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.

Background: Hepatocellular carcinoma (HCC) is a life-threatening human malignancy and the fourth leading cause of cancer-related deaths worldwide. Patients with HCC are often diagnosed at an advanced stage with a poor prognosis. Sorafenib is a multikinase inhibitor used as the first-line treatment for patients with advanced HCC. However, acquired resistance to sorafenib in HCC leads to tumor aggression and limits the drug’s survival benefits; the underlying molecular mechanisms for this resistance remain unclear. Methods: This study aimed to examine the role of the tumor suppressor RBM38 in HCC, and its potential to reverse sorafenib resistance. In addition, the molecular mechanisms underlying the binding of RBM38 and the lncRNA GAS5 were examined. The potential involvement of RBM38 in sorafenib resistance was examined using both in vitro and in vivo models. Functional assays were performed to assess whether RBM38: binds to and promotes the stability of the lncRNA GAS5; reverses the resistance of HCC to sorafenib in vitro; and suppresses the tumorigenicity of sorafenib-resistant HCC cells in vivo. Results: RBM38 expression was lower in HCC cells. The IC₅₀ value of sorafenib was significantly lower in cells with RBM38 overexpression than in control cells. RBM38 overexpression improved sorafenib sensitivity in ectopic transplanted tumors and suppressed the growth rate of tumor cells. RBM38 could bind to and stabilize GAS5 in sorafenib-resistant HCC cells. In addition, functional assays revealed that RBM38 reversed sorafenib resistance both in vivo and in vitro in a GAS5-dependent manner. Conclusions: RBM38 is a novel therapeutic target that can reverse sorafenib resistance in HCC by combining and promoting the lncRNA GAS5.

Overexpression of Golgi membrane protein 1 (GOLM1) is closely associated with hepatocellular carcinoma (HCC) vascular invasion. How GOLM1 may be involved in angiogenesis in HCC remains unclear. We explored how GOLM1 promotes angiogenesis in HCC and potential prognostic value. Expression levels of GOLM1 in HCC patients and healthy controls were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between HCC patients and controls were compared. GOLM1 was knocked out in the HCC cell line, and RNA sequencing and DEG expression analysis were performed compared with control cells. Based on TCGA data and cell line RNA sequencing data, DEGs affected by a high expression of GOLM1 were identified. Subsequently, enrichment analysis was performed to explore the functions and pathways of the DEGs affected by a high expression of GOLM1. A relevant network analysis was built. Cox regression, genomic variance analysis scores, minimum absolute shrinkage and selection operator regression, and random forest regression models were applied to determine the best prognostic model and validated using the GSE54236 dataset from the Gene Expression Omnibus (GEO). We determined the effect of GOLM1 expression on immune cell infiltration in liver cancer. GOLM1 was overexpressed in HCC tissues compared with controls, and its level correlated with tumor purity and prognosis. 400 DEGs affected by highly expressed GOLM1 were identified in TCGA and cell line RNA sequencing data. Enrichment analysis revealed that these DEGs may be related to biological processes of oxidative stress and angiogenesis and involved in the VEGF signaling pathway and protein processing in endoplasmic reticulum. We predicted a comprehensive regulatory network in which GOLM1 activated VEGF signaling to promote HCC angiogenesis. GOLM1 may interact with E2F1 and IGF2BP3 to promote angiogenesis. GOLM1 overexpression was associated with greater immune cell infiltration. A random forest regression model was the best prognostic model. Our study reveals a potential molecular mechanism of GOLM1 in promoting HCC. We developed two prognostic models based on DEG associated with GOLM1 overexpression to help stratify HCC prognosis and improve individualized treatment.

Hepatocellular carcinoma (HCC), a highly malignant digestive system tumor, poses substantial challenges due to its intricate underlying causes and pronounced post-surgery recurrence. Consequently, the prognosis for HCC remains notably unfavorable. The endorsement of sorafenib and PD-L1 inhibitors for HCC signifies the onset of a new era embracing immunotherapy and targeted treatment approaches for this condition. Hence, comprehending the mechanisms underpinning targeted immune combination therapy has become exceedingly vital for the prospective management of HCC patients. This article initially presents a triumphant instance of curative treatment involving the combination of TKI and PD-1 inhibitor subsequent to liver resection, targeting an advanced stage HCC as classified by the BCLC staging system. The case patient carries a decade-long history of hepatitis B, having undergone a regimen of 20 courses of treatments involving apatinib and camrelizumab. Throughout the treatment period, no occurrences of grade 3 or 4 adverse events (AE) were noted. Subsequently, the patient underwent a left hepatectomy. Following the hepatectomy, their serum AFP levels have consistently remained within normal limits, and CT imaging has indicated the absence of tumor recurrence over a span of 36 months. The patient had been reviewed on time for two years after the operation. The last time a CT was performed for this patient in our hospital was 7 May 2021, and no new tumors were found. Follow-up is still ongoing. When applying combined targeted immune transformation therapy using TKI and ICI for a patient with BCLC advanced stage HCC, apatinib treatment serves a dual purpose. It inhibits the survival and angiogenesis of tumor cells, while also enhancing the efficacy of camrelizumab in obstructing the interaction between PD-1 and PD-L1. This restoration of T cell cytotoxicity subsequently facilitates the elimination of tumor cells, leading to an enhanced anticancer effect.