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

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.

By characterizing metabolic and immune microenvironment reveal potential prognostic markers in the development of colorectal cancer

“…To characterize the dynamic changes of metabolism in the development of COAD, GSVA was used to calculate the activity of 85 metabolic pathways collected from the MsigDB database in four clinical stages. We found that tumor tissue have activated energy supply compared with normal tissues (Figure 2A), which was consistent with previous studies showing that the activated metabolic microenvironment could supply tumor proliferation and metastasis (Wang et al, 2021). In different stages of tumor development, there are obvious differences in the activity of metabolic pathways.…”

Consolidation Tumor Ratio Combined With Pathological Features Could Predict Status of Lymph Nodes of Early-Stage Lung Adenocarcinoma

“…Nevertheless, there are still some factors we did not involve, including CEA level, SUVmax, and so on, for the lack of data (not all early-stage lung cancer patients need to take PET-CT and CEA examination). Current data showed that the combined application of multiomics, including liquid biopsy, proteomics, and metabolomics, has widened in predicting the malignancy of small pulmonary nodules (29). We believe that a prospective cohort will be established in the future to develop a more complete model to predict the incidence of lymph node metastasis of early-stage lung adenocarcinoma.…”

Single-Cell RNA Sequencing Analysis of Gene Regulatory Network Changes in the Development of Lung Adenocarcinoma

“…To validate the regulatory network changes during lung adenocarcinoma development another set of single cell transcriptome data (GSE123902 [ 27 ], GSE117570 [ 28 ], GSE148071 [ 29 ]) was used as a validation dataset. After quality control, a total of 34,920 cells were selected for subsequent analysis (including 15,701, 14,984, and 4235 cells from distant normal regions of lung samples, early LUAD, and advanced LUAD).…”