Simple Summary This comprehensive review focuses on the role of epigenetics in oral squamous cell carcinoma (OSCC), a prevalent type of oral cancer. The review highlights the importance of epigenetic changes, including DNA methylation, histone modifications, and miRNAs, in OSCC development and progression. Aberrant DNA methylation of tumor suppressor genes (TSGs) promotes tumor growth, making gene methylation patterns potential biomarkers for OSCC detection. Histone modifications, such as acetylation, methylation, phosphorylation, and ubiquitination, impact gene expression by modifying chromatin structure. Dysregulated miRNAs also contribute to OSCC progression. Epigenetic-targeted therapies, such as DNMT and HDAC inhibitors, show promise in modifying abnormal gene expression patterns, potentially leading to improved treatment outcomes for OSCC. However, challenges remain in biomarker identification and developing effective combination treatments. Understanding and targeting these epigenetic processes offer potential strategies to overcome drug resistance and improve OSCC treatment efficacy. Overall, the review highlights the potential of understanding and targeting epigenetic processes to overcome challenges and improve the efficacy of OSCC treatment, offering valuable insights for society in the diagnosis and treatment of oral cancer. Abstract Oral squamous cell carcinoma (OSCC) is a prevalent and significant type of oral cancer that has far-reaching health implications worldwide. Epigenetics, a field focused on studying heritable changes in gene expression without modifying DNA sequence, plays a pivotal role in OSCC. Epigenetic changes, encompassing DNA methylation, histone modifications, and miRNAs, exert control over gene activity and cellular characteristics. In OSCC, aberrant DNA methylation of tumor suppressor genes (TSG) leads to their inactivation, subsequently facilitating tumor growth. As a result, distinct patterns of gene methylation hold promise as valuable biomarkers for the detection of OSCC. Oral cancer treatment typically involves surgery, radiation therapy, and chemotherapy, but even with these treatments, cancer cells cannot be effectively targeted and destroyed. Researchers are therefore exploring new methods to target and eliminate cancer cells. One promising approach is the use of epigenetic modifiers, such as DNA methyltransferase (DNMT) inhibitors and histone deacetylase (HDAC) inhibitors, which have been shown to modify abnormal epigenetic patterns in OSCC cells, leading to the reactivation of TSGs and the suppression of oncogenes. As a result, epigenetic-targeted therapies have the potential to directly alter gene expression and minimize side effects. Several studies have explored the efficacy of such therapies in the treatment of OSCC. Although studies have investigated the efficacy of epigenetic therapies, challenges in identifying reliable biomarkers and developing effective combination treatments are acknowledged. Of note, epigenetic mechanisms play a significant role in drug resistance in OSCC and other cancers. Aberrant DNA methylation can silence tumor suppressor genes, while alterations in histone modifications and chromatin remodeling affect gene expression related to drug metabolism and cell survival. Thus, understanding and targeting these epigenetic processes offer potential strategies to overcome drug resistance and improve the efficacy of cancer treatments in OSCC. This comprehensive review focuses on the complex interplay between epigenetic alterations and OSCC cells. This will involve a deep dive into the mechanisms underlying epigenetic modifications and their impact on OSCC, including its initiation, progression, and metastasis. Furthermore, this review will present the role of epigenetics in the treatment and diagnosis of OSCC.

Epigenetic Regulation in Oral Squamous Cell Carcinoma Microenvironment: A Comprehensive Review

Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.

/pdf/deep-learning-techniques-with-genomic-data-in-cancer-51seyli5.pdf

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature

The Scientific Investigation Committee of the American Academy of Restorative Dentistry offers this review of the 2021 dental literature in restorative dentistry to inform busy dentists regarding noteworthy scientific and clinical progress over the past year. Each member of the committee brings discipline-specific expertise to coverage of this broad topical area. Specific subject areas addressed, in order of the appearance in this report, include COVID-19 and the dental profession (new); prosthodontics; periodontics, alveolar bone, and peri-implant tissues; implant dentistry; dental materials and therapeutics; occlusion and temporomandibular disorders; sleep-related breathing disorders; oral medicine and oral and maxillofacial surgery; and dental caries and cariology. The authors focused their efforts on reporting information likely to influence daily dental treatment decisions with an emphasis on future trends in dentistry. With the tremendous volume of dentistry and related literature being published daily, this review cannot possibly be comprehensive. Rather, its purpose is to update interested readers and provide important resource material for those interested in pursuing greater details on their own. It remains our intent to assist colleagues in negotiating the extensive volume of important information being published annually. It is our hope that readers find this work useful in successfully managing the patients and dental problems they encounter.

Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry.

The function of non-coding RNAs (ncRNAs) in the pathogenesis and development of cancer is indisputable. Molecular mechanisms underlying carcinogenesis involve the aberrant expression of ncRNAs, including circular RNAs (circRNAs), and microRNAs (miRNAs). CircRNAs are a class of single-stranded, covalently closed RNAs responsible for maintaining cellular homeostasis through their diverse functions. As a part of the competing endogenous RNA (ceRNAs) network, they play a central role in the regulation of accessibility of miRNAs to their mRNA targets. The interplay between these molecular players is based on the primary role of circRNAs that act as miRNAs sponges, and the circRNA/miRNA imbalance plays a central role in different pathologies including cancer. Herein, we present the latest state of knowledge about interactions between circRNAs and miR-141, a well-known member of the miR-200 family, in malignant transformation, with emphasis on the biological role of circRNA/miR-141/mRNA networks as a future target for novel anti-cancer therapies.

Interactions between circRNAs and miR-141 in Cancer: From Pathogenesis to Diagnosis and Therapy

Background Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. Although immunotherapy provides hope for advanced HCC patients, the outcomes are not satisfactory and vary by individual case. In this study, we sought to establish novel molecular subtypes and a stable model based on tumor-related pathways for guiding the immunotherapy in HCC patients. Methods A total of 15 pathways including immune pathways, stromal pathways, oncogenic pathways, and DNA damage repair pathways were used to construct molecular subtypes through consensus clustering. Immune characteristics, gene mutations, and genomic alterations including copy number variations and homologous recombination deficiency (HRD) were analyzed in different clusters. The Tumor Immune Dysfunction and Exclusion (TIDE) framework was used to predict the response to immunotherapy. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were employed to screen prognostic genes for constructing a risk model. Results Three clusters/subtypes were constructed including Immune-E, Immune-D and Stromal-E. Immune-D had the worst prognosis and high enrichment of HRD pathways. Immune-E had higher immune infiltration, higher expression of major histocompatibility complex (MHC)-related genes, and higher expression of PD1, PDL1, CTLA4, and LAG3. TP53 alterations frequently occurred in Immune-D. Immune-E had a relatively high response to immunotherapy and was sensitive to chemotherapeutic drugs. Moreover, we constructed an IMScore model that was effective to classify HCC patients into different risk groups, and the IMScore had a better performance than the TIDE score. Conclusions This study revealed the complex interaction among the tumor microenvironment (TME), genomic alterations, and tumor-related pathways by exploring the molecular difference of 3 subtypes. The IMScore model has potential to provide guidance for immunotherapy in HCC patients.

Molecular subtyping and IMScore based on immune-related pathways, oncogenic pathways, and DNA damage repair pathways for guiding immunotherapy in hepatocellular carcinoma patients

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCC-related genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumor-infiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area.

/pdf/molecular-subtypes-of-oral-squamous-cell-carcinoma-based-on-1fffww2fg5.pdf

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach.

Objective. To identify the key genetic and epigenetic mechanisms involved in the wound healing process after injury of the oral mucosa. Materials and Methods. Gene expression profiling datasets pertaining to rapid wound healing of oral mucosa were identified using the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed to identify differentially expressed genes (DEGs) during oral mucosal wound healing. Next, functional enrichment analysis was performed to identify the biological processes (BPs) and signaling pathways relevant to these DEGs. A protein-protein interaction (PPI) network was constructed to identify hub DEGs. Interaction networks were constructed for both miRNA-target DEGs and DEGs-transcription factors. A DEGs-chemical compound interaction network and a miRNA-small molecular interaction network were also constructed. Results. DEGs were found significantly enriched in several signaling pathways including arachidonic acid metabolism, cell cycle, p53, and ECM-receptor interaction. Hub genes, GABARAPL1, GABARAPL2, HDAC5, MAP1LC3A, AURKA, and PLK1, were identified via PPI network analysis. Two miRNAs, miR-34a-5p and miR-335-5p, were identified as pivotal players in the miRNA-target DEGs network. Four transcription factors FOS, PLAU, BCL6, and RORA were found to play key roles in the TFs-DEGs interaction network. Several chemical compounds including Valproic acid, Doxorubicin, Nickel, and tretinoin and small molecular drugs including atorvastatin, 17β-estradiol, curcumin, and vitamin D3 were noted to influence oral mucosa regeneration by regulating the expression of healing-associated DEGs/miRNAs. Conclusion. Genetic and epigenetic mechanisms and specific drugs were identified as significant molecular mechanisms and entities relevant to oral mucosal healing. These may be valuable potential targets for experimental research.

/pdf/identification-of-the-potential-biomarkers-involved-in-the-2mh2k0t9b4.pdf

Identification of the Potential Biomarkers Involved in the Human Oral Mucosal Wound Healing: A Bioinformatic Study

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Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach.

Identification of the Potential Biomarkers Involved in the Human Oral Mucosal Wound Healing: A Bioinformatic Study