Evidence map›Paper›PMID 42702809›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Tumor Exposomics: A New Paradigm for Individualized Continuous Exposure Monitoring.

Kaicheng Shen, Weiyi Wang, Yang Wang, Yiqiang Wu, Xiaohong Liu, Wei Zhang, Juanjuan Ou

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kaicheng Shen *Sichuan Clinical Research Center For Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.ORCID https://orcid.org/0000-0001-9529-7764
Weiyi Wang *Sichuan Clinical Research Center For Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Yang WangSichuan Clinical Research Center For Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Yiqiang WuState Key Laboratory of Woody Oil Resource Utilization, Central South University of Forestry and Technology, Changsha, China.ORCID https://orcid.org/0000-0002-3477-2135
Xiaohong LiuChongqing Key Laboratory of Trusted Perception and Interaction Technology For Intelligent and Connected Vehicles, National University of Singapore (Chongqing) Research Institute, Chongqing, China.
Wei ZhangState Key Laboratory of Woody Oil Resource Utilization, Central South University of Forestry and Technology, Changsha, China.ORCID https://orcid.org/0000-0002-9756-9994
Juanjuan OuSichuan Clinical Research Center For Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.ORCID https://orcid.org/0000-0003-1665-4300

Funding

National Key Research and Development Program of China 2024YFA1109103National Natural Science Foundation of China 32494790National Natural Science Foundation of China 82150109National Natural Science Foundation of China 82573015Natural Science Foundation of Chongqing CSTB2023NSCQ-MSX0768Natural Science Foundation of Qinghai Province 2024-ZJ-937
6 · The paper itself

Abstract

Exposomics provides a systems-level framework to characterize the environmental exposures experienced across the life course and their biological consequences, offering critical insights into tumor initiation and precision prevention. Advances in sensing technologies, intelligent materials, and data science now enable continuous acquisition of external exposures alongside endogenous molecular and phenotypic responses. In this emerging paradigm, exposure is conceptualized not as an isolated variable statistically associated with disease, but as a temporally structured driver embedded within multiscale biological processes. By integrating multimodal monitoring with AI-enabled causal modeling, exposomics moves cancer risk assessment beyond population averages toward individualized, dynamically updated exposure-informed risk assessment. This Perspective highlights key technological directions in external-internal monitoring integration, intelligent sensing ecosystems, and causal data fusion, and outlines a translational framework aimed at supporting precision cancer prevention and early risk management.

Indexed as

integrated continuous monitoring of internal and external exposuresmultimodal data integrationpersonalized exposure assessmenttumor exposomicswearable devices

Identifiers

PMID42702809
PMCPMC13547710

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.