Survival Analysis Of Cancer Patients Research Articles

The ceramide synthase (CERS/LASS) family: Functions involved in cancer progression.

Ceramide synthases (CERSes) are also known longevity assurance (LASS) genes. CERSes play important roles in the regulation of cancer progression. The CERS family is expressed in a variety of human tumours and is involved in tumorigenesis. They are closely associated with the progression of liver, breast, cervical, ovarian, colorectal, head and neck squamous cell, gastric, lung, prostate, oesophageal, pancreatic and blood cancers. CERSes play diverse and important roles in the regulation of cell survival, proliferation, apoptosis, migration, invasion, and drug resistance. The differential expression of CERSes in tumour and nontumour cells and survival analysis of cancer patients suggest that some CERSes could be used as potential prognostic markers. They are also important potential targets for cancer therapy. In this review, we summarize the available evidence on the inhibitory or promotive roles of CERSes in the progression of many cancers. Furthermore, we summarize the identified upstream and downstream molecular mechanisms that may regulate the function of CERSes in cancer settings.

Survival analysis of cancer patients in Portugal following the reference centre model implementation.

Cancer has affected around eighteen million people all over the world in 2018. In Portugal, cancer was diagnosed in sixty thousand individuals during 2018, being the second leading cause of death (one in every four deaths). Following the European Directive 2011/24/EU, the Portuguese Health System has been recognizing oncology Reference Centres (RCs), which are focused on delivering best-in-class treatment for cancer patients. This paper performs a survival analysis of cancer patients in Portugal, having hospital episodes with discharge date after the official recognition, in 2016, of the first RCs for hepatobiliary, pancreatic, sarcomas and oesophageal cancer. The aim is to assess the impact of RCs on the survival probability of these patients. For each cancer type, survival curves are estimated using the Kaplan-Meier methodology, and hazard ratios are estimated for different covariates, using multivariate Extended Cox models. The results obtained support the implementation and encourage the further extension of the RC model for oncology in Portugal, as cancer patients treated in an oncology RC, overall, have a better survival probability when compared to patients who had no episode in an RC. These results are clearer for hepatobiliary and pancreatic cancer, but also visible for sarcomas and oesophageal cancer.

Systematical analysis reveals a strong cancer relevance of CREB1-regulated genes

The transcription factor cyclic-AMP response element-binding protein 1 (CREB1) responds to cAMP level and controls the expression of target genes, which regulates nutrition partitioning. The promoters of CREB1-targeted genes responsive to cAMP have been extensively investigated and characterized with the presence of both cAMP response element and TATA box. Compelling evidence demonstrates that CREB1 also plays an essential role in promoting tumor development. However, only very few genes required for cell survival, proliferation and migration are known to be constitutively regulated by CREB1 in tumors. Their promoters mostly do not harbor any cAMP response element. Thus, it is very likely that CREB1 regulates the expressions of distinct sets of target genes in normal tissues and tumors. The whole gene network constitutively regulated by CREB1 in tumors has remained unrevealed. Here, we employ a systematical and integrative approach to decipher this gene network in the context of both tissue cultured cancer cells and patient samples. We combine transcriptomic, Rank-Rank Hypergeometric Overlap, and Chipseq analysis, to define and characterize CREB1-regulated genes in a multidimensional fashion. A strong cancer relevance of those top-ranked targets, which meet the most stringent criteria, is eventually verified by overall survival analysis of cancer patients. These findings strongly suggest the importance of genes constitutively regulated by CREB1 for their implicative involvement in promoting tumorigenesis.

The impact of socio-demographic factors on the survival of cancer patients in Zimbabwe

We provide a survival analysis of cancer patients in Zimbabwe. Our results show that young cancer patients have lower but not significant hazard rate compared to old cancer patients. Male cancer patients have lower but not significant hazard rate compared to female cancer patients. Race and marital status are significant risk factors for cancer patients in Zimbabwe.

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

BackgroundSurvival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions.ResultsIn this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients’ survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients’ survival time.ConclusionThe proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients’ survival by integrating multi-omics data and clinical factors.

Survival Analysis of Cancer Patients at Teaching Hospitals in Ethiopia

Survival analysis today is widely implemented in the fields of medical and biological sciences. The basic principle behind the survival analysis implies to a statistical approach designed to take into account the amount of time utilized for a study period, or the study of time between entry into observation and a subsequent event. The event of interest pertains to death and the analysis consists of following the subject until death. This study aims to analysis the survival status of cancer patients, to identify the main cancer in women at study area and to determine the survival time of women with cancer after undergoing certain treatments in the selected teaching hospital(s). A random sample of 460 cancer patients was selected using purposive sampling from the study area, in 2016 to 2018. Parametric and non-parametric models, Logistic regression models, Statistical distributions and tests methods of data analysis were used in this study. The demographic information: age, educational level, treatments, economic level, marital status and stages of cancer were included and Comparisons were made among major cancers in patients. The findings of the study suggest that among major types of cancer in patients, breast cancer was the highly affecting the females in the study area. There are 62.15 % survival and 37.35 % of not survived patients in the data and the majority of patients (about 88 %) are diagnosed when they are in stages 1 and 2 and very few (about 8 %) of them are diagnosed in advanced stage of cancer. It also reveals that the average tumor sizes are significantly different for all ages and stages. By grouping ages into groups of 5, I also stratified the number of patients diagnosed with cancer in different stages and the diagnoses at different stages were different. Finally, Results of survival time shows that the surviving of cancer patient were different at different ages and treatments level.

MLsurvival: an automated machine learning tool for survival analysis of cancer patients.

e13555 Background: Survival analysis is used to establish a connection between covariates and the time of event with censored data. Compared with traditional statistical methods, machine learning approaches based on sophisticated and effective computational algorithms are more capable for handling complex multi-dimensional medical data. Methods: We developed an automated machine learning tool MLsurvival to analyze survival data of cancer patients, algorithms of which include the statistical cox regression and machine learning based on linear model (elastic net), ensemble model (gradient boosting with least squares or regression trees and random forest) and support vector kernel (linear and non-linear). The workflow of MLsurvival is comprised with four modules: preprocessing (missing data remove or imputation and feature standardization), feature selection (unsupervised multi-statistics and supervised machine recursive feature elimination with cross-validation), modeling (hyperparameter and performance evaluation) and prediction. To evaluate the performance of this tool, we analyzed medical data for 222 hepatocellular carcinoma (HCC) patients at stage II-III who underwent surgical resection and developed five machine learning approach based estimation models for overall survival (OS). Models were trained on 155 patients with 300 features, including clinical information, somatic mutation and copy number variation, and independently validated on the rest 67 patients. Results: The ensemble model of gradient boosting fitted by MLsurvival using 48 selected features for the data of 155 HCC patients possessed the highest mean AUC and C-Index value. For 67 patients in validation set, this model predicted half year mortality of patients with an AUC of 0.9 (95% CI, 0.771-1.029) and one year mortality with an AUC of 0.897 (95% CI, 0.816-0.978). In addition to that, this model was also predictive for the time of recurrence (pvalue < 0.0001). Furthermore, we also utilized this tool in survival analysis for extensive real data from patients with breast, lung, and esophagus cancers, while most of results showed superior accuracy and stable performance. Conclusions: MLsurvial is an automate tool for survival analysis of cancer patients with well performance. The risk scoring system implemented in this tool offers a novel strategy for incorporating multi-dimensional risk factors to predict clinical outcome, contributes to the better understanding of disease background and helps to optimize the clinical follow-up and therapeutic treatment for cancer patients.

Survival Analysis of Cancer Patients in North Eastern Nigeria from 2004 - 2017 - A Kaplan - Meier Method.

BACKGROUND:Cancer is a deadly malignant disease and is prevalent in Sub Saharan Africa. The North East part of Nigeria in particular and the country, in general, are struggling to cope with the increasing burden of cancer and other communicable and non-communicable diseases. The situation is worsened by the ongoing insurgency and terrorist activities in the area.AIM:The aim of this paper is to present the research findings from a cohort study aimed at the analysis of the estimation of the survivorship time of the real data of cancer patients in the North-eastern part of Nigeria and to establish if the insurgency in the region has contributed negatively to the life expectancy of its inhabitants.MATERIAL AND METHODS:The record of 1,090 patients from medical records departments of the University of Maiduguri Teaching Hospital (UMTH), located in Maiduguri, the capital city of Borno State in northeast Nigeria was obtained. The record showed patients that were diagnosed and died of one type of cancer or the other from 2004 to 2017. All the cancer cases included in the present study were grouped into sex, age, marital status, occupation, date admitted and date of death/discharge. Descriptive statistics and Kaplan-Meier method were used to analyse the data using SPSS version 23 while Microsoft EXCEL and Minitab 16.0 were used for data cleansing and organisation.RESULTS:Of the 1,090 patients analysed, 920 (84.40%) experienced the event, i.e. death, while 170 (15.60%) patients were censored. The data were analysed based on the ages and sex of the patients. 50.20% of the patients were of ages 21-50 years. The proportions of patients in this age bracket surviving past 7 days are 75%, while those between ages 80 years and above is 12 days. Others are of survival time of 5 days (ages 0-20 years) and 7 days (51-79 years). Using sex, 75% of the patients’ survival time is 7 days in the case of male and 6 days for females. It is safe to say that the survival time for cancer patients of the university the Maiduguri is 6 days and the result reflects the Northeastern part of Nigeria. This is because the hospital is one of few tertiary healthcare facilities in that area and consequently, cancer cases are often referred there.CONCLUSION:Cancer incidence is high, and the probability of survival reduces as the survival time increases. This is a dire situation in need of urgent intervention from the government, groups and individuals to tackle the scourge of cancer, thereby improving on the life expectancy battered by the ongoing Boko Haram insurgency in that region.

Survival Analysis of Cancer Patients of Differing Payer Type in South West Virginia, Between 2000 and 2013.

IntroductionThe effect of insurance coverage on the health of at-risk populations is poorly understood in the Appalachian region of the United States. The goal of this study is to examine how different types of insurance coverage (Private Insurance, Medicare under 65, Medicare 65 or over, Medicaid and Self Pay) may influence cancer survival over time. This study analyzes colon, bladder, as well as combines anal, rectal, and esophageal cancers.MethodsWe systematically analyzed all West Virginia Cancer Registry patients between the years of 2000 and 2013 who was diagnosed with colon, bladder, anal, rectal, and esophageal cancers. Separate analysis examined colon (n = 927), bladder (n = 269), and combined anal, rectum, and esophageal cancers (n = 398). Cox proportional hazards models investigated the effect of insurance types on survival while controlling for age, sex, tobacco use, alcohol use, and cancer stage.ResultsOverall, tobacco use marginally significantly decreased colon cancer survival. Tobacco use had a suggestive relationship at hazards ratio at 1.150, 95% confidence interval: 0.9990-1.235, p = 0.052. The type of payer group did not alter survival. Older individuals tend to have a lower survival rate compared to those that are younger at the time of diagnosis. Also, late-stage cancer faced lower survival compared to those with early-stage cancer. Other results within stage groups corresponded to existing literature.ConclusionFor the three differing cancer groupings, there was no significant survival difference for patients by insurance type. The effect of tobacco usage on colon cancer survival merits further research. The study design could be improved by considering more risk factors such as patient comorbidities that might affect patient care and survival.

Flexible objective Bayesian linear regression with applications in survival analysis

ABSTRACTWe study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general non-informative, scale-invariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in [16]. We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.

EPIDEMIOLOGY AND SURVIVAL OF PATIENTS WITH MALIGNANT TUMORS OF THE NASAL CAVITY, MIDDLE EAR AND ACCESSORY SINUSES ACCORDING TO THE STAGE OF DISEASE AND HISTOLOGICAL STRUCTURE OF TUMORS

Introduction. The population-level survival analysis for cancer patients was not carried out in Russia due to the absence of population-based cancer registries, meeting the requirements of international Agency for Research on Cancer. This opportunity has appeared only with the establishment of the population-based cancer registry in St. Petersburg. Cancer survival estimation is possible only under conditions of strict adherence to the principles of the study. Analysis of survival of cancer patients is the main criterion of the objective assessment of the effectiveness of anticancer therapy. Malignant tumors of the nasal cavity, middle ear and accessory sinuses are classified by two ICD-10 diagnosis codes: С30 and С31 (С30: malignant tumors of the nasal cavity and middle ear; C31: malignant tumors of accessory sinuses). Database of the population-based cancer registry promotes the study of survival by any parameter including the fourth character of the ICD-10 code and histological type of the tumor. Purposes and objectives. To estimate the observed and relative survival rates in patients with malignant tumors of the nasal cavity, middle ear and accessory sinuses and to compare the results obtained with the EU average (according to the EuroCare program), as well as to estimate survival of this group of patients according to sex, age, stage of disease and histological type of the tumor. Material and methods. Between 1994 and 2009, 565 cases with malignant tumors of the nasal cavity, middle ear and accessory sinuses were selected from the population-based cancer registry. There were used the observed and relative survival rates calculated according to the international standards. Results. We estimated the 5-year observed and relative survival rates in patients with cancers of the nasal cavity, middle ear and sinuses, using ICD-10 codes С30 and 31 and taking into account sex, age, stage of disease and histological structure of tumors. A clear trend toward increase in the 5-year observed and relative survival rates in patients with malignant tumors of the nasal cavity, middle ear and accessory sinuses was observed.

Survival Analysis of Cancer Patients With FIGO Stage IIIA Endometrial Cancer

Endometrial cancer patients with positive serosa and/or adnexae (FIGO stage IIIA) have a variable prognosis and are at a significant risk for recurrence. We investigated how tumor characteristics and adjuvant treatments influence the overall survival (OS) and recurrence patterns in these patients and patients with positive cytology alone (previously classified as stage IIIA before 2009). This multi-institution retrospective study reviewed 55 patients with positive serosa and/or adnexae and 18 patients with positive cytology only, surgically staged from 1990 to 2010. The study cohort was evaluated using the Kaplan-Meier estimates of OS and Cox proportional hazards modeling. The 5-year OS for all IIIA patients was 55%. Administration of adjuvant therapy was associated with improved OS when compared with surgery alone (P=0.0018). The 5-year OS was 20% for patients treated with surgery alone (n=10), 55% with surgery and radiation therapy (n=26), 75% with surgery and chemotherapy (n=7), and 79% with surgery followed by both radiation therapy and chemotherapy (n=12; P=0.005). The tumor characteristics showed that nonendometrioid histology (P=0.0143) and lymph vascular space invasion (P=0.0483) had a poorer OS. Recurrence occurred in 29% of IIIA patients, with 9% locoregional failures and 20% distant failures. Patients with positive cytology only had a similar OS to patients with positive serosa and/or adnexae (76% vs. 55%; P=0.104) and recurrence rate (22% vs. 29%; P=0.4101). This retrospective study suggests benefit from the use of adjuvant radiotherapy and chemotherapy for stage IIIA patients. We recommend further investigation of adjuvant therapies for IIIA patients in prospective studies and randomized clinical trials.

Survival Analysis of Cancer Patients Treated with Traditional Korean Medicine Recent 10 years

Purpose: We aimed to analyze survival rates and duration of cancer patients who treated with Traditional Korean Medicine (TKM). Methods: We analyzed the medical records of 971 patients whowere treatedmore than 1month of out-patient or 20 days of hospitalization at the Daejeon Oriental Hospital from Oct. 2004 to Sep. 2014. The survival rates andmedian survival duration (MSD) were estimated by the Kaplan-Meier method and Log-rank test. Seven different kinds of cancers patients who were suffered from liver, thyroid, colon & rectum, stomach, breast, pancreas, or lung cancer were analyzed by gender, age, and cancer stage-specific. We also estimated survival rates and MSD of 203 terminally ill cancer patients. Results: The 5-year survival rates and MSD of 971 patients was 38.6% (95%CI 34.8 – 42.3), 3.28-year (95%CI 2.87 3.65) respectively. The 5-year survival rates of liver, colon & rectum, stomach, breast, pancreas and lung cancer patients were 30.0% (95% CI 19.5 – 40.5), 25.0%(95% CI 16.0 – 34.0), 24.4%(95% CI 16.2 – 32.6), 79.3%(95% CI 70.4 – 88.1), 3.4% (95% CI 0.0 – 9.3), 22.2%(95% CI 12.6 – 31.8), respectively. The MSD for liver, colon & rectum, stomach, breast, pancreas and lung cancer patients was 2.27-year (95%CI 1.21 3.50), 3.11-years (95%CI 2.67 3.58), 1.96-year (95%CI 1.71 2.52), 8.04-year (95%CI 7.01 10.69), 1.01-year (95% CI 0.80 1.21), 2.07-years (95%CI 1.47 2.53), respectively. Moreover, the MSD of terminally ill cancer patients was 0.39-years (about 20weeks, 95%CI 0.33 0.44) and 0.5-year survival rates for themwere 40.7% (95%CI 33.6 47.7), respectively. Conclusion: All cancer types were not obviously extended the survival duration by treatment with TKM, the survival rate, especially for the liver, breast, pancreas and lung cancer patients are relatively higher than other treatment. Well-designed and prospective clinical trials should be recommended to provide more reliable evidence. Contact: Jun-young Kim, jy-057@hanmail.net

The L1/2 regularization approach for survival analysis in the accelerated failure time model

The analysis of high-dimensional and low-sample size microarray data for survival analysis of cancer patients is an important problem. It is a huge challenge to select the significantly relevant bio-marks from microarray gene expression datasets, in which the number of genes is far more than the size of samples. In this article, we develop a robust prediction approach for survival time of patient by a L1/2 regularization estimator with the accelerated failure time (AFT) model. The L1/2 regularization could be seen as a typical delegate of Lq(0<q<1) regularization methods and it has shown many attractive features. In order to optimize the problem of the relevant gene selection in high-dimensional biological data, we implemented the L1/2 regularized AFT model by the coordinate descent algorithm with a renewed half thresholding operator. The results of the simulation experiment showed that we could obtain more accurate and sparse predictor for survival analysis by the L1/2 regularized AFT model compared with other L1 type regularization methods. The proposed procedures are applied to five real DNA microarray datasets to efficiently predict the survival time of patient based on a set of clinical prognostic factors and gene signatures.

Cumhuriyet Üniversitesi Tıp Fakültesi Onkoloji Merkezi’ne başvuran kanserli hastaların demografik dağılımı ve sağkalım verileri

Aim. The aim of this study is to investigate the demographic characteristics and survival analysis of cancer patients. Method. Three thousand four hundred and seven cases of cancer diagnosed between 2006-2011 were reviewed retrospectively from patient’s files. Results. One thousand eighty one (55.2%) patients were male and one thousand twenty six (44.8%) were female. The detected most frequent 5 types of cancers were as follow; breast (18.0%), lung (16.7%), colorectal (12.1%), gastric (9.1%), prostat (4.0%). The most frequent cancer for male were lung (26.2%), colorectal (13.5%), stomach (12.2%), prostate (7.2%) and brain (4.6%). The most frequent cancer for females were breast (39.8%), colorectal (10.4%), ovarian (5.6%), stomach (5.2%), lung (4.9%). In all cases, there were 33.4% local, 25.6% locoregional, 38.2% metastatic disease. Conclusion. The 3-year relative survival rates among patients with the most frequent cancers were breast 87%, lung 22%, colorectal 61%, stomach 38%, prostate 66%.

Cancer Survival in Korea 1993-2002: A Population-Based Study

Population-based survival reflect the average prognosis of unselected patients with a variety of natural histories as well as treatment patterns and are also useful for evaluating effectiveness and efficiency of cancer-directed health services in a given region. Although survival data have been reported based on hospital data, the survival data from population-based registry have been rarely reported in Korea. Based on the Korea National Cancer Incidence Database, we report the results from survival analysis for cancer patients diagnosed during 1993-2002 and followed up until 31 December 2005 at primary cancer sites. The five-year relative survival rates (RSR) were calculated using the Ederer II method. The Kaplan-Meier method was used to estimate median survival and the 95% confidence intervals. In males, the five-year RSR for all cancers was 32.5% during 1993-1997 and was 37.8% during 1998-2002. In females, the five-year RSR for all cancers was 53.7% during 1993-1997 and was 57.0% during 1998-2002. The largest improvement in survival was shown in prostate cancer in males and breast and stomach cancer in females. The median survival durations were 16.3 months in males and 81.6 months in females. This result will be useful for evaluation of cancer treatment outcomes in Korea.

582 POSTER Survival analysis of Cancer patients in North coastal Andhra Pradesh, India

582 POSTER Survival analysis of Cancer patients in North coastal Andhra Pradesh, India