Dataset Information

Identification and Characterization of Cancer-Associated Fibroblast Subpopulations in Lung Adenocarcinoma

ABSTRACT:

Simple Summary

Considering that cancer-associated fibroblasts (CAFs) facilitate cancer cell motility, invasiveness, and drug resistance, heterogeneity within the CAF population may be of major significance during cancer progression and metastasis. Through pseudotime trajectory analysis of single-cell RNA sequencing data, we revealed several subpopulations of lung CAFs with distinct gene expression patterns, namely, immunosuppressive, neoantigen-presenting, myofibroblastic, and proliferative CAFs. The knockdown of KPNA2, one of the neoantigen-presenting CAF-specific markers, attenuated CAF invasiveness, suggesting that CAF subtype markers may represent therapeutic targets within the tumor microenvironment of lung cancer patients.

Abstract

Cancer-associated fibroblasts (CAFs) reside within the tumor microenvironment, facilitating cancer progression and metastasis via direct and indirect interactions with cancer cells and other stromal cell types. CAFs are composed of heterogeneous subpopulations of activated fibroblasts, including myofibroblastic, inflammatory, and immunosuppressive CAFs. In this study, we sought to identify subpopulations of CAFs isolated from human lung adenocarcinomas and describe their transcriptomic and functional characteristics through single-cell RNA sequencing (scRNA-seq) and subsequent bioinformatics analyses. Cell trajectory analysis of combined total and THY1 + CAFs revealed two branching points with five distinct branches. Based on Gene Ontology analysis, we denoted Branch 1 as “immunosuppressive”, Branch 2 as “neoantigen presenting”, Branch 4 as “myofibroblastic”, and Branch 5 as “proliferative” CAFs. We selected representative branch-specific markers and measured their expression levels in total and THY1 + CAFs. We also investigated the effects of these markers on CAF activity under coculture with lung cancer cells. This study describes novel subpopulations of CAFs in lung adenocarcinoma, highlighting their potential value as therapeutic targets.

SUBMITTER: Kim D

PROVIDER: S-EPMC9324153 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCancer-associated fibroblasts (CAFs) comprise a heterogeneous population of stromal cells within the tumour microenvironment. CAFs exhibit both tumour-promoting and tumour-suppressing functions, making them exciting targets for improving cancer treatments. Careful isolation, identification, and characterisation of CAF heterogeneity is thus necessary for ex vivo validation and future implementation of CAF-targeted strategies in cancer.MethodsMurine 4T1 (metastatic) and 4T07 (poorly/non-metastatic) orthotopic triple negative breast cancer tumours were collected after 7, 14, or 21 days. The tumours were analysed via flow cytometry for the simultaneous expression of six CAF markers: alpha smooth muscle actin (αSMA), fibroblast activation protein alpha (FAPα), platelet derived growth factor receptor alpha and beta (PDGFRα and PDGFRβ), CD26/DPP4 and podoplanin (PDPN). All non-CAFs were excluded from the analysis using a lineage marker cocktail (CD24, CD31, CD45, CD49f, EpCAM, LYVE-1, and TER-119). In total 128 murine tumours and 12 healthy mammary fat pads were analysed.ResultsWe have developed a multicolour flow cytometry strategy based on exclusion of non-CAFs and successfully employed this to explore the temporal heterogeneity of freshly isolated CAFs in the 4T1 and 4T07 mouse models of triple-negative breast cancer. Analysing 128 murine tumours, we identified 5-6 main CAF populations and numerous minor ones based on the analysis of αSMA, FAPα, PDGFRα, PDGFRβ, CD26, and PDPN. All markers showed temporal changes with a distinct switch from primarily PDGFRα+ fibroblasts in healthy mammary tissue to predominantly PDGFRβ+ CAFs in tumours. CD26+ CAFs emerged as a large novel subpopulation, only matched by FAPα+ CAFs in abundance.ConclusionWe demonstrate that multiple subpopulations of CAFs co-exist in murine triple negative breast cancer, and that the abundance and dynamics for each marker differ depending on tumour type and time. Our results form the foundation needed to isolate and characterise specific CAF populations, and ultimately provide an opportunity to therapeutically target specific CAF subpopulations.

Project description:Objective Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. Methods LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. Results A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. Conclusion Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.

Dataset Information

Identification and Characterization of Cancer-Associated Fibroblast Subpopulations in Lung Adenocarcinoma

Simple Summary

Abstract

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets