Open AccessAnalysis of potential genes and pathways associated with the colorectal normal mucosa–adenoma–carcinoma sequence
Author(s) -
Wu Zhuoxuan,
Liu Zhen,
Ge Weiting,
Shou Jiawei,
You Liangkun,
Pan Hongming,
Han Weidong
Publication year - 2018
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.1484
Subject(s) - kegg , colorectal adenoma , colorectal cancer , biology , gene , adenoma , intestinal mucosa , gene expression , cancer research , bioinformatics , medicine , genetics , cancer , transcriptome
This study aimed to identify differentially expressed genes ( DEG s) related to the colorectal normal mucosa–adenoma–carcinoma sequence using bioinformatics analysis. Raw data files were downloaded from Gene Expression Omnibus ( GEO ) and underwent quality assessment and preprocessing. DEG s were analyzed by the limma package in R software (R version 3.3.2). Gene Ontology ( GO ) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway analysis were performed with the DAVID online tool. In a comparison of colorectal adenoma ( n = 20) and colorectal cancer ( CRC ) stage I ( n = 31), II ( n = 38), III ( n = 45), and IV ( n = 62) with normal colorectal mucosa ( n = 19), we identified 336 common DEG s. Among them, seven DEG s were associated with patient prognosis. Five ( HEPACAM 2, ITLN 1, LGALS 2, MUC 12, and NXPE 1 ) of the seven genes presented a sequentially descending trend in expression with tumor progression. In contrast, TIMP 1 showed a sequentially ascending trend. GCG was constantly downregulated compared with the gene expression level in normal mucosa. The significantly enriched GO terms included extracellular region, extracellular space, protein binding, and carbohydrate binding. The KEGG categories included HIF ‐1 signaling pathway, insulin secretion, and glucagon signaling pathway. We discovered seven DEG s in the normal colorectal mucosa–adenoma–carcinoma sequence that was associated with CRC patient prognosis. Monitoring changes in these gene expression levels may be a strategy to assess disease progression, evaluate treatment efficacy, and predict prognosis.