Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data

Abstract

The multiple types of high throughput genomics data create a potential opportunity to identify driver pattern in ovarian cancer, which will acquire some novel and clinical biomarkers for appropriate diagnosis and treatment to cancer patients. However, it is a great challenging work to integrate omics data, including somatic mutations, Copy Number Variations (CNVs) and gene expression profiles, to distinguish interactions and regulations which are hidden in drug response dataset of ovarian cancer. To distinguish the candidate driver genes and the corresponding driving pattern for resistant and sensitive tumor from the heterogeneous data, we combined gene co-expression modules and mutation modulators and proposed the identification driver patterns method. Firstly, co-expression network analysis is applied to explore gene modules for gene expression profiles via weighted correlation network analysis (WGCNA). Secondly, mutation matrix is generated by integrating the CNVs and somatic mutations, and a mutation network is constructed from this mutation matrix. The candidate modulators are selected from the significant genes by clustering the vertex of the mutation network. At last, regression tree model is utilized for module networks learning in which the achieved gene modules and candidate modulators are trained for the driving pattern identification and modulator regulatory exploring. Many of the candidate modulators identified are known to be involved in biological meaningful processes associated with ovarian cancer, which can be regard as potential driver genes, such as CCL11, CCL16, CCL18, CCL23, CCL8, CCL5, APOB, BRCA1, SLC18A1, FGF22, GADD45B, GNA15, GNA11 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.

Next‐generation sequencing unravels extensive genetic alteration in recurrent ovarian cancer and unique genetic changes in drug‐resistant recurrent ovarian cancer

“…Gene mutations and copy number variations have been identified in ovarian cancer, which contribute to oncogenesis, ovarian cancer progression, and acquired chemoresistance (Lu et al., ; Zhao, Sun, & Zhao, ). Delineation of gene mutations and copy number variations in ovarian cancer could not only provide insight into the interplay of mutated genes and their encoded proteins in driving tumorigenesis and tumor progression, but it could also lead to identification of diagnostic and prognostic biomarkers for primary and recurrent ovarian cancer.…”

Integrated Analysis of Transcriptome and Prognosis Data Identifies FGF22 as a Prognostic Marker of Lung Adenocarcinoma

“…29 Another study has shown that FGF22 and BRCA1 affect the thyroid hormone pathway in ovarian cancer. 30 Our results suggested that FGF22 and related TFs may affect signaling transduction pathways and carcinogenic pathways. However, to our knowledge, no study has reported the effect of FGF22 on lung cancer.…”