Dataset Information

A Temporal Examination of Platelet Counts as a Predictor of Prognosis in Lung, Prostate, and Colon Cancer Patients.

ABSTRACT: Platelets, components of hemostasis, when present in excess (>400?K/?L, thrombocytosis) have also been associated with worse outcomes in lung, ovarian, breast, renal, and colorectal cancer patients. Associations between thrombocytosis and cancer outcomes have been made mostly from single-time-point studies, often at the time of diagnosis. Using laboratory data from the Department of Veterans Affairs (VA), we examined the potential benefits of using longitudinal platelet counts in improving patient prognosis predictions. Ten features (summary statistics and engineered features) were derived to describe the platelet counts of 10,000+?VA lung, prostate, and colon cancer patients and incorporated into an age-adjusted LASSO regression analysis to determine feature importance, and predict overall or relapse-free survival, which was compared to the previously used approach of monitoring for thrombocytosis near diagnosis (Postdiag AG400 model). Temporal features describing acute platelet count increases/decreases were found to be important in cancer survival and relapse-survival that helped stratify good and bad outcomes of cancer patient groups. Predictions of overall and relapse-free survival were improved by up to 30% compared to the Postdiag AG400 model. Our study indicates the association of temporally derived platelet count features with a patients' prognosis predictions.

SUBMITTER: Sylman JL

PROVIDER: S-EPMC5920102 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Temporal Examination of Platelet Counts as a Predictor of Prognosis in Lung, Prostate, and Colon Cancer Patients.

Sylman Joanna L JL Boyce Hunter B HB Mitrugno Annachiara A Tormoen Garth W GW Thomas I-Chun IC Wagner Todd H TH Lee Jennifer S JS Leppert John T JT McCarty Owen J T OJT Mallick Parag P

Scientific reports 20180426 1

Platelets, components of hemostasis, when present in excess (>400 K/μL, thrombocytosis) have also been associated with worse outcomes in lung, ovarian, breast, renal, and colorectal cancer patients. Associations between thrombocytosis and cancer outcomes have been made mostly from single-time-point studies, often at the time of diagnosis. Using laboratory data from the Department of Veterans Affairs (VA), we examined the potential benefits of using longitudinal platelet counts in improving patie ...[more]

PMID: 29700384

Similar Datasets

Project description:BackgroundChronic inflammation is related to the development of IgA nephropathy (IgAN). Emerging studies have reported that platelet-related parameters including platelet (PLT), platelet-to-albumin ratio (PAR), and platelet-to-lymphocyte ratio (PLR) are proved to be novel prognostic indicators for several inflammatory diseases. Whether platelet-related parameters could serve as predictors for IgAN remains unknown.MethodsA total of 966 IgAN patients were enrolled in this retrospective study and were divided into several groups based on the optimal cut-off value of the platelet-related parameters. End-stage renal disease was used as the renal endpoint. A 1:2 propensity score (PS) match was then carried out to eliminate significant differences at baseline. The area under the receiver operating characteristic curve (AUROC), Kaplan-Meier (K-M) curve, and Cox proportional hazards analyses were performed to evaluate their predictive effect.ResultsWithout considering the effect of covariates, the K-M curve showed that PLT, PLR, and PAR were strongly correlated with the renal outcomes of IgAN. However, the AUROC revealed that the PAR and PLR had better predictive power than the PLT. Multivariate Cox regression adjusting for demographic data, pathological findings, treatment, and laboratory results indicated that compared with PLR, albumin and PLT, PAR seemed to be a better marker of adverse renal outcome, implying that PAR was the only platelet-related parameter that could be used as an independent risk factor. Notably, high PAR patients seemed to have more severe clinical manifestations and pathological lesions. However, after eliminating the influence of different baselines on outcome variables, the PAR could still predict the poor prognosis of IgAN. To more accurately evaluate the predictive power of the PAR, we analyzed the predictive effect of the PAR on patients with different clinicopathological characteristics through subgroup analysis. It was indicated that the PAR might better predict the prognosis and outcome of patients whose disease was already very severe.ConclusionPAR might be used as an independent risk factor for IgAN progression.

Project description:BackgroundPlatelet counts of less than 150,000 per cubic millimeter during uncomplicated pregnancies are described as gestational thrombocytopenia if no alternative cause is identified. Platelet counts may be even lower in women with pregnancy-related complications. However, the occurrence and severity of thrombocytopenia throughout pregnancy are not defined.MethodsWe evaluated platelet counts throughout pregnancy in women who delivered at Oklahoma University Medical Center between 2011 and 2014. These platelet counts were compared with those of nonpregnant women who were included in the National Health and Nutrition Examination Survey from 1999 through 2012.ResultsAmong the 15,723 deliveries that occurred during the study period, 7351 women had sufficient data for our analyses. Of these women, 4568 had uncomplicated pregnancies, 2586 had pregnancy-related complications, and 197 had preexisting disorders associated with thrombocytopenia. Among the women who had uncomplicated pregnancies, the mean platelet count in the first trimester (mean gestation, 8.7 weeks) was 251,000 per cubic millimeter, which was lower than the mean platelet count in the 8885 nonpregnant women (273,000 per cubic millimeter) (P<0.001). At the time of delivery, 9.9% of the women with uncomplicated pregnancies had a platelet count below 150,000 per cubic millimeter. During the course of the uncomplicated pregnancies and deliveries, only 45 women (1.0%) had a platelet count below 100,000 per cubic millimeter. Among the 12 women with uncomplicated pregnancies who had a platelet count below 80,000 per cubic millimeter, only 5 (0.1%, among whom the range of platelet counts was 62,000 to 78,000 per cubic millimeter; median, 65,000) were identified by medical record review as having no alternative cause for the thrombocytopenia. Platelet counts of less than 150,000 per cubic millimeter at the time of delivery were more common among women who had pregnancy-related complications than among women who had uncomplicated pregnancies (11.9% vs. 9.9%, P=0.01). Throughout their pregnancies and deliveries, 59 women (2.3%) with pregnancy-related complications had a platelet count below 100,000 per cubic millimeter, and 31 (1.2%) had a platelet count below 80,000 per cubic millimeter.ConclusionsMean platelet counts decreased during pregnancy in all the women, beginning in the first trimester. In women who have a platelet count of less than 100,000 per cubic millimeter, a cause other than pregnancy or its complications should be considered. (Funded by the National Heart, Lung, and Blood Institute.).

Project description:BackgroundColon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary.MethodsWe implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients.ResultsA five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis.ConclusionsTranscription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.

Project description:BackgroundColon adenocarcinoma (COAD) is one of the most lethal cancers. It is particularly important to accurately predict prognosis and to provide individualized treatment. Several lines of evidence suggest that genetic factors and clinicopathological characteristics are related to cancer onset and progression. The aim of this study was to identify potential prognostic genes and to develop a nomogram to predict survival and recurrence of COAD.MethodsTo identify potential prognostic genes in COAD, microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained from GEO2R. Venn diagram was drawn to select those genes that were overexpressed in all datasets, and survival analyses were performed to determine the prognostic values of the selected genes. New nomograms were developed based on the genes that were significantly associated with prognosis. Clinicopathological data were obtained from The Cancer Genome Atlas (TCGA). Finally, the new nomograms were compared head-to-head comparison with the TNM nomogram.ResultsFrom GSE21510, GSE110223, GSE113513 and GSE110224, a total of 834, 218, 236 and 613 overexpressed DEGs were screened out, respectively. The Venn diagram revealed that 12 genes appeared in all four profiles. After survival analyses, only INHBA expression was associated with both overall survival (OS) and disease-free survival (DFS). Multivariate analyses revealed that age, pathological N and pathological M were significant independent risk factors for OS. Age, pathological N, pathological M and INHBA were significant independent risk factors for DFS. Two prediction models predicted the probability of 3-year survival and 5-year survival for OS and DFS, respectively. The concordance indexes were 0.785 for 3-year overall survival, 0.759 for 5-year overall survival, 0.789 for 3-year disease-free survival and 0.757 for 5-year disease-free survival. The head-to-head comparison according to time-dependent ROC curves indicated that the new models had higher predictive accuracy. Decision curve analyses (DCA) indicated that the clinical value of the new models were higher than TNM models for predicting disease-free survival.ConclusionThe combination of INHBA expression with a clinical nomogram improves prognostic power in colon adenocarcinoma, especially for predicting recurrence.

Dataset Information

A Temporal Examination of Platelet Counts as a Predictor of Prognosis in Lung, Prostate, and Colon Cancer Patients.

Publications

A Temporal Examination of Platelet Counts as a Predictor of Prognosis in Lung, Prostate, and Colon Cancer Patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets