Tumor Heterogeneity in Gastrointestinal Cancer Based on Multimodal Data Analysis.

Autor:	Ai D; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China., Du Y; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China., Duan H; Department of Statistics and Financial Mathematics, School of Mathematics, South China University of Technology, Guangzhou 510641, China., Qi J; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China., Wang Y; School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Jazyk:	angličtina
Zdroj:	Genes [Genes (Basel)] 2024 Sep 13; Vol. 15 (9). Date of Electronic Publication: 2024 Sep 13.
DOI:	10.3390/genes15091207
Abstrakt:	Background: Gastrointestinal cancer cells display both morphology and physiology diversity, thus posing a significant challenge for precise representation by a single data model. We conducted an in-depth study of gastrointestinal cancer heterogeneity by integrating and analyzing data from multiple modalities. Methods: We used a modified Canny algorithm to identify edges from tumor images, capturing intricate nonlinear interactions between pixels. These edge features were then combined with differentially expressed mRNA, miRNA, and immune cell data. Before data integration, we used the K-medoids algorithm to pre-cluster individual data types. The results of pre-clustering were used to construct the kernel matrix. Finally, we applied spectral clustering to the fusion matrix to identify different tumor subtypes. Furthermore, we identified hub genes linked to these subtypes and their biological roles through the application of Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Ontology (GO) enrichment analysis. Results: Our investigation categorized patients into three distinct tumor subtypes and pinpointed hub genes associated with each. Genes MAGI2-AS3 , MALAT1 , and SPARC were identified as having a differential impact on the metastatic and invasive capabilities of cancer cells. Conclusion: By harnessing multimodal features, our study enhances the understanding of gastrointestinal tumor heterogeneity and identifies biomarkers for personalized medicine and targeted treatments. Competing Interests: The authors declare no conflicts of interest.
Databáze:	MEDLINE
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