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

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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.

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Data availability

All data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

AUC:

Area under curve

CAF:

Cancer-associated fibroblast

DEGs:

Differentially expressed genes

EMT:

Epithelial–mesenchymal transition

EPIC:

Estimating the Proportions of Immune and Cancer cells

ESCC:

Esophageal squamous cell carcinoma

EGFR:

Epidermal growth factor receptor

FBLIM1:

Filamin binding LIM protein 1

GO:

Gene ontology

GSVA:

Gene set variation analysis

GSEA:

Gene set enrichment analysis

irAEs:

Immune-related adverse events

iCAFs:

Inflammatory CAFs

IGF:

Insulin-like growth factor

ICBs:

Immune checkpoint blockades

KEGG:

Kyoto encyclopedia of genes and genomes

LUSC:

Lung squamous carcinoma

LUAD:

Lung adenocarcinoma

MCP-counter:

Microenvironment Cell Populations-counter

MDSCs:

Myeloid-derived suppressor cells

myCAFs:

Myofibroblastic CAFs

NSCLC:

Non-small cell lung cancer

OS:

Overall survival

OSCC:

Oral squamous cell carcinoma

PD1:

Programmed cell death protein 1

PD-L1:

Programmed death-ligand 1

PFS:

Progression-free survival

RNA-seq:

RNA sequence

RPPA:

Reverse phase protein array

ROC:

Receiver operating characteristic curve

scRNA-seq:

Single-cell RNA sequence

ssGSEA:

Single sample gene set enrichment analysis

TME:

Tumor microenvironment

TCPA:

The Cancer Proteome Atlas

TPM:

Transcripts per million

TIDE:

Tumor Immune Dysfunction and Exclusion

TGFβ:

Transforming growth factor beta

TIME:

Tumor immune microenvironment

UMAP:

Uniform manifold approximation and projection

WGCNA:

Weighted gene co-expression network analysis

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Acknowledgements

The authors thank the specimen donors and research groups for the TCGA, GSE37745, GSE41271, GSE42127, GSE126044, GSE135222, GSE78220, GSE131907, and CHCAMS cohort, which provided valuable data resources for this article.

Funding

This work was supported by the grant from the National Natural Science Foundation of China (No.81871739, No.82172856). The funders had no role in the study design, data extraction, statistical analysis, and manuscript writing.

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    Authors and Affiliations

    1. 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

      Shasha Wang, Ning Lou & Liyuan Dai

    2. 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

      Guangyu Fan, Tongji Xie, Ruyun Gao, Mengwei Yang & Yuankai Shi

    3. 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

      Lin Li

    4. Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA

      Yajun He

    5. Clinical 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

      Xiaohong Han

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    Contributions

    XH, YS, SW, and GF were responsible for the conception and design of the study. SW, GF, YH, TX, NL, LD, RG, and MY conducted the bioinformatic analysis and prepared all the figures and tables. LL was involved in the collection of tumor tissue samples and experimental operations. XH, YS, SW, GF, LL, and YH contributed significantly to data interpretation, statistical analysis, and manuscript writing. All authors reviewed, revised, and approved the final manuscript.

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    Correspondence to Yuankai Shi or Xiaohong Han.

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    All authors declare no potential conflicts of interest.

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    Study of clinical samples from included NSCLC patients treated with immunotherapy were approved by the medical ethics committee of Cancer Hospital, CAMS and PUMC (No.19–019/1804).

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    Shasha Wang, Guangyu Fan and Lin Li have contributed equally to this work

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    Wang, S., Fan, G., Li, L. et al. Integrative analyses of bulk and single-cell RNA-seq identified cancer-associated fibroblasts-related signature as a prognostic factor for immunotherapy in NSCLC. Cancer Immunol Immunother 72, 2423–2442 (2023). https://doi.org/10.1007/s00262-023-03428-0

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