---
title: "Intratumoral Microbiome in Cancer: Current Findings and Evolving Detection Technologies"
id: "pubmed-42764218"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42764218"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42764218/"
doi: "10.3760/cma.j.cn112152-20260303-00105"
published_at: "2026-09-23T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Intratumoral Microbiome in Cancer: Current Findings and Evolving Detection Technologies
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42764218
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42764218/)
- **DOI:** [10.3760/cma.j.cn112152-20260303-00105](https://doi.org/10.3760%2Fcma.j.cn112152-20260303-00105)
- **Published At:** 2026-09-23T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The **intratumoral microbiome** is a core functional component of the **tumor microenvironment** and a growing frontier in oncology research. - Microorganisms detected within tumors include **bacteria**, **fungi**, and **viruses**, which can influence tumorigenesis and progression. - Microbial effects on tumors occur via multiple pathways such as secretion of metabolites, induction of genomic instability, and remodeling of the immune microenvironment. - Species composition and microbial abundance show **cancer-type specificity**; associations with patient prognosis are highly context dependent. - Current detection approaches for intratumoral microbes include **in situ detection technologies**, **metagenomic sequencing**, and **computational pathology–driven intelligent detection**. - Each detection modality has distinct strengths and limitations: in situ methods provide spatial resolution, metagenomics enables broad taxonomic profiling, and computational pathology/AI aims to overcome signal detection and quantification bottlenecks. - Deep learning–centered intelligent detection is increasingly addressing key challenges such as identifying low-abundance microbial signals, precise quantification, and spatial distribution analysis. - Future directions emphasize deep integration of **three-dimensional pathological imaging**, **spatial omics**, and **multi-modal foundation models** to enable multi-dimensional data integration. - Advances in multimodal integration are expected to clarify microbe–host regulatory mechanisms and support development of precision diagnostics and therapeutic strategies based on individual tumor microecology. - The reviewed article reports no conflicts of interest and lists funding sources from the National Natural Science Foundation of China and the Healthy Zhejiang One Million People Cohort grant, as noted in the source metadata.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Department of Big Data Health Sciences, School of Public Health, Zhejiang University, Hangzhou 310058, China Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou 310058, China. * 2 Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou 310058, China Center of Clinical Big Data and Analytics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou 310058, China. * PMID: **42764218** * DOI: [ 10.3760/cma.j.cn112152-20260303-00105 ](https://doi.org/10.3760/cma.j.cn112152-20260303-00105) Item in Clipboard Review # [Current research status of the intratumoral microbiome and evolution of detection technologies] [Article in Chinese] Q Y Huang et al. Zhonghua Zhong Liu Za Zhi. 2026. Show details Display options Display options Format Abstract PubMed PMID Zhonghua Zhong Liu Za Zhi Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Zhonghua+Zhong+Liu+Za+Zhi%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Zhonghua+Zhong+Liu+Za+Zhi%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42764218/) . 2026 Sep 23;48(9):1124-1138. doi: 10.3760/cma.j.cn112152-20260303-00105. ### Authors [Q Y Huang](https://pubmed.ncbi.nlm.nih.gov/?term=Huang+QY&cauthor_id=42764218)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42764218/#short-view-affiliation-1 "Department of Big Data Health Sciences, School of Public Health, Zhejiang University, Hangzhou 310058, China Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou 310058, China."), [H R Xiang](https://pubmed.ncbi.nlm.nih.gov/?term=Xiang+HR&cauthor_id=42764218)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42764218/#short-view-affiliation-1 "Department of Big Data Health Sciences, School of Public Health, Zhejiang University, Hangzhou 310058, China Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou 310058, China."), [H K Tu](https://pubmed.ncbi.nlm.nih.gov/?term=Tu+HK&cauthor_id=42764218)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42764218/#short-view-affiliation-2 "Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou 310058, China Center of Clinical Big Data and Analytics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou 310058, China.") ### Affiliations * 1 Department of Big Data Health Sciences, School of Public Health, Zhejiang University, Hangzhou 310058, China Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou 310058, China. * 2 Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou 310058, China Center of Clinical Big Data and Analytics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou 310058, China. * PMID: **42764218** * DOI: [ 10.3760/cma.j.cn112152-20260303-00105 ](https://doi.org/10.3760/cma.j.cn112152-20260303-00105) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract in [ English, ](https://pubmed.ncbi.nlm.nih.gov/42764218/#eng-abstract) [ Chinese ](https://pubmed.ncbi.nlm.nih.gov/42764218/#zho-abstract) As a core functional component of the tumor microenvironment, the regulatory role of intratumoral microbiome in tumorigenesis and progression has become as a frontier research direction in oncology. Microorganisms such as bacteria, fungi, and viruses participate in the regulation of tumor biological mechanisms through multiple pathways, including metabolite secretion, induction of genomic instability, and remodeling of the immune microenvironment; their species composition and abundance characteristics exhibit distinct cancer-type specificity, and their impact on patient prognosis is highly context-dependent. Current detection systems for the intratumoral microbiome mainly encompass in situ detection technologies, metagenomic sequencing, and computational pathology-driven intelligent detection, each with its own advantages and limitations, among which intelligent detection centered on deep learning is gradually overcoming the technical bottlenecks of identifying low-abundance microbial signals, achieving accurate quantification, and resolving spatial distribution. In the future, with the deep integration of three-dimensional pathological imaging, spatial omics, and multi-modal foundation models, intratumoral microbiome research will advance toward the in-depth development of multi-dimensional data integration, providing innovative ideas and technical pathways for elucidating the regulatory mechanisms between microorganisms and the host and for developing precision diagnostic and treatment strategies based on individual microecological characteristics. 瘤内微生物组作为肿瘤微环境的核心功能单元，其对肿瘤发生、发展的调控作用已成为肿瘤学领域的前沿研究方向。细菌、真菌、病毒等微生物通过代谢产物分泌、基因组不稳定性诱导及免疫微环境重塑等多重途径，参与肿瘤生物学机制调控，物种组成与丰度特征呈现显著癌种特异性，对患者预后的影响具有高度背景依赖性。当前瘤内微生物检测体系主要涵盖原位探测技术、宏基因组测序及计算病理学驱动的智能化检测，各类技术各有优劣，其中以深度学习为核心的智能化检测正逐步突破低丰度微生物信号识别、精准定量及空间分布解析的技术瓶颈。未来，随着三维病理成像、空间组学与多模态基础模型的深度融合，瘤内微生物组研究将向多维数据整合方向纵深发展，为阐明微生物与宿主的调控机制、开发基于个体微生态特征的肿瘤精准诊疗策略提供创新思路和技术路径。. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement 所有作者声明无利益冲突 ## Similar articles * [ Personality Theories. ](https://pubmed.ncbi.nlm.nih.gov/42475469/) Gallios JM, Iyer V, Kaylor LE.Gallios JM, et al.2026 Jun 20. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–.2026 Jun 20. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–.PMID: 42475469Free Books & Documents. * [ Crosstalk Between Intratumoral Microbes and Tumor Immunity: Implications for Tumor Therapy. ](https://pubmed.ncbi.nlm.nih.gov/41589006/) Li F, Qiao L, Liang X, Zhang Y, Liang N, Xie J, Deng G, Hao Y, Hu P, Wu X, Ding F, Feng C, Mu Y, Zhang J.Li F, et al.Cancer Med. 2026 Feb;15(2):e71575. doi: 10.1002/cam4.71575.Cancer Med. 2026.PMID: 41589006Free PMC article.Review. * [ Emerging technologies and current challenges in intratumoral microbiota research. ](https://pubmed.ncbi.nlm.nih.gov/41552720/) Wang Z, Zhang T, Liu Y.Wang Z, et al.Front Cell Infect Microbiol. 2026 Jan 2;15:1685862. doi: 10.3389/fcimb.2025.1685862. eCollection 2025.Front Cell Infect Microbiol. 2026.PMID: 41552720Free PMC article.Review. * [ Intratumoral microbiota in tumorigenesis: A double-edged sword. ](https://pubmed.ncbi.nlm.nih.gov/42409802/) Fu X, Chen H, Du M, Li P, Zhao Y, Yang H, Li H, Zhang G, He P, Wu S, Zhang M, An Z, Li Z, Ying B, Silva JRH, An L, Luo H.Fu X, et al.Chin Med J (Engl). 2026 Sep 20;139(18):2706-2720. doi: 10.1097/CM9.0000000000004178. Epub 2026 Jul 3.Chin Med J (Engl). 2026.PMID: 42409802Free PMC article.Review. * [ The intratumoral microbiota in breast cancer: from basic research to clinical translation. ](https://pubmed.ncbi.nlm.nih.gov/40948461/) Xu Y, Wang MC.Xu Y, et al.Gut Microbes. 2025 Dec;17(1):2560695. doi: 10.1080/19490976.2025.2560695. Epub 2025 Sep 15.Gut Microbes. 2025.PMID: 40948461Free PMC article.Review. 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