Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma.
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ABSTRACT:
SUBMITTER: Li G
PROVIDER: S-EPMC8322950 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Hepatocellular carcinoma (HCC) is one of the most aggressive malignant tumors, and the prognosis of HCC patients with lymph node metastasis (LNM) is poor. However, robust biomarkers for predicting the prognosis of HCC LNM are still lacking. This study used weighted gene coexpression network analysis of GSE28248 (N = 80) microarray data to identify gene modules associated with HCC LNM and validated in GSE40367 dataset (N = 18). The prognosis-related genes in the HCC LNM module were further screened based on the prognostic curves of 371 HCC samples from TCGA. We finally developed a prognostic signature, PSG-30, as a prognostic-related biomarker in HCC LNM. The HCC subtypes identified by PSG-30-based consensus clustering analysis showed significant differences in prognosis, clinicopathological stage, m6A modification, ferroptosis activation, and immune characteristics. In addition, RAD54B was selected by regression model as an independent risk factor affecting the prognosis of HCC patients with LNM, and its expression was significantly positively correlated with tumor mutational burden and microsatellite instability in high-risk subtypes. Patients with high RAD54B expression had a better prognosis in the immune checkpoint inhibitor-treated cohorts but had a poor prognosis in the HCC sorafenib-treated group. The association of high RAD54B expression with LNM in breast cancer (BRCA) and cholangiocarcinoma and its prognostic effect in BRCA LNM cases suggest the value of RAD54B at the pancancer level. In conclusion, PSG-30 can effectively identify HCC LNM population with poor prognosis, and high-risk patients with high RAD54B expression may be more suitable for immunotherapy.
| S-EPMC9279092 | biostudies-literature
Project description:Bladder cancer (BC) is the ninth most common malignancy worldwide. Bladder urothelial carcinoma (BLCA) constitutes more than 90% of bladder cancer (BC). The five-year survival rate is 5-70%, and patients with BLCA have a poor clinical outcome. The identification of novel clinical molecular markers in BLCA is still urgent to allow for predicting clinical outcomes. This study aimed to identify a novel signature integrating the three-dimension transcriptome of protein coding genes, long non-coding RNAs, microRNAs that is related to the overall survival of patients with BLCA, contributing to earlier prediction and effective treatment selection, as well as to the verification of the established model in the subtypes identified. Gene expression profiling and the clinical information of 400 patients diagnosed with BLCA were retrieved from The Cancer Genome Atlas (TCGA) database. A univariate Cox regression analysis, robust likelihood-based survival modelling analysis and random forests for survival regression and classification algorithms were used to identify the critical biomarkers. A multivariate Cox regression analysis was utilized to construct a risk score formula with a maximum area under the curve (AUC = 0.7669 in the training set). The significant signature could classify patients into high-risk and low-risk groups with significant differences in overall survival time. Similar results were confirmed in the test set (AUC = 0.645) and in the entire set (AUC = 0.710). The multivariate Cox regression analysis indicated that the five-RNA signature was an independent predictive factor for patients with BLCA. Non-negative matrix factorization and a similarity network fusion algorithm were applied for identifying three molecular subtypes. The signature could separate patients in every subtype into high- and low- groups with a distinct difference. Gene set variation analysis of protein-coding genes associated with the five prognostic RNAs demonstrated that the co-expressed protein-coding genes were involved in the pathways and biological process of tumourigenesis. The five-RNA signature could serve as to some degree a reliable independent signature for predicting outcome in patients with BLCA.
| S-EPMC7380276 | biostudies-literature
Project description:BackgroundThe abnormal expression of genes is an essential factor affecting the prognosis of cancer. RNA modification is a way of regulating post-transcriptional levels, including m6A, m5C, m1A RNA methylation. Studies have found that RNA methylation regulates tumorigenesis development and stem cell regeneration. However, there are few studies on lung adenocarcinoma. This study aims to explore the clinical value of RNA methylation for lung adenocarcinoma.MethodsWe summarized thirty-one RNA methylation regulators. The training set was obtained from The Cancer Genome Atlas (TCGA) database, and the test set was obtained from the Gene Expression Omnibus (GEO) database. The Wilcoxon test was used to analyze the expression of RNA methylation regulators. We constructed tumor subgroup models and risk models based on the expression of those regulators. Principal component analysis (PCA) and the receiver operating characteristic (ROC) confirmed the accuracy of the models. Real-time polymerase chain reaction (PCR) validates the results in vitro.ResultsMost RNA methylation regulators had distinct expressions in tumor tissues and adjacent tissues (P<0.05). All the models showed high predictive performance (AUC: 0.65-0.82), and the five-year survival of patients in each group was statistically different (P<0.05). The patients in the high-risk group were more likely to have a higher stage, more lymph node metastases, and distant metastases, showing a poor clinical outcome. Patients with high expression of NOP2 or HNRNP were more likely to have a poorly differentiated in vitro experiment.ConclusionsWith our study, we found that the expressions of most RNA methylation regulators were significantly different in cancer and para-cancerous tissues. Different molecular phenotypes constructed by RNA methylation regulators can be independent risk factors for the prognosis of lung adenocarcinoma. Our study demonstrates the critical role of RNA methylation in lung adenocarcinoma, and it is expected to supply a reference for the prognostic stratification and treatment strategy development of lung adenocarcinoma.
| S-EPMC7333141 | biostudies-literature
Project description:Liver hepatocellular carcinoma (LIHC) comprises most cases of liver cancer with a poor prognosis. N6 -methyladenosine (m6A) plays important biological functions in cancers. Thus, the present research was aimed to determine biomarkers of m6A regulators that could effectively predict the prognosis of LIHC patients. Based on the data collected from the Cancer Genome Atlas (TCGA) database, the correlation between the mRNA expression levels and copy number variation (CNV) patterns were determined. Higher mRNA expression resulted from the increasing number of 9 genes. Using the univariate Cox regression analysis, 11 m6A regulators that had close correlations with the LIHC prognosis were identified. In addition, under the support of the multivariate Cox regression models and the least absolute shrinkage and selection operator, a 4-gene (YTHDF2, IGF2BP3, KIAA1429, and ALKBH5) signature of m6A regulators was constructed. This signature was expected to present a prognostic value in LIHC (log-rank test p value < 0.0001). The GSE76427 (n = 94) and ICGC-LIRI-JP (n = 212) datasets were used to validate the prognostic signature, suggesting strong power to predict patients' prognosis for LIHC. To sum up, genetic alterations in m6A regulatory genes were identified as reliable and effective biomarkers for predicting the prognosis of LIHC patients.
| S-EPMC8849219 | biostudies-literature
Project description:PurposeHepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC.MethodsWe selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), and gene set enrichment analysis (GSEA) was used to explore bioinformatics information and further screen genes. We then built a gene signature based on the Cox proportional hazards regression model.ResultsGSEA revealed that the hallmark DNA repair gene set was significantly upregulated in the tumor phenotype. A set of seven genes, namely, ADA, FEN1, POLR2G, SAC3D1, SEC61A1, SF3A3, and UPF3B, were significantly a ssociated with overall survival (OS) and used to form a gene signature. The signature risk score was calculated and used to divide patients into high- and low-risk groups. The high-risk group showed worse prognosis (log-rank test p<0.0001). Univariate and multivariate Cox regression analysis showed that the prognostic performance of this risk score signature was robust in different subgroups based on clinicopathological features, with p-values <0.05 (HR=2.38, 95% CI (confidence interval) =1.355-4.184), indicating that it can serve as an independent prognostic indicator.ConclusionWe developed and identified a seven-gene signature related to the DNA repair process that can predict survival in HCC. It can be used as an effective classification tool and to guide clinical treatment.
| S-EPMC6689532 | biostudies-literature
Project description:Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed AS (DEAS) were compared between HCC and adjacent normal samples. Univariate Cox regression analysis was used to determine changes in DEAS associated with overall survival (OS). A PI was generated from OS-associated DEASs using Kaplan-Meier curves, receiver operating characteristic (ROC) curves, multivariate Cox regression, and cluster analysis. Then, the correlation between DEASs and splicing factors was assessed, followed by functional and pathway enrichment analysis. We identified 34 163 ASs of 8985 genes in HCC, and 153 OS-ASs were identified using univariate Cox regression analysis. Low- and high-PI groups were determined based on the median "PI-ALL" value according to significantly different survival (P = 2.2e - 16). The ROC curve of all PI (PI-ALL) had an area under the curve (AUC) of 0.993 for survival status in patients with HCC. A potential regulatory network associated with prognosis of patients with HCC was established. Enrichment analysis also resulted in the identification of several pathways potentially associated with carcinogenesis and progression of HCC. Four clusters were identified that were associated with clinical features and prognosis. Our study generated comprehensive profiles of ASs in HCC. The interaction network and functional connections were used to elucidate the underlying mechanisms of AS in HCC.
| S-EPMC7064038 | biostudies-literature
Project description:Objective: This study intends to identify potential prognostic marker genes associated with the prognosis of patients suffering from hepatocellular carcinoma (HCC) based on TCGA and GEO analysis. Methods: TCGA-LIHC cohort was downloaded and the data related to HCC were extracted from The Cancer Genome Atlas (TCGA) database and subjected to differential analysis. HCC-related gene expression datasets were retrieved from the GEO database, followed by differential analysis. After intersection of the results of TCGA and GEO databases, gene interaction analysis was performed to obtain the core genes. To identify the genes related to the prognosis of HCC patients, we conducted univariate and multivariate Cox analyses. Results: Based on differential analysis of TCGA database, 854 genes were differentially expressed in HCC, any of which might link to the occurrence and progression of HCC. Meanwhile, joint analysis of HCC-related gene expression datasets in the GEO database screened 214 genes. Five core genes CDC20, TOP2A, RRM2, UBE2C and AOX1 were significantly associated with the prognosis of HCC patients and the risk model based on these five genes effectively predicted the prognosis of HCC patients. Conclusion: Collectively, our data suggest that CDC20, TOP2A, RRM2, UBE2C and AOX1 may be the key genes affecting the prognosis of patients with HCC. The five-gene signature could accurately predict the prognosis of HCC patients.
| S-EPMC8455941 | biostudies-literature
Project description:Due to the high mortality of hepatocellular carcinoma (HCC), its prognostic models are urgently needed. Bile acid (BA) metabolic disturbance participates in hepatocarcinogenesis. We aim to develop a BA-related gene signature for HCC patients. Research data of HCC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) online databases. After least absolute shrinkage and selection operator (LASSO) regression analysis, we developed a BA-related prognostic signature in TCGA cohort based on differentially expressed prognostic BA-related genes. Then, the predictive performance of the signature was evaluated and verified in TCGA and ICGC cohort respectively. We obtained the risk score of each HCC patient according to the model. The differences of immune status and drug sensitivity were compared in patients that were stratified based on risk score. The protein and mRNA levels of the modeling genes were validated in the Human Protein Atlas database and our cell lines, respectively. In TCGA cohort, we selected 4 BA-related genes to construct the first BA-related prognostic signature. The risk signature exhibited good discrimination and predictive ability, which was verified in ICGC cohort. Patients were classified into high- and low-risk groups according to their median scores. The occurrence of death increased with increasing risk score. Low-risk patients owned favorable overall survival. High-risk patients possessed high immune checkpoint expression and low IC50 values for sorafenib, cisplatin and doxorubicin. Real-time quantitative PCR and immunohistochemical results validate expression of modeling genes in the signature. We constructed the first BA-related gene signature, which might help to identify HCC patients with poor prognosis and guide individualized treatment.
| S-EPMC9792463 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) has a dismal long-term outcome. We aimed to construct a multi-gene model for prognosis prediction to inform HCC management. The cancer-specific differentially expressed genes (DEGs) were identified using RNA-seq data of paired tumor and normal tissue. A prognostic signature was built by LASSO regression analysis. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. A 10-gene signature was constructed to stratify the TCGA and ICGC cohorts into high- and low-risk groups where prognosis was significantly worse in the high-risk group across cohorts (P < 0.001 for all). The 10-gene signature outperformed all previously reported models for both C-index and the AUCs for 1-, 3-, 5-year survival prediction (C-index, 0.84 vs 0.67 to 0.73; AUCs for 1-, 3- and 5-year OS, 0.84 vs 0.68 to 0.79, 0.81 to 0.68 to 0.80, and 0.85 vs 0.67 to 0.78, respectively). Multivariate Cox regression analysis revealed risk group and tumor stage to be independent predictors of survival in HCC. A nomogram incorporating tumor stage and signature-based risk group showed better performance for 1- and 3-year survival than for 5-year survival. GSEA revealed enrichment of pathways related to cell cycle regulation among high-risk samples and metabolic processes in the low-risk group. Our 10-gene model is robust for prognosis prediction and may help inform clinical management of HCC.
| S-EPMC7372135 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the leading causes of cancer deaths worldwide. Recently, microRNAs (miRNAs) are reported to be altered and act as potential biomarkers in various cancers. However, miRNA biomarkers for predicting the stage of HCC are limitedly discovered. Hence, we sought to identify a novel miRNA signature associated with cancer stage in HCC. We proposed a support vector machine (SVM)-based cancer stage prediction method, SVM-HCC, which uses an inheritable bi-objective combinatorial genetic algorithm for selecting a minimal set of miRNA biomarkers while maximizing the accuracy of predicting the early and advanced stages of HCC. SVM-HCC identified a 23-miRNA signature that is associated with cancer stages in patients with HCC and achieved a 10-fold cross-validation accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve (AUC) of 92.59%, 0.98, 0.74, 0.80, and 0.86, respectively; and test accuracy and test AUC of 74.28% and 0.73, respectively. We prioritized the miRNAs in the signature based on their contributions to predictive performance, and validated the prognostic power of the prioritized miRNAs using Kaplan-Meier survival curves. The results showed that seven miRNAs were significantly associated with prognosis in HCC patients. Correlation analysis of the miRNA signature and its co-expressed miRNAs revealed that hsa-let-7i and its 13 co-expressed miRNAs are significantly involved in the hepatitis B pathway. In clinical practice, a prediction model using the identified 23-miRNA signature could be valuable for early-stage detection, and could also help to develop miRNA-based therapeutic strategies for HCC.
| S-EPMC7467934 | biostudies-literature