Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

Simple Summary Currently, there is no effective method to detect the prognosis for hepatocellular carcinoma (HCC). This study used bioinformatics techniques to determine HCC molecular subtypes and prognosis-related biomarkers. A total of 3330 intersectional differentially expressed genes (DEGs) with the same differential direction in four datasets were identified by differential expression analysis. Intersectional DEGs were involved in the cell cycle, FOXO signaling pathway, and complement and coagulation cascades. Then, two subtypes were identified using a non-negative matrix decomposition method. Subtype C2 displayed better overall survival than subtype C1. Moreover, 217 prognostic related-genes were identified using the Cox regression and Kaplan-Meier curves. The area under the curve >0.80 of prognostic relate-genes were selected to construct random survival forest and the least absolute shrinkage and selection operator model and obtained seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14 and FANCI). Risk score model and recurrence model were constructed based on feature genes, and FANCI was inferred as a key gene by univariate Cox model. High expression of FANCI was mainly involved in cell cycle, DNA replication and mismatch repair. Interestingly, Single Sample Gene Set Enrichment Analysis was used to quantify immune infiltration and showed that Th2 cells and T helper cells were significantly up regulated in HCC compared to controls. Furthermore, we found the presence of two mutation sites as well as methylation modifications occurred in FANCI. Overall, we identified two types of HCC and identified that FANCI will serve as a potential biomarker for HCC prognosis and be important to the diagnosis and treatment of HCC. Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.

Automated summary

Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.