Fan Yang, Yan Zhao, Xiaohan Huang, Jin Zhang, Ting Zhang
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引用次数: 1
Abstract
Objective: To screen the cell differentiation trajectory-related genes and build a cell differentiation trajectory-related signature for predicting the prognosis of lung adenocarcinoma (LUAD).
Methods: LUAD single cell mRNA expression profile, TCGA-LUAD transcriptome data were obtained from GEO and TCGA databases. Single-cell RNA-seq data were used for cell clustering and pseudotime analysis after dimensionality reduction analysis, and the cell differentiation trajectory-related genes were acquired after differential expression analysis conducted between the main branches. Then, the consensus clustering analysis was carried out on TCGA-LUAD samples, and the GSEA analysis was performed, then the differences on the expression levels of immune checkpoint genes and immunotherapy response were compared among clusters. The prognostic model was constructed, and the GSE42127 dataset was used to validate. A nomogram evaluation model was used to predict prognosis.
Results: Two subsets with distinct differentiation states were found after cell differentiation trajectory analysis. TCGA-LUAD samples were divided into two cell differentiation trajectory-related gene-based clusters, GSEA found that cluster 1 was significantly related to 20 pathways, cluster 2 was significantly enriched in three pathways, and it was also shown that clusters could better predict immune checkpoint gene expression and immunotherapy response. A six cell differentiation-related genes-based prognostic signature was constructed, and the patients in the high-risk group had poorer prognosis than those in the low-risk group. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features, and this nomogram had strong predictive performance and high accuracy.
Conclusion: The cell differentiation-related signature and the prognostic nomogram could accurately predict survival.
期刊介绍：
Genetics Research is a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations and molecular interactions, extending out to developmental, evolutionary, and population genetics as well as ethical, legal and social aspects. Our aim is to lead to a better understanding of genetic processes in health and disease. The journal focuses on the use of new technologies, such as next generation sequencing together with bioinformatics analysis, to produce increasingly detailed views of how genes function in tissues and how these genes perform, individually or collectively, in normal development and disease aetiology. The journal publishes original work, review articles, short papers, computational studies, and novel methods and techniques in research covering humans and well-established genetic organisms. Key subject areas include medical genetics, genomics, human evolutionary and population genetics, bioinformatics, genetics of complex traits, molecular and developmental genetics, Evo-Devo, quantitative and statistical genetics, behavioural genetics and environmental genetics. The breadth and quality of research make the journal an invaluable resource for medical geneticists, molecular biologists, bioinformaticians and researchers involved in genetic basis of diseases, evolutionary and developmental studies.
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