Fatty acid-binding protein 4 as a biomarker for colon adenocarcinoma risk and prognosis: Challenges and future directions

Abstract

In this letter, we have commented on the study by Zhang et al, which utilized bioinformatics and immunohistochemistry to assess the value of fatty acid-binding protein 4 (FABP4) as a biomarker for colon adenocarcinoma (COAD). Their findings improve our understanding of FABP4 in cancer cell adhesion and immune cell infiltration. However, differential expression analysis was insufficient to demonstrate a direct association between FABP4 expression and the occurrence and progression of COAD. Using Mendelian randomization for causal inferences can provide a solid biological foundation for model construction. Furthermore, integrating machine and deep learning approaches may yield more robust and precise prognostic outcomes than using a single Cox regression model. In addition, integrating genome-wide association study data to identify additional pathogenic genes involved in the regulation of fatty acid metabolism may facilitate the development of a multi-target strategy. This approach could potentially mitigate the compensatory effects associated with targeting FABP4 alone, and enhance therapeutic efficacy. Enhancing experimental validation would further improve the reliability of the results. With the continuous advancement of machine learning, multi-omics technologies, and experimental techniques, future studies may systematically integrate diverse sequencing datasets to offer novel insights into the early diagnosis, individualized treatment, and prognostic evaluation of COAD.

Keywords: Fatty acid-binding protein 4; Colon adenocarcinoma; Mendelian randomization; Machine learning; Deep learning; Multiple targets

Core Tip: Fatty acid-binding protein 4 (FABP4) is a promising biomarker and potential therapeutic target for colon adenocarcinoma diagnosis and prognosis. Our letter calls for a stronger biological rationale for FABP4’s role and advocates using Mendelian randomization to confirm its causal links. Incorporating machine learning and deep learning can yield more precise prognostic models. We also propose exploring multi-target strategies related to FABP4 and encourage future studies to combine experimental validation to reduce bioinformatics false positives. This allows us to elucidate underlying molecular biological mechanisms, offering fresh insights for personalized colon adenocarcinoma treatment.