Evidence map›Paper›PMID 41760596›Full record

ArticleCell death discovery2026

Exosomal S100A9 promotes lung metastasis of adenoid cystic carcinoma via activating cancer-associated fibroblasts.

Chu-Wen Chen, Shen-Rong Zhang, Yu-Meng Yan, Kuang-Wu Pan, Fa-Rong Ou, Kai Su, Bin Cheng

Abstract read
In one paragraph

Article in Cell death discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Emerging Nanoplatforms are Effective Against Tumor Hypoxia.International journal of nanomedicine · 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

7 authors.

Chu-Wen Chen *Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.ORCID http://orcid.org/0009-0002-7437-3670
Shen-Rong Zhang *Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.
Yu-Meng YanHospital of Stomatology, Sun Yat-sen University, Guangzhou, China.
Kuang-Wu PanDepartment of Stomatology, The Third People's Hospital of Chengdu, Sichuan, China.
Fa-Rong OuHospital of Stomatology, Sun Yat-sen University, Guangzhou, China.
Kai SuHospital of Stomatology, Sun Yat-sen University, Guangzhou, China. suk@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0003-3441-3084
Bin ChengHospital of Stomatology, Sun Yat-sen University, Guangzhou, China. chengbin@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0001-7288-806X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adenoid cystic carcinoma (ACC) is a malignant epithelial neoplasm that arises in secretory glands and commonly metastasizes to the lungs, remaining the primary cause of patient mortality. Here, we observed that S100A9, a calcium-binding protein, was significantly upregulated in highly metastatic ACC cells and markedly enriched in their derived exosomes. Single-cell RNA sequencing was employed to comprehensively map the tumor microenvironment (TME), revealing robust intercellular communication between epithelial cells and cancer-associated fibroblasts (CAFs). Mechanistically, S100A9-enriched exosomes reprogrammed normal fibroblasts, resulting in the acquisition of an activated CAF phenotype characterized by the upregulation of FAP and α-SMA expression, and enrichment of IL-17, TNF-α, and NF-κB signaling pathways. Functionally, these exosome-educated CAFs promoted the epithelial-mesenchymal transition in ACC cells and facilitated lung metastasis in vivo via an IL-17-dependent signaling axis. Overall, these findings establish exosomal S100A9 as a crucial mediator of TME reprogramming, suggesting that targeting the S100A9-IL-17 axis may serve as a promising therapeutic strategy for disrupting ACC lung metastasis.

Identifiers

PMID41760596
PMCPMC13004868

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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.