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

Abstract

PurposeWe aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients.Materials and MethodsWe 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.ResultsWe 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.ConclusionMolecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

The role of CCN4/WISP-1 in the cancerous phenotype

“…Another role for CCN4 emerged from a study published in 2017, in which the researchers were seeking a molecular signature for lymphatic invasion of ovarian cancer. 20 They found that a decrease in CCN4 mRNA expression was associated with lymphatic invasion and poorer survival. Additional studies on the role of CCN4 in ovarian cancer are needed before any conclusions can be drawn.…”

“…In the compiled data, these authors noted a slight elevation of CCN4 mRNA in ovarian tumor samples. Another role for CCN4 emerged from a study published in 2017, in which the researchers were seeking a molecular signature for lymphatic invasion of ovarian cancer 20. They found that a decrease in CCN4 mRNA expression was associated with lymphatic invasion and poorer survival.…”

Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms

“…Unlike recently emerging classification models, there were not too many survival prediction models available; however, the random survival forest model has been gradually utilized by researchers for survival prediction. For instance, Paik et al developed a 21-gene-based random survival forest model to predict progression-free survival of ovarian cancer [ 42 ]. Jung et al identified insulin resistance SNPs in combination with lifestyle factors for breast cancer risk prediction via the random survival forest model [ 43 ].…”

Using machine learning approaches for multi-omics data analysis: A review

“…It is evident from Table 8 that most of the multi-omics studies focus on different forms of cancer. In particular, the presence of many multi-omics studies related to breast List et al, 2014;Ma et al, 2016;Nam et al, 2009) and ovarian (Anděl et al, 2015;Mankoo et al, 2011;Paik et al, 2017;Zhang et al, 2014) cancer highlights the research thrust by the scientific community in these domains.…”