Distinctive Craniofacial and Oral Anomalies in MN1 C-terminal Truncation Syndrome.

MN1 C-terminal truncation (MCTT) syndrome was first reported in 2020 and only 28 patients have been recorded to date. Since MCTT syndrome is a newly defined and rare syndrome with many clinical features, the present study reviewed the manifestations and management of oral and dental anomalies. Gene variants of MCTT syndrome and their positive phenotypes were summarised. The phenotypes of variants in two exons differed from each other mainly in the craniomaxillofacial region, including brain MRI abnormalities and palatal morphology. Pathogenic mechanisms, especially in craniofacial and oral anomalies, were discussed. Appropriate treatments in the stomatology and respiratory departments could improve the symptoms of MCTT syndrome. The different sites of MN1 gene variants may influence the clinical symptoms and there may be racial differences in MCTT syndrome. We recommend oral and pulmonary evaluations for the multidisciplinary treatment of MCTT syndrome.

[Construction of a Prognostic Model of Multiple Myeloma Based on Metabolism-Related Genes].

OBJECTIVE: To screen the prognostic biomarkers of metabolic genes in patients with multiple myeloma (MM), and construct a prognostic model of metabolic genes. METHODS: The histological database related to MM patients was searched. Data from MM patients and healthy controls with complete clinical information were selected for analysis.The second generation sequencing data and clinical information of bone marrow tissue of MM patients and healthy controls were collected from human protein atlas (HPA) and multiple myeloma research foundation (MMRF) databases. The gene set of metabolism-related pathways was extracted from Molecular Signatures Database (MSigDB) by Perl language. The biomarkers related to MM metabolism were screened by difference analysis, univariate Cox risk regression analysis and LASSO regression analysis, and the risk prognostic model and Nomogram were constructed. Risk curve and survival curve were used to verify the grouping effect of the model. Gene set enrichment analysis (GSEA) was used to study the difference of biological pathway enrichment between high risk group and low risk group. Multivariate Cox risk regression analysis was used to verify the independent prognostic ability of risk score. RESULTS: A total of 8 mRNAs which were significantly related to the survival and prognosis of MM patients were obtained (P<0.01). As molecular markers, MM patients could be divided into high-risk group and low-risk group. Survival curve and risk curve showed that the overall survival time of patients in the low-risk group was significantly better than that in the high risk group (P<0.001). GSEA results showed that signal pathways related to basic metabolism, cell differentiation and cell cycle were significantly enriched in the high-risk group, while ribosome and N polysaccharide biosynthesis signaling pathway were more enriched in the low-risk group. Multivariate Cox regression analysis showed that the risk score composed of the eight metabolism-related genes could be used as an independent risk factor for the prognosis of MM patients, and receiver operating characteristic curve (ROC) showed that the molecular signatures of metabolism-related genes had the best predictive effect. CONCLUSION: Metabolism-related pathways play an important role in the pathogenesis and prognosis of patients with MM. The clinical significance of the risk assessment model for patients with MM constructed based on eight metabolism-related core genes needs to be confirmed by further clinical studies.

Identification of Hypoxia-Related Molecular Classification and Associated Gene Signature in Oral Squamous Cell Carcinoma.

The high heterogeneity of oral squamous cell carcinoma (OSCC) is the main obstacle for individualized treatment. Recognizing the characteristics of different subtypes and investigating the promising strategies for each subclass are of great significance in precise treatment. In this study, we systematically evaluated hypoxia-mediated patterns together with immune characteristics of 309 OSCC patients in the TCGA training set and 97 patients in the GSE41613 testing set. We further identified two different hypoxia subtypes with distinct immune microenvironment traits and provided treatment programs for the two subclasses. In order to assess hypoxia level individually, we finally constructed a hypoxia-related risk score, which could predict the clinical outcome and immunotherapy response of OSCC patients. In summary, the recognition of different hypoxia patterns and the establishment of hypoxia-related risk score might enhance our understanding of the tumor microenvironment of OSCC and provide more personalized treatment strategies in the future.

[Bioinformatics Analysis of the Influence of Coronavirus Infection on Hematopoietic System and Potential Intervention Drugs and Their Significance for COVID-19].

OBJECTIVE: To analyze and predict the effect of coronavirus infection on hematopoietic system and potential intervention drugs, and explore their significance for coronavirus disease 2019 (COVID-19). METHODS: The gene expression omnibus (GEO) database was used to screen the whole genome expression data related with coronavirus infection. The R language package was used for differential expression analysis and KEGG/GO enrichment analysis. The core genes were screened by PPI network analysis using STRING online analysis website. Then the self-developed apparent precision therapy prediction platform (EpiMed) was used to analyze diseases, drugs and related target genes. RESULTS: A database in accordance with the criteria was found, which was derived from SARS coronavirus. A total of 3606 differential genes were screened, including 2148 expression up-regulated genes and 1458 expression down-regulated genes. GO enrichment mainly related with viral infection, hematopoietic regulation, cell chemotaxis, platelet granule content secretion, immune activation, acute inflammation, etc. KEGG enrichment mainly related with hematopoietic function, coagulation cascade reaction, acute inflammation, immune reaction, etc. Ten core genes such as PTPRC, ICAM1, TIMP1, CXCR5, IL-1B, MYC, CR2, FSTL1, SOX1 and COL3A1 were screened by protein interaction network analysis. Ten drugs with potential intervention effects, including glucocorticoid, TNF-α inhibitor, salvia miltiorrhiza, sirolimus, licorice, red peony, famciclovir, cyclosporine A, houttuynia cordata, fluvastatin, etc. were screened by EpiMed plotform. CONCLUSION: SARS coronavirus infection can affect the hematopoietic system by changing the expression of a series of genes. The potential intervention drugs screened on these grounds are of useful reference significance for the basic and clinical research of COVID-19.

[Bioinformatics analysis of genes related to poor prognosis of human hepatocellular carcinoma and its clinical significance].

OBJECTIVE: To screen genes associated with poor prognosis of hepatocellular carcinoma (HCC) and to explore the clinical significance of these genes. METHODS: The proper expression profile data of HCC was obtained from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by differential expression analysis. The DAVID and String database were used for function enrichment analysis and to construct the protein-protein interaction (PPI) network respectively. The Cancer Genome Atlas (TCGA) database and the Cox Proportional Hazard Model were used for prognosis analysis of the DEGs. RESULTS: A eligible human HCC data set (GSE84402) met the requirements. A total of 1141 differentially expressed genes were identified, including 720 up-regulated and 421 down-regulated genes. The results of function enrichment analysis and PPI network performed that CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11 and CYP2B6 were prognosis key genes. And the prognosis analysis showed that the expressions of CDC6、PIK3R1、KIF11 and RACGAP1 were increased, and the expression of CENPE was decreased, which was closely related to prognosis of HCC. CONCLUSION: CDC6、CENPE、PIK3R1、KIF11 and RACGAP1 may be closely related to poor prognosis of HCC, and can be used as molecular biomarkers for future research of HCC prognosis.

[Analysis of Unfavorable Prognosis Gene Markers in Patients with Acute Myeloid Leukemia by Multiomics].

OBJECTIVE: To analyze the molecular markers associated with occurrence, development and poor prognosis of acute myeloid leukemia (AML) by using the data of GEO and TCGA database, as well as multiomics analysis. METHODS: The transcriptome data meeting requirements were down-loaded from GEO database, the differentially expressed genes were screened by using the R language limma package, and the GO function enrichment analysis and KEGG pathway analysis were performed for differentially expressed genes, at the same time, the protein interaction network was contracted by using STRING database and cytoscape software to screen out the hub gene, then the prognosis analysis was carried out for hub gene by combination with the clinical information affected in TCGA database. RESULTS: 620 differentially expressed genes were screened out, among which 162 differentially expressed genes were up-regulated, and 458 differentially expressed genes were down-regulated. Based on the results of GO functional enrichment, the KEGG pathway enrichment and protein interaction network, CXCL4, CXCR4, CXCR1, CXCR2, CCL5 and JUN were selected as hub genes. The survival analysis showed that the high expression of CXCL4, CXCR1, and CCL5 was a risk factor for poor prognosis of patiants. CONCLUSION: CXCL4, CXCR1 and CCL5 can be used as biomarkers for the occurrence and development of AML, which relateds with the unfavorable prognosis and can provide a basis for further study.

[Prognosis-related miRNA bioinformatics screening of lung adenocarcinoma and its clinical significance].

OBJECTIVE: To investigate the prognosis-related miRNA histological features and clinical significance of lung adenocarcinoma. METHODS: Using The Cancer Genome Atlas (TCGA) data, the miRNA expression profile data of human lung adenocarcinoma were searched for differential analysis, and the prognosis-related miRNAs were screened by Cox risk regression model. The targeted miRNAs were predicted by mirwalk analysis platform, KEGG functional enrichment analysis, and finally, predict the function of prognosis-related miRNAs. RESULTS: A total of 46 differential miRNAs in lung adenocarcinoma were screened, including 19 up-regulated and 27 down-regulated. Six prognostic-related miRNAs were screened by Cox survival analysis, namely hsa-mir-21, hsa-mir-142, hsa-mir-200a high expression, hsa-mir-101, hsa-let-7c, hsa-mir-378e low expression, hsa-mir-21 and hsa-mir-378e were associated with poor prognosis in patients with lung adenocarcinoma, and the survival time was shortened significantly (P<0.05, AUC=0.618). KEGG analysis showed that the above prognosis-related miRNA targeting regulatory genes were related with immune response pathways, miRNA and cancer pathways, metabolic pathways and so on. CONCLUSIONS: Hsa-mir-21 and hsa-mir-378e are associated with poor prognosis of lung adenocarcinoma, and may be used as a molecular marker for prognosis of lung adenocarcinoma after further clinical verification.