H] NA 3485 (PMID: 24885658) Hollingshead et al. (2014) GSE48433; 171-PPARγ Agonist Gene ID stomach

H] NA 3485 (PMID: 24885658) Hollingshead et al. (2014) GSE48433; 171-PPARγ Agonist Gene ID stomach cancer [stomach] NA 3282 (PMID: 24885658) Hollingshead et al. (2014) GSE118897; 1- stomach cancer [stomach] NA 628 (PMID: 30404039) Yang et al. (2019) 1-gastric adenocarcinoma (STAD) [stomach] NA 4052 Ingenuity Know-how Base 10-gastric adenocarcinoma (STAD) [stomach] NA 4053 Ingenuity Knowledge Base 102-gastric adenocarcinoma (STAD) [stomach] NA 4056 Ingenuity Information Base 111-gastric adenocarcinoma (STAD) [stomach] NA 4066 Ingenuity Knowledge Base 1.604 0.728 1.155 2.121 2.138 1.342 1.134 0.447 -log10(p) 1.86E00 N/A 1.64E00 1.45E00 2.29E00 0 0 0 24 5 five 10 70 16 20 21 37 five five ten 36 71 71 71 N (tumor samples) N (handle samples)Frontiers in Pharmacology | www.frontiersin.orgMarch 2021 | Volume 12 | ArticleRabben et al.Repositioning Ivermectin in Gastric CancerFIGURE 2 | Gene expression signature and connectivity map (cMAP). (A) Heatmap of human GC gene expression signature that constitutes an activation of cancer disease determined by differential expression of 22,000 genes. Size of square is proportional for the variety of genes contained within the specific function and colour represent activity state (z-score; orange: activated, blue: decreased). (B) Connectivity map (cMap) displaying associations among a large-scale compendium of functional perturbations in cancer cell lines coupled for the human GC gene expression signature based on the L1000 assay (Subramanian et al., 2017). Note: Ivermectin and other known drugs are visualized.regulatory z-scores for canonical pathways and illnesses and biofunctions that overlapped with all the experimental information on the present study had been calculated utilizing the formula described previously (Sitarz et al., 2018). IPA has sophisticated algorithms to calculate predicted functional activation/ inhibition of canonical pathways, diseases and functions, transcription regulators and regulators according to their downstream molecule expressions (TLR2 Antagonist manufacturer QIAGEN Inc., https://www. qiagenbioinformatics.com/products/ingenuitypathway-analysis). Fischer’s precise test was employed to calculate a p-value determining the probability that the association among the genes within the datasets from human GC and mouse GC as well as the canonical pathway or disease/function by opportunity alone.Connectivity Map and Data/Pathway MiningThe notion of a Connectivity Map (cMap) was lately developed, whereby genes, drugs, and illness states are connected by virtue of prevalent gene expression signatures (Qu and Rajpal, 2012; Subramanian et al., 2017; Musa et al., 2018). To determine candidate drugs, the gene expression signature of GC was generated determined by the gene expression profile of human GC. A optimistic cMap score indicates there is a optimistic similarity involving a offered perturbagen’s signature, i.e., genes which might be elevated by treatment (in reference datasets) are also upregulated within the human GC dataset, while a unfavorable score indicates that the two signatures are opposing. cMap was performed employing thegene expression signature of human GC (n 7 GC vs. n 6 typical tissue). Data mining was performed making use of the gene expression profile information of 61 samples from 16 individuals, 26 samples from 26 mice, and 324 samples from seven independent datasets in the TCGA database. Moreover, knowledge-based pathway mining was applied determined by previous research that showed WNT/-catenin signaling pathway as one of the essential pathways in gastric tumorigenesis (Zhao et al., 2014; Rabben et al., 2021). Custom-made molecu.