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. 2021 Sep 14;11:705939. doi: 10.3389/fonc.2021.705939

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Copyright © 2021 Zhou, Li, Morita, Hachimura, Miyakawa, Takahashi and Tanokura

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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DEG mining between non-CRC tissues and CRC tissues based on the GEO database. (A) Heatmap of the differential gene expression in all the samples from non-CRC and CRC at the datasets GSE21510, GSE24514, and GSE8671; the color bar represents differential gene expression magnitude. (B) Identification of the differentially expressed genes between non-CRC and CRC at the datasets GSE21510, GSE24514, and GSE8671. (The red and green spots in the CRC were classified by the upregulated and downregulated genes compared with non-CRC, respectively.) (C) Venn plot of DEGs at the datasets GSE21510, GSE24514, and GSE8671 (6,544 genes were mapped in 7,173 DEGs of GSE21510; 3,790 genes were mapped in 3,883 DEGs of GSE24514; and 7,446 genes were mapped in 8,479 DEGs of GSE8671). (D) KEGG pathways and GO functional analysis of overlapped DEGs at the datasets GSE21510, GSE24514, and GSE8671 based on the Enrichr online tool.