Oral Lichen Planus and Oral Squamous Cell Carcinoma share key oncogenic signatures

Increased expression of keratin 17 in oral lichen planus and its correlation with disease severity

Background/purpose

Oral lichen planus (OLP) is a chronic inflammatory disease with unknown mechanisms of pathogenesis. Keratin 17 (KRT17) is a protein that regulates numerous cellular processes. This study aimed to explore the expression of KRT17 in OLP and its correlation with the severity of OLP.

Materials and methods

RNA sequencing using epithelium from 5 OLP patients and 5 health control (HC) was performed, followed by functional analysis. The validation cohort of 20 OLP and 20 HC tissues were used to investigate positive area value of KRT17 by immunohistochemical analysis. Reticular, erosive and ulcerative (REU) scores were used for measuring the severity of OLP.

Results

A total of 15493 genes were detected, of which 1492 genes were significantly up-regulated in OLP and 622 were down-regulated. The mRNA expression of KRT17 was elevated by 13.09-fold in OLP compared to that in HC. Pathway analysis demonstrated high KRT17 expression was associated with multiple biological processes. The median of percentage of KRT17 positive area value was 19.30 % in OLP and 0.01 % in HC (P < 0.001). Percentage of KRT17 positive area value was higher in erosive OLP patients (27.25 %) compared to that in non-erosive patients (15.02 %, P = 0.006). REU scores were positively correlated with percentage of KRT17 positive area value (r = 0.628, P = 0.003).

Conclusion

The mRNA expression of KRT17 was elevated in OLP tissues compared to that in HC. KRT17 was positively correlated with the severity of OLP, indicating KRT17 might play a vital role in the pathogenesis of OLP.

Identification of the differentially expressed activated memory CD4+ T-cells-related genes and ceRNAs in oral lichen planus

Background

Oral lichen planus (OLP) is a common chronic oral mucosal disease with 1.4 % malignant transformation rate, and its etiology especially immune pathogenesis remains unclear. This study was aimed at investigating the immune cells related molecular underlying the pathophysiology of OLP through bioinformatics analysis.

Methods

The dataset GSE52130 obtained from the Gene Expression Omnibus (GEO) database was conducted a comprehensive analysis in this study. The CIBERSORTx was used for investigating immune cells infiltration. The gene set enrichment analysis (GSEA) and gene ontology (GO) enrichment were performed for exploring the biological functions and gene annotation. The protein-protein interactions (PPI) were constructed by STRING database and visualized by Cytoscape software. The cytohubba plugin was utilized for screening hub genes. The receiver operating characteristic (ROC) was performed for evaluating diagnostic value of hub genes. The miRNAs, lncRNAs and drugs were respectively predicted by NetworkAnalyst, miRTarbase, ENCORI, and DGIdb database.

Results

This study identified 595 differentially expressed genes (DEGs). The GSEA indicated keratinization, innate immune system and biological oxidation were involved in OLP. GO analysis showed extracellular matrix and keratinocyte were mainly enriched. And we found the activated memory CD4+ T cells were lowly infiltrated in OLP. We identified 101 activated memory CD4+ T-cells-related DEGs. Three hub genes (APP, IL1B, TF) were selected. APP and IL1B were significantly up-regulated, whereas TF was down-regulated in OLP. The three hub genes show high diagnostic value in OLP. Additionally, they were involved in MAPK signal, NF-kappaB signal and iron metabolism in OLP. What's more, NEAT1/XIST - miR - 15a - 5p/miR - 155-5p - APP/IL1B signal axis was focused in competing endogenous RNA (ceRNA) network. In addition, 35 drugs were predicted for OLP.

Conclusion

Three activated memory CD4+ T-cells-related DEGs were identified by integrative analysis. It may provide novel insight into the pathogenesis of OLP and suggest potential therapeutic targets for OLP.

Development of an immune-related diagnostic predictive model for oral lichen planus

Oral lichen planus (OLP) was a chronic inflammatory disease of unknown etiology with a 1.4% chance of progressing to malignancy. However, it has been suggested in several studies that immune system disorders played a dominant role in the onset and progression of OLP. Therefore, this experiment aimed to develop a diagnostic prediction model for OLP based on immunopathogenesis to achieve early diagnosis and treatment and prevent cancer. In this study, 2 publicly available OLP datasets from the gene expression omnibus database were filtered. In the experimental group (GSE52130), the level of immune cell infiltration was assessed using MCPcounter and ssGSEA algorithms. Subsequently, differential expression analysis and gene set enrichment analysis were performed between the OLP and control groups. The resulting differentially expressed genes were intersected with immunologically relevant genes provided on the immunology database and analysis portal database (ImmPort) website to obtain differentially expressed immunologically relevant genes (DEIRGs). Furthermore, the gene ontology and kyoto encyclopedia of genes and genomes analyses were carried out. Finally, protein-protein interaction network and least absolute shrinkage and selection operator regression analyses constructed a model for OLP. Receiver operating characteristic curves for the experimental and validation datasets (GSE38616) were plotted separately to validate the model's credibility. In addition, real-time quantitative PCR experiment was performed to verify the expression level of the diagnostic genes. Immune cell infiltration analysis revealed a more significant degree of inflammatory infiltration in the OLP group compared to the control group. In addition, the gene set enrichment analysis results were mainly associated with keratinization, antibacterial and immune responses, etc. A total of 774 differentially expressed genes was obtained according to the screening criteria, of which 65 were differentially expressed immunologically relevant genes. Ultimately, an immune-related diagnostic prediction model for OLP, which was composed of 5 hub genes (BST2, RNASEL, PI3, DEFB4A, CX3CL1), was identified. The verification results showed that the model has good diagnostic ability. There was a significant correlation between the 5 hub diagnostic biomarkers and immune infiltrating cells. The development of this model gave a novel insight into the early diagnosis of OLP.

Non-Habit-Related Oral Squamous Cell Carcinoma: A Review

Oral squamous cell carcinoma (OSCC) is a serious and potentially life-threatening condition that can have a profound impact on an individual's health and well-being. Its etiology is commonly known to be habit induced, such as tobacco consumption, smoking, or alcohol abuse. Apart from these etiologies, certain factors that lead to OSCC are also present but are less frequently encountered in hospitals and clinics. However, these non-habitual factors, with their pathogenesis, can lead to OSCC, which may be confusing to certain medical practitioners. This article discusses the various non-habitual causes that can lead to OSCC, as well as their pathophysiology, molecular expression, and related indicators and prognostic factors.

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.

Rank aggregation of independent genetic screen results highlights new strategies for adoptive cellular transfer therapy of cancer

Efficient intratumoral infiltration of adoptively transferred cells is a significant barrier to effectively treating solid tumors with adoptive cellular transfer (ACT) therapies. Our recent forward genetic, whole-genome screen identified T cell-intrinsic gene candidates that may improve tumor infiltration of T cells. Here, results are combined with five independent genetic screens using rank aggregation to improve rigor. This resulted in a combined total of 1,523 candidate genes - including 1,464 genes not currently being evaluated as therapeutic targets - that may improve tumor infiltration of T cells. Gene set enrichment analysis of a published human dataset shows that these gene candidates are differentially expressed in tumor infiltrating compared to circulating T cells, supporting translational potential. Importantly, adoptive transfer of T cells overexpressing gain-of-function candidates (AAK1ΔN125, SPRR1B, and EHHADH) into tumor-bearing mice resulted in increased T cell infiltration into tumors. These novel gene candidates may be considered as potential therapeutic candidates that can aid adoptive cellular therapy in improving T cell infiltration into solid tumors.