2283-PUB: Metformin Treatment and Prostate Cancer Risk: Cox Regression Analysis with Time-Dependent Covariates of 150,000 Men with Incident Diabetes Mellitus

ObjectiveGrowing evidence has highlighted that the immune and stromal cells that infiltrate in pancreatic cancer microenvironment significantly influence tumor progression. However, reliable microenvironment-related prognostic gene signatures are yet to be established. The present study aimed to elucidate tumor microenvironment-related prognostic genes in pancreatic cancer.MethodsWe applied the ESTIMATE algorithm to categorize patients with pancreatic cancer from TCGA dataset into high and low immune/stromal score groups and determined their differentially expressed genes. Then, univariate and LASSO Cox regression was performed to identify overall survival-related differentially expressed genes (DEGs). And multivariate Cox regression analysis was used to screen independent prognostic genes and construct a risk score model. Finally, the performance of the risk score model was evaluated by Kaplan-Meier curve, time-dependent receiver operating characteristic and Harrell’s concordance index.ResultsThe overall survival analysis demonstrated that high immune/stromal score groups were closely associated with poor prognosis. The multivariate Cox regression analysis indicated that the signatures of four genes, including TRPC7, CXCL10, CUX2, and COL2A1, were independent prognostic factors. Subsequently, the risk prediction model constructed by those genes was superior to AJCC staging as evaluated by time-dependent receiver operating characteristic and Harrell’s concordance index, and both KRAS and TP53 mutations were closely associated with high risk scores. In addition, CXCL10 was predominantly expressed by tumor associated macrophages and its receptor CXCR3 was highly expressed in T cells at the single-cell level.ConclusionsThis study comprehensively investigated the tumor microenvironment and verified immune/stromal-related biomarkers for pancreatic cancer.

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Diabetes Mellitus and Prostate Cancer Risk; A Nationwide Case–Control Study within PCBaSe Sweden

Cancer Epidemiology Biomarkers & Prevention ◽

10.1158/1055-9965.epi-12-1046 ◽

2013 ◽

Vol 22 (6) ◽

pp. 1102-1109 ◽

Cited By ~ 34

Author(s):

Katja Fall ◽

Hans Garmo ◽

Soffia Gudbjörnsdottir ◽

Pär Stattin ◽

Björn Zethelius

Keyword(s):

Prostate Cancer ◽

Diabetes Mellitus ◽

Cancer Risk ◽

Case Control Study ◽

Prostate Cancer Risk ◽

Case Control ◽

Control Study

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Bleeding risk of haemodialysis and peritoneal dialysis patients

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa216 ◽

2020 ◽

Vol 36 (1) ◽

pp. 170-175

Author(s):

Anita van Eck van der Sluijs ◽

Alferso C Abrahams ◽

Maarten B Rookmaaker ◽

Marianne C Verhaar ◽

Willem Jan W Bos ◽

...

Keyword(s):

Regression Analysis ◽

Peritoneal Dialysis ◽

Cox Regression ◽

Bleeding Risk ◽

Time Dependent ◽

Antiplatelet Drug ◽

Dialysis Modality ◽

Limited Information ◽

Dialysis Patients ◽

Cox Regression Analysis

Abstract Background Dialysis patients have an increased bleeding risk as compared with the general population. However, there is limited information whether bleeding risks are different for patients treated with haemodialysis (HD) or peritoneal dialysis (PD). From a clinical point of view, this information could influence therapy choice. Therefore the aim of this study was to investigate the association between dialysis modality and bleeding risk. Methods Incident dialysis patients from the Netherlands Cooperative Study on the Adequacy of Dialysis were prospectively followed for major bleeding events over 3 years. Hazard ratios with 95% confidence intervals (CIs) were calculated for HD compared with PD using a time-dependent Cox regression analysis, with updates on dialysis modality. Results In total, 1745 patients started dialysis, of whom 1211 (69.4%) received HD and 534 (30.6%) PD. The bleeding rate was 60.8/1000 person-years for HD patients and 34.6/1000 person-years for PD patients. The time-dependent Cox regression analysis showed that after adjustment for age, sex, primary kidney disease, prior bleeding, cardiovascular disease, antiplatelet drug use, vitamin K antagonist use, erythropoietin use, arterial hypertension, residual glomerular filtratin rate, haemoglobin and albumin levels, bleeding risk for HD patients compared with PD increased 1.5-fold (95% CI 1.0–2.2). Conclusions In this large prospective cohort of incident dialysis patients, HD patients had an increased bleeding risk compared with PD patients. In particular, HD patients with a history of prior bleeding had an increased bleeding risk.

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Metformin Treatment and Cancer Risk: Cox Regression Analysis, With Time-Dependent Covariates, of 320,000 Persons With Incident Diabetes Mellitus

American Journal of Epidemiology ◽

10.1093/aje/kwz157 ◽

2019 ◽

Vol 188 (10) ◽

pp. 1794-1800 ◽

Cited By ~ 5

Author(s):

Rachel Dankner ◽

Nirit Agay ◽

Liraz Olmer ◽

Havi Murad ◽

Lital Keinan Boker ◽

...

Keyword(s):

Breast Cancer ◽

Lung Cancer ◽

Cancer Risk ◽

Time Dependent ◽

Incident Diabetes ◽

Diabetic Patients ◽

Defined Daily Dose ◽

Metformin Treatment ◽

Health Maintenance ◽

Cox Regression Analysis

Abstract There is conflicting evidence regarding the association between metformin use and cancer risk in diabetic patients. During 2002–2012, we followed a cohort of 315,890 persons aged 21–87 years with incident diabetes who were insured by the largest health maintenance organization in Israel. We used a discrete form of weighted cumulative metformin exposure to evaluate the association of metformin with cancer incidence. This was implemented in a time-dependent covariate Cox model, adjusting for treatment with other glucose-lowering medications, as well as age, sex, ethnic background, socioeconomic status, smoking (for bladder and lung cancer), and parity (for breast cancer). We excluded from the analysis metformin exposure during the year before cancer diagnosis in order to minimize reverse causation of cancer on changes in medication use. Estimated hazard ratios associated with exposure to 1 defined daily dose of metformin over the previous 2–7 years were 0.98 (95% confidence interval (CI): 0.82, 1.18) for all-sites cancer (excluding prostate and pancreas), 1.05 (95% CI: 0.67, 1.63) for colon cancer, 0.98 (95% CI: 0.49, 1.97) for bladder cancer, 1.02 (95% CI: 0.59, 1.78) for lung cancer, and 0.88 (95% CI: 0.56, 1.39) for female breast cancer. Our results do not support an association between metformin treatment and the incidence of major cancers (excluding prostate and pancreas).

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Patient experience with service quality: Implications for survival outcomes in prostate cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.34_suppl.39 ◽

2012 ◽

Vol 30 (34_suppl) ◽

pp. 39-39

Author(s):

Christopher G. Lis ◽

Maurie Markman ◽

Mark Rodeghier ◽

Digant Gupta

Keyword(s):

Prostate Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Service Quality ◽

Patient Experience ◽

Cox Regression ◽

Cox Regression Analysis ◽

Risk Of Mortality ◽

Patient Reported

39 Background: Prostate cancer is the second leading cause of cancer death among U.S. men. While self-reported quality of life has been shown to be prognostic of survival, there has been limited exploration of whether a patient’s assessment of the overall quality-of-care received might influence survival in prostate cancer. We evaluated the relationship between patient-reported experience with service quality and overall survival in prostate cancer. Methods: 832 returning prostate cancer patients treated at Cancer Treatment Centers of America between July 2007 and December 2010. Overall patient experience (“considering everything, how satisfied are you with your overall experience?”) was measured on a 7-point Likert scale ranging from “completely dissatisfied” to “completely satisfied”. It was dichotomized into 2 categories: top box response (7) versus all others (1-6). Cox regression was used to evaluate the association between patient experience and survival. Results: 560 patients were newly diagnosed while 272 had been previously treated. Majority of patients (n=570, 68.5%) had stage II disease at diagnosis. The mean age was 63.6 years. By the time of this analysis, 93 (11.2%) patients had expired. 710 (85.3%) patients were “completely satisfied” with the service quality they received while 122 (14.7%) patients were not. Median overall survival was 47.9 months. On univariate Cox regression analysis, “completely satisfied” patients had a significantly lower risk of mortality compared to those not “completely satisfied” (HR=0.48; 95% CI: 0.30-0.78; p=0.003). On multivariate Cox regression analysis controlling for stage at diagnosis, treatment history and age, “completely satisfied” patients demonstrated significantly lower mortality (HR=0.50; 95% CI: 0.29-0.87; p=0.01) compared to those not “completely satisfied”. Conclusions: Patient experience with service quality was an independent predictor of survival in prostate cancer. Based on this provocative observation, it is reasonable to suggest that further exploration of a possible meaningful relationship between patient perceptions of the care they have received and outcome in prostate cancer is indicated.

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Diabetes mellitus and prostate cancer risk

The Prostate ◽

10.1002/pros.20777 ◽

2008 ◽

Vol 68 (10) ◽

pp. 1126-1132 ◽

Cited By ~ 38

Author(s):

Brandon L. Pierce ◽

Stephen Plymate ◽

Elaine A. Ostrander ◽

Janet L. Stanford

Keyword(s):

Prostate Cancer ◽

Diabetes Mellitus ◽

Cancer Risk ◽

Prostate Cancer Risk

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Association between metabolic syndrome, diabetes mellitus and prostate cancer risk

Prostate Cancer and Prostatic Diseases ◽

10.1038/pcan.2012.54 ◽

2013 ◽

Vol 16 (2) ◽

pp. 181-186 ◽

Cited By ~ 18

Author(s):

Y R Lawrence ◽

O Morag ◽

M Benderly ◽

V Boyko ◽

I Novikov ◽

...

Keyword(s):

Prostate Cancer ◽

Diabetes Mellitus ◽

Metabolic Syndrome ◽

Cancer Risk ◽

Prostate Cancer Risk

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Systematic Analysis of the Relationship Between Ovarian Cancer Prognosis and Alternative Splicing

10.21203/rs.3.rs-488874/v1 ◽

2021 ◽

Author(s):

Di Zhang ◽

Dan Zou ◽

Yue Deng ◽

Lihua Yang

Keyword(s):

Regression Analysis ◽

Operating Characteristic ◽

Cox Regression ◽

Risk Groups ◽

Time Dependent ◽

Survival Curves ◽

Cox Regression Analysis ◽

Prognostic Analysis ◽

Independent Analysis ◽

The Relationship

Abstract Background: Ovarian cancer(OC) is the gynecological tumor with the highest mortality rate, effective biomarkers are of great significance in improving its prognosis. In recent years, there have been many studies on alternative splicing (AS) events, and the role of AS events in tumor has become a focus of attention.Methods: Data were downloaded from the TCGA database and Univariate Cox regression analysis was performed to determine AS events associated with OC prognosis. Eight prognostic models of OC were constructed in R package, and the accuracy of the models were evaluated by the time-dependent receiver operating characteristic (ROC) curves. Eight types of survival curves were drawn to evaluate the differences between the high and low risk groups. Independent prognostic factors of OC were analyzed by single factor independent analysis and multi-factor independent prognostic analysis. Again, Univariate Cox regression analysis was used to analyze the relationship between splicing factors(SF) and AS events, and Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed on OS-related SFs to understand the pathways.Results: Univariate Cox regression analysis showed that among the 15,278 genes, there were 31,286 overall survival (OS) related AS events, among which 1524 AS events were significantly correlated with OS. The area under the time-dependent receiver operating characteristic curve (AUC) of AT and ME were the largest and the RI was the smallest ,which were 0.757 and 0.68 respectively. The constructed models have good value for the prognosis assessment of OC patients. Among the eight survival curves, AP was the most significant difference between the high and low risk groups, with a P value of 1.61e−1.The results of single factor independent analysis and multi-factor independent prognostic analysis showed that risk score calculated by the model and age could be used as independent risk factors. According to univariate COX regression analysis ,109 SFs were correlated with AS events and adjusted in two ways: positive and negative.Conclusions: SFs and AS events can directly or indirectly affect the prognosis of OC patients. It is very important to find effective prognostic markers to improve the survival rate of OC.

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