Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis

Abstract

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.

Keywords: GSEA; HCC; TMB; cytokine signaling in immune; hepatocellular carcinoma; immune microenvironment; prediction model; scRNA-seq; single cell.

Figure 1

Workflow of this study.

Figure 2

Identification of cytokine signaling in immune-related genes (CSIRG) in the GSE149614 dataset. (A) Nonlinear dimensionality reduction by the t-SNE algorithm. (B) Differentially expressed genes and CSIRG from Reactome. (C) A volcano map involving the up- and downregulated CSIRG. (D) The expression of 26 CSIRG in two cell populations. The primary HCC tissue was denoted as group 1, and metastatic HCC tissue was denoted as group 2.

Figure 3

LASSO regression results. (A) LASSO coefficient profile of four overall survival (OS)-related CSIRG. (B) LASSO deviation profile of four OS-related CSIRG.

Figure 4

The immunohistochemistry results of PPIA and SQSTM1 from the Human Protein Atlas showed higher expression levels of PPIA and SQSTM1 in HCC tissues than in normal tissues.

Figure 5

Validation of the three-gene signature in HCC. (A,B) Time-dependent ROC curves of the three-gene signature in two datasets. (C,D) Kaplan-Meier curves of risk subgroups in the two datasets. (E,F) The association between survival status and RS. Integration nomogram based on risk score and cancer status for HCC prediction.

Figure 6

(A) Forest plot of univariate regression analysis. (B) Forest plot of multivariate regression analysis. (C) A nomogram incorporating cancer status and risk score for prognosis. (D) ROC curves of the nomogram with AUCs of 0.667, 0.698, and 0.747 at 1, 3, and 5 years, respectively. (E) Comparison of predicted and actual OS in the calibration curve. (F–H) Decision curve analyses of models in the TCGA-LIHC dataset.

Figure 7

Comparison of genomic mutations between the CSIRG high-risk group (A) and the CSIRG low-risk group (B) of the TCGA-LIHC dataset.

Figure 8

Comparison of enriched signaling pathways between the risk subgroups of the TCGA-LIHC dataset.

Figure 9

Comparison of tumor-infiltrating immune cells between the risk subgroups of the TCGA-LIHC dataset.