Machine learning based on biological context facilitates the identification of microvascular invasion in intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a rare disease associated with a poor prognosis, primarily due to early recurrence and metastasis. An important feature of this condition is microvascular invasion (MVI). However, current predictive models based on imaging have limited efficacy in this regard. This study employed a random forest model to construct a predictive model for MVI identification and uncover its biological basis. Single-cell transcriptome sequencing, whole exome sequencing, and proteome sequencing were performed. The area under the curve of the prediction model in the validation set was 0.93. Further analysis indicated that MVI-associated tumor cells exhibited functional changes related to epithelial-mesenchymal transition and lipid metabolism due to alterations in the NF-kappa B and MAPK signaling pathways. Tumor cells were also differentially enriched for the IL-17 signaling pathway. There was less infiltration of SLC30A1+ CD8+ T cells expressing cytotoxic genes in MVI-associated ICC, whereas there was more infiltration of myeloid cells with attenuated expression of the MHC II pathway. Additionally, MVI-associated intercellular communication was closely related to the SPP1-CD44 and ANXA1-FPR1 pathways. These findings resulted in a brilliant predictive model and fresh insights into MVI.