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Characterizing the Metabolic and Immune Landscape of Non-small Cell Lung Cancer Reveals Prognostic Biomarkers Through Omics Data Integration

Authors
  • Wang, Fengjiao1
  • Zhang, Yuanfu2
  • Hao, Yangyang2
  • Li, Xuexin3
  • Qi, Yue2
  • Xin, Mengyu2
  • Xiao, Qifan1
  • Wang, Peng2
  • 1 Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin , (China)
  • 2 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin , (China)
  • 3 Department of Urinary Surgery, Harbin Medical University Cancer Hospital, Harbin , (China)
Type
Published Article
Journal
Frontiers in Cell and Developmental Biology
Publisher
Frontiers Media SA
Publication Date
Jul 06, 2021
Volume
9
Identifiers
DOI: 10.3389/fcell.2021.702112
Source
Frontiers
Keywords
Disciplines
  • Cell and Developmental Biology
  • Original Research
License
Green

Abstract

Non-small cell lung cancer (NSCLC) is one of the most common malignancies worldwide. The development of high-throughput single-cell RNA-sequencing (RNA-seq) technology and the advent of multi-omics have provided a solid basis for a systematic understanding of the heterogeneity in cancers. Although numerous studies have revealed the molecular features of NSCLC, it is important to identify and validate the molecular biomarkers related to specific NSCLC phenotypes at single-cell resolution. In this study, we analyzed and validated single-cell RNA-seq data by integrating multi-level omics data to identify key metabolic features and prognostic biomarkers in NSCLC. High-throughput single-cell RNA-seq data, including 4887 cellular gene expression profiles from NSCLC tissues, were analyzed. After pre-processing, the cells were clustered into 12 clusters using the t-SNE clustering algorithm, and the cell types were defined according to the marker genes. Malignant epithelial cells exhibit individual differences in molecular features and intra-tissue metabolic heterogeneity. We found that oxidative phosphorylation (OXPHOS) and glycolytic pathway activity are major contributors to intra-tissue metabolic heterogeneity of malignant epithelial cells and T cells. Furthermore, we constructed T-cell differentiation trajectories and identified several key genes that regulate the cellular phenotype. By screening for genes associated with T-cell differentiation using the Lasso algorithm and Cox risk regression, we identified four prognostic marker genes for NSCLC. In summary, our study revealed metabolic features and prognostic markers of NSCLC at single-cell resolution, which provides novel findings on molecular biomarkers and signatures of cancers.

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