Chinese Medical Sciences Journal ›› 2024, Vol. 39 ›› Issue (2): 111-121.doi: 10.24920/004354
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PPP1R14A is Associated with Immunotherapy Resistance in Head and Neck Squamous Cell Carcinoma Identified by Single-Cell and Bulk RNA-Sequencing
Jun-Jie Ma1#, Lei Zhang2#, Jin Lu3, 4, Hao-Xuan Zhang3, 4, *(
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- 1Department of Clinical Medicine, Bengbu Medical University, Bengbu 233030, Anhui, China
2Department of General Surgery, The Second Affiliated Hospital of Bengbu Medical University, Bengbu 233030, Anhui, China
3Department of Human Anatomy, Bengbu Medical University, Bengbu 233030, Anhui, China
4Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical University, Bengbu 233030, Anhui, China
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Received:2024-03-04Accepted:2024-04-18Published:2024-06-30Online:2024-06-18 -
Contact:*zhxww@foxmail.com -
About author:#co-first author:Contributed equally to this work.
Cite this article
Jun-Jie Ma, Lei Zhang, Jin Lu, Hao-Xuan Zhang. PPP1R14A is Associated with Immunotherapy Resistance in Head and Neck Squamous Cell Carcinoma Identified by Single-Cell and Bulk RNA-Sequencing[J].Chinese Medical Sciences Journal, 2024, 39(2): 111-121.
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Figure 1.
Identification of nivolumab resistance-associated differentially expressed genes (DEGs) between nivolumab resistant and sensitive patients in the Gene Expression Omnibus (GEO) database. (A) Volcano plot of DEGs between nivolumab resistant and sensitive patients in single-cell and bulk RNA-sequencing data. (B) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of function annotation of DEGs. (C) Gene set enrichment analysis (GSEA) of biological function of DEGs. (D) The Uniform Manifold Approximation and Projection (UMAP) method shows the distribution of five immune-infiltrating cells in tumor tissues. (E, F) Scatter plot (E) and violin plot (F) of resistance-related scores for five immune-infiltrating cells in tumor tissues (n = 26,324)."
Figure 1.
Figure 2.
Identification of key genes for drug resistance. (A, B) Screening of key genes for drug resistance using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis (A) and Recursive Feature Elimination (RFE) algorithm analysis (B). (C) Venn diagram shows overlapping DEGs identified by RFE and LASSO. (D, E) Comparisons of expression of each key gene in the sensitive patients (D, n = 42) and the resistant patients (E, n = 54) before and after nivolumab immunotherapy. ***P < 0.001, **P < 0.01, *P < 0.05."
Figure 2.
Figure 3.
Correlation of PPP1R14A gene expression and immune response. (A) Differences in tumor purity and immune score between the high and low PPP1R14A gene expression groups (n = 96, *P < 0.05, **P < 0.01). (B) Correlation between PPP1R14A expression and immune cell infiltration. (C) Heatmap of the correlation between PPP1R14A gene and immunoregulatory genes. (D) Expression of PPP1R14A gene in five types of immune cells (n = 9,347). (E) Comparisons of PPP1R14A gene expression in five immune cell subtypes between the nivolumab resistant group and the nivolumab sensitive group (n = 9,347, *P < 0.05, ***P < 0.001). (F) Scatter plot of UMAP distribution of T cell subtypes. (G) Proportion of T cells in different immunotherapy response groups (χ2 = 33.56, P < 0.001). (H) Differential PPP1R14A expression in CD8+ cells, CD4+ cells, and regulatory T cells (Tregs) in the immunotherapy sensitive and resistant groups. (*P < 0.05, ***P < 0.001)."
Figure 3.
Figure 4.
The funicton enrichment of PPP1R14A. (A) Heatmap of gene set variation analysis (GSVA) scores for 84 metabolism pathways in the high and low PPP1R14A expression groups. (B) Distribution of GSVA scores of different metabolism pathways in the high and low PPP1R14A expression groups (n = 53, *P < 0.05, **P < 0.01, ***P < 0.001)."
Figure 4.
Figure 5.
Identification of pseudotime and cellular communication of immune cells infiltrated in tumor tissues on single cell dataset. (A) Cellular heterogeneity identification with CytoTRACE method. (B) Cellular heterogeneity identification with Monocel3. (C, D) Comparisons of CytoTRACE score (C) and pseudotime score (D) between the high and low PPP1R14A gene expression groups (***P < 0.001). (E) Potential ligand-receptor communication associated with PPP1R14A. (F) GSEA analysis of the MHC-Ⅰ and MHC-Ⅱ pathways associated with PPP1R14A expression."
Figure 5.
Figure 6.
Screening potentially sensitive drug for nivolumab resistant HNSCC patients. (A) The correlation between PPP1R14A gene expression and the half maximal inhibitory concentration (IC50) of 565 FDA approved immunotherapeutics. (B, C) Comparisons of IC50 of AXITINIB in bulk (B) and single-cell (C) sequencing data between the high and low PPP1R14A expression groups (***P < 0.001). (D) Scatter plots of IC50 of AXITINIB for immune cells."
Figure 6.
Figure 7.
Validation of PPP1R14A expression in external cohorts. (A) The tumor immune dysfunction and exclusion (TIDE) score distribution of the TCGA cohort samples. (B) Correlation between the PPP1R14A expression and TIDE scores in the nivolumab-sensitive and -resistant groups. (C) Differential PPP1R14A expression in nivolumab-sensitive and -resistant groups in the TCGA (n = 552), GSE21551 (n = 100), and GSE226134 (n = 49) cohorts. (*P < 0.05, ***P < 0.001). (D) Relationship between PPP1R14A expression and immunotherapy response in the TCGA, GSE21551, and GSE226134 cohorts."
Figure 7.
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