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Updated: May 11, 2026
In Vivo Inhibition of MicroRNA to Decrease Tumor Growth in Mice
Published on: August 23, 2019
Combining single cell and bulk transcriptomics with Mendelian randomization identifies gut microbiota related
Xiangyu Wang1, Bing Wang1, Shimei Ding2
1Department of Nuclear Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, No.157 West Fifth Street, Xi'an, 710004, Shaanxi, China.
Background:
Imbalance in gut microbiota (GM) may play a role in the development of thyroid cancer (TC), but the specific mechanisms remain unclear. This study aimed to identify prognostic genes associated with both TC and GM and investigate the underlying molecular mechanisms.
Methods:
Public datasets were leveraged to perform differential expression, Mendelian randomization (MR), and machine learning analyses to pinpoint prognostic genes. A nomogram model was developed for survival prediction. Functional roles of candidate genes were explored through gene set enrichment analysis (GSEA), immune infiltration profiling, drug sensitivity prediction, and regulatory network construction. Single-cell RNA sequencing was employed to investigate cell-specific mechanisms in TC.
Results:
MR analysis identified 27 GM traits causally linked to TC, corresponding to 124 genes. Three key prognostic genes (LRP1B, MCM6, and PPARG) were selected to construct a robust prognostic model, which demonstrated high predictive accuracy. GSEA revealed the involvement of lysosomal and oxidative phosphorylation pathways in TC pathogenesis. Immune profiling showed significant variations in five immune cell types, including monocytes, between risk groups, correlating with prognostic gene expression. Drug prediction suggested 138 potentially effective compounds, alongside regulatory elements such as hsa-miR-20a-5p. Single-cell analysis highlighted the pivotal role of fibroblasts in TC progression.
Conclusion:
This study identified three prognostic genes (LRP1B, MCM6, PPARG) linked to TC and GM, offering new insights into the molecular mechanisms and presenting potential biomarkers and therapeutic targets for TC management.
