Transcriptomic Profiling Predicts Multiple Pathways and Molecules Associated With the Metastatic Phenotype of Oral Cancer Cells

MYEOV with High Frequencies of Mutations in Head and Neck Cancers Facilitates Cancer Cell Malignant Behaviors

Cancer driver genes (CDGs) and the driver mutations disrupt the homeostasis of numerous critical cell activities, thereby playing a critical role in tumor initiation and progression. In this study, integrative bioinformatics analyses were performed based on a series of online databases, aiming to identify driver genes with high frequencies of mutations in head and neck cancers. Higher myeloma overexpressed (MYEOV) genetic variation frequency and expression level were connected to a poorer prognosis in head and neck cancer patients. MYEOV was dramatically upregulated within head and neck tumor samples and cells. Consistently, MYEOV overexpression remarkably enhanced the aggressiveness of head and neck cancer cells by promoting colony formation, cell invasion, and cell migration. Conversely, MYEOV knockdown attenuated cancer cell aggressiveness and inhibited tumor growth and metastasis in the oral orthotopic tumor model. In conclusion, MYEOV is overexpressed in head and neck cancer, with greater mutation frequencies correlating to a poorer prognosis in head and neck cancer patients. MYEOV serves as an oncogene in head and neck cancer through the promotion of tumor cell colony formation, invasion, and migration, as well as promoting tumor growth and metastasis in the oral orthotopic tumor model.

Cytomorphology and Gene Expression Signatures of Anchorage-independent Aggregations of Oral Cancer Cells

BACKGROUND/AIM

Cancer cells with high anchorage independence can survive and proliferate in the absence of adhesion to the extracellular matrix. Under anchorage-independent conditions, cancer cells adhere to each other and form aggregates to overcome various stresses. In this study, we investigated the cytomorphology and gene expression signatures of oral cancer cell aggregates.

MATERIALS AND METHODS

Two oral cancer-derived cell lines, SAS and HSC-3 cells, were cultured in a low-attachment plate and their cytomorphologies were observed. The transcriptome between attached and detached SAS cells was examined using gene expression microarrays. Subsequently, gene enrichment analysis and Ingenuity Pathway Analysis were performed. Gene expression changes under attached, detached, and re-attached conditions were measured via RT-qPCR.

RESULTS

While SAS cells formed multiple round-shaped aggregates, HSC-3 cells, which had lower anchorage independence, did not form aggregates efficiently. Each SAS cell in the aggregate was linked by desmosomes and tight junctions. Comparative transcriptomic analysis revealed 1,698 differentially expressed genes (DEGs) between attached and detached SAS cells. The DEGs were associated with various functions and processes, including cell adhesion. Moreover, under the detached condition, the expression of some epithelial genes (DSC3, DSP, CLDN1 and OCLN) were up-regulated. The changes in both cytomorphology and epithelial gene expression under the detached condition overall returned to their original ones when cells re-attached.

CONCLUSION

The results suggest specific cytomorphological and gene expression changes in oral cancer cell aggregates. Our findings provide insights into the mechanisms underlying anchorage-independent oral cancer cell aggregation and reveal previously unknown potential diagnostic and therapeutic molecules.

Arecoline Is Associated With Inhibition of Cuproptosis and Proliferation of Cancer-Associated Fibroblasts in Oral Squamous Cell Carcinoma: A Potential Mechanism for Tumor Metastasis

Background

Metastatic disease remains the primary cause of death in patients with oral squamous cell carcinoma (OSCC), especially those who use betel nut. The different steps of the metastatic cascade rely on reciprocal interactions between cancer cells and the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the TME of OSCC. However, the precise mechanisms regulating CAFs in OSCC are poorly understood.

Methods

Thirteen genes related to the arecoline were analyzed to explore the significant ones involved in arecoline-related OSCC metastasis. The GSE139869 (n = 10) and The Cancer Genome Atlas (TCGA)-OSCC data (n = 361) were mined for the identification of the differentially expressed genes. The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the independent prognostic signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the functional enrichment of selected genes, and gene set enrichment analysis of cuproptosis-related genes was completed. Spearman’s analysis and Tumor Immune Estimation Resource (TIMER) were used to visualize the correlation between the infiltration of CAFs and the gene expression. The correlation analysis of the cells and different genes, including CAF infiltration and transcripts per million expression, was assessed. The relationship between arecoline and CAFs was confirmed by cell counting kit-8 assay (CCK-8). CancerSEA was searched to identify the single-cell phenotype.

Result

Arecoline-associated fibrosis-related OSCC differentially expressed genes (AFOC-DEGs), namely, PLAU, IL1A, SPP1, CCL11, TERT, and COL1A2, were screened out and selected from the Gene Expression Omnibus (GEO) database and TCGA database. AFOC-DEGs were highly expressed in OSCC, which led to poor survival of patients. Functional enrichment analysis, protein–protein interaction network construction, and Spearman’s correlation analysis all suggested that AFOC-DEGs were closely associated with cuproptosis. Cellular experiments demonstrated that arecoline stimulation could significantly increase the cell viability of CAFs. Single-sample Gene Set Enrichment Analysis (ssGSEA) results showed that GLS and MTF1 were highly expressed when fibroblasts proliferated at high enrichment levels. In addition, analysis of single-cell sequencing results suggested that OSCC cells with high expression of AFOC-DEGs were associated with OSCC metastasis.

Conclusion

We found a close association between arecoline, cuproptosis, and CAFs, which might play an important role in the metastasis of OSCC.

Pan-Cancer Metastasis Prediction Based on Graph Deep Learning Method

Tumor metastasis is the major cause of mortality from cancer. From this perspective, detecting cancer gene expression and transcriptome changes is important for exploring tumor metastasis molecular mechanisms and cellular events. Precisely estimating a patient’s cancer state and prognosis is the key challenge to develop a patient’s therapeutic schedule. In the recent years, a variety of machine learning techniques widely contributed to analyzing real-world gene expression data and predicting tumor outcomes. In this area, data mining and machine learning techniques have widely contributed to gene expression data analysis by supplying computational models to support decision-making on real-world data. Nevertheless, limitation of real-world data extremely restricted model predictive performance, and the complexity of data makes it difficult to extract vital features. Besides these, the efficacy of standard machine learning pipelines is far from being satisfactory despite the fact that diverse feature selection strategy had been applied. To address these problems, we developed directed relation-graph convolutional network to provide an advanced feature extraction strategy. We first constructed gene regulation network and extracted gene expression features based on relational graph convolutional network method. The high-dimensional features of each sample were regarded as an image pixel, and convolutional neural network was implemented to predict the risk of metastasis for each patient. Ten cross-validations on 1,779 cases from The Cancer Genome Atlas show that our model’s performance (area under the curve, AUC = 0.837; area under precision recall curve, AUPRC = 0.717) outstands that of an existing network-based method (AUC = 0.707, AUPRC = 0.555).