A Pipeline to Call Multilevel Expression Changes between Cancer and Normal Tissues and Its Applications in Repurposing Drugs Effective for Gastric Cancer

Table 1

Gene expression differential analysis in interleukin-4 and interleukin-13 signaling pathways.

zGroup	Gene_id	Log2FoldChange	Padj
Normal	FCGR1A	2.160463
	TNFAIP2	1.704628
	GBP5	1.813918
	IL18BP	0.929973
	FOXO4	-0.55515
	VEGFA	0.853914
	SOCS7	0.550096	0.000314
	IL17F	-1.71267	0.003176
	ITGB2	0.647619	0.010578
	STAT3	0.250713	0.018252
	IL24	0.668153	0.065269
	FUT7	-0.54276	0.076443
	IRF5	0.322352	0.103439
	IL10RA	-0.43151	0.114833
	BATF3	0.294055	0.130277
	POPDC3	-0.75591	0.130528
	VEPH1	0.581702	0.140707
	ALOX5	0.292276	0.237381
	ITGB3BP	0.152357	0.309558
	CCL24	-0.39966	0.395529
	MNDA	0.125587	0.62581
	HIF1AN	-0.03136	0.795816
	IL12RB1	-0.06538	0.826025
	ALOX5AP	-0.05154	0.866366
	IL23R	-0.06508	0.866881
	JARID2	0.016533	0.921854
	STAT4	-0.01968	0.942641
Cancer	SBNO2	0.799014
	LBP	3.152698
	ICAM1	1.209842
	IL17C	2.409849
	SOD2	0.739123
	NFKBIA	-0.49383
	TNFAIP3	0.363209	0.043201
	CSF3	0.661918

The results of 35 gene expression differences were extracted from the results of DESeq2. According to the WGCNA clustering results, they were divided into two groups and ranked according to the expression multiples.