scholarly journals An Accurate Prognostic Survival Model of Survival Based on Expression of Genes Involved in Plasma Cell Metabolism

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 14-15
Author(s):  
Yang Liang ◽  
Han-ying Huang ◽  
Yun Wang ◽  
Weida Wang ◽  
Jin-Yuan Li ◽  
...  
Keyword(s):  
High Risk ◽  
Plasma Cell ◽  
Risk Score ◽  
Prediction Accuracy ◽  
Cox Regression ◽  
Cell Metabolism ◽  
Metabolic Risk ◽  
Expression Of Genes ◽  
Metabolic Risk Score

Background: Models to predict survival of persons with plasma cell myeloma are mostly use clinical and laboratory co-variates including cytogenetics and mutation topography. These models have AUCs of 0.55 to 0.77 indicating considerable inaccuracy. We tested whether data of plasma cell metabolism might improve prediction accuracy. Methods: RNA matrix, clinical and laboratory data from datasets GSE13633 (training cohort) and GSE57317, GSE24080 and GSE2658 (validation cohorts) were downloaded from the Gene Expression Omnibus (GEO) database. Genes regulating plasma cell metabolism correlating with survival were identified in uni-variable Cox regression analyses and a metabolic risk score built by Lasso Cox regression analysis. Survivals was compared by Kaplan-Meier curves with log-rank tests. Prediction accuracy of the metabolic risk score was quantified by time-dependent receiver operating characteristic (ROC) curves and the area under the curve. A nomogram was developed including metabolic risk score and ISS stage. Enriched pathways in different metabolic risk score cohorts were evaluated by gene set enrichment analyses (GSEA). We interrogated validity of 3 genes with the largest risk coefficients in vitro using small molecule inhibitors to test effects on cell proliferation and apoptosis by CCK-8 and multi-parameter flow cytometry (MPFC). Results: 7 genes regulating plasma cell metabolism with high risk coefficients were used to develop a metabolic risk score. The score had robust predictive performance with AUCs for 3-year survival in the 4 cohorts of 0.71 [95% Confidence Interval, 0.65, 0.79], 0.88 [0.71, 1.03], 0.70 [0.63,0.76] and 0.71 [0.65, 0.79]. Subjects in the high-risk cohort had worse 3-year survivals compared with those in the low-risk cohort (62% [55, 68%] vs. 85% [80, 90%], P < 0.001; 54% [33, 76%] vs. 92% [82, 102%], P <0.001; 58% [52, 65%] vs. 77% [71, 82%], P <0.001; and 51% [38, 64%] vs. 74% [62, 85%], P <0.001). The metabolic risk score remained an independent prognostic factor for survival in multi-variable regression analyses. A nomogram combining metabolic risk score with ISS score increased prediction accuracy of 5-year survival (AUCs of 0.72 [0.69,0.81] vs. 0.66 [0.60, 0.72]; P = 0.015). In GSEA most gene enrichment pathways in high-risk cohort were metabolism-related. In vitro, the results showed that the three small molecule inhibitors combined with bortezomib had a synergistic inhibitory effect on the growth of myeloma cells H929, MM.1S, U266B1 and RPMI 8226, with an effective synergistic CI value; and enhanced the apoptosis of myeloma cell lines effect. Conclusion: A risk score based on expression of genes involved in plasma cell metabolism predicted survival and significantly improved prediction accuracy of the ISS score. Predictions based on gene expression were validated in vitro. Keywords GEO, plasma cell myeloma, metabolism, prognostic model Disclosures No relevant conflicts of interest to declare.

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

Dietary factors associated with metabolic risk score in Finnish children aged 6–8 years: the PANIC study

2013 ◽  
Vol 53 (6) ◽  
pp. 1431-1439 ◽  
Author(s):  
A. M. Eloranta ◽  
V. Lindi ◽  
U. Schwab ◽  
S. Kiiskinen ◽  
T. Venäläinen ◽  
...  
Keyword(s):  
Risk Score ◽  
Dietary Factors ◽  
Metabolic Risk ◽  
Factors Associated ◽  
Metabolic Risk Score

A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

2021 ◽  
Author(s):  
Yanjia Hu ◽  
Jing Zhang ◽  
Jing Chen
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongjie Chen ◽  
Hui Huang ◽  
Longjun Zang ◽  
Wenzhe Gao ◽  
Hongwei Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Prognostic Signature ◽  
Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p &lt; 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p &lt; 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

scholarly journals A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

2021 ◽  
Author(s):  
Shaopei Ye ◽  
Wenbin Tang ◽  
Ke Huang
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

scholarly journals A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

scholarly journals DNA methylation in blood as a mediator of the association of mid-childhood body mass index with cardio-metabolic risk score in early adolescence

Epigenetics ◽  
2018 ◽  
Vol 13 (10-11) ◽  
pp. 1072-1087 ◽  
Author(s):  
Jian V. Huang ◽  
Andres Cardenas ◽  
Elena Colicino ◽  
C. Mary Schooling ◽  
Sheryl L. Rifas-Shiman ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Dna Methylation ◽  
Body Mass ◽  
Risk Score ◽  
Early Adolescence ◽  
Metabolic Risk ◽  
Childhood Body Mass Index ◽  
Metabolic Risk Score

scholarly journals Patterns of body mass index milestones in early life and cardiometabolic risk in early adolescence

2019 ◽  
Vol 48 (1) ◽  
pp. 157-167 ◽  
Author(s):  
Izzuddin M Aris ◽  
Sheryl L Rifas-Shiman ◽  
Ling-Jun Li ◽  
Ken P Kleinman ◽  
Brent A Coull ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Body Mass Index ◽  
Risk Score ◽  
Fat Mass ◽  
Early Adolescence ◽  
Cardiometabolic Risk ◽  
Metabolic Risk ◽  
Fat Mass Index ◽  
Cardiometabolic Profile ◽  
Metabolic Risk Score

Abstract Background Few studies have examined the independent and combined relationships of body mass index (BMI) peak and rebound with adiposity, insulin resistance and metabolic risk later in life. We used data from Project Viva, a well-characterized birth cohort from Boston with repeated measures of BMI, to help fill this gap. Methods Among 1681 children with BMI data from birth to mid childhood, we fitted individual BMI trajectories using mixed-effects models with natural cubic splines and estimated age, and magnitude of BMI, at peak (in infancy) and rebound (in early childhood). We obtained cardiometabolic measures of the children in early adolescence (median 12.9 years) and analysed their associations with the BMI parameters. Results After adjusting for potential confounders, age and magnitude at infancy BMI peak were associated with greater adolescent adiposity, and earlier adiposity rebound was strongly associated with greater adiposity, insulin resistance and metabolic risk score independently of BMI peak. Children with a normal timing of BMI peak plus early rebound had an adverse cardiometabolic profile, characterized by higher fat mass index {β 2.2 kg/m2 [95% confidence interval (CI) 1.6, 2.9]}, trunk fat mass index [1.1 kg/m2 (0.8, 1.5)], insulin resistance [0.2 units (0.04, 0.4)] and metabolic risk score [0.4 units (0.2, 0.5)] compared with children with a normal BMI peak and a normal rebound pattern. Children without a BMI peak (no decline in BMI after the rise in infancy) also had adverse adolescent metabolic profiles. Conclusions Early age at BMI rebound is a strong risk factor for cardiometabolic risk, independent of BMI peak. Children with a normal peak-early rebound pattern, or without any BMI decline following infancy, are at greatest risk of adverse cardiometabolic profile in adolescence. Routine monitoring of BMI may help to identify children who are at greatest risk of developing an adverse cardiometabolic profile in later life and who may be targeted for preventive interventions.

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