Formation, contents, functions of exosomes and their potential in lung cancer diagnostics and therapeutics

Xia, Zhenkun; Qing, Bei; Wang, Wei; Gu, Linguo; Chen, Hongzuo; Yuan, Yunchang

doi:10.1111/1759-7714.14217

Cited by 17 publications

(11 citation statements)

References 165 publications

(282 reference statements)

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“…Crosstalk between cancer cells and the TME has been proven to play an important role in tumor progression and metastasis ( 43 ). And TIICs are an important component of the TME, and their composition and distribution are closely related to tumorigenesis and development ( 44 ).…”

Section: Resultsmentioning

confidence: 99%

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Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

Chi

Peng²,

Yang³

et al. 2022

Front. Endocrinol.

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BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM.MethodsIn this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals.Results4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy.Conclusions4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies.

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Section: Resultsmentioning

confidence: 99%

“…(C) GSVA analysis between the high-risk cohort and the low-risk cohort with KEGG. (43). And TIICs are an important component of the TME, and their composition and distribution are closely related to tumorigenesis and development (44).…”

Section: Functional Enrichment Analysis Of Smgsmentioning

confidence: 99%

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

Chi

Peng²,

Yang³

et al. 2022

Front. Endocrinol.

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“…It has been established that interaction between cancer cells and the TME is crucial for the development and spread of tumors ( 27 ). Additionally, TIICs represent a significant part of the TME, and the composition and location of these cells have a direct impact on the formation and occurrence of tumors ( 31 ).…”

Section: Resultsmentioning

confidence: 99%

“…The R package “SingleR” is used for the annotation of different cell types ( 26 ). In addition, we used the “analyze_sc_clusters” function of the R package “ReactomeGSA” ( 27 ) for enrichment analysis and the “pathways” function to extract the results from different cells. Finally, the R packages “CellChat” ( 28 ) and “patchwork” were used for intercellular communication analysis and network visualization.…”

Section: Methodsmentioning

confidence: 99%

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

et al. 2023

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BackgroundHepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients.MethodsThe International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR.Result11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong.ConclusionTEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

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“…Containing various bioactive molecules including proteins, lipids, and nucleic acids, EVs not only mediate signal transduction in cellular communication but also participate in the physiological process including immune regulation and cancer progression [ 12 ]. Due to these characteristics, EVs could be a perfect mode of liquid biopsy, as reported by substantial evidence focusing on its clinical application in tumor diagnosis and prognosis [ 13 , 14 ]. However, no study has reported that EVs are associated with irAEs.…”

Section: Introductionmentioning

confidence: 99%

Extracellular Vesicle-Derived Protein File from Peripheral Blood Predicts Immune-Related Adverse Events in Gastric Cancer Patients Receiving Immunotherapy

Jiang

Zhang

Chong

et al. 2022

Cancers

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Immune checkpoint inhibitors (ICIs) initiate a new stage for gastric cancer (GC) therapeutics, and plenty of patients have already benefited from ICIs. Liquid biopsy promotes the development of precision medicine of GC. However, due to the lack of precision biomarkers of immune-related adverse events (irAEs), the safety of ICIs-treated GC patients cannot be guaranteed. In our study, GC patients treated with ICIs were included for investigating the correlation between irAEs of ICIs and corresponding outcomes. We also explored the potential of biomarkers of irAEs via EV-derived proteins. Dynamic plasma was taken from 102 ICIs-treated GC patients generated retrospectively or prospectively, who were divided into discovery and validating cohorts. Plasma EV-derived protein profiles were described, and two EV-proteins, inducible T-cell co-stimulator (EV-ICOS) and indoleamine 2,3-dioxygenase 1(EV-IDO1), from 42 vital proteins were screened to predict the prognosis of ICIs with irAEs. Our work is the first to propose that EV-proteins can predict ICIs-corresponding irAEs, which can be conducive to the diagnosis and treatment of GC patients, and to facilitate the screening of beneficiaries.

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Formation, contents, functions of exosomes and their potential in lung cancer diagnostics and therapeutics

Cited by 17 publications

References 165 publications

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

Extracellular Vesicle-Derived Protein File from Peripheral Blood Predicts Immune-Related Adverse Events in Gastric Cancer Patients Receiving Immunotherapy

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