Evidence map›Paper›PMID 37010552›Full record

ArticleCancer immunology, immunotherapy : CII2023

Integrative analyses of bulk and single-cell RNA-seq identified cancer-associated fibroblasts-related signature as a prognostic factor for immunotherapy in NSCLC.

Shasha Wang, Guangyu Fan, Lin Li, Yajun He, Ning Lou, Tongji Xie, Liyuan Dai, Ruyun Gao, Mengwei Yang, Yuankai Shi and 1 more

Open access · greenAbstract read
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
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

16 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  4. A Metastasis-competent CAF Subpopulation Defined byRecent patents on anti-cancer drug discovery · 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

11 authors at 2 institutions in 2 countries.

Shasha Wang *Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, China.
Guangyu Fan *Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China.
Lin Li *Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, China.
Yajun HeGangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA.
Ning LouDepartment of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, China.
Tongji XieDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China.
Liyuan DaiDepartment of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, China.
Ruyun GaoDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China.
Mengwei YangDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China.
Yuankai ShiDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, 100021, China. syuankai@cicams.ac.cn.
Xiaohong HanClinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China. hanxiaohong@pumch.cn.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNEmory University · US

Funding

National Natural Science Foundation of China 81871739National Natural Science Foundation of China 82172856
6 · The paper itself

Abstract

An emerging view regarding cancer-associated fibroblast (CAF) is that it plays a critical role in tumorigenesis and immunosuppression in the tumor microenvironment (TME), but the clinical significance and biological functions of CAFs in non-small cell lung cancer (NSCLC) are still poorly explored. Here, we aimed to identify the CAF-related signature for NSCLC through integrative analyses of bulk and single-cell genomics, transcriptomics, and proteomics profiling. Using CAF marker genes identified in weighted gene co-expression network analysis (WGCNA), we constructed and validated a CAF-based risk model that stratifies patients into two prognostic groups from four independent NSCLC cohorts. The high-score group exhibits a higher abundance of CAFs, decreased immune cell infiltration, increased epithelial-mesenchymal transition (EMT), activated transforming growth factor beta (TGFβ) signaling, and a limited survival rate compared with the low-score group. Considering the immunosuppressive feature in the high-score group, we speculated an inferior clinical response for immunotherapy in these patients, and this association was successfully verified in two NSCLC cohorts treated with immune checkpoint blockades (ICBs). Furthermore, single-cell RNA sequence datasets were used to clarify the molecular mechanisms underlying the aggressive and immunosuppressive phenotype in the high-score group. We found that one of the genes in the risk model, filamin binding LIM protein 1 (FBLIM1), is mainly expressed in fibroblasts and upregulated in CAFs compared to fibroblasts from normal tissue. FBLIM1-positive CAF subtype was correlated with increased TGFβ expression, higher mesenchymal marker level, and immunosuppressive tumor microenvironment. Finally, we demonstrated that FBLIM1 might serve as a poor prognostic marker for immunotherapy in clinical samples. In conclusion, we identified a novel CAF-based classifier with prognostic value in NSCLC patients and those treated with ICBs. Single-cell transcriptome profiling uncovered FBLIM1-positive CAFs as an aggressive subtype with a high abundance of TGFβ, EMT, and an immunosuppressive phenotype in NSCLC.

Indexed as

Cancer-Associated FibroblastsCarcinoma, Non-Small-Cell LungLung NeoplasmsCell Adhesion MoleculesCytoskeletal ProteinsHumansImmunotherapyPrognosisSingle-Cell Gene Expression AnalysisTransforming Growth Factor betaTumor MicroenvironmentCell Adhesion MoleculesCytoskeletal ProteinsFBLIM1 protein, humanTransforming Growth Factor betaCancer-associated fibroblastImmunotherapyNon-small cell lung cancerPrognosisSingle-cellTumor microenvironment

Identifiers

PMID37010552
PMCPMC10992286
OpenAlexW4362505865

What OpenQuestion holds

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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.