Identification of a Gene Signature for Renal Cell Carcinoma–Associated Fibroblasts Mediating Cancer Progression and Affecting Prognosis

Liu, Bitian; Chen, Xiaonan; Zhan, Yunhong; Wu, Bin; Shen, Pan

doi:10.3389/fcell.2020.604627

Cited by 38 publications

(30 citation statements)

References 43 publications

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“…This is a novel method to explore the relationship between numerous genes and clinical phenotypes. WGCNA has been applied to identify CAF markers, such as in gastric cancer (13), bladder cancer (14), and renal cell carcinoma (15). However, to date, CAFs have not been analyzed by WGCNA in large-sample multicenter OC cohorts.…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Feng

Dai

et al. 2022

Front. Immunol.

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Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

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Section: Introductionmentioning

confidence: 99%

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Feng

Dai

et al. 2022

Front. Immunol.

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“…We first calculated the CAFs score for each patient based on 56 CAFs markers reported in literature through the GSVA algorithm. Subsequently, WGCNA was applied to construct gene co-expression networks, which has the potential to identify CAFs-related genes ( 59 ),and the green module from networks was of the highest correlation with the CAFs score. By intersecting green module genes and the DEGs, we obtained 27 response-associated CDEGs for anti-PD-1 therapy.…”

Section: Discussionmentioning

confidence: 99%

A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning

et al. 2022

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BackgroundMelanoma is a highly aggressive skin cancer with a poor prognosis and mortality. Immune checkpoint blockade (ICB) therapy (e.g., anti-PD-1 therapy) has opened a new horizon in melanoma treatment, but some patients present a non-responsive state. Cancer-associated fibroblasts (CAFs) make up the majority of stromal cells in the tumor microenvironment (TME) and have an important impact on the response to immunotherapy. There is still a lack of identification of CAFs-related predictors for anti-PD-1 therapy, although the establishment of immunotherapy biomarkers is well underway. This study aims to explore the potential CAFs-related gene panel for predicting the response to anti-PD-1 therapy in melanoma patients and elucidating their potential effect on TME.MethodsThree gene expression datasets from melanoma patients without anti-PD-1 treatment, in a total of 87 samples, were downloaded from Gene Expression Omnibus (GEO) as the discovery sets (GSE91061) and validation sets (GSE78220 and GSE122220). The CAFs-related module genes were identified from the discovery sets by weighted gene co-expression network analysis (WGCNA). Concurrently, we utilized differential gene analysis on the discovery set to obtain differentially expressed genes (DEGs). Then, CAFs-related key genes were screened with the intersection of CAFs-related module genes and DEGs, succeeded by supervised machine learning-based identification. As a consequence of expression analysis, gene set enrichment analysis, survival analysis, staging analysis, TME analysis, and correlation analysis, the multidimensional systematic characterizations of the key genes were uncovered. The diagnostic performance of the CAFs-related gene panel was assessed by receiver operating characteristic (ROC) curves in the validation sets. Eventually, the CAFs-related gene panel was verified by the expression from the single-cell analysis.ResultsThe six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI. The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively.ConclusionConfirmed by clinical treatment outcomes, the identified CAFs-related genes can be used as a promising biomarker panel for prediction to anti-PD-1 therapy response, which may serve as new immunotherapeutic targets to improve survival outcomes of melanoma patients.

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“…The EPIC (http://epic.gfellerlab.org/) tool was used to estimate the fraction of CAFs and explore the changes in the matrix components of PeCa and normal tissues (internal set) (31). EPIC establishes reference gene expression profiles for major tumorinvasive immune cell types (CD4+ T, CD8+ T, B, natural killer, and macrophages) and further deduces the reference spectra of CAFs and endothelial cells (32).…”

Section: Cancer-associated Fibroblast Scorementioning

confidence: 99%

Interplay Between Immune and Cancer-Associated Fibroblasts: A Path to Target Metalloproteinases in Penile Cancer

Cury

Kuasne²,

Souza³

et al. 2022

Front. Oncol.

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Extracellular matrix (ECM) remodeling and inflammation have been reported in penile carcinomas (PeCa). However, the cell types and cellular crosstalk involved in PeCa are unexplored. We aimed to characterize the complexity of cells and pathways involved in the tumor microenvironment (TME) in PeCa and propose target molecules associated with the TME. We first investigated the prognostic impact of cell types with a secretory profile to identify drug targets that modulate TME-enriched cells. The secretome analysis using the PeCa transcriptome revealed the enrichment of inflammation and extracellular matrix pathways. Twenty-three secreted factors were upregulated, mainly collagens and matrix metalloproteinases (MMPs). The deregulation of collagens and MMPs was confirmed by Quantitative reverse transcription - polymerase chain reaction (RT-qPCR). Further, the deconvolution method (digital cytometry) of the bulk samples revealed a high proportion of macrophages and dendritic cells (DCs) and B cells. Increased DCs and B cells were associated with better survival. A high proportion of cancer-associated fibroblasts (CAFs) was observed in low-survival patients. Patients with increased CAFs had decreased immune cell proportions. The treatment with the MMP inhibitor GM6001 in CAF cells derived from PeCa resulted in altered cell viability. We reported a crosstalk between immune cells and CAFs, and the proportion of these cell populations was associated with prognosis. We demonstrate that a drug targeting MMPs modulates CAFs, expanding the therapeutic options of PeCa.

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Identification of a Gene Signature for Renal Cell Carcinoma–Associated Fibroblasts Mediating Cancer Progression and Affecting Prognosis

Cited by 38 publications

References 43 publications

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning

Interplay Between Immune and Cancer-Associated Fibroblasts: A Path to Target Metalloproteinases in Penile Cancer

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