Journal of thoracic disease · 2025
An integrated analysis of scRNA-seq and RNA-seq data revealed metastasis-related regulators as prognostic indicators in lung adenocarcinoma.
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Jiang Yang, Ye Danrong, Zhou Yongxin
Аннотация
BACKGROUND: The incidence and mortality rates of lung cancer are exceptionally high. Many patients are diagnosed with early stage lung cancer but experience rapid recurrence post-surgery. Many research studies have shown that the unfavorable prognosis of patients may be associated with micro-metastasis in the lymph nodes. Our research aimed to develop a nomogram to predict the prognosis of lung adenocarcinoma (LUAD).
METHODS: Single-cell RNA sequencing (scRNA-seq) data were analyzed to identify 11 cell clusters. Patterns of incoming and outgoing signals were identified across the entire cell population. A weighted gene co-expression network analysis (WGCNA) was conducted to uncover critical genes in LUAD. The intersecting marker genes were used to construct the prognostic model.
RESULTS: scRNA-seq data were analyzed to identify 19 cell clusters. We identified 3,464 marker genes from the scRNA-seq dataset, 1,994 differentially expressed genes from the bulk RNA sequencing (RNA-seq) dataset, and 1,863 genes associated with a key module identified by the WGCNA. After performing the intersection, univariate Cox, and least absolute shrinkage and selection operator analyses, a prognostic model was established based on the expression levels of 13 signature genes. Subsequent functional experiments confirmed the role of selected regulated genes.
CONCLUSIONS: Through the integration of scRNA-seq data and bulk RNA-seq data, we developed an innovative model to predict the prognosis of patients. The risk score was found to be a significant independent predictor and clinical-pathological features of LUAD.
METHODS: Single-cell RNA sequencing (scRNA-seq) data were analyzed to identify 11 cell clusters. Patterns of incoming and outgoing signals were identified across the entire cell population. A weighted gene co-expression network analysis (WGCNA) was conducted to uncover critical genes in LUAD. The intersecting marker genes were used to construct the prognostic model.
RESULTS: scRNA-seq data were analyzed to identify 19 cell clusters. We identified 3,464 marker genes from the scRNA-seq dataset, 1,994 differentially expressed genes from the bulk RNA sequencing (RNA-seq) dataset, and 1,863 genes associated with a key module identified by the WGCNA. After performing the intersection, univariate Cox, and least absolute shrinkage and selection operator analyses, a prognostic model was established based on the expression levels of 13 signature genes. Subsequent functional experiments confirmed the role of selected regulated genes.
CONCLUSIONS: Through the integration of scRNA-seq data and bulk RNA-seq data, we developed an innovative model to predict the prognosis of patients. The risk score was found to be a significant independent predictor and clinical-pathological features of LUAD.