An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules

Abstract

Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on the hypothesis that the gene co-expression network may reveal relationships among genes, some communities in the network could influence the prognosis of cancer patients and all the prognosis-related communities could fully reveal the prognosis of cancer patients. To predict the prognosis for cancer patients in each subtype, we adopted an ensemble classifier based on the gene co-expression network of the corresponding subtype. Using the gene expression data of ovarian cancer patients in TCGA (The Cancer Genome Atlas), three subtypes were identified. Survival analysis showed that patients in different subtypes had different survival risks. Three ensemble classifiers were constructed for each subtype. Leave-one-out and independent validation showed that our method outperformed control and literature methods. Furthermore, the function annotation of the communities in each subtype showed that some communities were cancer-related. Finally, we found that the current drug targets can partially support our method.

MSC-CSMC: A multi-objective semi-supervised clustering algorithm based on constraints selection and multi-source constraints for gene expression data

“…Generally, there are two approaches to determine the value of k; one is to directly set it as the true number of clusters (Yu et al, 2018;Zhao et al, 2021;Liu et al, 2022;Wu and Ma, 2022); The other approach is applicable to the case where the true number of clusters is unknown, in which the variation range of k is determined firstly, and the k corresponding to the optimal value of an index (Silhouette index, Dunn index, Davies-Bouldin index, etc.) can be chosen as the optimal number of clusters (Gao et al, 2019;Acharya et al, 2020;López-Cortés et al, 2020;Zhang et al, 2022). In this paper, we adopt the first approach, and the number of clusters k is selected according to Table 1.…”

KAZN as a diagnostic marker in ovarian cancer: a comprehensive analysis based on microarray, mRNA-sequencing, and methylation data

“…Epigenetic changes are integral to all aspects of cancer genomics, such as DNA methylation, which is among the common epigenetic mechanisms involved in the formation and development of cancer [11,12]. A large body of studies has shown that aberrant methylation of global DNA or specific genes may affect the progression [13] and prognosis of ovarian cancer [14], but the mechanisms for the aberrant DNA methylations to be involved in ovarian cancer remain largely unknown.…”