Dataset Information

GeneExpressScore Signature: a robust prognostic and predictive classifier in gastric cancer.

ABSTRACT: Although several prognostic signatures have been developed for gastric cancer (GC), the utility of these tools is limited in clinical practice due to lack of validation with large and multiple independent cohorts, or lack of a statistical test to determine the robustness of the predictive models. Here, a prognostic signature was constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model and a training dataset with 300 GC patients. The signature was verified in three independent datasets with a total of 658 tumors across multiplatforms. A nomogram based on the signature was built to predict disease-free survival (DFS). Based on the LASSO model, we created a GeneExpressScore signature (GESGC ) classifier comprised of eight mRNA. With this classifier patients could be divided into two subgroups with distinctive prognoses [hazard ratio (HR) = 4.00, 95% confidence interval (CI) = 2.41-6.66, P < 0.0001]. The prognostic value was consistently validated in three independent datasets. Interestingly, the high-GESGC group was associated with invasion, microsatellite stable/epithelial-mesenchymal transition (MSS/EMT), and genomically stable (GS) subtypes. The predictive accuracy of GESGC also outperformed five previously published signatures. Finally, a well-performed nomogram integrating the GESGC and four clinicopathological factors was generated to predict 3- and 5-year DFS. In summary, we describe an eight-mRNA-based signature, GESGC , as a predictive model for disease progression in GC. The robustness of this signature was validated across patient series, populations, and multiplatform datasets.

SUBMITTER: Zhu X

PROVIDER: S-EPMC6210036 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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GeneExpressScore Signature: a robust prognostic and predictive classifier in gastric cancer.

Zhu Xiaoqiang X Tian Xianglong X Sun Tiantian T Yu Chenyang C Cao Yingying Y Yan Tingting T Shen Chaoqin C Lin Yanwei Y Fang Jing-Yuan JY Hong Jie J Chen Haoyan H

Molecular oncology 20180928 11

Although several prognostic signatures have been developed for gastric cancer (GC), the utility of these tools is limited in clinical practice due to lack of validation with large and multiple independent cohorts, or lack of a statistical test to determine the robustness of the predictive models. Here, a prognostic signature was constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model and a training dataset with 300 GC patients. The signature was verified ...[more]

PMID: 29957874

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Project description:BackgroundAn increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated.MethodsUnivariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300; GSE15459, n = 191; and GSE26901, n = 109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n = 600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432; GSE84437, n = 431; and TCGA, n = 336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method.ResultsA risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC > 0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p < 0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p < 0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p = 0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p = 0.00085). The patients' risk score increased with the progression of the clinicopathological stage.ConclusionIn this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

Project description:Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs).We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates.Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients.RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.

Dataset Information

GeneExpressScore Signature: a robust prognostic and predictive classifier in gastric cancer.

Publications

GeneExpressScore Signature: a robust prognostic and predictive classifier in gastric cancer.

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