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

Author:

Xu Shuaishuai12,Wan Mingyu2,Ye Chanqi2,Chen Ruyin2,Li Qiong2,Zhang Xiaochen2,Ruan Jian12ORCID

Affiliation:

1. Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University , 25 Taiping Road, Luzhou 646000, Sichuan Province , China

2. Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, and Key Laboratory of Cancer Prevention and Intervention, Ministry of Education , 79 Qingchun Road, Hangzhou 310000, Zhejiang Province , China

Abstract

Abstract Intrahepatic cholangiocarcinoma 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 nuclear factor-kappa B and mitogen-activated protein kinase signaling pathways. Tumor cells were also differentially enriched for the interleukin-17 signaling pathway. There was less infiltration of SLC30A1+ CD8+ T cells expressing cytotoxic genes in MVI-associated intrahepatic cholangiocarcinoma, whereas there was more infiltration of myeloid cells with attenuated expression of the major histocompatibility complex 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.

Funder

R&D Program of Zhejiang

Beijing Science and Technology Innovation Medical Development Foundation

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Beijing Xisike Clinical Oncology Research Foundation

Publisher

Oxford University Press (OUP)

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