Journal of Diagnostics Concepts & Practice >
Advances in novel tumor markers for cancer diagnosis and treatment in an era of integration of multi-omics technologies and artificial intelligence
Received date: 2025-10-15
Revised date: 2025-11-03
Accepted date: 2026-02-10
Online published: 2026-06-27
The integration of multi-omics technologies and artificial intelligence is jointly promoting the application of more precise and dynamic novel tumor markers in cancer diagnosis and treatment. In terms of genomics, high-throughput sequencing technologies have promoted in-depth research into tumor driver genes, epigenetic reprogramming, and circulating cell-free DNA fragmentomics, providing actionable biomarkers for early cancer screening, molecular subtyping, and targeted therapy. Examples include the use of mismatch repair (MMR) deficiency to predict the efficacy of programmed death-1 (PD-1) inhibitors, and the combination of methylation markers with protein markers for the early detection of hepatocellular carcinoma. Transcriptomics, by analyzing mRNA and non-coding RNA combined with single-cell sequencing technologies, reveals tumor heterogeneity and identifies biomarkers for efficacy evaluation. Proteomics utilizes mass spectrometry to analyze protein expression levels and post-translational modifications (phosphorylation, glycosylation, acetylation), overco-ming the sensitivity bottleneck in early detection and providing new approaches for tumor subtyping and the discovery of therapeutic targets. Metabolomics systematically quantifies metabolic reprogramming processes, including glucose metabolism, amino acid metabolism, lipid metabolism, and nucleotide metabolism, providing a basis for tumor diagnosis and intervention strategies. Microbiomics confirms that the intratumoral microbiota can serve as functional biomarkers to aid in tumor diagnosis and prognosis assessment. Despite challenges such as biological interference, technical standardization, and the risk of false positives, artificial intelligence technologies, particularly machine learning algorithms, provide powerful computational capabilities for multi-omics data mining and pathological image assessment. This is driving the development of tumor markers towards high sensitivity and high specificity, and facilitating a comprehensive breakthrough in precision oncology.
LU Renquan , HU Ling . Advances in novel tumor markers for cancer diagnosis and treatment in an era of integration of multi-omics technologies and artificial intelligence[J]. Journal of Diagnostics Concepts & Practice, 2026 , 25(03) : 278 -286 . DOI: 10.16150/j.1671-2870.2026.03.004
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