Independent Prognostic Factor For Hepatocellular Carcinoma Research Articles

Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm.

The purpose of this study was to identify the potential diagnostic biomarkers in hepatocellular carcinoma (HCC) by machine learning (ML) and to explore the significance of immune cell infiltration in HCC. From GEO datasets, the microarray datasets of HCC patients were obtained and downloaded. Differentially expressed genes (DEGs) were screened from five datasets of GSE57957, GSE84402, GSE112790, GSE113996, and GSE121248, totalling 125 normal liver tissues and 326 HCC tissues. In order to find the diagnostic indicators of HCC, the LASSO regression and the SVM-RFE algorithms were utilized. The prognostic value of VIPR1 was analyzed. Finally, the difference of immune cell infiltration between HCC tissues and normal liver tissues was evaluated by CIBERSORT algorithm. In this study, a total of 232 DEGs were identified in 125 normal liver tissues and 326 HCC tissues. 11 diagnostic markers were identified by LASSO regression and SVM-RFE algorithms. FCN2, ECM1, VIRP1, IGFALS, and ASPG genes with AUC>0.85 were regarded as candidate biomarkers with high diagnostic value, and the above results were verified in GSE36376. Survival analyses showed that VIPR1 and IGFALS were significantly correlated with the OS, while ASPG, ECM1, and FCN2 had no statistical significance with the OS. Multivariate assays indicated that VIPR1 gene could be used as an independent prognostic factor for HCC, while FCN2, ECM1, IGFALS, and ASPG could not be used as independent prognostic factors for HCC. Immune cell infiltration analyses showed that the expression of VIPR1 in HCC was positively correlated with the levels of several immune cells. Overall, VIPR1 gene can be used as a diagnostic feature marker of HCC and may be a potential target for the diagnosis and treatment of liver cancer in the future.

The Landscape of Using Glycosyltransferase Gene Signatures for Overall Survival Prediction in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a heterogeneous disease that occurs in the setting of chronic liver diseases. The role of glycosyltransferase (GT) genes has recently been the focus of research associated with tumor development. However, the prognostic value of GT genes in HCC remains unclear. Therefore, this study aimed to identify GT genes related to HCC prognosis through bioinformatics analysis. We firstly constructed a prognostic signature based on four GT genes using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses in The Cancer Genome Atlas (TCGA) dataset. Next, the risk score of each patient was calculated, and HCC patients were divided into high- and low-risk groups. Kaplan–Meier analysis showed that the survival rate of high-risk patients was significantly lower than that of low-risk patients. Receiver operating characteristic (ROC) curves assessed that risk scores calculated with a four-gene signature could predict 3- and 5-year overall survival (OS) of HCC patients, revealing the prognostic ability of this gene signature. Moreover, univariate and multivariate Cox regression analyses demonstrated that the risk score was an independent prognostic factor of HCC. Finally, functional analysis revealed that immune-related pathways were enriched and the immune status was different between the two risk groups in HCC. In summary, the novel GT gene signature could be used for prognostic prediction of HCC. Thus, targeting the GT genes may serve as an alternative treatment strategy for HCC.

The Prognostic and Immunotherapeutic Significance of AHSA1 in Pan-Cancer, and Its Relationship With the Proliferation and Metastasis of Hepatocellular Carcinoma.

The AHSA1 is a main activator of ATPase of Hsp90. Hsp90 is involved in various metabolic and developmental processes of tumor cells. Although, the role of AHSA1 in tumor cells is still unrecognized. In the current research, the RNA-seq of 33 tumors were downloaded using The Cancer Genome Atlas (TCGA) database for the analysis of AHSA1 expression in tumors. The Kaplan-Meier method was used for the evaluation of the prognostic significance of AHSA1 in patients with pan-cancer. Additionally, the correlation between AHSA1 and immune cell infiltration, immune checkpoint, pyroptosis-related molecules, epithelial cell transformation-related molecules, and autophagy-related molecules were analyzed by co-expression. Furthermore, we examined the effect of AHSA1 knockdown on cell function in Huh7 and HCCLM3 cells of hepatocellular carcinoma (HCC) cell lines.According to the finding of this study, up-regulation of AHSA1 expression was observed in numerous tumor tissues, and its over-expression in liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), and esophageal carcinoma (ESCA) could affect the overall survival and disease-specific survival of the patients. Meanwhile, as per the correlation analysis the expression of AHSA1 was greatly correlated with the expression of various immune cell infiltrates, immune checkpoint inhibitors, tumor mutation load, and microsatellite instability. Moreover, this study focused on analyzing the association of AHSA1 expression with multiple pathological stages in HCC, and confirmed that AHSA1 was an independent prognostic factor of HCC by univariate and multivariate COX regression in TCGA and The International Cancer Genome Consortium (ICGC) cohorts. At the same time, cellular experiments proved that the AHSA1 knockdown could decrease the proliferation activity, cell migration and invasion ability of HCC cells. Therefore, the results of this study indicated that AHSA1 can be used as a potential prognostic biomarker of tumors and it may have a significant role in the proliferation as well as migration of HCC cells.

RAB42 is a Potential Biomarker that Correlates With Immune Infiltration in Hepatocellular Carcinoma

Backgrounds: Hepatocellular carcinoma (HCC) is a malignant cancer with high mortality. Previous studies have reported that RAB42 is associated with prognosis and progression in glioma. However, the role of RAB42 in HCC is still unknown. Therefore, we aimed to elucidate the value of RAB42 in the predicting prognosis of HCC, and its relationship with immune cells infiltration. Methods: UALCAN, HCCDB, and MethSurv databases were used to examine the expression and methylation levels of RAB42 in HCC and normal samples. cBioPortal and MethSurv were used to identify genetic alterations and DNA methylation of RAB42, and their effect on prognosis. The correlations between RAB42 and the immune cells and cancer-associated fibroblasts infiltration were analyzed by TIMER, TISIDB, and GEPIA database. The LinkedOmics database was used to analyze the enriched pathways associated with genes co-expressed with RAB42. EdU assay was used to evaluate the proliferation ability of liver cancer cells, and transwell assay was used to detect the invasion and migration ability of liver cancer cells. Results: The expression levels of RAB42 were increased in HCC tissues than that in normal tissues. Highly expressed RAB42 was significantly correlated with several clinical parameters of HCC patients. Moreover, increased RAB42 expression clearly predicted poor prognosis in HCC. Furthermore, multivariate Cox regression analysis showed that RAB42 was an independent prognostic factor in HCC. The RAB42 genetic alteration rate was 5%. RAB42 DNA methylation in HCC tissues was lower than that in normal tissues. Among the 7 DNA methylation CpG sites, two were related to the prognosis of HCC. The results of gene set enrichment analysis (GSEA) showed that RAB42 was associated with various immune cells and cancer-associated fibroblasts infiltration in HCC. Meanwhile, we found RAB42 methylation was strongly correlated with immune infiltration levels, immunomodulators, and chemokines. Experiments in vitro indicated that knockdown of RAB42 inhibited the proliferation, invasion, and migration of liver cancer cells. Conclusions: Our study highlights the clinical importance of RAB42 in HCC and explores the effect of RAB42 on immune infiltration in the tumor microenvironment, and RAB42 may act as a pro-oncogene that promotes HCC progression.

NAP1L1 Functions as a Novel Prognostic Biomarker Associated With Macrophages and Promotes Tumor Progression by Influencing the Wnt/β-Catenin Pathway in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is regarded as one of the universal cancers in the world. Therefore, our study is based on clinical, molecular mechanism and immunological perspectives to analyze how NAP1L1 affects the progression of HCC. To begin with, the gene expression datasets and clinical data of GSE14520, GSE76427, ICGC, and TCGA are originated from GEO, ICGC, and TCGA databases. Subsequently, DEG screening was performed on data using R studio, and we finally found that 2,145 overlapping DEGs were screened from four datasets at the end. Then, we used R studio to filter the survival-related genes of the GSE76427 and ICGC datasets, and we screened out 101 survival-related genes. Finally, 33 common genes were screened out from 2,145 overlapping DEGs and 101 survival-related genes. Then, NAP1L1 was screened from 33 common genes using the CytoHubba plug-in in Cytoscape software. Furthermore, ground on GEO, ICGC, and TCGA databases, the survival analysis, clinical feature analysis, univariate/multivariate regression analysis, and multiple GSEA were used to study NAP1L1. The Conclusion claimed that HCC patients with higher expression levels of NAP1L1 had a poorer prognosis than those with lower expression levels. Thus, we believe that NAP1L1 is an independent prognostic factor for HCC. In order to shed light on NAP1L1’s molecular mechanism promoting the progression of HCC closely, the GSEA tool was applied to complete the GSEA of the four datasets. Furthermore, the results confirmed that NAP1L1 could promote HCC progression by regulating the G2/M transition of the cell cycle and Wnt signaling pathway. Western blot and flow cytometry were also performed to understand those mechanisms in this study. The result of Western blot showed that NAP1L1 silencing led to downregulation of CDK1 and β-catenin proteins; the result of flow cytometry showed that cell numbers in the G2 phase were significantly increased when NAP1L1 was silenced. Thus, we claimed that NAP1L1 might promote HCC progression by activating the Wnt signaling pathway and promoting cell cycle G2/M transition. In addition, ground on GSE14520 and GSE76427 datasets, and ICGC and TCGA databases, the correlation between NAP1L1 and immune cells was analyzed in HCC patients. At the same time, the TISIDB online database and the TIMER online database were testified to the association between NAP1L1 and immune cells. Hence, the summary shows that NAP1L1 was connected with a certain amount of immune cells. We can speculate that NAP1L1 may influence macrophages to promote HCC progression through some potential mechanisms.

High Expression of TGF-β1 Contributes to Hepatocellular Carcinoma Prognosis via Regulating Tumor Immunity.

BackgroundTransforming growth factor-beta (TGF-β) signaling is essential in initialization and progression of hepatocellular carcinoma (HCC). Therefore, a treatment targeting TGF-β pathway may be a promising option for HCC control.MethodsFirst, publicly available RNA-seq datasets and clinical characteristics of 374 HCC patients in The Cancer Genome Atlas (TCGA) database were downloaded. Then, Cox regression analysis and LASSO analysis were used to construct a prognostic model for TGF-β family genes. The area under the curve (AUC) of the risk signature was calculated to evaluate the predictive power of the model. Cox regression analysis was applied to predict whether TGF-β1 can be an independent prognosis factor for HCC. Next, hazard ratio and survival analyses were performed to investigate the correlation between TGF-β1 expression and survival time. Furthermore, differential expression level of TGF-β1 in HCC tissues and cells was determined. In addition, Gene Set Enrichment Analysis (GSEA) identified the top significantly activated and inhibited signal pathways related to high expression of TGF-β1. Finally, the CIBERSORT tool was adopted to correlate the tumor-infiltrating immune cells (TICs) with TGF-β1 expression in HCC cohorts.ResultsCox regression analysis and LASSO analysis revealed that seven TGF-β family members (including TGF-β1) could be used as prognostic factors for HCC. Interestingly, TGF-β1 was demonstrated to be an independent prognostic factor of HCC. RT-qPCR and immunofluorescence staining confirmed the high expression of TGF-β1 in HCC cell lines and tissues, which is significantly related to pathological classifications, poor prognosis, and short survival time. Finally, GSEA and CIBERSORT analyses suggested that TGF-β1 may interact with various immune cells and influence the prognosis of HCC patients through Tregs and γδ T cells.ConclusionWe established a novel prognostic prediction method to predict the risk scores of TGF-β genes in HCC prognosis. TGF-β1 is highly expressed in HCC cell lines and tissues, correlates to poor prognosis, and thus can be used as a potential biomarker to predict HCC prognosis. We showed that TGF-β1 may play its roles in HCC prognosis by modulating the immune microenvironment of tumor cells. Our data may shed more light on better understanding the role of TGF-β1 in HCC prognosis.

Reappraisal of the Role of Alkaline Phosphatase in Hepatocellular Carcinoma.

Background: Alkaline phosphatase (ALP) is a marker of liver function and is associated with biliary tract disease. It was reported as a prognostic factor for hepatocellular carcinoma (HCC). The genetic expression in tumor-tissue microarrays and the perioperative serologic changes in ALP have never been studied for their correlation with HCC prognosis. Methods: The genetic expression of ALP isoforms (placental (ALPP), intestinal (ALPI) and bone/kidney/liver (ALPL)) was analyzed in tumor and non-cancerous areas in 38 patients with HCC after partial hepatectomy. The perioperative change in ALP was further analyzed in a cohort containing 525 patients with HCC to correlate it with oncologic outcomes. A total of 43 HCC patients were enrolled for a volumetry study after major and minor hepatectomy. Results: The genetic expression of the bone/kidney/liver isoform was specifically and significantly higher in non-cancerous areas than in tumors. Patients with HCC with a higher ALP (>81 U/dL) had significantly more major hepatectomies, vascular invasion, and recurrence. Cox regression analysis showed that gender, major hepatectomies, the presence of satellite lesions, higher grades (III or IV) and perioperative changes in liver function tests were independent prognostic factors for recurrence-free survival, and a postoperative increase in the ALP ratio at postoperative day (POD) 7 vs. POD 0 > 1.46 should be emphasized. A liver regeneration rate more than 1.8 and correlation analysis revealed that the ALP level at POD 7 and 30 was significantly higher and correlated with remnant liver growth. Conclusions: This study demonstrated that the perioperative ALP change was an independent prognostic factor for HCC after partial hepatectomies, and the elevation of ALP represented a functional biomarker for the liver but not an HCC biomarker. The higher regeneration capacity was possibly associated with the elevation of ALP after operation.

A Novel Prognostic Model for Identifying the Risk of Hepatocellular Carcinoma Based on Angiogenesis Factors.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with poor prognosis. An optimized stratification of HCC patients to discriminate clinical benefit regarding different degrees of malignancy is urgently needed because of no effective and reliable prognostic biomarkers currently. HCC is typically characterized by rich vascular. The dysregulated vascular endothelial growth factor was proved a pivotal regulator of the development of HCC. Therefore, we investigated the capability of angiogenic factors (AFs) in stratifying patients and constructed a prognostic risk model. A total of 6 prognostic correlated AFs (GRM8, SPC25, FSD1L, SLC386A, FAM72A and SLC39A10) were screened via LASSO Cox regression, which provided the basis for developing a novel prognostic risk model. Based on the risk model, HCC patients were subdivided into high-risk and low-risk groups. Kaplan-Meier curve indicated that patients in the high-risk group have a lower survival rate compared with those in the low-risk group. The prognostic model showed good predictive efficacy, with AUCs reaching 0.802 at 1 year, 0.694 at 2 years, and 0.672 at 3 years. Univariate and multivariate cox regression analysis demonstrated that the risk score had significant prognostic value and was an independent prognostic factor for HCC. Moreover, this model also showed a good diagnostic positive rate in the ICGC-LIRI-JP and GSE144269. Finally, we demonstrated the efficacy of the AF-risk model in HCC patients following sorafenib adjuvant chemotherapy. And revealed the underlying molecular features involving tumor stemness, immune regulation, and genomic alterations associated with the risk score. Based on a large population, we established a novel prognostic model based on 6 AFs to help identify HCC patients with a greater risk of death. The model may provide a reference for better clinical management of HCC patients in the era of cancer precision medicine.

Expression of FOXM1 and PLK1 predicts prognosis of patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most frequently encountered malignant tumor types and to improve its treatment, effective prognostic biomarkers are urgently required. Cell cycle dysregulation is a significant feature of cancer progression. The aim of the present study was to estimate the expression levels of forkhead box protein M1 (FOXM1) and polo-like kinase 1 (PLK1), both of which have essential roles in cell cycle regulation, and determine their prognostic value in HCC. To this end, FOXM1 and PLK1 expression levels were assessed in The Cancer Genome Atlas and International Cancer Genome Consortium Japan HCC cohorts, and the associations between their co-expression were determined via Pearson's correlation analysis. Furthermore, the overall survival and disease-free survival in these cohorts for different FOXM1 and PLK1 expression statuses were analyzed. In vitro knockdown experiments were also performed using Huh7 cells. The results obtained indicated overexpression of FOXM1 and PLK1 in HCC tumor tissues as well as a positive correlation between FOXM1 and PLK1 expression. The results also suggested that both FOXM1 and PLK1 are required for HCC cell proliferation. In addition, upregulation of FOXM1 and PLK1 was indicated to be associated with poor prognosis of patients with HCC. However, only their coordinated overexpression was identified as an independent prognostic factor for HCC.

The Detection and Verification of Two Heterogeneous Subgroups and a Risk Model Based on Ferroptosis-Related Genes in Hepatocellular Carcinoma.

# Background. Because of the heterogeneity of hepatocellular carcinoma (HCC) and the complex nature of the tumor microenvironment (TME), the long-term efficacy of therapy continues to be a clinical challenge. It is necessary to classify and refine the appropriate treatment intervention decision-making in this kind of tumor. Methods. We used “ConsensusClusterPlus” to establish a stable molecular classification based on the ferroptosis-related genes (FRGs) expression obtained from FerrDb. The clinical features, immune infiltration, DNA damage, and genomic changes of different subclasses were evaluated. The least absolute shrinkage and selection operator regression (LASSO) method and univariate Cox regression were utilized to construct the ferroptosis-related prognosis risk score (FPRS) model, and the association between the FPRS model and HCC molecular characteristics, immune features, and immunotherapy was studied. Results. We identified two ferroptosis subclasses, C1 with poor prognosis and a higher proportion of patients in the middle and late stages infected with HBV and HCV, having higher DNA damage including aneuploidy, HRD, fraction altered, and the number of segments, and higher probability of gene mutation and copy number mutation. FPRS model was constructed on the basis of differentially expressed genes (DEGs) between C1 and C2, which showed a higher area under the curve (AUC) in predicting overall survival rate in the training set and independent verification cohort and could reflect the clinical characteristics and response to immunotherapy of different patients, being an independent prognostic factor of HCC. Conclusion. Here, we revealed two novel molecular subgroups based on FRGs and develop an FPRS model consisting of six genes that can help predict prognosis and select patients suitable for immunotherapy.

Pyroptosis-Related Risk Signature Exhibits Distinct Prognostic, Immune, and Therapeutic Landscapes in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a common abdominal cancer. The existing therapeutic approaches often fail to achieve satisfactory results. Pyroptosis, an inflammatory form of programmed cell death, provides new ideas for anticancer treatment. However, the roles of pyroptosis-related (PR) genes (PRGs) in HCC remain elusive. Methods: Differentially expressed genes (DEGs) (n = 22) were screened out using TCGA and GTEx databases. A novel PR risk signature was constructed through Lasso regression analysis. Its prognostic value was evaluated through a series of survival analyses and was tested in ICGC and GSE14520 cohorts. CIBERSORT, ssGSEA, and ESTIMATE methods were employed to determine the effects of the PR risk score on the tumor immune microenvironment (TIM). The TIDE scoring system, IMvigor210 cohort, GSE109211 dataset, and GSDC database were applied to explore the associations of the PR risk score with therapeutic effects. The biofunctions of WNK1 in hepatocellular cancer (HC) cells were confirmed through qPCR, colony formation, and Transwell assays. Results: Overall, 22 of 45 PRGs (48.9%) were abnormally expressed in HCC samples. Then, a PR risk signature consisting of eight PRGs was constructed. A high PR risk score led to an unfavorable prognosis. The PR risk score was identified as an independent prognostic factor of HCC and could increase the decision-making benefit of the traditional TNM model. In addition, we established a nomogram containing the clinical stage and PR risk score to predict the survival rates of HCC patients. The prognostic value of the PR model was successfully validated in ICGC and GSE14520 cohorts. Moreover, high PR risk conferred the decreased infiltration level of CD8+ T cells and weakened the activities of “cytolytic activity” pathways. As for therapeutic correlation, a high PR risk score seemed to imply a poor efficacy of PD-1/L1 inhibitors and sorafenib. Finally, the overexpression of WNK1 could promote the proliferation, migration, and invasion of HC cells. Conclusions: The PR risk score was closely related to the prognosis, antitumor immune process, therapeutic outcomes, and malignant progression of HCC. WNK1, the core regulator of pyroptosis, possesses pro-oncogenic abilities, showing promise as a novel treatment target.

Identification of the Expression Patterns and Potential Prognostic Role of 5-Methylcytosine Regulators in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer with a poor prognosis. 5-methylcytosine (m5C) modification plays a nonnegligible role in tumor pathogenesis and progression. However, little is known about the role of m5C regulators in HCC. Methods: Based on 9 m5C regulators, the m5C modification patterns of HCC samples extracted from public databases were systematically evaluated and correlated with tumor immune and prognosis characteristics. An integrated model called the “m5Cscore” was constructed using principal component analysis, and its prognostic value was evaluated. Results: Almost all m5C regulators were differentially expressed between HCC and normal tissues. Through unsupervised clustering, three different m5Cclusters were ultimately uncovered; these clusters were characterized by differences in prognosis, immune cell infiltration, and pathway signatures. The m5Cscore was constructed to quantify the m5C modifications of individual patients. Subsequent analysis revealed that the m5Cscore was an independent prognostic factor of HCC and could be a novel indicator to predict the prognosis of HCC. Conclusion: This study comprehensively explored and systematically profiled the features of m5C modification in HCC. m5C modification patterns play a crucial role in the tumor immune microenvironment (TIME) and prognosis of HCC. The m5Cscore provides a more holistic understanding of m5C modification in HCC and provides a practical tool for predicting the prognosis of HCC. This study will help clinicians identify effective indicators of HCC to improve the poor prognosis of this disease.

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma.

BackgroundThe evaluation of the nodal status of hepatocellular carcinoma (HCC) is a classic but controversial topic. This study aimed to investigate the incidence of lymph node metastasis (LNM), explore the role of lymph node dissection (LND), and develop and validate a novel model to predict LNM in patients with HCC, not other specified (NOS).MethodsThe study cohort was taken from the Surveillance, Epidemiology, and End Results database. The annual percent change (APC) was calculated using the Joinpoint regression. Survival analyses adopted the competing risk model. The nomogram was constructed based on the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and validated by calibration curves. The area under the receiver operating characteristic curve (AUROC) was obtained to compare prognostic performance. Decision curve and clinical impact curve analyses were introduced to examine the clinical value of the models.ResultsA total of 8,829 patients were finally enrolled in this study, and 1,346 (15.2%) patients received LND. The LND rate showed no noticeable fluctuation in the last decade, with an APC of 0.5% (P=0.593). LNM was identified in 56 (4.2%) patients and confirmed an independent prognostic factor of HCC patients (P=0.005). There were 2,497 lymph nodes retrieved, and 93 (3.7%) of them were positive. After propensity score matching, LND indicated no direct oncologic benefit and did not worsen competing risks. Moreover, an increased number of lymph nodes retrieved could not improve prognoses. 1,346 patients with LND were further randomly divided into the training and validation sets with the ratio of 1:1. Race, tumor size, clinical T stage, extrahepatic bile duct invasion, and tumor grade were independent risk factors for LNM. The constructed model was well calibrated and showed good discrimination power and net benefits in clinical practice.ConclusionLNM is an independent prognostic factor in HCC, but routine LND seems to be unnecessary in HCC patients. The constructed model could predict the presence of LNM in HCC patients with good performance, which is meaningful to patient stratification and individual treatment strategies optimization.

Cyclin-Dependent Kinase 4 is expected to be a therapeutic target for hepatocellular carcinoma metastasis using integrated bioinformatic analysis

ABSTRACT Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. HCC cells possess biological characteristics of high invasion and metastasis. In this respect, to prevent cancer cell invasion and metastasis and early active intervention, we herein screened through the TCGA database for further prognostic analysis including overall survival and disease-free survival . The Kaplan-Meier curve suggested that Cyclin-Dependent Kinase 4 (CDK4) might be an independent prognostic factor for HCC. Moreover, we performed mRNA expression analysis to measure CDK4 levels in normal liver tissues and HCC tissues, and immunohistochemistry analysis to detect protein level of CDK4 in Non-tumor tissue and HCC tissues . Our findings indicated that the expression of CDK4 was significantly higher in tumor tissues compared with Non-tumor tissue in HCC, which increased from HCC stage 1 to 3. Furthermore, the results of transwell-assay indicated that knocking down CDK4 significantly suppresses the invasion and migration of HCC cells, and the results of bioinformatics analysis revealed that genes closely associated with CDK4 are potentially worthy of further investigation. Additionally, the results of Western Blot indicated CDK4 regulates epithelial mesenchymal transition in HCC,and CDK4 appears to regulate EMT and HCC progression via the Wnt/β-catenin pathway. Collectively, this study found the key target gene through bioinformatic analysis and further functional validation through cell experiments. In particular, CDK4 is anticipated to become a crucial hub gene to snipe the metastasis of cancer cells in HCC. Abbreviations: Hepatocellular carcinoma (HCC);Cyclin-Dependent Kinase 4(CDK4);Genomic Data Commons (GDC); genes; EC, Endometrial cancer; GEO, gene expression omnibus; GO, Gene Ontology; GSEA, Gene set enrichment analysis; KEGG, Database; TCGA, The Cancer Genome Atlas; TSGs, tumor suppressor genes;epithelial mesenchymal transition (EMT).

Use of 6 m6A-relevant lncRNA genes as prognostic markers of primary liver hepatocellular carcinoma based on TCGA database.

BackgroundHepatocellular carcinoma (HCC) is diagnosed at the middle and advanced stages, negating radical treatment. Identifying specific and effective prognostic HCC biomarkers is important and can facilitate the discovery of potential therapeutic targets. N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) are associated with the development of multiple tumors. The role of m6A-relevant lncRNAs in the initiation and progression of HCC is unclear. The aim of the present study was to investigate the expression of m6A-relevant lncRNAs in HCC and to identify new prognostic markers of the disease.MethodsGene expression and clinical data were retrieved from The Cancer Genome Atlas database. m6A-relevant lncRNAs were identified by co-expression analysis and were screened by univariate Cox regression analysis. Different HCC patient clusters were established via consensus clustering. Gene set enrichment analysis (GSEA) was used to determine the cluster enrichment pathways. A risk score model was constructed, and Kaplan-Meier analysis of the overall survival (OS) between cluster 1 (high risk) and cluster 2 (low risk) was performed. Relationships between the clusters, risk scores, and clinicopathological characteristics were clarified.ResultsOf the 1,852 m6A-relevant lncRNAs identified, 68 had prognostic relevance. The pathological grade, American Joint Committee on Cancer stage, and T stage of cluster 1 were significantly more advanced than those of cluster 2. Based on GSEA, mitotic spindle, G2M_CHECKPOINT, glycolysis, the phosphoinositide 3-kinase (PI3K) protein kinase B (AKT) mammalian target of rapamycin (mTOR) pathway, and DNA repair were more enriched in cluster 1. Six key m6A-relevant lncRNAs were selected to build a risk score model predicting the prognosis of HCC. The OS of patients in the high-risk group was shorter than that of patients in the low-risk group. Risk score was an independent prognostic factor of HCC patients.ConclusionsThe findings indicated that m6A-relevant lncRNAs may be important in the progression of HCC. The risk score model based on the 6 key m6A-relevant lncRNAs can accurately predict the prognosis of patients with HCC.

8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma

BackgroundHypoxia plays a crucial role in immunotherapy of hepatocellular carcinoma (HCC) by changing the tumor microenvironment. Until now the association between hypoxia genes and prognosis of HCC remains obscure. We attempt to construct a hypoxia model to predict the prognosis in HCC.ResultsWe screened out 3 hypoxia genes (ENO1, UGP2, TPI1) to make the model, which can predict prognosis in HCC. And this model emerges as an independent prognostic factor for HCC. A Nomogram was drawn to evaluate the overall survival in a more accurate way. Furthermore, immune infiltration state and immunosuppressive microenvironment of the tumor were detected in high-risk patients.ConclusionWe establish and validate a risk prognostic model developed by 3 hypoxia genes, which could effectively evaluate the prognosis of HCC patients. This prognostic model can be used as a guidance for hypoxia modification in HCC patients undergoing immunotherapy.

The prognostic value of TPM1-4 in hepatocellular carcinoma.

BackgroundDespite advances in multiple disciplinary diagnoses and treatments, the prognosis of hepatocellular carcinoma (HCC) remains poor. Some evidence has identified that the aberrant expression of tropomyosins (TPMs) is involved with some cancers development. However, prognostic values of TPMs in HCC have not been thoroughly investigated.MethodsOriginal TPM1–4 mRNA expression of TCGA HCC data and GTEx was downloaded from UCSC XENA. Oncomine database and GSE46408 were used for verification. Clinical stages and survival analysis of TPM1–4 in HCC were performed by GEPIA2. cBioPortal was utilized to assess TPM1–4 gene alteration in HCC. TIMER2.0 was used for investigating the relevance of TPM1–4 to tumor‐infiltrating immune cells in HCC. Additionally, we constructed a TPM1–4 prognostic model to explore the value of TPM1–4 for prognostic evaluation in HCC. LinkedOmics was applied to elucidate TPM3 co‐expression networks in HCC.ResultsThis present study showed that TPM1–4 was upregulated in all HCC tissues, and TPM3 overexpression was correlated with poor survival outcomes in patients with HCC. Besides, TPM3 amplification was the main altered type in TPM1–4 genetic alteration, which affected the prognosis of HCC patients. The risk model revealed that TPM1, TPM2, and TPM3 were applied to risk assessment of HCC prognosis, among which TPM3 expression was significantly higher in the high‐risk group than that in the low‐risk group. Univariate and multivariate cox regression analyses indicated that TPM3 may be an independent prognostic factor of HCC prognosis. In addition, TPM3 co‐expression genes mainly participated in the cell cycle by maintaining microtubule cytoskeleton in HCC progression. TPM1–4 was associated with some tumor‐infiltrating immune cells in HCC.ConclusionOur study detected that the expression level of TPM1–4 was all remarkably elevated in HCC, suggesting that TPM1–4 may serve an important role in HCC development. High TPM3 expression was found to be correlated with poor overall survival, and TPM3 may be an independent prognostic factor for HCC.

Combination of psoas muscle mass index and neutrophil/lymphocyte ratio as a prognostic predictor for patients undergoing nonsurgical hepatocellular carcinoma therapy.

Background and AimReliable predictors for hepatocellular carcinoma (HCC) are urgently needed. The psoas muscle index (PMI) is a simple and rapid method for evaluating muscle atrophy. Furthermore, the neutrophil/lymphocyte ratio (NLR) is a prognostic factor that is easy to calculate in everyday clinical practice. We aimed to investigate the value of the PMI and NLR as prognostic factors for patients receiving nonsurgical HCC therapy, hepatic arterial infusion chemotherapy (HAIC), transcatheter arterial chemoembolization (TACE), or molecular targeted drugs such as sorafenib (SOR) and lenvatinib (LEN).MethodsWe enrolled 87 patients with HCC who were treated with HAIC, TACE, SOR, or LEN. The primary endpoint was overall survival (OS) with variable PMI or NLR status. For Barcelona Clinic Liver Cancer (BCLC)‐B patients, useful prognostic factors were examined by comparing the OS between stratified groups. Prognostic factors including PMI and NLR were evaluated by univariate and multivariate analysis.ResultsAnalysis of HAIC or TACE (HAIC/TACE) and SOR or LEN (SOR/LEN) patients showed significant differences in OS between low and high PMI. In patients treated with TACE, there was a significant difference in OS between low and high NLR. For BCLC‐B and low PMI, the prognosis was significantly worse for SOR/LEN than for TACE, although there was no difference for high PMI, suggesting that PMI may be useful for treatment selection. In addition, the prognostic formula composed of PMI, NLR, and up‐to‐seven criteria developed in the present study may be useful.ConclusionPMI and NLR are considered to be independent prognostic factors for HCC.

Genomic Instability of Mutation-Derived Gene Prognostic Signatures for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the major cancer-related deaths worldwide. Genomic instability is correlated with the prognosis of cancers. A biomarker associated with genomic instability might be effective to predict the prognosis of HCC. In the present study, data of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used. A total of 370 HCC patients from the TCGA database were randomly classified into a training set and a test set. A prognostic signature of the training set based on nine overall survival (OS)–related genomic instability–derived genes (SLCO2A1, RPS6KA2, EPHB6, SLC2A5, PDZD4, CST2, MARVELD1, MAGEA6, and SEMA6A) was constructed, which was validated in the test and TCGA and ICGC sets. This prognostic signature showed more accurate prediction for prognosis of HCC compared with tumor grade, pathological stage, and four published signatures. Cox multivariate analysis revealed that the risk score could be an independent prognostic factor of HCC. A nomogram that combines pathological stage and risk score performed well compared with an ideal model. Ultimately, paired differential expression profiles of genes in the prognostic signature were validated at mRNA and protein level using HCC and paratumor tissues obtained from our institute. Taken together, we constructed and validated a genomic instability–derived gene prognostic signature, which can help to predict the OS of HCC and help us to explore the potential therapeutic targets of HCC.

Systematic Construction and Validation of a Prognostic Model for Hepatocellular Carcinoma Based on Immune-Related Genes.

Hepatocellular carcinoma (HCC), a highly aggressive tumor, has high incidence and mortality rates. Recently, immunotherapies have been shown to be a promising treatment in HCC. The results of either the CheckMate-040 or IMbrave 150 trials demonstrate the importance of immunotherapy in the systemic treatment of liver cancer. Thus, in this study, we tried to establish a reliable prognostic model for liver cancer based on immune-related genes (IRGs) and to provide a new insight for immunotherapy of HCC. In this study, we used four datasets that incorporated 851 HCC samples, including 340 samples with complete clinical information from the cancer genome atlas (TCGA) database, to establish an effective model for predicting the prognosis of HCC patients based on the differential expression of IRGs and validated the prognostic model using the data from International Cancer Genome Consortium (ICGC). The top 6 characteristic IRGs identified by protein-protein interaction (PPI) network analysis, MMP9, FOS, CAT, ESR1, ANGPTL3, and KLKB1, were selected for further study. In addition, we assessed the correlations of the six characteristic IRGs with the tumor immune microenvironment, clinical stage, and sensitivity to anti-cancer drugs. We also explored whether the differential expression of the characteristic IRGs was specific to HCC or present in pan-cancer. The expression levels of the six characteristic IRGs were significantly different between most tumor tissues and adjacent normal tissues. In addition, these characteristic IRGs showed a strong association with immune cell infiltration in HCC patients. We found that MMP9 and ESR1 were independent prognostic factors for HCC, while CAT, ESR1, and KLKB1 were associated with the clinical stage. We collected HCC paraffin sections from 24 patients from Xijing hospital to identify the differential expression of the five genes (MMP9, ESR1, CAT, FOS, and KLKB1). Finally, the results of decision curve analysis (DCA) and nomogram revealed that our models provided a prognostic benefit for most HCC patients and the predicted overall survival (OS) was consistent with the actual OS. In conclusion, we systemically constructed a novel prognostic model that provides new insights into HCC.