Cancer Res Treat.  2018 Apr;50(2):461-473. 10.4143/crt.2017.104.

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer

• Paik ES
• Choi HJ
• Kim TJ
• Lee JW
• Kim BG
• Bae DS
• Choi CH

• KMID: 2411135
• DOI: http://doi.org/10.4143/crt.2017.104

Abstract

PURPOSE
We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients.
MATERIALS AND METHODS
We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation, we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database.
RESULTS
We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival.
CONCLUSION
Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

Keyword

Gene expression signature; Ovarian neoplasm; Human genome; Lymphatic metastasis

MeSH Terms

Disease-Free Survival
DNA
Forests
Gene Expression
Genome
Genome, Human
Humans
Logistic Models
Lymphatic Metastasis
Ovarian Neoplasms*
RNA, Messenger
ROC Curve
Support Vector Machine
Transcriptome
DNA
RNA, Messenger

Figure

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  Fig. 1. Flowchart of analysis. TCGA, The Cancer Genome Atlas.

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  Fig. 2. Expression heat map of mRNAs included in the predictive model. Each column represents one patient and each row represents an mRNA included in the model (n=21), sorted on the basis of the established prognostic index. The plot above the heat map shows the specific risk score index for each sample. In the heatmap, green represents low expression and red represents high expression.

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  Fig. 3. Accuracy of predicting lymphatic invasion. (A, B) Receiver operating characteristic curve of the prediction of lymphatic invasion using molecular signatures. (C, D) Logistic regression with C-index. (E) Linear discrimination analysis (LDA). (F) Supporter vector machine (SVM). The Y-axis represents the accuracy of the C-index. AUC, area under the curve.

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  Fig. 4. Random survival forest model for prediction of survival. (A, B) Progression-free survival. (C, D) Overall survival.

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  Fig. 5. Kaplan-Meier survival curve according to lymphatic invasion (A) and pathway deregulation score (PDS) (B). The graph according to PDS shows Kaplan-Meier plots of two groups defined by the level of deregulation (high and low) of primary tumor samples. High deregulation scores are associated with better prognosis.

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  Fig. 6. Validation with microarray data from 26 ovarian cancer samples from Samsung Medical Center. (A) Hierarchical clustering of gene expression according to lymphatic invasion. (B) Prediction accuracy of magnetic resonance imaging (MRI), molecular signature, and combination. (C, D) Random survival forest model showing incorporating molecular data and clinical variables improved the prediction of progression-free survival and overall survival.

Reference

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