Abstract 3528: Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4

Abstract

Abstract N6-methyladenosine (m6A) is the most abundant mRNA modification with a crucial role in cellular processes. Its involvement in cancers is well-established. Dysregulation of m6A is linked to cancer initiation, progression, and therapy resistance, impacting the tumor microenvironment (TME) and promoting metastasis. Understanding m6A's regulatory role in cancer is essential for identifying new therapeutic targets. We considered the construction of a knowledge graph (KG) representing molecular regulatory mechanisms (MRMs) of m6A in healthy conditions and different cancers from PubMed papers. Such a KG consists of interconnected triplets of (head node, regulatory relationship, tail node), each denoting regulation between genes or a gene and a phenotype/process. Advances in natural language processing (NLP) have provided tools such as SemRep and GNBR to automate the extraction of these triplets from the literature. However, these methods don’t extract contextual information, such as the type of cancers or cells in which these MRMs occur, leading to contradictory regulations in the constructed KG. In addition, existing approaches were designed to extract generic regulatory relations and therefore struggled in properly capturing unique concepts associated with m6A regulatory mechanisms. To tackle this challenge, we designed a novel ChatGPT-4 prompt to extract relational graphs from papers tailored for m6A regulatory mechanisms. An annotated dataset of 400 titles on m6A-related MRMs were created to evaluate the performance. We then applied the prompt to 1023 papers to create a m6A Molecular Regulatory Mechanism Knowledge Graph (m6A-MRM-KG). This graph illuminates m6A's roles in gene expression regulation, especially in cancer and immunity. Anticipated to enhance our understanding of cancer development, it provides insights into potential therapeutic strategies. The m6A-MRM-KG not only organizes information but also empowers researchers to uncover novel insights in the dynamic relationships of m6A modifications in diverse biological contexts. Citation Format: Sumin Jo, Xidong Wu, Yiming Zeng, Arun Das, Ting-He Zhang, David Alexander Spellman, Adam Ferris, Shou-Jiang Gao, Jianqiu (Michelle) Zhang, Yu-Chiao Chiu, Yufei Huang. Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3528.