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.

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 Integrated omics analysis based on transcriptome and DNA methylation data combined with machine learning methods is very promising for the diagnosis, prognosis, and classification of cancer. In this study, the DNA methylation and gene expression data of ovarian cancer (OC) were analyzed to identify abnormally methylated differentially expressed genes (DEGs), screen potential therapeutic agents for OC, and construct a risk model based on the abnormally methylated DEGs to predict patient prognosis. Methods The gene expression and DNA methylation data of primary OC samples with tumor protein 53 (TP53) wild-type and normal samples were obtained from The Cancer Genome Atlas (TCGA) database. DEGs with aberrant methylation were analyzed by screening the intersection between DEGs and differentially methylated genes (DMGs). We attempted to search for potential drugs targeting DEGs with aberrant methylation by employing a network medicine framework. A gene signature based on the DEGs with aberrant methylation was constructed by regularized least absolute shrinkage and selection operator (LASSO) regression analysis. Results A total of 440 aberrant methylated DEGs were screened. Based on their gene expression profiles and methylation data from different regions, the results of both discriminative pattern recognition analysis and principal component analysis (PCA) showed a significant separation between tumor tissue and healthy ovarian tissue. In total, 126 potential therapeutic drugs were identified for OC by network-based proximity analysis. Five genes were identified in 440 aberrant methylated DEGs, which formed an aberrant methylated DEGs-driven gene signature. This signature could significantly distinguish the different overall survivals (OS) of OC patients and showed better predictive performance in both the training and validation sets. Conclusions In this study, the DNA methylation and gene expression data of OC were analyzed to identify abnormally methylated DEGs and potential therapeutic drugs, and a gene signature based on five aberrant methylation DEGs was constructed, which could better predict the risk of death in patients.

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A gene signature driven by abnormally methylated DEGs was developed for TP53 wild-type ovarian cancer samples by integrative omics analysis of DNA methylation and gene expression data

Background Macrophages are the main immune components in the microenvironment of osteosarcoma. The treatment strategy centered on macrophages has become a hot topic to improve cancer treatment. However, the research on the role of macrophages in the treatment of osteosarcoma is still in its infancy. Methods The data of osteosarcoma samples were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and GSE21257 datasets, and the macrophage enrichment fraction of osteosarcoma samples in TARGET was calculated by single-sample gene set enrichment analysis (ssGSEA) method to screen macrophage-related genes for consensus clustering. Differential expression analysis, univariable Cox, and least absolute shrinkage and selection operator (LASSO) regression were conducted to select reliable predictors and create a risk score system. The GSE21257 dataset was used as a verification set to verify the accuracy of risk score system. Results We identified 2 osteosarcoma clusters mediated by 22 macrophage score-related genes, namely cluster 1 (C1) and cluster 2 (C2). Compared with C2, C1 had a significant advantage in prognosis, and the degree of immune cell infiltration in tumor microenvironment (TME) was significantly higher, the expression of immune checkpoint molecules was significantly enhanced, and the Tumor Immune Dysfunction and Exclusion (TIDE) score was also significantly down-regulated. A robust risk score system was presented and validated, which demonstrated accuracy and independence in assessing the risk of death of osteosarcoma. The risk score system could also monitor TME infiltration in osteosarcoma samples and showed a close relationship with osteosarcoma biology, including metastasis and immunity. Conclusions We identified 2 types of clusters mediated by macrophage-related genes and helped to analyze the cluster suitable for immunotherapy. A new prognostic risk score system was created to quantitatively evaluate the prognosis and TME of osteosarcoma, and to provide a new entry point for the design of personalized treatment.

Characterization of osteosarcoma subtypes mediated by macrophage-related genes and creation and validation of a risk score system to quantitatively assess the prognosis of osteosarcoma and reflect the tumor microenvironment

Colorectal cancer (CRC) is a major global health concern, with rising incidence and mortality rates. Conventional treatments often come with significant complications, prompting the exploration of natural compounds like curcumin as potential therapeutic agents. Using bioinformatic tools, this study investigated the role of curcumin in CRC treatment. Significant protein interactions between curcumin and target proteins were identified in the STITCH database. Differentially expressed genes (DEGs) associated with CRC were then analyzed from GEO databases. Comparing curcumin targets and CRC-related DEGs, nine significant common targets were identified: DNMT1, PCNA, CCND1, PLAU, MMP3, SOX9, FOXM1, CXCL2, and SERPINB5. Pathway enrichment analyses revealed that curcumin-targeted pathways were primarily related to p53, IL-17, NF-kappa B, TNF, and cell cycle signaling, all crucial in CRC development and progression. Further analyses using DAID and EnrichR algorithms showed that the curcumin targets exhibited greater specificity to bronchial epithelial cells and colorectal adenocarcinoma than other diseases. Analyses via the DSigDB database indicated that curcumin ranks highly among other drugs targeting the identified CRC-related genes. Docking studies revealed favorable binding interactions between curcumin and the key CRC-related proteins, suggesting potential molecular mechanisms by which curcumin may exert its effects. In summary, this study provides bioinformatic and docking evidence that curcumin may exert beneficial effects on CRC by modulating the expression or activity of multiple CRC-susceptibility genes involved in critical signaling pathways. These findings warrant further experimental validation and support the potential of curcumin as a therapeutic agent for CRC. 

Bioinformatic analysis of the molecular targets of curcumin in colorectal cancer

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.

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

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