RESUMEN

Enhancer of Zeste Homolog 1 (EZH1) and Enhancer of Zeste Homolog 2 (EZH2) are the key components of polycomb repressive complex 2 (PRC2); however, the roles of these proteins in oral squamous cell carcinoma (OSCC) have yet to be elucidated. In this study, we aimed to determine the respective roles of these proteins in OSCC by investigating the expression levels of EZH1 and EZH2 in OSCC tissues (N = 63) by immunohistochemistry. In addition, we used lentiviruses to construct stable OSCC cell lines that overexpressed EZH1 and EZH2. Then, we investigated these cell lines for cell viability, colony formation capacity, stemness, and epithelial-mesenchymal transition (EMT). Binding competition between EZH1 and EZH2 with PRC2 was further evaluated using Co-immunoprecipitation (Co-IP). Compared with normal tissues, the expression levels of EZH2 in OSCC tissues was up-regulated, while the expression of EZH1 was down-regulated. EZH2 enhanced cell viability, colony formation capacity, stemness, and EMT, while EZH1 did not. Furthermore, analysis indicated that EZH1 and EZH2 bound competitively to PRC2 and influenced the methylation status of H3K27. In conclusion, our findings verified that EZH1 and EZH2 play opposing roles in OSCC and that EZH1 and EZH2 compete as the key component of PRC2, thus affecting the characteristics of OSCC via the methylation of H3K27.

Asunto(s)

Carcinoma de Células Escamosas , Neoplasias de Cabeza y Cuello , Neoplasias de la Boca , Humanos , Proteína Potenciadora del Homólogo Zeste 2/genética , Carcinoma de Células Escamosas/genética , Carcinoma de Células Escamosas de Cabeza y Cuello , Neoplasias de la Boca/genética , Complejo Represivo Polycomb 2/genética

RESUMEN

PURPOSE: The present study aimed to explore the potential components and mechanisms of Rhodiola rosea-Euonymus alatus drug pair (TY) that ameliorate rheumatoid arthritis (RA). METHODS: The main active components, core targets, and important pathways of TY against RA were predicted by network pharmacology analysis. The binding activity between the main active components and the core targets was verified by the molecular docking technique. Collagen-induced arthritis (CIA) rat model and tumor necrosis factor (TNF)-α-induced fibroblast-like synovial cells in human RA (HFLS-RA) model were established, respectively. The core targets were verified by cell counting kit-8 assay, hematoxylin eosin, enzyme-linked immunosorbent assay, real-time polymerase chain reaction, and Western blot analysis, and the therapeutic effect of TY was evaluated. RESULTS: A total of 18 possible components and 34 core targets were obtained by network pharmacology, among which inflammatory response, phosphatidylinositide 3-kinases (PI3K)-AKT and MAPK pathways were involved in the therapeutic effect of TY on RA. The results of molecular docking showed that kaempferol and quercetin had high binding affinity to interleukin (IL)-1ß, IL-6, matrix metalloproteinase (MMP)9, and TNF-α. In vivo and in vitro experiments showed that TY dose-dependently inhibited the proliferation of HFLS-RA cells induced by TNF-α, and significantly reduced the paw swelling and arthritis scores in CIA rats. At the same time, TY inhibited the production of inflammatory factors in CIA rat serum and TNF-α-induced HFLS-RA cells. It also decreased the expression of PI3K, phospho-protein kinase B, MMP1, MMP3, MMP9, and increased the protein and mRNA levels of tissue inhibitors of MMPs (TIMP)1 in synovial tissue. CONCLUSION: TY can inhibit the PI3K/AKT signaling pathway and regulate the balance between MMPs and TIMP, thus playing a therapeutic role in RA.

Asunto(s)

Artritis Experimental , Artritis Reumatoide , Euonymus , Rhodiola , Humanos , Ratas , Animales , Euonymus/metabolismo , Rhodiola/metabolismo , Factor de Necrosis Tumoral alfa/metabolismo , Proteínas Proto-Oncogénicas c-akt , Simulación del Acoplamiento Molecular , Farmacología en Red , Fosfatidilinositol 3-Quinasas , Artritis Reumatoide/tratamiento farmacológico , Artritis Reumatoide/genética , Artritis Experimental/tratamiento farmacológico , Artritis Experimental/patología , Metaloproteinasas de la Matriz/uso terapéutico

RESUMEN

Multiple primary cancers (MPCs) refer to cancers that occur simultaneously or metachronously in the same individual. The incidence of MPC has increased recently, as the survival time of malignant tumor patients has been greatly prolonged. It is difficult to differentiate MPC from primary cancers (PCs) in the same anatomical region from the clinical manifestation alone. However, their biological behaviors appear to be distinct. In this study, we show that the prognosis of multiple primary oral cancers (MP-OCs) is worse than primary oral cancers (P-OCs). To better understand the molecular mechanisms of MP-OC, we used whole exome sequencing (WES) to analyze samples from 9 patients with MP-OC and 21 patients with P-OC. We found more somatic mutations in MP-OC than in P-OC. MP-OC had more complicated mutation signatures, which were associated with age-related and Apolipoprotein B mRNA Editing Catalytic Polypeptide-like (APOBEC) activity-related signatures. Tumor mutational burden (TMB) and mutant-allele tumor heterogeneity (MATH) of MP-OC trended higher compared to P-OC. KEGG and GO analysis showed the differential pathways of MP-OC versus P-OC. In addition, MP-OC took amplification, not loss, as the main pattern of copy number variation (CNV), while P-OC took both. Lastly, we did not find significantly different mutant germline genes, but MSH-6 mutation may be a potential MP-OC driver. In short, our preliminary results show that MP-OC and P-OC have different molecular characteristics.

RESUMEN

BACKGROUND: Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging. METHODS: We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis. FINDINGS: We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79-84%), specificity of 84% (82-87%) and AUC of 0·90 (0·87-0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83-90%) for machine learning and 86% (82-89%) for deep learning, and a pooled specificity of 89% (82-93%) for machine learning, and 87% (82-91%) for deep learning. INTERPRETATION: AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field. FUNDING: College students' innovative entrepreneurial training plan program .