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Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer. | LitMetric

Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer.

Adv Cancer Res

Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, VA, United States; VCU Institute of Molecular Medicine, Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States. Electronic address:

Published: September 2024

AI Article Synopsis

  • Cancer involves complex cellular processes and gene expression, and advancements in techniques like single-cell sequencing and seqFISH have enabled precise mapping of cells based on gene expression.
  • The integration of machine learning and artificial intelligence with these datasets allows for comprehensive analysis of any tissue, offering insights into how cells communicate in the tumor microenvironment and identifying potential biomarkers for treatment.
  • This review focuses on pancreatic cancer, detailing how spatial transcriptomics and AI aid in understanding the disease and suggesting that combining these tools with other data can lead to earlier diagnoses and more personalized treatment approaches for patients.

Article Abstract

Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach.

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Source
http://dx.doi.org/10.1016/bs.acr.2024.06.007DOI Listing

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