Development of Sex-Stratified Prediction Models for Recurrent Venous Thromboembolism: A Danish Nationwide Cohort Study

2020 ◽  
Vol 120 (05) ◽  
pp. 805-814 ◽  
Author(s):  
Ida Ehlers Albertsen ◽  
Mette Søgaard ◽  
Samuel Zachary Goldhaber ◽  
Gregory Piazza ◽  
Flemming Skjøth ◽  
...  
Keyword(s):  
Venous Thromboembolism ◽  
Treatment Duration ◽  
Prediction Models ◽  
Cox Regression ◽  
External Validation ◽  
Risk Scores ◽  
Major Surgery ◽  
Anticoagulant Treatment ◽  
Cox Regression Analysis ◽  
Recurrent Venous Thromboembolism

Abstract Objective To optimize decision making for anticoagulant treatment duration after incident venous thromboembolism, we derived and internally validated two clinically applicable sex-specific prediction models for venous thromboembolism recurrence, discarding the traditional categorization of provoked and unprovoked venous thromboembolism. Methods This study was based on data from Danish nationwide registries. We identified all routine care in- and outpatients with completed anticoagulant treatment for incident venous thromboembolism from 2012 through 2017. The outcome was recurrent venous thromboembolism within 2 years. Risk scores were derived using Cox regression analysis and a backward selection process on a set of 24 potential predictors. Performance was assessed through calibration and discrimination using bootstrap techniques to internally validate the scores. Results The study included 11,519 patients. Risk scores under the joint acronym AIM-SHA-RP were developed. Age, Incident pulmonary embolism, and recent Major surgery were predictors for both sexes; Statin treatment, Heart disease and Antiplatelet treatment were predictors specifically for men, while chronic Renal disease and recent Pneumonia or sepsis were predictors specifically for women. The risk scores were well calibrated and identified a low- (< 5%), intermediate- (5–10%), and high-risk (> 10%) group for both sexes. Generally, discriminative capacities, as measured by the c-statistic, were limited. Conclusion We developed two clinically applicable risk scores to estimate the risk of recurrent venous thromboembolism after completed anticoagulant treatment. The risk scores can potentially guide treatment duration of anticoagulation after incident venous thromboembolism but require further external validation before implemented in clinical practice.

scholarly journals A 5-mRNA-Based Prognostic Signature of Survival in Lung Adenocarcinoma

2021 ◽  
Author(s):  
qianlin xia ◽  
Weimo Yu ◽  
Qiuyue Li ◽  
Jin Wang ◽  
Yuzhen Du
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Prediction Models ◽  
Risk Model ◽  
Cox Regression ◽  
External Validation ◽  
Assessment Tools ◽  
Normal Lung ◽  
Survival Prognosis ◽  
Cox Regression Analysis

Abstract Background: Lung adenocarcinoma (LUAD) is the most common non-small cell lung cancer, with an increasing incidence and poor prognosis. To evaluate the prognosis of LUAD patients and optimize treatment, effective clinical research prediction models are urgently needed. Methods : In this study, we thoroughly mined LUAD genomic data from GEO (GSE43458, GSE32863, and GSE27262) and TCGA datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Single-factor Cox and LASSO regression analyses were used to screen DEGs related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic (ROC) curve and Kaplan-Meier (KM) survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram. Results: A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (HR = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (AUC = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively. Conclusion: A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

scholarly journals A Signature of Seven Lipid Metabolism Genes to Predict Hepatocellular Carcinoma Prognosis

2021 ◽  
Author(s):  
Muqi Li ◽  
Xiwen Wu ◽  
Shufen Liao ◽  
shutong wang ◽  
shuirong lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Lipid Metabolism ◽  
Cox Regression ◽  
External Validation ◽  
Metabolic Process ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Lipid Metabolism Genes

Abstract BackgroundLipid metabolism is important in tumor progression. However, its role in hepatocellular carcinoma (HCC) remains unknown. We attempt to build a lipid metabolism-related signature to evaluate its role in predicting the prognosis of HCC patients. MethodsWe obtained differential expression genes (DEGs) through differential analysis of mRNA expression between tumor tissues and paraneoplastic tissue of patients with HCC. The lipid metabolism-related genes were obtained from KEGG and MisDB. The corresponding gene expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database. Prognosis-related genes were obtained by COX regression analysis. Intersecting genes were defined as genes shared by DEGs and prognosis-related genes. The least absolute shrinkage and selection operator (LASSO) technique was used to calculate the prognostic genes and coefficients for forming a prognostic assessment signature. Kaplan–Meier survival analysis was applied to assess the model’s credibility. ICGC database was also used for external validation. ResultsA total of 39 lipid metabolism-related DEGs were analyzed that showed significant enrichment in the phospholipid metabolic process, glycerolipid metabolic process and glycerophospholipid pathways. Seven lipid metabolism genes (ELOVL3, LCLAT1, ME1, PPARGC1A, PTDSS2, SRD5A3, SLC2A1) closely related with prognosis were identified to construct the signature. Patients with low-risk scores showed better survival rates, which was also validated in the ICGC database. ConclusionWe established a signature composed of seven lipid metabolism-related genes to predict the prognosis of HCC patients, providing a new biomarker for the diagnosis and treatment of HCC.

scholarly journals Identification of Genetic Aberrations in Thrombomodulin Gene in Patients With Recurrent Venous Thromboembolism

2017 ◽  
Vol 23 (4) ◽  
pp. 319-328 ◽  
Author(s):  
Abrar Ahmad ◽  
Kristina Sundquist ◽  
Bengt Zöller ◽  
Peter J. Svensson ◽  
Jan Sundquist ◽  
...  
Keyword(s):  
Venous Thromboembolism ◽  
Cox Regression ◽  
Population Based ◽  
Large Sample Size ◽  
Genetic Changes ◽  
Genetic Aberrations ◽  
Cox Regression Analysis ◽  
Vein Thrombosis ◽  
Recurrent Venous Thromboembolism ◽  
Deep Vein

Thrombomodulin (THBD) serves as a cofactor for thrombin-mediated activation of anticoagulant protein C pathway. Genetic aberrations in THBD have been studied in arterial and venous thrombosis. However, genetic changes in THBD and their role in the risk assessment of recurrent venous thromboembolism (VTE) are not well understood. The aim of the present study was to identify the genetic aberrations in THBD and their association with the risk of VTE recurrence in a prospective population-based study. We sequenced the entire THBD gene, first in selected patients with VTE (n = 95) by Sanger sequencing and later validated those polymorphisms with minor allele frequency (MAF) ≥5% in the whole study population (n = 1465 with the follow-up period of 1998-2008) by Taqman polymerase chain reaction. In total, we identified 8 polymorphisms in THBD, and 3 polymorphisms with MAF ≥5% were further validated. No significant association between THBD polymorphisms and risk of VTE recurrence on univariate or multivariate Cox regression analysis was found (hazard ratio [HR] = 0.89, 95% confidence interval [CI] = 0.62-1.28, HR = 1.27, 95% CI = 0.88-1.85, and HR = 1.15, 95% CI = 0.80-1.66 for THBD rs1962, rs1042580, and rs3176123 polymorphisms, respectively), adjusted for family history, acquired risk factors for VTE, location of deep vein thrombosis, and risk of thrombophilia. Subanalysis of patients with unprovoked first VTE also showed no significant association of identified THBD polymorphisms with the risk of VTE recurrence. Our results show that aberrations in the THBD gene may not be useful for the assessment of VTE recurrence; however, further studies with large sample size are needed to confirm these findings.

scholarly journals Comprehensive Analysis of Survival-Related lncRNAs, miRNAs, and mRNAs Forming a Competing Endogenous RNA Network in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yanjie Zhao ◽  
Heng Zhang ◽  
Qiang Ju ◽  
Xinmei Li ◽  
Yuxin Zheng
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Lymphatic Metastasis ◽  
Prediction Models ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Prognostic Prediction

To analyze and construct a survival-related endogenous RNA (ceRNA) network in gastric cancer (GC) with lymph node metastasis, we obtained expression profiles of long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs) in GC from The Cancer Genome Atlas database. The edgeR package was used to screen differentially expressed lncRNAs, mRNAs, and miRNAs between GC patients with lymphatic metastasis and those without lymphatic metastasis. Then, we used univariate Cox regression analysis to identify survival-related differentially expressed RNAs. In addition, we used multivariate Cox regression analysis to screen lncRNAs, miRNAs, and mRNAs for use in the prognostic prediction models. The results showed that 2,247 lncRNAs, 155 miRNAs, and 1,253 mRNAs were differentially expressed between the two patient groups. Using univariate Cox regression analysis, we found that 395 lncRNAs, eight miRNAs, and 180 mRNAs were significantly related to the survival time of GC patients. We next created a survival-related network consisting of 59 lncRNAs, seven miRNAs, and 36 mRNAs. In addition, we identified eight RNAs associated with prognosis by multivariate Cox regression analysis, comprising three lncRNAs (AC094104.2, AC010457.1, and AC091832.1), two miRNAs (miR-653-5p and miR-3923), and three mRNAs (C5orf46, EPHA8, and HPR); these were used to construct the prognostic prediction models, and their risk scores could be used to assess GC patients’ prognosis. In conclusion, this study provides new insights into ceRNA networks in GC and the screening of prognostic biomarkers for GC.

scholarly journals Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi-Ming Chu ◽  
Huan-Ming Hsu ◽  
Chi-Wen Chang ◽  
Yuan-Kuei Li ◽  
Yu-Jia Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Models ◽  
Reference Model ◽  
External Validation ◽  
Risk Scores ◽  
Consistent Finding ◽  
Hazard Ratios ◽  
Genetic Predictors ◽  
Novel Biomarkers ◽  
Cox Proportional Hazard Regression

AbstractGenetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0–81.4% and 74.6–78% respectively (rfm, ACC 63.2–65.5%, AUC 61.9–74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10–8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

scholarly journals Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Yuhan Zhang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Identification and Validation of a Glycolysis Related Metabolic Reprogramming Gene Signature for Predicting Survival in Colon Cancer Patients

2021 ◽  
Author(s):  
Gang Liu ◽  
Xiaowang WU ◽  
Jian Chen
Keyword(s):  
Colon Cancer ◽  
Prediction Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Prognosis Model ◽  
The Relationship

Abstract Background Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with high mortality rate. Because of malignancy and easily metastasis feather, and limited treatments, the prognosis of CC remains poor. Glycolysis is a metabolic process of glucose in anoxic environments which is an important way to provide energy for tumor. The role of glycolysis in CC largely remains unknown and is necessary to be explored. Method In our study, we analyzed glycolysis related genes expression in CC, patients gene expression and corresponding clinical data were downloaded from GEO dataset, glycolysis related genes sets were collected from Msigdb. Through COX regression analysis, prognosis model based on glycolysis-related genes was established. The efficacy of gene model was tested by Survival analysis, ROC analysis and PCA analysis. Furthermore, the relationship between risk scores and clinical characteristic was researched. Results Our findings identified 13 glycolysis related genes (NUP107, SEC13, ALDH7A1, ALG1, CHPF, FAM162A, FBP2, GALK1, IDH1, TGFA, VLDLR, XYLT2 and OGDHL) consisted prognostic prediction model with relative high accuracy. The relationship between prediction model and clinical feathers were specifically studied, results showed age > 65years, TNM III-IV, T3-4, N1-3, M1 and high-risk score were independent prognostic risk factors with poorer prognosis. Finally, model genes were significantly expressed and EMT were activated in CC patients. Conclusion This study provided a new aspect to advance our understanding in the potential mechanism of glycolysis in CC.

scholarly journals Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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