Dataset Information

A Combined ULBP2 and SEMA5A Expression Signature as a Prognostic and Predictive Biomarker for Colon Cancer.

ABSTRACT: Background: Prognostic biomarkers for cancer have the power to change the course of disease if they add value beyond known prognostic factors, if they can help shape treatment protocols, and if they are reliable. The aim of this study was to identify such biomarkers for colon cancer and to understand the molecular mechanisms leading to prognostic stratifications based on these biomarkers. Methods and Findings: We used an in house R based script (SSAT) for the in silico discovery of stage-independent prognostic biomarkers using two cohorts, GSE17536 and GSE17537, that include 177 and 55 colon cancer patients, respectively. This identified 2 genes, ULBP2 and SEMA5A, which when used jointly, could distinguish patients with distinct prognosis. We validated our findings using a third cohort of 48 patients ex vivo. We find that in all cohorts, a combined ULBP2/SEMA5A classification (SU-GIB) can stratify distinct prognostic sub-groups with hazard ratios that range from 2.4 to 4.5 (p?0.01) when overall- or cancer-specific survival is used as an end-measure, independent of confounding prognostic parameters. In addition, our preliminary analyses suggest SU-GIB is comparable to Oncotype DX colon(®) in predicting recurrence in two different cohorts (HR: 1.5-2; p?0.02). SU-GIB has potential as a companion diagnostic for several drugs including the PI3K/mTOR inhibitor BEZ235, which are suitable for the treatment of patients within the bad prognosis group. We show that tumors from patients with worse prognosis have low EGFR autophosphorylation rates, but high caspase 7 activity, and show upregulation of pro-inflammatory cytokines that relate to a relatively mesenchymal phenotype. Conclusions: We describe two novel genes that can be used to prognosticate colon cancer and suggest approaches by which such tumors can be treated. We also describe molecular characteristics of tumors stratified by the SU-GIB signature.

SUBMITTER: Demirkol S

PROVIDER: S-EPMC5463424 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A Combined ULBP2 and SEMA5A Expression Signature as a Prognostic and Predictive Biomarker for Colon Cancer.

Demirkol Secil S Gomceli Ismail I Isbilen Murat M Dayanc Baris Emre BE Tez Mesut M Bostanci Erdal Birol EB Turhan Nesrin N Akoglu Musa M Ozyerli Ezgi E Durdu Sevi S Konu Ozlen O Nissan Aviram A Gonen Mithat M Gure Ali Osmay AO

Journal of Cancer 20170409 7

Background: Prognostic biomarkers for cancer have the power to change the course of disease if they add value beyond known prognostic factors, if they can help shape treatment protocols, and if they are reliable. The aim of this study was to identify such biomarkers for colon cancer and to understand the molecular mechanisms leading to prognostic stratifications based on these biomarkers. Methods and Findings: We used an in house R based script (SSAT) for the in silico disco ...[more]

PMID: 28607584

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Project description:BackgroundColon cancer (CC) is a common gastrointestinal malignant tumor with high heterogeneity in clinical behavior and response to treatment, making individualized survival prediction challenging. Ferroptosis is a newly discovered iron-dependent cell death that plays a critical role in cancer biology. Therefore, identifying a prognostic biomarker with ferroptosis-related genes provides a new strategy to guide precise clinical decision-making in CC patients.MethodsAlteration in the expression profile of ferroptosis-related genes was initially screened in GSE39582 dataset involving 585 CC patients. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were combined to further identify a novel ferroptosis-related gene signature for overall survival prediction. The prognostic performance of the signature was validated in the GSE17536 dataset by Kaplan-Meier survival curve and time-dependent ROC curve analyses. Functional annotation of the signature was explored by integrating GO and KEGG enrichment analysis, GSEA analysis and ssGSEA analysis. Furthermore, an outcome risk nomogram was constructed considering both the gene signature and the clinicopathological features.ResultsThe prognostic signature biomarker composed of 9 ferroptosis-related genes accurately discriminated high-risk and low-risk patients with CC in both the training and validation datasets. The signature was tightly linked to clinicopathological features and possessed powerful predictive ability for distinct clinical subgroups. Furthermore, the risk score was confirmed to be an independent prognostic factor for CC patients by multivariate Cox regression analysis (p < 0.05). Functional annotation analyses showed that the prognostic signature was closely correlated with pivotal cancer hallmarks, particularly cell cycle, transcriptional regulation, and immune-related functions. Moreover, a nomogram with the signature was also built to quantify outcome risk for each patient.ConclusionThe novel ferroptosis-related gene signature biomarker can be utilized for predicting individualized prognosis, optimizing survival risk assessment and facilitating personalized management of CC patients.

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Dataset Information

A Combined ULBP2 and SEMA5A Expression Signature as a Prognostic and Predictive Biomarker for Colon Cancer.

Publications

A Combined ULBP2 and SEMA5A Expression Signature as a Prognostic and Predictive Biomarker for Colon Cancer.

Similar Datasets

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