Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma

Machine-Learning Applications in Oral Cancer: A Systematic Review

Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.

Binary Response Analysis Using Logistic Regression in Dentistry

Multivariate analysis with binary response is extensively utilized in dental research due to variations in dichotomous outcomes. One of the analyses for binary response variable is binary logistic regression, which explores the associated factors and predicts the response probability of the binary variable. This article aims to explain the statistical concepts of binary logistic regression analysis applicable to the field of dental research, including model fitting, goodness of fit test, and model validation. Moreover, interpretation of the model and logistic regression are also discussed with relevant examples. Practical guidance is also provided for dentists and dental researchers to enhance their basic understanding of binary logistic regression analysis.

An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.

Integrated multiplex network based approach for hub gene identification in oral cancer

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

Human Beta Defensins and Cancer: Contradictions and Common Ground

Human beta-defensins (hBDs, −1, 2, 3) are a family of epithelial cell derived antimicrobial peptides (AMPs) that protect mucosal membranes from microbial challenges. In addition to their antimicrobial activities, they possess other functions; e.g., cell activation, proliferation, regulation of cytokine/chemokine production, migration, differentiation, angiogenesis, and wound healing processes. It has also become apparent that defensin levels change with the development of neoplasia. However, inconsistent observations published by various laboratories make it difficult to reach a consensus as to the direction of the dysregulation and role the hBDs may play in various cancers. This is particularly evident in studies focusing on oral squamous cell carcinoma (OSCC). By segregating each hBD by cancer type, interrogating methodologies, and scrutinizing the subject cohorts used in the studies, we have endeavored to identify the “take home message” for each one of the three hBDs. We discovered that (1) consensus-driven findings indicate that hBD-1 and−2 are down- while hBD-3 is up-regulated in OSCC; (2) hBD dysregulation is cancer-type specific; (3) the inhibition/activation effect an hBD has on cancer cell lines is related to the direction of the hBD dysregulation (up or down) in the cancer from which the cell lines derive. Therefore, studies addressing hBD dysregulation in various cancers are not generalizable and comparisons should be avoided. Systematic delineation of the fate and role of the hBDs in a specific cancer type may lead to innovative ways to use defensins as prospective biomarkers for diagnostic/prognostic purposes and/or in novel therapeutic modalities.

Identification of key candidate genes and pathways in oral squamous cell carcinoma by integrated Bioinformatics analysis

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignant head and neck tumor, which poses a serious threat to human health. In recent years, the incidence of OSCC has been increasing, while the prognosis has not significantly improved. Elucidation of the molecular mechanisms underlying the development of OSCC may provide novel therapeutic strategies. In the present study, the gene expression profiles from 4 datasets, including 244 OSCC and 95 normal oral mucosa samples, were subjected to statistical and Bioinformatics analysis. A total of 34 differentially expressed genes (DEGs) were identified, among which 14 were upregulated and 20 were downregulated in OSCC compared with normal oral mucosa tissues. Gene Ontology enrichment analysis indicated that the DEGs were mainly involved in regulation of the immune response, cell adhesion and cell proliferative processes. The Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the DEGs were mainly associated with the phosphoinositide-3 kinase Akt and Toll-like receptor signaling pathway. The key candidate DEGs were identified from the complex protein-protein interaction network, and secreted phosphoprotein 1 (SPP1), integrin subunit α 3 and plasminogen activator, urokinase (PLAU) were confirmed to be significantly associated with the survival rate. Cell Counting Kit-8 and Transwell assays demonstrated that SPP1 and PLAU regulate cell proliferation, migration and invasion. The candidate genes/pathways identified in the present study may include promising diagnostic biomarkers or therapeutic targets for OSCC.

Identification of ITGA3 as an Oncogene in Human Tongue Cancer via Integrated Bioinformatics Analysis

Human tongue cancer (TC) is an aggressive malignancy with a very poor prognosis. There is an urgent need to elucidate the underlying molecular mechanisms involved in TC progression. mRNA expression profiles play a vital role in the exploration of cancer-related genes. Therefore, the purpose of our study was to identify the progression associated candidate genes of TC by bioinformatics analysis. Five microarray datasets of TC samples were downloaded from the Gene Expression Omnibus (GEO) database and the data of 133 TC patients were screened from The Cancer Genome Atlas (TCGA) head and neck squamous cell carcinoma (HNSC) database. The integrated analysis of five microarray datasets and the RNA sequencing data of TC samples in TCGA-HNSC was performed to obtain 1023 overlapping differentially expressed genes (DEGs) in TC and adjacent normal tissue (ANT) samples. Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to enrich the significant pathways of the 1023 DEGs and PI3KAkt signaling pathway (P=0.011) was selected to be the candidate pathway. A total of 23 DEGs with |log2 fold change (FC)| ≥1.0 in phosphatidylinositol 3-kinase-serine/threonine kinase (PI3K-Akt) signaling pathway were subjected to survival analysis of 125 eligible TC samples in TCGA database, indicating increased integrin-α3 gene (ITGA3) expression was significantly associated with poorer prognosis. Taken together, our study suggested ITGA3 may facilitate the development of TC via activating PI3K-Akt signaling pathway.

Complex integrated analysis of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma

OBJECTIVES

This study aims to reveal regulatory network of lncRNAs-miRNAs-mRNAs in oral squamous cell carcinoma (OSCC) through gene expression data.

MATERIAL AND METHODS

Differentially expressed lncRNAs, miRNAs and mRNAs (cut-off: False discovery rate (FDR)<0.05 and |fold change|>1.5) were unveiled by package edgeR of R. Cox regression analysis was performed to screen prognostic factors in OSCC related with overall survival (OS) and relapse-free survival (RFS). Protein-protein interaction (PPI) network was constructed for differentially expressed mRNAs using BioGRID, HPRD and DIP. Key hub genes were identified from top 100 differentially expressed mRNAs ranked by betweenness centrality using recursive feature elimination. LncRNA-miRNA and miRNA-mRNA regulatory network were constructed and combined into ceRNAs regulatory network. Gene ontology biological terms and Kyoto Encyclopedia of Genes and Genomes pathways were identified using Fisher's exact test.

RESULTS

A total of 929 differentially expressed mRNAs, 23 differentially expressed lncRNAs and 29 differentially expressed miRNAs were identified. 59 mRNAs, 6 miRNAs (hsa-mir-133a-1, hsa-mir-1-2, hsa-mir-486, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 6 lncRNAs (C10orf91, C2orf48, SFTA1P, FLJ41941,PART1,TTTY14) were related with OS; and 52 mRNAs, 4 miRNAs (hsa-mir-133a-1, hsa-mir-135b, hsa-mir-196b, hsa-mir-193b) and 2 lncRNAs (PART1, TTTY14) were associated with RFS. A support vector machine (SVM) classifier containing 37 key hub genes was obtained. A ceRNA regulatory network containing 417 nodes and 696 edges was constructed. ECM-receptor interaction, cytokine-cytokine receptor interaction, focal adhesion, arachidonic acid metabolism, and p53 signaling pathway were significantly enriched in the network.

CONCLUSION

These findings uncover the pathogenesis of OSCC and might provide potential therapeutic targets.

Susceptibility of Human Oral Squamous Cell Carcinoma (OSCC) H103 and H376 cell lines to Retroviral OSKM mediated reprogramming

Although numbers of cancer cell lines have been shown to be successfully reprogrammed into induced pluripotent stem cells (iPSCs), reprogramming Oral Squamous Cell Carcinoma (OSCC) to pluripotency in relation to its cancer cell type and the expression pattern of pluripotent genes under later passage remain unexplored. In our study, we reprogrammed and characterised H103 and H376 oral squamous carcinoma cells using retroviral OSKM mediated method. Reprogrammed cells were characterized for their embryonic stem cells (ESCs) like morphology, pluripotent gene expression via quantitative real-time polymerase chain reaction (RT-qPCR), immunofluorescence staining, embryoid bodies (EB) formation and directed differentiation capacity. Reprogrammed H103 (Rep-H103) exhibited similar ESCs morphologies with flatten cells and clear borders on feeder layer. Reprogrammed H376 (Rep-H376) did not show ESCs morphologies but grow with a disorganized morphology. Critical pluripotency genes Oct4, Sox2 and Nanog were expressed higher in Rep-H103 against the parental counterpart from passage 5 to passage 10. As for Rep-H376, Nanog expression against its parental counterpart showed a significant decrease at passage 5 and although increased in passage 10, the level of expression was similar to the parental cells. Rep-H103 exhibited pluripotent signals (Oct4, Sox2, Nanog and Tra-1-60) and could form EB with the presence of three germ layers markers. Rep-H103 displayed differentiation capacity into adipocytes and osteocytes. The OSCC cell line H103 which was able to be reprogrammed into an iPSC like state showed high expression of Oct4, Sox2 and Nanog at late passage and may provide a potential iPSC model to study multi-stage oncogenesis in OSCC.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer

Network-based and cheminformatics approaches identify novel lead molecules forCXCR4, a key gene prioritized in oral cancer.