Dataset Information

Gene Expression Signature to Predict Prognosis and Adjuvant Chemosensitivity of Colorectal Cancer Patients.

ABSTRACT: Purpose:Molecular characteristics using gene-expression profiling can undoubtedly improve the prediction of treatment responses, and ultimately, the clinical outcome of cancer patients. We aimed at developing a genetic signature to improve the prediction of chemosensitivity and prognosis of patients with colorectal cancer (CRC). Patients and Methods:We analyzed microarray data of 32 CRC patients to explore the potential functions and pathways involved in the disease relapse in CRC. Gene expression profiles and clinical follow-up information of GSE39582, GSE17536, and GSE103479 were downloaded from the Gene Expression Omnibus database (GEO) to identify prognostic genes. Eventually, a model of 15-mRNA signature was established, in which its efficacy for predicting chemosensitivity and prognosis was examined. Results:Based on the proposed model of 15-mRNA signature, the test series patients could be classified into high-risk or low-risk subgroup with significantly different overall survival (OS) rate (?hazard ratio [HR]=1.48, 95% confidence interval [CI]=1.30-1.70, P?0.001). The prognostic value of this 15-mRNA signature was confirmed in another validation series. Further analysis revealed that the prognostic value of this signature was independent of the TNM stage and can predict adjuvant chemosensitivity of patients with early?-stage CRC. Conclusion:We identified a novel 15-mRNA signature in patients with CRC, which could be clinically helpful in the prognosis evaluation and the process of selection of patients with early?-stage CRC for undergoing adjuvant chemotherapy.

SUBMITTER: Li J

PROVIDER: S-EPMC7227814 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Gene Expression Signature to Predict Prognosis and Adjuvant Chemosensitivity of Colorectal Cancer Patients.

Li Jianxia J Zhang Jianwei J Hu Huabin H Cai Yue Y Ling Jiayu J Wu Zehua Z Deng Yanhong Y

Cancer management and research 20200511

<h4>Purpose</h4>Molecular characteristics using gene-expression profiling can undoubtedly improve the prediction of treatment responses, and ultimately, the clinical outcome of cancer patients. We aimed at developing a genetic signature to improve the prediction of chemosensitivity and prognosis of patients with colorectal cancer (CRC).<h4>Patients and methods</h4>We analyzed microarray data of 32 CRC patients to explore the potential functions and pathways involved in the disease relapse in CRC ...[more]

PMID: 32494194

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Project description:BackgroundSeveral studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC) patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes.Methods and findingsWe investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p?=?0.002). Similar results were obtained with reversed training and testing datasets (p?=?0.007). Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p?=?0.006), but not beneficial to the low-risk patients (p?=?0.491).ConclusionsThe NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network-based data integration method provides a convergence between biological relevance and clinical usefulness in gene signature development.

Project description:A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM) stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA). The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets: GSE39582 includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics; GSE17538 is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS): Kaplan Meier (KM) Log Rank p = 0.0034; overall survival (OS): KM Log Rank p = 0.0336) in GSE17538. For patients with proficient mismatch repair system (pMMR) in GSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rank p = 0.005; Relapse free survival (RFS): KM Log Rank p = 0.022). Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher's exact test p = 0.0003). After stage stratification, the signature could still distinguish poor prognosis patients in GSE17538 from good prognosis within stage II (Log Rank p = 0.01) and stage II & III (Log Rank p = 0.017) in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT) and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rank p = 0.046; III & II, OS: KM Log Rank p = 0.041). Among stage II/III pMMR patients with lower 12-gene scores in GSE39582, the subgroup receiving ACT showed significantly longer OS time compared with those who received no ACT (Log Rank p = 0.021), while there is no obvious difference between counterparts among patients with higher 12-gene scores (Log Rank p = 0.12). Besides COAD, our 12-gene signature is multifunctional in several other cancer types including kidney cancer, lung cancer, uveal and skin melanoma, brain cancer, and pancreatic cancer. Functional classification showed that seven of the twelve genes are involved in immune system function and regulation, so our 12-gene signature could potentially be used to guide decisions about adjuvant therapy for patients with stage II/III and pMMR COAD.

Dataset Information

Gene Expression Signature to Predict Prognosis and Adjuvant Chemosensitivity of Colorectal Cancer Patients.

Publications

Gene Expression Signature to Predict Prognosis and Adjuvant Chemosensitivity of Colorectal Cancer Patients.

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