Dataset Information

Deeper insights into transcriptional features of cancer-associated fibroblasts: An integrated meta-analysis of single-cell and bulk RNA-sequencing data.

ABSTRACT: Cancer-associated fibroblasts (CAFs) have long been known as one of the most important players in tumor initiation and progression. Even so, there is an incomplete understanding of the identification of CAFs among tumor microenvironment cells as the list of CAF marker genes varies greatly in the literature, therefore it is imperative to find a better way to identify reliable markers of CAFs. To this end, we summarized a large number of single-cell RNA-sequencing data of multiple tumor types and corresponding normal tissues. As a result, for 9 different types of cancer, we identified CAF-specific gene expression signatures and found 10 protein markers that showed strongly positive staining of tumor stroma according to the analysis of IHC images from the Human Protein Atlas database. Our results give an insight into selecting the most appropriate combination of cancer-associated fibroblast markers. Furthermore, comparison of different approaches for studying differences between cancer-associated and normal fibroblasts (NFs) illustrates the superiority of transcriptome analysis of fibroblasts obtained from fresh tissue samples. Using single-cell RNA sequencing data, we identified common differences in gene expression patterns between normal and cancer-associated fibroblasts, which do not depend on the type of tumor.

SUBMITTER: Kazakova AN

PROVIDER: S-EPMC9574913 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Deeper insights into transcriptional features of cancer-associated fibroblasts: An integrated meta-analysis of single-cell and bulk RNA-sequencing data.

Kazakova Anastasia N AN Anufrieva Ksenia S KS Ivanova Olga M OM Shnaider Polina V PV Malyants Irina K IK Aleshikova Olga I OI Slonov Andrey V AV Ashrafyan Lev A LA Babaeva Nataliya A NA Eremeev Artem V AV Boichenko Veronika S VS Lukina Maria M MM Lagarkova Maria A MA Govorun Vadim M VM Shender Victoria O VO Arapidi Georgij P GP

Frontiers in cell and developmental biology 20221003

Cancer-associated fibroblasts (CAFs) have long been known as one of the most important players in tumor initiation and progression. Even so, there is an incomplete understanding of the identification of CAFs among tumor microenvironment cells as the list of CAF marker genes varies greatly in the literature, therefore it is imperative to find a better way to identify reliable markers of CAFs. To this end, we summarized a large number of single-cell RNA-sequencing data of multiple tumor types and ...[more]

PMID: 36263012

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Project description:ObjectiveThis study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC).Methods421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7.Results12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro.ConclusionThe risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.

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Dataset Information

Deeper insights into transcriptional features of cancer-associated fibroblasts: An integrated meta-analysis of single-cell and bulk RNA-sequencing data.

Publications

Deeper insights into transcriptional features of cancer-associated fibroblasts: An integrated meta-analysis of single-cell and bulk RNA-sequencing data.

Similar Datasets

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