Biomarker For Hepatocellular Carcinoma Research Articles

Recent research progress of circular RNAs in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is an extremely heterogeneous malignant tumor with a high morbidity and mortality. Circular RNAs (circRNAs) are noncoding RNAs with high stability, organ/tissue/cell-specific expression and are conserved across species. Accumulating evidence suggested that circRNAs play crucial roles as microRNA sponges, protein sponges, scaffolds, recruiters and could even polypeptide encoders. Many studies have since revealed that circRNAs were aberrantly expressed in HCC and acted as crucial modulators of HCC carcinogenesis and progression. Furthermore, circRNAs have also been identified as potential diagnostic and prognostic biomarkers for HCC. In this review, we thoroughly outline and evaluate the function of circRNAs in HCC development, with an emphasis on the specific molecular pathways by which they participated in the formation and progression of HCC, and we address their potential for serving as clinical biomarkers in HCC.

Screening immune-related blood biomarkers for DKD-related HCC using machine learning.

Diabetes mellitus is a significant health problem worldwide, often leading to diabetic kidney disease (DKD), which may also influence the occurrence of hepatocellular carcinoma (HCC). However, the relationship and diagnostic biomarkers between DKD and HCC are unclear. Using public database data, we screened DKD secretory RNAs and HCC essential genes by limma and WGCNA. Potential mechanisms, drugs, and biomarkers for DKD-associated HCC were identified using PPI, functional enrichment, cMAP, and machine learning algorithms, and a diagnostic nomogram was constructed. Then, ROC, calibration, and decision curves were used to evaluate the diagnostic performance of the nomograms. In addition, immune cell infiltration in HCC was explored using CIBERSORT. Finally, the detectability of critical genes in blood was verified by qPCR. 104 DEGs associated with HCC using WGCNA were identified. 101 DEGs from DKD were predicated on secreting into the bloodstream with Exorbase datasets. PPI analysis identified three critical modules considered causative genes for DKD-associated HCC, primarily involved in inflammation and immune regulation. Using lasso and RM, four hub genes associated with DKD-associated HCC were identified, and a diagnostic nomogram confirmed by DCA curves was established. The results of immune cell infiltration showed immune dysregulation in HCC, which was associated with the expression of four essential genes. PLVAP was validated by qPCR as a possible blood-based diagnostic marker for DKD-related HCC. We revealed the inflammatory immune pathways of DKD-related HCC and developed a diagnostic nomogram for HCC based on PLVAP, C7, COL15A1, and MS4A6A. We confirmed with qPCR that PLVAP can be used as a blood marker to assess the risk of HCC in DKD patients.

The combination of CD8 and TIM3 expression to predict survival outcomes in hepatocellular carcinoma.

543 Background: CD8+ T cells have anti-tumor immunity in a variety of cancers including hepatocellular carcinoma (HCC). Tumor-infiltrating CD8+ T cells express immune checkpoint proteins and focus on a therapeutic targeting. T cell immunoglobulin and mucin domain protein 3 (TIM3) has been reported to be associated with suppression of anti-tumor immunity, poor prognosis, and tumor progression in HCC, resulting in up-regulation on exhausted CD8+ T cells. As the exhausted T cells showed a decrease of effector cytokine production and cytolytic activity, leading to the tumor progression, TIM3 is one of the most important exhaustion markers. In the present study, we investigated the role of CD8 and TIM3 as a prognostic biomarker in HCC. Methods: We analyzed 474 HCC patients with clinical and transcriptomic data from The Cancer Genome Atlas (TCGA: n=359) and GSE 76427 (n=115) to examine associations between CD8, TIM3, and programmed death receptor-1/programmed cell death ligand 1 (PD-1/PD-L1) expression and survival outcomes. Each gene was divided into low- and high-expression groups using the tertiles of gene expression. Results: The patients with high CD8 and low TIM3 expression were associated with better overall survival (OS) (P=0.021 and P=0.025, respectively) in TCGA, while PD-1/PD-L1 were not associated with OS (P=0.306 and P=0.318, respectively). Moreover, the patients with high CD8 and low TIM3 expression were validated in GSE dataset (P=0.014 and P=0.009, respectively). The patients with high CD8 and low TIM3 expression were associated with improved OS compared with the overall HCC patient (P=0.008). Among the patients with high CD8 expression, low TIM3 expression (non-exhausted CD8+ T cells) was associated with better OS (P=0.009), but PD-1/PD-L1 were not associated with OS (P=0.491 and P=0.772, respectively). Finally, multivariate analysis showed that the patients withnon-exhausted CD8+ T cells were significantly superior for the survival outcomes (Hazard ratio=2.80, 95% CI=1.27–6.19, P=0.01). Conclusions: High CD8 and low TIM3 expression is associated with better OS in HCC than PD-1/PD-L1, suggesting that CD8 and TIM3 are useful biomarkers for predicting HCC prognosis. Restoring exhausted T cells via the suppression of TIM3 is a promising strategy for cancer treatment, which become a breakthrough in the cancer immunotherapy.

Agrin as a prognostic biomarker in hepatocellular carcinoma.

559 Background: Hepatocellular carcinoma (HCC), the predominant form of hepatic cancer is associated with high mortality rates, both in the United States & globally. Alpha-fetoprotein (AFP) & Glypican-3 have been proposed as biomarkers for HCC, although they do not offer any prognostic benefit to model disease progression. Immunotherapy combinations increase patient survival to ~18 months but are not selected based on biomarkers. Agrin, a secreted proteoglycan is frequently overexpressed & secreted in HCC & plays a prominent role in liver tumor microenvironment to promote hepatocarcinogenesis. In this single center, retrospective study using banked materials, we assessed the role of agrin as a prognostic biomarker in HCC. Methods: With IRB approval, we studied 90 newly diagnosed unresectable hepatobiliary cancer patients, & compared them to healthy controls matched for age, gender, & BMI between February 2006 & September 2013. Serum concentration (conc.) of agrin was assessed using enzyme-linked immunosorbent assay (ELISA). We correlated the level of agrin with AFP, a known biomarker in hepatocellular cancer. Comparisons between groups were made using Mann-Whitney U & Kruskal Wallis tests. Survival analysis used both univariate & multivariate Cox regression models to investigate the relationship between survival outcomes & agrin concentration, in comparison to AFP. Results: Mean circulatory plasma agrin levels were higher in HCC patients (n=79) (7.97 ng/ml, SD 5.8) when compared to controls (4.27 ng/ml, SD 1.03). ECOG PS > 2 was linked to higher agrin conc. (mean: 10.5 ng/ml, p 0.007). While agrin levels in healthy individuals did not change with smoking history, the mean agrin levels were significantly higher in smokers (9.49 ng/ml) vs non-smokers (6.84 ng/ml) (p 0.0001) amongst HCC patients. Agrin displayed a strong positive correlation with AFP (correlation coefficient (r) 0.31944, p 0.0137). Multivariate cox regression models indicated that agrin conc. of > 9.3 ng/ml had greater predictable survival outcomes (Hazard ratio (HR) 8.51, p <0.001) when compared to elevated AFP levels (HR 1.55, p 0.25) (Table). HCC patients with lower agrin conc. (4.9 to 6.3 ng/ml) exhibited a one-year survival rate of 63% & a median survival time of 16 months, while those with higher agrin conc. exceeding 9.3 ng/ml had significantly lower one-year survival rate of 14% & significantly shorter median survival time of 3 months (p 0.002). Conclusions: Our results suggest that agrin levels as a prognostic indicator fare better than AFP levels. Thus, further studies should be done to explore this as a therapeutic target and as a biomarker of response to targeted therapies. [Table: see text]

Combinatorial Gene Expression Profiling of Serum HULC, HOTAIR, and UCA1 lncRNAs to Differentiate Hepatocellular Carcinoma from Liver Diseases: A Systematic Review and Meta-Analysis.

Hepatocellular carcinoma (HCC) presents a significant global health challenge due to limited early detection methods, primarily relying on conventional approaches like imaging and alpha-fetoprotein (AFP). Although non-coding RNAs (ncRNAs) show promise as potential biomarkers in HCC, their true utility remains uncertain. We conducted a comprehensive review of 76 articles, analyzing 88 circulating lncRNAs in 6426 HCC patients. However, the lack of a standardized workflow protocol has hampered holistic comparisons across the literature. Consequently, we herein confined our meta-analysis to only a subset of these lncRNAs. The combined analysis of serum highly upregulated in liver cancer (HULC) gene expression with homeobox transcript antisense intergenic RNA (HOTAIR) and urothelial carcinoma-associated 1 (UCA1) demonstrated markedly enhanced sensitivity and specificity in diagnostic capability compared to traditional biomarkers or other ncRNAs. These findings could have substantial implications for the early diagnosis and tailored treatment of HCC.

Value of computed tomography-based lipiodol retention pattern in the assessment of hepatocellular carcinoma complete response following conventional transarterial chemoembolization

BackgroundConventional transarterial chemoembolization (cTACE) has emerged as a major treatment modality for unresectable hepatocellular carcinoma (HCC). Efforts have recently been made to identify biomarkers of HCCs that can potentially predict the response of lesions to transarterial chemoembolization (TACE) therapy. The goal of these efforts is to simplify the decision-making process on whether a patient should choose cTACE or pursue other treatment options. Among these, computed tomography (CT) based lipiodol retention analysis has shown promise in providing valuable insights into post-TACE outcomes. This study aimed to investigate the value of CT-based lipiodol retention pattern in predicting recurrence after a state of complete response (CR) according to modified Response Evaluation Criteria in Solid Tumors (mRECIST) guideline post-cTACE.MethodsFrom January 2022 to June 2023, a cohort of 30 patients with 40 HCCs who received a primary cTACE session were identified. The study included patients who underwent 1st cTACE session with no more than three HCCs. Ten HCC lesions were eliminated owing to different reasons (illustrated in the flow chart). Tumor response was evaluated based on mRECIST on 1st follow-up CT with a particular focus on tumors demonstrating a complete response. Patterns of lipiodol retention within these tumors were categorized as complete lipiodol retention pattern (C-Lip), covering the whole tumor, or incomplete lipiodol retention pattern (I-Lip). Recurrence was defined when reappearance of enhancement on arterial-phase images of the treated lesion with subsequent washout on portal/delayed phase images occurred corresponding to findings of restricted diffusion on follow-ups DCE-MRI.ResultsA total of 30 HCCs with a CR were classified into 21 (70%) with C-Lip and 9 (30%) with I-Lip. After a median follow-up of 6.00 ± 2.27 months (3–9 months), 8/9 (88.9%) I-Lip and 5/21 (23.8%) C-Lip showed recurrence on follow-up (DCE-MRI), (p < 0.01), with no significant difference in the time of progression (mean 5.57 ± 2.69 for C-Lip vs. 6.00 ± 2.27 months for I-Lip, with (p < 0.5).ConclusionsHCCs exhibit complete lipiodol retention alongside mRECIST CR possess a reduced susceptibility of recurrence, thereby potentially benefiting from MRI during subsequent evaluations. Conversely, the presence of incomplete lipiodol retention in conjunction with mRECIST CR is an indication of a high risk of recurrence and may require further treatment.

The sorafenib resistance-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma

ABSTRACT Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide. Most patients with advanced HCC acquire sorafenib resistance. Drug resistance reflects the heterogeneity of tumors and is the main cause of tumor recurrence and death.We identified and validated sorafenib resistance related-genes (SRGs) as prognostic biomarkers for HCC. We obtained SRGs from the Gene Expression Omnibus and selected four key SRGs using the least absolute shrinkage and selection operator, random forest, and Support Vector Machine-Recursive feature elimination machine learning algorithms. Samples from the The Cancer Genome Atlas (TCGA)-HCC were segregated into two groups by consensus clustering. Following difference analysis, 19 SRGs were obtained through univariate Cox regression analysis, and a sorafenib resistance model was constructed for risk stratification and prognosis prediction. In multivariate Cox regression analysis, the risk score was an independent predictor of overall survival (OS). Patients classified as high-risk were more sensitive to other chemotherapy drugs and showed a higher expression of the common immune checkpoints. Additionally, the expression of drug-resistance genes was verified in the International Cancer Genome Consortium cohort. A nomogram model with a risk score was established, and its prediction performance was verified by calibration chart analysis of the TCGA-HCC cohort. We conclude that there is a significant correlation between sorafenib resistance and the tumor immune microenvironment in HCC. The risk score could be used to identify a reliable prognostic biomarker to optimize the therapeutic benefits of chemotherapy and immunotherapy, which can be helpful in the clinical decision-making for HCC patients.

KIAA1429 facilitates metastasis via m6A-YTHDC1-dependent RND3 down-regulation in hepatocellular carcinoma cells

N6-methyladenosine (m6A), a dynamically reversible modification in eukaryotic RNAs, modulates gene expression and pathological processes in various tumors. KIAA1429, the largest component of the m6A methyltransferase complex, plays an important role in m6A modification. However, the underlying mechanism of KIAA1429 in hepatocellular carcinoma (HCC) remains largely unknown. Immunohistochemical assay was performed to examine the expression of KIAA1429 in HCC tissues. Transwell, wound healing and animal experiments were used to investigate the influence of KIAA1429 on cell migration and invasion. The mRNA high‐throughput sequencing (RNA-seq) and methylated RNA immunoprecipitation sequencing (MeRIP-seq) were performed to screen the downstream target of KIAA1429. RNA stability assays, RNA immunoprecipitation assay (RIP), MeRIP-qPCR and luciferase assay were used to evaluate the relationship between KIAA1429 and the m6A-modified genes. Results showed that the expression level of KIAA1429 was significantly higher in HCC tissues than in adjacent tissues, and the upregulation of KIAA1429 could promote HCC metastasis in vitro and in vivo. Mechanistically, we confirmed that KIAA1429 negatively regulated the tumor suppressor, Rho family GTPase 3 (RND3), by decreasing its mRNA stability in coordination with the m6A reader YTHDC1. Moreover, we demonstrated that KIAA1429 could regulate the m6A modification of RND3 mRNA via its RNA binding domain. Our data indicated that KIAA1429 exerted its oncogenic role by inhibiting RND3 expression in an m6A-dependent manner, suggesting that KIAA1429 might be a potential prognostic biomarker and therapeutic target in HCC.

Integrating bioinformatics and experimental validation to unveil disulfidptosis-related lncRNAs as prognostic biomarker and therapeutic target in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) stands as a prevalent malignancy globally, characterized by significant morbidity and mortality. Despite continuous advancements in the treatment of HCC, the prognosis of patients with this cancer remains unsatisfactory. This study aims at constructing a disulfidoptosis‑related long noncoding RNA (lncRNA) signature to probe the prognosis and personalized treatment of patients with HCC.MethodsThe data of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) databases. Univariate, multivariate, and least absolute selection operator Cox regression analyses were performed to build a disulfidptosis-related lncRNAs (DRLs) signature. Kaplan–Meier plots were used to evaluate the prognosis of the patients with HCC. Functional enrichment analysis was used to identify key DRLs-associated signaling pathways. Spearman’s rank correlation was used to elucidate the association between the DRLs signature and immune microenvironment. The function of TMCC1-AS1 in HCC was validated in two HCC cell lines (HEP3B and HEPG2).ResultsWe identified 11 prognostic DRLs from the TCGA dataset, three of which were selected to construct the prognostic signature of DRLs. We found that the survival time of low-risk patients was considerably longer than that of high-risk patients. We further observed that the composition and the function of immune cell subpopulations were significantly different between high- and low-risk groups. Additionally, we identified that sorafenib, 5-Fluorouracil, and doxorubicin displayed better responses in the low-score group than those in the high-score group, based on IC50 values. Finally, we confirmed that inhibition of TMCC1-AS1 impeded the proliferation, migration, and invasion of hepatocellular carcinoma cells.ConclusionsThe DRL signatures have been shown to be a reliable prognostic and treatment response indicator in HCC patients. TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.

Liquid-liquid phase separation-related lncRNA prognostic signature and ZNF32-AS2 as a novel biomarker in hepatocellular carcinoma

BackgroundLiquid-liquid phase separation (LLPS) enhances oncogenic signaling pathways and advances cancer progression, and has been proposed as a promising cancer biomarker and intervention target. Nevertheless, doubts remain about the prognostic importance of LLPS-related long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC). Methods: An LLPS-related lncRNA prognostic signature was generated by drivers and regulators of LLPS, and was validated in external datasets. The underlying genetic changes and functional enrichment of the signature were assessed. The drug sensitivity and response to immunotherapy were predicted in patients categorized as high-risk and low-risk. Clinical samples, phase separation agonist, and dispersant were used to identify lncRNAs with the most significant expression change. Cancer cells with ZNF32-AS2 expression regulation were subjected to colony formation assay, scratch test assay, migration and invasion assay, sorafenib resistance assay, and xenograft tumor model. Results: The signature of LLPS-related hub lncRNAs identified through Weighted Gene Co-Expression Network Analysis showed outstanding performance in training and external validation cohorts consistently, and the molecular characteristics varied between different risk groups. Potential drugs for high-risk individuals were identified, and low-risk individuals demonstrated a more favorable reaction to immunotherapy. ZNF32-AS2 showed the most significant expression change in phase separation agonist and dispersant treatment. ZNF32-AS2 promoted the proliferation, mobility, and sorafenib resistance of liver cancer cells. Conclusions: The LLPS-related lncRNA signature may help assess prognosis and predict treatment efficacy in clinical settings. LLPS-related ZNF32-AS2 promoted the proliferation, mobility, and sorafenib resistance of liver cancer cells, and may be a novel potential biomarker in hepatocellular carcinoma.

Assessment of gankyrin mRNA expression in tissue and liquid biopsy as a sensitive biomarker for hepatocellular carcinoma

Assessment of gankyrin mRNA expression in tissue and liquid biopsy as a sensitive biomarker for hepatocellular carcinoma

Elucidating B4GALNT1 as potential biomarker in hepatocellular carcinoma using machine learning models and mutational dynamics explored through MD simulation

Liver hepatocellular carcinoma (LIHC) is considered one of the primary contributors to cancer-related mortality on a global scale. The identification of new biomarkers is of utmost importance due to the fact that patients with LIHC are frequently detected at advanced stages, leading to an increased mortality rate. The study utilized TCGA-LIHC gene expression datasets to identify biomarkers and to address the complexity of datasets. A combination of feature selection (FS) techniques was used, and the performance of this strategy was assessed using ten machine learning classifiers. The findings were integrated, revealing biomarkers identified through at least five FS techniques. Through our proposed approach, we identified 55 potential biomarkers for LIHC. The Gaussian Naive Bayes Classifier (AUC = 0.99) was found to be the most effective classifier, achieving 98.67% accuracy when utilizing the 55 identified biomarkers in the test dataset. Additionally, we conducted differential gene expression, survival analysis, and enrichment analysis for all the identified biomarkers. Subsequently, Lasso-penalized Cox regression further refined the identified biomarkers to thirteen. Out of thirteen genes, we singled out B4GALNT1 because of its statistical significance in differential expression analysis and increasing importance across various cancer types, including LIHC. We carried out comprehensive bioinformatics and molecular dynamics simulation studies along with other structural analysis of B4GALNT1 in LIHC. In LIHC, six mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) have been predicted to be probably damaging by evaluating in-silico prediction algorithms. In comparison to the wild type, the B4GALNT1 variations, specifically P64Q and S131F, demonstrate increased stability. However, these mutations lead to decreased atomic fluctuations, indicating a rigid protein structure. Again, mutations like A311S and P507Q induce increased flexibility, highlighting their structural impact on B4GALNT1. The study demonstrated the combination of various feature selection methods effectively reveals new biomarkers, thereby directly impacting their biological significance. Furthermore, our findings indicate a link between increased B4GALNT1 expression in individuals with liver cancer and a poorer prognosis, highlighting its potential as a promising therapeutic target.

Investigation of the feasibility of NRAV as a biomarker for hepatocellular carcinoma.

The long non-coding RNA, Negative Regulator of Antiviral Response (NRAV) has been identified as a participant in both respiratory virus replication and immune checkpoints, however, its involvement in pan-cancer immune regulation and prognosis, particularly those of hepatocellular carcinoma (HCC), remains unclear. To address this knowledge gap, we analyzed expression profiles obtained from The Cancer Genome Atlas (TCGA) database, comparing normal and malignant tumor tissues. We found that NRAV expression is significantly upregulated in tumor tissues compared to adjacent nontumor tissues. Kaplan-Meier (K-M) analysis revealed the prognostic power of NRAV, wherein overexpression was significantly linked to reduced overall survival in a diverse range of tumor patients. Furthermore, noteworthy associations were observed between NRAV, immune checkpoints, immune cell infiltration, genes related to autophagy, epithelial-mesenchymal transition (EMT), pyroptosis, tumor mutational burden (TMB), and microsatellite instability (MSI) across different cancer types, including HCC. Moreover, NRAV upregulation expression was associated with multiple pathological stages by clinical observations. Furthermore, our investigation revealed a substantial elevation in the expression of NRAV in both HCC tumor tissues and cells compared to normal tissues and cells. The inhibition of NRAV resulted in the inhibition of cell proliferation, migration, and invasion in HCC cells, while also influencing the expression of CD274 (PD-L1) and CD44, along with various biomarkers associated with EMT, autophagy, and pyroptosis. The aforementioned results propose NRAV as a promising prognostic biomarker for HCC.

Bioinformatics analysis and experimental validation of cystathionine-gamma-lyase as a potential prognosis biomarker in hepatocellular carcinoma

Bioinformatics analysis and experimental validation of cystathionine-gamma-lyase as a potential prognosis biomarker in hepatocellular carcinoma

Comprehensive analysis identifies long non-coding RNA RNASEH1-AS1 as a potential prognostic biomarker and oncogenic target in hepatocellular carcinoma.

RNASEH1-AS1, a long non-coding RNA (lncRNA) divergently transcribed from the antisense strand of its neighboring protein-coding gene ribonuclease H1 (RNASEH1), has recently been demonstrated to be involved in tumor progression. However, the association between RNASEH1-AS1 and hepatocellular carcinoma (HCC) remains unclear. In the present study, first, the expression of RNASEH1-AS1 in HCC and its correlation with clinicopathological features, prognosis, diagnosis, immune cell infiltration of HCC patients was inspected using relevant R packages based on The Cancer Genome Atlas (TCGA) data. RNASEH1-AS1 was found to be up-regulated in most cancer types, including HCC, and its overexpression was significantly associated with histologic grade and AFP level as well as poor prognosis, and was an independent risk factor affecting overall survival with good diagnostic and prognostic values for HCC. RNASEH1-AS1 was inversely associated with the infiltration of most immune cell types, including plasmacytoid dendritic cells (pDC), B cells and neutrophils. Second, a total of 1109 positively co-expressed genes (PCEGs) of RNASEH1-AS1 were screened out in HCC by correlation analysis in batches (|Spearman's r| >0.4 and adjusted P value <0.01). GO and KEGG enrichment analysis indicated that PCEGs of RNASEH1-AS1 were mainly related to RNA processing, ribosome biogenesis, transcription and histone acetylation. The top 10 hub genes (EIF4A3, WDR43, WDR12, DKC1, NAT10, UTP18, DDX18, BYSL, DDX10, PDCD11) were identified by constructing the protein-protein interaction (PPI) network, and they were all highly expressed in HCC and positively correlated with histological grade. Third, a risk model was constructed based on four RNASEH1-AS1-related hub genes (EIF4A3, WDR12, DKC1, and NAT10) with good prognostic predictive potential via univariate Cox and the least absolute selection operator (LASSO) regression analysis. Fourth, experimental validation revealed that RNASEH1-AS1 was significantly elevated in HCC tissues and several cell lines, and its knockdown could suppress the proliferation, migration, and invasion of HCC cells. Finally, mechanistic studies demonstrated that the stability of RNASEH1-AS1 could be regulated by DKC1 via their direct interaction. Taken together, RNASEH1-AS1 may serve as a potential prognostic and diagnostic biomarker and oncogenic lncRNA for HCC.

Construction of immune-related molecular diagnostic and predictive models of hepatocellular carcinoma based on machine learning

BackgroundTo exploit hepatocellular carcinoma (HCC) diagnostic substances, we identify potential predictive markers based on machine learning and to explore the significance of immune cell infiltration in this pathology. MethodThree HCC gene expression datasets were used for weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were applied to identify candidate biomarkers. The diagnostic value of HCC diagnostic gene biomarkers was further assessed by the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from HCC patients and to analyze their correlation with diagnostic markers. In addition, the prognostic value of the markers and the sensitivity of the drugs were analyzed. ResultWGCNA and differential expression analysis were used to screen 396 distinct gene signatures in HCC tissues. They were mostly engaged in cytoplasmic fusion and the cell division cycle, according to gene enrichment analyses. Five genes were shown to have a high diagnostic value for use as diagnostic biomarkers for HCC, including EFHD1 (AUC = 0.77), KIF4A (AUC = 0.97), UBE2C (AUC = 0.96), SMYD3 (AUC = 0.91), and MCM7 (AUC = 0.93). T cells, NK cells, macrophages, and dendritic cells were found to be related to diagnostic markers in HCC tissues by immune cell infiltration analysis, indicating that these cells are intimately linked to the onset and spread of HCC. Concurrently, these five genes and their constructed models have considerable prognostic value. ConclusionThese five genes (EFHD1, KIF4A, UBE2C, SMYD3, and MCM7) may serve as new candidate molecular markers for HCC, providing new insights for future diagnosis, prognosis, and molecular therapy of HCC.

Circ-AKT3 Inhibits Malignant Progression of Hepatocellular Carcinoma by Negatively Regulating PDK1

This study investigated the clinical and functional significance of circ-AKT3 in hepatocellular carcinoma (HCC) and its impact on tumor progression. Using qRT-PCR, we found reduced circ-AKT3 levels in HCC tissues compared to adjacent liver samples. Clinical data revealed that low circ-AKT3 expression correlated with increased lymphatic and distant metastasis. Functional experiments in MHCC-97H and Hep3B cells, chosen due to their low circ-AKT3 levels, demonstrated that circ-AKT3 overexpression suppressed HCC cell proliferation, invasion, and migration. Bioinformatics analysis and luciferase assays indicated that circ-AKT3 potentially targets PDK1. PDK1 expression was elevated in HCC tissues and cells, inversely correlated with circ-AKT3, and reduced upon circ-AKT3 overexpression. Rescue experiments illustrated that PDK1 overexpression reversed circ-AKT3-induced inhibition of HCC cell proliferation and migration. In conclusion, downregulated circ-AKT3 in HCC, associated with aggressive clinical features, hinders tumor progression by modulating PDK1. This suggests circ-AKT3′s potential as a diagnostic and prognostic biomarker for HCC.

Prognostic Significance of Iron Metabolism and Immune-Related Genes as Risk Markers in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly lethal liver cancer with significant heterogeneity, which poses challenges in predicting prognosis and treatment outcomes. The impact of iron metabolism and immune-related genes (IMRGs) on HCC patient prognoses remains elusive. We utilized The Cancer Genome Atlas (TCGA) dataset to obtain mRNA expression data and clinical information from HCC patients. Through the application of LASSO regression and univariate/multivariate Cox regression analyses, we identified five IMRGs significantly associated with survival of HCC patients. We constructed a prognostic model comprising these five genes. The model demonstrated excellent predictive performance, not only within TCGA dataset but also when validated using the Gene Expression Omnibus (GEO) dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses presented significant variations in functional categories, such as apical plasma membrane and collagen-containing extracellular matrix. Several pathways, including the PI3K-AKT signaling pathway and the calcium signaling pathway, exhibited significant variations among HCC patients with varying prognoses (P < 0.05). Immune infiltration analysis indicated significantly lower levels of various immune cells, immune functions, and immune checkpoints, such as B cells, CD8+ T cells, and TILs, in the high-risk group (P < 0.05). Immunophenoscore results suggested that the low-risk group may exhibit a more favorable response to immune therapy. Furthermore, the CellMiner database predicted anti-tumor drugs significantly associated with prognostic genes (P < 0.001). In conclusion, our findings highlight the predictive role of IMRGs in prognosis and immune treatment of HCC, indicating that ADAMTS13, CRHBP, VIPR1, FCN3, and CLEC1B may serve as potential prognostic biomarkers for HCC.

Serum Aldo-Keto Reductase Family 1 Member B10 (AKR1B10) as a Potential Biomarker for Diagnosis of Hepatocellular Carcinoma.

To evaluate the diagnostic performance of aldo-keto reductase family 1 member B10 (AKR1B10) in a Beijing cohort with hepatocellular carcinoma (HCC). This study included 521 subjects who visited Peking Union Medical College Hospital from June 2017 to May 2023, including 109 cases of HCC, 165 cases of healthy controls, 106 cases of benign liver diseases, and 141 cases of other cancers. Serum AKR1B10 levels were measured and compared across various groups. Diagnostic performances of serum AKR1B10 and other tumor markers were assessed using receiver operator characteristic (ROC) curves. In addition, a subset of HCC patients who underwent surgical resection were recruited for clinical follow-up study. We found that serum AKR1B10 expression was higher in patients with HCC relative to other control groups. The association between serum AKR1B10 and clinical features of HCC was not observed. Serum AKR1B10 showed a high diagnostic performance for HCC, and when combined with AFP, the diagnostic effectiveness was significantly improved. Specifically, serum AKR1B10 showed superior diagnostic effectiveness for AFP-negative HCC. The clinical follow-up study indicated a gradual decrease in serum AKR1B10 after surgery. Our study demonstrated that serum AKR1B10 is a promising biomarker for HCC, and when used in combination with AFP can significantly improve the detection rate of HCC.

CircMYBL2 facilitates hepatocellular carcinoma progression by regulating E2F1 expression.

Circular RNAs (circRNAs) have been recognized as pivotal regulators in tumorigenesis, yet the biological functions as well as molecular mechanisms of the majority of circRNAs in hepatocellular carcinoma (HCC) remain elusive. We sought to unveil the expression profile and biological role of circMYBL2 in HCC. Initial microarray analyses were conducted to probe the expression profile of circMYBL2 in HCC cells, and qRT‒PCR analysis was then performed in HCC cell lines and tissues, revealing significant upregulation of circMYBL2. Subsequent experiments were conducted to evaluate the biological function of circMYBL2 in HCC progression. Furthermore, bioinformatics analysis, qRT‒PCR analysis, luciferase reporter assays, and western blot analysis were employed to investigate the interplay among circMYBL2, miR-1205, and E2F1. CircMYBL2 was found to exhibit marked upregulation in tumor tissues as well as HCC cell lines. Elevated expression of circMYBL2 increased the proliferation and migration of HCC cells, whereas circMYBL2 knockdown elicited contrasting effects. Mechanistically, our results indicated that circMYBL2 promoted E2F1 expression and facilitated HCC progression by sponging miR-1205. Our findings revealed that circMYBL2 contributed to HCC progression through the circMYBL2/miR-1205/E2F1 axis, suggesting the potential of circMYBL2 as a novel target for HCC treatment or a prognostic biomarker for HCC.