Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients

SMG5 Inhibition Restrains Hepatocellular Carcinoma Growth and Enhances Sorafenib Sensitivity

Hepatocellular carcinoma (HCC) has a pathogenesis that remains elusive with restricted therapeutic strategies and efficacy. This study aimed to investigate the role of SMG5, a crucial component in nonsense-mediated mRNA decay (NMD) that degrades mRNA containing a premature termination codon, in HCC pathogenesis and therapeutic resistance. We demonstrated an elevated expression of SMG5 in HCC and scrutinized its potential as a therapeutic target. Our findings revealed that SMG5 knockdown not only inhibited the migration, invasion, and proliferation of HCC cells but also influenced sorafenib resistance. Differential gene expression analysis between the control and SMG5 knockdown groups showed an upregulation of methionine adenosyltransferase 1A in the latter. High expression of methionine adenosyltransferase 1A, a catalyst for S-adenosylmethionine (SAM) production, as suggested by The Cancer Genome Atlas data, was indicative of a better prognosis for HCC. Further, an ELISA showed a higher concentration of SAM in SMG5 knockdown cell supernatants. Furthermore, we found that exogenous SAM supplementation enhanced the sensitivity of HCC cells to sorafenib alongside changes in the expression of Bax and Bcl-2, apoptosis-related proteins. Our findings underscore the important role of SMG5 in HCC development and its involvement in sorafenib resistance, highlighting it as a potential target for HCC treatment.

A prognostic model based on DNA methylation-related gene expression for predicting overall survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) continues to increase in morbidity and mortality among all types of cancer. DNA methylation, an important epigenetic modification, is associated with cancer occurrence and progression. The objective of this study was to establish a model based on DNA methylation risk scores for identifying new potential therapeutic targets in HCC and preventing cancer progression.

Methods

Transcriptomic, clinical, and DNA methylation data on 374 tumor tissues and 50 adjacent normal tissues were downloaded from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma database. The gene expression profiles of the GSE54236 liver cancer dataset, which contains data on 161 liver tissue samples, were obtained from the Gene Expression Omnibus database. We analyzed the relationship between DNA methylation and gene expression levels after identifying the differentially methylated and expressed genes. Then, we developed and validated a risk score model based on the DNA methylation-driven genes. A tissue array consisting of 30 human hepatocellular carcinoma samples and adjacent normal tissues was used to assess the protein and mRNA expression levels of the marker genes by immunohistochemistry and qRT-PCR, respectively.

Results

Three methylation-related differential genes were identified in our study: GLS, MEX3B, and GNA14. The results revealed that their DNA methylation levels were negatively correlated with local gene expression regulation. The gene methylation levels correlated strongly with the prognosis of patients with liver cancer. This was confirmed by qRT-PCR and immunohistochemical verification of the expression of these genes or proteins in tumors and adjacent tissues. These results revealed the relationship between the level of relevant gene methylation and the prognosis of patients with liver cancer as well as the underlying cellular and biological mechanisms. This allows our gene signature to provide more accurate and appropriate predictions for clinical applications.

Conclusion

Through bioinformatics analysis and experimental validation, we obtained three DNA methylation marker: GLS, MEX3B, and GNA14. This helps to predict the prognosis and may be a potential therapeutic target for HCC patients.

N6-methyladenosine methylation-related genes YTHDF2, METTL3, and ZC3H13 predict the prognosis of hepatocellular carcinoma patients

Background

Hepatocellular carcinoma (HCC) is a common primary malignant tumor and cause of cancer-related death in humans. Increasing evidence indicates that an imbalance in N6-methyladenosine (m6A) methylation is strongly linked to the occurrence and development of cancer. We used comprehensive bioinformatics to establish a potential prognostic model of HCC based on m6A methylation-related genes. And case analyses were used to verify the results.

Methods

The clinical data and gene expressions were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The prognostic value of m6A methylation-related genes in HCC patients and their relationship with the immune microenvironment were explored by comprehensive bioinformatics analyses. We also collected pathological specimens from 70 patients with HCC from the Department of Pathology, Affiliated Hospital of Qingdao University, and performed immunohistochemical staining on the specimens. We compared tumor specimens from 27 patients positive for METTL3, YTHDF2, and ZC3H13 staining with their adjacent normal tissues and against 27 patient specimens negative for METTL3, YTHDF2, and ZC3H13. The influence of METTL3, YTHDF2, and ZC3H13 on survival was analyzed, and the prognostic model for METTL3, YTHDF2, and ZC3H13 in HCC was verified by clinical data.

Results

Most m6A methylation-related genes showed significantly different expressions between cancer and normal tissues. Three candidate m6A methylation-related genes (YTHDF2, METTL3, and ZC3H13) were significantly correlated with the overall survival (OS) of HCC patients. A Kaplan-Meier survival analysis indicated a worse prognosis of high-risk patients than that of low-risk patients. Immunological analysis showed that the high-risk group was more likely to have higher follicular helper T cell counts and lower resting memory CD4 T cell counts. The expression of YTHDF2, METTL3, and ZC3H13 was validated by other databases, including the Oncomine database, the Human Protein Atlas (HPA), and the Kaplan-Meier plotter.

Conclusions

Our prognostic model based on m6A methylation-related genes effectively predicted the prognosis of HCC patients.

Construction and validation of a prognostic model with RNA binding protein-related mRNAs for the HBV-related hepatocellular carcinoma patients

Hepatocellular carcinoma (HCC) is a common malignancy worldwide with poor clinical outcomes, and the infection of hepatitis B virus (HBV) is the leading cause of this disease. Mounting evidence shows that RNA binding proteins (RBPs) can modulate the progression of cancers. However, the functions and clinical implications of RBP-related mRNAs in HBV-related HCC remain largely unclear. Therefore, we aim to develop a prognostic model based on the RBP-related mRNAs for HBV-related HCC patients. Firstly, we identified 626 differentially expressed RBP-related mRNAs in the HBV-related HCC through the Pearson correlation analysis. Subsequently, the Kaplan-Meier survival, univariate, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses were used to construct a prognostic model comprised of five RBP-related mRNAs. Furthermore, the patients were categorized into the high- and low-risk groups by the prognostic model and the patients in the high-risk group had a poor prognosis. Additionally, the prognostic model was an independent predictor of prognosis, and the accuracy of the prognostic model was proved by the receiver operator characteristic (ROC) analysis. Furthermore, the functional enrichment analysis revealed that various cancer-promoting processes were enriched in the high-risk group. Taken together, our study may provide the HBV-related HCC biomarkers of prognosis to improve the clinical outcomes of patients.

A novel ferroptosis-related gene signature for clinically predicting recurrence after hepatectomy of hepatocellular carcinoma patients

High recurrence rate in HCC is the primary cause of the poor prognosis after hepatectomy. Therefore, in this study, we aimed to construct a gene signature for predicting the recurrence rate in HCC. The mRNA expression profiles and clinical information of HCC patients from GEO and TCGA databases were used, and ferroptosis-related gene list was obtained from the FerrDb database. We identified 39 ferroptosis-related genes (FDEGs) that were differentially expressed between HCC samples and normal tissues from the GSE14520 dataset. The univariate and multivariate Cox regression analyses were employed to construct a prognostic signature. Seven FDEGs (MAPK9, SLC1A4, PCK2, ACSL3, STMN1, CDO1, and CXCL2) were included to construct a risk model, which was validated in the TCGA dataset. Patients in high-risk groups exhibited a significantly poor prognosis compared with patients in low-risk groups in both the training set (GSE14520 cohort) and the validation set (TCGA cohort). Multivariate cox regression analyses demonstrated that the 7-gene signature was an independent risk factor for RFS in HCC patients. KEGG analysis showed that FDEGs were mainly enriched in Ferroptosis, Hepatocellular carcinoma pathway, and MAPK signaling pathway. GSEA analysis suggested that the high-risk group was correlated with multiple oncogenic signatures and invasive-related pathways. These results indicated that this risk model can accurately predict recurrence after hepatectomy and offer novel research directions for personalized treatment in HCC patients.

Development and Validation of a Gene Mutation-Associated Nomogram for Hepatocellular Carcinoma Patients From Four Countries

Background: Genomic alteration is the basis of occurrence and development of carcinoma. Specific gene mutation may be associated with the prognosis of hepatocellular carcinoma (HCC) patients without distant or lymphatic metastases. Hence, we developed a nomogram based on prognostic gene mutations that could predict the overall survival of HCC patients at early stage and provide reference for immunotherapy.

Methods: HCC cohorts were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The total patient was randomly assigned to training and validation sets. Univariate and multivariate cox analysis were used to select significant variables for construction of nomogram. The support vector machine (SVM) and principal component analysis (PCA) were used to assess the distinguished effect of significant genes. Besides, the nomogram model was evaluated by concordance index, time-dependent receiver operating characteristics (ROC) curve, calibration curve and decision curve analysis (DCA). Gene Set Enrichment Analysis (GSEA), CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS) were utilized to explore the potential mechanism of immune-related process and immunotherapy.

Results: A total of 695 HCC patients were selected in the process including 495 training patients and 200 validation patients. Nomogram was constructed based on T stage, age, country, mutation status of DOCK2, EYS, MACF1 and TP53. The assessment showed the nomogram has good discrimination and high consistence between predicted and actual data. Furthermore, we found T cell exclusion was the potential mechanism of malignant progression in high-risk group. Meanwhile, low-risk group might be sensitive to immunotherapy and benefit from CTLA-4 blocker treatment.

Conclusion: Our research established a nomogram based on mutant genes and clinical parameters, and revealed the underlying association between these risk factors and immune-related process.

A Novel RNA-Binding Protein Signature to Predict Clinical Outcomes and Guide Clinical Therapy in Gastric Cancer

Objective: This study aimed to develop an RNA-binding protein (RBP)-based signature for risk stratification and guiding clinical therapy in gastric cancer. Methods: Based on survival-related RBPs, an RBP-based signature was established by LASSO regression analysis in TCGA dataset. Kaplan-Meier curves were drawn between high- and low-risk groups. The predictive efficacy of this signature was assessed via ROCs at 1-, 3-, and 5-year survival. Its generalizability was verified in an external dataset. Following adjustment with other clinicopathological characteristics, the independency of survival prediction was evaluated via multivariate Cox regression and subgroup analyses. GSEA was utilized in identifying activated pathways in two groups. Stromal score, immune score, tumor purity, and infiltration levels of 22 immune cells were determined in each sample via the ESTIMATE and CIBERSORT algorithms. The sensitivity to chemotherapy drugs was assessed through the GDSC database. Results: Data showed that patients with high risk exhibited unfavorable clinical outcomes than those with low risk. This signature possessed good performance in predicting 1-, 3-, and 5-year survival and can be independently predictive of patients' survival. Calcium, ECM receptor interaction, and focal adhesion were highly enriched in high-risk samples. High-risk samples presented increased stromal and immune scores and reduced tumor purity. Moreover, this signature presented close relationships with immune infiltrations. Low-risk specimens were more sensitive to sorafenib, gefitinib, vinorelbine, and gemcitabine than high-risk specimens. Conclusion: This RBP-based signature may be a promising tool for predicting clinical outcomes and guiding clinical therapy in gastric cancer.

Ferroptosis-Related Gene Signature Accurately Predicts Survival Outcomes in Patients With Clear-Cell Renal Cell Carcinoma

As a type of regulated cell death induced by Ras selective lethal (RSL) compounds such as erasti, ferroptosis is characterized by iron-dependent lipid peroxide accumulation to lethal levels. At present, little is known about the role of ferroptosis-related genes in clear-cell renal cell carcinoma (ccRCC). In the present study, the expression data of ferroptosis-related genes in ccRCC were obtained from the Cancer Genome Atlas (TCGA), and COX regression analysis was performed to construct a risk model of ferroptosis prognostic signature. The GEO database was used to verify the accuracy of the model. The following findings were made: the results reveal that the prognostic signature constructed by 11 ferroptosis genes (CARS, CD44, DPP4, GCLC, HMGCR, HSPB1, NCOA4, SAT1, PHKG2, GOT1, HMOX1) was significantly related to the overall survival (OS) of ccRCC patients based on the lowest Akaike information criterion (AIC); multivariate analysis indicates that ferroptosis-related gene prognostic signature was an independent prognostic factor in ccRCC patients; the calibration curve and c-index value (0.77) demonstrate that the nomogram with the signature could predict the survival of ccRCC patients; and enrichment analysis shows that the high-risk group were enriched in humoral immunity and receptor interaction pathways. The aforementioned findings indicate that the ferroptosis-related gene signature can accurately predict the prognosis of ccRCC patients and provide valuable insights for individualized treatment.