Evidence map›Paper›PMID 40276602›Full record

ArticleFrontiers in pharmacology2025

From COPD to cancer: indacaterol's unexpected role in combating NSCLC.

Chenghao Liu, Jiaqi Huang, Pengjie Cai, Min Jiang, Honglei Chen

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. 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. Article
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

5 authors.

Chenghao Liu *Department of Pathology, School of Basic Medical Sciences, Wuhan University, Wuhan, China.
Jiaqi Huang *Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Pengjie CaiDepartment of Pathology, School of Basic Medical Sciences, Wuhan University, Wuhan, China.
Min JiangKaramay Central Hospital of Xinjiang, Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay, China.
Honglei ChenDepartment of Pathology, School of Basic Medical Sciences, Wuhan University, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Non-small cell lung cancer (NSCLC) is one of the most prevalent and deadly malignancies worldwide. In previous studies, indacaterol, a drug used to manage chronic obstructive pulmonary disease, has shown antitumor activity. However, its role in the context of NSCLC remains underexplored. This study aimed to investigate indacaterol's mechanisms and potential therapeutic effects in lung cancer treatment. Methods: Expression profiles and clinical information from the TCGA database were analyzed to explore the potential impact of the Results: Analysis of TCGA data revealed that GLUT1 has a potential role in promoting NSCLC and may work in concert with MCT4. Indacaterol significantly inhibited the viability of NSCLC cells in a concentration-dependent manner. Molecular modeling and CETSA experiments further indicated that indacaterol may bind to GLUT1 and affect GLUT1 expression. Immunohistochemistry suggested that indacaterol also reduces the expression of MCT4, suggesting its potential to diminish the capacity of tumors to reprogram stromal metabolism. Conclusion: Indacaterol, a potential inhibitor of GLUT1, has significant antitumor effects on NSCLC. Moreover, the combination of indacaterol with immune checkpoint inhibitors may further enhanced the inhibitory effects of indacaterol on NSCLC cells. Our study provides scientific evidence supporting the clinical application of indacaterol as a novel therapeutic strategy for NSCLC treatment.

Indexed as

combination therapyGLUT1indacaterollung cancerPD-L1 inhibitors

Identifiers

PMID40276602
PMCPMC12018804

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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.