Identification and validation of a prognostic four-genes signature for hepatocellular carcinoma: integrated ceRNA network analysis

How CLSPN could demystify its prognostic value and potential molecular mechanism for hepatocellular carcinoma: A crosstalk study

BACKGROUND & AIMS

CLSPN, a critical component of the S-phase checkpoint in response to DNA replication stress, has been implicated in the pathogenesis of multiple tumor types. The rising incidence of hepatocellular carcinoma (HCC) poses a significant challenge to global public health. Despite this, the specific functions of CLSPN in the development of HCC remain poorly understood.

METHODS

We systematically evaluated the expression of CLSPN, prognosis and immune infiltration in patients with HCC and identified a competing endogenous RNA (ceRNA) network by using public database. The RT-qPCR, western blot, CCK8, transwell, flow cytometry, animal experiments, proteasome inhibition experiment, Co-IP assay and mass spectrometry were applied to explore its biological functions, post-transcriptional modifications and potential molecular mechanisms of CLSPN in HCC.

RESULTS

We verified the expression of CLSPN, and its high expression is an independent prognostic factor in HCC. The expression of CLSPN is also associated with the immune microenvironment of HCC. CLSPN silencing inhibited the proliferation, migration, invasion and cell cycle progression of HCC cells. We established a PSMA3-AS1/hsa-miR-101-3p/CLSPN regulator axis in HCC. CLSPN was influenced by ubiquitination and was involved in the Wnt/β-catenin pathway to regulate HCC progression.

CONCLUSIONS

It was the first time to comprehensively discover and identify the expression, prognosis, immunotherapy, RNAs regulator, posttranscriptional modification, and molecular mechanisms of CLSPN in HCC. These novel insights have the potential to expedite the development of personalized treatment strategies and translational medicine approaches for HCC patients.

Cuproptosis-related ceRNA axis triggers cell proliferation and cell cycle through CBX2 in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has high morbidity and mortality. Despite substantial advances in treatment, the prognosis of patients with LUAD remains unfavorable. The ceRNA axis has been reported to play an important role in the pathogenesis of LUAD. In addition, cuproptosis is considered an important factor in tumorigenesis. The expression of CBX2 has been associated with the development of multiple tumors, including LUAD. However, the precise molecular mechanisms through which the cuproptosis-related ceRNA network regulates CBX2 remain unclear.

METHODS

The DEGs between tumor and normal samples of LUAD were identified in TCGA database. The "ConsensusClusterPlus" R package was used to perform consensus clustering based on the mRNA expression matrix and cuproptosis-related gene expression profile. Then, LASSO-COX regression analysis was performed to identify potential prognostic biomarkers associated with cuproptosis, and the ceRNA network was constructed. Finally, the mechanisms of ceRNA in LUAD was studied by cell experiments.

RESULTS

In this study, the AC144450.1/miR-424-5p axis was found to promote the progression of LUAD by acting on CBX2. The expression of AC144450.1 and miR-424-5p can be altered to regulate CBX2 and is correlated with cell proliferation and cell cycle of LUAD. Mechanistically, AC144450.1 affects the expression of CBX2 by acting as the ceRNA of miR-424-5p. In addition, a cuproptosis-related model were constructed in this study to predict the prognosis of LUAD.

CONCLUSIONS

This study is the first to demonstrate that the AC144450.1/miR-424-5p/CBX2 axis is involved in LUAD progression and may serve as a novel target for its diagnosis and treatment.

Deciphering essential druggable genes reveals potential immune-inflammatory axis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant tumor with a high mortality rate and poor prognosis in patients. Its pathogenesis is a complex process of multi-factors and multi-steps. However, the etiology and exact molecular mechanism are not completely clear.

METHODS

Here, we constructed a specific-expressed network based on RNA sequencing data. Gene and miRNA expression profiles and clinical evidence were integrated to detect hepatocellular carcinoma survival modules. Finally, we attempted to identify potential key biomarkers and drug targets by integrating drug sensitivity analysis and immune infiltration analysis.

RESULTS

A total of 42 prognostic modules for hepatocellular carcinoma were detected. The prognostic modules were significantly enriched with known cancer-related molecules and 12.93 % molecules of prognostic modules had been found were the targets of small molecule drug. In addition, we found that 38 of 42 (90.48 %) essential genes were associated with the proportions of at least one of the 7 immune cell types.

CONCLUSION

We integrated clinical prognosis information, RNA sequencing data, and drug activity data to explore risk modules of hepatocellular carcinoma. Through drug sensitivity analysis and immune infiltration analysis, we assessed the value of hub genes in the modules as potential biomarkers and drug targets for hepatocellular carcinoma. The protocol provides new insight into parsing the molecular mechanism and theoretical basis of hepatocellular carcinoma.

Competing endogenous RNA network characterization of lymph node metastases in Leuran gastric cancer subtypes

Gastric cancer is a kind of tumor with strong heterogeneity. Long noncoding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors. In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. The performance of SVM model was good in the intestinal and diffuse cohort, while the model in the whole cohort was relatively poor. Some important co-expression patterns between immune cells and ceRNAs network indicated significant correlation CD70 with dendritic cells and so on. Our research inferred competing endogenous RNA network of lymph node metastasis and built an excellent predicting model.

Construction and validation of prognostic signature for hepatocellular carcinoma basing on hepatitis B virus related specific genes

BACKGROUND

Hepatocellular carcinoma (HCC) is a frequent primary liver cancer, and it is one of the leading cause of cancer-related deaths. Hepatitis B virus (HBV) infection is a crucial risk factor for HCC. Thus, this study aimed to explore the prognostic role of HBV-positive HCC related specific genes in HCC.

METHODS

The HCC related data were downloaded from three databases, including The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO). Univariate Cox regression analysis and LASSO Cox regression analysis were conducted to build the Risk score. Multivariate Cox regression analysis and survival analysis determined the independent prognostic indicators.

RESULTS

After cross analysis of differentially expressed genes (DEGs), we have identified 106 overlapped DEGs, which were probably HBV-positive HCC related specific genes. These 106 DEGs were significantly enriched in 213 GO terms and 8 KEGG pathways. Among that, 11 optimal genes were selected to build a Risk score, and Risk score was an independent prognostic factor for HCC. High risk HCC patients had worse OS. Moreover, five kinds of immune cells were differentially infiltrated between high and low risk HCC patients.

CONCLUSION

The prognostic signature, based on HMMR, MCM6, TPX2, KIF20A, CCL20, RGS2, NUSAP1, FABP5, FZD6, PBK, and STK39, is conducive to distinguish different prognosis of HCC patients.

Identification of cell senescence molecular subtypes in prediction of the prognosis and immunotherapy of hepatitis B virus-related hepatocellular carcinoma

Hepatitis B virus (HBV)-infected hepatocellular carcinoma (HCC) has a high incidence and fatality rate worldwide, being among the most prevalent cancers. The growing body of data indicating cellular senescence (CS) to be a critical factor in hepatocarcinogenesis. The predictive value of CS in HBV-related HCC and its role in the immune microenvironment are unknown. To determine the cellular senescence profile of HBV-related HCC and its role in shaping the immune microenvironment, this study employed a rigorous evaluation of multiple datasets encompassing 793 HBV-related HCC samples. Two novel distinct CS subtypes were first identified by nonnegative matrix factorization, and we found that the senescence-activated subgroup had the worst prognosis and correlated with cancer progression. C1 and C2 were identified as the senescence-suppressed and senescence-activated subgroups. The immune microenvironment indicated that C2 exhibited a relatively low immune status, higher tumor purity, and lower immune scores and estimated scores, while the C1 subgroup possessed a better prognosis. The CS score signature based on five genes (CENPA, EZH2, G6PD, HDAC1, and PRPF19) was established using univariate Cox regression and the lasso method. ICGC-LIRI and GSE14520 cohorts were used to validate the reliability of the CS scoring system. In addition, we examined the association between the risk score and hallmark pathways through gene set variation analysis and gene set enrichment analysis. The results revealed a high CS score to be associated with the activation of cell senescence-related pathways. The CS score and other clinical features were combined to generate a CS dynamic nomogram with a better predictive capacity for OS at 1, 2, and 3 years than other clinical parameters. Our study demonstrated that cellular senescence patterns play a non-negligible role in shaping the characteristics of the immune microenvironment and profoundly affecting tumor prognosis. The results of this study will help predict patient prognosis more accurately and may assist in development of personalized immunotherapy for HBV-related HCC patients.

Role of 5-methylcytosine in determining the prognosis, tumor microenvironment, and applicability of precision medicine in patients with hepatocellular carcinoma

Background: 5-methylcytosine has a profound impact on the development and progression of hepatocellular carcinoma. The aim of this study was to investigate the usefulness of 5-methylcytosine in determining the prognosis, tumor microenvironment, and applicability of precision medicine in hepatocellular carcinoma.

Methods: We collected data of seven hepatocellular carcinoma cohorts (The Cancer Genome Atlas, International Cancer Genome Consortium, GSE14520, GSE6764, GSE9843, GSE63898, GSE76427). An unsupervised clustering method was used to identify novel subtypes of hepatocellular carcinoma based on the expression 5-methylcytosine gene signatures. The 5-methylcytosine score was determined using the least absolute shrinkage and selection operator method based on the differential expression of genes in the identified subtypes. Subsequently, we investigated the association between 5-methylcytosine-based clusters (according to the 5-methylcytosine score) and clinical outcomes, immunophenotypes, classical molecular subtypes, and therapeutic opportunities in hepatocellular carcinoma. Finally, we examined the sensitivity of patients with high 5-methylcytosine score to drugs.

Results: We identified two hepatocellular carcinoma-specific, 5-methylcytosine-based subtypes (clusters 1 and 2). Cluster 1 exhibited significantly higher 5-methylcytosine scores versus cluster 2. The 5-methylcytosine-based subtypes accurately predicted classical molecular subtypes, immunophenotypes, prognosis, and therapeutic opportunities for patients with hepatocellular carcinoma. Cluster 1 (high 5-methylcytosine score) was characterized by lower anticancer immunity and worse prognosis versus cluster 2 (low 5-methylcytosine score). Moreover, cluster 1 (high 5-methylcytosine score) exhibited low sensitivity to cancer immunotherapy, but high sensitivity to radiotherapy and targeted therapy with lenvatinib.

Conclusion: The novel 5-methylcytosine-based subtypes (according to the 5-methylcytosine score) may reflect the prognosis, tumor microenvironment, and applicability of precision medicine in patients with hepatocellular carcinoma.

Integrated Analysis of the lncRNA-Associated ceRNA Network in Wilms Tumor via TARGET and GEO Databases

Wilms tumor (WT) is the most common genitourinary renal tumor that typically occurs in children under 15 and is thought to be linked to somatic and germline mutations. However, the specific functional role of competing endogenous RNAs (ceRNAs) and their potential implications in WT remain unclear. In this study, we developed an lncRNA-mediated (long noncoding RNA-mediated) ceRNA network via the R packages for WT with expression data obtained from the tumor alterations relevant for genomics-driven therapy (TARGET) database. Unsupervised hierarchical clustering analysis revealed that the WT specimens could be clearly distinguished from healthy specimens with respect to the expression of disordered RNAs. A total of 1,607 differentially expressed (DE) lncRNAs, 116 DE microRNAs (DEmiRNAs), and 3,262 DE messenger RNAs (DEmRNAs) were identified as WT-specific RNAs, and a lncRNA-miRNA-mRNA ceRNA network with 159 DElncRNAs, 18 DEmiRNAs, 131 DEmRNAs, and 792 interactions was constructed. According to the clinical survival data, 12 DElncRNAs, 5 DEmRNAs, and 2 DEmiRNAs were selected from the ceRNA network that could significantly impact the overall survival of WT patients (P < 0.05). Functional enrichment analysis showed that the biological processes and pathways of DEmRNAs, such as cell cycle and virus infection, may be associated with WT. The present study constructed a dysregulated lncRNA-mediated ceRNA network in WT and discovered that lncRNA-mediated ceRNAs may serve as important regulators in WT development and progression. Survival-associated RNAs may serve as new potential biomarkers, suggesting that the constructed ceRNA network in WT might be important for determining optimal therapeutic strategies.

Systematic analyses to explore immune gene sets-based signature in hepatocellular carcinoma, in which IGF2BP3 contributes to tumor progression

Tumor immune microenvironment (TIME) is of critical importance for the development and therapeutic response of hepatocellular carcinoma (HCC). However, limited studies have investigated immune-related indicators for clinical supervision and decision. The current study aimed to develop an improved prognostic signature based on TIME. HCC patients from TCGA and ICGC database were classified into three subtypes (Immunity High, Immunity Medium and Immunity Low) according to ssGSEA scores of 29 immune gene sets. Differentially expressed immune-related genes (DE IRGs) between Immune High and Low groups were screened with an adjusted P < 0.05. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of differentially expressed genes (DEGs) between tumor and normal tissues. 45 survival-related immune genes (SRIGs) were identified at points of intersection between hub genes and DE IRGs. By performing Cox regression and LASSO analysis, 3 of the 45 SRIGs were screened to establish a prognostic model. Patients with high risk scores exhibited worse survival outcome and poorer response to chemotherapy. Potential mechanisms of chemotherapy resistance also have been discussed. More significantly, high -risk patients showed increased immune cell infiltration and checkpoints, which suggested a benefit of immunotherapy. In addition, knockdown of IGF2BP3 was determined to significantly inhibit cell proliferation and migration in HCC. Our immune-related model may be an effective tool for precise diagnosis and treatment of HCC. It may help to select patients suitable for chemotherapy, and immunotherapy.

LRP1B mutation associates with increased tumor mutation burden and inferior prognosis in liver hepatocellular carcinoma

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is the most common primary liver cancer and the main cause of death in patients with cirrhosis. LRP1B is found to involve in a variety of cancers, but the association of LRP1B mutation with tumor mutation burden (TMB) and prognosis of LIHC is rarely studied.

METHODS AND RESULTS

Herein, we analyzed the somatic mutation data of 364 LIHC patients from The Cancer Genome Atlas (TCGA) and found that LRP1B showed elevated mutation rate. Calculation of the TMB in LRP1B mutant and LRP1B wild-type groups showed that LRP1B mutant group had higher TMB compared with that in LRP1B wild-type group. Then survival analysis was performed and the survival curve showed that LRP1B mutation was associated with poor survival outcome, and this association remained to be significant after adjusting for multiple confounding factors including age, gender, tumor stage, mutations of BRCA1, BRCA2, and POLE.

CONCLUSION

Collectively, our results revealed that LRP1B mutation was related to high TMB value and poor prognosis in LIHC, indicating that LRP1B mutation is probably helpful for the selection of immunotherapy and prognosis prediction in LIHC.

Machine Learning Screens Potential Drugs Targeting a Prognostic Gene Signature Associated With Proliferation in Hepatocellular Carcinoma

Background: This study aimed to screen potential drugs targeting a new prognostic gene signature associated with proliferation in hepatocellular carcinoma (HCC). Methods: CRISPR Library and TCGA datasets were used to explore differentially expressed genes (DEGs) related to the proliferation of HCC cells. Differential gene expression analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct a prognostic gene signature. Then the predictive power of the gene signature was validated in the TCGA and ICGC datasets. Furthermore, potential drugs targeting this gene signature were screened. Results: A total of 640 DEGs related to HCC proliferation were identified. Using univariate Cox analysis and random forest algorithm, 10 hub genes were screened. Subsequently, using multiplex combinatorial screening, five hub genes (FARSB, NOP58, CCT4, DHX37 and YARS) were identified. Taking the median risk score as a cutoff value, HCC patients were divided into high- and low-risk groups. Kaplan-Meier analysis performed in the training set showed that the overall survival of the high-risk group was worse than that of the low-risk group (p < 0.001). The ROC curve showed a good predictive efficiency of the risk score (AUC > 0.699). The risk score was related to gene mutation, cancer cell stemness and immune function changes. Prediction of immunotherapy suggetsted the IC50s of immune checkpoint inhibitors including A-443654, ABT-888, AG-014699, ATRA, AUY-922, and AZ-628 in the high-risk group were lower than those in the low-risk group, while the IC50s of AMG-706, A-770041, AICAR, AKT inhibitor VIII, Axitinib, and AZD-0530 in the high-risk group were higher than those in the low-risk group. Drug sensitivity analysis indicated that FARSB was positively correlated with Hydroxyurea, Vorinostat, Nelarabine, and Lomustine, while negatively correlated with JNJ-42756493. DHX37 was positively correlated with Raltitrexed, Cytarabine, Cisplatin, Tiotepa, and Triethylene Melamine. YARS was positively correlated with Axitinib, Fluphenazine and Megestrol acetate. NOP58 was positively correlated with Vorinostat and 6-thioguanine. CCT4 was positively correlated with Nerabine. Conclusion: The five-gene signature associated with proliferation can be used for survival prediction and risk stratification for HCC patients. Potential drugs targeting this gene signature deserve further attention in the treatment of HCC.

Comprehensive analysis of competitive endogenous RNAs network: Identification and validation of prediction model composed of mRNA signature and miRNA signature in gastric cancer

Gastric cancer (GC), one of the most lethal malignant tumors, is highly aggressive with a poor prognosis, while the molecular mechanisms underlying it remain largely unknown. Although advanced imaging techniques and comprehensive treatment facilitate the diagnosis and survival of some GC patients, the precise diagnosis and prognosis are still a challenge. The present study used publicly available gene expression profiles from The Cancer Genome Atlas and Gene Expression Omnibus datasets including mRNA, micro (mi)RNA and circular (circ)RNA of GC to establish a competing endogenous RNA network (ceRNA). Further, the present study performed least absolute shrinkage and selector operator regression analysis on the hub RNAs to establish a prediction model with mRNA and miRNA. The ceRNA network contained 109 edges and 56 nodes and the visible network contains 13 miRNAs, 9 circRNAs and 34 mRNAs. The five mRNA-based signature were CTF1, FKBP5, RNF128, GSTM2 and ADAMTS1. The area under curve (AUC) value of the diagnosis training cohort was 0.9975. The prognosis of the high-risk group (RiskScore >4.664) was worse compared with that of the low-risk group (RiskScore ≤4.664; P<0.05) in the training cohort. The five miRNA-based signature were miR-145-5p, miR-615-3p, miR-6507-5p, miR-937-3p and miR-99a-3p. The AUC value of the diagnosis training cohort was 0.9975. The prognosis of the high-risk group (RiskScore >1.621) was worse compared with that of the low-risk group (RiskScore ≤1.621; P<0.05) in the training cohort. The validation cohorts indicated that both five mRNA and five miRNA-based signatures had strong predictive power in diagnosis and prognosis for GC. In conclusion, a ceRNA network was established for GC and a five mRNA-based signature and a five miRNA-based signature was identified that enabled diagnosis and prognosis of GC by assigning patient to a high-risk group or low-risk group.

Integrated analysis of ceRNA network reveals potential prognostic Hint1-related lncRNAs involved in hepatocellular carcinoma progression

BACKGROUND

Hint1 is a novel tumor suppressor gene, and inactivation of its expression is closely associated with the carcinogenesis of a variety of malignancies. The effects of Hint1 deficiency on the competing endogenous RNA (ceRNA) regulatory network in the context of HCC remains to be fully characterized. This study aims to explore Hint1-related hub lncRNAs in HCC and to establish a reliable prognostic model for HCC patients based on these hub lncRNAs.

METHODS

lncRNA + mRNA microarray was used to identify differentially expressed (DE) lncRNAs and mRNAs in Huh7 cells before and after Hint1 knockdown. A Hint1-related ceRNA network was mapped by bioinformation technology. The DEmRNAs in the network were analyzed via GO and KEGG enrichment analyses. Hub DElncRNAs associated with HCC patient prognosis were then detected through univariate and multivariate Cox regression analyses and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, and nomograms. We also utilized Kaplan-Meier curves to validate the relationship between hub lncRNAs and the overall survival (OS) of HCC patients. Finally, A Hint1-related core ceRNA network based on the hub DElncRNAs and DEmRNAs was mapped.

RESULTS

We identified 417 differentially expressed DElncRNAs and 2096 DEmRNAs in Huh7 cells before and after Hint1 knockdown. Three hub DElncRNAs (LINC00324, SNHG3, and DIO3OS) in the Hint1-associated ceRNA network were screened out using univariate and multivariate Cox regression analyses. A hepatocellular carcinoma (HCC) prognostic risk-scoring model and nomogram were constructed using these three hub lncRNAs, and it was confirmed that the risk score of the model could be used as an independent predictor of HCC prognosis. A Hint1-related core ceRNA network based on the hub DElncRNAs and DEmRNAs was also mapped.

CONCLUSION

We constructed a reliable prognostic model for HCC patients based on three Hint1-related hub lncRNAs, and we believe these three hub lncRNAs may play critical roles in hepatocarcinogenesis, and progression.

Gene signature to predict prognostic survival of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis and is the second most fatal cancer, and certain HCC patients also show high heterogeneity. This study developed a prognostic model for predicting clinical outcomes of HCC. RNA and microRNA (miRNA) sequencing data of HCC were obtained from the cancer genome atlas. RNA dysregulation between HCC tumors and adjacent normal liver tissues was examined by DESeq algorithms. Survival analysis was conducted to determine the basic prognostic indicators. We identified competing endogenous RNA (ceRNA) containing 15,364 pairs of mRNA–long noncoding RNA (lncRNA). An imbalanced ceRNA network comprising 8 miRNAs, 434 mRNAs, and 81 lncRNAs was developed using hypergeometric test. Functional analysis showed that these RNAs were closely associated with biosynthesis. Notably, 53 mRNAs showed a significant prognostic correlation. The least absolute shrinkage and selection operator’s feature selection detected four characteristic genes (SAPCD2, DKC1, CHRNA5, and UROD), based on which a four-gene independent prognostic signature for HCC was constructed using Cox regression analysis. The four-gene signature could stratify samples in the training, test, and external validation sets (p <0.01). Five-year survival area under ROC curve (AUC) in the training and validation sets was greater than 0.74. The current prognostic gene model exhibited a high stability and accuracy in predicting the overall survival (OS) of HCC patients.

Development of an Eight-gene Prognostic Model for Overall Survival Prediction in Patients with Hepatocellular Carcinoma

BACKGROUND AND AIMS

The overall survival (OS) of hepatocellular carcinoma (HCC) remains dismal. Bioinformatic analysis of transcriptome data could identify patients with poor OS and may facilitate clinical decision. This study aimed to develop a prognostic gene model for HCC.

METHODS

GSE14520 was retrieved as a training set to identify differential expressed genes (DEGs) between tumor and adjacent liver tissues in HCC patients with different OS. A DEG-based prognostic model was then constructed and the TCGA-LIHC and ICGC-LIRI datasets were used to validate the model. The area under the receiver operating characteristic curve (AUC) and hazard ratio (HR) of the model for OS were calculated. A model-based nomogram was established and verified.

RESULTS

In the training set, differential expression analysis identified 80 genes dysregulated in oxidation-reduction and metabolism regulation. After univariate Cox and LASSO regression, eight genes (LPCAT1, DHRS1, SORBS2, ALDH5A1, SULT1C2, SPP1, HEY1 and GOLM1) were selected to build the prognostic model. The AUC for 1-, 3- and 5-year OS were 0.779, 0.736, 0.754 in training set and 0.693, 0.689, 0.693 in the TCGA-LIHC validation set, respectively. The AUC for 1- and 3-year OS were 0.767 and 0.705 in the ICGC-LIRI validation set. Multivariate analysis confirmed the model was an independent prognostic factor (training set: HR=4.422, p<0.001; TCGA-LIHC validation set: HR=2.561, p<0.001; ICGC-LIRI validation set: HR=3.931, p<0.001). Furthermore, a nomogram combining the model and AJCC stage was established and validated, showing increased OS predictive efficacy compared with the prognostic model (p=0.035) or AJCC stage (p<0.001).

CONCLUSIONS

Our eight-gene prognostic model and the related nomogram represent as reliable prognostic tools for OS prediction in HCC patients.

Lidocaine Inhibits Hepatocellular Carcinoma Development by Modulating circ_ITCH/miR-421/CPEB3 Axis

BACKGROUND

Lidocaine plays an anticancer role in hepatocellular carcinoma. Nevertheless, the mechanism of lidocaine in hepatocellular carcinoma remains largely unclear.

AIMS

This study aims to assess the function of lidocaine and explore the potential regulatory mechanism.

METHODS

Hepatocellular carcinoma cells were challenged via lidocaine. Cell proliferation, apoptosis, migration, and invasion were detected via colony formation, 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide, flow cytometry, Western blot, and transwell analyses. Circular RNA itchy E3 ubiquitin protein ligase (circ_ITCH), microRNA-421 (miR-421), and cytoplasmic polyadenylation element-binding protein 3 (CPEB3) abundances were detected via quantitative reverse transcription polymerase chain reaction or Western blot. The relationship between miR-421 and circ_ITCH or CPEB3 was tested via dual-luciferase reporter analysis. The role of circ_ITCH in lidocaine-challenged cell growth in vivo was assessed via xenograft model.

RESULTS

Lidocaine inhibited hepatocellular carcinoma cell proliferation by decreasing colony formation and cell viability. Lidocaine suppressed hepatocellular carcinoma cell migration and invasion and promoted apoptosis. circ_ITCH and CPEB3 levels were decreased in hepatocellular carcinoma tissues and cells, and were restored in cells via lidocaine treatment. circ_ITCH knockdown weakened the suppressive effect of lidocaine on hepatocellular carcinoma development, which was abolished via CPEB3 overexpression. circ_ITCH could modulate CPEB3 by competitively binding with miR-421. miR-421 knockdown mitigated the effect of circ_ITCH silence in lidocaine-challenged cells. circ_ITCH knockdown increased xenograft tumor growth.

CONCLUSIONS

Lidocaine represses hepatocellular carcinoma cell proliferation, migration, and invasion and promotes apoptosis via regulating circ_ITCH/miR-421/CPEB3 axis, indicating a new insight into the mechanism of lidocaine in hepatocellular carcinoma.

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.

Molecular classification of hepatocellular carcinoma: prognostic importance and clinical applications

Hepatocellular carcinoma (HCC) is a lethal human malignancy with a very low overall and long-term survival rate. Poor prognostic outcomes are predominantly associated with HCC due to a huge landscape of heterogeneity found in the deadliest disease. However, molecular subtyping of HCC has significantly improved the knowledge of the underlying mechanisms that contribute towards the heterogeneity and progression of the disease. In this review, we have extensively summarized the current information available about molecular classification of HCC. This review can be of great significance for providing the insight information needed for development of novel, efficient and personalized therapeutic options for the treatment of HCC patients globally.

A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma

Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice.

Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients’ survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks.

Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy.

Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery.

Characterization of the Tumor Immune Microenvironment Identifies M0 Macrophage-Enriched Cluster as a Poor Prognostic Factor in Hepatocellular Carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and a high recurrence rate. The tumor immune microenvironment in HCC has been characterized as shifted toward immunosuppression. We conducted a genomic data-driven classification of immune microenvironment HCC subtypes. In addition, we demonstrated their prognostic value and suggested a potential therapeutic targeting strategy.

METHODS

RNA sequencing data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma was used (n = 366). Abundance of immune cells was imputed using CIBERSORT and visualized using unsupervised hierarchic clustering. Overall survival (OS) was analyzed using Kaplan-Meier estimates and Cox regression. Differential expression and gene set enrichment analyses were conducted on immune clusters with poor OS and high programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) coexpression. A scoring metric combining differentially expressed genes and immune cell content was created, and its prognostic value and immune checkpoint blockade response prediction was evaluated.

RESULTS

Two clusters were characterized by macrophage enrichment, with distinct M0^Hi and M2^Hi subtypes. M2^Hi (P = .038) and M0^Hi (P = .018) were independently prognostic for OS on multivariable analysis. Kaplan-Meier estimates demonstrated that patients in M0^Hi and M2^Hi treated with sorafenib had decreased OS (P = .041), and angiogenesis hallmark genes were enriched in the M0^Hi group. CXCL6 and POSTN were overexpressed in both the M0^Hi and the PD-1^Hi/PD-L1^Hi groups. A score consisting of CXCL6 and POSTN expression and absolute M0 macrophage content was discriminatory for OS (intermediate: hazard ratio [HR], 1.59; P ≤ .001; unfavorable: HR, 2.08; P = .04).

CONCLUSION

Distinct immune cell clusters with macrophage predominance characterize an aggressive HCC phenotype, defined molecularly by angiogenic gene enrichment and clinically by poor prognosis and sorafenib response. This novel immunogenomic signature may aid in stratification of unresectable patients to receive checkpoint inhibitor and antiangiogenic therapy combinations.

Investigation of Potential Molecular Biomarkers for Diagnosis and Prognosis of AFP-Negative HCC

Background

Alpha-fetoprotein (AFP) is the most important diagnostic and prognostic index of hepatocellular carcinoma (HCC). AFP-positive HCC can be easily diagnosed based on the serum AFP level and typical imaging features, but a number of HCC patients are negative (AFP < 20 ng/mL) for AFP. Therefore, it is necessary to develop novel diagnostic and prognostic biomarkers for AFP-negative HCC.

Methods

RNA data from TCGA and differential expression of lncRNAs, miRNAs, and mRNAs were downloaded to analyze the differential RNA expression patterns between AFP-negative HCC tissues and normal tissues. A lncRNA-miRNA-mRNA ceRNA regulatory network was constructed to elucidate the interaction mechanism of RNAs. Functional enrichment analysis of these DEmRNAs was performed to indirectly reveal the mechanism of action of lncRNAs. A PPI network was built using STRING, and the hub genes were identified with Cytoscape. The diagnostic value of hub genes was assessed with receiver operating characteristic (ROC) analysis. And the prognostic value of RNAs in the ceRNA was estimated with Kaplan-Meier curve analysis.

Results

A total of 131 lncRNAs, 185 miRNA, and 1309 mRNAs were found to be differentially expressed in AFP-negative HCC. A ceRNA network consisting of 12 lncRNA, 23 miRNA, and 74 mRNA was constructed. The top ten hub genes including EZH2, CCNB1, E2F1, PBK, CHAF1A, ESR1, RRM2, CCNE1, MCM4, and ATAD2 showed good diagnostic power under the ROC curve; and 2 lncRNAs (LINC00261, LINC00482), 3 miRNAs (hsa-miR-93, hsa-miR-221, hsa-miR-222), and 2 mRNAs (EGR2, LPCAT1) were found to be associated with the overall survival of AFP-negative patients.

Conclusion

This study could provide a novel insight into the molecular pathogenesis of AFP-negative HCC and reveal some candidate diagnostic and prognostic biomarkers for AFP-negative HCC.

Construction of a novel mRNA-miRNA-lncRNA network and identification of potential regulatory axis associated with prognosis in colorectal cancer liver metastases

Liver metastasis is a leading cause of death in patients with colorectal cancer (CRC). Increasing evidence demonstrates that competing endogenous RNA (ceRNA) networks play important roles in malignant cancers. The purpose of this study was to identify molecular markers and build a ceRNA network as a significant predictor of colorectal liver metastases (CRLM). By integrated bioinformatics analysis, we found that apolipoprotein C1 (APOC1) was upregulated in CRLM and associated with prognosis in patients with CRC and thereby established an APOC1-dependent ceRNA network. By survival analysis, expression analysis, and correlation analysis of each element in the ceRNA network, we identified that ZEB1-AS1, miR-335-5p and APOC1 regulated each other. We further experimentally confirmed that ZEB1-AS1 promoted a CRC progression via regulating the expression of miR-335-5p that controlled the expression of APOC1. Our findings indicate that the ZEB1-AS1-miR-335-5p-APOC1 ceRNA regulatory network is significantly valuable for better prognosis of patients with CRC and as a new therapeutic target for the treatment of CRLM.

Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

Background

Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers.

Methods

We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively.

Results

With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients.

Conclusions

In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08173-0.

Prognostic Power of a Chaperonin Containing TCP-1 Subunit Genes Panel for Hepatocellular Carcinoma

Chaperonin containing TCP-1 (T-complex protein 1) (CCT) is a large molecular weight complex that contains nine subunits (TCP1, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT6B, CCT7, CCT8). This study aimed to reveal key genes which encode CCT subunits for prognosis and establish prognostic gene signatures based on CCT subunit genes. The data was downloaded from The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus. CCT subunit gene expression levels between tumor and normal tissues were compared. Corresponding Kaplan-Meier analysis displayed a distinct separation in the overall survival of CCT subunit genes. Correlation analysis, protein-protein interaction network, Gene Ontology analysis, immune cells infiltration analysis, and transcription factor network were performed. A nomogram was constructed for the prediction of prognosis. Based on multivariate Cox regression analysis and shrinkage and selection method for linear regression model, a three-gene signature comprising CCT4, CCT6A, and CCT6B was constructed in the training set and significantly associated with prognosis as an independent prognostic factor. The prognostic value of the signature was then validated in the validation and testing set. Nomogram including the signature showed some clinical benefit for overall survival prediction. In all, we built a novel three-gene signature and nomogram from CCT subunit genes to predict the prognosis of hepatocellular carcinoma, which may support the medical decision for HCC therapy.

Transcriptomic identification of HBx-associated hub genes in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies around the world. Among the risk factors involved in liver carcinogenesis, hepatitis B virus (HBV) X protein (HBx) is considered to be a key regulator in hepatocarcinogenesis. Whether HBx promotes or protects against HCC remains controversial, therefore exploring new HBx-associated genes is still important.

METHODS

HBx was overexpressed in HepG2, HepG2.2.15 and SMMC-7721 cell lines, primary mouse hepatocytes and livers of C57BL/6N mice. High-throughput RNA sequencing profiling of HepG2 cells with HBx overexpression and related differentially-expressed genes (DEGs), pathway enrichment analysis, protein-protein interaction networks (PPIs), overlapping analysis were conducted. In addition, Gene Expression Omnibus (GEO) and proteomic datasets of HBV-positive HCC datasets were used to verify the expression and prognosis of selected DEGs. Finally, we also evaluated the known oncogenic role of HBx by oncogenic array analysis.

RESULTS

A total of 523 DEGs were obtained from HBx-overexpressing HepG2 cells. Twelve DEGs were identified and validated in cells transiently transfected with HBx and three datasets of HBV-positive HCC transcription profiles. In addition, using the Kaplan-Meier plotter database, the expression levels of the twelve different genes were further analyzed to predict patient outcomes.

CONCLUSION

Among the 12 identified HBx-associated hub genes, HBV-positive HCC patients expressing ARG1 and TAT showed a good overall survival (OS) and relapse-free survival (RFS). Thus, ARG1 and TAT expression could be potential prognostic markers.

Construction of a disease-specific lncRNA-miRNA-mRNA regulatory network reveals potential regulatory axes and prognostic biomarkers for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with a high incidence and poor prognosis. Exploration of the underlying mechanisms and effective prognostic indicators is conducive to clinical management and optimization of treatment. The RNA‐seq and clinical phenotype data of HCC were retrieved from The Cancer Genome Atlas (TCGA), and differential expression analysis was performed. Then, a differential lncRNA‐miRNA‐mRNA regulatory network was constructed, and the key genes were further identified and validated. By integrating this network with the online tool‐based ceRNA network, an HCC‐specific ceRNA network was obtained, and lncRNA‐miRNA‐mRNA regulatory axes were extracted. RNAs associated with prognosis were further obtained, and multivariate Cox regression models were established to identify the prognostic signature and nomogram. As a result, 198 DElncRNAs, 120 DEmiRNAs, and 2827 DEmRNAs were identified, and 30 key genes identified from the differential network were enriched in four cancer‐related pathways. Four HCC‐specific lncRNA‐miRNA‐mRNA regulatory axes were extracted, and SNHG11, CRNDE, MYLK‐AS1, E2F3, and CHEK1 were found to be related with HCC prognosis. Multivariate Cox regression analysis identified a prognostic signature, comprised of CRNDE, MYLK‐AS1, and CHEK1, for overall survival (OS) of HCC. A nomogram comprising the prognostic signature and pathological stage was established and showed some net clinical benefits. The AUC of the prognostic signature and nomogram for 1‐year, 3‐year, and 5‐year survival was 0.777 (0.657‐0.865), 0.722 (0.640‐0.848), and 0.630 (0.528‐0.823), and 0.751 (0.664‐0.870), 0.773 (0.707‐0.849), and 0.734 (0.638‐0.845), respectively. These results provided clues for the study of potential biomarkers and therapeutic targets for HCC. In addition, the obtained 30 key genes and 4 regulatory axes might also help elucidate the underlying mechanism of HCC.

Development and validation of a novel pseudogene pair-based prognostic signature for prediction of overall survival in patients with hepatocellular carcinoma

BACKGROUND

There is growing evidence that pseudogenes may serve as prognostic biomarkers in several cancers. The present study was designed to develop and validate an accurate and robust pseudogene pairs-based signature for the prognosis of hepatocellular carcinoma (HCC).

METHODS

RNA-sequencing data from 374 HCC patients with clinical follow-up information were obtained from the Cancer Genome Atlas (TCGA) database and used in this study. Survival-related pseudogene pairs were identified, and a signature model was constructed by Cox regression analysis (univariate and least absolute shrinkage and selection operator). All individuals were classified into high- and low-risk groups based on the optimal cutoff. Subgroups analysis of the novel signature was conducted and validated in an independent cohort. Pearson correlation analyses were carried out between the included pseudogenes and the protein-coding genes based on their expression levels. Enrichment analysis was performed to predict the possible role of the pseudogenes identified in the signature.

RESULTS

A 19-pseudogene pair signature, which included 21 pseudogenes, was established. Patients in high-risk group demonstrated an increased the risk of adverse prognosis in the TCGA cohort and the external cohort (all P < 0.001). The novel pseudogene signature was independent of other conventional clinical variables used for survival prediction in HCC patients in the two cohorts revealed by the multivariate Cox regression analysis (all P < 0.001). Subgroup analysis further demonstrated the diagnostic value of the signature across different stages, grades, sexes, and age groups. The C-index of the prognostic signature was 0.761, which was not only higher than that of several previous risk models but was also much higher than that of a single age, sex, grade, and stage risk model. Furthermore, functional analysis revealed that the potential biological mechanisms mediated by these pseudogenes are primarily involved in cytokine receptor activity, T cell receptor signaling, chemokine signaling, NF-κB signaling, PD-L1 expression, and the PD-1 checkpoint pathway in cancer.

CONCLUSION

The novel proposed and validated pseudogene pair-based signature may serve as a valuable independent prognostic predictor for predicting survival of patients with HCC.

Development and validation of a RNA binding protein gene pair-associated prognostic signature for prediction of overall survival in hepatocellular carcinoma

BACKGROUND

Increasing evidence has demonstrated the correlation between hepatocellular carcinoma (HCC) prognosis and RNA binding proteins (RBPs) dysregulation. Thus, we aimed to develop and validate a reliable prognostic signature that can estimate the prognosis for HCC.

METHODS

Gene expression profiling and clinical information of 374 HCC patients were derived from the TCGA data portal. The survival-related RBP pairs were determined using univariate cox-regression analysis and the signature was built based on LASSO analysis. All patients were divided patients into high-and low-risk groups according to the optimal cut off of the signature score determined by time-dependent receiver operating characteristic (ROC) curve analysis. The predictive value of the signature was further validated in an independent cohort.

RESULTS

A 37-RBP pairs signature consisting of 61 unique genes was constructed which was significantly associated with the survival. The RBP-related signature accurately predicted the prognosis of HCC patients, and patients in high-risk groups showed poor survival in two cohorts. The novel signature was an independent prognostic factor of HCC in two cohorts (all P < 0.001). Furthermore, the C-index of the prognostic model was 0.799, which was higher than that of many established risk models. Pathway and process enrichment analysis showed that the 61 unique genes were mainly enriched in translation, ncRNA metabolic process, RNA splicing, RNA modification, and translational termination.

CONCLUSION

The novel proposed RBP-related signature based on relative expression orderings could serve as a promising independent prognostic biomarker for patients with HCC, and could improve the individualized survival prediction in HCC.

Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology

TP53 mutation is one of the most common genetic changes in hepatocellular carcinoma (HCC). It is of great clinical significance to tailor specialized prognostication approach and to explore more therapeutic options for TP53-mutant HCCs. In this study, a total of 1135 HCC patients were retrospectively analyzed. We developed a random forest-based prediction model to estimate TP53 mutational status, tackling the problem of limited sample size in TP53-mutant HCCs. A multi-step process was performed to develop robust poor prognosis-associated signature (PPS). Compared with previous established population-based signatures, PPS manifested superior ability to predict survival in TP53-mutant patients. After in silico screening of 2249 drug targets and 1770 compounds, we found that three targets (CANT1, CBFB and PKM) and two agents (irinotecan and YM-155) might have potential therapeutic implications in high-PPS patients. The results of drug targets prediction and compounds prediction complemented each other, presenting a comprehensive view of potential treatment strategy. Overall, our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy.

The significance of m6A RNA methylation regulators in predicting the prognosis and clinical course of HBV-related hepatocellular carcinoma

Background

Hepatocarcinogenesis is reportedly correlated with abnormal m6A modifications; however, it is unknown whether m6A RNA methylation regulators facilitate the occurrence of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Thus, we constructed an m6A-related model that may enhance HBV-related HCC prognosis.

Methods

Gene signatures of HNRNPA2B1 and RBM15 were generated by univariate and Lasso Cox regression analyses using the gene set and clinical information from The Cancer Genome Atlas (TCGA) database. High-risk and low-risk groups were confirmed based on the gene signature model. Furthermore, we validated the predictive roles of the two genes for overall survival (OS) in the GSE14520 dataset. The relative expression of 22 paired mRNAs was measured using quantitative real-time polymerase chain reaction (qRT-PCR) analysis to determine whether the two genes had a predictive role in our Guilin cohort.

Results

The differences in OS between the high-risk and low-risk groups were statistically significant in the TCGA (p = 0.003) and GSE14520 (p = 0.045) datasets, but not in the Guilin cohort, owing to differences in clinical information among the three cohorts (mainly the TNM stage and survival state). Stratified analysis of TNM stages showed that the two-gene signature acted as a prognostic indicator of HBV-related HCC patients in the early TNM stage; both TCGA and GSE14520 cohorts showed statistical significance. Moreover, multivariate Cox regression analysis indicated that the two-gene signature was an independent factor for predicting prognosis (HR = 1.087, 95% CI: 1.007–1.172). Correlation analysis between the gene signature and clinical features revealed that the risk stratification was significantly correlated with grade and survival state. Finally, Gene Set Enrichment Analysis (GSEA) revealed that the KEGG pathways associated with the cell cycle, DNA replication, the spliceosome, repair, and metabolism-related processes were all significantly enriched in the high-risk group. Among the enriched genes, the expression levels of the replication protein RPA1 and the pre-mRNA splicing factor SF3B1 were significantly upregulated in the high-risk group. These results might help in elucidating the underlying molecular mechanisms of HBV-related HCC.

Conclusions

Our data may provide new predictive signatures and potential therapeutic targets to identify and treat HBV-related HCC patients in the early disease stage.

Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma

Background

Due to the complicated molecular and cellular heterogeneity in hepatocellular carcinoma (HCC), the morbidity and mortality still remains high level in the world. However, the number of novel metabolic biomarkers and prognostic models could be applied to predict the survival of HCC patients is still small. In this study, we constructed a metabolic gene signature by systematically analyzing the data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC).

Methods

Differentially expressed genes (DEGs) between tumors and paired non-tumor samples of 50 patients from TCGA dataset were calculated for subsequent analysis. Univariate cox proportional hazard regression and LASSO analysis were performed to construct a gene signature. The Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC), Univariate and Multivariate Cox regression analysis, stratification analysis were used to assess the prognostic value of the gene signature. Furthermore, the reliability and validity were validated in four types of testing cohorts. Moreover, the diagnostic capability of the gene signature was investigated to further explore the clinical significance. Finally, Go enrichment analysis and Gene Set Enrichment Analysis (GSEA) have been performed to reveal the different biological processes and signaling pathways which were active in high risk or low risk group.

Results

Ten prognostic genes were identified and a gene signature were constructed to predict overall survival (OS). The gene signature has demonstrated an excellent ability for predicting survival prognosis. Univariate and Multivariate analysis revealed the gene signature was an independent prognostic factor. Furthermore, stratification analysis indicated the model was a clinically and statistically significant for all subgroups. Moreover, the gene signature demonstrated a high diagnostic capability in differentiating normal tissue and HCC. Finally, several significant biological processes and pathways have been identified to provide new insights into the development of HCC.

Conclusion

The study have identified ten metabolic prognostic genes and developed a prognostic gene signature to provide more powerful prognostic information and improve the survival prediction for HCC.

Integrated nomogram based on five stage-related genes and TNM stage to predict 1-year recurrence in hepatocellular carcinoma

Background

The primary tumor, regional lymph nodes and distant metastasis (TNM) stage is an independent risk factor for 1-year hepatocellular carcinoma (HCC) recurrence but has insufficient predictive efficiency. We attempt to develop and validate a nomogram to predict 1-year recurrence in HCC and improve the predictive efficiency of the TNM stage.

Methods

A total of 541 HCC patients were enrolled in the study. The risk score (RS) model was established with the logistic least absolute shrinkage and selector operation algorithm. The predictive nomogram was further validated in the internal testing cohort and external validation cohort. The area under the receiver operating characteristic curves (AUCs), decision curves and clinical impact curves were used to evaluate the predictive accuracy and clinical value of the nomogram.

Results

In the training cohort, we identified a RS model consisting of five stage-related genes (NUP62, EHMT2, RANBP1, MSH6 and FHL2) for recurrence at 1 year. The 1-year disease-free survival of patients was worse in the high-risk group than in the low-risk group (P < 0.0001), and 1-year recurrence was more likely in the high-risk group (Hazard ratio: 3.199, P < 0.001). The AUC of the nomogram was 0.739, 0.718 and 0.693 in the training, testing and external validation cohort, respectively, and these values were larger than the corresponding AUC of the TNM stage (0.681, 0.688 and 0.616, respectively).

Conclusions

A RS model consisting of five stage-related genes was successfully identified for predicting 1-year HCC recurrence. Then, a novel nomogram based on the RS model and TNM stage to predict 1-year HCC recurrence was also developed and validated.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

BACKGROUND

Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown.

OBJECTIVE

To develop a gene signature in OA.

METHOD

In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA.

RESULTS AND DISCUSSION

Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8).

CONCLUSION

The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

Transcriptomic profiling of peroxisome-related genes reveals a novel prognostic signature in hepatocellular carcinoma

Emerging evidence suggests that peroxisomes play a role in the regulation of tumorigenesis and cancer progression. However, the prognostic value of peroxisome-related genes has been rarely investigated. This study aimed to establish a peroxisome-related gene signature for overall survival (OS) prediction in patients with hepatocellular carcinoma (HCC). First, univariate Cox regression analysis was employed to identify prognostic peroxisome-related genes in The Cancer Genome Atlas liver cancer cohort, and least absolute shrinkage and selection operator Cox regression analysis was used to construct a 10-gene signature. The risk score based on the signature was positively correlated with poor prognosis (HR = 4.501, 95% CI = 3.021–6.705, P = 1.39e−13). Second, multivariate Cox regression incorporating additional characteristics revealed that the signature was an independent predictor. Time-dependent ROC curves demonstrated good performance of the signature in predicting the OS of HCC patients. The prognostic performance was validated using International Cancer Genome Consortium HCC cohort data. Gene set enrichment analysis revealed that the signature-related alterations in biological processes mainly involved peroxisomal functions. Finally, we developed a nomogram model based on the gene signature and TNM stage, which showed a superior prognostic power (C-index = 0.702). Thus, our study revealed a novel peroxisome-related gene signature that may help improve personalized OS prediction in HCC patients.

Identification of Hub Genes in Hepatocellular Carcinoma Related to Progression and Prognosis by Weighted Gene Co-Expression Network Analysis

BACKGROUND Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in the world. Bioinformatics studies have been widely used for screening genes involved in the initiation and progression of HCC. MATERIAL AND METHODS We obtained liver cancer microarray raw data from the GEO database (GSE54238). Next, weighted gene co-expression network analysis (WGCNA) was used to assess the critical modules. Then, we assessed the gene significance by calculating survival, expression level, and receiver operating characteristic (ROC) in the TCGA database. We also validated the expression of selected genes in the Oncomine database and calculated the relationship between 4 hub genes and immune infiltration. Finally, GSEA enrichment analysis was used to explore the potential mechanism. RESULTS We identified the red and blue modules as the critical modules, and found 176 candidate genes by assessing gene significance. GO and KEEG results suggested that the candidate genes are involved in the cell cycle. Four hub genes - SOX4, STK39, TARBP1, and TDRKH - were eventually screened after validating their expression and power in diagnosing HCC in the TCGA database. Immune infiltration analysis and GSEA enrichment analysis showed that these 4 hub genes were correlated with the immune cell populations infiltration and that multiple mechanisms were involved, such as angiogenesis and epithelial-mesenchymal transition. CONCLUSIONS Our findings revealed that these 4 genes can be regarded as potential prognosticators and therapeutic targets for HCC.

lncRNA SNHG6 functions as a ceRNA to up-regulate c-Myc expression via sponging let-7c-5p in hepatocellular carcinoma

Emerging evidence has revealed that dysregulation of lncRNAs correlate with the development and progression of hepatocellular carcinoma (HCC). In the present study, we globally investigated the expression of SNHG6 in 31 cancer type, and we found that SNHG6 was highly expressed in various cancers, especially in HCC. High expression of SNHG6 was associated with progression and poor prognosis in patients with HCC. Gain of function and loss of function assays showed that SNHG6 promoted HCC cell proliferation. Gene Set Enrichment Analysis (GSEA) and correlation analysis suggested that SNHG6 positively correlated with c-Myc and its downstream targets. Ectopic overexpression of SNHG6 markedly increased the expression of c-Myc and its downstream targets, whereas silencing SNHG6 had the opposite effect on the expression of c-Myc and its downstream targets. Mechanistic assays revealed that SNHG6 acted as a competing endogenous RNA (ceRNA) to sponge let-7c-5p and thereby modulating the depression of c-Myc by let-7c-5p. Taken together, SNHG6 promotes HCC cell proliferation via competitively binding let-7c-5p in hepatocellular carcinoma.