Molecular-level effects of eribulin and paclitaxel on breast cancer based on differential co-expression network analysis

Abstract

ABSTRACT. We investigated the effects of eribulin and paclitaxel on breast cancer (BC) by exploring molecular biomarkers and pathways. Co-expression networks were constructed by differentially coexpressed genes and links, and centralities were analyzed to explore the hub genes. Pathway-enrichment analysis was performed. The hub genes were validated using the polymerase chain reaction and western blotting. A total of 132 and 153 differentially expressed genes were identified in BC cell lines treated with eribulin and paclitaxel, respectively. Six hub genes were identified in two co-expression networks. The spliceosome pathway was the mutually significant pathway. The validation analysis was basically consistent with the bioinformatics. We successfully identified several hub genes and pathways relevant to the effects of eribulin and paclitaxel on BC based on the network analysis.

Insights into Breast Cancer Prognosis: A Differential Regulatory Network Approach to Identify Key Transcription Factor Biomarkers

“…Also, the DCGL package was used to reconstruct co-expression networks in the BC treatment groups (eribulin and paclitaxel) compared to the untreated BC subjects. Finally, USP8, FSTL3, TUBA1C, KLF6, EIF3B, and UBR2 are hub genes in eribulin treatment, and KIF20A, PTPRK, ZSCAN20, DEPDC1, UNG, and AURKA are hub genes in paclitaxel treatment based on degree centrality [29].…”

“…Previously, enormous research efforts have been made to understand the mechanisms of BC pathogenesis and to identify diagnostic and prognostic targets based on WGCNA [19][20][21][22]. In several studies, the DCGL package was used to identify biomarkers related to diseases including schizophrenia [23], bladder cancer [24], Parkinson's disease [25], cholangiocarcinoma [26], hepatocellular carcinoma [27], gastric carcinoma [28], and breast cancer [29,30]. Despite these signi cant network-based analyses of BC, to date, to the best our knowledge, there are no research reports on the analysis of gene expression pro les at different stages of BC using the DCEA method.…”

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

“…To explore DEGs, most of the studies considered the LIMMA approach [1,14,. Some studies considered SAM [68,69], t-test [70,71], WGCNA [72,73], and some other tools [66,67,[74][75][76][77][78][79] for detecting DEGs between BC and control samples. However, most of these techniques, including LIMMA, SAM, t, and WGCNA, are sensitive to outlying observations, for which sometimes they produce misleading results [16][17][18][19][20][21][22].…”

Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

“…Among the numerous differential co-expression tools, a software package “differentially co-expressed genes and links” (DCGL) [ 20 ] is capable of identifying differentially co-expressed gene pairs (links) and differentially co-expressed genes, and another software “gene sets net correlations analysis” (GSNCA) [ 21 ] commits to evaluating the disruption (rewiring) of internal co-expression within a biologically relevant gene set. Both tools have contributed to a wide range of human disease studies, including many on cancer [ 22 , 23 , 24 , 25 , 26 ] and a few on mental disorders [ 27 , 28 , 29 ].…”