Applications and advances of multi-omics technologies in gastrointestinal tumors

Abstract

Gastrointestinal tumors pose a significant clinical challenge due to their high heterogeneity and the difficulties in early diagnosis. The article systematically reviews the latest advances in multi-omics technologies in gastrointestinal tumor research, focusing on their contributions to early screening, biomarker discovery, and treatment optimization. Genomics reveals genetic characteristics and heterogeneity of tumors; transcriptomics helps identify molecular subtypes and potential therapeutic targets; proteomics provides important information on core proteins and the immune microenvironment; and metabolomics offers promising biomarkers for early diagnosis. Furthermore, emerging fields such as epigenomics, metagenomics, and lipidomics, through the construction of multi-scale frameworks, have opened new paths for molecular subtyping and targeted therapy. By integrating these multi-dimensional data, multi-omics integration enables a panoramic dissection of driver mutations, dynamic signaling pathways, and metabolic-immune interactions. However, challenges such as data heterogeneity, insufficient algorithm generalization, and high costs limit clinical translation. In the future, the integration of single-cell multi-omics, artificial intelligence, and deep learning technologies with multi-omics may offer more efficient strategies for the precise diagnosis and personalized treatment of gastrointestinal tumors.

Keywords: biomarkers; early screening; gastrointestinal tumors; multi-omics technologies; single-cell genomics; treatment optimization.

FIGURE 1

Schematic diagram of the integrated application of basic omics technologies in digestive tract tumors: information extracted from digestive tract tumors is processed using genomics, transcriptomics, proteomics, and metabolomics methods. The processed information is aggregated, analyzed, and uploaded to cloud storage, providing data support for early diagnosis and precision treatment of tumors.

FIGURE 2

Workflow of integrated analysis of multi-omics data. Integrating foundational omics data (such as genomics and metabolomics) with emerging fields like epigenomics, followed by standardized preprocessing including normalization and noise reduction, then employing multi-omics factor analysis and deep learning algorithms to construct AI models, ultimately achieving precision medicine applications such as tumor molecular subtyping and treatment response prediction.