Prognosis Of Liver Cancer Patients Research Articles

MiR-3680-3p is a novel biomarker for the diagnosis and prognosis of liver cancer and is involved in regulating the progression of liver cancer.

MicroRNAs (miRNAs) are small non-coding RNAs that can actively participate in post-transcriptional regulation of genes. A number of studies have shown that miRNAs can serve as important regulators of cancer cell growth, differentiation, and apoptosis. They can also act as markers for the diagnosis and prognosis of certain cancers. To explore the potential prognosis-related miRNAs in liver cancer patients, to provide theoretical basis for early diagnosis and prognosis of liver cancer, as well as to provide a new direction for the targeted therapy of liver cancer. The miRNA expression profiles of liver cancer patients in the the Cancer Genome Atlas database were comprehensively analyzed and various prognostic-related miRNAs of liver cancer were screened out. The data was further subjected to survival analysis, prognostic analysis, gene ontology and kyoto encyclopedia of genes and genomes enrichment analysis, microenvironment analysis, and drug sensitivity analysis by R Language version 4.2.0. Finally, the screened miRNAs were further validated by different experiments. Thus, miNRAs involved in liver cancer diagnosis and prognosis were identified. MiRNA-3680-3p was found to be significantly different in 10 different cancers, including liver cancer, and was significantly associated with the microenvironment, survival, and prognosis of liver cancer patients. In addition, drug sensitivity analysis revealed that miRNA-3680-3p can provide a useful reference for drug selection in targeted therapy for liver cancer. MiRNA-3680-3p can serve as a biomarker for the diagnosis and prognosis of liver cancer patients and down-regulation of miRNA-3680-3p could significantly inhibit both the proliferation and migration of liver cancer cells.

Exosomal IGFBP2 derived from highly metastatic promotes hepatocellular carcinoma metastasis by inducing epithelial mesenchymal transition

Liver cancer metastasis is the main cause of death in liver cancer patients. Exosomes, which are small vesicles released by cancer cells, play a crucial role in the metastasis of cancer. The aim of this study was to investigate the effect of exosomes derived from high metastatic potential liver cancer cells acting as cell to cell communication on liver cancer metastasis. Bioinformatics analysis was used to obtain the differential expression of exosomal mRNAs from the plasma of both liver cancer patients and healthy volunteers. Transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and protein blot were employed to characterize the exosomes. The molecular mechanisms and were explored by conducting CCK8, Transwell, Tunel, RTqPCR, western blot, and immunofluorescence staining. We examined IGFBP2 special expression in the plasma exosomes of both liver cancer patients and healthy volunteers, and its presence was associated with a poor prognosis in liver cancer patients. Furthermore, we observed that exosomes from highly metastatic liver cancer cells (MHCC97H) contained high levels of IGFBP2 and could enhance the metastatic potential of less aggressive liver cancer cells (Hep3B). Additionally, we discovered that IGFBP2 in MHCC97H-derived exosomes activated ERK signaling pathway, which triggered epithelial-mesenchymal transition (EMT) in Hep3B cells. Our study underscores the significance of exosomal IGFBP2 from highly metastatic liver cancer cells as a driver of metastasis in less invasive liver cancer cells. This suggests that targeting IGFBP2 in exosomes could be a promising strategy for the treatment and prognosis of liver cancer patients.

Integrated multi-omics analyses reveal the TM4SF family genes with prognostic and therapeutic relevance in hepatocellular carcinoma.

TM4SF family members (TM4SFs) have been shown to be aberrantly expressed in multiple types of cancer. However, a comprehensive investigation of the TM4SFs has yet to be performed in LIHC. The study comprehensively investigated the expression and prognostic value of TM4SFs. Then, a TM4SFs-based risk model and nomogram were constructed for prognostic prediction. Finally, functional loss of TM4SFs was performed to verify the potential role of TM4SFs in LIHC. We found that TM4SFs were significantly up-regulated in LIHC. High expression and hypomethylation of TM4SFs were associated with poor prognosis of LIHC patients. Then, a TM4SFs-based risk model was constructed that could effectively classify LIHC patients into high and low-risk groups. In addition, we constructed a prognostic nomogram that could predict the long-term survival of LIHC patients. Based on immune infiltration analysis, high-risk patients had a relatively higher immune status than low-risk patients. Moreover, the prediction module could predict patient responses to immunotherapy and chemotherapy. Finally, loss-of-function studies showed that TM4SF4 knockdown could substantially suppress the growth, migratory, and invasive abilities of LIHC cells. Targeting TM4SFs will contribute to effective immunotherapy strategies and improve the prognosis of liver cancer patients.

Identification and Analysis of Subtypes of Liver Cancer Based on Genes Related to E3 Ubiquitin Ligases and Deubiquitinating Enzymes.

Recent studies have reported a correlation between ubiquitination or deubiquitination and cancer development. But mechanisms underlying the roles of genes associated with E3 ubiquitin ligases and deubiquitinating enzymes (DUB) in liver cancer remain to be explored. We analyzed and screened differentially expressed genes related to E3 ubiquitin ligases and DUB in liver cancer on the basis of public databases. Cluster analysis was utilized to classify liver cancer samples into different subtypes. Survival analysis, immune analysis, and pathway enrichment analysis were performed on the subtypes. We constructed a protein-protein interaction network using STRING to screen hub genes. Finally, we used the Connectivity Map (CMap) database to predict targeted small molecules. The results show that a total of 139 differentially expressed E3/DUB genes in liver cancer were screened. Then, liver cancer was classified into two subtypes, cluster 1 and cluster 2, based on E3-related and DUB-related genes. Patients in cluster 1 had higher survival rates and immune levels than those in cluster 2. Four hub genes (RPSA, RPS5, RPL30, and RPL8) significantly affecting the survival of the two subtypes of liver cancer patients were identified based on cluster 1 and cluster 2. Finally, the CMap database predicted that small-molecule drugs including probenecid, dexamethasone, and etomidate may improve the prognosis of liver cancer patients. These findings may offer a reference for risk stratification studies and drug development in liver cancer.

Serial circulating tumor DNA profiling predicts tumor recurrence after liver transplantation for liver cancer.

Minimal residual disease (MRD) is proposed to be responsible for tumor recurrence. The role of circulating tumor DNA (ctDNA) to detect MRD, monitor recurrence, and predict prognosis in liver cancer patients undergoing liver transplantation (LT) remains unrevealed. Serial blood samples were collected to profile ctDNA mutational changes. Baseline ctDNA mutational profiles were compared with those of matched tumor tissues. Correlations between ctDNA status and recurrence rate (RR) and recurrence-free survival (RFS) were analyzed, respectively. Dynamic change of ctDNA was monitored to predict tumor recurrence. Baseline mutational profiles of ctDNA were highly concordant with those of tumor tissues (median, 89.85%; range 46.2-100%) in the 74 patients. Before LT, positive ctDNA status was associated with higher RR (31.7% vs 11.5%; p = 0.001) and shorter RFS than negative ctDNA status (17.8 vs 19.4months; p = 0.019). After LT, the percentage of ctDNA positivity decreased (17.6% vs 47.0%; p < 0.001) and patients with positive ctDNA status had higher RR (46.2% vs 21.3%; p < 0.001) and shorter RFS (17.2 vs 19.2months; p = 0.010). Serial ctDNA profiling demonstrated patients with decreased or constant negative ctDNA status had lower RR (33.3% vs 50.0%; p = 0.015) and favorable RFS (18.2 vs 15.0months, p = 0.003) than those with increased or constant positive ctDNA status. Serial ctDNA profiling predicted recurrence months ahead of imaging evidence and serum tumor biomarkers. ctDNA could effectively detect MRD and predict tumor recurrence in liver cancer patients undergone LT.

The integration of machine learning and multi-omics analysis provides a powerful approach to screen aging-related genes and predict prognosis and immunotherapy efficacy in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly malignant tumor with high incidence and mortality rates. Aging-related genes are closely related to the occurrence and development of cancer. Therefore, it is of great significance to evaluate the prognosis of HCC patients by constructing a model based on aging-related genes. Non-negative matrix factorization (NMF) clustering analysis was used to cluster the samples. The correlation between the risk score and immune cells, immune checkpoints, and Mismatch Repair (MMR) was evaluated through Spearman correlation test. Real Time Quantitative PCR (RT-qPCR) and immunohistochemistry were used to validate the expression levels of key genes in tissue and cells for the constructed model. By performing NMF clustering, we were able to effectively group the liver cancer samples into two distinct clusters. Considering the potential correlation between aging-related genes and the prognosis of liver cancer patients, we used aging-related genes to construct a prognostic model. Spearman correlation analysis showed that the model risk score was closely related to MMR and immune checkpoint expression. Drug sensitivity analysis also provided guidance for the clinical use of chemotherapy drugs. RT-qPCR showed that TFDP1, NDRG1, and FXR1 were expressed at higher levels in different liver cancer cell lines compared to normal liver cells. In summary, we have developed an aging-related model to predict the prognosis of hepatocellular carcinoma and guide clinical drug treatment for different patients.

Lamin B1 regulates the growth of hepatocellular carcinoma cells by influencing telomerase activity

Lamin B1 (LMNB1) is highly expressed in liver cancer tissues, and its influence and mechanism on the proliferation of hepatocellular carcinoma cells were explored by knocking down the expression of the protein. In liver cancer cells, siRNAs were used to knock down LMNB1. Knockdown effects were detected by Western blotting. Changes in telomerase activity were detected by telomeric repeat amplification protocol assay (TRAP) experiments. Telomere length changes were detected by quantitative real-time polymerase chain reaction (qPCR). CCK8, cloning formation, transwell and wound healing were performed to detect changes in its growth, invasion and migration capabilities. The lentiviral system was used to construct HepG2 cells that steadily knocked down LMNB1. Then the changes of telomere length and telomerase activity were detected, and the cell aging status was detected by SA-β-gal senescence staining. The effects of tumorigenesis were detected by nude mouse subcutaneous tumorigenesis experiments, subsequent histification staining of tumors, SA-β-gal senescence staining, fluorescence in situ hybridization (FISH) for telomere analysis and other experiments. Finally, the method of biogenesis analysis was used to find the expression of LMNB1 in clinical liver cancer tissues, and its relationship with clinical stages and patient survival. Knockdown of LMNB1 in HepG2 and Hep3B cells significantly reduced telomerase activity, cell proliferation, migration and invasion abilities. Experiments in cells and tumor formation in nude mice had demonstrated that stable knockdown of LMNB1 reduced telomerase activity, shortened telomere length, senesced cells, reduced cell tumorigenicity and KI-67 expression. Bioinformatics analysis showed that LMNB1 was highly expressed in liver cancer tissues and correlated with tumor stage and patient survival. In conclusion, LMNB1 is overexpressed in liver cancer cells, and it is expected to become an indicator for evaluating the clinical prognosis of liver cancer patients and a target for precise treatment.

Iron deficiency downregulates ENPEP to promote angiogenesis in liver tumors

The abnormal iron metabolism in liver cancer leads to iron deficiency in tumor tissues. We previously found that iron deficiency promoted liver cancer metastasis, but the mechanisms were not fully understood. In the present study, we identified that the angiogenesis-associated glutamyl aminopeptidase (ENPEP) was consistently decreased in iron-deficient liver tissues, iron-deficient liver tumors, and iron-deprived liver cancer cells. Interestingly, the lower expression of ENPEP was correlated with the poor prognosis of liver cancer patients, while the biomarkers of angiogenesis, CD31 and CD34, were increased in tumor tissues. In vivo imaging of liver-orthotopically implanted and tail vein-injected liver cancer cells showed that iron deficiency increased the pulmonary metastasis of liver cancer. The angiogenesis in iron-deficient tumors was enhanced, and the expression of ENPEP was decreased. Silencing ENPEP expression increased the migration of liver cancer cells and the proliferation of cocultured HUVECs. By sequence analysis, we found that the transcription factor SP1 possessed abundant binding sites in the ENPEP promoter region. Its combination with ENPEP promoters was verified by chromatin immunoprecipitation. The inhibition of SP1 by mithramycin A effectively restored the expression of ENPEP, which was decreased by iron deficiency. In conclusion, these results revealed that iron deficiency in liver tumors decreased the expression of ENPEP by SP1 and increased the angiogenesis and metastasis of liver tumors, which further explained the mechanism by which iron deficiency promoted liver cancer metastasis.

Identification of ASF1B as a prognostic marker for liver cancer by meta-analysis and its immune value revealed by a comprehensive pan-cancer analysis of 33 human cancers.

As one of the most common malignant tumours, liver cancer is difficult to detect in the early stage, with strong metastasis and poor prognosis. Anti-silencing function protein 1 was originally discovered in yeast as a histone H3-H4 chaperone, and studies have shown that ASF1B may be a target for inhibiting the growth of hepatocellular carcinoma cells. To evaluate the diagnostic and prognostic significance of ASF1B expression in human LIHC on the basis of TCGA data. A meta-analysis revealed that high ASF1B expression was strongly associated with better overall survival. A comprehensive pan-cancer analysis of 33 human cancers revealed the immunotherapeutic value of ASF1B. In this study, we observed a significant upregulation of ASF1B expression in LIHC samples compared to non-cancer samples. Clinical analysis showed that high expression of ASF1B was associated with age, tumour status, and clinical stage. Survival analysis showed that patients with high ASF1B expression had worse overall survival and progression-free survival than patients with low ASF1B expression. The AUCs of the 1-year, 3-year, and 5-year survival-related ROC curves were 0.672, 0.590, and 0.591, respectively. Our study shows that ASF1B may provide new ideas for the diagnosis and prognosis of liver cancer patients, as well as providing a new direction for the application of ASF1B in tumour immunotherapy.

Clinical Value of Growth Differentiation Factor 15 Detection in the Diagnosis of Early Liver Cancer Based on Data Mining.

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P < 0.01), there was a significant difference (P < 0.01) compared with adjacent tissue (P < 0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients.

A new acidic microenvironment related lncRNA signature predicts the prognosis of liver cancer patients.

The acidic microenvironment (AME), like hypoxia, inflammation, or immunoreaction, is a hallmark of the tumor microenvironment (TME). This work aimed to develop a prediction signature dependent on AME-associated lncRNAs in order to predict the prognosis of LC individuals. We downloaded RNA-seq information and the corresponding clinical and predictive data from The Cancer Genome Atlas (TCGA) dataset and conducted univariate and multivariate Cox regression analyses to identify AME-associated lncRNAs for the construction of a prediction signature The Kaplan-Meier technique was utilized to determine the overall survival (OS) rate of the high (H)-risk and low (L)-risk groups. Using gene set enrichment analysis (GSEA) the functional variations between the H- and L-risk groups were investigated. The association between the prediction signature and immunological state was investigated using single-sample GSEA (ssGSEA). Additionally, the association between the predicted signature and the therapeutic response of LC individuals was evaluated. Lastly, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. We generated a signature comprised of seven AME-associated lncRNAs (LINC01116, AC002511.2, LINC00426, ARHGAP31-AS1, LINC01060, TMCC1-AS1, AC012065.1). The H-risk group had a worse prognosis than the L- risk group. The AME-associated lncRNA signature might determine the prognosis of individuals with LC independently. The AME-related lncRNA signature shows a greater predictive effectiveness than clinic-pathological factors, with an area under the receiver operating characteristic (ROC) curve of 0.806%. When participants were categorized based on several clinico-pathological characteristics, the OS of high-risk individuals was shorter compared to low-risk patients. GSEA demonstrated that the metabolism of different acids and the PPAR signaling pathway are closely associated with low-risk individuals. The prognostic signature was substantially associated with the immunological status of LC individuals, as determined by ssGSEA. High risk individuals were more sensitive to some immunotherapies (including anti-TNFSF4 anti-SIRPA, anti-CD276 and anti-TNFSF15) and some conventional chemotherapy drugs (including lapatinib and paclitaxel). Finally, the expression levels of the seven lncRNAs comprising the signature were tested by qRT-PCR. A basis for the mechanism of AME-associated lncRNAs in LC is provided by the prediction signature, which also offers clinical therapeutic recommendations for LC individuals.

Construction of a model for predicting the prognosis of liver cancer patients based on CuProtosis-related LncRNA

Introduction: Liver cancer is one of the most common malignant tumors in the world, and patients with liver cancer are often in the middle and late stages of cancer when they are diagnosed. Copper death is a newly discovered new cell death method. It is a copper-dependent and regulated cell death method. At the same time, Long noncoding RNAs (LncRNAs) also play an important regulatory role in the pathological process of tumors such as liver cancer. Materials and methods: First, the expression levels of CuProtosis-related genes in liver cancer samples were extracted, and a CuProtosis- related LncRNA prognostic model was constructed. C-index curve and ROC curve were drawn by survival analysis, PFS analysis, and independent prognosis analysis. The model was also validated by clinical grouping and PCA principal component analysis. To ensure its accuracy, enrichment analysis, immune analysis and tumor mutational burden analysis further explored the potential function of this model, and finally discussed potential drugs targeting this model. Results: A prognostic model for predicting survival was constructed and its high predictive ability in liver cancer patients was validated. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment showed that the differential genes were mainly enriched in 5 pathways. Meanwhile, six differentially expressed immune functions were found in the high-risk and low-risk groups. The survival rate of patients in the high mutation group was significantly lower than that of the patients with liver cancer in the low mutation group. Twelve drugs with significant differences in drug sensitivity between high- and low-risk groups were explored. Conclusions: The risk-prognosis model based on CuProtosis LncRNA established in this study is expected to be used to predict the prognosis and immunotherapy response of liver cancer patients. It provides new clues and methods for predicting the survival time of liver cancer patients, and also provides new ideas for guiding individualized immunotherapy strategies for liver cancer patients in the future.

Cluster classification and clinical prognostic modeling based on m6A RNA methylation regulators in liver cancer

Objective: Cluster classification based on m6A methylation regulators and construct prognostic evaluation model. Methods: Utilizing consensus cluster to classify the liver cancer samples form TCGA based on the expression of 13 m6A methylation regulators, and verify the function and prognostic significance of the clustered subtypes. Marker genes were further screened to construct a risk prediction model for evaluating the prognosis of liver cancer patients. Results: The two clustered subtypes based on m6A methylation regulators showed significant differences in the prognosis value of liver cancer patients (P=0.048), and 38 prognostic markers related to m6A methylation in liver cancer were screened from the subgroup with poor prognosis. Two m6A regulatory genes, YTHDF1 and YTHDF2, are proved with adverse prognosis by univariate cox analysis (P<0.05, Hazard ratio>1). We used Lasso regression method to build risk assessment model and effectively predicted the prognosis status of liver cancer patients within 4 years (4-year AUC=0.685, 3-year AUC=0.669). Moreover, the assessment model was validated in another dataset of Asia liver cancer patients. Conclusion: The study provided ideas for studying m6A methylation in liver cancer, and the risk prediction model can be used to evaluate the short-term prognosis of liver cancer patients.

Hepatic ARID3A facilitates liver cancer malignancy by cooperating with CEP131 to regulate an embryonic stem cell-like gene signature

Liver cancer stemness refers to the stem cell-like phenotype of hepatocarcinoma cells and is closely related to a high degree of tumour malignancy. Here, we identified AT-rich interacting domain 3A (ARID3A) as one of the most upregulated stemness-related transcription factors in liver cancer by an in vitro functional screen. ARID3A can promote liver cancer cell viability and metastasis both in vitro and in vivo. Mechanistically, ARID3A interacts with CEP131 and transcriptionally activates KDM3A by co-occupying its promoter element, further upregulating the expression of downstream embryonic stem (ES) signature genes via demethylation of H3K9me2. ARID3A and CEP131 promote an ES cell gene signature through activation of KDM3A and contribute to the poor prognosis of liver cancer patients. Collectively, these results provide evidence highlighting a transcription-dependent mechanism of ARID3A in stemness regulation in liver cancer. The ARID3A/CEP131-KDM3A regulatory circuit could serve as a prognostic indicator and potential therapeutic target for liver cancer.

Development and validation of cuproptosis-related gene signature in the prognostic prediction of liver cancer.

Liver cancer is a generic term referring to several cancer types arising from the liver. Every year, liver cancer causes lots of deaths and other burdens to the people all over the world. Though the techniques in the diagnosis and therapy of liver cancer have undergone significant advances, the current status of treating liver cancer is not satisfactory enough. The improvement of techniques for the prognosis of liver cancer patients will be a great supplement for the treatment of liver cancer. Cuproptosis is a newly identified regulatory cell death type, which may have a close connection to liver cancer pathology. Here, we developed a prognostic model for liver cancer based on the cuproptosis-related mRNAs and lncRNAs. This model can not only effectively predict the potential survival of liver cancer patients, but also be applied to evaluate the infiltration of immune cell, tumor mutation burden, and sensitivity to anti-tumor drugs in liver cancer. In addition, this model has been successfully validated in lots of liver cancer patients’ data. In summary, we wish this model can become a helpful tool for clinical use in the therapy of liver cancer.

Establishment and verification of a prognostic model of liver cancer by RNA-binding proteins based on the TCGA database.

BackgroundGlobally, liver cancer is one of the most common malignant tumors and is the third leading cause of cancer deaths. RNA-binding protein (RBP) is a general term for a class of proteins that bind to RNA to regulate metabolic processes. The expression of RNA-binding proteins is related to the prognosis of liver cancer patients.MethodsThe RBP gene expression data of liver cancer were extracted from the TCGA database. First, the differentially expressed RBPs (DE RBPs) were selected through enrichment analysis and volcano mapping. Then, the prognosis-related RBP genes were selected through single-factor Cox regression analysis. The key prognosis-related RBPs were further screened by multifactor Cox regression analysis, and a formula for the patient’s risk coefficient was obtained. Finally, based on the patient’s risk score, a nomogram was established and verified.ResultsWe extracted 374 cancer tissue samples and 50 normal tissue samples with the clinical information from each sample. Through enrichment analysis, we screened 208 upregulated RBPs and 122 downregulated RBPs. Prognosis-related high-risk genes were EEF1E1, NOP56, UPF3B, SF3B4, SMG5, CD3EAP, BRCA1, BARD1, XPO5, CSTF2, EZH2, EXO1, RRP12, PRIM1, LIN28B, NROB1 and TCOF1, and the low-risk genes were MRPL46, RCL1, MRPL54, CPEB3, IFIT5, PPARGC1A, EIF2AK4, SEPSECS, ACO1, SECISBP2 L and ZCCHC24. Further multivariate Cox regression analysis was performed on the prognosis-related RBPs, and the three key prognosis-related RBPs were screened out, which were BARD1, NR0B1 and EIF2AK4. A patient risk coefficient calculation formula was obtained: risk score = (1.207×BARD1 Exp) + (0.483×NR0B1 Exp) + (-0.720×EIF2AK4 Exp). Finally, a nomogram was established based on the risk score to predict the survival time of patients from 1 to 5 years.ConclusionsThe nomogram has good predictive value for the survival time of liver cancer patients.

Dihydroartemisinin promoted FXR expression independent of YAP1 in hepatocellular carcinoma.

Loss of FXR, one of bile acid receptors, enlarged livers. Yes-associated protein 1 (YAP1), a dominant oncogene, promotes hepatocellular carcinoma (HCC). However, the relationship between FXR and YAP1 was unspecified in bile acid homeostasis in HCC. Here, we used TIMER2.0, the Cancer Genome Atlas (TCGA) Database, and Kaplan-Meier Plotter Database and discovered that FXR was positively correlated with better prognosis in liver cancer patients. Our previous research showed that dihydroartemisinin (DHA) inhibited cell proliferation in HepG2 and HepG22215 cells. However, the relationship of YAP1 and the bile acid receptor FXR remains elusive during DHA treatment. Furthermore, we showed that DHA improved FXR and reduced YAP1 in the liver cancer cells and mice. Additionally, the expression of nucleus protein FXR was enhanced in Yap1LKO mice with liver cancer. DHA promoted the expression level of whole and nuclear protein FXR independent of YAP1 in Yap1LKO mice with liver cancer. DHA declined cholesterol 7α-hydroxylase, but not sterol 27-hydroxylase, and depressed cholic acid and chenodeoxycholic acid of liver tissue in Yap1LKO mice with liver cancer. Generally, our results suggested that DHA improved FXR and declined YAP1 to suppress bile acid metabolism. Thus, we suggested that FXR acted as a potential therapeutic target in HCC.

Sphingomyelin Phodiesterase Acid-Like 3A Promotes Hepatocellular Carcinoma Growth Through the Enhancer of Rudimentary Homolog.

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, with unclear pathogenesis. Sphingomyelin phodiesterase acid-like 3A (SMPDL3A) affects cell differentiation and participates in immune regulation. However, its molecular biological function in HCC has not yet been elucidated.MethodsData from 180 HCC patients were analyzed the relationship between the expression of SMPDL3A in liver cancer tissues and the prognosis of liver cancer patients. Crispr-Cas9 dual vector lentivirus was used to knock out SMPDL3A in HCC cell lines. The effects of SMPDL3A on cell viability were determined by CCK8 assay, clone formation experiment, cell cycle assay, cell scratch, TUNEL experiment and flow cytometry. Xenograft tumor assays in BALB/c nude mice confirmed that SMPDL3A promoted tumor growth and in vivo. Preliminary exploration of SMPDL3A interacting protein by mass spectrometry analysis and co-immunoprecipitation.ResultsThis study showed that the expression of SMPDL3A in HCC tissue differed from that in tumor-adjacent tissues. Moreover, the overall survival rate and tumor-free survival rate of patients with high-SMPDL3A expression were significantly lower than those with low-SMPDL3A expression. SMPDL3A expression was closely related to the level of protein induced by PIVKA-II, liver cirrhosis, tumor diameter, microvascular invasion, and Barcelona clinic liver cancer staging. Thus, SMPDL3A is an independent risk factor that affects the tumor-free survival rate and overall survival rate of HCC patients. In vitro study using Crispr-Cas9 genome editing technology revealed the knockout effect of SMPDL3A on cell proliferation, apoptosis, and migration. Cell counting kit-8 assay and clone formation experiment showed that sgSMPDL3A inhibited tumor cell proliferation and migration. Flow cytometry and TUNEL assay showed that sgSMPDL3A promoted apoptosis in tumors. Moreover, sgSMPDL3A inhibited tumor growth during subcutaneous tumor formation in nude mice. Immunohistochemistry of Ki67 and PNCA also indicated that sgSMPDL3A inhibited subcutaneous tumor proliferation in tumor-bearing nude mice. Further experiments showed that SMPDL3A interacts with the enhancer of rudimentary homolog (ERH).ConclusionsHigh-SMPDL3A expression was related to poor prognosis of patients with HCC. Knockout of SMPDL3A inhibited the proliferation and migration and accelerated the migration of HCC cells. SMPDL3A interacted with ERH to affect the tumorigenesis and progression of HCC.

Novel Prognostic Signatures of Hepatocellular Carcinoma Based on Metabolic Pathway Phenotypes.

Hepatocellular carcinoma is a disastrous cancer with an aberrant metabolism. In this study, we aimed to assess the role of metabolism in the prognosis of hepatocellular carcinoma. Ten metabolism-related pathways were identified to classify the hepatocellular carcinoma into two clusters: Metabolism_H and Metabolism_L. Compared with Metabolism_L, patients in Metabolism_H had lower survival rates with more mutated TP53 genes and more immune infiltration. Moreover, risk scores for predicting overall survival based on eleven differentially expressed metabolic genes were developed by the least absolute shrinkage and selection operator (LASSO)-Cox regression model in The Cancer Genome Atlas (TCGA) dataset, which was validated in the International Cancer Genome Consortium (ICGC) dataset. The immunohistochemistry staining of liver cancer patient specimens also identified that the 11 genes were associated with the prognosis of liver cancer patients. Multivariate Cox regression analyses indicated that the differentially expressed metabolic gene-based risk score was also an independent prognostic factor for overall survival. Furthermore, the risk score (AUC = 0.767) outperformed other clinical variables in predicting overall survival. Therefore, the metabolism-related survival-predictor model may predict overall survival excellently for HCC patients.

Identification of Energy Metabolism-Related Gene Signatures From scRNA-Seq Data to Predict the Prognosis of Liver Cancer Patients.

The increasingly common usage of single-cell sequencing in cancer research enables analysis of tumor development mechanisms from a wider range of perspectives. Metabolic disorders are closely associated with liver cancer development. In recent years, liver cancer has been evaluated from different perspectives and classified into different subtypes to improve targeted treatment strategies. Here, we performed an analysis of liver cancer from the perspective of energy metabolism based on single-cell sequencing data. Single-cell and bulk sequencing data of liver cancer patients were obtained from GEO and TCGA/ICGC databases, respectively. Using the Seurat R package and protocols such as consensus clustering analysis, genes associated with energy metabolism in liver cancer were identified and validated. An energy metabolism-related score (EM score) was established based on five identified genes. Finally, the sensitivity of patients in different scoring groups to different chemotherapeutic agents and immune checkpoint inhibitors was analyzed. Tumor cells from liver cancer patients were found to divide into nine clusters, with cluster 4 having the highest energy metabolism score. Based on the marker genes of this cluster and TCGA database data, the five most stable key genes (ADH4, AKR1B10, CEBPZOS, ENO1, and FOXN2) were identified as energy metabolism-related genes in liver cancer. In addition, drug sensitivity analysis showed that patients in the low EM score group were more sensitive to immune checkpoint inhibitors and chemotherapeutic agents AICAR, metformin, and methotrexate.