Advances in novel tumor markers for cancer diagnosis and treatment in an era of integration of multi-omics technologies and artificial intelligence

doi:10.16150/j.1671-2870.2026.03.004

Abstract

Abstract:

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.

Key words: Tumor markers, Multi-omics technology, Artificial intelligence, Clinical translation

CLC Number:

R446
R730.43

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.

Figures/Tables 2

Figure 1

Table 1

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标志物	肿瘤类型	临床意义	参考文献
基因组学
MMR	直肠癌	预测PD-1抑制剂疗效	[6]
ARID1A	泛癌	改善免疫治疗耐药	[7]
甲基化+蛋白标志物	早期肝细胞癌	早期筛查	[12]
CpG岛甲基化表型	结直肠癌	早期筛查与治疗靶向性	[13]
特异性DNA甲基化谱	急性白血病	精准分类	[14]
cfDNA片段	肺癌	早期筛查	[16]
cfDNA片段及5hmC数据	肝细胞癌	早期筛查	[17]
转录组学
TRGC2⁺自然杀伤细胞	食管鳞癌	评估疗效	[20]
TPX2	胰腺癌	抑制TPX2表达可增强奥拉帕尼与吉西他滨的联合疗效	[21]
蛋白组学
47个核心蛋白标志物	泛癌	早期筛查与监测手术疗效	[22]
GUK1	肺癌	细胞增殖靶点	[23]
SRC	肝细胞癌	恶性亚型靶点	[24]
糖蛋白	非小细胞肺癌	早期筛查	[26]
H2BK120ac、H3.3K18ac和H4K77ac	肝细胞癌	预后	[27]
EP300	宫颈癌	治疗靶点	[28]
代谢组学
谷氨酰胺	泛癌	调控抗肿瘤免疫	[35]
谷胱甘肽	胃癌	早期筛查与预后评估	[37]
天冬氨酸	乳腺癌	促进肿瘤远端转移	[38]
n-6多不饱和脂肪酸	结直肠癌	早期筛查	[39]
胞嘧啶脱氨酶	胰腺导管腺癌	改善临床获得性耐药	[43]
微生物组学
11种细菌，4种真菌和1种古菌	结直肠癌	早期筛查	[46]
口腔微生物群	胰腺癌等	早期筛查	[47]
脆弱拟杆菌和解没食子酸链球菌等	结直肠癌	早期筛查	[48]