A comprehensive genome-wide analysis of the long noncoding RNA expression profile in metastatic lymph nodes of oral mucosal melanoma

Long Noncoding RNA SAMMSON Promotes Melanoma Progression by Inhibiting FOXA2 Expression

Long noncoding RNAs (lncRNAs) play crucial roles in melanoma initiation and development, serving as potential therapeutic targets and prognostic markers for melanoma. lncRNA survival-associated mitochondrial melanoma-specific oncogenic noncoding RNA (SAMMSON) is upregulated in many types of human cancers. However, the functions of SAMMSON in melanoma have not been fully elucidated. This study is aimed at investigating the expression and functions of SAMMSON in melanoma development. Bioinformatics analysis was performed to determine the expression of SAMMSON and its correlation with the 10-year overall survival (OS) in melanoma patients. Cell proliferation, migration, invasion, and tumorigenesis were detected by MTT, colony formation, Transwell assays, and mouse xenograft model. The expression of cell cycle-related factors, epithelial-to-mesenchymal transition (EMT) makers, and matrix metalloproteinases (MMPs) was assessed by RT-qPCR and western blotting analysis. The results demonstrated that SAMMSON expression was upregulated in melanoma tissues and cells, and lower SAMMSON expression was correlated with longer 10-year OS. SAMMSON knockdown decreased the proliferation, migration, and invasion of melanoma cells by regulating the expression of proliferation-related genes, EMT factors, and MMPs, respectively. Additionally, Forkhead box protein A2 (FOXA2) was confirmed to be a target of SAMMSON, and the biological effects induced by FOXA2 overexpression were similar to those induced by SAMMSON silencing in melanoma cells. Further studies showed that SAMMSON downregulated FOXA2 expression in melanoma cells by modulating the EZH2/H3K27me3 axis. Taken together, our data indicate that SAMMSON plays an important role in melanoma progression and can be a valuable biomarker and therapeutic target in melanoma.

Review on Canine Oral Melanoma: An Undervalued Authentic Genetic Model of Human Oral Melanoma?

Oral melanoma (OM) is a highly aggressive tumor of the oral cavity in humans and dogs. Here we review the phenotypic similarities between the disease in these 2 species as the basis for the view that canine OM is a good model for the corresponding human disease. Utility of the "canine model" has likely been hindered by a paucity of information about the extent of the molecular genetic similarities between human and canine OMs. Current knowledge of the somatic alterations that underpin human tumorigenesis and metastatic progression is relatively limited, primarily due to the rarity of the disease in humans and consequent lack of opportunity for large-scale molecular analysis. The molecular genetic comparisons between human and canine OMs that have been completed indicate some overlap between the somatic mutation profiles of canine OMs and a subset of human OMs. However, further comparative studies featuring, in particular, larger numbers of human OMs are required to provide substantive evidence that canine OMs share mechanisms of tumorigenesis with at least a subset of human OMs. Future molecular genetic investigations of both human and canine OMs should investigate how primary tumors develop a metastatic gene expression signature and the genetic and epigenetic alterations specific to metastatic sites. Such studies may identify genetic alterations and pathways specific to the metastatic disease which could be targetable by new drugs.

Network analysis and transcriptome profiling in peripheral blood mononuclear cells of patients with rheumatoid arthritis

The present study aimed to investigate the differential expression of long non-coding RNAs (lncRNAs) in rheumatoid arthritis (RA). High-throughput gene sequencing technology was used to detect the expression of lncRNA and mRNA in three patients with RA (RA group) and normal controls (NC group). A Bioinformatics analysis was used to assess the effects of differentially expressed mRNAs on signaling pathways and biological functions. The selected dysregulated lncRNAs were verified by reverse transcription-quantitative (RT-q)PCR in the peripheral blood mononuclear cells (PBMCs) of patients with RA and age- and sex-matched controls. A correlation analysis was used to analyze the relationship between lncRNAs and clinical indexes. From the lncRNA sequencing data, significantly differentially expressed lncRNAs between the RA and NC groups were identified by a fold change ≥2 and P<0.05. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis suggested that the differentially expressed mRNAs were mainly involved in organelle composition, intracellular regulation, signaling pathways, cancer, virus and inflammation. A total of four of these lncRNAs were confirmed by RT-qPCR to be significantly differentially expressed (LINC00304, MIR503HG, LINC01504 and FAM95B1). Through the correlation analysis, it was confirmed that there was a strong correlation between these lncRNAs and clinical laboratory indicators and indexes such as course of disease, arthrocele and joint tenderness. Overall, the present results suggested that the expression levels of LINC00304, MIR503HG, LINC01504 and FAM95B1 in PBMCs from patients with RA may serve as potential biomarkers for RA diagnosis, influencing the occurrence and progress of RA.

Long non‑coding RNAs are novel players in oral inflammatory disorders, potentially premalignant oral epithelial lesions and oral squamous cell carcinoma (Review)

In recent years, a large number of studies have shown that the abnormal expression of long non‑coding (lnc)RNAs can lead to a variety of different diseases, including inflammatory disorders, cardiovascular disease, nervous system diseases, and cancers. Recent research has demonstrated the biological characteristics of lncRNAs and the important functions of lncRNAs in oral inflammation, precancerous lesions and cancers. The present review aims to explore and discuss the potential roles of candidate lncRNAs in oral diseases by summarizing multiple lncRNA profiles in diseased and healthy oral tissues to determine the altered lncRNA signatures. In addition, to highlight the exact regulatory mechanism of lncRNAs in oral inflammatory disorders, potentially premalignant oral epithelial lesions and oral squamous cell carcinoma. The detection of lncRNAs in oral samples has the potential to be used as a diagnostic and an early detection tool for oral diseases. Furthermore, lncRNAs are promising future therapeutic targets in oral diseases, and research in this field may expand in the future.

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.

Cancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by specific therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous sources of information into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction software.

Genome-wide analysis of canine oral malignant melanoma metastasis-associated gene expression

Oral malignant melanoma (OMM) is the most common canine melanocytic neoplasm. Overlap between the somatic mutation profiles of canine OMM and human mucosal melanomas suggest a shared UV-independent molecular aetiology. In common with human mucosal melanomas, most canine OMM metastasise. There is no reliable means of predicting canine OMM metastasis, and systemic therapies for metastatic disease are largely palliative. Herein, we employed exon microarrays for comparative expression profiling of FFPE biopsies of 18 primary canine OMM that metastasised and 10 primary OMM that did not metastasise. Genes displaying metastasis-associated expression may be targets for anti-metastasis treatments, and biomarkers of OMM metastasis. Reduced expression of CXCL12 in the metastasising OMMs implies that the CXCR4/CXCL12 axis may be involved in OMM metastasis. Increased expression of APOBEC3A in the metastasising OMMs may indicate APOBEC3A-induced double-strand DNA breaks and pro-metastatic hypermutation. DNA double strand breakage triggers the DNA damage response network and two Fanconi anaemia DNA repair pathway members showed elevated expression in the metastasising OMMs. Cross-validation was employed to test a Linear Discriminant Analysis classifier based upon the RT-qPCR-measured expression levels of CXCL12, APOBEC3A and RPL29. Classification accuracies of 94% (metastasising OMMs) and 86% (non-metastasising OMMs) were estimated.