| Literature DB >> 35058933 |
Lushan Peng1, Dan Wang1, Yingying Han1, Tao Huang1, Xiaoyun He2, Junpu Wang1,3,4, Chunlin Ou1,5.
Abstract
Cancer-associated fibroblasts (CAFs) are the most important component of the stromal cell population in the tumor microenvironment and play an irreplaceable role in oncogenesis and cancer progression. Exosomes, a class of small extracellular vesicles, can transfer biological information (e.g., proteins, nucleic acids, and metabolites as messengers) from secreting cells to target recipient cells, thereby affecting the progression of human diseases, including cancers. Recent studies revealed that CAF-derived exosomes play a crucial part in tumorigenesis, tumor cell proliferation, metastasis, drug resistance, and the immune response. Moreover, aberrant expression of CAF-derived exosomal noncoding RNAs and proteins strongly correlates with clinical pathological characterizations of cancer patients. Gaining deeper insight into the participation of CAF-derived exosomes in tumorigenesis may lead to novel diagnostic biomarkers and therapeutic targets in human cancers.Entities:
Keywords: biomarkers; cancer-associated fibroblasts; exosomes; immune response; metastasis; therapy
Mesh:
Year: 2022 PMID: 35058933 PMCID: PMC8764452 DOI: 10.3389/fimmu.2021.795372
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 7.561
Figure 1Roles of CAF-derived exosomes in tumorigenesis. (A) Activated NFs can be converted into CAFs. (B) CAF-derived exosomes can regulate tumor cell proliferation; (C) CAF-derived exosomes can facilitate the conversion of drug-sensitive cancer cells into drug-resistant cancer cells; (D) CAF-derived exosomes are able to enhance the metastatic capacity of cancer cells; (E) CAF-derived exosomes can induce an antitumor immune response by regulating the activity of immune cells, including T cells, M1 macrophages, M2 macrophages, and dendritic cells.
Overview of the most popular exosome isolation techniques.
| Exosome isolation techniques | Methods | Advantages | Limitations | Ref. |
|---|---|---|---|---|
| Ultracentrifugation techniques | Differential ultracentrifugation | Easy to use | Time consuming | ( |
| Little sample pretreatment | Requires large starting sample volumes | |||
| Affordability over time | Low exosome recovery | |||
| Density gradient centrifugation | Effective for exosomes from protein aggregates and non-membranous particles | Low exosome recovery | ( | |
| Useful for separating exosomes and other EVs from body fluids | ||||
| Size-based isolation techniques | Ultrafiltration | Less time consuming | Particle deformation | ( |
| Requires no special instrumentation | Lysis of exosomes | |||
| Sequential filtration | Automatable | Rigid components associated with cellular debris are filtered away | ( | |
| Produces intact and biologically active exosome material | ||||
| Size exclusion chromatography | Preserves vesicle structure, integrity, and biological activity | Requires run times of several hours | ( | |
| Not easily scalable | ||||
| Cannot be used for high throughput applications. | ||||
| Immunoaffinity capture-based techniques | Magneto-immunoprecipitation | Higher isolation efficiency | Protein/antigen used to capture the exosomes must be expressed on the surface of exosomes | ( |
| Can handle large sample volumes | ||||
| Preserves the activity of exosomal proteins | Specificity of the assay is limited to specificity of the antibody. | |||
| Exosome precipitation | Polyethylene glycol precipitation | Quick | Lack of selectivity | ( |
| Simple | ||||
| Requires little technical expertise or expensive equipment | ||||
| Can be used for various starting volumes |
Exosome-related databases.
| Database | Introduction | Characteristics | Website | Ref. |
|---|---|---|---|---|
| ExoCarta | The first comprehensive database of exosomal markers, containing 286 research results on several species, e.g., humans, rats, mice, sheep, guinea pigs, fruit flies, horses, rabbits, and cattle; data on various tissue-derived exosomal proteins, mRNA, miRNAs, and lipids and other information from organ sources are available. | ExoCarta covers the protein–protein interaction network and biological pathways with exosomal protein dynamics. Users can download the most commonly used protein data from a large number of studies. The downloaded file can be directly imported into the FunRich tool for other function enrichment analysis and correlation network analysis. |
| ( |
| ExoRBase | A long-chain RNA-seq database of human blood exosomes. Currently, the database includes 92 blood samples, 58,330 circRNAs, 15,501 lncRNAs and 18,333 mRNAs, with annotations, expression levels and possible source tissues. | ExoRBase integrates and visualizes the RNA expression profiles based on normalized RNA-seq data spanning both normal individuals and patients with various diseases. |
| ( |
| EVmiRNA | A miRNA database of EVs, curating and analyzing 462 miRNA expression profile datasets on EVs in 17 tissues/diseases. EVmiRNA provides several functional modules—miRNA expression profiles and the sample information about EVs from different sources; specifically expressed miRNAs in different EVs that would be helpful for biomarker identification; miRNA annotations, including miRNA expression in EVs and TCGA cancer types, miRNA pathway regulation mechanisms, and miRNA functions and literary references. | EVmiRNA provides detailed miRNA expression profiles in EVs as well as valuable and comprehensive resources, including EV samples classification (source/cancer and exosome/MV), miRNA expression profile for each sample, the most expressed miRNAs, specifically expressed miRNAs for each EV type, and miRNA functions and regulation mechanisms. |
| ( |
| EV-TRACK | A crowdsourcing knowledge base that centralizes data on EV biology and methodology and comprises methodological specifications on 3,383 EV experiments in 1,699 documents. EV-TRACK evaluates EV separation and identification-related parameters based on Minimum Experimental Requirements for EV Research. | EV-TRACK collects the original data on EV separation and characterization and increases the authenticity and repeatability of the data. For each experiment, the website explains and sort out general and specific method information to help reproduce the experiment and evaluate it. |
| ( |
| EVpedia | A high-throughput comprehensive database of prokaryotic and eukaryotic EVs. EVpedia provides databases of prokaryotes, nonmammalian eukaryotes and mammalian vesicular mRNAs, miRNAs, and lipids. | EVpedia is an integrated and comprehensive proteome, transcriptome, and lipidome database of EVs derived from archaea, bacteria, and eukaryotes, including humans. EVpedia may serve as a useful community resource to trigger the advancement of systematic and comprehensive studies on EVs and for unveiling the fundamental roles of EVs |
| ( |
| Vesiclepedia | A manually curated compendium of molecular data on lipids, RNAs, and proteins identified in various classes of EVs. Currently, Vesiclepedia comprises 35,264 protein, 18,718 mRNA, 1,772 miRNA, and 342 lipid entries encompassing 341 independent studies published in the past several years. | Users can query and download EV cargo data, EV separation details, characterization methods, biophysical and molecular characteristics, and EV-METRIC according to various search criteria. This information helps biomedical scientists evaluate the quality of EV preparations and obtain the corresponding data. FunRich can help users directly analyze data. |
| ( |
| EMBL-EBI | A comprehensive annotation database for the functional analysis of human exosomal proteins according to Gene Ontology information. | EMBL-EBI can identify the target protein used for focus annotation and annotation of exosomal experimental methodology. |
| ( |
| ExRNA Atlas | A data repository of the Extracellular RNA Communication Consortium (ERCC). This database includes small RNA sequencing and RT-qPCR-derived extracellular-RNA profiles from human and mouse biofluids. | All RNA-seq datasets are processed using version 4 of the exceRpt small RNA-seq pipeline, and ERCC-developed quality metrics are uniformly applied to these datasets. |
| ( |
Figure 2Roles of CAF-derived exosomal proteins in tumorigenesis. (A) CAF-derived exosomal TGF-β can activate the TGF-β–SMAD signaling pathway to promote EMT in ovarian cancer. (B) CAF-derived exosomal CD81 can trigger the WNT signaling cascade contributing to the metastasis of breast cancer. (C) CAF-derived exosomal CD9 can activate MMP2 signaling enhancing the migration and invasiveness of gastric cancer cells. (D) CAF-derived exosomal SHH can launch the SHH signaling pathway thus increasing the proliferation and metastasis of esophageal squamous cell cancer (ESCC) cells.
Figure 3CAF-derived exosomes can regulate tumorigenesis. (A) CAF-derived exosomal ncRNAs (such as miR-210, miR-93-5p, miR-500a-5p, and lncRNAs LINC00659 and SNHG3) or proteins (such as SHH, CD97, CD81, and CD9) affect the proliferation of tumors. (B) CAF-derived exosomal ncRNAs (such as miR-181d-5p, miR-148b, and miR-369) or proteins (TGF-β) control the metastasis of tumors. (C) CAF-derived exosomal ncRNAs (such as miR-106b, miR-423-5p, miR-21, miR-130a, and lncRNAs UCA1 and H19) or proteins (TGF-β) influence drug resistance of tumors. (D) CAF-derived exosomal miR-92 and PD-L1 regulate the immune response to tumors by inducing apoptosis and impairing proliferation of T cells.
Correlation between CAF-derived exosomal content and clinical pathological characterizations of cancer patients.
| Exosomal contents | Tumor type | Sample sources | Dyregulation | Relationship with clinicopathology | Ref. |
|---|---|---|---|---|---|
| miR-382-5p | Oral squamous cell carcinoma | Tissue | Upregulation | TNM stage, lymph node metastasis | ( |
| miR196a | Head and neck cancer | Plasma | Upregulation | Poor prognosis | ( |
| miR-3188 | Head and neck cancer | Plasma and tissue | Downregulation | TNM stage, tumor size, poor survival | ( |
| miR-150-3p | Hepatocellular carcinoma | Plasma | Downregulation | Long survival | ( |
| miR369 | Lung squamous cell carcinoma | Tissue | Upregulation | Poor prognosis | ( |
| miR-17-5p | Colorectal cancer | Tissue | Upregulation | Poor prognosis | ( |
| lncRNA H19 | Colorectal cancer | Tissue | Upregulation | TNM stage | ( |
| lncRNA UCA1 | Vulvar squamous cell carcinoma | Tissue | Upregulation | T stage, Clinical stage, and lymph node metastasis status | ( |
| MMP11 | Gastric cancer | Tissue | Upregulation | Poor prognosis | ( |
Figure 4Applications of CAF-derived exosomes as therapeutic targets in cancer in vivo. In a mouse xenograft model, (A) CAF-derived exosomal miR-148b can inhibit the metastasis of endometrial cancer cells. (B) CAF-derived exosomal miR-139 can suppress the metastasis of gastric cancer cells. (C) CAF-derived exosomal miR-3188 is able to slow down the growth of head and neck squamous cell carcinoma cells. (D) CAF-derived exosomal miR-34a-5p can promote the proliferation and metastasis of oral squamous cell carcinoma cells.