Cox Proportional Risk Model and Its Application in Environmental Survival Analysis

2020 ◽  
pp. 271-279
Author(s):  
Zhina Yu ◽  
Xiaojing Zhou ◽  
Shaoqin Liu ◽  
Xiaoshuang Wang
Keyword(s):  
Survival Analysis ◽  
Risk Model ◽  
Environmental Survival

scholarly journals Seven Autophagy-Related Genes are Associated with the Tumor Immune Microenvironment in Predicting Survival Risk of Non-Small Cell Lung Cancer

2021 ◽  
Author(s):  
Huihui Jiang ◽  
Aiqun Xu ◽  
Min Li ◽  
Rui Han ◽  
Enze Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Survival Analysis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Small Cell ◽  
Gene Set Enrichment Analysis ◽  
Small Cell Lung

Abstract Background: Non-small cell lung cancer (NSCLC) ranks first among global cancer-related deaths. Despite the emergence of various immunological and targeted therapies, immune tolerance remains a barrier to treatment. Methods: It has been found that this obstacle can be overcome by targeting autophagy-related genes (ATGs). ATGs were screened by coexpression analysis and the genes related to the prognosis of lung cancer were screened using Kaplan–Meier (K-M) survival analysis, univariate Cox regression, and multivariate Cox regression. The prognostic risk model of ATGs was constructed and verified using K-M survival analysis and receiver operating characteristic (ROC) curve analysis. Results: The prognostic risk model of ATGs was constructed. Gene set enrichment analysis (GSEA) showed that the function and pathway of ATG enrichment were closely related to immune cell function. CIBERSORT, LM22 matrix, and Pearson correlation analysis showed that risk signals were significantly correlated with immune cell infiltration and immune checkpoint genes. Conclusions: We identified and independently verified the ATG (AL691432.2, MMP2-AS1, AC124067.2, CRNDE, ABALON, AL161431.1, NKILA) in NSCLC patients and found that immune regulation in the tumor microenvironment is closely related to this gene.

scholarly journals Integrated analysis of autophagy-related genes(ATGs) reveals the prognostic value of oral squamous cell carcinoma

2020 ◽  
Author(s):  
Guangzhao Huang ◽  
Zhi-yun Li ◽  
Yu Rao ◽  
Xiao-zhi Lv
Keyword(s):  
Survival Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Score Model

Abstract Background: Increasing evidence demonstrated that autophagy paly a crucial role in initiation and progression of OSCC. The aim of this study was to explore the prognostic value of autophagy-related genes(ATGs) in patients with OSCC. RNA-seq and clinical data were downloaded from TCGA database following extrating ATGs expression profiles. Then, differentially expressed analysis was performed in R software EdgeR package, and the potential biological function of differentially expressed ATGs were explored by GO and KEGG enrichment analysis. Furthermore, a risk score model based on ATGs was constructed to predict the overall survival. Moreover, univariate, multivariate cox regression and survival analysis were used to select autophagy related biomarkers which were identified by RT-qPCR in OSCC cell lines, OSCC tissues and matched normal mucosal tissues. Results: Total of 232 ATGs were extrated and 37 genes were differentially expressed in OSCC. GO and KEGG analysis indicated that these differentially expressed genes were mainly located in autophagosome membrane, and associated with apoptosis, platinum drug resistance, ErbB signaling pathway and TNF signaling pathway. Furthermore, a risk score model including 9 variables was constructed and subsequently identified with univariate, multivariate cox regression, survival analysis and Receiver Operating Characteristic curve(ROC). Moreover, ATG12 and BID were identified as potential autophagy related biomakers. Conclusion: This study successfully constructed a risk model to predict the prognosis of patients with OSCC, and the risk score may be as a independent prognostic biomarker in OSCC. ATG12 and BID were identified as potential biomarkers in tumor diagnosis and treatment of OSCC.

scholarly journals Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kang-Wen Xiao ◽  
Zhi-Bo Liu ◽  
Zi-Hang Zeng ◽  
Fei-Fei Yan ◽  
Ling-Fei Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Risk Group ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Set ◽  
Cox Regression Analysis

Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( p  < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

scholarly journals Introduction to Competing Risk Model in the Epidemiological Research

2018 ◽  
Vol 5 (3) ◽  
pp. 98-102
Author(s):  
Abbas Alipour ◽  
Abolghasem Shokri ◽  
Fatemeh Yasari ◽  
Soheila Khodakarim
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Risk Model ◽  
Cox Model ◽  
Premature Death ◽  
General Purpose ◽  
Competing Risk ◽  
Risk Modeling ◽  
Competing Risk Model ◽  
Hazards Model

Background and aims: Chronic kidney disease (CKD) is a public health challenge worldwide, with adverse consequences of kidney failure, cardiovascular disease (CVD), and premature death. The CKD leads to the end-stage of renal disease (ESRD) if late/not diagnosed. Competing risk modeling is a major issue in epidemiology research. In epidemiological study, sometimes, inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In these situations, competing risk analysis is preferred to other models in survival analysis studies. The purpose of this study was to describe the bias resulting from the use of standard survival analysis to estimate the survival of a patient with ESRD and to provide alternate statistical methods considering the competing risk. Methods: In this retrospective study, 359 patients referred to the hemodialysis department of Shahid Ayatollah Ashrafi Esfahani hospital in Tehran, and underwent continuous hemodialysis for at least three months. Data were collected through patient’s medical history contained in the records (during 2011-2017). To evaluate the effects of research factors on the outcome, cause-specific hazard model and competing risk models were fitted. The data were analyzed using Stata (a general-purpose statistical software package) software, version 14 and SPSS software, version 21, through descriptive and analytical statistics. Results: The median duration of follow-up was 3.12 years and mean age at ESRD diagnosis was 66.47 years old. Each year increase in age was associated with a 98% increase in hazard of death. In this study, statistical analysis based on the competing risk model showed that age, age of diagnosis, level of education (under diploma), and body mass index (BMI) were significantly associated with death (hazard ratio [HR]=0.98, P<0.001, HR=0.99, P<0.001, HR=2.66, P=0.008, and HR=0.98, P<0.020, respectively). Conclusion: In analysis of competing risk data, it was found that providing both the results of the event of interest and those of competing risks were of importance. The Cox model, which ignored the competing risks, presented the different estimates and results as compared to the proportional sub-distribution hazards model. Thus, it was revealed that in the analysis of competing risks data, the sub-distribution proportion hazards model was more appropriate than the Cox model.

scholarly journals Screening of miRNAs as Prognostic Biomarkers for Colon Adenocarcinoma and Biological Function Analysis of Their Target Genes

2021 ◽  
Vol 11 ◽  
Author(s):  
Guoliang Zheng ◽  
GuoJun Zhang ◽  
Yan Zhao ◽  
Zhichao Zheng
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
High Risk ◽  
Prognostic Model ◽  
Target Genes ◽  
Risk Model ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Training Set ◽  
Cox Regression Analysis

We constructed a prognostic risk model for colon adenocarcinoma (COAD) using microRNAs (miRNAs) as biomarkers. Clinical data of patients with COADs and miRNA-seq data were from TCGA, and the differential expression of miRNAs (carcinoma vs. para-carcinoma tissues) was assessed using R software. COAD data were randomly divided into Training and Testing Sets. A linear prognostic risk model was constructed using Cox regression analysis based on the Training Set. Patients were classified as high-risk or low-risk according to the score of the prognostic model. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate model performance. The gene targets in the prognostic model were identified and their biological functions were analyzed. Analysis of COAD and normal cell lines using qPCR was used to verify the model. There were 134 up-regulated and 140 down-regulated miRNAs. We used the Training Set to develop a prognostic model based on the expression of seven miRNAs. ROC analysis indicated this model had acceptable prediction accuracy (area under the curve=0.784). Kaplan-Meier survival analysis showed that overall survival was worse in the high-risk group. Cox regression analysis showed that the 7-miRNA Risk Score was an independent prognostic factor. The 2,863 predicted target genes were mainly enriched in the MAPK, PI3K-AKT, proteoglycans in cancer, and mTOR signaling pathways. For unknown reasons, expression of these miRNAs in cancerous and normal cells differed somewhat from model predictions. Regardless, the 7-miRNA Risk Score can be used to predict COAD prognosis and may help to guide clinical treatment.

scholarly journals Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhengxin Wu ◽  
Jinshui Tan ◽  
Yifan Zhuang ◽  
Mengya Zhong ◽  
Yubo Xiong ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Clinical Samples ◽  
Pyrimidine Metabolism ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis

Abstract Background Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear. Methods Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR. Results Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan–Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model. Conclusion In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

scholarly journals Improving the Lung Cancer Clinical Trial Development by Incorporating Competing Risk Factors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhu Wenbo ◽  
Zhao Qing ◽  
Wang Li ◽  
Zhu Hangju ◽  
Zhang Junying ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Overall Survival ◽  
Survival Analysis ◽  
Risk Model ◽  
Survival Rates ◽  
Progression Free Survival ◽  
Competing Risk ◽  
Analysis Model ◽  
Overall Survival Rates

Introduction. Distinct from other diseases, as cancer progresses, both the symptoms and treatments evolve, resulting in a complex, time-dependent relationship. Many competing risk factors influence the outcome of cancer. An improved method was used to evaluate the data from 6 non-small-cell lung cancer (NSCLC) clinical trials combined in our center since 2016 to deal with the bias caused by competing risk factors. Material and Methods. Data of 118 lung cancer patients were collected from 2016 to 2020. Fine and Gray’s model for competing risk was used to evaluate survival of different treatment group compares with the classic survival analysis model. Results. Immunotherapy had better progression-free survival than chemotherapy. (HR: 0.62, 95% CI: 0.41-0.95, p = 0.0260 ). However, there were no significant differences in patients who withdrew due to treatment-related adverse events from different groups. ( Z = 0.0508 , p = 0.8217 ). The PD-1/PD-L1 inhibitors in our study did not significantly improve overall survival compared with chemotherapy (HR:0.77, 95% CI:0.48-1.24, p = 0.2812 ), estimated 1-year overall survival rates were 55% and 46%, and 3-year overall survival rates were 17% and 10%, respectively. Conclusion. When the outcome caused by competing risk exists, the corresponding competing risk model method should be adopted to eliminate the bias caused by the classic survival analysis.

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