Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 30:14:1189570.
doi: 10.3389/fendo.2023.1189570. eCollection 2023.

A possible genetic association between obesity and colon cancer in females

Xiao-Li Zhang  1 , Xin-Feng Zhang  1 , Yuan Fang  2 , Meng-Li Li  3 , Ruo Shu  1 , Yi Gong  4 , Hua-You Luo  1 , Yan Tian  1
Affiliations

A possible genetic association between obesity and colon cancer in females

Xiao-Li Zhang et al. Front Endocrinol (Lausanne). .

Abstract

Object: There is mounting clinical evidence that an increase in obesity is linked to an increase in cancer incidence and mortality. Although studies have shown a link between obesity and colon cancer, the particular mechanism of the interaction between obesity and colon cancer in females remains unknown. The goal of this work is to use bioinformatics to elucidate the genetic link between obesity and colon cancer in females and to investigate probable molecular mechanisms.

Methods: GSE44076 and GSE199063 microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. In the two microarray datasets and healthy controls, the online tool GEO2R was utilized to investigate the differential genes between obesity and colon cancer. The differential genes (DEGs) identified in the two investigations were combined. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies were performed on the DEGs. The STRING database and Cytoscape software were then used to build protein-protein interaction (PPI) networks to discover hub genes. NetworkAnalyst was also used to build networks of target microRNAs (miRNAs) and hub genes, as well as networks of transcriptions.

Results: Between the two datasets, 146 DEGs were shared. The DEGs are primarily enriched in inflammatory and immune-related pathways, according to GO analysis and KEGG. 14 hub genes were identified via PPI building using the Cytoscape software's MCODE and CytoNCA plug-ins: TYROBP, CD44, BGN, FCGR3A, CD53, CXCR4, FN1, SPP1, IGF1, CCND1, MMP9, IL2RG, IL6 and CTGF. Key transcription factors for these hub genes include WRNIP1, ATF1, CBFB, and NR2F6. Key miRNAs for these hub genes include hsa-mir-1-3p, hsa-mir-26b-5p, hsa-mir-164a-5p and hsa-mir-9-5p.

Conclusion: Our research provides evidence that changed genes are shared by female patients with colon cancer and obesity. Through pathways connected to inflammation and the immune system, these genes play significant roles in the emergence of both diseases. We created a network between hub genes and miRNAs that target transcription factors, which may offer suggestions for future research in this area.

Keywords: GEO; colon cancer; genes; obesity; women.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Volcano and Venn plots of differentially expressed genes. (A), genes differentially expressed between obese and control in the GSE199063 dataset (red dots represent up-regulated genes, blue dots represent down-regulated genes). (B), genes differentially expressed between colon cancer and healthy control in the GSE44076 dataset (red dots represent up-regulated genes, blue dots represent down-regulated genes). (C), Number of genes overlapping differentially expressed genes in the above two datasets. (CC, colon cancer).
Figure 2
Figure 2
Bubble plot of GO enrichment analysis results. x-axis indicates the proportion of genes per functional term. y-axis indicates the annotated terms of gene enrichment. The circle size represents the number of genes: the larger the circle, the higher the number of genes. The circle color represents the adjusted P value: the redder the color, the higher the degree of gene enrichment. MF, Molecular Function; BP, Biological Process; CC, Cellular Component.
Figure 3
Figure 3
Bubble plot of KEGG enrichment analysis results. x-axis indicates the proportion of genes per functional term. y-axis indicates the annotated terms of gene enrichment. The circle size represents the number of genes: the larger the circle, the higher the number of genes. The circle color represents the adjusted P value: the redder the color, the higher the degree of gene enrichment.
Figure 4
Figure 4
Network diagram of KEGG enrichment analysis results. The line segments connect genes and enrichment pathways, and different colors represent different enrichment pathways. The size of the circles represents the different number of connected line segments, the larger the circle, the more genes and pathways are connected, the gray circle represents genes, the yellow circle represents pathways.
Figure 5
Figure 5
The protein interaction network obtained from the analysis of PPI with Cytoscape plugin cytoNCA, the circles represent the proteins and the lines connect the interacting proteins.
Figure 6
Figure 6
The protein interaction network obtained by analyzing PPI with Cytoscape plugin MCODE, (A–D) represent the four sub-modules obtained by MCODE plugin.
Figure 7
Figure 7
Transcription factor-gene network of 12 hub genes, red circles represent hub genes and blue diamonds represent transcription factors.
Figure 8
Figure 8
miRNA-gene network of 9 hub genes, red circles represent hub genes, yellow diamonds represent miRNAs.
Figure 9
Figure 9
Transcription factor-miRNA-gene network of 13 hub genes, red circles represent hub genes, blue diamonds represent transcription factors, and orange diamonds represent miRNAs.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Caballero B. Humans against obesity: who will win? Adv Nutr (2019) 10(suppl_1):S4–9. doi: 10.1093/advances/nmy055 - DOI - PMC - PubMed
    1. Flier JS. Obesity wars: molecular progress confronts an expanding epidemic. Cell (2004) 116(2):337–50. doi: 10.1016/s0092-8674(03)01081-x - DOI - PubMed
    1. Broughton DE, Moley KH. Obesity and female infertility: potential mediators of obesity’s impact. Fertil Steril (2017) 107(4):840–7. doi: 10.1016/j.fertnstert.2017.01.017 - DOI - PubMed
    1. Sung MK, Yeon JY, Park SY, Park JH, Choi MS. Obesity-induced metabolic stresses in breast and colon cancer. Ann N Y Acad Sci (2011) 1229:61–8. doi: 10.1111/j.1749-6632.2011.06094.x - DOI - PubMed
    1. Wells JC, Marphatia AA, Cole TJ, McCoy D. Associations of economic and gender inequality with global obesity prevalence: understanding the female excess. Soc Sci Med (2012) 75(3):482–90. doi: 10.1016/j.socscimed.2012.03.029 - DOI - PubMed

Publication types

Grants and funding

This work was supported by the National Natural Science Foundation of China (82060525).