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Small nucleolar RNA H/ACA Box 51 (SNORA51) is involved in progression of multiple cancers. However, its role in hepatocellular carcinoma (HCC) is still unclear. The aim of the present study was to analyze the expression of SNORA51 in HCC and its clinical significance. A total of 136 patients with HCC who underwent surgery from January 1, 2016 to December 31, 2018 were included. The expression of SNORA51 in cancer tissues and adjacent tissues was compared using reverse transcription-quantitative PCR and bioinformatics methods. Methylation of the SNORA51 promoter in cancer and adjacent tissues was compared using bioinformatics. The relationship between SNORA51 expression levels and clinicopathological characteristics of patients with HCC, in addition to prognosis, was analyzed. The expression of SNORA51 in HCC was significantly higher compared with that in adjacent tissues (P<0.05). starBase demonstrated that higher expression levels of SNORA51 were associated with a significantly worse prognosis of patients with HCC compared with those who had lower expression levels of SNORA51 (P<0.05). Bioinformatics analysis using The University of Alabama at Birmingham Cancer Data Analysis Portal demonstrated that methylation of the SNORA51 promoter region in HCC was significantly decreased compared with adjacent tissues (P<0.05). A high expression of SNORA51 was significantly associated with portal vein tumor thrombus, vascular invasion and TNM stage (P<0.05). The median survival time of patients with high SNORA51 expression was significantly lower compared with those who had low SNORA51 expression (P<0.05). Both uni- and multivariate Cox regression analysis demonstrated that SNORA51 expression was an independent risk factor that significantly worsened the prognosis of patients with HCC (P<0.05). The overexpression of SNORA51 in patients with HCC was significantly associated with a poor prognosis and may be related to the reduced methylation of the SNORA51 promoter region. Therefore, SNORA51 may be a promising biomarker for prediction of the prognosis of patients with HCC and may be a therapeutic target for the treatment of HCC in future. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study was to develop a prognostic model for chronic lymphocytic leukemia (CLL). Methods: GEO2R was used to retrieve the gene expression data of CLL and normal B cells from the Gene Expression Omnibus (GEO; GSE22529 and GSE50006 datasets) database. Practical Extraction and Report Language was used to extract the gene expression and overall survival (OS) data of CLL patients from the Chronic Lymphocytic Leukemia – ES (CLLE-ES) project in the International Cancer Genome Consortium (ICGC) database. Cox regression with Lasso was used to create and validate a prognostic model for CLL. Results: A total of 267 genes exhibited differential expression between CLL and normal B cells. Cox univariate analysis identified 14 DEGs that correlated with OS. Lasso multivariate evaluation demonstrated that AKAP12 and IGFBP4 are independent prognostic factors for CLL. Kaplan-Meier survival analysis revealed a significant association between the estimated risk score and survival. The area under the receiver operating characteristic curve was calculated to be 0.97, indicating high predictive accuracy. In addition, high AKAP12 and IGFBP4 risk scores were associated with the high incidence of trisomy 12q. Conclusion: Taken together, AKAP12 and IGFBP4 are independent prognostic factors for CLL. [ABSTRACT FROM AUTHOR]

Background: Microsatellite instability‐high (MSI‐H) subgroup of gastric cancer (GC) is characterized by a high tumor mutational burden, increased lymphocytic infiltration, and enhanced inflammatory cytokines. GC patients with MSI‐H status have a good response to immune checkpoint blockade management. However, heterogeneity within the subtype and the underlying mechanisms of shaping tumor microenvironments remain poorly understood. Methods: RNA expression levels and clinical parameters of GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The data were analyzed using single‐sample Gene Set Enrichment Analysis (ssGSEA), univariate Cox regression, multivariate Cox regression, and Least Absolute Shrinkage Selection Operator (LASSO) regression. In addition, multiplex immunohistochemistry (mIHC) was used in our clinical cohort for the tumor microenvironment study. Results: By ssGSEA and survival analysis, the EMT signaling pathway was identified as a representative pathway, which can stratify the patients with MSI‐H GC with significant survival predictive power. Then, a novel representative EMT‐related five‐gene signature (namely CALU, PCOLCE2, PLOD2, SGCD, and THBS2) was established from EMT signaling gene set, which sensitivity and specificity were further validated in the independent GEO database (GSE62254) cohort for disease outcome prediction. Based on public single‐cell data and in situ immunohistochemistry, we found that most of these five genes were abundantly expressed in cancer‐associated fibroblasts. Furthermore, patients with high or low risk divided by this five‐gene signature exhibited a strong correlation of the distinct patterns of tumor immune microenvironment. By mIHC staining of sections from 30 patients with MSI‐H status, we showed that the patients with better prognoses had the increased infiltration of CD8+ cells in the primary tumoral tissue. Conclusion: Our study developed a simple five‐gene signature for stratifying MSI‐H GC patients with survival predictive power. [ABSTRACT FROM AUTHOR]