Abstract:
Lung adenocarcinoma (LUAD) is the most common type of lung cancer and a leading
cause of cancer-related mortality worldwide. LUAD exhibits high heterogeneity and
complexity at both molecular and cellular levels and often metastasizes to distant
organs, such as lymph nodes and the brain, which poses challenges for diagnosis,
prognosis and treatment. Single-cell RNA sequencing (scRNA-seq) is a powerful
technology that enables the characterization of individual cells within a complex tissue
or tumour. This study applied scRNA-seq to fifteen primary LUAD samples, seven
lymph node metastases and ten brain metastases and analyzed 107,757 cells. Seurat v4.0
pipeline was used for data preprocessing, quality control, normalisation, integration,
clustering, annotation, and differential expression analysis. Unsupervised clustering
analysis was performed to identify cell types and characterize cellular differences
between tumour and metastatic cells. Fifteen distinct cell types and subtypes were
identified, including epithelial, stromal, immune and endothelial cells. Gene expression
patterns and functional states of each cell type and subtype uncovered the cellular
diversity and dynamics of LUAD and its metastases. A comparison was also made of
the molecular and cellular features of primary and metastatic LUAD and identified the
essential genes and pathways (TGF-β, NF1 and EGFR) involved in LUAD metastasis.
Moreover, potential biomarkers and therapeutic targets for LUAD and its metastases
were identified. The research demonstrates the power of scRNA-seq for capturing the
complexity and dynamics of cancer evolution and progression. The study provides a
comprehensive single-cell atlas of LUAD and its metastases and reveals novel insights
into the cellular and molecular mechanisms underlying LUAD metastasis and
heterogeneity.
