Identification and validation of EPHX2 as a prognostic biomarker in hepatocellular carcinoma.
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ABSTRACT:
SUBMITTER: Zhan K
PROVIDER: S-EPMC8299194 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Liver hepatocellular carcinoma (LIHC) is one of the most lethal tumors worldwide, and while its detailed mechanism of occurrence remains unclear, an early diagnosis of LIHC could significantly improve the 5-years survival of LIHC patients. It is therefore imperative to explore novel molecular markers for the early diagnosis and to develop efficient therapies for LIHC patients. Currently, DEPDC1B has been reported to participate in the regulation of cell mitosis, transcription, and tumorigenesis. To explore the valuable diagnostic and prognostic markers for LIHC and further elucidate the mechanisms underlying DEPDC1B-related LIHC, numerous databases, such as Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, Kaplan-Meier plotter, and The Cancer Genome Atlas (TCGA) were employed to determine the association between the expression of DEPDC1B and prognosis in LIHC patients. Generally, the DEPDC1B mRNA level was highly expressed in LIHC tissues, compared with that in normal tissues (p < 0.01). High DEPDC1B expression was associated with poor overall survival (OS) in LIHC patients, especially in stage II, IV, and grade I, II, III patients (all p < 0.05). The univariate and multivariate Cox regression analysis showed that DEPDC1B was an independent risk factor for OS among LIHC patients (HR = 1.3, 95% CI: 1.08-1.6, p = 0.007). In addition, the protein expression of DEPDC1B was validated using Human Protein Atlas database. Furthermore, the expression of DEPDC1B was confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) assay using five pairs of matched LIHC tissues and their adjacent noncancerous tissues. The KEGG pathway analysis indicated that high expression of DEPDC1B may be associated with several signaling pathways, such as MAPK signaling, the regulation of actin cytoskeleton, p53 signaling, and the Wnt signaling pathways. Furthermore, high DEPDC1B expression may be significantly associated with various cancers. Conclusively, DEPDC1B may be an independent risk factor for OS among LIHC cancer patients and may be used as an early diagnostic marker in patients with LIHC.
| S-EPMC8793833 | biostudies-literature
Project description:BackgroundCELSR2 is postulated to be a receptor involved in contact-mediated communication; however, the specific function of this particular member has not been determined in hepatocellular carcinoma (HCC).MethodsHere, we explored the expression and function of CELSR2 in HCC patients through data mining and examined the results using clinical samples and in vitro experiments.ResultsIt was found that CELSR2 mRNA and protein expression levels were significantly higher in cancerous tissue than in normal tissue. The increased mRNA expression of CELSR2 was significantly associated with overall survival (OS) in HCC patients. Moreover, the genetic alteration rate of CELSR2 gene in HCC can reach 8%, and these alterations would deeply influence its neighboring genes, then jointly affecting the occurrence and development of tumor through cell adhesion and numerous common carcinogenic pathways. Our in vitro results indicated that the depletion of CELSR2 inhibited liver cancer cell proliferation and invasion. Univariate and multivariate Cox regression analyses showed that CELSR2 could be viewed as an independent risk factor for HCC patients.ConclusionsThis study demonstrated that data mining could efficiently reveal the roles of CELSR2 in HCC and its potential regulatory networks. The CELSR2 protein level may serve as a novel prognostic biomarker for HCC.
| S-EPMC7161135 | biostudies-literature
Project description:Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P-value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238-5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.
| S-EPMC7298074 | biostudies-literature
Project description:BackgroundCancer stem cells (CSCs) are typically related to metastasis, recurrence, and drug resistance in malignant tumors. However, the biomarker and mechanism of CSCs need further exploration. This study is aimed at comprehensively depicting the stemness characteristics and identify a potential stemness-associated biomarker in hepatocellular carcinoma (HCC).MethodsThe data of HCC patients from The Cancer Genome Atlas (TCGA) were collected and divided based on the mRNA expression-based stemness index (mRNAsi) in this study. Weighted gene coexpression network analysis (WGCNA) and the protein-protein interaction (PPI) network were performed, and the genes were screened through the Cytoscape software. Then, we constructed a prognostic expression signature using the multivariable Cox analysis and verified using the GEO and ICGC databases. Even more importantly, we used the three-dimensional (3D) fibrin gel to enrich the tumor-repopulating cells (TRCs) to validate the expression of the signature in CSCs by quantitative RT-PCR.ResultsmRNAsi was significantly elevated in tumor and high-mRNAsi score was associated with poor overall survival in HCC. The positive stemness-associated (blue) module with 737 genes were screened based on WGCNA, and Budding uninhibited by benzimidazoles 1 (BUB1) was identified as the hub gene highly related to stemness in HCC. Then, the prognostic value and stemness characteristics were well validated in the ICGC and GSE14520 cohorts. Further analysis showed the expression of BUB1 was elevated in TRCs.ConclusionBUB1, as a potential stemness-associated biomarker, could serve as a therapeutic CSCs-target and predicted the clinical outcomes of patients with HCC.
| S-EPMC9293570 | biostudies-literature
Project description:Many molecular classification and prognostic gene signatures for hepatocellular carcinoma (HCC) patients have been established based on genome-wide gene expression profiling; however, their generalizability is unclear. Herein, we systematically assessed the prognostic effects of these gene signatures and identified valuable prognostic biomarkers by integrating these gene signatures. With two independent HCC datasets (GSE14520, N=242 and GSE54236, N=78), 30 published gene signatures were evaluated, and 11 were significantly associated with the overall survival (OS) of postoperative HCC patients in both datasets. The random survival forest models suggested that the gene signatures were superior to clinical characteristics for predicting the prognosis of the patients. Based on the 11 gene signatures, a functional protein-protein interaction (PPI) network with 1406 nodes and 10,135 edges was established. With tissue microarrays of HCC patients (N=60), we determined the prognostic values of the core genes in the network and found that RAD21, CDK1, and HDAC2 expression levels were negatively associated with OS for HCC patients. The multivariate Cox regression analyses suggested that CDK1 was an independent prognostic factor, which was validated in an independent case cohort (N=78). In cellular models, inhibition of CDK1 by siRNA or a specific inhibitor, RO-3306, reduced cellular proliferation and viability for HCC cells. These results suggest that the prognostic predictive capacities of these gene signatures are reproducible and that CDK1 is a potential prognostic biomarker or therapeutic target for HCC patients.
| S-EPMC5440601 | biostudies-literature
Project description:Immune-related genes (IRGs) have been identified as critical drivers of the initiation and progression of hepatocellular carcinoma (HCC). This study is aimed at constructing an IRG signature for HCC and validating its prognostic value in clinical application. The prognostic signature was developed by integrating multiple IRG expression data sets from TCGA and GEO databases. The IRGs were then combined with clinical features to validate the robustness of the prognostic signature through bioinformatics tools. A total of 1039 IRGs were identified in the 657 HCC samples. Subsequently, the IRGs were subjected to univariate Cox regression and LASSO Cox regression analyses in the training set to construct an IRG signature comprising nine immune-related gene pairs (IRGPs). Functional analyses revealed that the nine IRGPs were associated with tumor immune mechanisms, including cell proliferation, cell-mediated immunity, and tumorigenesis signal pathway. Concerning the overall survival rate, the IRGPs distinctly grouped the HCC samples into the high- and low-risk groups. Also, we found that the risk score based on nine IRGPs was related to clinical and pathologic factors and remained a valid independent prognostic signature after adjusting for tumor TNM, grade, and grade in multivariate Cox regression analyses. The prognostic value of the nine IRGPs was further validated by forest and nomogram plots, which revealed that it was superior to the tumor TNM, grade, and stage. Our findings suggest that the nine-IRGP signature can be effective in determining the disease outcomes of HCC patients.
| S-EPMC7081044 | biostudies-literature
Project description:BackgroundARID1A is a commonly mutated tumor suppressor gene found in all human cancer types, but its clinical significance, oncogenic functions, and relevant mechanisms in hepatocellular carcinoma (HCC) are not well understood.ObjectiveWe aimed to improving the prognosis risk classification of HCC from the perspective of ARID1A mutations.Materials and methodsWe examined the interaction between ARID1A mutations and the overall survival via Kaplan-Meier survival analysis. We used gene set enrichment analysis (GSEA) to elucidate the influence of ARID1A mutations on signaling pathways. A prognostic model was constructed using LASSO and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was used to estimate the performance and accuracy of the model.ResultsHCC patients with ARID1A mutations presented poor prognosis. By GSEA, we showed that genes upregulated by reactive oxygen species (ROS) and regulated by MYC were positively correlated with ARID1A mutations. A prognostic signature consisting of 5 genes (SRXN1, LDHA, TFDP1, PPM1G, and EIF2S1) was constructed in our research. The signature showed good performance in predicting overall survival (OS) for HCC patients by internal and external validation.ConclusionOur research proposed a novel and robust approach for the prognostic risk classification of HCC patients, and this approach may provide new insights to improve the treatment strategy of HCC.
| S-EPMC8004398 | biostudies-literature
Project description:Background and aimsThe immune system plays vital roles in hepatocellular carcinoma (HCC) initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients.MethodsGene expression data were retrieved from The Cancer Genome Atlas database. The IRPS was established via least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset.ResultsA total of 62 genes were identified as candidate immune-related prognostic genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in the ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be independent risk factors influencing prognosis of HCC patients. The relationships between the IRPS and infiltration of immune cells demonstrated that the IRPS was associated with immune cell infiltration. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.ConclusionsThe IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.
| S-EPMC8666365 | biostudies-literature
Project description:The intimate interaction between redox signaling and immunity has been widely revealed. However, the clinical application of relevant therapeutic is unavailable due to the absence of validated markers that stratify patients. Here, we identified novel biomarkers for prognosis prediction in hepatocellular carcinoma (HCC). Prognostic redox-immune-related genes for predicting overall survival (OS) of HCC were identified using datasets from TCGA, LIRI-JP, and GSE14520. LASSO Cox regression was employed to construct the signature model and generate a risk score in the TCGA cohort. The signature contained CDO1, G6PD, LDHA, GPD1L, PPARG, FABP4, CCL20, SPP1, RORC, HDAC1, STC2, HDGF, EPO, and IL18RAP. Patients in the high-risk group had a poor prognosis compared to the low-risk group. Univariate and multivariate Cox regressions identified this signature as an independent factor for predicting OS. Nomogram constructed by multiple clinical parameters showed good performance for predicting OS indicated by the c-index, the calibration curve, and AUC. GSEA showed that oxidoreductase activity and peroxisome-related metabolic pathways were enriched in the low-risk group, while glycolysis activity and hypoxia were higher in the high-risk group. Furthermore, immune profiles analysis showed that the immune score and stromal score were significantly decreased in the high-risk group in the TCGA cohort. There was a considerably lower infiltration of anti-tumor immune cells while a higher proportion of pro-tumor immune cells in silico. Immune markers were distinctly expressed between the subgroups, and redox-sensitive immunoregulatory biomarkers were at higher levels in the high-risk group. Altogether, we identified a redox-immune prognostic signature. A more severe redox perturbation-driven immunosuppressive environment in the high-risk group stratified by the signature may account for poor survival. This may provide a clue to the combined therapy targeting redox and immune in HCC.
| S-EPMC8040390 | biostudies-literature
Project description:BackgroundDeregulated purine metabolism is critical for fast-growing tumor cells by providing nucleotide building blocks and cofactors. Importantly, purine antimetabolites belong to the earliest developed anticancer drugs and are still prescribed in clinics today. However, these antimetabolites can inhibit non-tumor cells and cause undesired side effects. As liver has the highest concentration of purines, it makes liver cancer a good model to study important nodes of dysregulated purine metabolism for better patient selection and precisive cancer treatment.MethodsBy using a training dataset from TCGA, we investigated the differentially expressed genes (DEG) of purine metabolism pathway (hsa00230) in hepatocellular carcinoma (HCC) and determined their clinical correlations to patient survival. A prognosis model was established by Lasso-penalized Cox regression analysis, and then validated through multiple examinations including Cox regression analysis, stratified analysis, and nomogram using another ICGC test dataset. We next treated HCC cells using chemical drugs of the key enzymes in vitro to determine targetable candidates in HCC.ResultsThe DEG analysis found 43 up-regulated and 2 down-regulated genes in the purine metabolism pathway. Among them, 10 were markedly associated with HCC patient survival. A prognostic correlation model including five genes (PPAT, DCK, ATIC, IMPDH1, RRM2) was established and then validated using the ICGC test dataset. Multivariate Cox regression analysis found that both prognostic risk model (HR = 4.703 or 3.977) and TNM stage (HR = 2.303 or 2.957) independently predicted HCC patient survival in the two datasets respectively. The up-regulations of the five genes were further validated by comparing between 10 pairs of HCC tissues and neighboring non-tumor tissues. In vitro cellular experiments further confirmed that inhibition of IMPDH1 significantly repressed HCC cell proliferation.ConclusionIn summary, this study suggests that purine metabolism is deregulated in HCC. The prognostic gene correlation model based on the five purine metabolic genes may be useful in predicting HCC prognosis and patient selection. Moreover, the deregulated genes are targetable by specific inhibitors.
| S-EPMC7841304 | biostudies-literature