Integrating single-cell and bulk RNA sequencing to develop a cancer-associated fibroblast-related signature for immune infiltration prediction and prognosis in lung adenocarcinoma

Abstract

Background: An accumulating amount of studies are highlighting the impacts of cancer-associated fibroblasts (CAFs) on the initiation, metastasis, invasion, and immune evasion of lung cancer. However, it is still unclear how to tailor treatment regimens based on the transcriptomic characteristics of CAFs in the tumor microenvironment of patients with lung cancer.Methods: Our study examined single-cell RNA-sequencing data from the Gene Expression Omnibus (GEO) database to identify expression profiles for CAF marker genes and constructed a prognostic signature of lung adenocarcinoma using these genes in The Cancer Genome Atlas (TCGA) database. The signature was validated in 3 independent GEO cohorts. Univariate and multivariate analyses were used to confirm the clinical significance of the signature. Next, multiple differential gene enrichment analysis methods were used to explore the biological pathways related to the signature. Six algorithms were used to assess the relative proportion of infiltrating immune cells, and the relationship between the signature and immunotherapy response of lung adenocarcinoma (LUAD) was explored based on the tumor immune dysfunction and exclusion (TIDE) algorithm. Results:The signature related to CAFs in this study showed good accuracy and predictive capacity. In all clinical subgroups, the high-risk patients had a poor prognosis. The univariate and multivariate analyses confirmed that the signature was an independent prognostic marker. Moreover, the signature was closely associated with particular biological pathways related to cell cycle, DNA replication, carcinogenesis, and immune response. The 6 algorithms used to assess the relative proportion of infiltrating immune cells indicated that a lower infiltration of immune cells in the tumor microenvironment was associated with highrisk scores. Importantly, we found a negative correlation between TIDE, exclusion score, and risk score.Conclusions: Our study constructed a prognostic signature based on CAF marker genes useful for prognosis and immune infiltration estimation of lung adenocarcinoma. This tool could enhance therapy efficacy and allow individualized treatments.

Single-cell data analysis of malignant epithelial cell heterogeneity in lung adenocarcinoma for patient classification and prognosis prediction

“…8 H–I). Additionally, we compared the predictive performance of our model with previously published prognostic models [ [46] , [47] , [48] , [49] ] constructed using LUAD scRNA-seq data in testing dataset. Our model demonstrated better predictive ability ( Figs.…”

Correlation between chromatin epigenetic-related lncRNA signature (CELncSig) and prognosis, immune microenvironment, and immunotherapy in non-small cell lung cancer

“…We downloaded RNA sequence transcription data (FPKM), RNA transcription data (n = 19508), and lncRNA transcription data (n = 13481) from the TCGA website ( https://portal.gdc.cancer.gov /). The Cancer Genome Atlas (TCGA) database is a comprehensive collection of genomic, epigenomic, and clinical data from cancer patients [ 12 , 13 ]. In addition, we also obtained relevant clinical samples of lung cancer, including the following information: age, sex, tumor grade, TNM, stage, survival time, and survival status.…”

Development of a CD8+ T cell associated signature for predicting the prognosis and immunological characteristics of gastric cancer by integrating single-cell and bulk RNA-sequencing

“…However, the TME is a complex environment with high heterogeneity, conventional transcriptomic investigation may ignore the biologically relevant differences between distinct cells 9 . Compared to traditional RNA sequencing, the single-cell RNA-sequencing (scRNA-seq) technology enables researchers to determine the heterogenicity of tumor and stromal cells from the perspective of cellular level, and discriminate the gene expression characteristics of distinct cell types, thereby identifying feature genes for each cell 10 . As far as we know, there were no studies focused on constructing prognostic signatures for GC from the perspective of CD8+ T cell marker genes.…”