Recent Perspectives on the Mechanism of Recurrence After Ablation of Hepatocellular Carcinoma: A Mini-Review

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification

BACKGROUND

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC.

METHODS

The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic.

RESULTS

Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events.

CONCLUSION

In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

The tumor microenvironment in the postsurgical liver: Mechanisms and potential targets of postoperative recurrence in human hepatocellular carcinoma

Surgery remains to be the mainstay of treatment for hepatocellular carcinoma (HCC). Nonetheless, its therapeutic efficacy is significantly impaired by postoperative recurrence, which occurs in more than half of cases as a result of intrahepatic metastasis or de novo tumorigenesis. For decades, most therapeutic strategies on inhibiting postoperative HCC recurrence have been focused on the residual tumor cells but satisfying therapeutic outcomes are barely observed in the clinic. In recent years, a better understanding of tumor biology allows us to shift our focus from tumor cells toward the postoperative tumor microenvironment (TME), which is gradually identified to play a pivotal role in tumor recurrence. In this review, we describe various surgical stress and surgical perturbation on postoperative TME. Besides, we discuss how such alternations in TME give rise to postoperative recurrence of HCC. Based on its clinical significance, we additionally highlight the potential of the postoperative TME as a target for postoperative adjuvant therapeutics.

Transmembrane protein 147, as a potential Sorafenib target, could expedite cell cycle process and confer adverse prognosis in hepatocellular carcinoma

TMEM147 was identified as a core component of ribosome-bound translocon complex at ER/NE. So far, sparse studies reported its expression profiling and oncological implications in hepatocellular carcinoma (HCC) patients. Here we inspected TMEM147 expression levels in HCC cohorts from public databases and tumor tissues. TMEM147 was augmented at transcriptional levels (p < 0.001) and protein levels in HCC patients. A series of bioinformatics tools implemented in R studio were orchestrated in TCGA-LIHC to evaluate the prognostic significance, compile relevant gene clusters, and explore the oncological functions and therapy responses. It is suggested that TMEM147 could predict poor clinical outcomes effectively and independently (p < 0.001, HR = 2.231 for overall survival (OS) vs. p = 0.04, HR = 2.296 for disease-specific survival), and was related to risk factors including advanced histologic tumor grade (p < 0.001), AFP level (p < 0.001) and vascular invasion (p  = 0.007). Functional enrichment analyses indicated that TMEM147 was involved in cell cycle, WNT/MAPK signaling pathways and ferroptosis. Expression profiling in HCC cell lines, mouse model, and a clinical trial revealed that TMEM147 was a considerable target and marker for adjuvant therapy in vitro and in vivo. Subsequentially, in vitro wet-lab experimentation authenticated that TMEM147 would be downregulated by Sorafenib administration in hepatoma cells. Lentivirus-mediated overexpression of TMEM147 could promote cell cycle progression from S phase into G2/M phase, and enhance cell proliferation, thus attenuating drug efficacy and sensitivity of Sorafenib. Further explorations into TMEM147 may inspire a fresh perspective to predict clinical outcomes and improve therapeutic efficacy for HCC patients.