Dementia-Predicting Cognitive Risk Score and Its Correlation with Cortical Thickness in Parkinson Disease

2017 ◽  
Vol 44 (3-4) ◽  
pp. 203-212 ◽  
Author(s):  
Byoung Seok Ye ◽  
Seun Jeon ◽  
Jee Hyun Ham ◽  
Jae Jung Lee ◽  
Jong Min Lee ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Risk Score ◽  
Neuropsychological Tests ◽  
Cox Regression ◽  
Cingulate Cortex ◽  
Data Set ◽  
Score System ◽  
Cognitive Risk ◽  
Dementia Risk ◽  
Risk Score System

Background: We developed a risk score system to predict risks of developing dementia in individual Parkinson disease (PD) patients using baseline neuropsychological tests. Methods: A total of 216 nondemented PD patients underwent a baseline neuropsychological evaluation and were followed up for a mean of 2.7 (±1.1) years. Univariate Cox regression models controlled for age, gender, and education selected neuropsychological tests individually predicting dementia risk. Then, a multivariate Cox regression model combined them into a cognitive risk score system. Cortical areas correlating with cognitive risk score were investigated using a separate MRI data set from 207 nondemented PD patients. Results: Fifty-two patients (23.9%) developed dementia. The univariate Cox regression analyses identified the confrontational naming and semantic fluency tests, frontal/executive function tests, immediate verbal memory test, and visuospatial function test as predicting dementia risk. The calculated cognitive risk score (range 53-188) predicted future dementia with moderate accuracy (integrated area under the curve = 0.79; 95% CI: 0.73-0.85). A higher cognitive risk score correlated with cortical thinning in the right anteromedial temporal cortex, bilateral posterior cingulate cortex, right anterior cingulate cortex, left parahippocampal gyrus, and right superior frontal cortex in a separate MRI data set. Conclusion: The cognitive risk score system is a useful approach to predict the dementia risk among PD patients.

Comprehensive analysis of dysregulated long non-coding RNA – microRNA – mRNA involved in the progression of human lung adenocarcinoma

2020 ◽  
Author(s):  
Dan Yang ◽  
Yang He ◽  
Bo Wu ◽  
Yan Deng ◽  
Ruxi Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Score System ◽  
Cerna Network ◽  
Risk Score System

Abstract Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to identify a competing endogenous RNA (ceRNA) network in LUAD.Methods: Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC| > 1.0 and a false discovery rate (FDR) < 0.05. Then, these DELs, DEMs, and DEGs were used to construct the initial ceRNA network. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves were utilized to validate the reliability of the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs. GEPIA2 was further used to verify the correlations between DEGs and DELs.Results: A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were significantly enriched in the GO terms “nucleoplasm”, “transcription factor complex”, “protein binding”, and “metal ion binding”, whereas these DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P < 0.05). Furthermore, 15 LUAD drugs interacting with 29 DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, 4 DEGs, PRKCE, DLC1, LATS2, and DPY19L1, were incorporated into the risk score system. The area under the curve (AUC) values of the time-dependent ROC curves at 3 years and 5 years were both higher than 0.5. Finally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 DEL-DEG pairs, NAV2-AS2 – PRKCE (r = 0.430, P < 0.001) and NAV2-AS2 – LATS2 (r = 0.338, P < 0.001). Considering the previously constructed ceRNA network, NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were identified.Conclusions: The lncRNA-miRNA-mRNA ceRNA network plays an essential role in LUAD. These results may improve our understanding and provide novel mechanistic insights to explore diagnostics, tumourigenesis, prognosis, and therapeutic drugs for LUAD patients.

scholarly journals Comprehensive Analysis of Autophagy-Associated lncRNAs Reveal Potential Prognostic Prediction in Pancreatic Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Guangyu Chen ◽  
Gang Yang ◽  
Junyu Long ◽  
Jinshou Yang ◽  
Cheng Qin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Risk Score ◽  
Expression Profile ◽  
Cox Regression ◽  
Risk Groups ◽  
Cancer Genome ◽  
Low Risk ◽  
Score System ◽  
Prognostic Prediction ◽  
Risk Score System

Pancreatic cancer (PC) is a highly malignant tumor in the digestive system. Both long noncoding RNAs (lncRNAs) and autophagy play vital roles in the development and progress of PC. Here, we constructed a prognostic risk score system based on the expression profile of autophagy-associated lncRNAs for prognostic prediction in PC patients. Firstly, we extracted the expression profile of lncRNA and clinical information from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The autophagy-associated genes were from The Human Autophagy Database. Through Cox regression and survival analysis, we screened out seven autophagy-associated lncRNAs and built the risk score system in which the patients with PC were distinguished into high- and low-risk groups in both training and validation datasets. PCA plot displayed distinct discrimination, and risk score system displayed independently predictive value for PC patient survival time by multivariate Cox regression. Then, we built a lncRNA and mRNA co-expression network via Cytoscape and Sankey diagram. Finally, we analyzed the function of lncRNAs in high- and low-risk groups by gene set enrichment analysis (GSEA). The results showed that autophagy and metabolism might make significant effects on PC patients of low-risk groups. Taken together, our study provides a new insight to understand the role of autophagy-associated lncRNAs and finds novel therapeutic and prognostic targets in PC.

Predictions of the dysregulated competing endogenous RNA signature involved in the progression of human lung adenocarcinoma

2020 ◽  
Vol 29 (3) ◽  
pp. 399-416
Author(s):  
Dan Yang ◽  
Yang He ◽  
Bo Wu ◽  
Ruxi Liu ◽  
Nan Wang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Score System ◽  
Cerna Network ◽  
Risk Score System

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to construct a competing endogenous RNA (ceRNA) network to predict the progression in LUAD. METHODS: Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC|> 1.0 and a false discovery rate (FDR) < 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Meanwhile, the GEPIA2 database and immunohistochemical (IHC) results were utilized to validate the expression levels of selected DEGs. RESULTS: A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P< 0.05). Furthermore, 15 LUAD drugs interacting with 29 significant DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, PRKCE, DLC1, LATS2, and DPY19L1 were incorporated into the risk score system, and the results suggested that LUAD patients who had the high-risk score always suffered from a poorer overall survival. Additionally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 significant DEL-DEG pairs, NAV2-AS2 – PRKCE (r= 0.430, P< 0.001) and NAV2-AS2 – LATS2 (r= 0.338, P< 0.001). And NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were finally identified as ceRNA network involved in the progression of LUAD. CONCLUSIONS: The lncRNA-miRNA-mRNA ceRNA network plays an essential role in predicting the progression of LUAD. These results may improve our understanding and provide novel mechanistic insights to explore prognosis and therapeutic drugs for LUAD patients.

scholarly journals Establishment of a Prognostic Prediction System Based on Tumor Microenvironment of Pancreatic Cancer

2021 ◽  
Author(s):  
Bo Liu ◽  
Tingting Fu ◽  
Ping He ◽  
Ke Xu
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Prediction System ◽  
Score System ◽  
Cox Regression Analysis ◽  
Prognostic Prediction ◽  
Risk Score System

Abstract Purpose: Pancreatic cancer (PC) is an inflammatory tumor. Tumor microenvironment (TME) plays an important role in the development of PC. This study aims to explore hub genes of TME and establish a prognostic prediction system for PC.Methods: High throughput RNA-sequencing and clinical data of PC were downloaded from TCGA and ICGC database, respectively. PC Patients were divided into High- and low-score group by using stromal, immune scores system based on ESTIMATE. Differentially expressed genes (DEGs) between High- and low-score patients were screened and survival related DEGs were identified as candidate genes by univariate COX regression analysis. Final variables for establishment of the prognostic prediction system were determined by LASSO analysis and multivariate COX regression analysis. The predictive power of the prognostic system was evaluated by internal and external validation. Results: A total of 210 candidate genes were identified by stromal, immune scores system and survival analyses. Finally, the prognostic risk score system was constructed by the following genes: FAM57B, HTRA3, CXCL10, GABRP, SPRR1B, FAM83A, LY6D. In process of internal validation, Harrell's C-index of this prognostic risk score system was 0.73, and the area under the receiver operating characteristic curve (AUC) value of 1-year, 2-year and 3-year OS period was 0.67, 0.76 and 0.86, respectively. In the external validation set, the survival prediction C-index was 0.71, and the AUC was 0.81, 0.72, 0.78 at 1-year, 2-year and 3-year, respectively.Conclusion: This prognostic risk score system based on TME demonstrated a good predictive capacity to the prognosis of PC.

scholarly journals Comprehensive analysis of the putative competing endogenous RNA network in human lung adenocarcinoma

2020 ◽  
Author(s):  
Dan Yang ◽  
Yang He ◽  
Bo Wu ◽  
Yan Deng ◽  
Ruxi Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Roc Curves ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Competing Endogenous Rna ◽  
Score System ◽  
Cerna Network ◽  
Risk Score System

Abstract Background Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer worldwide. Until now, the molecular mechanisms underlying LUAD progression have not been fully explained. This study aimed to identify a competing endogenous RNA (ceRNA) network in LUAD. Methods Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs) were identified from The Cancer Genome Atlas (TCGA) database with a |log2FC| > 1.0 and a false discovery rate (FDR) < 0.05. Then, these DELs, DEMs, and DEGs were used to construct the initial ceRNA network. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network, and survival analyses were performed to analyse these DEGs involved in the ceRNA network. Subsequently, the drug-gene interaction database (DGIdb) was utilized to select candidate LUAD drugs interacting with significant DEGs. Then, lasso-penalized Cox regression and multivariate Cox regression models were used to construct the risk score system. Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves were utilized to validate the reliability of the risk score system. Finally, based on the correlations between DELs and DEGs involved in the risk score system, the final ceRNA network was identified. Results A total of 340 DELs, 29 DEMs, and 218 DEGs were selected to construct the initial ceRNA network. Functional enrichment analyses indicated that 218 DEGs were significantly enriched in the GO terms “nucleoplasm”, “transcription factor complex”, “protein binding”, and “metal ion binding”, whereas these DEGs were associated with the KEGG pathway terms “microRNAs in cancer”, “pathways in cancer”, “cell cycle”, “HTLV-1 infection”, and the “PI3K-Akt signalling pathway”. K-M survival analysis of all differentially expressed genes involved in the ceRNA network identified 24 DELs, 4 DEMs, and 29 DEGs, all of which were significantly correlated with LUAD progression (P < 0.05). Furthermore, 15 LUAD drugs interacting with 29 DEGs were selected. After lasso-penalized Cox regression and multivariate Cox regression modelling, 4 DEGs, PRKCE, DLC1, LATS2, and DPY19L1, were incorporated into the risk score system. The area under the curve (AUC) values of the time-dependent ROC curves at 3 years and 5 years were both higher than 0.5. Finally, the correlation coefficients between these 4 DEGs and their corresponding DELs involved in the ceRNA network suggested that there were 2 DEL-DEG pairs, NAV2-AS2 – PRKCE (r = 0.430, P < 0.001) and NAV2-AS2 – LATS2 (r = 0.338, P < 0.001). Considering the previously constructed ceRNA network, NAV2-AS2 – mir-31 – PRKCE and NAV2-SA2 – mir-31 – LATS2 were identified. Conclusions The lncRNA-miRNA-mRNA ceRNA network plays an essential role in LUAD. These results may improve our understanding and provide novel mechanistic insights to explore diagnostics, tumourigenesis, prognosis, and therapeutic drugs for LUAD patients.

A risk score system for identification of patients with upper-GI bleeding suitable for outpatient management

2004 ◽  
Vol 59 (7) ◽  
pp. 772-781 ◽  
Author(s):  
Pedro Almela ◽  
Adolfo Benages ◽  
Salvador Peiró ◽  
Ramón Añón ◽  
Miguel Minguez Pérez ◽  
...  
Keyword(s):  
Risk Score ◽  
Gi Bleeding ◽  
Outpatient Management ◽  
Score System ◽  
Upper Gi Bleeding ◽  
Upper Gi ◽  
Risk Score System

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

2020 ◽  
Author(s):  
Hao Zhao ◽  
Xuening Zhang ◽  
Zhan Shi ◽  
Songhe Shi
Keyword(s):  
Tumor Microenvironment ◽  
Gleason Score ◽  
Risk Score ◽  
Regression Models ◽  
Cox Regression ◽  
External Validation ◽  
Disease Free Survival ◽  
Data Set ◽  
Free Survival ◽  
Disease Free

Abstract Background Tumor microenvironment (TME) and immune checkpoint inhibitors has been shown to promote active immune responses through different mechanisms. We aimed to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).Methods The gene transcriptome profiles and clinical information of PCa patients were obtained from the TCGA database, and the immune, stromal and estimate scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of risk score (RS) model based on univariate Cox and LASSO Cox regression models analysis, and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 data set was used for external validation. Finally, 22 subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the Cibersort algorithm.Results In this study, the patients with higher immune, stromal, and estimate scores were associated with poorer DFS, higher Gleason score, and higher AJCC T stage. Based on the immune and stromal scores, the Venny diagram screened out 515 cross DEGs. The univariate COX and Lasso Cox regression models were used to select 18 DEGs from 515 DEGs, and constructed a RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUC of 1-year, 3-year and 5-year DFS rates in RS model were 0.778, 0.754 and 0.750, respectively. In addition, the RS model constructed from 18 genes was found to be more sensitive than Gleason score (1, 3, 5 year AUC= 0.704, 0.677 and 0.682). The nomograms of DFS were established based on RS and Gleason scores. The AUC of the nomograms in the first, third, and fifth years were 0.802, 0.808, and 0.796, respectively. These results have been further validated in GEO. In addition, the proportion of Tregs was higher in high-RS patients (P<0.05), and the expression of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) was higher in high-RS patients (P<0.05).Conclusion We identified 18 TME-related genes from the TCGA database, which were significantly related to DFS in PCa patients.

scholarly journals Risk score system for late aortic events in patients with uncomplicated type B aortic dissection

2020 ◽  
Vol 159 (6) ◽  
pp. 2173-2183.e1 ◽  
Author(s):  
Akihito Matsushita ◽  
Minoru Tabata ◽  
Wahei Mihara ◽  
Takeshi Shimamoto ◽  
Tatsuhiko Komiya ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Risk Score ◽  
Type B ◽  
Type B Aortic Dissection ◽  
Score System ◽  
Risk Score System

scholarly journals Serum ghrelin level is associated with cardiovascular risk score

2015 ◽  
Vol 53 (2) ◽  
pp. 140-145 ◽  
Author(s):  
Dana Pop ◽  
P. Peter ◽  
Alexandra Dădârlat ◽  
Adela Sitar-Tăut ◽  
D. Zdrenghea
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Risk Score ◽  
Wide Distribution ◽  
Score System ◽  
Cardiology Department ◽  
Significant Difference ◽  
Risk Score System ◽  
Serum Ghrelin

Abstract Ghrelin, a newly discovered bioactive peptide, was originally reported to induce growth hormone release. Recent studies have shown beneficial hemodynamic effects of ghrelin in the cardiovascular system to support the wide distribution of its receptors in cardiovascular tissues. The aim of the study was to determine whether cardiovascular risk factors influence plasma ghrelin levels. Methods. We evaluated in the Rehabilitation Hospital Cluj-Napoca, Cardiology - Department 88 consecutive subjects, 65 (73.86%) being women, with mean age 61.7±10.33 years. We assessed the presence of cardiovascular risk factors (obesity, arterial hypertension, diabetes mellitus, metabolic syndrome, smoking and lipid fractions). Plasma ghrelin levels were determined with a commercial ELISA kit (pg/ml). Results. After the evaluation of cardiovascular risk factors, we found no statistically significant difference between ghrelin levels in the patients with vs those without cardiovascular risk factors (p>0.05). A negative correlation was found between ghrelin levels and age, r = −0.32 (p <0.05). Using the HeartScore Internet tool we calculated the cardiovascular risk for each patient according to the risk score system (SCORE) for high cardiovascular risk countries. Statistically, the risk of fatal cardiovascular events in the next 10 years was indirectly correlated with the ghrelin levels in each patient - correlation between ghrelin levels and SCORE system r=−0.25, p=0.015. In conclusion, low serum ghrelin concentrations are associated with an increased global cardiovascular risk, calculated based on the European SCORE scale. However, we could not demonstrate a direct relationship between any of the major risk factors and ghrelin.

Sign in / Sign up

Export Citation Format

Share Document