Annals of surgical oncology · 2025
Causal Inference and Prognostic Modeling in Colorectal Cancer through Integration of Computational Pathology and Mendelian Randomization.
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Bao Wenhao, Wang Tianying, Wang Yulin, Chen Long, Qin Fuyao, Chen Shuang, Miao Lei, Zhou Shaofei
Аннотация
BACKGROUND: While traditional pathology supports the diagnosis and staging of colorectal cancer (CRC), computational pathology provides novel prognostic insights. Mendelian randomization (MR) is effective in uncovering causal relationships in cancer research; however, studies that integrate MR with pathology to investigate gut microbiota (GM), immune cells, and CRC remain limited.
MATERIALS AND METHODS: We analyzed whole-slide images from The Cancer Genome Atlas Colon Adenocarcinoma/Rectal Adenocarcinoma (TCGA-COAD/READ) datasets using ResNet-50 and CellProfiler to extract pathological features. MR analysis and mediation analyses were then performed to identify causal links between GM, immune cells, and CRC. Causal single-nucleotide polymorphisms (SNPs) were mapped to corresponding genes (microbiome-immune genes), and the expression levels of these genes were correlated with the extracted image features. Finally, a prognostic model was constructed using machine learning algorithms.
RESULTS: We identified 55 causal relationships, 6 mediating effects, and 15 microbiome-immune genes. Among the extracted pathological features, 21 were found to be associated with these microbiome-immune genes. The prognostic model developed in this study demonstrated accurate performance in predicting CRC prognosis.
CONCLUSIONS: By integrating MR with computational pathology, we elucidated the causal relationships among GM, immune cells, and CRC, and successfully constructed a prognostic prediction model. This work provides new targets for the precision treatment of CRC.
MATERIALS AND METHODS: We analyzed whole-slide images from The Cancer Genome Atlas Colon Adenocarcinoma/Rectal Adenocarcinoma (TCGA-COAD/READ) datasets using ResNet-50 and CellProfiler to extract pathological features. MR analysis and mediation analyses were then performed to identify causal links between GM, immune cells, and CRC. Causal single-nucleotide polymorphisms (SNPs) were mapped to corresponding genes (microbiome-immune genes), and the expression levels of these genes were correlated with the extracted image features. Finally, a prognostic model was constructed using machine learning algorithms.
RESULTS: We identified 55 causal relationships, 6 mediating effects, and 15 microbiome-immune genes. Among the extracted pathological features, 21 were found to be associated with these microbiome-immune genes. The prognostic model developed in this study demonstrated accurate performance in predicting CRC prognosis.
CONCLUSIONS: By integrating MR with computational pathology, we elucidated the causal relationships among GM, immune cells, and CRC, and successfully constructed a prognostic prediction model. This work provides new targets for the precision treatment of CRC.