Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2018.9758 mmr-19-02-1004 Articles Identification of putative drugs for gastric adenocarcinoma utilizing differentially expressed genes and connectivity map ChenZu-Xuan 1 ZouXiao-Ping 2 YanHuang-Qun 2 ZhangRui 2 PangJin-Shu 2 QinXin-Gan 3 HeRong-Quan 4 MaJie 4 FengZhen-Bo 2 ChenGang 2 GanTing-Qing 1 Department of Medical Oncology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China Correspondence to: Professor Ting-Qing Gan, Department of Medical Oncology, The Second Affiliated Hospital of Guangxi Medical University, 166 Daxuexi Road, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China, E-mail: gantingqing_gxmu@163.com 022019 13122018 19 2 1004 1015 19042018 20112018 Copyright: © Chen et al. 2019 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Gastric adenocarcinoma (GAC) is a challenging disease with dim prognosis even after surgery; hence, novel treatments for GAC are in urgent need. The aim of the present study was to explore new potential compounds interfering with the key pathways related to GAC progression. The differentially expressed genes (DEGs) between GAC and adjacent tissues were identified from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Connectivity Map (CMap) was performed to screen candidate compounds for treating GAC. Subsequently, pathways affected by compounds were overlapped with those enriched by the DEGs to further identify compounds which had anti-GAC potential. A total of 843 DEGs of GAC were identified. Via Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, 13 pathways were significantly enriched. Moreover, 78 compounds with markedly negative correlations with DEGs were revealed in CMap database (P<0.05 and Enrichment <0). Subpathways of cell cycle and p53 signaling pathways, and core genes of these compounds, cyclin B1 (CCNB1) and CDC6, were identified. This study further revealed seven compounds that may be effective against GAC; in particular methylbenzethonium chloride and alexidine have never yet been reported for GAC treatment. In brief, the candidate drugs identified in this study may provide new options to improve the treatment of patients with GAC. However, the biological effects of these drugs need further investigation.

 differentially expressed genes pathways connectivity map gastric adenocarcinoma Introduction

Globally, gastric cancer is the fifth leading cause of cancer and the third leading cause of death from cancer (1,2). In 2015, 679,100 new cases of gastric cancer were diagnosed in China, accounting for 15.8% of the total number of newly occurred cancer cases. In addition, gastric cancer resulted in 498,000 deaths, 17.7% of all cancer-related deaths, and the incidence of gastric cancer has been steadily increasing (3). Among these cases, gastric adenocarcinoma (GAC) accounts for 95% of all gastric cancer cases. Research indicates that, even after surgery, the outcome of GAC patients remains dim (4–6). Therefore, other novel treatments for GAC should be developed. The study of small-molecule drugs aiming at multiple protein pathways modulating tumor progression, invasion, and metastasis formation, has received much interest in recent years (7–9). The purpose of this study was to discover new, potential small-molecule drugs by using multiple online databases.

Connectivity Map (CMap) is one of the gene expression profile databases used to process the genetic data. CMap was developed by Lamb and his colleagues from Broad Institute of MIT, Whitehead Institute and Harvard Medical School, (Boston, MA, USA) (10). CMap utilizes the differential gene expression of human cells which are treated with small-molecule drugs, to construct a biological application database based on connection of small-molecule drugs, gene expression and different diseases. CMap allows scholars of drug development to take advantage of gene expression profiling data and, therefore, identify the drugs highly correlated with disease, infer the main chemical structure of most drug molecules, and summarize the mechanism of possible action of drug molecules.

To explore new drugs for GAC, based on the integrated subpathway analysis, we implemented an in silico method for the reuse of GAC drugs. First, we identified the differentially expressed genes (DEGs) between GAC and non-tumor tissues identified in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, and then determined the potential pathways affecting the progression of GAC. Next, CMap was used to verify the pathways of GAC affected by small-molecule treatment. Finally, small-molecule drugs that can target subpathways related to GAC were considered as potential new agents in the treatment of GAC (Fig. 1). The candidate drugs identified in our approach may provide a new direction for improving the treatment of patients with GAC.

 Materials and methods <sec> <title>DEG analysis of GAC

Using the GEPIA online analysis website (http://gepia.cancer-pku.cn/), the expression data of mRNA of GAC in TCGA and GTEx databases were performed with the value of fold change (FC). Among these data, only the genes with logFC >2 and logFC <-2 were defined as DEGs, including upregulated and downregulated ones.

 Enrichment analysis of DEGs

DEGs were performed with Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis with the WebGestalt database (http://www.webgestalt.org/). Also, pathway analysis was conducted by Gene List Analysis (http://www.pantherdb.org/) to obtain possible pathways during the development of GAC. Finally, we used the STRING database (https://string-db.org/) to analyze the protein-protein interaction (PPI) of the ultimate DEGs as previously reported (11–16). In this study, GO outcomes were analyzed visibly with Cytoscape software (version 3.7.0, U.S. National Institute of General Medical Sciences (NIGMS), http://cytoscape.org/).

 CMap for DEG analysis of drug molecule cures for GAC

The CMap database (https://portals.broadinstitute.org/CMap/) (build 02) contains over 7,000 gene expression profiles and 1,309 chemicals. To analyze this potential mechanism for the development of GAC, we first set up the files in query signature format for DEGs obtained from the TCGA (https://cancergenome.nih.gov/) and GTEx databases (https://gtexportal.org/home/). We then entered the CMap quick query interface to import the files of upregulated and downregulated genes and ran them with CMap analysis. In this way, we analyzed the drug molecules for the DEGs of GAC (17). The negatively related drugs (P<0.05 and Enrichment <0) for anti-GAC were then screened.

 Correlation data between drug molecules and subpathways

The chip expression profiles of 1,309 drugs and the genes affected by the drugs using the CMap database were downloaded. Furthermore, we identified the subpathways that obtain significant enrichment for each small-molecule drug with the affected genes according to the method reported by a previous publication (18). Consistent with the reference, 196 small-molecular drugs and 104 subpathways were also achieved. The overlapped pathways between those from CMap and those enriched by DEGs were determined, which were identified as potential pathways related to both the treatment and pathogenesis of GAC. Finally, the drug-pathway network was constructed for GAC.

 Results <sec> <title>Screening results of DEGs

Altogether, 843 DEGs in mRNA expression of GAC were obtained, which included 638 upregulated genes and 205 downregulated ones. The next analysis was based on this screening result.

 Functional annotation, pathway enrichment and PPI network analysis

Through GO analysis, in the annotations of biological progress, the top three most significant processes were mitotic sister chromatid segregation, mitotic cell cycle and nuclear division. In the terms of cellular component, the top most significant annotations were extracellular space, chromosome, centromeric region and spindle. As for analysis of molecular function, the top three most significant functions were serine hydrolase activity, chemokine activity and serine-type peptidase activity (Table I and Fig. 2). KEGG pathway analysis indicated that DEGs were obviously centralized in 13 pathways, including cell cycle, protein digestion and absorption, Staphylococcus aureus infection, and the p53 signaling pathway (Table II and Fig. 3). From the PPI network analysis, we acquired the following hub genes: CCNB1, AURKA, CDC6, KIF11, OIP5, NCAPG, KIF23, DLGAP5 and NDC80 (nodes ≥100) (Fig. 4).

 CMap analysis to achieve potential compounds for GAC

The 843 DEGs of GAC mentioned above led to 78 compounds by CMap (Table III) when P<0.05 and Enrichment <0.

 Intersection of small-molecule drug correlative pathways and KEGG pathways

According to a previous method (18), we performed subpathway analysis and obtained 104 subpathways. After integrating these 104 subpathways with 13 KEGG pathways generated by the DEGs, two pathways related to anti-GAC drug molecules were finally achieved (Table IV and Fig. 5), including cell cycle and p53 signaling pathways. These two pathways were related to 32 genes and seven CMap small-molecule drugs. The genes involved in these two KEGG pathway were CDKN2A, DBF4, CHEK1, ORC6, SFN, MAD2L1, MCM2, MCM4, MCM5, PCNA, PLK1, CCND1, BUB1, BUB1B, TTK, CDC45, CCNA2, CCNB1, PKMYT1, CCNB2, PTTG1, ESPL1, CDK1, CDC6, CDC20, CDC25C, IGFBP3, GTSE1, SERPINB5, RPRM, RRM2 and BID. The PPI analysis with the above 32 genes demonstrated two hub genes (CCNB1 and CDC6). The seven CMap small-molecule drugs were troglitazone, methylbenzethonium chloride, thiostrepton, alexidine, vorinostat, methotrexate and etoposide (Fig. 6).

 Expression levels of CCNB1 and CDC6 mRNA in GAC tissues

The expression levels of CCNB1 and CDC6 mRNA in GACs were queried from GEPIA database (http://gepia.cancer-pku.cn/). The results showed that the two genes were both highly expressed in GAC tissues compared to non-cancerous gastric tissues (Fig. 7).

 Verification of predicting small-molecule drugs of GAC with online literature retrieval

Using PubMed, we identified studies that investigated the effect of relevant drugs on GAC. We found 268 articles related to the effect of methotrexate on GAC, 403 articles related to etoposide, and 17 articles related to troglitazone, which is a diabetes drug that may inhibit GAC. Nine studies concerned vorinostat and three studies were related to thiostrepton. Most importantly, methylbenzethonium chloride and alexidine have never been addressed in the literature of GAC.

 Discussion

In the present study, we identified DEGs of GAC and found several pathways and hub genes that may play a critical role in the pathogenesis and development of GAC. Also, through the connectivity mapping approach, some known compounds were found to share similar pathways of those generated from the DEGs of GAC, including methotrexate, etoposide, troglitazone, thiostrepton, vorinostat, methylbenzethonium chloride and alexidine. The findings from the present study suggest that methylbenzethonium chloride and alexidine could act as novel potential drugs for the treatment of GAC and warrant further investigation, as they have never been tested previously.

The CMap database reveals the connection between disease, genes and drugs, using gene expression data and the ‘similarity’ concept with a small-molecular compound or the gene expression spectrum of the drug as the core (19). CMap database provides a unique method for drug development through comparison to filter candidate compounds curing diseases, and it has been adopted by several scholars (20,21). For instance, Xiao et al used gene expression profile chip technology and the CMap database to study molecular mechanisms of Hirschsprung disease (HD) and potential drugs. They found differences in the neuronal developmental disorders of HD genes and signaling pathways, and discovered that some compounds may offset the damage of HD development (22).

In this study, the DEGs between GAC and adjacent tissues were compared with the expression profiles in CMap to identify negatively correlative compounds that are potential compounds for GAC. Among the candidate compounds determined in the present study, two compounds (alexidine and methylbenzethonium) are particularly important. Alexidine is an antimicrobial agent with high affinity for bacteria, which can be used in the root canal irrigation solution of oral treatment (23). Feng et al, using high-throughput drug screening tests, identified that alexidine is an antitumor drug that can inhibit cytokines and growth factors necessary for multiple myeloma (24). Meanwhile, methylbenzethonium chloride, a broad spectrum antibiotic, was found to be able to specifically induce apoptosis in undifferentiated embryonic stem cells of mice (25). The effect could be applied to prevent reoccurrence of the tumor after stem cell transplantation therapy. Methylbenzethonium chloride may become another novel anticancer agent (25).

The present study showed that alexidine had the lowest connectivity score (−0.996), indicating a highly negative correlation with the DEGs of GAC. The connectivity score of methylbenzethonium chloride also suggests that it has the capacity to inhibit the growth of GAC. In addition, this study predicted that both alexidine and methylbenzethonium chloride can play a vital role in inhibiting GAC by regulating the p53 signaling pathway. Previous studies have shown that the p53 signaling pathway regulates various cellular functions, including apoptosis, induction of aging, and inhibition of cell growth, migration and invasion (26–28). However, the specific molecular mechanisms of alexidine and methylbenzethonium chloride for antitumor activity need to be further explored.

Five other compounds achieved in the present study have been mentioned in other studies. Troglitazone hinders BGC-823 GAC cell proliferation and promotes its apoptosis by inducing expression of the non-steroidal anti-inflammatory drug-activated gene (NAG) (29). In addition, thiostrepton was found to reverse drug resistance in GAC by inhibiting the forkhead box transcription factor 1 (FOXM1) (30). Vorinostat (31), methotrexate (32) and etoposide (33) are proven to inhibit the proliferation of GAC cells. This evidence indicates that the predictive method in this study is convincing and worth being used for drug exploration.

In this study, we used bioinformatic methods to screen differentially expressing potential genetic biomarkers based on RNA-seq data. The results of pathway enrichment analysis indicated 13 pathways which were evidently enriched with DEGs, including the cell cycle, protein digestion and absorption, Staphylococcus aureus infection and the p53 signaling pathway. In addition, these DEGs were analyzed with CMap and subpathways, and two (cell cycle and p53 signaling pathway) were found to be closely related to the treatment potential and occurrence of GAC. CCNB1 and CDC6 in these pathways were also hub genes in the PPI network.

The clinical role of these hub genes was analyzed also based on publicly available RNA-seq data, and it was found that CCNB1 was upregulated in patients with GAC. CCNB1 is a member of the cell cycle protein B family; it is a regulatory protein involved in mitosis, mostly expressed in the G2/M period, and plays a significant role in the S-to-G2/M phases (34). Therefore, overexpression of CCNB1 in GAC leads to chaos in the cell cycle, mitosis promotion and cell proliferation. Previous research has shown that silencing of CDKN3 stimulates cell cycle arrest by reducing the expression of CDK2, CDC25, CCNB1 and CCNB2 in human GAC cells, thus, inhibits the proliferation of tumor cells (35). It was found in vivo that dipalmitoyl phosphatidic acid could dramatically inhibit the growth of tumors in a mouse subcutaneous tumor model, and suppress cell proliferation and angiogenesis in triple-negative breast cancer. The suppressing effect was mediated partly due to reduction in the expression of CCNB1 (36). Therefore, CCNB1 may be an important target gene in the treatment of GAC, and the present study predicted that compounds aimed at this target gene may be reasonable and effective in treating GAC. Recent studies have shown that knockdown of CDC6 expression levels can interfere with the cell cycle and inhibit the proliferation of prostate and ovarian cancer cells (37,38). This evidence suggests that CDC6 may also be a potential biomarker for GAC therapy.

The present study comprehensively analyzed the possible mechanism of treating GAC by data mining in the public gene chip databases and bioinformatic analyses. We discovered cell cycle and p53 signaling pathways and key gene targets CCNB1 and CDC6 as potential targets of GAC treatment. We further predicted that seven known compounds may be effective in curing GAC, including methylbenzethonium chloride and alexidine, which have never been previously reported to treat GAC. However, several limitations should be admitted. Firstly, the current findings were based on in silico methods and validations are certainly needed. Secondly, CMap did not cover GAC cell lines and only provided general DEGs post treatment of existing drugs. The overlapping pathways of DEGs from TCGA and pathways from Cmap also need to be confirmed. Thirdly, the precise mechanism of the drugs we recommended remains to be investigated. Hence, further clinical, in vitro and in vivo experiments are needed to verify the definite effects and molecular mechanism of the potential drugs on GAC.

 Acknowledgements

Not applicable.

 Funding

The present study was supported by a fund from the Promoting Project of Basic Capacity for Young and Middle-Aged University Teachers in Guangxi, China (KY2016YB077).

 Availability of data and materials

The datasets used during the present study are available from the corresponding author upon reasonable request.

 Authors' contributions

ZXC, XPZ, HQY, RZ and JSP analyzed and interpreted the data and wrote the draft of the manuscript. XGQ, RQH, JM, ZBF, GC and TQG conceived and designed the study, supervised the data mining, corrected and revised the draft. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

 Ethics approval and consent to participate

Not applicable.

 Patient consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

 Abbreviations GAC

gastric adenocarcinoma

 CMap

connectivity map

 TCGA

The Cancer Genome Atlas

 DEGs

differentially expressed genes

 KEGG

Kyoto Encyclopedia of Genes and Genomes

 FC

fold change

 GO

Gene Ontology

 PPI

protein-protein interaction

 HD

Hirschsprung disease

 NAG

non-steroidal anti-inflammatory drug-activated gene

 FOXM1

forkhead box transcription factor 1

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The flow chart of the study process.

Gene Ontology (GO) enrichment analysis of the differentially expressed genes in gastric adenocarcinoma.

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the differentially expressed genes in gastric adenocarcinoma.

The top 9 hub genes with most interaction lines in protein-protein interaction (PPI) analysis.

Small-molecular drugs and their perturbed pathways in gastric adenocarcinoma.

The 3D conformers of the five compounds that counteracted the molecular signature effect in gastric adenocarcinoma. The 3D structures of the five compounds were provided by PubChem (https://pubchem.ncbi.nlm.nih.gov/compound). (A) Methotrexate, (B) etoposide, (C) troglitazone, (D) vorinostat and (E) methylbenzethonium chloride.

Verification of CCNB1 and CDC6 mRNA expression levels. The data were provided by GEPIA database based on 408 GAC (T; red) and 211 controls (N; grey). GAC, gastric adenocarcinoma, *P<0.05.

Top 10 of the most significantly enriched GO terms.

Pathway ID Terms Gene count FDR P-value
BP
  GO:0000070 Mitotic sister chromatid segregation 36 0 0
  GO:0000278 Mitotic cell cycle 112 0 0
  GO:0000280 Nuclear division 76 0 0
  GO:0000819 Sister chromatid segregation 47 0 0
  GO:0007049 Cell cycle 142 0 0
  GO:0007059 Chromosome segregation 58 0 0
  GO:0007067 Mitotic nuclear division 69 0 0
  GO:0008283 Cell proliferation 165 0 0
  GO:0022402 Cell cycle process 125 0 0
  GO:0042127 Regulation of cell proliferation 127 0 0
CC
  GO:0005615 Extracellular space 129 0 0
  GO:0000775 Chromosome, centromeric region 34 1.14E-13 2.22E-16
  GO:0005819 Spindle 42 1.52E-13 4.44E-16
  GO:0000793 Condensed chromosome 35 2.86E-13 1.11E-15
  GO:0098687 Chromosomal region 42 2.54E-12 1.23E-14
  GO:0000779 Condensed chromosome, centromeric region 25 2.88E-12 1.68E-14
  GO:0000776 Kinetochore 26 1.18E-11 8.04E-14
  GO:0000777 Condensed chromosome kinetochore 23 1.39E-11 1.20E-13
  GO:0009986 Cell surface 64 1.39E-11 1.21E-13
  GO:0005694 Chromosome 71 1.40E-10 1.36E-12
MF
  GO:0017171 Serine hydrolase activity 30 2.76E-07 1.77E-10
  GO:0008009 Chemokine activity 14 2.76E-07 3.88E-10
  GO:0008236 Serine-type peptidase activity 29 2.76E-07 5.77E-10
  GO:0004252 Serine-type endopeptidase activity 27 2.76E-07 6.04E-10
  GO:0042379 Chemokine receptor binding 15 3.08E-07 8.91E-10
  GO:0004175 Endopeptidase activity 42 3.08E-07 1.01E-09
  GO:0045236 CXCR chemokine receptor binding 9 4.33E-07 1.66E-09
  GO:0001664 G-protein coupled receptor binding 27 6.08E-05 2.66E-07
  GO:0032395 MHC class II receptor activity 6 7.59E-05 3.74E-07
  GO:0042802 Identical protein binding 82 1.18E-04 6.44E-07

GO, Gene Ontology; BP, biological progress; CC, cellular component; MF, molecular function; FDR, false discovery rate.

Significantly enriched KEGG pathway.

Pathway ID Terms Gene count FDR P-value
hsa04110 Cell cycle 26 2.83E-08 9.34E-11
hsa04974 Protein digestion and absorption 17 1.33E-04 8.80E-07
hsa05150 Staphylococcus aureus infection 12 9.58E-04 9.49E-06
hsa04115 p53 signaling pathway 13 1.35E-03 1.79E-05
hsa05140 Leishmaniasis 12 9.11E-03 1.50E-04
hsa05323 Rheumatoid arthritis 13 1.40E-02 3.07E-04
hsa04610 Complement and coagulation cascades 12 1.40E-02 3.24E-04
hsa05416 Viral myocarditis 10 1.56E-02 4.13E-04
hsa05310 Asthma 7 1.73E-02 5.12E-04
hsa05164 Influenza A 18 4.47E-02 1.63E-03
hsa04512 ECM-receptor interaction 11 4.47E-02 1.64E-03
hsa04060 Cytokine-cytokine receptor interaction 24 4.47E-02 1.77E-03
hsa04640 Hematopoietic cell lineage 12 4.86E-02 2.09E-03

KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate.

CMap compounds matched by the DEGs of gastric adenocarcinoma.

Rank CMap name Cell line N Enrichment P-value Specificity Percent non-null
1 Phenoxybenzamine MCF7 3 −0.984 0 0 100
2 Vorinostat MCF7 7 −0.844 0 0.1262 100
3 Trichostatin A PC3 55 −0.705 0 0.1149 96
4 Trichostatin A MCF7 92 −0.59 0 0.1881 88
5 Trichostatin A HL60 34 −0.465 0 0.1946 52
6 LY-294002 MCF7 34 −0.454 0 0.1625 70
7 Resveratrol MCF7 6 −0.865 0.00002 0.0082 100
8 Alexidine PC3 2 −0.996 0.00004 0 100
9 15-Delta prostaglandin J2 MCF7 8 −0.695 0.00018 0.0414 87
10 Meticrane PC3 2 −0.991 0.00026 0 100
11 Astemizole PC3 2 −0.99 0.00026 0.0192 100
12 Thiostrepton MCF7 2 −0.973 0.00141 0.0283 100
13 Clemizole PC3 2 −0.973 0.00141 0 100
14 Sulconazole MCF7 2 −0.973 0.00157 0 100
15 Mefloquine PC3 2 −0.971 0.00167 0.0431 100
16 MG-262 PC3 2 −0.968 0.00223 0.0738 100
17 Cloperastine PC3 2 −0.968 0.00223 0.0149 100
18 Thioridazine PC3 5 −0.736 0.0027 0.102 100
19 Methotrexate MCF7 3 −0.877 0.00379 0.0853 100
20 Valproic acid HL60 14 −0.448 0.00403 0.2883 64
21 Cloperastine MCF7 3 −0.873 0.00415 0.0196 100
22 Fludroxycortide PC3 2 −0.954 0.00453 0.0171 100
23 Pyrantel PC3 2 −0.946 0.00644 0.0144 100
24 Thioguanosine MCF7 2 −0.945 0.00658 0.0455 100
25 6-Bromoindirubin-3′-oxime methylbenzethonium PC3 4 −0.755 0.00732 0.0498 100
26 Chloride PC3 2 −0.939 0.00767 0.0598 100
27 Chlorpromazine PC3 4 −0.749 0.0079 0.0168 100
28 Vorinostat HL60 3 −0.839 0.00837 0.1705 100
29 Vitexin MCF7 2 −0.936 0.00861 0.0051 100
30 Acetazolamide MCF7 2 −0.931 0.00984 0 100
31 Pyrvinium MCF7 4 −0.731 0.0105 0.1304 100
32 5224221 MCF7 2 −0.927 0.01097 0.1429 100
33 Methacholine chloride MCF7 2 −0.924 0.01181 0.0278 100
34 Cortisone MCF7 2 −0.921 0.01262 0.0117 100
35 Carbachol MCF7 2 −0.919 0.01318 0.0058 100
36 Clotrimazole MCF7 3 −0.807 0.01444 0.0556 100
37 Dipyridamole MCF7 3 −0.799 0.01671 0.04 100
38 Abamectin MCF7 2 −0.907 0.01746 0.05 100
39 LY-294002 PC3 12 −0.423 0.01802 0.3669 66
40 Troglitazone PC3 4 −0.696 0.01804 0.1159 100
41 Luteolin MCF7 2 −0.904 0.01839 0.0476 100
42 Hydroflumethiazide MCF7 2 −0.902 0.01913 0.0601 100
43 Homochlorcyclizine MCF7 2 −0.898 0.02066 0.0968 100
44 Gemfibrozil PC3 2 −0.896 0.02167 0.0208 100
45 Withaferin A PC3 2 −0.894 0.02223 0.0917 100
46 Tanespimycin PC3 12 −0.414 0.02239 0.3382 58
47 Prochlorperazine MCF7 9 −0.472 0.0231 0.1892 66
48 Ciclosporin MCF7 4 −0.679 0.02349 0.0576 75
49 Disulfiram PC3 2 −0.891 0.02382 0.0667 100
50 Procaine PC3 2 −0.89 0.024 0.0294 100
51 0173570-0000 PC3 4 −0.677 0.02407 0.1349 75
52 Tretinoin MCF7 13 −0.395 0.02531 0.3655 61
53 Fluphenazine PC3 3 −0.769 0.02534 0.1026 100
54 Loperamide MCF7 3 −0.767 0.026 0.087 100
55 Dilazep PC3 2 −0.886 0.02612 0.0784 100
56 Trifluoperazine PC3 3 −0.765 0.02656 0.1379 100
57 3-Acetylcoumarin MCF7 3 −0.764 0.02692 0.022 100
58 Flunarizine MCF7 2 −0.884 0.02712 0.068 100
59 Sulfaguanidine PC3 2 −0.878 0.02972 0.0202 100
60 Ethaverine MCF7 2 −0.878 0.03004 0.0133 100
61 Amiodarone MCF7 3 −0.754 0.03043 0.1039 100
62 Picotamide PC3 2 −0.875 0.03127 0.0162 100
63 Felodipine MCF7 5 −0.594 0.0318 0.1376 80
64 Prestwick-1084 MCF7 2 −0.873 0.03201 0.0545 100
65 Monobenzone MCF7 2 −0.871 0.03306 0.0548 100
66 Pioglitazone PC3 5 −0.586 0.03585 0.3436 60
67 Levocabastine MCF7 2 −0.866 0.03626 0.0615 100
68 Noretynodrel MCF7 2 −0.865 0.03628 0.0822 100
69 Trifluoperazine MCF7 9 −0.448 0.03655 0.2308 55
70 15-Delta prostaglandin J2 HL60 3 −0.738 0.03684 0.1429 100
71 Etoposide MCF7 2 −0.864 0.03712 0.1 100
72 Bufexamac MCF7 2 −0.863 0.0376 0.0556 100
73 0179445-0000 PC3 4 −0.644 0.03853 0.0685 75
74 15-Delta prostaglandin J2 PC3 3 −0.734 0.03856 0.1507 100
75 Minaprine PC3 2 −0.858 0.04008 0.031 100
76 Oxymetazoline PC3 2 −0.855 0.04181 0.0345 100
77 Nortriptyline MCF7 2 −0.852 0.04338 0.0901 100
78 CP-690334-01 MCF7 4 −0.633 0.04418 0.1027 50
79 SB-203580 PC3 2 −0.85 0.04515 0.0464 100
80 Scriptaid PC3 2 −0.849 0.04537 0.1596 100
81 Esculetin MCF7 2 −0.848 0.04609 0.0671 100
82 Fluspirilene MCF7 2 −0.848 0.0464 0.1748 100
83 Sulfadoxine MCF7 2 −0.845 0.04829 0.0481 100
84 Monorden PC3 5 −0.562 0.04932 0.106 60
85 Ivermectin MCF7 2 −0.843 0.04937 0.1404 100
86 Norethisterone MCF7 2 −0.842 0.04994 0.0263 100

CMap, Connectivity Map; DEGs, differentially expressed genes. N, number of all instances of the same perturbagen made in the same cell line. A total of 78 compounds were included, among which, four compounds were administered to two different cell lines and two compounds were administered to three different cell lines. Thus, there are 86 rows in the table.

CMap negatively correlated compounds matched by pathway.

Drug name Pathway name Subpathway ID
Alexidine p53 signaling pathway path:04115_2; path:04115_1; path:04115_7
Mefloquine Toll-like receptor signaling pathway path:04620_17; path:04620_18; path:04620_22; path:04620_9
Mefloquine Steroid hormone biosynthesis path:00140_3; path:00140_19; path:00140_16; path:00140_8
Astemizole Toll-like receptor signaling pathway path:04620_12; path:04620_9; path:04620_18; path:04620_17
Thiostrepton p53 signaling pathway path:04115_1
Methotrexate p53 signaling pathway path:04115_7; path:04115_1; path:04115_4; path:04115_3; path:04115_2
Sulconazole Metabolism of xenobiotics by cytochrome P450 path:00980_3
Resveratrol Tryptophan metabolism path:00380_5
Resveratrol Toxoplasmosis path:05145_18
Thioguanosine Steroid hormone biosynthesis path:00140_7; path:00140_8
MG-262 Steroid hormone biosynthesis path:00140_1; path:00140_9; path:00140_8; path:00140_6; path:00140_5
Methylbenzethonium chloride p53 signaling pathway path:04115_1
Monobenzone MAPK signaling pathway path:04010_30
Trifluoperazine Protein processing in endoplasmic reticulum path:04141_18: path:04141_1
5224221 Steroid hormone biosynthesis path:00140_18; path:00140_27; path:00140_9; path:00140_8; path:00140_4
Vitexin Steroid hormone biosynthesis path:00140_19
Disulfiram Protein processing in endoplasmic reticulum path:04141_1
Thioridazine Pathways in cancer path:05200_29; path:05200_18; path:05200_11
Vorinostat p53 signaling pathway path:04115_1; path:04115_2; path:04115_4; path:04115_3
Etoposide p53 signaling pathway path:04115_7; path:04115_1; path:04115_3
Withaferin A Steroid hormone biosynthesis path:00140_25; path:00140_5; path:00140_10; path:00140_4
Pyrvinium Steroid hormone biosynthesis path:00140_6; path:00140_16; path:00140_19; path:00140_17; path:00140_18; path:00140_4
Scriptaid Steroid hormone biosynthesis path:00140_9; path:00140_6; path:00140_17; path:00140_16; path:00140_5; path:00140_1
Trichostatin A Steroid hormone biosynthesis path:00140_10; path:00140_19; path:00140_6; path:00140_8; path:00140_9
0173570-0000 Steroid hormone biosynthesis path:00140_16; path:00140_4; path:00140_17; path:00140_3; path:00140_6; path:00140_10; path:00140_18; path:00140_13; path:00140_7; path:00140_8
Troglitazone Cell cycle path:04110_17
Prochlorperazine Protein processing in endoplasmic reticulum path:04141_1
LY-294002 Steroid hormone biosynthesis path:00140_6; path:00140_27
Tanespimycin MAPK signaling pathway path:04010_15
Monorden Steroid hormone biosynthesis path:00140_3; path:00140_7; path:00140_18

CMap, connectivity map.