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BACKGROUND: Although there are many treatments for Multiple myeloma (MM), patients with MM still unable to escape the recurrence and aggravation of the disease. OBJECTIVE: We constructed a risk model based on genes closely associated with MM prognosis to predict its prognostic value. METHODS: Gene function enrichment and signal pathway enrichment analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, univariate and multivariate Cox regression analysis, Kaplan-Meier (KM) survival analysis and Receiver Operating Characteristic (ROC) analysis were used to identify the prognostic gene signature for MM. Finally, the prognostic gene signature was validated using the Gene Expression Omnibus (GEO) database. RESULTS: Thirteen prognostic genes were screened by univariate Cox analysis and LASSO regression analysis. Multivariate Cox analysis revealed risk score to be an independent prognostic factor for patients with MM [Hazard Ratio (HR) = 2.564, 95% Confidence Interval (CI) = 2.223–2.958, P < 0.001]. The risk score had a high level of predictive value according to ROC analysis, with an area under the curve (AUC) of 0.744. CONCLUSIONS: The potential prognostic signature of thirteen genes were assessed and a risk model was constructed that significantly correlated with prognosis in MM patients. [ABSTRACT FROM AUTHOR]

BACKGROUND: Acute myeloid leukemia (AML) has a poor prognosis, and the current 5-year survival rate is less than 30%. OBJECTIVE: The present study was designed to identify the significant genes closely related to AML prognosis and predict the prognostic value by constructing a risk model based on their expression. METHODS: Using bioinformatics (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, univariate and multivariate Cox regression analysis, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) analysis) to identify a prognostic gene signature for AML. Finally, The Cancer Genome Atlas (TCGA) database was used to validate this prognostic signature. RESULTS: Based on univariate and multivariate Cox regression analysis, eighteen prognostic genes were identified, and the gene signature and risk score model were constructed. Multivariate Cox analysis showed that the risk score was an independent prognostic factor [hazard ratio (HR) = 1.122, 95% confidence interval (CI) = 1.067–1.180, P < 0.001]. ROC analysis showed a high predictive value of the risk model with an area under the curve (AUC) of 0.705. CONCLUSIONS: This study evaluated a potential prognostic signature with eighteen genes and constructed a risk model significantly related to the prognosis of AML patients. [ABSTRACT FROM AUTHOR]

Aimin District, Mudanjiang, Heilongjiang, 157011, People's Republic of ChinaTel +86-13945395825Email [email protected] Purpose: Long noncoding RNAs have been studied more and more as potential prognostic markers. However, the prognostic of LINC00460 in clear cell renal cell carcinoma (ccRCC) has not been explored. In this study, the potential role of LINC00460 was investigated in ccRCC. Patients and Methods: One hundred thirteen pairs of ccRCC tissues and para-normal tissues were collected. The expressions of LINC00460 in these tissues and ccRCC cells were evaluated via qRT-PCR. The prognostic value of LINC00460 was accessed with the use of Kaplan–Meier analysis and Cox proportional hazards model analysis. The influence of LINC00460 on ccRCC cell proliferation, migration, and invasion was determined via cell counting kit-8 (CCK-8) and Transwell assays. Results: The results revealed that LINC00460 was significantly enhanced in ccRCC tissues, as well as in ccRCC cell lines. The overexpression of LINC00460 was significantly associated with lymph node metastasis and TNM stage, and lead to poor overall survival. Knockdown of LINC00460 reduces the cell ability of proliferation, migration, and invasion. LINC00460 could sponge to miR-149-5p. Conclusion: LINC00460 may be developed as a prognostic biomarker and molecular therapy target for ccRCC. [ABSTRACT FROM AUTHOR]

Background: Cholangiocarcinoma (CCA) is the second most common fatal primary hepatobiliary malignant carcinoma, characterized by early invasion and extremely poor outcomes. It is therefore necessary to identify a novel biomarker to better diagnose CAA and predict its prognosis. Recently, emerging evidence has revealed that some lncRNAs play an important role in the tumorigenesis and progression of CAA. In order to support this search for novel diagnostic and prognostic biomarkers for CAA, we conducted a meta-analysis to analyze the published association between lncRNA expression and its clinical value in CAA. Methods: Eligible studies were pooled and analyzed according to our inclusion and exclusion criteria after a comprehensive literature search. Stata 14.0 software was used to analyze the data from relevant studies and to construct a forest plot. Different effect sizes were selected for the meta-analysis. Results: In total, 24 publications were included in this meta-analysis. After review of their full-text, 16 articles studied the association between lncRNAs and clinicopathological characteristics, 2 discussing diagnosis and 16 discussing prognosis. Our results showed that overexpression of CCAT1 was significantly correlated with tumor stage (I + II vs III + IV) (OR, 4.99; 95% CI 2.77–8.99; P<0.001) and lymph node metastasis in CCA (OR, 4.75; 95% CI 2.65–8.52; P<0.001). Furthermore, elevated CCAT lncRNA family expression predicted a shorter overall survival (HR, 2.09; 95% CI 1.17–3.00; P<0.001), especially CCAT2. Upregulation of CCAT2 was also obviously associated with tumor stage in CCA (OR, 5.29; 95% CI 2.64–10.58; P=0.001). Conclusion: This is the first meta-analysis to assess the relationship between expression of lncRNAs and the clinical values of patients with CCA. lncRNAs can function as potential molecular biomarkers of the clinicopathology and prognosis of CCA. [ABSTRACT FROM AUTHOR]