High-Throughput In Vitro Gene Expression Profile to Screen of Natural Herbals for Breast Cancer Treatment - Kui, Ling; Kong, Qinghua; Yang, Xiaonan; Pan, Yunbing; Xu, Zetan; Wang, Shouling; Chen, Jian; Wei, Kunhua; Zhou, Xiaolei; Yang, Xingzhi; Wu, Tingqin; Mastan, Anthati; et al. (Frontiers in Oncology, 2021)
Breast cancer has surpassed lung cancer as the most commonly diagnosed cancer in women worldwide. Some therapeutic drugs and approaches could cause side-effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in breast cancer; however, the chemical composition and underlying antitumor mechanisms of TCM still need to be investigated. The primary aim of this study is to provide unique insights to screen the natural components for breast cancer therapy using high-throughput transcriptome analysis. Differentially expressed genes were identified based on two conditions: single samples and groups classified according pharmaceutical effect. Subsequently, the sample treated with E. cochinchinensis Lour. generated most significant DEGs set, including 1,459 DEGs, 805 up-regulated and 654 down-regulated. Similarly, group 3 treatment contained the most DEGs (414 DEGs, 311 up-regulated and 103 down-regulated). KEGG pathway analyses showed five significant pathways associated with inflammatory and metastasis processes in cancer, which include TNF, IL−17, NF-kB, MAPK signaling pathway and Transcriptional misregulation in cancer. Classified samples into 13 groups based on their pharmaceutical effects, the results of KEGG pathway analyses remained consistent with signal samples, group 3 present high significance. 21 genes were significantly regulated in these five pathways, interestingly, IL6, TNFAIP3 and BRIC3 enriched on at least two pathways, seven genes (FOSL1, S100A9, CXCL12, ID2, PRS6KA3, AREG, DUSP6) have been reported as the target biomarkers even the diagnostic tools in cancer therapy. In addition, weighted correlation network analysis (WGCNA) was used to identify 18 modules. Among them, blue and thistle2 were the most relevant modules. 26 hub genes in blue and thistle2 modules were identified as hub genes. In conclusion, we screened out three new TCM (R. communis L., E. cochinchinensis Lour., B. fruticosa) that have the potential to develop natural drugs for breast cancer therapy, and obtained the therapeutic targets.