Hepatocellular Carcinoma Liver Tissues Research Articles

Expression of Fascin-1 and its diagnostic value in liver cancer

Although some studies have reported on the expression and clinical significance of Fascin-1 (FSCN1) in liver cancer, the clinical application and differential diagnosis value of FSCN1 in liver cancer are still unclear. The aim of this study was to analyze the expression level of FSCN1 protein in liver cancer tissues and explore its diagnostic and application value in differentiating between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The immunehistochemical analysis was used to detect the expression of FSCN1 in 108 cases of HCC, 26 cases of ICC, 23 cases of liver cirrhosis, and 11 cases of normal liver tissues. The differences in the positive expression rate and strong positive expression rate of FSCN1 among different groups were analyzed. The positive rate of FSCN1 in normal liver tissues, liver cirrhosis, HCC, and ICC tissues was 0.0% (0/11), 0.0% (0/23), 13.9% (15/108), and 92.3% (24/26), respectively, while the strong positive rate was 0.0% (0/11), 0.0% (0/23), 0.9% (1/108), and 69.2% (18/26), respectively. Both the positive rate and strong positive rate of FSCN1 in ICC tissues were significantly higher than those in HCC, liver cirrhosis, and normal liver tissues. Additionally, the positive rate of FSCN1 in moderately to poorly differentiated HCC tissues was 18.8% (15/80), significantly higher than in well-differentiated HCC (0.0%, 0/28) (P = 0.031). In liver cancer, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FSCN1 positive prediction for ICC were 92.3%, 86.1%, 61.5%, and 97.9%, respectively, whereas the sensitivity, specificity, PPV, and NPV of FSCN1 strong positive prediction for ICC were 69.2%, 99.1%, 94.7%, and 93.0%, respectively. These results suggest that FSCN1 may play an important role in the occurrence and progression of liver cancer, and it can be used as a novel diagnostic marker for ICC.

The comprehensive roles of lncRNA FAM99A/FAM99B in hepatocellular carcinoma: Expressions, regulatory mechanisms and functional pathway analysis

AimsThe incidence and mortality of liver hepatocellular carcinoma (LIHC) were increasing year by year. The aim of this study was to investigate the comprehensive roles of lncRNA FAM99A and FAM99B in LIHC. Main methodsAccording to the data of TCGA and GTEx, the expression levels of FAM99A and FAM99B in LIHC were evaluated, and the overall survival (OS), disease-free survival (DFS), immune cell infiltration and tumor stage were analyzed. The subcellular localization of FAM99A and FAM99B in various cancer cell lines was predicted by lncATLAS database. In addition, we also used ENCORI, KEGG, LinkedOmics, Metascape and other databases. It was verified by in vivo and in vitro experiments. Key findingsCompared with adjacent normal tissues, FAM99A and FAM99B were down-regulated in LIHC tissues, and significantly correlated with immune cell infiltration. With the progression of tumor stage and grade, the expression of FAM99A and FAM99B showed a decreasing trend, and the prognosis of patients were also poor. In addition, the biological functions, signaling pathways and protein interactions of FAM99A and FAM99B in LIHC were enriched to study the potential molecular mechanisms. The overlapping RNA binding proteins (RBP) of FAM99A and FAM99B mainly included CSTF2T, BCCIP, RBFOX2 and SF3B4. Finally, experiments showed that overexpression of FAM99A attenuated the proliferation, invasion, colony formation and tumor growth of LIHC cells. SignificanceTaken together, the above studies demonstrated that FAM99A and FAM99B had an inhibitory effect on the progression of LIHC, which might be promising diagnostic biomarkers and therapeutic targets for LIHC patients.

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis.

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

HHLA2 promotes hepatoma cell proliferation, migration, and invasion via SPP1/PI3K/AKT signaling pathway.

Hepatocellular carcinoma (HCC) stands as one of the most malignant tumors characterized by poor prognosis and high mortality rates. Emerging evidence underscores the crucial role of the B7 protein family in various cancers, including HCC. However, the involvement of the human endogenous retrovirus H long-terminal repeat-associated protein 2 (HHLA2,or B7-H5) in HCC remains unclear.Immunohistochemistry was employed to assess the differential expression of HHLA2 between HCC and normal liver tissues. A battery of assays, including CCK8, EdU, tablet clone-forming, Transwell, and wound healing assays, were conducted to elucidate the function and potential mechanisms of HHLA2 in the malignant biological behaviors of HCC. Additionally, a xenograft mouse model was established to evaluate the tumorigenicity of hepatoma cell lines exhibiting different HHLA2 expression levels in vivo. Western blot analysis was used to analyze HHLA2, secretory phosphoprotein 1 (SPP1), and PI3K/AKT/mTOR levels.HHLA2 exhibited elevated expression in HCC tissues, correlating with poor tumor differentiation and shortened overall survival in HCC patients. In vitro experiments demonstrated that HHLA2 overexpression (OE) promoted the proliferation, migration, and invasion of hepatoma cells, while in vivo experiments revealed that HHLA2 OEenhanced HCC tumor growth. Conversely, inhibition of HHLA2 expression yielded the opposite effect. Downregulation of SPP1 inhibited the proliferation, migration, and invasion induced by HHLA2 OE, and this effect was linked to the PI3K/AKT/mTOR signaling pathway.Our findings indicate that HHLA2 promotes the proliferation, migration, and invasion of hepatoma cells via the SPP1/PI3K/AKT signaling pathway, establishing it as a potential therapeutic target for HCC.

PDCL3 is a prognostic biomarker associated with immune infiltration in hepatocellular carcinoma

BackgroundPhosducin-like 3 (PDCL3) is a member of the photoreceptor family, characterized by a thioredoxin-like structural domain and evolutionary conservation. It plays roles in angiogenesis and apoptosis. Despite its significance, research on the biological role of PDCL3 in liver hepatocellular carcinoma (LIHC) remains limited. This study aims to explore the prognostic value and potential mechanisms of PDCL3 in cancer, particularly in LIHC, through bioinformatics analysis.MethodsRNA-seq data and corresponding clinical information for pan-cancer and LIHC were extracted from the TCGA database to analyze PDCL3 expression and survival prognosis. Differential expression of PDCL3 was analyzed using the HPA database. GO and KEGG enrichment analysis were performed for PDCL3-associated genes. The relationship between PDCL3 expression and various immune cell types was examined using the TIMER website. Clinical samples were collected, and immunohistochemistry and immunofluorescence experiments were conducted to validate the differential expression of PDCL3 in LIHC and normal tissues. In vitro assays, including CCK-8, wound healing, Transwell, and colony formation experiments, were employed to determine the biological functions of PDCL3 in LIHC cells.ResultsAnalysis from TIMER, GEPIA, UALCAN, and HPA databases revealed differential expression of PDCL3 in various tumors. Prognostic analysis from GEPIA and TCGA databases indicated that high PDCL3 expression was associated with poorer clinical staging and prognosis in LIHC. Enrichment analysis of PDCL3-associated genes revealed its involvement in various immune responses. TCGA and TIMER databases showed that high PDCL3 expression in LIHC decreased tumor immune activity by reducing macrophage infiltration. PDCL3 exhibited positive correlations with multiple immune checkpoint genes. Immunohistochemistry (IHC) and immunofluorescence (IF) experiments confirmed elevated PDCL3 expression in LIHC tissues compared to adjacent normal tissues. In vitro experiments demonstrated that PDCL3 promoted LIHC cell proliferation, migration, invasion, and colony-forming ability.ConclusionPDCL3 is highly expressed in various cancer types. Our study suggests that elevated PDCL3 expression in hepatocellular carcinoma is associated with poorer prognosis and may serve as a potential diagnostic biomarker for LIHC. PDCL3 may regulate the biological functions of LIHC by modulating immune infiltration. However, the precise regulatory mechanisms of PDCL3 in cancer warrant further investigation.

Nuclear respiratory factor 1 regulates super enhancer-controlled SPIDR to protect hepatocellular carcinoma cells from oxidative stress

BackgroundCellular response to oxidative stress plays significant roles in hepatocellular carcinoma (HCC) development, yet the exact mechanism by which HCC cells respond to oxidative stress remains poorly understood. This study aimed to investigate the role and mechanism of super enhancer (SE)-controlled genes in oxidative stress response of HCC cells.MethodsThe GSE112221 dataset was used to identify SEs by HOMER. Functional enrichment of SE-controlled genes was performed by Metascape. Transcription factors were predicted using HOMER. Prognosis analysis was conducted using the Kaplan-Meier Plotter website. Expression correlation analysis was performed using the Tumor Immune Estimation Resource web server. NRF1 and SPIDR expression in HCC and normal liver tissues was analyzed based on the TCGA-LIHC dataset. ChIP-qPCR was used to detect acetylation of lysine 27 on histone 3 (H3K27ac) levels of SE regions of genes, and the binding of NRF1 to the SE of SPIDR. To mimic oxidative stress, HepG2 and Hep3B cells were stimulated with H2O2. The effects of NRF1 and SPIDR on the oxidative stress response of HCC cells were determined by the functional assays.ResultsA total of 318 HCC-specific SE-controlled genes were identified. The functions of these genes was significant association with oxidative stress response. SPIDR and RHOB were enriched in the “response to oxidative stress” term and were chosen for validation. SE regions of SPIDR and RHOB exhibited strong H3K27ac modification, which was significantly inhibited by JQ1. JQ1 treatment suppressed the expression of SPIDR and RHOB, and increased reactive oxygen species (ROS) levels in HCC cells. TEAD2, TEAD3, NRF1, HINFP and TCFL5 were identified as potential transcription factors for HCC-specific SE-controlled genes related to oxidative stress response. The five transcription factors were positively correlated with SPIDR expression, with the highest correlation coefficient for NRF1. NRF1 and SPIDR expression was up-regulated in HCC tissues and cells. NRF1 activated SPIDR transcription by binding to its SE. Silencing SPIDR or NRF1 significantly promoted ROS accumulation in HCC cells. Under oxidative stress, silencing SPIDR or NRF1 increased ROS, malondialdehyde (MDA) and γH2AX levels, and decreased superoxide dismutase (SOD) levels and cell proliferation of HCC cells. Furthermore, overexpression of SPIDR partially offset the effects of NRF1 silencing on ROS, MDA, SOD, γH2AX levels and cell proliferation of HCC cells.ConclusionNRF1 driven SPIDR transcription by occupying its SE, protecting HCC cells from oxidative stress-induced damage. NRF1 and SPIDR are promising biomarkers for targeting oxidative stress in the treatment of HCC.

Exploration of YBX1 role in the prognostic value and immune characteristics by single-cell and bulk sequencing analysis for liver hepatocellular carcinoma.

Y-box binding protein 1 (YBX1) plays a variety of roles in progression of multiple tumors. However, the role of YBX1 in prognostic value and immune regulation for liver hepatocellular carcinoma (LIHC) remains unclear. The present study aimed to examine the effect of YBX1 on the regulation of tumor immunity and survival prediction in LIHC patients. YBX1-related expression profiles and single-cell and bulk sequencing analysis were performed using online databases. YBX1 expression was validated by a quantitative real-time PCR (qRT-PCR), western blotting and immunohistochemistry. Univariate/multivariate Cox regression analysis was performed to determine independent predictors of overall survival (OS). The ESTIMATE (i.e., Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis were used to assess the relationships between YBX1 and LIHC immunity. YBX1 was over-expressed in LIHC tissues and cell lines. High YBX1 expression was significantly associated with poor OS. Univariate/multivariate Cox regression analysis revealed that YBX1 was an independent prognostic factor for LIHC. Gene set enrichment analysis revealed that YBX1 was associated with multiple signaling pathways correlated to LIHC. Additionally, YBX1 was expressed in multiple immune cells and was significantly correlated with immune cells, immune checkpoint markers and tumor immune microenvironment. The TIDE analysis demonstrated that LIHC patients with high YBX1 expression showed a higher T-cell dysfunction score and a higher exclusion score, as well as poorer immunotherapy response. YBX1 plays crucial oncogenic roles in LIHC and is closely associated with the immune defense system. YBX1 inhibition may serve as a potential treatment for LIHC.

AMPKα2 promotes tumor immune escape by inducing CD8+ T-cell exhaustion and CD4+ Treg cell formation in liver hepatocellular carcinoma

BackgroundAdenosine monophosphate-activated protein kinase (AMPK) is associated with the development of liver hepatocellular carcinoma (LIHC). AMPKα2, an α2 subunit of AMPK, is encoded by PRKAA2, and functions as the catalytic core of AMPK. However, the role of AMPKα2 in the LIHC tumor immune environment is unclear.MethodsRNA-seq data were obtained from the Cancer Genome Atlas and Genotype-Tissue Expression databases. Using the single-cell RNA-sequencing dataset for LIHC obtained from the China National Genebank Database, the communication between malignant cells and T cells in response to different PRKAA2 expression patterns was evaluated. In addition, the association between PRKAA2 expression and T-cell evolution during tumor progression was explored using Pseudotime analysis, and the role of PRKAA2 in metabolic reprogramming was explored using the R “scMetabolis” package. Functional experiments were performed in LIHC HepG2 cells.ResultsAMPK subunits were expressed in tissue-specific and substrate-specific patterns. PRKAA2 was highly expressed in LIHC tissues and was associated with poor patient prognosis. Tumors with high PRKAA2 expression displayed an immune cold phenotype. High PRKAA2 expression significantly promoted LIHC immune escape. This result is supported by the following evidence: 1) the inhibition of major histocompatibility complex class I (MHC-I) expression through the regulation of interferon-gamma activity in malignant cells; 2) the promotion of CD8+ T-cell exhaustion and the formation of CD4+ Treg cells in T cells; 3) altered interactions between malignant cells and T cells in the tumor immune environment; and 4) induction of metabolic reprogramming in malignant cells.ConclusionsOur study indicate that PRKAA2 may contribute to LIHC progression by promoting metabolic reprogramming and tumor immune escape through theoretical analysis, which offers a theoretical foundation for developing PRKAA2-based strategies for personalized LIHC treatment.

Kinetic 18F-FDG PET/CT imaging of hepatocellular carcinoma: a dual input four-compartment model.

The endoplasmic reticulum plays an important role in glucose metabolism and has not been explored in the kinetic estimation of hepatocellular carcinoma (HCC) via 18F-fluoro-2-deoxy-D-glucose PET/CT. A dual-input four-compartment (4C) model, regarding endoplasmic reticulum was preliminarily used for kinetic estimation to differentiate 28 tumours from background liver tissue from 24 patients with HCC. Moreover, parameter images of the 4C model were generated from one patient with negative findings on conventional metabolic PET/CT. Compared to the dual-input three-compartment (3C) model, the 4C model has better fitting quality, a close transport rate constant (K1) and a dephosphorylation rate constant (k6/k4), and a different removal rate constant (k2) and phosphorylation rate constant (k3) in HCC and background liver tissue. The K1, k2, k3, and hepatic arterial perfusion index (HPI) from the 4C model and k3, HPI, and volume fraction of blood (Vb) from the 3C model were significantly different between HCC and background liver tissues (all P < 0.05). Meanwhile, the 4C model yielded additional kinetic parameters for differentiating HCC. The diagnostic performance of the top ten genes from the most to least common was HPI(4C), Vb(3C), HPI(3C), SUVmax, k5(4C), k3(3C), k2(4C), v(4C), K1(4C) and Vb(4C). Moreover, a patient who showed negative findings on conventional metabolic PET/CT had positive parameter images in the 4C model. The 4C model with the endoplasmic reticulum performed better than the 3C model and produced additional useful parameters in kinetic estimation for differentiating HCC from background liver tissue.

Knockdown of liver cancer cell-secreted exosomal PSMA5 controls macrophage polarization to restrain cancer progression by blocking JAK2/STAT3 signaling.

Tumor-associated macrophages, a major component of the tumor microenvironment, undergo polarization into M2 macrophages (M2), and thereby exert an immunosuppressive effect to induce cancer metastasis. This study strives to uncover a molecular mechanism underlying this event in hepatocellular carcinoma (HCC). Proteasome subunit alpha 5 (PSMA5) expression in liver hepatocellular carcinoma (LIHC) tissues and its association with LIHC patients were predicted using StarBase. PSMA5 level in human HCC cells was manipulated via transfection. Exosomes were isolated from HCC cells, and internalized into macrophages which were cocultured with HCC cells. Exosome internalization was observed after fluorescence labeling. HCC cell migration and invasion were evaluated by wound healing and Transwell assays. Xenograft assay was performed to investigate the role of PSMA5 in in vitro tumorigenesis. M2 polarization was assessed by enzyme-linked immunosorbent assay,quantitative reverse transcription polymerase chain reaction,and immunohistochemistry. PSMA5 expression in exosomes and Janus Kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) activation in macrophages and tumors were detected by Western blot analysis. High PSMA5 expression was observed in LIHC tissues and associated with compromised survival of LIHC patients. PSMA5 knockdown inhibited HCC cell migration and invasion. PSMA5 knockdown in HCC cells downregulated PSMA5 level in exosomes from these HCC cells. HCC cell-isolated exosomes were successfully internalized into macrophages, and facilitated M2 polarization and JAK2/STAT3 pathway activation. HCC cell-secreted exosomal PSMA5 knockdown inhibited the exosome-induced effect on macrophages, and attenuated the promotion induced by exosome-treated macrophages on HCC cell migration/invasion and tumorigenesis along with in vivo M2 polarization and JAK2/STAT3 pathway activation. HCC cell-secreted exosomal PSMA5 knockdown hinders M2 polarization to suppress cancer progression by restraining JAK2/STAT3 signaling.

Fanconi Anemia Complementary Group A (FANCA) Facilitates the Occurrence and Progression of Liver Hepatocellular Carcinoma.

Liver hepatocellular carcinoma (LIHC) is a serious liver disease worldwide, and its pathogenesis is complicated. This study investigated the potential role of FANCA in the advancement and prognosis of LIHC. Public databases, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blot(WB)and immunohistochemistry (IHC) were employed to measure FANCA expression between tumor and normal samples. The relationship between FANCA expression and prognosis of LIHC patients were examined. Functional enrichment of FANCA-related genes was performed. Furthermore, univariate and multivariate analyses were conducted to determine the independent prognosis value of FANCA in LIHC. Finally, influence of FANCA knockout on the proliferation, migration, and invasion of HepG2 cell was validated with cloning formation, CCK8, and Transwell assays. Expression analysis presented that FANCA had high expression level in LIHC tissues and cells. Receiver operating characteristic (ROC) curve analysis showed that FANCA was of great diagnosis value in LIHC. Clinicopathological analysis revealed that FANCA was significantly greater expressed in the advanced stage than in the early stage of LIHC. Univariate, multivariate, and Kaplan-Meier survival analysis confirmed that high expression of FANCA was strongly associated with poor survival of LIHC patients. In addition, high level of FANCA in LIHC showed a negative association with immunoinfiltrated B cells, T cells, and stromal scores. Moreover, Knockout of FANCA significantly inhibited HepG2 cell proliferative activity, migration, and invasion ability. Our data revealed that high level of FANCA was closely associated with LIHC malignant progression, suggesting its potential utility as a diagnostic, predictive indicator, and therapeutic target.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma

Background & aimsThe present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model.MethodsWe recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison.ResultsSeven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973–0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835– 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively).ConclusionCT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

EPS15-AS1 Inhibits AKR1B1 Expression to Enhance Ferroptosis in Hepatocellular Carcinoma Cells.

Epidermal growth factor receptor substrate 15 (EPS15) is part of the EGFR pathway and has been implicated in various tumorigenesis. Increasing evidence suggests that long noncoding RNA (lncRNA) plays an essential role in liver hepatocellular carcinoma (LIHC) by regulating the expression of proteins and genes. Through analysis of the cancer genome atlas (TCGA) database, we found that EPS15 is highly expressed in LIHC tissue, and lncRNA EPS15-antisense1 (EPS15-AS1) decreased in LIHC cell lines. However, the function of EPS15-AS1 in LIHC is still unknown. When EPS15-AS1 was overexpressed in HepG2 cell lines, the expression of EPS15 was reduced and cell activity and invasiveness were inhibited. In addition, we observed an increase in Fe2+ ion and lipid peroxidation after overexpression of EPS15-AS1, and further analysis showed that the susceptibility to ferroptosis increased. Aldo-keto reductase family 1 member B 1 (AKR1B1) belongs to the aldo/keto reductase superfamily and is involved in maintaining the cellular redox balance. Survival analysis revealed that patients with a higher level of AKR1B1 have a lower survival rate in the TCGA database. We also found that EPS15 enhanced the AKR1B1 expression in LIHC, and AKR1B1 had the ability to promote cell invasiveness. Moreover, overexpression of AKR1B1 alleviated the promoting effect of EPS15-AS1 on ferroptosis. Therefore, EPS15-AS1 can induce ferroptosis in hepatocellular carcinoma cells by inhibiting the expression of EPS15 and AKR1B1 and disrupting the redox balance. EPS15 and AKR1B1 may serve as biomarkers for diagnosis and lncRNA EPS15-AS1 potential drug for LIHC.

Improving HCC Prognostic Models after Liver Resection by AI-Extracted Tissue Fiber Framework Analytics.

Despite advances in diagnostic and treatment technologies, predicting outcomes of patients with hepatocellular carcinoma (HCC) remains a challenge. Prognostic models are further obscured by the variable impact of the tumor properties and the remaining liver parenchyma, often affected by cirrhosis or non-alcoholic fatty liver disease that tend to precede HCC. This study investigated the prognostic value of reticulin and collagen microarchitecture in liver resection samples. We analyzed 105 scanned tissue sections that were stained using a Gordon and Sweet's silver impregnation protocol combined with Picric Acid-Sirius Red. A convolutional neural network was utilized to segment the red-staining collagen and black linear reticulin strands, generating a detailed map of the fiber structure within the HCC and adjacent liver tissue. Subsequent hexagonal grid subsampling coupled with automated epithelial edge detection and computational fiber morphometry provided the foundation for region-specific tissue analysis. Two penalized Cox regression models using LASSO achieved a concordance index (C-index) greater than 0.7. These models incorporated variables such as patient age, tumor multifocality, and fiber-derived features from the epithelial edge in both the tumor and liver compartments. The prognostic value at the tumor edge was derived from the reticulin structure, while collagen characteristics were significant at the epithelial edge of peritumoral liver. The prognostic performance of these models was superior to models solely reliant on conventional clinicopathologic parameters, highlighting the utility of AI-extracted microarchitectural features for the management of HCC.

METTL3 facilitates stemness properties and tumorigenicity of cancer stem cells in hepatocellular carcinoma through the SOCS3/JAK2/STAT3 signaling pathway.

Liver cancer stem cells (LCSCs) contribute to tumor recurrence and cancer cell proliferation in patients with hepatocellular carcinoma (HCC). METTL3-catalyzed m6A modification is relevant to the cancer stem cell (CSC) phenotype, including LCSCs. LCSCs were isolated from MHCC-97H and HepG2 cells through flow cytometry. UALCAN data were used to analyze the expression of METTL3 in liver hepatocellular carcinoma (LIHC) tissues. Loss- and gain-of-function experiments were utilized to assess the biological effects of METTL3 and SOCS3 on the proliferation and stemness phenotypes in vitro and in vivo. The mechanisms underlying the impact of METTL3 were explored using qPCR, MeRIP-qPCR, dual-luciferase reporter, and western blot assays. METTL3 was significantly upregulated in LIHC tissues according to the UALCAN database. METTL3 was highly expressed in LIHC and was significantly correlated with individual cancer stage, tumor grade and lymph node metastasis. Patients with low METTL3 expression had a longer overall survival time based on the data from UALCAN. In addition, the level of METTL3 was enhanced in LCSCs and decreased in non-LCSCs compared to HCC cells. Moreover, overexpression of METTL3 stimulated the proliferation and stemness of LCSCs in vitro and in vivo, while loss of METTL3 impeded it. Bioinformatics analysis combined with validation experiments determined that m6A was modified by METTL3-targeting SOCS3 mRNA. METTL3 had side effects regarding the stability of SOCS3 mRNA. SOCS3 overexpression impaired and SOCS3 depletion facilitated the development of LCSCs via the JAK2/STAT3 pathway. Furthermore, METTL3 depletion suppressed proliferation and stemness in LCSCs, which was restored by SOCS3 knockdown or colivelin treatment. We discovered that METTL3 facilitated the stemness and tumorigenicity of LCSCs by modifying SOCS3 mRNA with m6A.

Value of TMEM115 as a Potential Prognostic Biomarker in Liver Hepatocellular Carcinoma

Transmembrane protein 115 (TMEM115) is a membrane protein; considering the potential of membrane proteins as biomarkers for various pathological conditions, we aimed to examine the value of TMEM115 as a potential biomarker for improving the prognosis and treatment of liver hepatocellular carcinoma (LIHC) in this study. Online databases including the Tumor Immune Estimation Resource, UALCAN, Gene Expression Profiling Interactive Analysis version 2, OSlihc, and human Protein Atlas were used. The analysis suggested that TMEM115 expression in LIHC was higher compared to normal tissues; further, TMEM115 expression was confirmed to be related with poor prognosis in LIHC. Higher protein expression levels of TMEM115 were observed in LIHC tissues than in normal tissues. Tumor infiltration by immune cells was confirmed to be correlated with the expression of TMEM115. High TMEM115 expression and immune cell infiltration were further related to poor prognosis in LIHC. We also confirmed a correlation between TMEM115 and TP53 mutations. In conclusion, we confirmed that TMEM115 expression is correlated with tumor-infiltrating immune cells and poor prognosis in LIHC and that TMEM115 shows potential as a prognostic biomarker in LIHC.

EMT-related gene risk model establishment for prognosis and drug treatment efficiency prediction in hepatocellular carcinoma

This study was designed to evaluate the prognosis and pharmacological therapy sensitivity of epithelial mesenchymal transition-related genes (EMTRGs) that obtained from the EMTome database in hepatocellular carcinoma (HCC) using bioinformatical method. The expression status of EMTRGs were also investigated using the clinical information of HCC patients supported by TCGA database and the ICGC database to establish the TCGA cohort as the training set and the ICGC cohort as the validation set. Analyze the EMTRGs between HCC tissue and liver tissue in the TCGA cohort in the order of univariate COX regression, LASSO regression, and multivariate COX regression, and construct a risk model for EMTRGs. In addition, enrichment pathways, gene mutation status, immune infiltration, and response to drugs were also analyzed in the high-risk and low-risk groups of the TCGA cohort, and the protein expression status of EMTRGs was verified. The results showed a total of 286 differentially expressed EMTRGs in the TCGA cohort, and EZH2, S100A9, TNFRSF11B, SPINK5, and CCL21 were used for modeling. The TCGA cohort was found to have a worse outcome in the high-risk group of HCC patients, and the ICGC cohort confirmed this finding. In addition, EMTRGs risk score was shown to be an independent prognostic factor in both cohorts by univariate and multivariate COX regression. The results of GSEA analysis showed that most of the enriched pathways in the high-risk group were associated with tumor, and the pathways enriched in the low-risk group were mainly associated with metabolism. Patients in various risk groups had varying immunological conditions, and the high-risk group might benefit more from targeted treatments. To sum up, the EMTRGs risk model was developed to forecast the prognosis for HCC patients, and the model might be useful in assisting in the choice of treatment drugs for HCC patients.

Association of elevated autoantibody to high expression of GNAS in hepatocellular carcinoma

PurposeThis study was based on hepatocellular carcinoma (HCC) patients of early-stage to explore the diagnostic capability and possible production causes of anti-GNAS autoantibody. MethodsWe evaluated the frequency of anti-GNAS autoantibody in sera from patients with early-stage HCC by enzyme-linked immunosorbent assay (ELISA) and the expression of GNAS protein in early-stage HCC tissues by immunohistochemistry. Western blotting (WB) and real-time polymerase chain reaction (RT-PCR) were utilized to examine the expressions of GNAS protein and mRNA in cell lines. GEO and International Cancer Genome Consortium (ICGC) databases were inquired to explore mRNA expression and mutation of GNAS in HCC tissues. ResultsThe positive rates of anti-GNAS autoantibody in HCC patients at clinical stage I (78.1 %) and clinical stage II (57.1 %) were all significantly higher than that in healthy control (20 %). There was also a significant difference in GNAS protein expression between HCC and its adjacent normal liver tissues. The results from WB and RT-PCR showed a significant difference at the mRNA level but no statistical difference at the protein level between HCC and normal liver cell lines. The difference in mRNA level between HCC and adjacent normal liver tissues was verified to be significant. Furthermore, the ICGC database demonstrated a 10.6 % mutation frequency for GNAS in HCC patients. ConclusionThe coordination of elevated anti-GNAS autoantibody, high expression of GNAS in the mRNA and protein levels in HCC, and high frequency of GNAS mutation indicates that anti-GNAS autoantibody may be used as an early indicator of HCC.

Development and validation of multi-omic prognostic signature of anoikis-related genes in liver hepatocellular carcinoma.

Liver hepatocellular carcinoma (LIHC) is characterized by high morbidity, rapid progression and early metastasis. Although many efforts have been made to improve the prognosis of LIHC, the situation is still dismal. Inability to initiate anoikis process is closely associated with cancer proliferation and metastasis, affecting patients' prognosis. In this study, a corresponding gene signature was constructed to comprehensively assess the prognostic value of anoikis-related genes (ARGs) in LIHC. Using TCGA-LIHC dataset, the mRNA levels of the differentially expressed ARGs in LIHC and normal tissues were compared by Student t test. And prognostic ARGs were identified through Cox regression analysis. Prognostic signature was established and then externally verified by ICGC-LIRI-JP dataset and GES14520 dataset via LASSO Cox regression model. Potential functions and mechanisms of ARGs in LIHC were evaluated by functional enrichment analyses. And the immune infiltration status in prognostic signature was analyzed by ESTIMATE algorithm and ssGSEA algorithm. Furthermore, ARGs expression in LIHC tissues was validated via qRT-PCR and IHC staining from the HPA website. A total of 97 differentially expressed ARGs were detected in LIHC tissues. Functional enrichment analysis revealed these genes were mainly involved in MAP kinase activity, apoptotic signaling pathway, anoikis and PI3K-Akt signaling pathway. Afterward, the prognostic signature consisting of BSG, ETV4, EZH2, NQO1, PLK1, PBK, and SPP1 had a moderate to high predictive accuracy and served as an independent prognostic indicator for LIHC. The prognostic signature was also applicable to patients with distinct clinical parameters in subgroup survival analysis. And it could reflect the specific immune microenvironment in LIHC, which indicated high-risk group tended to profit from ICI treatment. Moreover, qRT-PCR and IHC staining showed increasing expression of BSG, ETV4, EZH2, NQO1, PLK1, PBK and SPP1in LIHC tissues, which were consistent to the results from TCGA database. The current study developed a novel prognostic signature comprising of 7 ARGs, which could stratify the risk and effectively predict the prognosis of LIHC patients. Furthermore, it also offered a potential indicator for immunotherapy of LIHC.

Genome-Wide Extrachromosomal Circular DNA Profiling of Paired Hepatocellular Carcinoma and Adjacent Liver Tissues.

Hepatocellular carcinoma (HCC) develops through multiple mechanisms. While recent studies have shown the presence of extrachromosomal circular DNA (eccDNA) in most cancer types, the eccDNA expression pattern and its association with HCC remain obscure. We aimed to investigate this problem. The genome-wide eccDNA profiles of eight paired HCC and adjacent non-tumor tissue samples were comprehensively elucidated based on Circle-seq, and they were further cross-analyzed with the RNA sequencing data to determine the association between eccDNA expression and transcriptome dysregulation. A total of 60,423 unique eccDNA types were identified. Most of the detected eccDNAs were smaller than 1 kb, with a length up to 182,363 bp and a mean sizes of 674 bp (non-tumor) and 813 bp (tumor), showing a greater association with gene-rich rather than with gene-poor regions. Although there was no statistical difference in length and chromosome distribution, the eccDNA patterns between HCC and adjacent non-tumor tissues showed significant differences at both the chromosomal and single gene levels. Five of the eight HCC tissues showed significantly higher amounts of chromosome 22-derived eccDNA expression compared to the non-tumor tissue. Furthermore, two genes, SLC16A3 and BAIAP2L2, with a higher transcription level in tumor tissues, were related to eccDNAs exclusively detected in three HCC samples and were negatively associated with survival rates in HCC cohorts from public databases. These results indicate the existence and massive heterogeneity of eccDNAs in HCC and adjacent liver tissues, and suggest their potential association with dysregulated gene expression.