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.

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.

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.

Development of a Third Trimester Contingent Prognostic Prediction Scheme for Suspected Early-Onset Pre-Eclampsia

2021 ◽  
pp. 1-9
Author(s):  
Enric Sabrià ◽  
Paula Lafuente-Ganuza ◽  
Paloma Lequerica-Fernández ◽  
Ana Isabel Escudero ◽  
Eduardo Martínez-Morillo ◽  
...  
Keyword(s):  
Early Onset ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Prediction Tool ◽  
Model Based ◽  
Prognostic Prediction ◽  
Short Term Prediction ◽  
Plgf Ratio

<b><i>Introduction:</i></b> Short-term prediction of pre-eclampsia (PE) using soluble FMS-like tyrosine kinase-1 (sFlt-1)/ placental growth factor (PlGF) ratio has high false-positive rate. Therefore, we developed a prognostic prediction tool that predicts early-onset PE leading to delivery within 1 week on pregnancies with an sFlt-1/PlGF ratio above 38 and compared it with an analogous model based on sFlt-1/PlGF ratio and with the 655 sFlt-1/PlGF ratio cutoff. <b><i>Methods:</i></b> Cohort study of 363 singleton pregnancies with clinical suspicion of PE before 34 weeks of gestation, allowing repeated assessments (522). 213 samples with an sFlt-1/PlGF ratio above 38 were assessed to construct and identify the best-fit linear mixed model. N-terminal pro-B-type natriuretic peptide (NT-proBNP), sFlt-1 MoM, PlGF MoM, and sFlt-1/PlGF ratio combined with gestational age (GA) were assessed. <b><i>Results:</i></b> None of the pregnancies with an sFlt-1/PlGF ratio of 38 or below developed early-onset PE (309 samples from 240 pregnancies). Conversely, 47 women of 213 assessments (22.1%) with an sFlt-1/PlGF ratio above 38 developed the assessed outcome. The selected model included sFlt-1 MoM, NT-proBNP, and GA. Differences in area under the curve were observed between the selected model and the GA + sFlt-1/PlGF model (<i>p</i> = 0.04). At an sFlt-1/PlGF ratio cutoff of 655, detection rate was 31.9% (15/47), while the selected model detection was 55.3% (26/47) (<i>p</i> = 0.008). <b><i>Discussion:</i></b> Considering repeated assessments, the sFlt-1/PlGF ratio of 38 or below adequately ruled out early-onset PE, leading to delivery within 1 week. However, when sFlt-1/PlGF ratio is above 38, the prediction tool derived from linear mixed model based on GA, NT-proBNP, and sFlt-1 MoM, provided a better prognosis prediction than the sFlt-1/PlGF ratio.

Usefulness of the abdominal fat area-based risk score for postoperative pancreatic fistula after pancreatoduodenectomy

2020 ◽  
Author(s):  
Yutaka Endo ◽  
Minoru Kitago ◽  
Masahiro Shinoda ◽  
Hiroshi Yagi ◽  
Yuta Abe ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Pancreatic Fistula ◽  
Risk Model ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
International Study ◽  
Abdominal Fat ◽  
The Body ◽  
Radiographic Findings ◽  
Model Based

Abstract BackgroundPancreatic fistulas remain a significant concern after pancreatectomy, and it is not possible to perform preoperative risk stratification for all patients. This study aimed to evaluate the usefulness of a unique risk model, based on the abdominal fat area (AFA) calculated by computed tomography, for pancreatic fistula development after pancreatoduodenectomy and compare it with models based on the body mass index (BMI) or abdominal thickness.Material and Methods Patient characteristics, preoperative laboratory data, radiographic findings, and their association with pancreatic fistula development after pancreaticoduodenectomy were analysed for 158 patients who underwent resection between 2011 and 2017. Clinically relevant postoperative pancreatic fistulas (CR-POPF) were defined as Grade B or C fistulas based on the International Study Group of Pancreatic Surgery (ISGPS) 2016 consensus.ResultsCR-POPF developed in 38 patients (24.2%). Multivariate logistic analysis indicated that the AFA, BMI, and intra-abdominal thickness were potential candidates for predictive models for pancreatic fistula development, small pancreatic duct diameter, diabetes mellitus development, and the pathology of non-pancreatic cancers. When comparing the three risk models (AFA-, BMI-, and intra-abdominal thickness-based), the AFA-derived risk model was superior to the BMI-based and intra-abdominal thickness-based risk stratification models (area under the curve 0.836 vs 0.824 vs 0.826).Conclusions The risk model based on AFA calculation was superior to that based on BMI or intra-abdominal thickness measurements. The model must be validated further to elucidate the efficacy of the risk scoring system in more detail.

scholarly journals Prognostic Value of Plasma hPG80 (Circulating Progastrin) in Metastatic Renal Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 375
Author(s):  
Manish Kohli ◽  
Winston Tan ◽  
Bérengère Vire ◽  
Pierre Liaud ◽  
Mélina Blairvacq ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Prognostic Impact ◽  
Significant Difference

Precise management of kidney cancer requires the identification of prognostic factors. hPG80 (circulating progastrin) is a tumor promoting peptide present in the blood of patients with various cancers, including renal cell carcinoma (RCC). In this study, we evaluated the prognostic value of plasma hPG80 in 143 prospectively collected patients with metastatic RCC (mRCC). The prognostic impact of hPG80 levels on overall survival (OS) in mRCC patients after controlling for hPG80 levels in non-cancer age matched controls was determined and compared to the International Metastatic Database Consortium (IMDC) risk model (good, intermediate, poor). ROC curves were used to evaluate the diagnostic accuracy of hPG80 using the area under the curve (AUC). Our results showed that plasma hPG80 was detected in 94% of mRCC patients. hPG80 levels displayed high predictive accuracy with an AUC of 0.93 and 0.84 when compared to 18–25 year old controls and 50–80 year old controls, respectively. mRCC patients with high hPG80 levels (>4.5 pM) had significantly lower OS compared to patients with low hPG80 levels (<4.5 pM) (12 versus 31.2 months, respectively; p = 0.0031). Adding hPG80 levels (score of 1 for patients having hPG80 levels > 4.5 pM) to the six variables of the IMDC risk model showed a greater and significant difference in OS between the newly defined good-, intermediate- and poor-risk groups (p = 0.0003 compared to p = 0.0076). Finally, when patients with IMDC intermediate-risk group were further divided into two groups based on hPG80 levels within these subgroups, increased OS were observed in patients with low hPG80 levels (<4.5 pM). In conclusion, our data suggest that hPG80 could be used for prognosticating survival in mRCC alone or integrated to the IMDC score (by adding a variable to the IMDC score or by substratifying the IMDC risk groups), be a prognostic biomarker in mRCC patients.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Sign in / Sign up

Export Citation Format

Share Document