Comprehensive analysis and validation of SNX7 as a novel biomarker for the diagnosis, prognosis, and prediction of chemotherapy and immunotherapy response in hepatocellular carcinoma

Abstract

Background: Studies have demonstrated that Sorting nexin 7 (SNX7) functions as an anti-apoptotic protein in liver tissue and plays a crucial role in the survival of hepatocytes during early embryonic development. However, its diagnostic and prognostic value as well as the predictive value of chemotherapy and immunotherapy have not been reported in hepatocellular carcinoma (HCC).

Methods: SNX7 mRNA expression and its diagnostic efficacy were examined in GEO datasets, and the findings were further confirmed in TCGA, ICGC cohorts, and cell lines. The protein level of SNX7 was determined using CPTAC and HPA databases, and the results were validated through immunohistochemistry (IHC). Survival analyses were performed in TCGA and ICGC cohorts, and the results were subsequently validated via Kaplan-Meier Plotter. The response to chemotherapy and immunotherapy was predicted via GDSC dataset and TIDE algorithm, respectively. R packages were employed to explore the relationship between SNX7 expression and immune infiltration, m6A modification, as well as the functional enrichment of differentially expressed genes (DEGs).

Results: The expression of SNX7 at both mRNA and protein levels was significantly upregulated in HCC tissues. SNX7 exhibited superior diagnostic efficacy compared to AFP alone for HCC detection, and combining it with AFP improved the diagnostic accuracy for HCC. High SNX7 was associated with unfavorable outcomes, including poor overall survival, disease-specific survival, progression-free survival, and advanced pathological stage, in patients with HCC, and SNX7 was identified as an independent risk factor for HCC. Moreover, elevated SNX7 expression was positively correlated with increased sensitivity to various chemotherapy drugs, including sorafenib, while it was associated with resistance to immunotherapy in HCC patients. Correlation analysis revealed a relationship between SNX7 and multiple m6A-related genes and various immune cells. Finally, enrichment analysis demonstrated strong associations of SNX7 with critical biological processes, such as cell cycle regulation, cellular senescence, cell adhesion, DNA replication, and mismatch repair pathway in HCC.

Conclusions: Our study highlights the association of SNX7 with the immune microenvironment and its potential influence on HCC progression. SNX7 emerges as a promising novel biomarker for the diagnosis, prognosis, and prediction of response to chemotherapy and immunotherapy in patients with HCC.

Keywords: Biomarker; Diagnostic; Hepatocellular carcinoma; Prognosis; Sorting nexin 7.

Fig. 1

Flow chart of the study

Fig. 2

SNX7 expression profiles in hepatocellular carcinoma. The SNX7 transcript levels in tissues of HCC and normal samples based on (A) GSE144269; B GSE45267; C GSE112790; D GSE14520. The SNX7 mRNA levels in HCC and adjacent normal liver tissues based on (E) GSE121248; F GSE10143; G GSE36376; H GSE76427; I GSE39791. J The protein expression of SNX7 in HCC and normal liver specimens from CPTAC data. K Validation of protein expression of SNX7 based on the HPA database. L Validation of gene expression of SNX7 between human normal liver cell line (LO2) and HCC cell lines. M Validation of protein expression of SNX7 based on IHC. Analysis between two groups of samples: Wilcoxon rank sum test

Fig. 3

SNX7 mRNA expression correlates with HCC pathologic stages. A-C High mRNA levels of SNX7 in HCC validated in the analysis of data from TCGA and GTEx database. D Box plot showing SNX7 mRNA expression in 1–3 pathologic stages of HCC specimens from TCGA datasets. E Validation of the mRNA levels of SNX7 in the ICGC cohort. F Validation of the SNX7 mRNA expression in 1–4 pathologic stages of HCC specimens from ICGC cohort. Analysis between two groups of samples: Wilcoxon rank sum test. Significances between multiple groups of samples were determined by Kruskal–Wallis rank-sum test and Dunn’s test

Fig. 4

The mRNA expression level of SNX7 between HCC patients and liver cirrhosis patients. The analysis of SNX7 levels in (A) GSE25097, (B) GSE46444, and (C) GSE54236 grouped by HCC and liver cirrhosis tissues. The difference between two groups was analyzed using the Wilcoxon rank sum test

Fig. 5

ROC curve of SNX7 mRNA expression in different LIHC cohorts. The ROC curves of SNX7 in distinguish and normal in GSE144269 (A), GSE45267 (B), GSE112790 (C), and (D) GSE14520. ROC curves of SNX7 for differentiating HCC tissues and adjacent normal tissues in GSE121248 (E), GSE10143 (F), GSE36376 (G), GSE39791 (H), and GSE76427 (I). ROC curves of SNX7 in validation sets of TCGA datasets (J). ROC curves of AFP for differentiating HCC tissues and adjacent normal tissues in GSE10143 (K), GSE36376 (L), and GSE39791 (M). ROC curves of SNX7&AFP for differentiating HCC tissues and adjacent normal tissues in GSE10143 (N), GSE36376 (O), and GSE39791 (P)

Fig. 6

High SNX7 expression predicts poor prognosis in HCC patients. The OS (A), DSS (B), and PFS (C) of HCC patients were analyzed in TCGA. Kaplan–Meier curves for the patients in high- and low-SNX7 groups from ICGC database (D). Kaplan–Meier analysis of OS (E), DSS (F), PFS (G), and RFS (H) in the validation cohort of the Kaplan Meier database. All survival curves, groups were defined using median split of SNX7 expression

Fig. 7

Different chemotherapeutic responses based on SNX7 expression levels in HCC. IC50 of response to chemotherapeutic drugs Bortezomib (A), Camptothecin (B), Cisplatin (C), Cytarabine (D), Gemcitabine (E), Vinorelbine (F), Docetaxel (G), Vinblastine (H), Rapamycin (I), Paclitaxel (J), Sorafenib (K), and Vorinostat (L) between the high and low SNX7 expression groups

Fig. 8

Correlation analysis between the level of SNX7 gene expression and immune cell infiltration. A The enrichment scores of 24 types of immune cells between SNX7^high and SNX7^low HCC groups, and Wilcoxon rank sum was applied for the significance test (^ns p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001). Correlation between SNX7 expression and (B) aDC, (C) CD8 + T cells, (D) Cytotoxic cells, (E) Macrophages, (F) NK cells, (G) p DC, (H) T helper cells, (I) Tcm cells, (J) TFH cells, and (K) Th2 cells

Fig. 9

Correlation analysis between the level of SNX7 and Immune checkpoint genes or immunotherapy response. A Box plot comparing the expression of checkpoint genes between SNX7^high and SNX7^low HCC groups. B The correlation between different expression of checkpoint genes between SNX7^high and SNX7.^low HCC groups. C-D The distribution of immune response scores between high and low expression of SNX7 in TCGA cohort (C) and ICGC cohort (D). (***p < 0.001)

Fig. 10

Correlations of SNX7 expression with m6A related genes in LIHC. A Expression of m6A-related genes in the high and low SNX7 expression groups in LIHC. B Heatmap shows the correlation between the expression of SNX7 and m6A-related genes. (.⁻p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001)

Fig. 11

Functional enrichment analysis of SNX7 in LIHC. A Volcano map of DEGs. B The heatmap of the top 100 differentially expressed genes (50 down-regulated and 50 up-regulated). C The top 40 significant KEGG pathways (20 down-regulated and 20 up-regulated) and the top 40 significant GO annotations (20 down-regulated and 20 up-regulated) were enriched for SNX7-related genes in LIHC