Though they carry some risk, emergency hepatectomy is still an important means of treatment for spontaneous HCC ruptures. For resectable HCC ruptures, emergency hepatectomy or staged hepatectomy are life-saving procedures, and efficient therapeutic methods. After the initial hemostasis, staged liver resection can often help patients achieve better long-term survival than emergency hepatectomy.

Background. Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with poor prognosis, making the prediction of the prognosis much challenges. Basement membrane-related genes (BMRGs) play an important role in the progression of cancer. Thus, they are often used as targets to inhibit tumor progression. However, the value of BMRGs in predicting prognosis of HCC still remains to be further elucidated. This study aimed to find the relationship between BMRGs and HCC and the value of BMRGs in predicting the prognosis of HCC. Methods. We acquired transcriptome and clinical data of HCC from The Cancer Genome Atlas (TCGA) and randomly divided the data into training and test sets in order to develop a reliable prognostic signature of BMRGs for HCC. The BMRGs model was built using multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), and univariate Cox regression. The risk signature was further validated and assessed using the principal component analysis (PCA), Kaplan-Meier analysis, and time-dependent receiver operating characteristics (ROC). To forecast the overall survival, a nomogram and calibration curves were created (OS). Functional enrichment analysis was used to evaluate the potential biological pathways. We also conducted immunological research and a pharmacological comparison between the high- and low-risk groups in this study. Results. We identified 16 differentially expressed genes and constructed a risk model of four BMRGs, including COL2A1, CTSA, LAMB1,P3H1. The PCA analysis showed that the signature could distinguish the high- and low-risk groups well. Patients in the low-risk group showed significantly better outcome compared with patients in the high-risk group. Receiver operating characteristic (ROC) curve analysis show predictive capacity. Moreover, the nomogram showed good predictability. Univariate and multivariate Cox regression analysis validated that the model results supported the hypothesis that BMRGs were independent risk factors for HCC. Furthermore, analysis of clinical characteristics and tumor microenvironment (TME) between risk groups showed significant difference. Functional analysis revealed different immune-related pathways were enriched, and immune status were different between two risk groups. Mediation analysis with IC50 revealed that the two risk group were significantly different, which could be a guidance of systemic treatment. Finally, we further verified in clinical samples that the mRNA and protein expression levels of the four genes in this model are significantly higher in liver cancer tissues than in adjacent tissues. Conclusion. A novel BMRGs signature can be used for prognostic prediction in HCC. This provide us with a potential progression trajectory as well as predictions of therapeutic response.

Background. Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with poor prognosis, making the prediction of the prognosis much challenges. N7-methylguanosine (m7G) is a common modification with roles in eukaryotes. However, the value of m7G-related LncRNAs in predicting prognosis of HCC still remains to be further elucidated. This study aimed to find new potential biomarkers for predicting prognosis and precision medication. Methods. To build reliable predictive models,we obtained transcriptome and clinical data of HCC from The Cancer Genome Atlas (TCGA). M7G-related prognostic lncRNAs were selected by coexpression analysis and univariate Cox regression. The least absolute shrinkage and selection operator (LASSO) was utilized to construct the m7G related lncRNA model. Patients in TCGA cohort were divided into train group and test group. Applying the muti-lncRNA model,the patients were divided into high risk group and low risk group .The Kaplan–Meier analysis,time-dependent receiver operating characteristics (ROC), univariate Cox (uni-Cox) regression, multivariate Cox (multi-Cox) regression, nomogram, clinical characteristics and calibration curves were made to verify and evaluated the model. Furthermore ,we used principal component analysis (PCA), t-distributed stochastic-neighbor embedding (tSNE), immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) to verify the signature in two risk groups .Results. A model with 4 m7G-related lncRNAs was constructed. The HCC patients were divided into two risk groups according to the model.Patients in the low-risk group showed significantly better outcome compared with patients in the high-risk group both in the train group and the test group (P < 0.001 in the TCGA train cohort and P = 0.001 in the test cohort). The risk score was an independent predictor for prognosis in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis show predictive capacity. Analysis of clinical characteristics and tumor microenvironment (TME) between risk groups showed significant difference (p=0.038). Functional analysis revealed different immune-related pathways were enriched, and immune status were different between two risk groups. Mediation analysis with IC50 revealed that the two risk group were significantly different, which could be a guidance of systemic treatment. Conclusion. A novel m7G-related LncRNAs signature can be used for prognostic prediction in HCC. Targeting m7G may be a therapeutic alternative for HCC.