Construction of a single nucleotide variant score-related gene-based prognostic model in hepatocellular carcinoma: analysis of multi-independent databases and validation in vitro - Yu-Jie Xu (BMC, 2021)
Abstract Background The accumulation of single nucleotide variants (SNVs) and the emergence of neoantigens can affect tumour proliferation and the immune microenvironment. However, the SNV-related immune microenvironment characteristics and key genes involved in hepatocellular carcinoma (HCC) are still unclear. We aimed to evaluate differences in the SNV-related immune microenvironment, construct a prognostic model and validate the key genes in vitro. Methods The categories of samples were defined by the expression of SNV score-related genes to evaluate the differences in mutational features, immune environment and prognosis. The survival model was constructed with survival-associated genes and verified in two independent test datasets. RCAN2, the key gene screened out for biofunction, was validated in vitro. Results IC2, among the three integrated clusters (IC1, IC2, IC3) classified by the 82 SNV score-related genes, was distinct from the rest in SNV score and immune cell infiltration, showing a better prognosis. Seven prognostic markers, HTRA3, GGT5, RCAN2, LGALS3, CXCL1, CLEC3B, and CTHRC1, were screened to construct a prognostic model. The survival model distinguished high-risk patients with poor prognoses in three independent datasets (log-rank P < 0.0001, 0.011, and 0.0068, respectively) with acceptable sensitivity and specificity. RCAN2 was inversely correlated with NK cell infiltration, and knockdown of RCAN2 promoted proliferation in HCC. Conclusions This study revealed the characteristics of the HCC SNV-associated subgroup and screened seven latent markers for their accuracy of prognosis. Additionally, RCAN2 was preliminarily proven to influence proliferation in HCC and it had a close relationship with NK cell infiltration in vitro. With the capability to predict HCC outcomes, the model constructed with seven key differentially expressed genes offers new insights into individual therapy.