Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Identification of Lifestyle Behaviors Associated with Recurrence and Survival in Colorectal Cancer Patients Using Random Survival Forests.

ABSTRACT: Current lifestyle recommendations for cancer survivors are the same as those for the general public to decrease their risk of cancer. However, it is unclear which lifestyle behaviors are most important for prognosis. We aimed to identify which lifestyle behaviors were most important regarding colorectal cancer (CRC) recurrence and all-cause mortality with a data-driven method. The study consisted of 1180 newly diagnosed stage I-III CRC patients from a prospective cohort study. Lifestyle behaviors included in the current recommendations, as well as additional lifestyle behaviors related to diet, physical activity, adiposity, alcohol use, and smoking were assessed six months after diagnosis. These behaviors were simultaneously analyzed as potential predictors of recurrence or all-cause mortality with Random Survival Forests (RSFs). We observed 148 recurrences during 2.6-year median follow-up and 152 deaths during 4.8-year median follow-up. Higher intakes of sugary drinks were associated with increased recurrence risk. For all-cause mortality, fruit and vegetable, liquid fat and oil, and animal protein intake were identified as the most important lifestyle behaviors. These behaviors showed non-linear associations with all-cause mortality. Our exploratory RSF findings give new ideas on potential associations between certain lifestyle behaviors and CRC prognosis that still need to be confirmed in other cohorts of CRC survivors.

SUBMITTER: van Zutphen M

PROVIDER: S-EPMC8157840 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data.

Project description:Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model's predictive performance. In addition, Cox PH predictive performance was better than RSF.

| S-EPMC8716055 | biostudies-literature

OBLIQUE RANDOM SURVIVAL FORESTS

Project description:We introduce and evaluate the oblique random survival forest (ORSF). The ORSF is an ensemble method for right-censored survival data that uses linear combinations of input variables to recursively partition a set of training data. Regularized Cox proportional hazard models are used to identify linear combinations of input variables in each recursive partitioning step. Benchmark results using simulated and real data indicate that the ORSF’s predicted risk function has high prognostic value in comparison to random survival forests, conditional inference forests, regression, and boosting. In an application to data from the Jackson Heart Study, we demonstrate variable and partial dependence using the ORSF and highlight characteristics of its 10-year predicted risk function for atherosclerotic cardiovascular disease events (ASCVD; stroke, coronary heart disease). We present visualizations comparing variable and partial effect estimation according to the ORSF, the conditional inference forest, and the Pooled Cohort Risk equations. The obliqueRSF R package, which provides functions to fit the ORSF and create variable and partial dependence plots, is available on the comprehensive R archive network (CRAN).

| S-EPMC9875945 | biostudies-literature

Consistency of Random Survival Forests.

Project description:We prove uniform consistency of Random Survival Forests (RSF), a newly introduced forest ensemble learner for analysis of right-censored survival data. Consistency is proven under general splitting rules, bootstrapping, and random selection of variables-that is, under true implementation of the methodology. Under this setting we show that the forest ensemble survival function converges uniformly to the true population survival function. To prove this result we make one key assumption regarding the feature space: we assume that all variables are factors. Doing so ensures that the feature space has finite cardinality and enables us to exploit counting process theory and the uniform consistency of the Kaplan-Meier survival function.

| S-EPMC2889677 | biostudies-literature

Random survival forests for competing risks.

Project description:We introduce a new approach to competing risks using random forests. Our method is fully non-parametric and can be used for selecting event-specific variables and for estimating the cumulative incidence function. We show that the method is highly effective for both prediction and variable selection in high-dimensional problems and in settings such as HIV/AIDS that involve many competing risks.

| S-EPMC4173102 | biostudies-literature

Pathway hunting by random survival forests.

Project description:MotivationPathway or gene set analysis has been widely applied to genomic data. Many current pathway testing methods use univariate test statistics calculated from individual genomic markers, which ignores the correlations and interactions between candidate markers. Random forests-based pathway analysis is a promising approach for incorporating complex correlation and interaction patterns, but one limitation of previous approaches is that pathways have been considered separately, thus pathway cross-talk information was not considered.ResultsIn this article, we develop a new pathway hunting algorithm for survival outcomes using random survival forests, which prioritize important pathways by accounting for gene correlation and genomic interactions. We show that the proposed method performs favourably compared with five popular pathway testing methods using both synthetic and real data. We find that the proposed methodology provides an efficient and powerful pathway modelling framework for high-dimensional genomic data.AvailabilityThe R code for the analysis used in this article is available upon request.

| S-EPMC3530909 | biostudies-literature

Gene selection using iterative feature elimination random forests for survival outcomes.

Project description:Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis.Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.

| S-EPMC3495190 | biostudies-literature

Lifestyle and lifestyle-related comorbidities independently associated with colorectal adenoma recurrence in elderly Chinese people.

Project description:The purpose of this study was to determine the lifestyle and lifestyle-related comorbidities independently associated with colorectal adenoma (CRA) recurrence in elderly Chinese people.During the 5-year follow-up after the initial colonoscopy, participants aged >60 years with the diagnosis and removal of CRA underwent a complete surveillance colonoscopy, and 152 participants with CRA recurrence plus 152 participants free of recurrence were included in this analysis.Participants with CRA recurrence were more likely to consume less vegetables and fruits, and more red meats compared with the control group (P<0.05 for all). Lifestyle-related comorbidities, including hypertension and nonalcoholic fatty liver disease (NAFLD), were more common in participants with CRA recurrence than in the control group (P<0.05 for all). In the multivariate analysis, pack-years of smoking were independently associated with an increased CRA recurrence (odds ratio [OR]: 1.03; P<0.05). Eating less vegetables (OR: 099; P<0.05) and fruits (OR: 0.98; P<0.05) was identified as a statistically independent factor influencing CRA recurrence, as was eating more red meats (OR: 1.01; P<0.05). Hypertension was also found to be a factor independently associated with an increased CRA recurrence (OR: 2.44; P<0.05). NAFLD had an independent association, with an increased CRA recurrence (OR: 3.43; P<0.05).Smoking cigarettes, high consumption of red meats, low intake of fruits and vegetables, and the presence of hypertension and NAFLD were independently associated with an increased CRA recurrence in elderly Chinese people. This conclusion helps elderly Chinese people to make effective behavioral changes, such as smoking cessation, substitution of fruits and vegetables for red meats, and timely treatment of hypertension and NAFLD, to reduce CRA recurrence and colorectal cancer risk.

| S-EPMC4918738 | biostudies-other

Identification of yeast transcriptional regulation networks using multivariate random forests.

Project description:The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes.

| S-EPMC2691601 | biostudies-literature

Lifestyle factors associated with survival after colorectal cancer diagnosis.

Project description:BACKGROUND: Aside from tumour stage and treatment, little is known about potential factors that may influence survival in colorectal cancer patients. The aim of this study was to investigate the associations between physical activity, obesity and smoking and disease-specific and overall mortality after a colorectal cancer diagnosis. METHODS: A cohort of 879 colorectal cancer patients, diagnosed in Western Australia between 2005 and 2007, were followed up to 30 June 2012. Cox's regression models were used to estimate the hazard ratios (HR) for colorectal cancer-specific and overall mortality associated with self-reported pre-diagnosis physical activity, body mass index (BMI) and smoking. RESULTS: Significantly lower overall and colorectal cancer-specific mortality was seen in females who reported any level of recent physical activity than in females reporting no activity. The colorectal cancer-specific mortality HR for increasing levels of physical activity in females were 0.34 (95% CI=0.15, 0.75), 0.37 (95% CI=0.17, 0.81) and 0.41 (95% CI=0.18, 0.90). Overweight and obese women had almost twice the risk of dying from any cause or colorectal cancer compared with women of normal weight. Females who were current smokers had worse overall and colorectal cancer-specific mortality than never smokers (overall HR=2.64, 95% CI=1.18, 5.93; colorectal cancer-specific HR=2.70, 95% CI=1.16, 6.29). No significant associations were found in males. CONCLUSION: Physical activity, BMI and smoking may influence survival after a diagnosis of colorectal cancer, with more pronounced results found for females than for males.

| S-EPMC3738138 | biostudies-other

Pathway analysis using random forests with bivariate node-split for survival outcomes.

Project description:MotivationThere is great interest in pathway-based methods for genomics data analysis in the research community. Although machine learning methods, such as random forests, have been developed to correlate survival outcomes with a set of genes, no study has assessed the abilities of these methods in incorporating pathway information for analyzing microarray data. In general, genes that are identified without incorporating biological knowledge are more difficult to interpret. Correlating pathway-based gene expression with survival outcomes may lead to biologically more meaningful prognosis biomarkers. Thus, a comprehensive study on how these methods perform in a pathway-based setting is warranted.ResultsIn this article, we describe a pathway-based method using random forests to correlate gene expression data with survival outcomes and introduce a novel bivariate node-splitting random survival forests. The proposed method allows researchers to identify important pathways for predicting patient prognosis and time to disease progression, and discover important genes within those pathways. We compared different implementations of random forests with different split criteria and found that bivariate node-splitting random survival forests with log-rank test is among the best. We also performed simulation studies that showed random forests outperforms several other machine learning algorithms and has comparable results with a newly developed component-wise Cox boosting model. Thus, pathway-based survival analysis using machine learning tools represents a promising approach in dissecting pathways and for generating new biological hypothesis from microarray studies.AvailabilityR package Pwayrfsurvival is available from URL: http://www.duke.edu/~hp44/pwayrfsurvival.htm.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC2804301 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data