Combination of tumour markers CEA and CA19-9 improves the prognostic prediction in patients with pancreatic cancer

2015 ◽  
Vol 68 (6) ◽  
pp. 427-433 ◽  
Author(s):  
Daniel Reitz ◽  
Armin Gerger ◽  
Julia Seidel ◽  
Peter Kornprat ◽  
Hellmut Samonigg ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Carbohydrate Antigen ◽  
Tumour Markers ◽  
Cancer Specific Survival ◽  
Receiver Operating Characteristic Curves ◽  
Prognostic Prediction ◽  
Medical University ◽  
Better Than

AimsTumour markers including carcinoembryonic antigen (CEA) or carbohydrate antigen 19-9 (CA19-9) are frequently determined at the time of diagnosis in patients with pancreatic cancer. Several studies indicate a prognostic relevance of these markers in pancreatic cancer, but space for improvement with regard to the predictive accuracy and ability is given. In this work, the main focus is on mathematical combinations of these two tumour markers in order to validate an improvement of prognostic test results in terms of sensitivity and specificity.MethodsThis retrospective study includes 393 patients with pancreatic cancer, who were treated between the years 2005 and 2012 at the Division of Oncology, Medical University of Graz, Austria. The goal of this study was to explore whether an appropriate combination of two tumour markers leads to a statistically significant improvement of the prognostic prediction.ResultsReceiver operating characteristic curves comparison analyses with the classification variable cancer-specific survival showed that the mathematical product of two tumour markers (TMproduct= (CEA×CA19-9); area under the curve (AUC)=0.727; 95% CI 0.680 to 0.770) is significantly better than CEA alone (AUC=0.644; 95% CI 0.594 to 0.691; p=0.003) but not significant compared with CA19-9 (AUC=0.710; 95% CI 0.662 to 0.754; p=0.1215). A linear combination of CEA and CA19-9 (TMlinear=(85×CEA+CA19-9); AUC=0.748; 95% CI 0.702 to 0.790) is significantly better than CEA (p<0.0001) as well as CA19-9 alone (p=0.0304).ConclusionsMathematical combinations of pretherapeutic tumour markers CEA and CA19-9 are feasible and can significantly improve the prognostic prediction in patients with pancreatic cancer.

scholarly journals The evaluative effectiveness of a combined index exceeded that of a single variable for the ovarian reserve and response of infertile women

2019 ◽  
Author(s):  
Shan-Jie Zhou ◽  
Ming-Jia Zhao ◽  
Cui Li ◽  
Xing Su
Keyword(s):  
Ovarian Reserve ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Ovarian Response ◽  
Antral Follicle ◽  
The Other ◽  
Single Variable ◽  
Infertile Women ◽  
Better Than

Abstract Aim of the present study was to explore the evaluative effectiveness of age, ovarian volume, antral follicle count (AFC), serum follicle-stimulating hormone (FSH), anti-Müllerian hormone (AMH), FSH/LH ratio and ovarian response prediction index (ORPI) to determine which could most advantageously assess ovarian reserve and response.Methods This research enrolled 319 consecutive infertile women who had undergone IVF-ET/ICSI treatments. Abovementioned variables were measured and calculated. Receiver Operating Characteristic Curve analysis was used to analyze the predictive accuracy of variables and to calculate cut-off values and corresponding sensitivity and specificity.Results Our study revealed that the significant variables for evaluating a decline in ovarian reserve include age, total volume of bilateral ovary, FSH, and ORPI. Moreover, the area under the curve (AUC) of ORPI was higher than other three variables (AUC = 0.903), and the cut-off value of ORPI was 0.245 (sensitivity 90.1%, specificity 73.9%). The significant variables forecasting excessive ovarian response were age, AFC, AMH, ORPI, FSH and FSH/LH ratio, and the significant variables forecasting low ovarian response were AMH and FSH/LH ratio. ORPI and FSH/LH ratio presented better effectiveness in evaluating ovarian response. When they were used to predict excessive response, the cut-off values of ORPI and FSH/LH ratio was 0.886 (sensitivity 84.7%, specificity 67.3%) and 1.753 (sensitivity 56.2%, specificity 67.6%), respectively. When used to predict low response, the cut-off value of FSH/LH ratio was 2.983 (sensitivity 75.0%, specificity 83.8%).Conclusions ORPI performed better than did the other variables in evaluating ovarian reserve and predicting excessive ovarian response, and the FSH/LH ratio performed better than did the other variables in predicting low ovarian response. Consequently, we agreed that the evaluative effectiveness of a combined index exceeded that of a single variable for evaluating the ovarian reserve and response of infertile women.

Correlation between tumor markers in pre and post treatment of pancreatic cancer patients

2021 ◽  
Vol 25 (1) ◽  
pp. 70-74
Author(s):  
N. Sivakumar ◽  
R. Arivazhagan ◽  
B. Prabasheela
Keyword(s):  
Pancreatic Cancer ◽  
Tumour Marker ◽  
Serum Marker ◽  
Carbohydrate Antigen ◽  
Tumour Markers ◽  
Post Treatment ◽  
Treatment Groups ◽  
Serum Tumour Marker ◽  
Before And After ◽  
Serum Tumour

One of the main causes of death in India is pancreas cancer. Various blood tumour indicators such as 19-9 carbohydrate antigen (CA19-9), antigen125 carbohydrate (CA125), antigen carcinoembryogenic (CEA) and alphaetoprotein (AFP) imbalance are observed in therapy for cancer. In disease predictions, thorough monitoring of the change in serum tumour markers was highly essential. The present investigation was thus conducted to examine serum marker tumour profiles before and after therapy of individuals with pancreatic cancer. The study comprised 400 individuals from both sexes suffering from pancreatic carcinogenic malignancy. In the pre and post-treatment of patients we detected serum tumour markers. In post-treatment groups, serum tumour marker levels were lower than before the individuals were treated. However, using pairs of samples t-testing at pfleg.0.05 these changes were statistically significant. Marker alterations in the serum tumour have shown risk for individuals. These alterations therefore enable the cancer individuals to predict and monitor properly.

scholarly journals Exploration of a Modified Stage for pN0 Colon Cancer Patients

2021 ◽  
Author(s):  
Yunxiao Liu ◽  
Hao Zhang ◽  
Yuliuming Wang ◽  
Mingyu Zheng ◽  
Chunlin Wang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Model Fitting ◽  
Cancer Specific Survival ◽  
T Stage ◽  
N Stage ◽  
Medical University

Abstract Purpose: Exploring a modified stage (mStage) for pN0 colon cancer patients.Methods: 39637 pN0 colon cancer patients were collected from the SEER database (2010-2015) (development cohort) and 455 pN0 colon cancer patients from the Second Affiliated Hospital of Harbin Medical University (2011-2015) (validation cohort). The optimal lymph nodes examined (LNE) stratification for cancer-specific survival (CSS) was obtained by X-tile software. LNE is combined with conventional T stage to form the mStage.Results: The novel N stage was built based on the LNE (N0a: LNE ≥ 26, N0b: LNE = 10-25 and N0c: LNE < 10). The mStage include mStageA (T1N0a, T1N0b, T1N0c and T2N0a), mStageB (T2N0b, T2N0c and T3N0a), mStageC (T3N0b), mStageD (T3N0c, T4aN0a and T4bN0a), mStageE (T4aN0b and T4bN0b) and mStageF (T4aN0c and T4bN0c). Cox regression model showed that mStage was an independent prognostic factor. AUC showed that the predictive accuracy of mStage was better than the conventional T stage for 5-year CSS in the development (0.700 vs 0.678, P < 0.001) and validation cohort (0.649 vs 0.603, P = 0.018). The C-index also showed that mStage had a superior model-fitting.Conclusions: For pN0 colon cancer patients, mStage might be superior to conventional T stage in predicting the prognosis.

scholarly journals Development of a KRAS-Associated Metabolic Risk Model for Prognostic Prediction in Pancreatic Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Zuyi Ma ◽  
Zhenchong Li ◽  
Zuguang Ma ◽  
Zixuan Zhou ◽  
Hongkai Zhuang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Risk Model ◽  
Area Under The Curve ◽  
Metabolic Reprogramming ◽  
Bioinformatics Analyses ◽  
Mutation Status ◽  
Metabolic Characteristics ◽  
Model Based ◽  
Metabolic Genes ◽  
Prognostic Prediction

Background. KRAS was reported to affect some metabolic genes and promote metabolic reprogramming in solid tumors. However, there was no comprehensive analysis to explore KRAS-associated metabolic signature or risk model for pancreatic cancer (PC). Methods. In the current study, multiple bioinformatics analyses were used to identify differentially expressed metabolic genes based on KRAS mutation status in PC. Then, we developed and validated a prognostic risk model based on the selected KRAS-associated metabolic genes. Besides, we explored the association between the risk model and the metabolic characteristics as well as gemcitabine-associated chemoresistance in PC. Results. 6 KRAS-associated metabolic genes (i.e., CYP2S1, GPX3, FTCD, ENPP2, UGT1A10, and XDH) were selected and enrolled to establish a prognostic risk model. The prognostic model had a high C-index of 0.733 for overall survival (OS) in TCGA pancreatic cancer database. The area under the curve (AUC) values of 1- and 3-year survival were both greater than 0.70. Then, the risk model was validated in two GEO datasets and also presented a satisfactory discrimination and calibration performance. Further, we found that the expression of some KRAS-driven glycolysis-associated genes (PKM, GLUT1, HK2, and LDHA) and gemcitabine-associated chemoresistance genes (i.e., CDA and RMM2) was significantly upregulated in high-risk PC patients evaluated by the risk model. Conclusions. We constructed a risk model based on 6 KRAS-associated metabolic genes, which predicted patients’ survival with high accuracy and reflected tumor metabolic characteristics and gemcitabine-associated chemoresistance in PC.

Copy number instability (CNI) combined with CA19-9 as a biomarker for postoperative prognostic prediction of pancreatic cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15730-e15730
Author(s):  
Qian Zhan ◽  
Dandan Ren ◽  
Beibei Mao ◽  
Xue Song ◽  
Yanna Ma ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Patients ◽  
Primary Tumor ◽  
Copy Number ◽  
Molecular Mechanisms ◽  
Tumor Tissue ◽  
Carbohydrate Antigen ◽  
Ductal Adenocarcinoma ◽  
Molecular Features ◽  
Prognostic Prediction

e15730 Background: Pancreatic cancer is one of the most aggressive human malignancies and has a poor prognosis. Large-scale studies have reported on its initiation, progression, diagnosis and prognosis. However, over the past several decades, the overall 5-year survival rate of pancreatic cancer has remained at less than 5%. Thus, the current major challenge in the postoperative management of pancreatic cancer is to identify high-risk recurrent patients. We retrospectively investigated the clinical, pathological and outcome data for 48 pancreatic cancer patients to clarify the associations of molecular mechanisms and prognosis. Methods: Eligible pancreatic cancer patients were included, and formalin-fixed paraffin-embedded (FFPE) tumor specimens and matched blood samples were collected at Ruijin Hospital, Shanghai Jiao tong University School of Medicine. Genome profiles were analyzed by using a designed 1408-gene panel based on next-generation sequencing (NGS). The copy number instability (CNI) score in primary tumor tissue was calculated to investigate the relationship between molecular features of the primary tumor and prognosis. Results: The CNI score in primary tumor tissue was positively correlated with lymph node metastasis, TP53 mutation, and early recurrence. Moreover, preoperative Carbohydrate antigen 19-9 (CA19-9) levels and CNI scores in primary tumor tissue were significant independent predictors associated with PFS in pancreatic ductal adenocarcinoma (PDAC). Finally, we performed an independent signature that includes CNI score and Carbohydrate antigen 19-9 (CA19-9) level to predict prognosis of pancreatic cancer. Conclusions: These results suggest that CNI score in primary tumor tissue is an independent predictive prognostic biomarker for PDAC. CNI combined with CA19-9 is a better predictor for postoperative prognostic prediction of pancreatic cancer.

scholarly journals The Evaluative Effectiveness of a Combined Index Exceeded That of a Single Variable for the Ovarian Reserve and Response of Infertile Women

2019 ◽  
Author(s):  
Shan-Jie Zhou ◽  
Ming-Jia Zhao ◽  
Cui Li ◽  
Xing Su
Keyword(s):  
Ovarian Reserve ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Ovarian Response ◽  
Antral Follicle ◽  
The Other ◽  
Single Variable ◽  
Infertile Women ◽  
Better Than

Abstract Background: Aim of the present study was to explore the evaluative effectiveness of age, ovarian volume, antral follicle count (AFC), serum follicle-stimulating hormone (FSH), anti-Müllerian hormone (AMH), FSH/LH ratio and ovarian response prediction index (ORPI) to determine which could most advantageously assess ovarian reserve and response. Methods: This research enrolled 319 consecutive infertile women who had undergone IVF-ET/ICSI treatments. Abovementioned variables were measured and calculated. Receiver Operating Characteristic Curve analysis was used to analyze the predictive accuracy of variables and to calculate cut-off values and corresponding sensitivity and specificity. Results: Our study revealed that the significant variables for evaluating a decline in ovarian reserve include age, total volume of bilateral ovary, FSH, and ORPI. Moreover, the area under the curve (AUC) of ORPI was higher than other three variables (AUC = 0.903), and the cut-off value of ORPI was 0.245 (sensitivity 90.1%, specificity 73.9%). The significant variables forecasting excessive ovarian response were age, AFC, AMH, ORPI, FSH and FSH/LH ratio, and the significant variables forecasting low ovarian response were AMH and FSH/LH ratio. ORPI and FSH/LH ratio presented better effectiveness in evaluating ovarian response. When they were used to predict excessive response, the cut-off values of ORPI and FSH/LH ratio was 0.886 (sensitivity 84.7%, specificity 67.3%) and 1.753 (sensitivity 56.2%, specificity 67.6%), respectively. When used to predict low response, the cut-off value of FSH/LH ratio was 2.983 (sensitivity 75.0%, specificity 83.8%). Conclusions: ORPI performed better than did the other variables in evaluating ovarian reserve and predicting excessive ovarian response, and the FSH/LH ratio performed better than did the other variables in predicting low ovarian response. Consequently, we agreed that the evaluative effectiveness of a combined index exceeded that of a single variable for evaluating the ovarian reserve and response of infertile women.

scholarly journals Plasma-Derived Exosome MiR-19b Acts as a Diagnostic Marker for Pancreatic Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Lei Wang ◽  
Jinxiang Wu ◽  
Naikuan Ye ◽  
Feng Li ◽  
Hanxiang Zhan ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Chronic Pancreatitis ◽  
Healthy Volunteers ◽  
Pancreatic Tumor ◽  
Diagnostic Marker ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Cancer Antigen ◽  
Diagnostic Values ◽  
Better Than

BackgroundDiagnosis of pancreatic cancer (Pca) is challenging. This study investigated the value of plasma-derived exosome miR-19b (Exo-miR-19b) in diagnosing patients with Pca.MethodsPlasma was collected from 62 patients with Pca, 30 patients with other pancreatic tumor (OPT), 23 patients with chronic pancreatitis (CP), and 53 healthy volunteers. MiR-19b levels in plasma-derived exosomes were detected.ResultsPlasma-derived Exo-miR-19b levels normalized using miR-1228 were significantly lower in Pca patients than in patients with OPT, CP patients, and healthy volunteers. The diagnostic values of Exo-miR-19b normalized using miR-1228 were superior to those of serum cancer antigen 19-9 (CA19-9) in differentiating Pca patients from healthy volunteers (area under the curve (AUC): 0.942 vs. 0.813, p = 0.0054), potentially better than those of CA19-9 in differentiating Pca patients from CP patients (AUC: 0.898 vs. 0.792, p = 0.0720), and equivalent to those of CA19-9 in differentiating Pca patients from patients with OPT (AUC: 0.810 vs. 0.793, p = 0.8206). When normalized using Caenorhabditis elegans miR-39 (cel-miR-39), Exo-miR-19b levels in Pca patients were significantly higher than those in patients with OPT, CP patients, and healthy volunteers. The diagnostic values of Exo-miR-19b normalized using cel-miR-39 were equivalent to those of CA19-9 in differentiating Pca patients from healthy volunteers (AUC: 0.781 vs. 0.813, p = 0.6118) and CP patients (AUC: 0.672 vs. 0.792, p = 0.1235), while they were inferior to those of CA19-9 in differentiating Pca patients from patients with OPT (AUC: 0.631 vs. 0.793, p = 0.0353).ConclusionPlasma-derived Exo-miR-19b is a promising diagnostic marker for Pca. The diagnostic value of plasma-derived Exo-miR-19b normalized using miR-1228 is superior to that of serum CA19-9 in differentiating patients with Pca from healthy volunteers.

scholarly journals Development of a KRAS-Associated Metabolic Risk Model for Prognostic Prediction in Pancreatic Cancer

2020 ◽  
Author(s):  
Zuyi Ma ◽  
Zhenchong Li ◽  
Zixuan Zhou ◽  
Hongkai Zhuang ◽  
Chunsheng Liu ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Risk Model ◽  
Area Under The Curve ◽  
Metabolic Reprogramming ◽  
Bioinformatics Analyses ◽  
Mutation Status ◽  
Metabolic Characteristics ◽  
Model Based ◽  
Metabolic Genes ◽  
Prognostic Prediction

Abstract Background: KRAS was reported to affect some metabolic genes and promote metabolic reprogramming in solid tumors. However, there is no comprehensive analysis to explore KRAS associated metabolic signature or risk model for Pancreatic cancer (PC).Methods: In current study, multiple bioinformatics analyses were used to identify differentially expressed metabolic genes based on KRAS mutation status in PC. Then we developed and validated a prognostic risk model based on the selected KRAS-associated metabolic genes. Besides, we explored the association of the risk model and the metabolic characteristics as well as Gemcitabine associated chemoresistance in PC.Results: 6 KRAS-associated metabolic genes (i.e. CYP2S1, GPX3, FTCD, ENPP2, UGT1A10, and XDH) were selected and were enrolled to establish a prognostic risk model. The prognostic model had a high C-index of 0.733 for overall survival (OS) in the TCGA pancreatic cancer database. The area under the curve (AUC) values of 1- and 3-year survival were both greater than 0.70. Then the risk model was validated in two GEO datasets and also presented a satisfactory discrimination and calibration performance. Further, we found that the expression of some KRAS-driven glycolysis associated genes (PKM, GLUT1, HK2, and LDHA) and Gemcitabine associated chemoresistance genes (i.e. CDA and RMM2) were significantly up-regulated in high-risk PC patients evaluated by the risk model.Conclusions: We constructed a risk model based on 6 KRAS associated metabolic genes, which predicts patients' survival with high accuracy and reflects tumor metabolic characteristics and Gemcitabine associated chemoresistance in PC.

scholarly journals Comparing the characteristics and predicting the survival of patients with head and neck melanoma versus body melanoma: a population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuxin Ding ◽  
Runyi Jiang ◽  
Yuhong Chen ◽  
Jing Jing ◽  
Xiaoshuang Yang ◽  
...  
Keyword(s):  
Head And Neck ◽  
Health Management ◽  
External Validation ◽  
Area Under The Curve ◽  
Population Based ◽  
Survival Prediction ◽  
Cancer Specific Survival ◽  
Cox Models ◽  
Lentigo Maligna Melanoma ◽  
Clinical Tools

Abstract Background Previous studies reported cutaneous melanoma in head and neck (HNM) differed from those in other regions (body melanoma, BM). Individualized tools to predict the survival of patients with HNM or BM remain insufficient. We aimed at comparing the characteristics of HNM and BM, developing and validating nomograms for predicting the survival of patients with HNM or BM. Methods The information of patients with HNM or BM from 2004 to 2015 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The HNM group and BM group were randomly divided into training and validation cohorts. We used the Kaplan-Meier method and multivariate Cox models to identify independent prognostic factors. Nomograms were developed via the rms and dynnom packages, and were measured by the concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration plots. Results Of 70,605 patients acquired, 21% had HNM and 79% had BM. The HNM group contained more older patients, male sex and lentigo maligna melanoma, and more frequently had thicker tumors and metastases than the BM group. The 5-year cancer-specific survival (CSS) and overall survival (OS) rates were 88.1 ± 0.3% and 74.4 ± 0.4% in the HNM group and 92.5 ± 0.1% and 85.8 ± 0.2% in the BM group, respectively. Eight variables (age, sex, histology, thickness, ulceration, stage, metastases, and surgery) were identified to construct nomograms of CSS and OS for patients with HNM or BM. Additionally, four dynamic nomograms were available on web. The internal and external validation of each nomogram showed high C-index values (0.785–0.896) and AUC values (0.81–0.925), and the calibration plots showed great consistency. Conclusions The characteristics of HNM and BM are heterogeneous. We constructed and validated four nomograms for predicting the 3-, 5- and 10-year CSS and OS probabilities of patients with HNM or BM. These nomograms can serve as practical clinical tools for survival prediction and individual health management.

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