Bioavailability, Health Benefits, and Delivery Systems of Allicin: A Review.

Garlic has been used worldwide as a spice due to its pungent taste and flavor-enhancing properties. As a main biologically active component of the freshly crushed garlic extracts, allicin (diallyl thiosulfinate) is converted from alliin by alliinase upon damaging the garlic clove, which has been reported to have many potent beneficial biological functions. In this work, allicin formation, stability, bioavailability, and metabolism process are examined and summarized. The biological functions of allicin and potential underlying mechanisms are reviewed and discussed, including antioxidation, anti-inflammation, antidiabetic, cardioprotective, antineurodegenerative, antitumor, and antiobesity effects. Novel delivery systems of allicin with enhanced stability, encapsulation efficiency, and bioavailability are also evaluated, such as nanoparticles, gels, liposomes, and micelles. This study could provide a comprehensive understanding of the physiochemical properties and health benefits of allicin, with great potential for further applications in the food and nutraceutical industries.

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

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.

Protective Effect of Triphala against Oxidative Stress-Induced Neurotoxicity.

BACKGROUND: Oxidative stress is implicated in the progression of many neurological diseases, which could be induced by various chemicals, such as hydrogen peroxide (H2O2) and acrylamide. Triphala is a well-recognized Ayurvedic medicine that possesses different therapeutic properties (e.g., antihistamine, antioxidant, anticancer, anti-inflammatory, antibacterial, and anticariogenic effects). However, little information is available regarding the neuroprotective effect of Triphala on oxidative stress. MATERIALS AND METHODS: An in vitro H2O2-induced SH-SY5Y cell model and an in vivo acrylamide-induced zebrafish model were established. Cell viability, apoptosis, and proliferation were examined by MTT assay, ELISA, and flow cytometric analysis, respectively. The molecular mechanism underlying the antioxidant activity of Triphala against H2O2 was investigated dose dependently by Western blotting. The in vivo neuroprotective effect of Triphala on acrylamide-induced oxidative injury in Danio rerio was determined using immunofluorescence staining. RESULTS: The results indicated that Triphala plays a neuroprotective role against H2O2 toxicity in inhibiting cell apoptosis and promoting cell proliferation. Furthermore, Triphala pretreatment suppressed the phosphorylation of the mitogen-activated protein kinase (MARK) signal pathway (p-Erk1/2, p-JNK1/2, and p-p38), whereas it restored the activities of antioxidant enzymes (superoxide dismutase 1 (SOD1) and catalase) in the H2O2-treated SH-SY5Y cells. Consistently, similar protective effects of Triphala were observed in declining neuroapoptosis and scavenging free radicals in the zebrafish central neural system, possessing a critical neuroprotective property against acrylamide-induced oxidative stress. CONCLUSION: In summary, Triphala is a promising neuroprotective agent against oxidative stress in SH-SY5Y cells and zebrafishes with significant antiapoptosis and antioxidant activities.

The Genetic and Epigenetic Mechanisms Involved in Irreversible Pulp Neural Inflammation.

AIM: To identify the critical genetic and epigenetic biomarkers by constructing the long noncoding RNA- (lncRNA-) related competing endogenous RNA (ceRNA) network involved in irreversible pulp neural inflammation (pulpitis). MATERIALS AND METHODS: The public datasets regarding irreversible pulpitis were downloaded from the gene expression omnibus (GEO) database. The differential expression analysis was performed to identify the differentially expressed genes (DEGs) and DElncRNAs. Functional enrichment analysis was performed to explore the biological processes and signaling pathways enriched by DEGs. By performing a weighted gene coexpression network analysis (WGCNA), the significant gene modules in each dataset were identified. Most importantly, DElncRNA-DEmRNA regulatory network and DElncRNA-associated ceRNA network were constructed. A transcription factor- (TF-) DEmRNA network was built to identify the critical TFs involved in pulpitis. RESULT: Two datasets (GSE92681 and GSE77459) were selected for analysis. DEGs involved in pulpitis were significantly enriched in seven signaling pathways (i.e., NOD-like receptor (NLR), Toll-like receptor (TLR), NF-kappa B, tumor necrosis factor (TNF), cell adhesion molecules (CAMs), chemokine, and cytokine-cytokine receptor interaction pathways). The ceRNA regulatory relationships were established consisting of three genes (i.e., LCP1, EZH2, and NR4A1), five miRNAs (i.e., miR-340-5p, miR-4731-5p, miR-27a-3p, miR-34a-5p, and miR-766-5p), and three lncRNAs (i.e., XIST, MIR155HG, and LINC00630). Six transcription factors (i.e., GATA2, ETS1, FOXP3, STAT1, FOS, and JUN) were identified to play pivotal roles in pulpitis. CONCLUSION: This paper demonstrates the genetic and epigenetic mechanisms of irreversible pulpitis by revealing the ceRNA network. The biomarkers identified could provide research direction for the application of genetically modified stem cells in endodontic regeneration.

Shared Genetic and Epigenetic Mechanisms between the Osteogenic Differentiation of Dental Pulp Stem Cells and Bone Marrow Stem Cells.

OBJECTIVE: To identify the shared genetic and epigenetic mechanisms between the osteogenic differentiation of dental pulp stem cells (DPSC) and bone marrow stem cells (BMSC). MATERIALS AND METHODS: The profiling datasets of miRNA expression in the osteogenic differentiation of mesenchymal stem cells from the dental pulp (DPSC) and bone marrow (BMSC) were searched in the Gene Expression Omnibus (GEO) database. The differential expression analysis was performed to identify differentially expressed miRNAs (DEmiRNAs) dysregulated in DPSC and BMSC osteodifferentiation. The target genes of the DEmiRNAs that were dysregulated in DPSC and BMSC osteodifferentiation were identified, followed by the identification of the signaling pathways and biological processes (BPs) of these target genes. Accordingly, the DEmiRNA-transcription factor (TFs) network and the DEmiRNAs-small molecular drug network involved in the DPSC and BMSC osteodifferentiation were constructed. RESULTS: 16 dysregulated DEmiRNAs were found to be overlapped in the DPSC and BMSC osteodifferentiation, including 8 DEmiRNAs with a common expression pattern (8 upregulated DEmiRNAs (miR-101-3p, miR-143-3p, miR-145-3p/5p, miR-19a-3p, miR-34c-5p, miR-3607-3p, miR-378e, miR-671-3p, and miR-671-5p) and 1 downregulated DEmiRNA (miR-671-3p/5p)), as well as 8 DEmiRNAs with a different expression pattern (i.e., miR-1273g-3p, miR-146a-5p, miR-146b-5p, miR-337-3p, miR-382-3p, miR-4508, miR-4516, and miR-6087). Several signaling pathways (TNF, mTOR, Hippo, neutrophin, and pathways regulating pluripotency of stem cells), transcription factors (RUNX1, FOXA1, HIF1A, and MYC), and small molecule drugs (curcumin, docosahexaenoic acid (DHA), vitamin D3, arsenic trioxide, 5-fluorouracil (5-FU), and naringin) were identified as common regulators of both the DPSC and BMSC osteodifferentiation. CONCLUSION: Common genetic and epigenetic mechanisms are involved in the osteodifferentiation of DPSCs and BMSCs.

Immune Checkpoint Gene Expression Profiling Identifies Programmed Cell Death Ligand-1 Centered Immunologic Subtypes of Oral and Squamous Cell Carcinoma With Favorable Survival.

OBJECTIVE: This study aimed to identify the programmed death ligand-1 (PDL1, also termed as CD274) and its positively correlated immune checkpoint genes (ICGs) and to determine the immune subtypes of CD274-centered ICG combinations in oral and squamous cell carcinoma (OSCC). MATERIALS AND METHODS: Firstly, the 95 ICGs obtained via literature reviews were identified in the Cancer Genome Atlas (TCGA) database in relation to OSCC, and such 88 ICG expression profiles were extracted. ICGs positively correlated with CD274 were utilized for subsequent analysis. The relationship between ICGs positively correlated with CD274 and immunotherapy biomarkers (tumor mutation burden (TMB), and adaptive immune resistance pathway genes) was investigated, and the relationships of these genes with OSCC clinical features were explored. The prognostic values of CD274 and its positively correlated ICGs and also their associated gene pairs were revealed using the survival analysis. RESULTS: Eight ICGs, including CTLA4, ICOS, TNFRSF4, CD27, B- and T-lymphocyte attenuator (BTLA), ADORA2A, CD40LG, and CD28, were found to be positively correlated with CD274. Among the eight ICGs, seven ICGs (CTLA4, ICOS, TNFRSF4, CD27, BTLA, CD40LG, and CD28) were significantly negatively correlated with TMB. The majority of the adaptive immune resistance pathway genes were positively correlated with ICGs positively correlated with CD274. The survival analysis utilizing the TCGA-OSCC data showed that, although CD274 was not significantly associated with overall survival (OS), the majority of ICGs positively correlated with CD274 (BTLA, CD27, CTLA4, CD40LG, CD28, ICOS, and TNFRSF4) were significantly correlated with OS, whereby their low-expression predicted a favorable prognosis. The survival analysis based on the gene pair subtypes showed that the combination subtypes of CD274_low/BTLA_low, CD274_low/CD27_low, CD274_low/CTLA4_low, CD8A_high/BTLA_low, CD8A_high/CD27_low, and CD8A_high/CTLA4_low predicted favorable OS. CONCLUSION: The results in this study provide a theoretical basis for prognostic immune subtyping of OSCC and highlight the importance of developing future immunotherapeutic strategies for treating oral cancer.

The molecular differences between human papillomavirus-positive and -negative oropharyngeal squamous cell carcinoma: A bioinformatics study.

OBJECTIVE: To investigate the genetic and epigenetic differences between human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) and HPV-negative OPSCC. METHODS: Microarray data of HPV-positive and -negative OPSCC were retrieved from NCBI GEO datasets. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) were identified by performing differential expression analysis. A functional enrichment analysis was performed to explore the biological processes and signaling pathways that DEGs and DE-miRNAs were involved in, respectively. A protein-protein interaction (PPI) network of DEGs was constructed to identify hub genes. miRNA-target network and miRNA-miRNA functional synergistic network were each constructed in order to identify risk-marker miRNAs. An miRNA-target-pathway network was constructed in order to explore the function of identified risk-marker miRNAs. RESULTS: Microarray data from 3 datasets (GSE39366, GSE40774, and GSE55550) was included and analyzed. The PPI network identified 3 hub genes (VCAM1, UBD, and RPA2). MiR-107 and miR-142-3p were found to play the most significant role in both the DE-miRNA-target network as well as in the miRNA-miRNA functional synergistic network. MiR-107 was involved in HPV-induced tumorigenesis by targeting many genes (CAV1, CDK6, MYB, and SERPINB5) and regulating the p53 signaling pathway, the PI3K-Akt signaling pathway, and the autophagy pathway. In addition, miR-142-3p was implicated in HPV-induced tumorigenesis by targeting the PPFIA1 gene and regulating transcriptional dysregulation and other cancerous pathways. CONCLUSION: Three genes (VCAM1, UBD, and RPA2), two miRNAs (miR-107 and miR-142-3p), and four pathways (p53, PI3K-Akt, autophagy, and transcription dysregulation in cancer) were identified to play critical roles in distinguishing HPV-positive OPSCC from HPV-negative OPSCC.

Shared genetic and epigenetic mechanisms between chronic periodontitis and oral squamous cell carcinoma.

OBJECTIVES: To analyze bioinformatic datasets for detecting genetic and epigenetic mechanisms shared by chronic periodontitis (CP) and oral squamous cell carcinoma (OSCC). MATERIALS AND METHODS: Datasets from GEO and TCGA databases reporting mRNAs, miRNAs or methylation expression in human CP and OSCC tissues were analyzed. Differential expression, functional enrichment and protein-protein interaction (PPI) network analyses were performed. Differentially expressed miRNAs (DEmiRNAs) and genes (DEG) in CP and OSCC were determined. DEmiRNA-target and DEmiRNA-DEG networks were constructed. Directly and indirectly interacting cross-talk genes were screened, and their prediction accuracy and association with OSCC prognosis was determined. RESULTS: 3 DE-miRNAs (miR-375, miR-3609 and miR-3652) expressed in both CP and OSCC critically regulated most DEGs. Among 12 directly interacting cross-talk genes, NCAPH was significantly related with the prognosis of OSCC. NR2F2 had highest differential expression in CP and OSCC. Among 4 cross-talk genes (FN1, MPPED1, NDEL1, and NR2F2) differentially expressed in CP, 3 (FN1, MPPED1, NDEL1) were also expressed in OSCC. Among 12 indirectly interacting cross-talk genes differentially expressed in OSCC, 3 genes (CDCA8, HIST1H3J, and RAD51) were significantly related to its prognosis. Significant pathways involved in CP and OSCC included: chemokine receptors, class I PI3K signaling events, epithelial-to-mesenchymal transition and signaling events by VEGFR1 and VEGFR2, EGF receptor (ErbB1). CONCLUSION: Bioinformatic analysis of available datasets implicated 1 directly interacting cross-talk gene (NCAPH), 4 indirectly interacting cross-talk genes (NCAPH, NR2F2, FN1, and MPPED1) and 3 DE-miRNAs (hsa-miR-375, miR-3609 and miR-3652) as shared genetic and epigenetic expression patterns between CP and OSCC.