Biomarker For Hepatocellular Carcinoma Research Articles

Construction a six-gene prognostic model for hepatocellular carcinoma based on WGCNA co-expression network

ObjectiveCurrently, the incidence of hepatocellular carcinoma remains high, and the prognosis of patients is poor. Prognostic biomarkers are still worth exploring. MethodsBased on The Cancer Genome Atlas (TCGA) database, the differentially expressed genes (DEGs) were screened. Subsequently, a modular analysis of these DEGs was performed using the weighted gene co-expression network analysis (WGCNA). A prognostic model for liver cancer patients was constructed employing the Cox proportional hazards model. Through univariate and multivariate Cox regression analyses, we developed a Cox proportional-hazards model specifically for hepatocellular carcinoma. Subsequently, International Cancer Genome Consortium (ICGC) cohort data were used to validate the accuracy of the Cox proportional-hazards model. Following this, we conducted further analyses of prognostic genes, encompassing functional enrichment analysis and survival analysis. Additionally, we utilized the BBcancer database to investigate whether these prognostic genes have the potential to serve as blood markers. Notably, in this six-gene prognostic model, we also analyzed the genes' drug susceptibility. ResultsLeveraging the candidate genes identified from the WGCNA analysis, we constructed a Cox proportional-hazards model with an AUC value greater than 0.7. This model incorporates HMMR, E2F2, WDR62, KIF11, MSH4, and KCNF1, revealing that patients with low expression levels of these genes had significantly better survival prognosis compared to those with high expression levels (P ​< ​0.05). The enrichment analysis revealed that these prognostic genes are enriched in pathways related to hepatitis B, hepatitis C, and hepatocellular carcinoma. Furthermore, we observed a strong association between HMMR, E2F2, WDR62, KIF11, MSH4, and KCNF1 with overall survival (OS) in hepatocellular carcinoma (HCC) patients, among which HMMR, E2F2, WDR62 and KIF11 genes were significantly differentially expressed in extracellular vesicles. Additionally, this six-gene prognostic model demonstrated sensitivity to drugs such as VX-680, TAE684, Sunitinib, S-Trityl-L-cysteine, Paclitaxel, and CGP-60474. ConclusionThe Cox risk prognostic model based on HMMR, E2F2, WDR62, KIF11, MSH4, and KCNF1 represents a valuable tool for predicting the prognosis of HCC patients and may serve as a target for drug development. In particular, HMMR, E2F2, WDR62, and KIF11 have potential as blood biomarkers for hepatocellular carcinoma, though their precise biological functions require further exploration.

High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies.In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications.We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use.In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling.

Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhibit a higher uptake of amino acids compared with their healthy counterparts.

The Role of Epithelial Cell Adhesion Molecule Cancer Stem Cell Marker in Evaluation of Hepatocellular Carcinoma.

Background and Objectives: Hepatocellular carcinoma (HCC) is a prevalent form of malignancy that is characterized by high mortality rates and prognosis that remain suboptimal, largely due to treatment resistance mechanisms. Recent studies have implicated cancer stem cells (CSCs), particularly those expressing epithelial cell adhesion molecule (EpCAM), in HCC progression and resistance. In the present study, we sought to assess EpCAM expression in HCC patients and its correlation with various clinicopathological parameters. Materials and Methods: Tissue samples from 42 HCC patients were subjected to immunohistochemical staining to evaluate EpCAM expression. Clinicopathological data were obtained including the size, grade and stage of tumors, vascular invasion status, alpha-fetoprotein levels, and cirrhosis status. The Chi square and Fisher's exact tests were employed to assess the association between categorical groups. Independent Student-t test or Mann-Whitney U test was used to investigate the association between continuous patient characteristics and survival. Results: Immunohistochemical analysis revealed EpCAM expression in 52.5% of HCC cases. EpCAM-positive tumors exhibited characteristics indicative of aggressive disease, including larger tumor sizes (p = 0.006), greater tumor multiplicity (p = 0.004), higher grades (p = 0.002), more advanced stages (p = 0.003), vascular invasion (p = 0.023), elevated alpha-fetoprotein levels (p = 0.013), and cirrhosis (p = 0.052). Survival analysis demonstrated that EpCAM expression was significantly associated with lower overall rates of survival and higher rates of recurrence in HCC patients. Conclusions: Our findings suggest that EpCAM expression may serve as a prognostic biomarker for HCC with a potential role in patient management. Targeting EpCAM-positive CSCs may represent a promising approach to overcome treatment resistance and improve clinical outcomes in HCC. However, further investigation into the molecular mechanisms underlying EpCAM's role in HCC progression is warranted to facilitate the development of personalized therapeutic interventions.

The elevated expression of serum glutathione reductase in hepatocellular carcinoma and its role in assessing the therapeutic efficacy and prognosis of transarterial chemoembolization

BackgroundCurrently, there is a scarcity of reliable biomarkers that can accurately forecast the outcome and prognosis of transarterial chemoembolization (TACE). In this study, we assessed the diagnostic efficacy of serum glutathione reductase (GR) as a biomarker for hepatocellular carcinoma (HCC) and its practicality in predicting TACE treatment response. MethodsThe baseline positive rate and level of serum GR were analyzed and compared between HCC group and control group. Serum GR levels were assessed at three specific time points in 181 patients with unresectable HCC who underwent TACE (HCC-TACE). The correlation between serum GR levels and clinical pathological factors, tumor reactivity, and prognosis was investigated. The modified Response Evaluation Criteria in Solid Tumors (mRECIST) was utilized for assessing the treatment response to TACE. A nomogram for predicting the response to TACE treatment efficacy was developed. ResultsSerum GR demonstrated superior diagnostic performance in HCC patients. The baseline levels of serum GR were associated with the patient's age, tumor size, BCLC staging, and tumor thrombi of the portal vein (TTPV) (p < 0.05). Elevated baseline levels of serum GR were also identified as independent prognostic factors for predicting lower overall survival (OS) and shorter time to radiological progression (TTP) (p < 0.001). Moreover, it is worth noting that non-responders group exhibited a substantial increase in median GR level in the fourth week following TACE treatment (p < 0.0001), whereas the median GR level of responders group did not display a significant augmentation (p > 0.05). Lastly, the changes in serum GRt1-t3 were negatively correlated with TTP (p < 0.001). The nomogram developed to predict the risk of mRECIST responsiveness in patients with HCC-TACE demonstrated excellent discriminatory ability. ConclusionSerum GR can serve as a valuable biomarker for the diagnosis of HCC and for predicting the therapeutic efficacy and prognosis of TACE treatment.

Research progress in the metabolic reprogramming of hepatocellular carcinoma (Review).

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and its morbidity is increasing worldwide due to increasing prevalence. Metabolic reprogramming has been recognized as a hallmark of cancer and serves a role in cancer progression. Glucose, lipids and amino acids are three major components whose altered metabolism can directly affect the energy production of cells, including liver cancer cells. Nutrients and energy are indispensable for the growth and proliferation of cancer cells, thus altering the metabolism of hepatoma cells can inhibit the progression of HCC. The present review summarizes recent studies on tumour regulatory molecules, including numerous noncoding RNAs, oncogenes and tumour suppressors, which regulate the metabolic activities of glucose, lipids and amino acids by targeting key enzymes, signalling pathways or interactions between the two. These regulatory molecules can regulate the rapid proliferation of cancer cells, tumour progression and treatment resistance. It is thought that these tumour regulatory factors may serve as therapeutic targets or valuable biomarkers for HCC, with the potential to mitigate HCC drug resistance. Furthermore, the advantages and disadvantages of metabolic inhibitors as a treatment approach for HCC, as well as possible solutions are discussed, providing insights for developing more effective treatment strategies for HCC.

Plasma CD24 level as a promising prognostic biomarker of hepatocellular carcinoma

BackgroundHepatocellular carcinoma constitutes the most common primary hepatic cancer and remains a major medical burden in both developing and developed world. It ranks fifth in terms of global cases and second in terms of deaths for males.CD24 is known as a heavily glycosylated cell surface molecule that is highly expressed in a wide variety of human malignancies. It plays an important role in self-renewal, proliferation, migration, invasion, and drug resistance. The aim of this work was to evaluate the potential role of serum CD24 in the diagnosis and prediction of response to interventional therapy among hepatocellular carcinomas.MethodsThis study included 40 adult Egyptian patients who had liver cirrhosis and hepatocellular carcinoma (HCC group). Another group of 20 patients with liver cirrhosis only served as controls (Cirrhosis group). All patients underwent standard laboratory tests and abdominal ultrasound. For HCC patients, a triphasic CT scan, alpha-fetoprotein was done. CD24 levels were measured in all patients, and in HCC patients at baseline and one month after intervention.ResultsBaseline CD24 was significantly higher among HCC group in comparison to cirrhosis group (19.463 ± 8.573 vs. 0.725 ± 0.125 mg/L) with an overall p value < 0.001. Serum CD24 levels significantly declined after locoregional treatment from 19.463 ± 8.573 mg/L to 3.569 ± 1.248 mg/L (p < 0.001). Baseline CD24 was a useful marker in eligibility for HCC intervention with 80% sensitivity and 74.29% specificity at a cutoff of ≤ 23 mg/L, and it also had 62.96% sensitivity and 100% specificity in prediction of cure after locoregional treatment at a cutoff of ≤ 19.5 mg/L.ConclusionCD24 could be a helpful diagnostic and prognostic marker for HCC, as its baseline level is useful in predicting both eligibility for intervention and cure after locoregional treatment.

Evaluating the diagnostic accuracy of heat shock proteins and their combination with Alpha-Fetoprotein in the detection of hepatocellular carcinoma: a meta-analysis

BackgroundA growing body of research suggests that heat shock proteins (HSPs) may serve as diagnostic biomarkers for hepatocellular carcinoma (HCC), but their results are still controversial. This meta-analysis endeavors to evaluate the diagnostic accuracy of HSPs both independently and in conjunction with alpha-fetoprotein (AFP) as novel biomarkers for HCC detection.MethodsPooled statistical indices, including sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) with 95% confidence intervals (CI), were computed to assess the diagnostic accuracy of HSPs, AFP, and their combinations. Additionally, the area under the summary receiver operating characteristic (SROC) curve (AUC) was determined.ResultsA total of 2013 HCC patients and 1031 control subjects from nine studies were included in this meta-analysis. The summary estimates for HSPs and AFP are as follows: sensitivity of 0.78 (95% CI: 0.69–0.85) compared to 0.73 (95% CI: 0.65–0.80); specificity of 0.89 (95% CI: 0.81–0.95) compared to 0.86 (95% CI: 0.77–0.91); PLR of 7.4 (95% CI: 3.7–14.9) compared to 5.1 (95% CI: 3.3–8.1); NLR of 0.24 (95% CI: 0.16–0.37) compared to 0.31 (95% CI: 0.24–0.41); DOR of 30.19 (95% CI: 10.68–85.37) compared to 16.34 (95% CI: 9.69–27.56); and AUC of 0.90 (95% CI: 0.87–0.92) compared to 0.85 (95% CI: 0.82–0.88). The pooled sensitivity, specificity, PLR, NLR, DOR and AUC were 0.90 (95% CI: 0.82–0.95), 0.94 (95% CI: 0.82–0.98), 14.5 (95% CI: 4.6–45.4), 0.11 (95% CI: 0.06–0.20), 133.34 (95% CI: 29.65–599.61), and 0.96 (95% CI: 0.94–0.98) for the combination of HSPs and AFP.ConclusionOur analysis suggests that HSPs have potential as a biomarker for clinical use in the diagnosis of HCC, and the concurrent utilization of HSPs and AFP shows notable diagnostic effectiveness for HCC.

LMNB1/CDKN1A Signaling Regulates the Cell Cycle and Promotes Hepatocellular Carcinoma Progression.

Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies in the world. Lamin B1 (LMNB1) is a key component of the nuclear skeleton structure. Recent studies have found that LMNB1 is overexpressed in tumor tissues and is associated with the prognosis of patients. However, the underlying mechanism remains unclear in HCC. This study aims to explore the clinical significance and molecular mechanisms of LMNB1 in HCC. The expression level of LMNB1 and its clinical values were analyzed with public databases, and the level of LMNB1 in HCC tissues and adjacent normal tissues was confirmed by qRT-PCR and IHC. Functional assays were conducted to explore the impact of LMNB1 knockdown on cell proliferation both in vivo and in vitro. Additionally, Genes and Genomes enrichment analysis, recovery analysis, and ChIP assays were employed to investigate its underlying molecular mechanisms. Finally, we carried out an analysis of the relationship between LMNB1 and immune cell infiltration in HCC. LMNB1 was found to be overexpressed in HCC and correlated with the pathological stage and unfavorable prognosis. Functional assays demonstrated that LMNB1 promotes HCC proliferation both in vitro and in vivo. Further analysis revealed that LMNB1 promotes the progression of HCC by regulating CDKN1A expression. Furthermore, the infiltration of immune cells in HCC tissues suggests a potential correlation between immune infiltration cell markers and the expression of LMNB1. LMNB1 emerged as a promising therapeutic target and prognostic biomarker for HCC, with its expression showing a correlation with several immune infiltration cell markers.

Angiopoietin-2 as a diagnostic biomarker for Liver cirrhosis and Hepatocellular Carcinoma

Angiopoietin-2 as a diagnostic biomarker for Liver cirrhosis and Hepatocellular Carcinoma

Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence

Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies.

Metabolomics biomarkers of hepatocellular carcinoma in a prospective cohort of patients with cirrhosis

Background & AimsThe effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of the screening modalities. MethodsWe developed a multicenter prospective cohort of cirrhotic patients undergoing surveillance with MRI, and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up. ResultsWe identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who didn’t develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine, had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, patatin-like phospholipase domain-containing protein 3 (PNPLA3) and transmembrane 6 superfamily member 2 (TMS6SF2) SNPs, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top 6 variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC=0.88 [0.83-0.93]). Finally, 23 metabolites distinguished Cases with Liver Imaging Reporting and Data System (LI-RADS)-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases-LR3. ConclusionsThis study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as main biomarkers of HCC risk, and microbiota-derived metabolites as main contributors to HCC development. Impact and ImplicationsThe effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited. There is an urgent need for improvement in risk stratification and new screening modalities, blood biomarkers in particular. Longitudinal collection of paired blood samples and MRI images from cirrhotic patients is particularly valuable in assessing how early blood and imaging markers become positive during the period when lesions are observed to obtain a diagnosis of HCC. We generated a multicenter prospective cohort of patients with cirrhosis under surveillance with contrast MRI, applied untargeted metabolomics on 612 serum samples from 203 patients and identified metabolites associated with risk of HCC development. Such biomarkers may significantly improve early stage HCC detection for cirrhotic patients undergoing HCC surveillance, a critical step to increasing curative treatment opportunities and reducing mortality.

PSMD9 promotes the malignant progression of hepatocellular carcinoma by interacting with c-Cbl to activate EGFR signaling and recycling

BackgroundMounting evidences shows that the ubiquitin‒proteasome pathway plays a pivotal role in tumor progression. The expression of 26S proteasome non-ATPase regulatory subunit 9 (PSMD9) is correlated with recurrence and radiotherapy resistance in several tumor types. However, the role and mechanism of PSMD9 in hepatocellular carcinoma (HCC) progression remain largely unclear.MethodsPSMD9 was identified as a prognosis-related biomarker for HCC based on analysis of clinical characteristics and RNA-seq data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and the JP Project of the International Cancer Genome Consortium (ICGC-LIRI-JP). PSMD9 expression was analyzed in cancer tissues and adjacent noncancerous tissues via immunohistochemistry and Western blotting. Multiple in vivo and in vitro experimental techniques (such as CCK-8, colony formation, EdU, and Transwell assays; flow cytometry; Western blotting; quantitative RT-PCR; Coimmunoprecipitation assay and immunofluorescence confocal imaging) were used to assess the functions of PSMD9 in the pathogenesis of HCC.ResultsWe found that the expression of PSMD9 was upregulated and associated with a poor prognosis in HCC patients. PSMD9 promoted HCC cell proliferation, migration, invasion and metastasis. Knockdown of PSMD9 significantly inhibited HCC cell proliferation by inducing G1/S cell cycle arrest and apoptosis. Mechanistically, we demonstrated that PSMD9 promoted HCC cell proliferation and metastasis via direct interaction with the E3 ubiquitin ligase c-Cbl, suppresses EGFR ubiquitination, influenced EGFR endosomal trafficking and degradation and subsequently activated ERK1/2 and Akt signaling. In addition, we showed that PSMD9 knockdown sensitized HCC cells to the tyrosine kinase inhibitor erlotinib in vitro and in vivo.ConclusionsCollectively, our results indicate that PSMD9 drives HCC progression and erlotinib resistance by suppressing c-Cbl mediated EGFR ubiquitination and therefore can be a potential therapeutic target for HCC.

Retinol-binding protein 4 as a promising serum biomarker for the diagnosis and prognosis of hepatocellular Carcinoma

BackgroundThe prognosis of hepatocellular carcinoma (HCC) is universally poor. Early diagnosis plays a pivotal role in determining the outcome of HCC. MethodsWe employed a comparative proteomics approach to identify potential biomarkers and validated the application of retinol-binding protein 4 (RBP4) as a biomarker for HCC. RBP4 protein expression was examined in liver tissues from 80 HCC patients through immunohistochemical analysis. Serum RBP4 concentrations were measured by ELISA in a cohort comprising 290 HCC patients, matched 202 chronic hepatitis B patients and 269 healthy controls. Survival data were collected from HCC patients. The diagnostic and prognostic values of RBP4 were evaluated using receiver operating curve (ROC) analysis. ResultsThe validation results demonstrated a significant reduction in RBP4 levels in both liver tissues and serum samples from HCC patients. ROC analysis of the diagnostic value of RBP4 revealed an AUC of 0.879 (95 % CI: 0.854∼0.903) for HCC. When combined with AFP, the AUC increased to 0.919, with a sensitivity of 87.9 % and specificity of 80 %. Survival analysis revealed significantly reduced overall survival time in individuals with low-expression of RBP4 compared to those with high-expression. The joint prognostic model exhibited an AUC of 0.926 (95 % CI: 0.888∼0.964), which was significantly higher than that of AFP alone (AUC=0.809; P <0.0001). ConclusionsRBP4 shows a great potential as a biomarker with appreciable diagnostic value, complementing the AFP in HCC diagnosis. Additionally, it holds promise as a prognostic biomarker that, when integrated into a combined prognostic model, could greatly improve HCC prognosis efficiency.

Identifying the Crucial Biomarker of MASH-Related HCC.

This study aimed to explore the key oncogenic factor of metabolicassociated steatohepatitis (MASH) to hepatocellular carcinoma (HCC). We utilized four differential GEO datasets (GSE164760, GSE139602, GSE197112, and GSE49541) to identify the key oncogenic factor for MASH-related HCC. The differential genes were analyzed using the GEO2R algorithm online. The GEPIA online website was used to explore the expression of selected four genes (SPP1, GNMT, CLDN11, and THBS2). The genetic alterations in genes were estimated by the cBioPortal website. The Kaplan-Meier Plotter online database was applied to explore the prognostic value of SPP1. Univariate and multivariate Cox analyses were carried out to further confirm the prognostic value of SPP1. The GO and KEGG enrichment analysis exported associated pathways with SPP1 expression. The positively or negatively related immune cells and immune checkpoint expressions were identified through Pearson correlation analysis. The lipogenesis-associated proteins were detected using western blotting and fluorescence. The high-fat diet (HFD) mouse model was constructed, and liver samples were collected. SPP1, GNMT, CLDN11, and THBS2 were determined in the transformation process of MASH to liver fibrosis. SPP1 and GNMT were upregulated in the HCC tumor tissue. SPP1, in particular, had the potential to be the prognostic factor through Cox analysis. Remarkably, SPP1 was highly expressed in HCC compared to normal tissues in three independent datasets (GSE121248, GSE14520, and GSE45267). SPP1 is mainly involved in the amplification and deep deletion mutations. SPP1 was found to be strongly correlated with ANXA2 expression, and ANXA2 was also highly expressed in HCC with significant prognostic performance. Moreover, SPP1 was found to participate in the carcinogenic mechanism and correlate with immune cells and immune checkpoint expression. SPP1 knockdown suppressed the SREBP1 and FASN expressions and increased the SIRT1 expression in vitro. Moreover, the HFD model validated the upregulation of SPP1 in the fatty liver in vivo. SPP1 may be the key oncogenic factor for the transformation of MASH to HCC, and it could be a potential immunotherapeutic target in HCC.

Clinical significance of PNO1 as a novel biomarker and therapeutic target of hepatocellular carcinoma.

The RNA-binding protein PNO1 plays an essential role in ribosome biogenesis. Recent studies have shown that it is involved in tumorigenesis; however, its role in hepatocellular carcinoma (HCC) is not well understood. The purpose of this study was to examine whether PNO1 can be used as a biomarker of HCC and also examine the therapeutic potential of PNO1 knockout for the treatment of HCC. PNO1 expression was upregulated in HCC and associated with poor prognosis. PNO1 expression was positively associated with tumour stage, lymph node metastasis and poor survival. PNO1 expression was significantly higher in HCC compared to that in fibrolamellar carcinoma or normal tissues. Furthermore, HCC tissues with mutant Tp53 expressed higher PNO1 than those with wild-type Tp53. PNO1 knockout suppressed cell viability, colony formation and EMT of HCC cells. Since activation of Notch signalling pathway promotes HCC, we measured the effects of PNO1 knockout on the components of Notch pathway and its targets. PNO1 knockout suppressed Notch signalling by modulating the expression of Notch ligands and their receptors, and downstream targets. PNO1 knockout also inhibited genes involved in surface adhesion, cell cycle, inflammation and chemotaxis. PNO1 knockout also inhibited colony and spheroid formation, cell migration and invasion, and markers of stem cells, pluripotency and EMT in CSCs. Overall, our data suggest that PNO1 can be used as a diagnostic and prognostic biomarker of HCC, and knockout of PNO1 by CRISPR/Cas9 can be beneficial for the management of HCC by targeting CSCs.

LARS1 is a Prognostic Biomarker and Exhibits a Correlation with Immune Infiltrates in Hepatocellular Carcinoma.

To study the relationship between LARS1 expression and immune infiltration and prognosis in hepatocellular carcinoma (HCC). The clinical characteristics together with LARS1 expression levels were obtained from the TCGA database. Immunohistochemistry confirmed LARS1 expression levels in paraneoplastic and tumor tissues. To investigate LARS1-related downstream molecules, a network of protein-protein interactions (PPIs) and the Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) were built. Furthermore, gene set enrichment analysis (GSEA) was used to analyze the pathways associated with LARS1 expression, whereas Single-sample GSEA (ssGSEA) was applied to perform an association study between immune infiltration and LARS1 gene expression. The TISCH Database and the TISIDB database were used to compare the difference of LARS1 expression in hepatocellular carcinoma and immunomodulators. In comparison to that in normal tissues, the LARS1 expression level was elevated in tumor tissues. LARS1 expression exhibited substantial correlation with AFP, Histologic grade, pathologic stage, Residual tumor, and Vascular invasion in HCC. Higher LARS1 expression in HCC was linked to lower progression-free survival (PFS), disease-specific survival (DSS), and overall survival (OS). According to the GO/KEGG study, the important biological process (neutral lipid metabolic process), cellular component (triglyceride-rich plasma lipoprotein), molecular functions (lipase inhibitor activity), and KEGG pathway (cholesterol metabolism) could be a probable function mechanism in promoting HCC. Various pathways as per GSEA revealed that they were enriched in samples with elevated LARS1 expression. The expression level of LARS1 in malignant tumor cells after immunotherapy was significantly higher than that before immunotherapy. LARS1 was also remarkably linked to the infiltration level and the immunomodulators. LARS1 can be used as a biomarker of HCC, which is associated to immune infiltration of HCC.

Establishment and Validation of a Blood Test-based Nomogram to Diagnose Patients with AFP-negative HCC.

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer death worldwide. Alpha-protein (AFP) is the most widely used blood biomarker for HCC. However, elevated serum AFP is only observed in part of HCC. This study aimed to develop an efficient nomogram model to distinguish patients with alpha- protein-negative HCC and liver cirrhosis. A total of 1130 patients (508 HCC patients + 622 cirrhosis patients) were enrolled in the training cohort. A total of 244 HCC patients and 246 cirrhosis patients were enrolled in the validation cohort. A total of 41 parameters about blood tests were analyzed with logistic regression. The nomogram was based on independent factors and validated both internally and externally. Independent factors were eosinophils %, hemoglobin concentration distribution width, fibrinogen, platelet counts, total bile acid, and mitochondria aspartate aminotransferase. The calibration curve for the probability of HCC showed good agreement between prediction by nomogram and actual observation. The concordance index was 0.851. In the validation cohort, the nomogram distinguished HCC from liver cirrhosis with an area under the curve of receiver operating characteristic of 0.754. This proposed nomogram was an accurate and useful method to distinguish patients with AFP-negative HCC from liver cirrhosis.

Basement membrane-related lncRNA signature for the prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the main type of primary liver cancer. This study aimed to develop a basement membrane (BM) related lncRNAs risk signature to evaluate the prognosis of HCC patients. We screened differentially expressed BM-related lncRNAs (DE-BMRlncRNAs) for risk evaluation, and identified six DE-BMRlncRNAs (AC072054.1, NUP50-DT, AC026412.3, AC109322.2, POLH-AS1 and LINC00595) for prognostic risk signature. HCC patients were divided to high or low risk according to median risk score. Our prognostic model predicted that patients with higher risk score had worse prognosis. We also created a nomogram to assist clinical decision-making according to risk score and clinicopathological features. Meanwhile, we confirmed the expression of six lncRNAs in HCC tissue and cells. POLH-AS1 knockdown inhibited the migration and invasion of HCC cells. In conclusion, we established a predictive model based on BMRlncRNAs to predict the prognosis of HCC. Our findings offer a rationale to further explore BM-related biomarkers for HCC.

Construction of a TP53 mutation-associated ceRNA network as prognostic biomarkers in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) continues to endanger human health worldwide. Regulatory networks of competing endogenous RNAs (ceRNAs) play important roles in HCC. TP53 is the second most often altered gene in HCC and has a significant role in regulating target genes such as miRNAs and lncRNAs. MethodsData from patients with TP53 mutation were collected through the cBioPortal database and differential analysis was performed to screen RNAs related to TP53 mutation. The lncRNA-miRNA–mRNA relationship was predicted by the miRcode, miRDB, and TargetScan databases. The ceRNA networks were screened and visualized by Cytoscape. Core ceRNA networks were generated by differential analysis, coexpression analysis, prognostic analysis and subcellular localization. Finally, methylation, mutation, PPI, GSEA, immunity and drug sensitivity analyses of MEX3A were performed to determine the role of MEX3A in HCC. ResultsWe identified 1508 DEmRNAs, 85 DEmiRNAs and 931 DElncRNAs and obtained a ceRNA network including 28 lncRNAs, 4 miRNAs and 31 mRNAs. Twenty hub DERNAs in the TP53-altered-related ceRNA network were screened out by Cytoscape and the core ceRNA network (LINC00491/TCL6-hsa-miR-139-5p-MEX3A) was obtained by multiple analyses. In addition, we discovered that the methylation level of MEX3A was decreased and the mutation frequency was raised in HCC. Furthermore, elevated MEX3A expression was associated with alterations in the HCC immunological microenvironment. ConclusionWe successfully constructed a reciprocal ceRNA network, which could provide new ideas for exploring HCC mechanisms and therapeutic approaches.