多组学技术与人工智能融合时代新型肿瘤标志物在肿瘤诊治中的研究进展

doi:10.16150/j.1671-2870.2026.03.004

摘要/Abstract

摘要：

多组学技术与人工智能融合时代共同推动着更精准、动态的新型肿瘤标志物在肿瘤诊治中的应用。在基因组学方面，高通量测序技术推动了肿瘤驱动基因、表观遗传重编程及循环游离DNA片段组学的深入研究，为肿瘤早筛、分子分型和靶向治疗提供了可落地的生物标志物，如错配修复（mismatch repair，MMR）缺陷预测程序性死亡蛋白1（programmed death-1， PD-1）抑制剂疗效、甲基化标志物联合蛋白标志物用于肝细胞癌早期检测等。转录组学通过分析mRNA及非编码RNA，结合单细胞测序技术，揭示了肿瘤异质性，并鉴定出疗效评估标志物。蛋白质组学利用质谱技术解析蛋白质表达水平及翻译后修饰（磷酸化、糖基化、乙酰化），突破了早期检测灵敏度瓶颈，为肿瘤分型和治疗靶点发现提供新途径。代谢组学系统定量糖代谢、氨基酸代谢、脂质代谢及核苷酸代谢等代谢重编程过程，为肿瘤诊断和干预策略提供依据。微生物组学则证实，肿瘤内微生物群可作为功能性生物标志物辅助肿瘤诊断与预后评估。尽管面临生物学因素干扰、技术标准化及假阳性风险等挑战，人工智能技术尤其是机器学习算法，为多组学数据挖掘和病理图像评估提供了强大算力，正推动肿瘤标志物向高灵敏度、高特异度方向发展，助力肿瘤精准医疗的全面突破。

关键词: 肿瘤标志物, 多组学技术, 人工智能, 临床转化

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

中图分类号:

R446
R730.43

卢仁泉, 胡玲. 多组学技术与人工智能融合时代新型肿瘤标志物在肿瘤诊治中的研究进展[J]. 诊断学理论与实践, 2026, 25(03): 278-286.

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.

图/表 2

图1

表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]