Evidence map›Paper›PMID 41133012›Full record

ArticleTranslational lung cancer research2025

Synergistic camrelizumab therapy inhibits P53-mutant osimertinib-resistant solid lung adenocarcinoma via the TGF-β signaling pathway.

Xiaoqin Shi, Min Zheng, Xiaolan Wang, Xiujun Chang, Kang'an Li, Le Cai, Yiran Cai

Abstract read
In one paragraph

Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaoqin Shi *Pathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Min Zheng *Department of Pulmonary and Critical Care Medicine, China Emergency General Hospital, Beijing, China.
Xiaolan WangPathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiujun ChangDepartment of Thoracic Surgery, Beijing Chest Hospital Affiliated to Capital Medical University, Beijing, China.
Kang'an LiDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Le CaiPathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yiran CaiPathology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung adenocarcinomas (LACs) are classified into several histological types. The clinical outcomes are not consistent in epidermal growth factor receptor-tyrosine kinase inhibitor ( Methods: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyse differentially expressed genes (DEGs) from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (GSE68465) cohorts. Expression of PD-L1 and cytokines were analyzed by immunohistochemistry and cytometric bead array. Osimertinib-resistant solid LAC xenografts were implanted into immune-humanized mice to demonstrate pathological responses after EGFR-TKI and ICI. TGF-β signaling proteins, pERK1/2, pSTAT3, and PD-L1 was detected by western blotting. Clinical responses were observed in 12 osimertinib-resistant patients treated by ICI. Results: GO and KEGG analyses highlighted "cytokine-cytokine receptor interaction" and "TGF-beta signaling pathway". Heterogeneous PD-L1 expression was regulated by synergistic IL-6/STAT3, TGF-β and EGFR pathways. Significant tumor remission and inhibition of TGF-β signaling pathway were found in EGFR-TKI plus ICI treatment in mice models. Osimertinib-resistant solid LACs treated by ICI had better clinical outcomes. Conclusions: TGF-β signaling pathway is associated with PD-L1 expression and the pathological remission treated by EGFR-TKI and ICI. Solid osimertinib-resistant LACs by ICI can be beneficial through the inhibition of TGF-β signaling pathway.

Indexed as

immune checkpoint inhibitor (ICI)Lung adenocarcinoma (LAC)osimertinib-resistancetransforming growth factor-β (TGF-β)

Identifiers

PMID41133012
PMCPMC12541876

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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.