Dataset Information

Predictors of Survival Among Colorectal Cancer Patients in a Low Incidence Area.

ABSTRACT: Background:Colorectal cancer is the third most common malignancy in Saudi Arabia. The best therapeutic regimen for colorectal cancer is a matter of ongoing debate and data on its treatment in Saudi Arabia are limited. Purpose:The objective of this study was to explore the predictors of survival and to compare the risk of mortality among colorectal cancer patients treated with different therapeutic modalities. Patients and Methods:The study utilized data from the electronic colorectal cancer registry of a university-affiliated tertiary care hospital. The Kaplan-Meier survival analysis was used to estimate the survival rates over 36 months of follow-up across rectal and colon cancer patients as well as different sociodemographic and medical characteristics. Bivariate and multiple Cox proportional-hazards regressions were conducted to estimate the risk of mortality among rectal and colon cancer patients undergoing different treatments. Results:The number of patients in the registry who were followed up for 36 months was 143 patients. The majority of patients had colon cancer (74.13%). Rectal cancer patients had generally better survival estimates compared to their colon cancer counterparts. Colon cancer patients treated with chemotherapy had a significantly lower risk of mortality controlling for the use of surgery, radiotherapy, and other variables including age, gender, stage of cancer, and family history of colorectal cancer (HR=0.33; P=0.03). Additionally, colon cancer patients with a family history of colorectal cancer had significantly higher risk of mortality (HR=3.40; P=0.02). Conclusion:The findings of this study highlight the value of chemotherapy in managing colon cancer patients.

SUBMITTER: Azzam N

PROVIDER: S-EPMC6982433 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictors of Survival Among Colorectal Cancer Patients in a Low Incidence Area.

Azzam Nahla N AlRuthia Yazed Y Alharbi Othman O Aljebreen Abdulrahman A Almadi Majid M Alarfaj Maryam M Alsaleh Khalid K Almasoud Abdulaziz A Alsharidah Muhannad M Alseneidi Sarah S Alali Fatimah F Alalwan Malak M

Cancer management and research 20200121

<h4>Background</h4>Colorectal cancer is the third most common malignancy in Saudi Arabia. The best therapeutic regimen for colorectal cancer is a matter of ongoing debate and data on its treatment in Saudi Arabia are limited.<h4>Purpose</h4>The objective of this study was to explore the predictors of survival and to compare the risk of mortality among colorectal cancer patients treated with different therapeutic modalities.<h4>Patients and methods</h4>The study utilized data from the electronic ...[more]

PMID: 32021457

Similar Datasets

Project description:BackgroundAn increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients.MethodsPatients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions.ResultsBased on univariate and multivariate analysis, some prognostic factors, such as "TNM stage", "tumor size" and "age at diagnosis," have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]).ConclusionsBased on this study, it's recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

Project description:ImportancePopulation-based East Asian data have corroborated reports from non-Asian settings on the association between low-dose aspirin and a lower risk of colorectal cancer (CRC).ObjectiveTo evaluate the association between duration and recency of low-dose aspirin use and CRC risk.Design, setting, and participantsThis nested case-control study included individuals who initiated aspirin use and matched individuals who did not use aspirin. Data were collected from Taiwan National Health Insurance and Taiwan Cancer Registry from 2000 through 2015. CRC cases were age- and sex-matched in a 1:4 ratio with individuals in a control group, identified from a cohort of individuals who used and did not use aspirin through risk-set sampling. Data analysis was conducted from June 2018 to July 2019.ExposuresLow-dose aspirin use was defined as receiving less than 150 mg per day, whereas 100 mg/d was most commonly used. Based on duration and recency of low-dose aspirin use between cohort entry (initiation date of low-dose aspirin for aspirin use group or randomly assigned date for those who did not use aspirin) and index date (CRC diagnosis date for individuals in the case group and the diagnosis date for the 4 corresponding matched individuals in the control group), the 3 following mutually exclusive exposure groups served as the basis for analysis: (1) long-term current low-dose aspirin use, (2) episodic low-dose aspirin use, and (3) no low-dose aspirin use (the reference group).Main outcomes and measuresCRC risk among the 3 exposure groups.ResultsAmong 4 710 504 individuals (2 747 830 [51.7%] men; median [interquartile range] age at cohort entry in initiator group, 61 [52-71] years; median [interquartile range] age at cohort entry in nonuse group, 59 [51-68] years), 79 095 CRC cases (1.7% of study cohort) were identified. Compared with no low-dose aspirin use, the adjusted odds ratio (OR) for long-term current low-dose aspirin use and CRC risk was 0.89 (95% CI, 0.85-0.93); for episodic use, 0.88 (95% CI, 0.86-0.89). Adjusted ORs of 0.69 (95% CI, 0.63-0.76) and 0.64 (95% CI, 0.61-0.67) were observed for long-term current use and episodic low-dose aspirin use within the subcohort of individuals who initiated low-dose aspirin between age 40 and 59 years.Conclusions and relevanceIn this study, low-dose aspirin use was associated with 11% lower CRC risk in an East Asian population, and this association was larger when low-dose aspirin use started before age 60 years.

Project description:This study aimed to explore the mechanisms of bridging integrator-3 dysregulation, its prognostic value, and related signaling pathways in colorectal cancer . Bioinformatic analysis was performed based on the data from The Cancer Genome Atlas–colorectal cancer and Human Protein Atlas. Colorectal cancer cell lines, LoVo and HT29 cells, were used as in vitro cell model to assess the effect of demethylation on bridging integrator-3 expression. Results showed that bridging integrator-3 was downregulated in colorectal cancer tissues compared to normal colon and rectum tissues. Heatmap of bridging integrator-3 messenger RNA expression, exon expression, and DNA methylation indicated a negative correlation between bridging integrator-3 expression and methylation of some CpG sites within the coding sequence. Demethylation treatment significantly increased bridging integrator-3 expression in LoVo and HT29 cells. Low bridging integrator-3 messenger RNA and exon expression were associated with significantly worse overall survival (P = .015 and .013, respectively). Multivariate analysis confirmed that low bridging integrator-3 messenger RNA expression was an independent prognostic factor of unfavorable overall survival (Hazard Ratio (HR) = 1.596, 95% confidence interval: 1.024-2.486; P = .039). High bridging integrator-3 DNA methylation was also associated with significantly worse overall survival (P = .013). Kyoto Encyclopedia of Genes and Genomes analysis indicated that the genes correlated with bridging integrator-3 (absolute Pearson r ≥ 0.3, n = 121) were enriched in sphingolipid signaling pathway, natural killer cell-mediated cytotoxicity, p53 signaling pathway, and apoptosis. Based on these findings, we infer that DNA hypermethylation might be an important mechanism of suppressed bridging integrator-3 expression in colorectal cancer . Its low expression is an independent predictor of unfavorable survival in patients with primary colorectal cancer . Bridging integrator-3 might act as a tumor suppressor via modulating natural killer cell-mediated cytotoxicity, p53 signaling pathway, and apoptosis.

Dataset Information

Predictors of Survival Among Colorectal Cancer Patients in a Low Incidence Area.

Publications

Predictors of Survival Among Colorectal Cancer Patients in a Low Incidence Area.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets