Hepatocellular Carcinoma Liver Tissues Research Articles

DNA methylation biomarker panels for differentiating various liver adenocarcinomas, including hepatocellular carcinoma, cholangiocarcinoma, colorectal liver metastases and pancreatic adenocarcinoma liver metastases

BackgroundDNA methylation biomarkers are one of the most promising tools for the diagnosis and differentiation of adenocarcinomas of the liver, which are among the most common malignancies worldwide. Their differentiation is important because of the different prognoses and treatment options. This study aimed to validate previously identified DNA methylation biomarkers that successfully differentiate between liver adenocarcinomas, including the two most common primary liver cancers, hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), as well as two common metastatic liver cancers, colorectal liver metastases (CRLM) and pancreatic ductal adenocarcinoma liver metastases (PCLM), and translate them to the methylation-sensitive high-resolution melting (MS-HRM) and digital PCR (dPCR) platforms.MethodsOur study included a cohort of 149 formalin-fixed, paraffin-embedded tissue samples, including 19 CRLMs, 10 PCLMs, 15 HCCs, 15 CCAs, 15 colorectal adenocarcinomas (CRCs), 15 pancreatic ductal adenocarcinomas (PDACs) and their paired normal tissue samples. The methylation status of the samples was experimentally determined by MS-HRM and methylation-specific dPCR. Previously determined methylation threshold were adjusted according to dPCR data and applied to the same DNA methylation array datasets (provided by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)) used to originally identify the biomarkers for the included cancer types and additional CRLM projects. The sensitivities, specificities and diagnostic accuracies of the panels for individual cancer types were calculated.ResultsIn the dPCR experiment, the DNA methylation panels identified HCC, CCA, CRC, PDAC, CRLM and PCLM with sensitivities of 100%, 66.7%, 100%, 86.7%, 94.7% and 80%, respectively. The panels differentiate between HCC, CCA, CRLM, PCLM and healthy liver tissue with specificities of 100%, 100%, 97.1% and 94.9% and with diagnostic accuracies of 100%, 94%, 97% and 93%, respectively. Reevaluation of the same bioinformatic data with new additional CRLM projects demonstrated that the lower dPCR methylation threshold still effectively differentiates between the included cancer types. The bioinformatic data achieved sensitivities for HCC, CCA, CRC, PDAC, CRLM and PCLM of 88%, 64%, 97.4%, 75.5%, 80% and 84.6%, respectively. Specificities between HCC, CCA, CRLM, PCLM and healthy liver tissue were 98%, 93%, 86.6% and 98.2% and the diagnostic accuracies were 94%, 91%, 86% and 98%, respectively. Moreover, we confirmed that the methylation of the investigated promoters is preserved from primary CRC and PDAC to their liver metastases.ConclusionsThe cancer-specific methylation biomarker panels exhibit high sensitivities, specificities and diagnostic accuracies and enable differentiation between primary and metastatic adenocarcinomas of the liver using methylation-specific dPCR. High concordance was achieved between MS-HRM, dPCR and bioinformatic data, demonstrating the successful translation of bioinformatically identified methylation biomarkers from the Illumina Infinium HumanMethylation450 BeadChip (HM450) and lllumina MethylationEPIC BeadChip (EPIC) platforms to the simpler MS-HRM and dPCR platforms.

The impact of cathepsins on liver hepatocellular carcinoma: Insights from genetic and functional analyses

Liver Hepatocellular Carcinoma (LIHC), ranked as the second deadliest cancer globally, poses a major health challenge because of its widespread occurrence and poor prognosis. The mechanisms underlying LIHC development and progression remain unclear. Cathepsins are linked to tumorigenesis in other cancers, but their role in LIHC is underexplored. This study employed integrative analyses, including Mendelian Randomization (MR), bulk RNA-sequencing (bulk-seq), single-cell RNA sequencing (scRNA-seq), immunohistochemical (IHC) analysis, and cellular experiments with siRNA technology, to investigate the role of cathepsin E (CTSE) in LIHC. MR analysis identified CTSE as a factor associated with increased LIHC risk. Prognostic analysis using TCGA data showed that higher CTSE levels are linked to poorer survival, establishing CTSE as an independent prognostic risk factor. Integrative transcriptome analysis revealed close relation of CTSE to the extracellular matrix. scRNA-seq from TISCH2 demonstrated that CTSE is predominantly expressed in malignant LIHC cells. IHC confirmed higher CTSE expression in LIHC tissues compared to peritumoral tissues. Functional assays, such as qRT-PCR, Western blot, cell proliferation, and colony formation experiments, demonstrated that siRNA-mediated CTSE knockdown in HepG2 and Huh7 cell lines notably suppressed cell proliferation and altered the FAK/Paxillin/Akt signaling cascade. This research enhances our comprehension of LIHC development, emphasizing CTSE as a promising prognostic marker and potential therapeutic target. Inhibiting CTSE could slow the progression of LIHC, presenting novel opportunities for therapeutic approaches.

Intratumoural microorganism can affect the progression of hepatocellular carcinoma.

Several recent studies have confirmed that intratumoural microorganisms can affect the occurrence and development of hepatocellular carcinoma (HCC); however, their role in tumor progression remains unclear. Hence, there is a need for further research on the role of intratumoural microorganisms in HCC. To investigate the changes in intratumoural microorganisms in HCC and the effect of Propionibacterium on HCC progression. HCC and normal liver tissue specimens were subjected to fluorescence in situ hybridization (FISH). After performing 16S rRNA sequencing on HCC and peritumoral tissues to analyze the differences between the two groups. Propionibacterium was cocultured with HCC cells in vitro. Changes in cell proliferation and migration capacity were evaluated. The expression of NF-κB pathway related proteins in tumor cells was compared. The orthotopic liver implantation model and the subcutaneous xenograft model were constructed. liver tissues and subcutaneous tumors were collected 2 weeks later. FISH demonstrated the presence of microorganisms in HCC and normal liver tissues. 16S rRNA sequencing revealed an abundance of Lysobacter, Lachnospiraceae, Pseudomonas, and Lactobacillus in HCC tissues. The distribution and abundance of Propionibacterium showed differences between HCC and peritumoral tissues (P < 0.05). In vitro studies demonstrated that Propionibacterium and its metabolite propionic acid (PA) inhibited the proliferation and migration of HCC cells (P < 0.05). The expression of the proteins in NF-κB signaling pathway also decreased in HCC cells (P < 0.05). Microorganisms in HCC and normal liver tissues displayed significant disparities. The PA-producing bacterium Propionibacterium in HCC exerts an effect on the NF-κB pathway, thereby affecting the biological behavior of HCC.

Study on the expression and prognostic relationship of MYL6B in liver cancer based on bioinformatics.

Primary liver cancer is a prevalent and deadly cancer type. Despite treatment advances, prognosis remains poor, with high recurrence rates. Early detection is crucial but challenging due to the disease's insidious nature. Myosin proteins play significant roles in cancer development, influencing cell migration, invasion, and tumor suppression. MYL6B, a myosin light chain, is involved in various cellular processes and has been associated with poor prognosis in colorectal adenocarcinoma and potential as a biomarker in breast cancer. To investigate the expression of MYL6B in liver hepatocellular carcinoma (LIHC) and its impact on prognosis and potential mechanisms of action using bioinformatics methods. The expression of MYL6B in pan-cancer and normal tissues was analyzed using the gene expression profiling interactive analysis 2 and tumor immune estimation resource databases. The expression level of MYL6B in LIHC tissues and its relationship with prognosis were analyzed, immunohistochemical analysis of MYL6B and its effect on immune cell infiltration, and the protein network were further studied. MYL6B was highly expressed in diffuse large b-cell lymphoma, LIHC, pancreatic adenocarcinoma, skin cutaneous melanoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, and lowly expressed in kidney chromophobe, acute myeloid leukemia, testicular germ cell tumors. The expression level of MYL6B was significantly different between cancer and normal tissues. It had a significant impact on both overall survival and disease-free survival. MYL6B is highly expressed in hepatocellular carcinoma and its expression level increases with cancer progression. High MYL6B expression is associated with poor prognosis in terms of overall survival and recurrence-free survival. The immunohistochemical level of MYL6B is high in hepatocellular carcinoma tissues, and MYL6B has a high level of immune infiltration inflammation. In protein network analysis, MYL6B is correlated with MYL2, MYL6, MYL9, MYLK4, MYLK2, MYL12A, MYL12B, MYH11, MYH9 and MYH10. The expression level of MYL6B in LIHC was significantly higher than in normal liver tissues, and it was correlated with the degree of differentiation survival rate, and immune infiltration. MYL6B is a potential target for LIHC treatment.

Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images.

Liver cancer, a leading cause of cancer mortality, is often diagnosed by analyzing the grayscale variations in liver tissue across different computed tomography (CT) images. However, the intensity similarity can be strong, making it difficult for radiologists to visually identify hepatocellular carcinoma (HCC) and metastases. It is crucial for the management and prevention strategies to accurately differentiate between these two liver cancers. This study proposes an automated system using a convolutional neural network (CNN) to enhance diagnostic accuracy to detect HCC, metastasis, and healthy liver tissue. This system incorporates automatic segmentation and classification. The liver lesions segmentation model is implemented using residual attention U-Net. A 9-layer CNN classifier implements the lesions classification model. Its input is the combination of the results of the segmentation model with original images. The dataset included 300 patients, with 223 used to develop the segmentation model and 77 to test it. These 77 patients also served as inputs for the classification model, consisting of 20 HCC cases, 27 with metastasis, and 30 healthy. The system achieved a mean Dice score of in segmentation and a mean accuracy of in classification, both in the test phase. The proposed method is a preliminary study with great potential in helping radiologists diagnose liver cancers.

Acetazolamide suppresses the progression of hepatocellular carcinoma induced by diethylnitrosamine in Wistar albino rats.

Hepatocellular carcinoma (HCC) continues to be the most prevalent type of liver cancer worldwide. Diethylnitrosamine (DEN)-induced HCC is an extensively used hepatic cancer model in experimental animals. Acetazolamide (AZA) is a carbonic anhydrase enzyme inhibitor. This study aimed to assess the therapeutic mechanism of AZA against DEN-induced HCC. Thirty male Wistar albino rats were divided equally into three groups. Group I (C): control group, Group II (HCC): DEN-induced HCC, and Group III (HCC/AZA): AZA-treated HCC. Verification of the HCC induced by DEN was confirmed by elevated liver enzymes' activities, and increased α-fetoprotein (AFP) levels, as well as distinct liver architecture changes. On the other hand, the AZA-treated HCC group experienced decreases in the activities of serum liver enzymes and AFP levels, as well as, regulated liver architecture. Additionally, it downregulated p-p38 MAPK/p-JNK1/JNK2/p-C-Jun/p-NF-κB p65 protein expressions. Moreover, it ameliorated autophagy by controlling the expression of the p-AMPK/p-mTOR1/LC3 I/II proteins. Furthermore, it downregulated the relative gene expressions of carbonic anhydrase-IX (CAIX) and hexokinase-II (HKII). Histopathological examination of AZA-treated HCC liver tissues supported these findings. Conclusion: AZA provides a new dimension in ameliorating experimentally induced HCC through regulation of hepatic biomarkers, antioxidant status, inflammatory markers, and autophagy, mediated by amelioration of CAIX and HKII gene expressions.

NFKBIE is a predictive factor of survival and is correlated with immune infiltration and antigen processing and presentation in hepatocellular carcinoma.

The important role of the nuclear factor κB (NFκB) pathway in tumour development has long been recognized; however, the role of the NFκB inhibitor family in liver cancer has not been elucidated. Hepatocellular carcinoma (HCC) is a serious public health burden with a high incidence, poor prognosis, and early detection, especially in Asia, where hepatitis is prevalent. In the present study, the mRNA expression level of the NFκB inhibitor family was assessed in HCC and normal tissues using the Metabolic Gene Rapid Visualizer, University of Alabama at Birmingham Cancer Data Analysis Portal, and the Tumor Immune Estimation Resource database (TIMER). Survival curves of nuclear factor of κ light polypeptide gene enhancer in B-cells inhibitor (NFKBI)E were obtained using the Kaplan-Meier method. Genes co-expressed with NFKBIE in HCC samples were studied using data from the LinkedOmics and the Hepatocellular Carcinoma Databases. Protein-protein interaction networks, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment pathway analyses were used to assess the NFKBIE mechanism in HCC. Using the TIMER database, the association between immune infiltration and NFKBIE was determined. RNA-sequencing (RNA-seq) was used to evaluate the function of NFKBIE in HCC and its impact on proliferation and migration. Western blotting was used to confirm the expression of NFKBIE in HCC cell lines. In addition, NFKBIE overexpression in HCC was demonstrated using tissue microarrays encompassing 80 pairs of HCC and normal liver tissues. NFKBIE was the only NFκB inhibitor with high expression and an improved prognosis in HCC compared with other NFκB inhibitors. NFKBIE was correlated with clinical characteristics, such as tumour grade, tumour protein P53 mutation status and tumour stage. Data obtained from Gene Set Cancer Analysis suggested that NFKBIE may inhibit the PI3K/AKT, RAS/MAPK, RTK and TSC/mTOR pathways. In addition, NFKBIE was significantly associated with B-cell immune infiltration and the RNA-seq data demonstrated that knockdown of NFKBIE significantly affected 'Antigen processing and presentation' and 'hepatocellular carcinoma' pathways. Immunohistochemistry of microarrays of tissue samples revealed that NFKBIE was overexpressed in several stages of HCC. Finally, inhibition of NFKBIE decreased the proliferation and migration of HCC cells. In conclusion, due to its prognostic value and overexpression in HCC, NFKBIE distinguished itself from other NFκB inhibitors. As such, it may provide a novel prognostic indicator and immunotherapeutic target for HCC.

Calenduloside E inhibits hepatocellular carcinoma cell proliferation and migration by down-regulating GPX4 and SLC7A11 expression through the autophagy pathway

To investigate the molecular mechanism through which calenduloside E inhibits hepatocellular carcinoma (HCC) cell proliferation and migration. HCC cell lines HepG2 and Huh7 treated with calenduloside E were examined for changes in cell viability using CCK-8 assay and expressions of GPX4, SLC7A11, LC3, P62 and phosphorylation of Akt/mTOR using Western blotting. The effects LY294002 and Rapamycin (the inhibitor and activator of autophagy, respectively) on proliferation and migration of calenduloside E-treated HCC cells were evaluated using EdU and Transwell assays. The TCGA database was used to explore the expression levels of GPX4 and SLC7A11 in HCC and normal liver tissues and their correlation with the patients'survival outcomes. GPX4 and SLC7A11 expressions were also detected in HCC cells and normal hepatocytes using RT-qPCR and Western blotting. Calenduloside E obviously inhibited the viability of HCC cells. GPX4 and SLC7A11 were highly expressed in HCC tissues and cell lines, and their expression levels were negatively correlated with the patients'survival. In HCC cell lines, calenduloside E significantly inhibited the expressions of GPX4 and SLC7A11 proteins, activated the Akt-mTOR pathway, and enhanced the expression of LC3 Ⅱ. The inhibitory effect of calenduloside E on GPX4 and SLC7A11 expressions was significantly enhanced by rapamycin but attenuated by LY294002. Inhibiting the autophagy pathway obviously diminished the inhibitory effect of calenduloside E on proliferation and migration of HCC cells, while activating this pathway produced the opposite effect. Calenduside E inhibits the proliferation and migration of HCC cells by down-regulating GPX4 and SLC7A11 expression via the autophagy pathway.

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.

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.

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.