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. 2025 Jan-Dec;19(1):e70027.
doi: 10.1049/syb2.70027.

Gut Microbiota Mediate Periampullary Cancer Through Extracellular Matrix Proteins: A Causal Relationship Study

Zeying Cheng  1   2 , Liqian Du  1   2 , Hongxia Zhang  3 , Zhongkun Zhou  1   2 , Yunhao Ma  1   2 , Baizhuo Zhang  1   2 , Lixue Tu  1   2 , Tong Gong  1   2 , Zhenzhen Si  1   2 , Hong Fang  1   2 , Jianfang Zhao  4 , Peng Chen  1   2
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Gut Microbiota Mediate Periampullary Cancer Through Extracellular Matrix Proteins: A Causal Relationship Study

Zeying Cheng et al. IET Syst Biol. 2025 Jan-Dec.

Abstract

Recent studies have reported that gut microbiota may play a role in the occurrence and development of digestive system cancers. Periampullary cancer is a relatively rare digestive system cancer which lacks effective targeted therapy and specific drugs. The purpose of this study is to elucidate the relationship between periampullary cancer and gut microbiota. This work collected public genome-wide association study (GWAS) data from 211 gut microbial taxa and three types of cancer related to periampullary cancer, which were used for two-sample Mendelian randomisation (MR) analysis. Based on the analysis of differentially expressed genes between periampullary cancer and adjacent normal tissue, extracellular matrix proteins were selected for further multivariable MR analysis. Finally, the Connectivity Map was used to screen potential therapeutic drugs for periampullary cancer. Two-sample MR results confirmed that nine microbial taxa, Tyzzerella, Alloprevotella, Holdemania, LachnospiraceaeUCG010, Terrisporobacter, Alistipes, Rikenellaceae, Anaerofilum and Dialister, were associated with periampullary cancer risk. Multivariable MR discovered extracellular matrix-related proteins [Collagen alpha-1(I) chain, Laminin, Fibronectin and Mucin] that may play a role in the association between gut microbiota and periampullary cancer. Finally, the Connectivity Map identified 27 potential candidate drugs. This study can provide theoretical basis for future prevention and diagnostic research on this rare cancer.

Keywords: bioinformatics; cancer; microorganisms.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Schematic diagram of Mendelian randomisation and differential expression analysis.
FIGURE 2
FIGURE 2
Results of differential gene enrichment analysis. (A) GO enrichment analysis. (B) KEGG pathway analysis.
FIGURE 3
FIGURE 3
PPI networks constructed based on DEGs. Genes with a degree algorithm score of no less than 10 are displayed. The deeper the colour and the larger the node, the higher the gene score.
See this image and copyright information in PMC

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