate, and invade, as well as limited the tumorous growth of oral squamous cell carcinoma cells in vivo. Bioinformatics analysis showed that Inhibin A expression positively interacted with KIAA1429 expression in The Cancer Genome Atlas database. The levels were also upregulated in our clinical samples. Furthermore, KIAA1429 silencing repressed the N6-methyladenosine level of Inhibin A in oral squamous cell carcinoma.

CONCLUSIONS

Inhibin A promotes the tumorigenesis of oral squamous cell carcinoma by KIAA1429-mediated N6-methyladenosine modification. This study adds to our current knowledge of the molecular mechanisms underlying oral squamous cell carcinoma malignancy.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

Cellular Impacts of Striatins and the STRIPAK Complex and Their Roles in the Development and Metastasis in Clinical Cancers (Review)

Striatins (STRNs) are generally considered to be cytoplasmic proteins, with lower expression observed in the nucleus and at cell-cell contact regions. Together with protein phosphatase 2A (PP2A), STRNs form the core region of striatin-interacting phosphatase and kinase (STRIPAK) complexes through the coiled-coil region of STRN proteins, which is crucial for substrate recruitment. Over the past two decades, there has been an increasing amount of research into the biological and cellular functions of STRIPAK members. STRNs and the constituent members of the STRIPAK complex have been found to regulate several cellular functions, such as cell cycle control, cell growth, and motility. Dysregulation of these cellular events is associated with cancer development. Importantly, their roles in cancer cells and clinical cancers are becoming recognised, with several STRIPAK components found to have elevated expression in cancerous tissues compared to healthy tissues. These molecules exhibit significant diagnostic and prognostic value across different cancer types and in metastatic progression. The present review comprehensively summarises and discusses the current knowledge of STRNs and core STRIPAK members, in cancer malignancy, from both cellular and clinical perspectives.

Unidirectional alteration of methylation and hydroxymethylation at the promoters and differential gene expression in oral squamous cell carcinoma

Background: Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Although overall losses of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) have been previously observed, a genome-wide, single-base-resolution, and simultaneous mapping of 5mC and 5hmC in OSCC is still unaccomplished. Similarly, the mechanism of how 5mC and 5hmC collectively lead to abnormal gene expression in OSCC is largely unexplored. Using parallel whole-genome bisulfite sequencing (WGBS) and whole-genome oxidative bisulfite sequencing (oxWGBS), we characterized 5mC- and 5hmC-profiles at single-nucleotide resolution in paired primary OSCC samples and their normal adjacent tissues (NATs). We also analyzed the effect of 5mC- and 5hmC-modifications on differential gene expression in OSCC using multi-omics analysis. Results: An overall reduction of both 5mC and 5hmC in various genomic regions have been observed in OSCC samples. At promoter regions, a total of 6,921 differentially methylated regions and 1,024 differentially hydroxymethylated regions were identified in OSCC. Interestingly, compared to bidirectional modification with 5mC and 5hmC, unidirectional modification with 5mC and 5hmC at the promoters is associated with bigger change in the gene expression. Additionally, genes bearing unidirectional modification with 5mC and 5hmC at the promoters are enriched in signaling pathways like cell proliferation, cell differentiation, and receptor tyrosine kinase pathway that are essential for the tumorigenesis. Finally, the grouped expression signature of top 20 genes bearing promoter-unidirectional-modification with 5mC and 5hmC tends to correlate with the clinical outcome of certain subtypes of head and neck squamous cell carcinoma. Conclusion: Using parallel WGBS and oxWGBS analyses, we observed an overall reduction of 5mC- and 5hmC-modifications at various genomic regions in OSCC. Unidirectional modification with 5mC and 5hmC at the promoters is associated with enhanced changes in gene expression in OSCC tissues. Furthermore, such differentially expressed genes bearing unidirectional modifications with 5mC and 5hmC at the promoters might have clinical relevance to the outcome of OSCC.

DNA Methylation as a Diagnostic, Prognostic, and Predictive Biomarker in Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) is a term collectively used to describe all cancers that develop in the oral and nasal cavities, the paranasal sinuses, the salivary glands, the pharynx, and the larynx. The majority (75%) of all newly diagnosed cases are observed in patients with locally advanced and aggressive disease, associated with significant relapse rates (30%) and poor prognostic outcomes, despite advances in multimodal treatment. Consequently, there is an unmet need for the identification and application of tools that would enable diagnosis at the earliest possible stage, accurately predict prognostic outcomes, contribute to the timely detection of relapses, and aid in the decision for therapy selection. Recent evidence suggests that DNA methylation can alter the expression of genes in a way that it favors tumorigenesis and tumor progression in HNSCC, and therefore represents a potential source for biomarker identification. This study summarizes the current knowledge on how abnormally methylated DNA profiles in HNSCC patients may contribute to the pathogenesis of HNSCC and designate the methylation patterns that have the potential to constitute clinically valuable biomarkers for achieving significant advances in the management of the disease and for improving survival outcomes in these patients.

Identification of Diagnostic Markers in Infantile Hemangiomas

Background

Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce.

Methods

The "limma" R package was applied to identify differentially expressed genes (DEGs) in developing IHs. Plugin ClueGO in Cytoscape software performed functional enrichment of DEGs. The Search Tool for Retrieving Interacting Genes (STRING) database was utilized to construct the PPI network. The least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were used to identify diagnostic genes for IHs. The receiver operating characteristic (ROC) curve evaluated diagnostic genes' discriminatory ability. Single-gene based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted by Gene Set Enrichment Analysis (GSEA). The chemicals related to the diagnostic genes were excavated by the Comparative Toxicogenomics Database (CTD). Finally, the online website Network Analyst was used to predict the transcription factors targeting the diagnostic genes.

Results

A total of 205 DEGs were singled out from IHs samples of 6-, 12-, and 24-month-old infants. These genes principally participated in vasculogenesis and development-related, endothelial cell-related biological processes. Then we mined 127 interacting proteins and created a network with 127 nodes and 251 edges. Furthermore, LASSO and SVM-RRF algorithms identified five diagnostic genes, namely, TMEM2, GUCY1A2, ISL1, WARS, and STEAP4. ROC curve analysis results indicated that the diagnostic genes had a powerful ability to distinguish IHs samples from normal samples. Next, the results of GSEA for a single gene illustrated that all five diagnostic genes inhibited the "valine, leucine, and isoleucine degradation" pathway in the development of IHs. WARS, TMEM2, and STEAP4 activated the "blood vessel development" and "vasculature development" in IHs. Subsequently, inhibitors targeting TMEM2, GUCY1A2, ISL1, and STEAP4 were mined. Finally, 14 transcription factors regulating GUCY1A2, 14 transcription factors regulating STEAP4, and 26 transcription factors regulating ISL1 were predicted.

Conclusion

This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.

Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Head and neck squamous cell carcinoma (HNSCC) is characterized by an immunosuppression environment and necessitates the development of new immunotherapy response predictors. The study aimed to build a prognosis-related competing endogenous RNA (ceRNA) network based on immune-related genes (IRGs) and analyze its immunological signatures. Differentially expressed IRGs were identified by bioinformatics analysis with Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and ImmPort databases. Finally, via upstream prognosis-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) prediction and co-expression analysis, we built an immune-related ceRNA network (LINC00052/hsa-miR-148a-3p/PLAU) related to HNSCC patient prognosis. CIBERSORT analysis demonstrated that there were substantial differences in 11 infiltrating immune cells in HNSCC, and PLAU was closely correlated with 10 type cells, including T cells CD8+ (R =  - 0.329), T cells follicular helper (R = - 0.342) and macrophage M0 (R = 0.278). Methylation and Tumor Immune Dysfunction and Exclusion (TIDE) analyses revealed that PLAU upregulation was most likely caused by hypomethylation and that high PLAU expression may be associated with tumor immune evasion in HNSCC, respectively.

Hub Gene Screening and Prognostic Modeling of Lung Cancer: An Integrated Bioinformatics Study

Background

One of the most frequent malignancies is lung carcinoma which poses heavy burden on the global health. The link among differentially expressed genes (DEGs) and lung cancer patients' clinical outcomes was still missing. In this study, we integrated transcriptome data with clinical data to investigate the relationship between them in lung carcinoma patients.

Methods

To begin, DEGs were identified using the Gene Expression Omnibus (GEO) gene expression pattern (GSE180347). Then, these DEGs are being searched in the TCGA database using the DEGs collected in the preceding phase. The Kaplan-Meier plotter was then used to assess the predictive value of these DEGs in patients with lung cancer.

Results

Our study revealed a total of 45 DEGs, 15 of which were up-regulated and 30 of which were down-regulated. These DEGs were mostly enriched in cytokine receptor binding and cytokine activity, according to GO enrichment analysis. These DEGs were mostly enriched in cytokine-cytokine receptor interaction, according to KEGG enrichment analysis. Based on the PPI network, which comprises of 12 DEGs, a major module was discovered. They are mostly interested in cytotoxicity mediated by natural killer cells. Among all 45 DEGs, the mutations of NCAM1 account for the most cases in TCGA database with a percentage above 15%. Among the 12 DEGs in the significant module, higher expression of FAS, GPR29, HAVCR2, and NCAM1 exhibits longer survival time with hazard ratio and 95% confident interval of 0.79 (0.69-0.89), 0.80 (0.70-0.90), 0.71 (0.60-0.84), and 0.73 (0.62-0.86), respectively. However, higher expression of FCGR3A and IFNG exhibits shorter survival time with hazard ratio and 95% confident interval of 1.50 (1.32-1.71) and 1.15 (1.02-1.31), respectively.

Conclusion

Our results demonstrate significant correlation between some DEGs and the survival outcome in lung adenocarcinomas patients, providing a comprehensive bioinformatics study in anticipation of future molecular mechanisms and biomarker studies.