Evidence map›Paper›PMID 39780215›Full record

ArticleJournal of translational medicine2025

An integrative analysis reveals cancer risk associated with artificial sweeteners.

Jumin Xie, Ying Zhu, Zixuan Yang, Zhang Yu, Mingzhi Yang, Qingzhi Wang

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  6. Long-term artificial sweetener exposure increases the risk of atherosclerosis.Naunyn-Schmiedeberg's archives of pharmacology · 2026
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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

6 authors.

Jumin XieHubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Guilin North Road No 16, Huangshi, 435003, Hubei, People's Republic of China. xiejumin@hbpu.edu.cn.ORCID 0000-0002-4493-4548
Ying Zhu *Hubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Guilin North Road No 16, Huangshi, 435003, Hubei, People's Republic of China.
Zixuan Yang *Hubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Guilin North Road No 16, Huangshi, 435003, Hubei, People's Republic of China.
Zhang Yu *Hubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Guilin North Road No 16, Huangshi, 435003, Hubei, People's Republic of China.
Mingzhi Yang *Hubei Key Laboratory of Renal Disease Occurrence and Intervention, Medical School, Hubei Polytechnic University, Guilin North Road No 16, Huangshi, 435003, Hubei, People's Republic of China.
Qingzhi WangMedical College of YiChun University, Xuefu Road No 576, Yichun, 336000, Jiangxi, People's Republic of China. wangqz1001@163.com.

Funding

Hubei polytechnic University · Huangshi Daye Lake high-tech Zone University Science Park joint open fund project 23xjz04AKKey scientific research projects of Hubei polytechnic University 23xjz08ALocal special projects in major health of Hubei Provincial Science and Technology Department 2022BCE054
6 · The paper itself

Abstract

backgroundArtificial sweeteners (AS) have been widely utilized in the food, beverage, and pharmaceutical industries for decades. While numerous publications have suggested a potential link between AS and diseases, particularly cancer, controversy still surrounds this issue. This study aims to investigate the association between AS consumption and cancer risk.

methodsTargets associated with commonly used AS were screened and validated using databases such as CTD, STITCH, Super-PRED, Swiss Target Prediction, SEA, PharmMapper, and GalaxySagittarius. Cancer-related targets were sourced from GeneCards, OMIM, and TTD databases. AS-cancer targets were identified through the intersection of these datasets. A network visualization ('AS-targets-cancer') was constructed using Cytoscape 3.9.0. Protein-protein interaction analysis was conducted using the STRING database to identify significant AS-cancer targets. GO and KEGG enrichment analyses were performed using the DAVID database. Core targets were identified from significant targets and genes involved in the 'Pathways in cancer' (map05200). Molecular docking and dynamics simulations were employed to verify interactions between AS and target proteins. Pan-cancer and univariate Cox regression analyses of core targets across 33 cancer types were conducted using GEPIA 2 and SangerBox, respectively. Gene chip datasets (GSE53757 for KIRC, GSE21354 for LGG, GSE42568 for BRCA, and GSE46602 for PRAD) were retrieved from the GEO database, while transcriptome and overall survival data were obtained from TCGA. Data normalization and identification of differentially expressed genes (DEGs) were performed on these datasets using R (version 4.3.2). Gene Set Enrichment Analysis (GSEA) was employed to identify critical pathways in the gene expression profiles between normal and cancer groups. A cancer risk prognostic model was constructed for key targets to further elucidate their significance in cancer initiation and progression. Finally, the HPA database was utilized to investigate variations in the expression of key AS-cancer target proteins across KIRC, LGG, BRCA, PRAD, and normal tissues.

resultsSeven commonly used AS (Aspartame, Acesulfame, Sucralose, NHDC, Cyclamate, Neotame, and Saccharin) were selected for study. A total of 368 AS-cancer intersection targets were identified, with 48 notable AS-cancer targets, including TP53, EGFR, SRC, PIK3R1, and EP300, retrieved. GO biological process analysis indicated that these targets are involved in the regulation of apoptosis, gene expression, and cell proliferation. Thirty-five core targets were identified from the intersection of the 48 significant AS-cancer targets and genes in the 'Pathways in cancer' (map05200). KEGG enrichment analysis of these core targets revealed associations with several cancer types and the PI3K-Akt signaling pathway. Molecular docking and dynamics simulations confirmed interactions between AS and these core targets. HSP90AA1 was found to be highly expressed across the 33 cancer types, while EGF showed the opposite trend. Univariate Cox regression analysis demonstrated strong associations of core targets with KIRC, LGG, BRCA, and PRAD. DEGs of AS-cancer core targets across these four cancers were analyzed. GSEA revealed upregulated and downregulated pathways enriched in KIRC, LGG, BRCA, and PRAD. Cancer risk prognostic models were constructed to elucidate the significant roles of key targets in cancer initiation and progression. Finally, the HPA database confirmed the crucial function of these targets in KIRC, LGG, BRCA, and PRAD.

conclusionThis study integrated data mining, machine learning, network toxicology, molecular docking, molecular dynamics simulations, and clinical sample analysis to demonstrate that AS increases the risk of kidney cancer, low-grade glioma, breast cancer, and prostate cancer through multiple targets and signaling pathways. This paper provides a valuable reference for the safety assessment and cancer risk evaluation of food additives. It urges food safety regulatory agencies to strengthen oversight and encourages the public to reduce consumption of foods and beverages containing artificial sweeteners and other additives.

Indexed as

NeoplasmsSweetening AgentsGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMolecular Docking SimulationProportional Hazards ModelsProtein Interaction MapsRisk FactorsSweetening AgentsArtificial sweetenerCancerCore targetsMachine learningMolecular dynamics simulationNetwork toxicologyPathways in cancer

Identifiers

PMID39780215
PMCPMC11708064

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LicenceCC BY-NC-ND
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Registered trials

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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.