In clinical practice, cancer stage (or grade) and some biomarkers, such as carcinoembryonic antigen (CEA) and CA199, are widely used to predict the prognosis of gastric carcinoma patients. Due to the limited role of prognostic indicators for gastric carcinoma, this condition remains one of the most fatal human malignancies with a dismal prognosis. Nicotinamide N-methyltransferase (NNMT, EC.2.1.1.1), a metabolizing enzyme, is involved in the development and progression of various carcinomas. However, the prognostic and biological functions of NNMT in gastric carcinoma are not yet clear. In the present study, NNMT was found to be overexpressed at the mRNA and protein levels in gastric carcinoma tissues compared with adjacent tissues. Importantly, the survival analysis verified that NNMT expression is an independent prognostic factor for overall survival of gastric cancer patients. Moreover, NNMT expression was related to primary tumor size, lymph node metastasis, distant metastasis, and TNM (tumor, node, and metastasis) stage. We also demonstrated that knockdown of NNMT inhibits cellular proliferation, invasion and migration in vitro and in vivo. Overall, the results of this study suggest that NNMT is a promising prognostic predictor for gastric cancer patients and could be used as a new target for gastric cancer therapy.

Nicotinamide N-methyltransferase: a potential biomarker for worse prognosis in gastric carcinoma.

miR-194 regulated AGK and inhibited cell proliferation of oral squamous cell carcinoma by reducing PI3K-Akt-FoxO3a signaling.

Several genes and pathways associated with oral squamous cell carcinoma (OSCC) are significant in terms of early detection and prognosis. The objective of this literature review is to evaluate the current research on molecular pathways and genes involved in oral cancer. Articles on the genes involved in oral cancer pathways were evaluated to identify potential biomarkers that can predict survival. In total, 36 articles were retrieved from internet databases, including EBSCO Host, Google Scholar, PubMed, and Science Direct, using the keywords "biomarker of oral cancer," "pathways of oral cancer," "genes involved in oral cancer," and "oral cancer pathways." A total of 36 studies related to OSCC were chosen. Most of the studies used cell lines, while others used archival tissues, few studies followed up the cases. Three major interlinked pathways found were the nuclear factor kappa B (NF-kB), PI3K-AKT, and Wnt pathways. The commonly mutated genes were cyclin D1 (CCND1), Rb, p53, FLJ10540, and TC21. The NF-kB, PI3K-AKT, and Wnt pathways are most frequently involved in the molecular pathogenesis of oral cancer. However, the CCND1, Rb, p53, FLJ10540, and TC21 genes were found to be more accurate in determining patients' overall survival. Polymerase chain reaction, immunohistochemistry, and immunoblotting were the commonly used detection methods.

Molecular pathways of oral cancer that predict prognosis and survival: A systematic review

Circular RNA (circRNA) is a type of non-coding RNA molecule that affects the cellular regulatory network by sequestering microRNA (miRNA) like a sponge. This study was performed to identify differentially-expressed circRNA in oral squamous cell carcinomas (OSCCs). By high-throughput sequencing, microarray circRNA expression profiles were acquired from patients with OSCCs (n = 8) and controls (n = 8), which totaled 1921 existing circRNA molecules and 10021 novel circRNA molecules. Most of the circular RNA is from exons and distributed in the No. 1 and 2 chromosomes. Eight up-regulated and down-regulated circRNA molecules were identified as differentially-expressed in OSCCs. Among this, the expression of circ_000334, circ_006740, and circ_006371 are significantly down-regulated in 42 pairs of samples, which means that these circRNA molecules might be implicated in oncogenesis and development of OSCCs.

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Circular RNA Expression in Oral Squamous Cell Carcinoma.

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential.

Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System.

Purpose: The purpose of our study was to explore the molecular mechanisms in the process of oral squamous cells carcinoma (OSCC) development. Method: We downloaded the affymetrix microarray data GSE31853 and identified differentially expressed genes (DEGs) between OSCC and normal tissues. Then Gene Ontology (GO) and Protein-Protein interaction (PPI) networks analysis was conducted to investigate the DEGs at the function level. Results: A total 372 DEGs with logFC| >1 and P value < 0.05 were obtained , including NNMT, BAX, MMP9 and VEGF. The enriched GO terms mainly were associated with the nucleoplasm, response to DNA damage stimuli and DNA repair. PPI network analysis indicated that GMNN and TSPO were significant hub proteins and steroid biosynthesis and synthesis and degradation of ketone bodies were significantly dysregulated pathways. Conclusion: It is concluded that the genes and pathways identified in our work may play critical roles in OSCC development. Our data provides a comprehensive perspective to understand mechanisms underlying OSCC and the significant genes (proteins) and pathways may be targets for therapy in the future.

Xiao-Jun Ding

Papers

Bioinformatics analysis reveals significant genes and pathways to target for oral squamous cell carcinoma.