Dataset Information

Identification and Clinical Validation of a Novel 4 Gene-Signature with Prognostic Utility in Colorectal Cancer.

ABSTRACT: Colorectal cancer (CRC) is a high burden disease with several genes involved in tumor progression. The aim of the present study was to identify, generate and clinically validate a novel gene signature to improve prediction of overall survival (OS) to effectively manage colorectal cancer. We explored The Cancer Genome Atlas (TCGA), COAD and READ datasets (597 samples) from The Protein Atlas (TPA) database to extract a total of 595 candidate genes. In parallel, we identified 29 genes with perturbations in > 6 cancers which are also affected in CRC. These genes were entered in cBioportal to generate a 17 gene panel with highest perturbations. For clinical validation, this gene panel was tested on the FFPE tissues of colorectal cancer patients (88 patients) using Nanostring analysis. Using multivariate analysis, a high prognostic score (composite 4 gene signature-DPP7/2, YWHAB, MCM4 and FBXO46) was found to be a significant predictor of poor prognosis in CRC patients (HR: 3.42, 95% CI: 1.71-7.94, p < 0.001 *) along with stage (HR: 4.56, 95% CI: 1.35-19.15, p = 0.01 *). The Kaplan-Meier analysis also segregated patients on the basis of prognostic score (log-rank test, p = 0.001 *). The external validation using GEO dataset (GSE38832, 122 patients) corroborated the prognostic score (HR: 2.7, 95% CI: 1.99-3.73, p < 0.001 *). Additionally, higher score was able to differentiate stage II and III patients (130 patients) on the basis of OS (HR: 2.5, 95% CI: 1.78-3.63, p < 0.001 *). Overall, our results identify a novel 4 gene prognostic signature that has clinical utility in colorectal cancer.

SUBMITTER: Ahluwalia P

PROVIDER: S-EPMC6696416 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Identification and Clinical Validation of a Novel 4 Gene-Signature with Prognostic Utility in Colorectal Cancer.

Ahluwalia Pankaj P Mondal Ashis K AK Bloomer Chance C Fulzele Sadanand S Jones Kimya K Ananth Sudha S Gahlay Gagandeep K GK Heneidi Saleh S Rojiani Amyn M AM Kota Vamsi V Kolhe Ravindra R

International journal of molecular sciences 20190805 15

Colorectal cancer (CRC) is a high burden disease with several genes involved in tumor progression. The aim of the present study was to identify, generate and clinically validate a novel gene signature to improve prediction of overall survival (OS) to effectively manage colorectal cancer. We explored The Cancer Genome Atlas (TCGA), COAD and READ datasets (597 samples) from The Protein Atlas (TPA) database to extract a total of 595 candidate genes. In parallel, we identified 29 genes with perturba ...[more]

PMID: 31387239

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Project description:AimsColon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk.MethodsThe Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (N = 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan-Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median. And survival ROC analysis was used to evaluate the prognostic model. Finally, the Gene Set Enrichment Analysis (GSEA) was performed for further functional enrichment analyses.ResultsA 10-gene model was obtained, including 7 negative impact factors (SLC39A14, AACS, ERP29, LAMP3, TMEM106C, TMED2, and SLC25A3) and 3 positive ones (CNPY2, GRB10, and PBK), which related with several important oncogenic pathways (KRAS signaling, TNF-α signaling pathway, and WNT signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis). By using colon cancer datasets from The Cancer Genome Atlas (TCGA), the model was validated in KM survival analysis (P ≤ 0.001) and significant analysis with recurrence time (P = 0.0018).ConclusionsThis study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.

Project description:Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC). Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC. Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB). Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

Project description:BackgroundHypermutated colorectal cancer (CRC) reportedly accounts for 15%-17% of all cases of CRC. However, the proportion and number of patients with hypermutated CRC cannot be unappreciated. Additionally, therapy options for these patients differ from those for CRC patients, with a greater potential benefit from immunotherapy.Materials and methodsWe sequenced the tumor mucosa of CRC patients with >24 months of follow-up data at our center and identified mutation profiles of hypermutated CRC as a training data set (Zhejiang University [ZJU]); we then collected patients from The Cancer Genome Atlas (TCGA) as a validation data set. Recurrently mutated genes were combined to calculate a compound score via Cox proportional hazards model. Patients with higher-than-median scores were segregated as the high-risk group. Outcomes were analyzed by Kaplan-Meier and Cox regression analyses using Python (3.6.0) and R (3.4.0).ResultsWe constructed a 4-gene signature (ACVR2A, APC, DOCK2, and POLE), with training in 45 hypermutated patients at ZJU and validation in 24 hypermutated patients from TCGA. Patients in the high-risk group showed poor survival (adjusted HR =9.85, 95% CI: 2.07-46.81, P=0.004). Further subgroup analysis was performed for stage II and III colon cancer (HR =10.91, 95% CI: 1.36-87.5, P=0.005) and high microsatellite instability (MSI-H) CRC (HR =12.57, 95% CI: 1.57-100.69, P=0.002) subgroups, which verified that our signature is universal. We then compared our prognostic signature with other risk factors (including MSI status, POLE driver mutation, BRAF-p.V600E, tumor mutational burden, and TNM staging). The results proved that our 4-gene signature is better than the other risk factor for prognosis in hypermutated CRC.ConclusionOur 4-gene signature is a good predictor of survival for hypermutated CRC, and this signature is powerful in stage II and III colon cancer and MSI-H CRC. Future prospective studies are needed to confirm the power of the 4-gene signature in patients receiving immunotherapy.

Dataset Information

Identification and Clinical Validation of a Novel 4 Gene-Signature with Prognostic Utility in Colorectal Cancer.

Publications

Identification and Clinical Validation of a Novel 4 Gene-Signature with Prognostic Utility in Colorectal Cancer.

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