The mRNA levels of SFKs in various types of cancer including HCC
As the working flow chart depicted in Fig. 1, we first used the TIMER2.0 database to explore the expression of SFKs in HCC patients (Fig. 2A). The data obtained from TIMER2.0 showed that the transcriptional levels of LYN, SRC and SRM were significantly elevated in HCC tissues, while BLK, FGR, FYN and HCK were reduced. Other members including BRK, FRK, LCK and YES did not show significant differences in HCC. We next utilized the UALCAN dataset to verify seven genes of SFKs with changed mRNA expression levels (Fig. 2B). The results indicated that the expression levels of LYN, SRC and SRM were significantly higher in HCC tissues than in normal tissues, and the expression levels of FYN were markedly lower. However, changes in the expression levels of BLK, FGR and HCK were not statistically significant. These results were partly consistent with those from TIMER2.0.
To further verify the above results, we detected the mRNA expression level of SFKs in 20 pairs of HCC and matched peritumor liver tissues using qPCR (Fig. 3). The results revealed that the expression levels of LYN, SRC and SRM were up-regulated, and FYN was down-regulated, whereas other SFK members did not show notable differences in HCC. The qPCR results were largely in agreement with the common parts of the TIMER2.0 database and the UALCAN database.
Genetic alteration analyses of SFKs genes in HCC
We then investigated the genetic alterations of SFKs in HCC patients by the use of cBioPortal and TIMER2.0 databases. As shown in Fig. 4A, alterations frequencies and types of SFKs were determined in 973 samples of four HCC studies. The SFKs mutations, amplifications, deep deletions and multiple alterations in 973 HCC samples occurred 10.08%, with frequencies of 3.08% (30 cases), 3.60% (35 cases), 3.19% (31 cases) and 0.21% (2 cases), respectively. The percentages of gene changes in individual SFKs in HCC were displayed in Fig. 4B, and the mutation frequency ranged from 0.4% to 4% (BLK, 3%; BRK, 0.7%; FGR, 0.6%; FRK, 0.8%; FYN, 1.3%; HCK, 0.4%; LCK, 0.6%; LYN, 4%; SRC, 0.7%; SRM, 1.1%; YES, 0.8%).
Next, the stacked bar plot from TIMER2.0 showed the relative proportion of different sCNA states of SFKs in HCC patients (Fig. 4C). The arm-level deletion and arm-level gain were the primary sCNA states, and the arm-level deletion was observed mainly in BLK, FGR, FRK, FYN, LCK and YES, while the arm-level gain was detected in BRK, HCK, LYN, SRC and SRM.
Furthermore, we explored the relationship between gene alteration and mRNA expression in 973 HCC samples (Fig. 4D). The decrease in the mRNA expression level of FYN might be partly caused by deep or shallow deletion of genes. Gain or amplification of genes could explain the increased mRNA expression levels of LYN, SRC and SRM. Combined with the above data, we speculated that genetic alterations of SFKs might lead to changes in their mRNA expression.
Correlations between SFKs expression and tumor stage in HCC patients
We then assessed the correlation between SFKs mRNA expression and the pathological stage in HCC using UALCAN databases. As shown in Fig. 5, SRC was highly expressed compared to normal tissues in tumor stage I-IV of HCC, and BRK, FRK, FYN, LYN and SRM were differentially expressed in several tumor stages. In comparison to normal tissues, the expression levels of BLK, LCK and YES were diverse in a certain tumor stage, but there was no difference in the expression levels of FGR and HCK in different tumor stages.
Prognostic values of SFKs in HCC
Furthermore, we analyzed the prognostic significance of SFKs expression in HCC using Kaplan-Meier Plotter, GEPIA and UALCAN database. The clinical characteristics of 364 HCC patients in Kaplan-Meier Plotter were shown in Table S2. OS curves were presented in Fig. 6A. The increased BLK (HR = 0.61, P = 0.01), BRK (HR = 0.68, P = 0.033), FYN (HR = 0.43, P = 2.7E-06), LCK (HR = 0.59, P = 0.0034), SRM (HR = 0.52, P = 0.00021), YES (HR = 0.68, P = 0.028) and decreased SRC (HR = 1.75, P = 0.0023) mRNA levels of HCC patients were strongly related to better OS. However, the expression levels of FGR (HR = 1.18, P = 0.4), FRK (HR = 0.75, P = 0.099), HCK (HR = 1.23, P = 0.24) and LYN (HR = 0.77, P = 0.15) had no effect on OS of HCC patients.
Next, GEPIA was used to verify above seven genes of SFKs that had an impact on the prognosis (Fig. 6B). HCC patients with higher transcriptional levels of SRC were significantly connected with shorter OS (p = 0.016), while those with increased transcriptional levels of FYN (p = 0.0034) or LCK (p = 0.044) were markedly associated with better long-term OS. There was no significant correlation between the expression levels of the rest four genes and OS in HCC patients. The results of FYN, LCK, SRC from Kaplan-Meier Plotter and GEPIA were consistent.
Then, UALCAN was used to further verify the influence of mRNA expression levels of FYN, LCK and SRC on OS (Fig. 6C). Among these three genes, only FYN (p = 0.043) and SRC (p = 0.00049) mRNA expression were significantly associated with OS. The above results from three databases indicated that the mRNA expression levels of FYN and SRC were remarkably correlated with prognosis in HCC patients.
Network analysis and functional enrichment analysis of SFKs
To explore the potential interactions at protein level of SFKs, the STRING database was employed in the protein-protein interaction (PPI) network analysis. As shown in Fig. 7A, the differentially expressed SFKs were connected with SH2D1A, CD4, CTTN, CTNNB1, CTNND1, CDH1, STAT3, PXN, CBL, and these proteins were primarily related to adaptive immunity, immunity, innate immunity, negative regulation of T cell receptor signaling pathway, positive regulation of natural killer cell mediated cytotoxicity, cell adhesion, positive regulation of interleukin-10 production. The results from GeneMANIA also indicated that the function of differentially expressed SFKs and their 20 related interactors (such as ABL1, GRAPL, MATK, SLA2, TXK, CSK, SLA, GRAP2, CRK, NCK2, CRKL, NCK1, GRB2, MAP3K21, MAP3K11 and ZAP70) were primarily correlated with immune response-regulating cell surface receptor signaling pathway involved in phagocytosis, positive regulation of T cell activation, positive regulation of lymphocyte activation, positive regulation of innate immune response, regulation of innate immune response and B cell apoptotic process (Fig. 7B).
Next, we used XIANTAO platform to perform Go enrichment and KEGG pathway analysis on SFKs and their 20 related genes from the GeneMANIA database (Table S3). As presented in Fig. 7C; Table 1, biological processes, such as protein autophosphorylation, immune response-activating cell surface receptor signaling pathway, positive regulation of T cell activation, T cell co-stimulation and lymphocyte co-stimulation were remarkably regulated by the SFKs and their 20 related genes. The extrinsic component of cytoplasmic side of plasma membrane was the primary cellular component of SFKs and their interactors. In addition, SFKs and their interactors also regulated the molecular functions, such as protein tyrosine kinase activity, SH3/SH2 adaptor activity, signaling adaptor activity, growth factor receptor binding and MAP kinase kinase kinase activity. By KEGG analysis, pathways including Chemokine signaling pathway, T cell receptor signaling pathway, ErbB signaling pathway, Focal adhesion, MAPK signaling pathway, Pathogenic Escherichia coli infection, Natural killer cell mediated cytotoxicity, Neurotrophin signaling pathway, Fc gamma R-mediated phagocytosis, NF-κB signaling pathway, Chronic myeloid leukemia, Adherens junction, Renal cell carcinoma, B cell receptor signaling pathway and Primary immunodeficiency were found to be correlated with SFKs and their 20 related genes (Fig. 7D; Table 2).
Correlation between SFKs expression and immune cell infiltration in HCC
The interaction network and functional enrichment analysis revealed that SFKs might be closely associated with tumorigenesis, progression and immune microenvironment. We further explored the relationship between SFKs and immune infiltration levels in HCC through the TIMER2.0 database. As shown in Fig. 8, the expression levels of FGR, HCK, LCK, LYN and YES were positively in connection with the infiltration of B cells (p < 0.001), macrophages (p < 0.001), dendritic cells (p < 0.001), neutrophils (p < 0.001), CD4+ T cells (p < 0.01) and CD8+ T cells (p < 0.001). Additionally, BLK was positively associated with the infiltration of B cells (p < 0.001), dendritic cells (p < 0.01), CD4+ T cells (p < 0.05) and CD8+ T cells (p < 0.001). BRK was positively correlated with the infiltration of macrophages (p < 0.01), neutrophils (p < 0.05) and CD4+ T cells (p < 0.001). FRK had significant positive correlations with the infiltration of macrophages (p < 0.001), dendritic cells (p < 0.001), neutrophils (p < 0.01), CD4+ T cells (p < 0.05) and CD8+ T cells (p < 0.001). FYN was positively connected with the infiltration of macrophages (p < 0.01), dendritic cells (p < 0.001), neutrophils (p < 0.01) and CD8+ T cells (p < 0.001). SRC was positively related to the infiltration of B cells (p < 0.001), macrophages (p < 0.001), dendritic cells (p < 0.001), neutrophils (p < 0.001) and CD4+ T cells (p < 0.001). SRM was positively relevant to the infiltration of B cells (p < 0.01), macrophages (p < 0.01), neutrophils (p < 0.05) and CD4+ T cells (p < 0.001). Taken together, the above results demonstrated that SFKs might play a dominant role in immune infiltration of HCC.
Overexpression of FYN suppresses tumor biological behavior in vitro and in vivo
Multiple databases combined with qPCR experiments suggested that among all SFK members, only FYN and SRC were dysregulated in expression, and were associated with OS, so we mainly considered these two genes as candidate genes for predicting prognostic markers in HCC patients. Many studies have shown that SRC has a tumor-promoting effect on HCC, but the impact of FYN on HCC is rarely reported. We then focused to identify the effect of FYN on HCC in vitro and in vivo.
The decreased protein expression level of FYN was observed in both HCC tissues (Fig. 9A) and human hepatoma cell line Huh7 by western blot analysis (Fig. 9B). We then generated stable FYN-overexpression Huh7 cell line (Fig. 9C). Cell proliferation ability, detected by CCK-8 proliferation assay and colony formation assay, decreased in FYN-overexpression Huh7 cells (p < 0.05, Fig. 9D; p < 0.01, Fig. 9E). Moreover, cell motility and invasion, as measured by migration and invasion assays, significantly inhibited upon FYN-overexpression group (p < 0.05, Fig. 9F; p < 0.001, Fig. 9G). Next, we examined the effect of FYN on HCC cells proliferation in vivo. In subcutaneous xenograft tumor model, tumor growth rate in mice injected with FYN-overexpression cells was significantly slower than with control Huh7 cells (Fig. 9H). FYN-overexpression Huh7 cells formed smaller tumors (365.5 ± 113.0 mm3 vs. 945.0 ± 156.7 mm3, P < 0.001, Fig. 9I; 412.8 ± 64.79 g vs. 918.3 ± 66.37 g, P < 0.001, Fig. 9J). Together, these data implied that overexpression of FYN, a potential tumor suppressor of HCC, suppresses tumor biological behavior in vitro and in vivo.