Liver Hepatocellular Carcinoma Patients Research Articles

Kinesin Family Member C1: Function in liver hepatocellular carcinoma and potential target for chemotherapeutic

MiR-105 exerts inhibitory effects on the development and progression of various cancers, including breast cancer, lung cancer, and gastric cancer. Through GEO data analysis, we observed decreased expression of miR-105 in liver cancer tissues compared to adjacent tissues. Furthermore, miR-105 downregulates KIFC1 expression levels by targeting its 3' UTR. KIFC1 (Kinesin Family Member C1), a Protein Coding gene, may play a role in mitotic metaphase plate polymerization and mitotic spindle assembly. However, our findings suggest that this gene could serve as a potential chemotherapeutic target for Liver hepatocellular carcinoma (LIHC). We obtained the LIHC dataset from the TCGA database and genotype Tissue Expression Project (GTEx) normal tissue data for differential analysis. Additionally, we utilized the cBioPortal database, tumor immune single-cell center (TISCH) database, gene set enrichment analysis (GSEA), and R software to investigate the possible functions and mechanisms of KIFC1. These findings were further validated through experiments such as immunohistochemistry and wound healing assays. Our results indicate that KIFC1 might be involved in DNA repair and cell cycle regulation in LIHC cells which subsequently impacts tumor cell proliferation; moreover, miR-105 influences hepatoma cell line proliferation via its interaction with KIFC1. Collectively, these results highlight the potential therapeutic significance of targeting KIFC1 for chemotherapy treatment in LIHC patients.

Ferritin light chain as a potential biomarker for the prognosis of liver hepatocellular carcinoma

High expression of the ferritin light chain (FTL) in cancer promotes its onset and progression and is associated with tumour evolution. However, the significance of FTL in pan-cancer progression and prognosis in humans remains unclear. Therefore, we selected various bioinformatics databases to perform a pan-cancer analysis on a public dataset. Our results showed that FTL was differentially expressed in pan-cancer tissues compared to normal tissues. High FTL expression significantly correlated with the clinicopathological characteristics of patients with liver hepatocellular carcinoma (LIHC). The subsequent validation experiments confirmed these observations. Notably, our study found for the first time that FTLs are closely associated with LIHC and that FTLs have important clinical diagnostic and prognostic value for patients with LIHC. We confirmed that FTL expression was closely associated with altered DNA cycles and immune infiltration in LIHC. In conclusion, high levels of FTL expression are associated with poor prognosis in LIHC patients and are expected to be a potential prognostic and immune marker for LIHC.

Unraveling the relationship between anoikis-related genes and cancer-associated fibroblasts in liver hepatocellular carcinoma

This study intended to determine the molecular subtypes of liver hepatocellular carcinoma (LIHC) on the strength of anoikis-related genes (ARGs) and to assess their prognostic value and prospective relationship with immune cell infiltration and cancer-associated fibroblasts (CAFs). Univariate Cox regression analysis yielded 66 prognosis-related ARGs and classified LIHC into two distinct subtypes, with subtype A demonstrating overexpression of most prognosis-related ARGs and a significant survival disadvantage. Furthermore, a reliable prediction model was developed using ARGs to evaluate the risk of LIHC patients. This model served as an independent prognostic indicator and a quantitative tool for clinical prognostic prediction. Additionally, subtype-specific differences in immune cell infiltration were observed, and the risk score was potentially linked to immune-related characteristics. Moreover, the study identified a significant association between CAF score and LIHC prognosis, with a low CAF score indicating a favorable patient prognosis. In conclusion, this study reveals the molecular mechanisms underlying the development and progression of LIHC and identifies potential therapeutic targets for the disease.

Gene signature developed based on programmed cell death to predict the therapeutic response and prognosis for liver hepatocellular carcinoma

BackgroundThe prognosis and therapeutic response of patients with liver hepatocellular carcinoma (LIHC) can be predicted based on programmed cell death (PCD) as PCD plays a crucial role during tumor progression. We developed a PCD-related gene signature to evaluate the therapeutic response and prognosis for patients with LIHC. MethodsMolecular subtypes of LIHC were classified using ConsensusClusterPlus according to the gene biomarkers related to PCD. To predict the prognosis of high- and low-risk LIHC patients, a risk model was established by LASSO regression analysis based on the prognostic genes. Functional enrichment analysis was conducted using clusterProfiler package, and relative abundance of immune cells was quantified applying CIBERSORT package. Finally, to determine drug sensitivity, oncoPredict package was employed. ResultsPCD was correlated with the clinicopathologic features of LIHC. Then, we defined four molecular subtypes (C1–C4) of LIHC using PCD-related prognostic genes. Specifically, subtype C1 had the worst prognosis with enriched T cells regulatory (Tregs) and Macrophage_M0 and higher expression of T cell exhaustion markers, meanwhile, C1 also had a relatively higher TIDE score and metastasis potential. A risk model was established using 5 prognostic genes. High-risk patients tended to have higher expression of T cell exhaustion markers and TIDE score and unfavorable outcomes, and they were more sensitive to small molecule drug 5.Fluorouracil. ConclusionA PCD-related gene signature was developed and verified to be able to accurately predict the prognosis and drug sensitivity of LIHC patients.

A Bioinformatics-Based Approach to Discover Novel Biomarkers in Hepatocellular Carcinoma

Background: Liver hepatocellular carcinoma (LIHC) is a common cancer with a poor prognosis and high recurrence rate. We aimed to identify potential biomarkers for LIHC by investigating the involvement of hub genes, microRNAs (miRNAs), transcription factors (TFs), and protein kinases (PKs) in its occurrence. Methods: we conducted a bioinformatics analysis using microarray datasets, the TCGA-LIHC dataset, and text mining to identify differentially expressed genes (DEGs) associated with LIHC. They then performed functional enrichment analysis and gene-disease association analysis. The protein-protein interaction network of the genes was established, and hub genes were identified. The expression levels and survival analysis of these hub genes were evaluated, and their association with miRNAs, TFs, and PKs was assessed. Results: The analysis identified 122 common genes involved in LIHC pathogenesis. Ten hub genes were filtered out, including CDK1, CCNB1, CCNB2, CCNA2, ASPM, NCAPG, BIRC5, RRM2, KIF20A, and CENPF. The expression level of all hub genes was confirmed, and high expression levels of all hub genes were correlated with poor overall survival of LIHC patients. Conclusion: Identifying potential biomarkers for LIHC can aid in the design of targeted treatments and improve the survival of LIHC patients. The findings of this study provide a basis for further research in the field of LIHC and contribute to the understanding of its molecular pathogenesis.

Targeting PRKDC activates the efficacy of antitumor immunity while sensitizing to chemotherapy and targeted therapy in liver hepatocellular carcinoma.

Liver hepatocellular carcinoma (LIHC) ranks among the malignancies with the highest mortality rates, primarily due to chemoresistance culminating in treatment failure. Despite its impact, predictive models addressing disease progression, tumor microenvironment, and drug sensitivity remain elusive for LIHC patients. Recognizing the significant influence of various programmed cell death (PCD) modes on tumor evolution, this study investigates PCD genes to elucidate their implications on the prognosis and immune landscape of LIHC. To develop the classification and model, we employed a total of 17 genes associated with PCD patterns. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA database, specifically from LIHC patients. Moreover, we obtained spatial transcriptome data and additional bulk datasets from the Gene Expression Omnibus (GEO) database to conduct further analysis. Various experiments were conducted to validate the role of the PCD gene PRKDC in proliferation, migration, invasion, EMT, cell cycle, therapeutic sensitivity, and antitumor immunity. A novel LIHC classification based on these genes divided patients into two clusters, C1 and C2. The C2 cluster exhibited characteristics indicative of poor prognosis and an immune-activated microenvironment. This group showed greater response potential to immune checkpoint inhibitors, displaying higher levels of certain immune signatures and receptors. A programmed cell death index (PCDI) was constructed using 17 selected PCD genes. This index could effectively predict patient prognosis, with higher PCDI indicating poorer survival rates and a more pro-tumor microenvironment. Immune landscapes revealed varying interactions with PCDI, suggesting therapeutic targets and insights into treatment resistance. Moreover, experiments results suggested that PRKDC can augment the invasive nature and growth of malignant cells and it can serve as a potential target for therapy, offering hope for ameliorating the prognosis of LIHC patients. The study uncovers the insights of programmed cell death in the prognosis and potential therapeutic interventions. And we found that PRKDC can serve as a target for enhancing the efficacy of antitumor immunity while sensitizing chemotherapy and targeted therapy in liver hepatocellular carcinoma.

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.

Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine.

Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC. Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation. Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts. This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients. The online version contains supplementary material available at 10.1007/s13167-024-00362-8.

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.

YBX1 as a prognostic biomarker and potential therapeutic target in hepatocellular carcinoma: A comprehensive investigation through bioinformatics analysis and in vitro study

BackgroundsY-box binding protein 1 (YBX1) is a DNA/RNA binding protein known to contribute to the progression of various malignancies, however, a comprehensive pan-cancer analysis to investigate YBX1 across a broad spectrum of cancer types has not yet been conducted. MethodsWe utilized the TIMER database for a comprehensive pan-cancer analysis and assessed YBX-1 expression via the TCGA and GEO databases. The relationship between YBX-1 expression and tumor-infiltrating cells was examined using TIMER and the R programming language. To evaluate the prognostic value of YBX1, we performed Kaplan-Meier plots and Cox regression analyses. Through LinkedOmics, we identified genes significantly correlated with YBX-1. The WEB-based Gene SeT AnaLysis Toolkit was used for KEGG pathway enrichment analysis. Additionally, using shRNA-mediated knockdown, we explored the potential role of YBX1 in tumor cell biology. ResultsOur study identifies pronounced overexpression of YBX-1 across multiple cancer types, correlating with adverse outcomes, notably in liver hepatocellular carcinoma (LIHC). A distinct association between elevated YBX-1 expression and heightened immune cell infiltration suggests YBX-1′s potential role in reshaping the tumor microenvironment. Intriguingly, our KEGG pathway analysis indicated a tight nexus between YBX-1 expression and lipid metabolism. Moreover, the suppression of YBX-1 via shRNA revealed diminished cellular proliferation and marked reductions in crucial molecules steering the fatty acid synthesis pathway, implicating YBX-1′s potential regulatory role in lipid metabolism within LIHC. ConclusionsYBX-1 serves as a favorable prognostic indicator in various cancers, particularly in liver hepatocellular carcinoma. Targeting YBX1 in HCC offers potential therapeutic strategies. This work paves the way for fresh insights into targeted therapeutic approaches for cancers, especially benefiting liver hepatocellular carcinoma patients.

Predicting the immunity landscape and prognosis with an NCLs signature in liver hepatocellular carcinoma.

Activated neutrophils release depolymerized chromatin and protein particles into the extracellular space, forming reticular Neutrophil Extracellular Traps (NETs). This process is accompanied by programmed inflammatory cell death of neutrophils, known as NETosis. Previous reports have demonstrated that NETosis plays a significant role in immune resistance and microenvironmental regulation in cancer. This study sought to characterize the function and molecular mechanism of NETosis-correlated long non-coding RNAs (NCLs) in the prognostic treatment of liver hepatocellular carcinoma (LIHC). We obtained the transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and evaluated the expression of NCLs in LIHC. A prognostic signature of NCLs was constructed using Cox and Last Absolute Shrinkage and Selection Operator (Lasso) regression, while the accuracy of model was validated by the ROC curves and nomogram, etc. In addition, we analyzed the associations between NCLs and oncogenic mutation, immune infiltration and evasion. Finally, LIHC patients were classified into four subgroups based on consensus cluster analysis, and drug sensitivity was predicted. After screening, we established a risk model combining 5 hub-NCLs and demonstrated its reliability. Independence checks suggest that the model may serve as an independent predictor of LIHC prognosis. Enrichment analysis revealed a concentration of immune-related pathways in the high-risk group. Immune infiltration indicates that immunotherapy could be more effective in the low-risk group. Upon consistent cluster analysis, cluster subgroup 4 presented a better prognosis. Sensitivity tests showed the distinctions in therapeutic effectiveness among various drugs in different subgroups. Overall, we have developed a prognostic signature that can discriminate different LIHC subgroups through the 5 selected NCLs, with the objective of providing LIHC patients a more precise, personalized treatment regimen.

Integrative analysis unveils ECM signatures and pathways driving hepatocellular carcinoma progression: A multi-omics approach and prognostic model development.

Liver hepatocellular carcinoma (LIHC) is a highly lethal form of cancer that is among the deadliest cancer types globally. In terms of cancer-related mortality rates, liver cancer ranks among the top three, underscoring the severity of this disease. Insufficient analysis has been conducted to fully understand the potential value of the extracellular matrix (ECM) in immune infiltration and the prognostic stratification of LIHC, despite its recognised importance in the development of this disease. The scRNA-seq data of GSE149614 was used to conduct single-cell analysis on 10 LIHC samples. CellChat scores were calculated for seven cell populations in the descending cohort to investigate cellular communication, while PROGENy scores were calculated to determine tumour-associated pathway scores in different cell populations. The pathway analysis using GO and KEGG revealed the enrichment of ECM-associated genes in the pathway, highlighting the potential role of the ECM in LIHC development. By utilizing the TCGA-LIHC cohort, an ECM-based prognostic model for LIHC was developed using Lasso regression. Immune infiltration scores were calculated using two methods, and the performance of the ECM-related risk score was evaluated using an independent cohort from the CheckMate study. To determine the precise expression of ECM-associated risk genes in LIHC, we evaluated hepatocellular carcinoma cell lines using a range of assays, including Western blotting, invasion assays and Transwell assays. Using single-cell transcriptome analysis, we annotated the spatially-specific distribution of major immune cell types in single-cell samples of LIHC. The main cell types identified and annotated included hepatocytes, T cells, myeloid cells, epithelial cells, fibroblasts, endothelial cells and B cells. The utilisation of cellchat and PROGENy analyses enabled the investigation and unveiling of signalling interactions, protein functionalities and the prominent influential pathways facilitated by the primary immune cell types within the LIHC. Numerous tumour pathways, including PI2K, EGFR and TGFb, demonstrated a close correlation with the involvement of ECM in LIHC. Moreover, an evaluation was conducted to assess the primary ECM-related functional changes and biological pathway enrichment in LIHC. Differential genes associated with ECM were identified and utilised to create prognostic models. The prognostic stratification value of these models for LIHC patients was confirmed through validation in multiple databases. Furthermore, through immune infiltration analysis, it was discovered that ECM might be linked to the irregular expression and regulation of numerous immune cells. Additionally, histone acetylation was mapped against gene mutation frequencies and differential expression profiles. The prognostic stratification efficacy of the ECM prediction model constructed in the context of PD-1 inhibitor therapy was also examined, and it exhibited strong stratification performance. Cellular experiments, including Western blotting, invasion and Transwell assays, revealed that ECM-associated risk genes have a promoting effect on the development of LIHC. The creation of biomarkers for LIHC using ECM-related genes unveiled substantial correlations with immune microenvironmental infiltration and functional mutations in various tumour pathways. This enlightens us to the possibility that the influence of ECM on tumours may extend beyond simply promoting the fibrotic process and the stromal composition of tumours.

Comprehensive prognostic and immune analysis of sterol O-acyltransferase 1 in patients with hepatocellular carcinoma.

Sterol O-acyltransferase 1 (SOAT1) is an important target in the diagnosis and treatment of liver cancer. However, the prognostic value of SOAT1 in patients with hepatocellular carcinoma (HCC) is still not clear. To investigate the correlation of SOAT1 expression with HCC, using RNA-seq and gene expression data of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) and pan-cancer. The correlation between SOAT1 expression and HCC was analyzed. Cox hazard regression models were conducted to investigate the prognostic value of SOAT1 in HCC. Overall survival and disease-specific survival were explored based on TCGA-LIHC data. Biological processes and functional pathways mediated by SOAT1 were characterized by gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes. In addition, the protein-protein interaction network and co-expression analyses of SOAT1 in HCC were performed to better understand the regulatory mechanisms of SOAT1 in this malignancy. SOAT1 and SOAT2 were highly expressed in unpaired samples, while only SOAT1 was highly expressed in paired samples. The area under the receiver operating characteristic curve of SOAT1 expression in tumor samples from LIHC patients compared with para-carcinoma tissues was 0.748, while the area under the curve of SOAT1 expression in tumor samples from LIHC patients compared with GTEx was 0.676. Patients with higher SOAT1 expression had lower survival rates. Results from GO/KEGG and gene set enrichment analyses suggested that the PI3K/AKT signaling pathway, the IL-18 signaling pathway, the calcium signaling pathway, secreted factors, the Wnt signaling pathway, the Jak/STAT signaling pathway, the MAPK family signaling pathway, and cell-cell communication were involved in such association. SOAT1 expression was positively associated with the abundance of macrophages, Th2 cells, T helper cells, CD56bright natural killer cells, and Th1 cells, and negatively linked to the abundance of Th17 cells, dendritic cells, and cytotoxic cells. Our findings demonstrate that SOAT1 may serve as a novel target for HCC treatment, which is helpful for the development of new strategies for immunotherapy and metabolic therapy.

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.

Analysis and Validation of Tyrosine Metabolism-related Prognostic Features for Liver Hepatocellular Carcinoma Therapy.

To explore tyrosine metabolism-related characteristics in liver hepatocellular carcinoma (LIHC) and to establish a risk signature for the prognostic prediction of LIHC. Novel prognostic signatures contribute to the mining of novel biomarkers, which are essential for the construction of a precision medicine system for LIHC and the improvement of survival. Tyrosine metabolism plays a critical role in the initiation and development of LIHC. Based on the tyrosine metabolism-related characteristics in LIHC, this study developed a risk signature to improve the prognostic prediction of patients with LIHC. To investigate the correlation between tyrosine metabolism and progression of LIHC and to develop a tyrosine metabolism-related prognostic model. Gene expression and clinicopathological information of LIHC were obtained from The Cancer Genome Atlas (TCGA) database. Distinct subtypes of LIHC were classified by performing consensus cluster analysis on the tyrosine metabolism-related genes. Univariate and Lasso Cox regression were used to develop a RiskScore prognosis model. Kaplan-Meier (KM) survival analysis with log-rank test and area under the curve (AUC) of receiver operating characteristic (ROC) were employed in the prognostic evaluation and prediction validation. Immune infiltration, tyrosine metabolism score, and pathway enrichment were evaluated using single-sample gene set enrichment analysis (ssGSEA). Finally, a nomogram model was developed with the RiskScore and other clinicopathological features. Based on the tyrosine metabolism genes in the TCGA cohort, we identified 3 tyrosine metabolism-related subtypes showing significant prognostic differences. Four candidate genes selected from the common differentially expressed genes (DEGs) between the 3 subtypes were used to develop a RiskScore model, which could effectively divide LIHC patients into high- and lowrisk groups. In both the training and validation sets, high-risk patients tended to have worse overall survival, less active immunotherapy response, higher immune infiltration and clinical grade, and higher oxidative, fatty, and xenobiotic metabolism pathways. Multivariate analysis confirmed that the RiskScore was an independent indicator for the prognosis of LIHC. The results from pan-- cancer analysis also supported that the RiskScore had a strong prognostic performance in other cancers. The nomogram demonstrated that the RiskScore contributed the most to the prediction of LIHC prognosis. Our study developed a tyrosine metabolism-related risk model that performed well in survival prediction, showing the potential to serve as an independent prognostic predictor for LIHC treatment.

The diagnostic, prognostic, drug sensitivity and ceRNA internet of PPP4R1 in liver hepatocellular carcinoma (LIHC).

Recent studies have reported a role of protein phosphatase 4 regulatory subunit 1 (PPP4R1) in cancer development. However, its expression, diagnostic significance, prognostic value and biological function in liver hepatocellular carcinoma (LIHC) are not known. The expression level of PPP4R1 in pan-cancer was evaluated by analyzing publicly accessible data from the University of California Santa Cruz (UCSC) Xena database. The diagnostic value of PPP4R1 for tumors was assessed using receiver operating characteristic (ROC) curves, whereas the impact of PPP4R1 on tumor prognosis was determined using Kaplan-Meier survival curves, and a prognostic model for LIHC was established using cox regression analysis. In addition, analysis of the correlation between PPP4R1 and anti-cancer drugs using Spearman's correlation coefficient was carried out. Four databases, miRWalk (mRNA-miRNA interactions), MicroT-CDS (mRNA-miRNA interactions), LncBase (miRNA-lncRNA interactions) and Encyclopedia of RNA Interactomes (ENCORI), were used to predict the competitive endogenous RNA (ceRNA) regulatory network of PPP4R1. Finally, the expression of PPP4R1 protein levels was verified using experiments. The findings indicated that the PPP4R1 expression level in cancerous tissues was notably greater than in adjacent tissues (P<0.05). PPP4R1 showed diagnostic significance for 14 tumors based on the ROC curves results area under the curve >0.7. Furthermore, the Kaplan-Meier survival plots demonstrated that PPP4R1 exhibited prognostic significance for all five tumors (P<0.05). According to the cox regression analysis, LIHC patients' prognosis was independently influenced by pathological stage, M stage, and PPP4R1 (P<0.05). The drug sensitivity analysis revealed a positive correlation between the expression level of PPP4R1 and the half maximal inhibitory concentration (IC50) of fludarabine. Additionally, the ceRNA network prediction indicated that the FGD5 antisense RNA 1 (FGD5-AS1)-hsa-miR-22-3p-PPP4R1 ceRNA network could potentially contribute to the progression of LIHC. The experimental results showed that the expression level of PPP4R1 protein was higher in cancer tissues than in paracancerous tissues. PPP4R1 has diagnostic value in most cancers, and high expression of PPP4R1 is associated with poor prognosis, drug resistance and natural killer cell-mediated toxicity, particularly in LIHC. Therefore, PPP4R1 may be a prognostic biomarker and a potential target for immunotherapy in LIHC.

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.

Fatty acid binding protein 5 is a novel therapeutic target for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is an aggressive subtype of liver cancer and is one of the most common cancers with high mortality worldwide. Reprogrammed lipid metabolism plays crucial roles in HCC cancer cell survival, growth, and evolution. Emerging evidence suggests the importance of fatty acid binding proteins (FABPs) in contribution to cancer progression and metastasis; however, how these FABPs are dysregulated in cancer cells, especially in HCC, and the roles of FABPs in cancer progression have not been well defined. To understand the genetic alterations and expression of FABPs and their associated cancer hallmarks and oncogenes in contributing to cancer malignancies. We used The Cancer Genome Atlas datasets of pan cancer and liver hepatocellular carcinoma (LIHC) as well as patient cohorts with other cancer types in this study. We investigated genetic alterations of FABPs in various cancer types. mRNA expression was used to determine if FABPs are abnormally expressed in tumor tissues compared to non-tumor controls and to investigate whether their expression correlates with patient clinical outcome, enriched cancer hallmarks and oncogenes previously reported for patients with HCC. We determined the protein levels of FABP5 and its correlated genes in two HCC cell lines and assessed the potential of FABP5 inhibition in treating HCC cells. We discovered that a gene cluster including five FABP family members (FABP4, FABP5, FABP8, FABP9 and FABP12) is frequently co-amplified in cancer. Amplification, in fact, is the most common genetic alteration for FABPs, leading to overexpression of FABPs. FABP5 showed the greatest differential mRNA expression comparing tumor with non-tumor tissues. High FABP5 expression correlates well with worse patient outcomes (P < 0.05). FABP5 expression highly correlates with enrichment of G2M checkpoint (r = 0.33, P = 1.1e-10), TP53 signaling pathway (r = 0.22, P = 1.7e-5) and many genes in the gene sets such as CDK1 (r = 0.56, P = 0), CDK4 (r = 0.49, P = 0), and TP53 (r = 0.22, P = 1.6e-5). Furthermore, FABP5 also correlates well with two co-expressed oncogenes PLK1 and BIRC5 in pan cancer especially in LIHC patients (r = 0.58, P = 0; r = 0.58, P = 0; respectively). FABP5high Huh7 cells also expressed higher protein levels of p53, BIRC5, CDK1, CDK2, and CDK4 than FABP5low HepG2 cells. FABP5 inhibition more potently inhibited the tumor cell growth in Huh7 cells than in HepG2 cells. We discovered that FABP5 gene is frequently amplified in cancer, especially in HCC, leading to its significant elevated expression in HCC. Its high expression correlates well with worse patient outcome, enriched cancer hallmarks and oncogenes in HCC. FABP5 inhibition impaired the cell viability of FABP5high Huh7 cells. All these support that FABP5 is a novel therapeutic target for treating FABP5high HCC.

A disulfidptosis-related lncRNAs signature in hepatocellular carcinoma: prognostic prediction, tumor immune microenvironment and drug susceptibility

Disulfidptosis, a novel type of programmed cell death, has attracted researchers’ attention worldwide. However, the role of disulfidptosis-related lncRNAs (DRLs) in liver hepatocellular carcinoma (LIHC) not yet been studied. We aimed to establish and validate a prognostic signature of DRLs and analyze tumor microenvironment (TME) and drug susceptibility in LIHC patients. RNA sequencing data, mutation data, and clinical data were obtained from the Cancer Genome Atlas Database (TCGA). Lasso algorithm and cox regression analysis were performed to identify a prognostic DRLs signature. Kaplan–Meier curves, principal component analysis (PCA), nomogram and calibration curve, function enrichment, TME, immune dysfunction and exclusion (TIDE), tumor mutation burden (TMB), and drug sensitivity analyses were analyzed. External datasets were used to validate the predictive value of DRLs. qRT-PCR was also used to validate the differential expression of the target lncRNAs in tissue samples and cell lines. We established a prognostic signature for the DRLs (MKLN1-AS and TMCC1-AS1) in LIHC. The signature could divide the LIHC patients into low- and high-risk groups, with the high-risk subgroup associated with a worse prognosis. We observed discrepancies in tumor-infiltrating immune cells, immune function, function enrichment, and TIDE between two risk groups. LIHC patients in the high-risk group were more sensitive to several chemotherapeutic drugs. External datasets, clinical tissue, and cell lines confirmed the expression of MKLN1-AS and TMCC1-AS1 were upregulated in LIHC and associated with a worse prognosis. The novel signature based on the two DRLs provide new insight into LIHC prognostic prediction, TME, and potential therapeutic strategies.