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Metabolic heterogeneity in early-stage lung adenocarcinoma revealed by RNA-seq and scRNA-seq

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Abstract

Purpose

Cancer cells maintain cell growth, division, and survival through altered energy metabolism. However, research on metabolic reprogramming in lung adenocarcinoma (LUAD) is limited

Methods

We downloaded TCGA and GEO sequencing data. Consistent clustering with the ConsensusClusterPlus package was employed to detect the scores for four metabolism-related pathways. The LUAD samples in the TCGA dataset were clustered with ConsensusClusterPlus, and the optimal number of clusters was determined according to the cumulative distribution function (CDF). The cell score for each sample in the TCGA dataset was calculated using the MCPcounter estimate function of the MCPcounter package.

Results

We identified two subtypes by scoring the samples based on the 4 metabolism-related pathways and cluster dimensionality reduction. The prognosis of cluster B was obviously poorer than that of cluster A in patients with LUAD. The analysis of single-nucleotide variation (SNV) data showed that the top 15 genes in the four metabolic pathways with the most mutations were TKTL2, PGK2, HK3, EHHADH, GLUD2, PKLR, TKTL1, HADHB, CPT1C, HK1, HK2, PFKL, SLC2A3, PFKFB1, and CPT1A. The IFNγ score of cluster B was significantly higher than that of cluster A. The immune T-cell lytic activity score of cluster B was significantly higher than that of cluster A. We further identified 5 immune cell subsets from single-cell sequencing data. The top 5 marker genes of B cells were IGHM, JCHAIN, IGLC3, IGHA1, and IGKC. The C0 subgroup of monocytes had a higher pentose phosphate pathway (PPP) score than the C6 subgroup.

Conclusions

Metabolism-related subtypes could be potential biomarkers in the prognosis prediction and treatment of LUAD.

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Data availability

The datasets analyzed during the current study are available from the corresponding authors on reasonable request.

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Funding

This work was funded by Henan Medical Science and Technology Joint Building Program (LHGJ20210308 and LHGJ20210328).

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Authors and Affiliations

  1. Department of Geriatric Respiratory and Sleep, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China

    Yang Zhang, Jiang Shi, Junfang Luo & Cong Liu

  2. Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China

    Lixu Zhu

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Contributions

YZ and LZ conceived and planned the study design; JS and YZ performed formal analysis and data interpretation; JL and YZ wrote the original draft; CL provided critical revisions and contributed to the editing of the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yang Zhang or Lixu Zhu.

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The authors have no conflict of interests related to this publication.

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The study has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

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Supplementary Information

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12094_2023_3082_MOESM1_ESM.tif

Supplementary file1 Differential genes and scores of metabolic subtypes in the GSE31210 dataset. A: Differential gene analysis of the two subtypes with respect to the four metabolism-related pathways in the GSE31210 dataset and heatmap of the four metabolism-related pathway scores (TIF 4351 KB)

12094_2023_3082_MOESM2_ESM.tif

Supplementary file2 Differential genes and scores of metabolic subtypes in the GSE50081 dataset. A: Differential gene analysis of the four metabolism-related pathways and heatmap of the four metabolism-related pathway scores for the two subtypes in the GSE50081 dataset (TIF 3337 KB)

12094_2023_3082_MOESM3_ESM.tif

Supplementary file3 Comparison of clinical features of metabolism-related subtypes. A: Comparison of different clinical features between the two molecular subtypes in the TCGA dataset. B: Comparison of different clinical features between the two molecular subtypes in the GSE31210 dataset. C: Comparison of different clinical features between the two molecular subtypes in the GSE50081 dataset (TIF 2624 KB)

12094_2023_3082_MOESM4_ESM.tif

Supplementary file4 The scores of immune cell, IFNγ, and immune T-cell lytic activity for metabolism-related subtypes in the GSE31210 dataset. A: Differences in immune cell scores predicted by MCPcounter between the two subtypes in the GSE31210 dataset. B: Difference in IFNγ score between the two subtypes in the GSE31210 dataset. C: Difference in immune T-cell lytic activity score between the two subtypes in the GSE31210 dataset (TIF 1035 KB)

12094_2023_3082_MOESM5_ESM.tif

Supplementary file5 The scores of immune cell, IFNγ, and immune T-cell lytic activity for metabolism-related subtypes in the GSE50081 dataset. A: Differences in immune cell scores predicted by MCPcounter between the two subtypes in the GSE50081 dataset. B: Difference in the IFNγ score between the two subtypes in the GSE50081 dataset. C: Difference in immune T-cell lytic activity score between the two subtypes in the GSE50081 dataset (TIF 947 KB)

Supplementary file6 tSNE plot of marker gene expression (TIF 4390 KB)

12094_2023_3082_MOESM7_ESM.tif

Supplementary file7 Analysis of the relationship between hypoxia type score, angiogenesis score, immune cells and metabolism-related subtypes A: Correlation analysis of the hypoxia type score and the four metabolism-related pathway scores. B: Correlation analysis of the angiogenesis score and the four metabolism-related pathway scores. C: Differential analysis of the metabolism-related subtypes in different cell types. D: Correlations of monocytes with the hypoxia score, angiogenesis score and four metabolism-related pathway scores (TIF 5595 KB)

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Zhang, Y., Shi, J., Luo, J. et al. Metabolic heterogeneity in early-stage lung adenocarcinoma revealed by RNA-seq and scRNA-seq. Clin Transl Oncol 25, 1844–1855 (2023). https://doi.org/10.1007/s12094-023-03082-z

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