Network Comparison of Inflammation in Colorectal Cancer and Alzheimer’s Disease

Table 3

Subpathways previously not associated with the two diseases. These subpathways were selected from the most significant 100 subpathways in each network. Subpathway (linear signaling flow) with fold-change (the numeral in parenthesis) of the disease group over the control group is represented in each dataset. The most significant 100 subpathways for each dataset are provided in Supplementary Tables S3–S5. The notation in the flow is “B <- A: A activates B” and “B ∣- A: A represses B.”

KEGG pathway	GSE4107 (CRC) subpathway; value	GSE1297 (AD) subpathway; value	GSE12685 (AD) subpathway; value
Hedgehog signaling (hsa04340)	PTCH1 (1.863) <- GLI2 (2.878) ∣- CSNK1G1 (0.587); 0.000035		PTCH2 (0.938) <- GLI3 (0.682) ∣- GSK3B (1.513); 0.0015
	WNT3 (3.147) <- GLI2 (2.878) ∣- CSNK1G1 (0.587); 0.000223
Axon guidance (hsa04360)		PAK3 (0.732) <- RAC1 (0.943) ∣- PLXNB3 (1.627) <- SEMA4C (1.283); 0.0008	CFL1 (1.157) ∣- LIMK1 (0.896) <- PAK4 (0.871) <- RAC3 (0.892) <- PLXNA3 (0.954) <- FES (0.841); 0.0011
WNT signaling (hsa04310)	JUN (4.179) <- TCF7L1 (2.735) <- CTNNB1 (2.562) ∣- GSK3B (0.735) ∣- DVL3 (1.608) <- FZD10 (6.256) <- WNT3 (3.147) <- PORCN (1.279); 0.000114
	JUN (4.179) <- TCF7L1 (2.735) <- CTNNB1 (2.562) ∣- GSK3B (0.735) ∣- DVL3 (1.608) <- APC2 (2.201) <- AXIN2 (2.307) <- CSNK1A1 (1.963); 0.00016
Pathways in cancer (hsa05200)	MMP2 (3.031) <- JUN (4.179) <- MAPK1 (2.425) <- MAP2K1 (1.162) <- ARAF (4.631) <- HRAS (1.027) <- SOS1 (1.624) <- GRB2 (1.613) <- IGF1R (2.299) <- IGF1 (2.529); 0.000022
ECM-receptor interaction (hsa04512)	SDC2 (3.091) <- TNC (9.557); 0.000026	SDC3 (0.849) <- COL5A2 (0.162); 0.003	SDC1 (0.865) <- COL3A1 (0.865); 0.0017
	SDC2 (3.091) <- FN1 (5.594); 0.000125
Neurotrophin signaling (hsa04722)			BAD (1.279) ∣- AKT2 (0.856) <- PDK1 (0.943) <- PIK3CD (0.576) <- GAB1 (0.997) <- SHC2 (0.844) <- NTRK1 (0.945) <- NTF3 (0.784); 0.0008