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Gene Regulatory Network Analysis with Drug Sensitivity Reveals Synergistic www.nature.com/scientificreports OPEN Gene regulatory network analysis with drug sensitivity reveals synergistic efects of combinatory chemotherapy in gastric cancer Jeong Hoon Lee1, Yu Rang Park 2, Minsun Jung 3 & Sun Gyo Lim 4* The combination of docetaxel, cisplatin, and fuorouracil (DCF) is highly synergistic in advanced gastric cancer. We aimed to explain these synergistic efects at the molecular level. Thus, we constructed a weighted correlation network using the diferentially expressed genes between Stage I and IV gastric cancer based on The Cancer Genome Atlas (TCGA), and three modules were derived. Next, we investigated the correlation between the eigengene of the expression of the gene network modules and the chemotherapeutic drug response to DCF from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The three modules were associated with functions related to cell migration, angiogenesis, and the immune response. The eigengenes of the three modules had a high correlation with DCF (−0.41, −0.40, and −0.15). The eigengenes of the three modules tended to increase as the stage increased. Advanced gastric cancer was afected by the interaction the among modules with three functions, namely cell migration, angiogenesis, and the immune response, all of which are related to metastasis. The weighted correlation network analysis model proved the complementary efects of DCF at the molecular level and thus, could be used as a unique methodology to determine the optimal combination of chemotherapy drugs for patients with gastric cancer. Although its incidence is decreasing in some parts of the world, gastric cancer is still the fourth most common cancer worldwide1,2. It poses a critical clinical challenge and is associated with poor prognosis, because a high percentage of patients are diagnosed at an advanced stage in some areas and due to its relatively chemoresistant properties and limited treatment options. Metastatic spread occurs frequently in advanced gastric cancer, and it is one of the major causes of death3,4. Systemic chemotherapy, which involves a combination of various cytotoxic agents, constitutes the main type of treatment in the adjuvant or palliative setting for patients with metastatic or recurrent cancer5–7. Tus far, the choices regarding chemotherapeutic agents or their combinations have typically been determined according to the results from clinical trials. Terefore, the accurate prediction of the response to combination chemotherapy is labor intensive, time consuming, and expensive because of the explosion in the number of combinations available8. Accordingly, a new methodology for conducting a molecular-level analysis in order to understand the association between advanced gastric cancer and chemotherapy sensitivity is required. Despite the increasing knowledge about tumor biology and pharmacology, our understanding of combination chemotherapy is limited due to the complex factors involved, such as the gene–drug interactions and gene reg- ulatory networks9. Nonetheless, various combinations of chemotherapeutic drugs have been extensively tested, because they can increase efcacy, lower dosages, and minimize drug resistance. Te combination of docetaxel, cisplatin, and 5-fuorouracil (DCF) is one of the most popular chemotherapy regimens in gastric cancer and is reported to provide a better clinical beneft than each of the agents alone10–12. Because of the limited number of studies, it is unclear whether the DCF regimen is the best among the chemotherapeutic agent combinations currently available. We postulated that gene–drug interactions have a critical and signifcant infuence on the efcacy of chemotherapy and that the efcacy of chemotherapy, in various combinations, can be analyzed using a 1Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, 110799, Republic of Korea. 2Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, 03722, South Korea. 3Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Korea. 4Department of Gastroenterology, Ajou University School of Medicine, Suwon, 16499, Korea. *email: [email protected] SCIENTIFIC REPORTS | (2020) 10:3932 | https://doi.org/10.1038/s41598-020-61016-z 1 www.nature.com/scientificreports/ www.nature.com/scientificreports Stage I Stage IV All No. of patients 59 46 105 Age 71.00 (62.00, 77.00) 63.00 (54.00, 69.00) 67.00 (58.00, 75.00) Status Alive 48 23 71 Dead 11 23 34 Histological type Stomach adeno. (NOS) 24 8 32 Intestinal adeno. (NOS) 14 13 27 Stomach adeno. difuse 7 7 14 Stomach intestinal adeno. tubular 9 10 19 Stomach intestinal adeno. mucinous 0 2 2 Stomach intestinal adeno. papillary 3 1 4 Stomach adeno. signet ring 2 4 6 Anatomic neoplasm subdivision Antrum/distal 14 19 23 Cardia/proximal 10 8 18 Fundus/body 23 12 35 Gastroesophageal junction 8 4 12 Others 3 3 6 Table 1. Te characteristics of the gastric cancer patients in stage I and IV. methodology based on these interactions. We believe that, if a methodology is identifed to demonstrate the com- plementary efect of the DCF combination therapy, the candidates for a new drug combination may be indicated by the individual’s genetic profle. There have been many studies assessing the effect of chemotherapy on cancers using machine learning approach13–18. However, investigations into chemotherapeutic drugs, such as 5-fuorouracil and docetaxel, at the molecular level are lacking due to the nature of the cytotoxic drugs19. However, with the advent of public data in the form of Te Cancer Genome Atlas (TCGA), many molecular-level studies of gastric cancer have been carried out20. In addition, the remarkable advances in bioinformatics have facilitated the analysis of these high-throughput data. For example, the diferential expression analysis packages DESeq, EdgeR, and limma and the network analysis packages GeneNetWeaver and WGCNA have enabled system-level analyses and the charac- terization of cancer genomics21–26. Drug sensitivity and patient genomic data are linked to inferred synthetic lethal gene pairs27. Te performance of molecular-based assays is expected to explain the synergistic efects of combina- tion drug therapies. However, no studies have linked TCGA and drug sensitivity databases to infer the synergistic efect of drug combinations, and research into the chemotherapeutic response according to molecular-level anal- yses is still, to the best of our knowledge, lacking. In this study, we performed a systematic analysis of the network structure dynamics in conjunction with the chemotherapeutic agents used in early- and advanced-stage gastric cancer patients based on the gastric cancer genome in the TCGA database with large-scale pharmacogenomic profles of GDSC. We hypothesize that gene regulatory network modules with independent functions in advanced-stage gastric cancer will play an important role in determining not only the prognosis of patients, but also their response to chemotherapeutic agents. Based on the function of each module and the sensitivity of each chemotherapeutic agent, here, our analysis enables us to explain the synergistic efects of DCF at the molecular level. Results The characteristics of the patients with gastric cancers. Te characteristics of 105 gastric cancer patients enrolled from the TCGA are shown in Table 1. Te median ages were 71 and 63 year old in stage I and IV, respectively. Te most common anatomical site was fundus/body. Linkage of diferentially expressed genes to the drug sensitivity of cancer cells. We imple- mented an analysis pipeline to interpret the complementary efects of the drug combinations at the molecular level. Te workfow scheme for the entire method is shown in Fig. 1A. Our method is divided into three stages. First, the genes that diferentiate between the early- and advanced-stage cancers were identifed. Second, the diferentially expressed genes were divided into three modules (Fig. 1B). Finally, the eigengenes, which are rep- resentative of the expression profle of each module, were calculated and linked to the sensitivities of the drugs (Fig. 1C). We compared the eigengenes of the three modules with the reactivity to the 265 drugs provided in the GDSC database. TCGA data processing and diferentially expressed genes. TCGA RNA-Seq expression data from 59 early-stage gastric cancer patients and 46 advanced-stage gastric cancer patients were used. From the above threshold of a counts per million (CPM) >1 in more than half of the samples, 6370 genes were excluded from the analysis and 14131 genes remained. We then performed a diferential expression analysis on the expression data. SCIENTIFIC REPORTS | (2020) 10:3932 | https://doi.org/10.1038/s41598-020-61016-z 2 www.nature.com/scientificreports/ www.nature.com/scientificreports Figure 1. Schematic showing the workfow of the research. (A) Te entire process used to identify the advanced- stage gastric cancer module and the correlation testing of the module with drug sensitivity. (B) Identifcation of three modules from the diferentially expressed genes. Functional enrichment tests were performed for each module. (C) Te eigengenes for each network were obtained from the COSMIC cell line project expression data. Each module shows a correlation with the sensitivities of 5-fuorouracil, docetaxel, and cisplatin. Overall, 357 diferentially expressed genes were derived (Table S1). Among the diferentially expressed genes, 335 were upregulated
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