Identification and Validation of Prognosis-Related Necroptosis Genes for Prognostic Prediction in Hepatocellular Carcinoma.
Ontology highlight
ABSTRACT: Background
The prediction of hepatocellular carcinoma (HCC) survival is challenging because of its rapid progression. In recent years, necroptosis was found to be involved in the progression of multiple cancer types. However, the role of necroptosis in HCC remains unclear.Methods
Clinicopathological parameters and transcriptomic data of 370 HCC patients were obtained from TCGA-LIHC dataset. Prognosis-related necroptosis genes (PRNGs) were identified and utilized to construct a LASSO risk model. The GEO cohorts (GSE54236 and GSE14520) were used for external validation. We evaluated the distribution of HCC patients, the difference in prognosis, and the accuracy of the prognostic prediction of the LASSO risk model. The immune microenvironment and functional enrichment of different risk groups were further clarified. Finally, we performed a drug sensitivity analysis on the PRNGs that constructed the LASSO model and verified their mRNA expression levels in vitro. Results: A total of 48 differentially expressed genes were identified, 23 of which were PRNGs. We constructed the LASSO risk model using nine genes: SQSTM1, FLT3, HAT1, PLK1, MYCN, KLF9, HSP90AA1, TARDBP, and TNFRSF21. The outcomes of low-risk patients were considerably better than those of high-risk patients in both the training and validation cohorts. In addition, stronger bile acid metabolism, xenobiotic metabolism, and more active immune cells and immune functions were observed in low-risk patients, and high expressions of TARDBP, PLK1, and FLT3 were associated with greater drug sensitivity. With the exception of FLT3, the mRNA expression of the other eight genes was verified in Huh7 and 97H cells. Conclusions. The PRNG signature provides a novel and effective method for predicting the outcome of HCC as well as potential targets for further research.
SUBMITTER: Gao X
PROVIDER: S-EPMC9259286 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background: The study focused on establishing a prognostic survival model with six necroptosis-related lncRNAs to predict overall survival (OS) in patients with hepatocellular carcinoma (HCC). Methods: The data of gene expression and clinical information of HCC patients were obtained from The Cancer Genome Atlas (TCGA). Cox regression with LASSO was used for constructing a necroptosis-related lncRNA survival model, which we further validated with qRT-PCR in vitro. The relative bioinformatics analysis and consensus cluster analysis were performed based on six differentially expressed lncRNAs. Results: The survival prognostic model was constructed by using data from TCGA. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. GSEA showed that several signaling pathways were related to HCC progression. Immune-related functional analysis showed that aDCs, macrophages, Th2 cells, and Tregs have stronger correlation with the high-risk group. The consensus cluster analysis further validated the 6-lncRNA prognostic model. Conclusion: A novel 6-lncRNA (AL606489.1, NRAV, LINC02870, DUXAP8, "ZFPM2-AS1," and AL031985.3) prognostic model had an accurately predictive power in HCC prognosis, which might be worthy of clinical application.
| S-EPMC9557293 | biostudies-literature
Project description:BackgroundNecroptosis is a necrotic programmed cell death with potent immunogenicity. Due to the dual effects of necroptosis on tumor growth, metastasis and immunosuppression, we evaluated the prognostic value of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC).MethodsWe first analyzed RNA sequencing and clinical HCC patient data obtained to develop an NRG prognostic signature based on the TCGA dataset. Differentially expressed NRGs were further evaluated by GO and KEGG pathway analyses. Next, we conducted univariate and multivariate Cox regression analyses to build a prognostic model. We also used the dataset obtained from the International Cancer Genome Consortium (ICGC) database to verify the signature. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to investigate the immunotherapy response. Furthermore, we investigated the relationship between the prediction signature and chemotherapy treatment response in HCC.ResultsWe first identified 36 differentially expressed genes out of 159 NRGs in hepatocellular carcinoma. Enrichment analysis showed that they were mainly enriched in the necroptosis pathway. Four NRGs were screened by Cox regression analysis to establish a prognostic model. The survival analysis revealed that the overall survival of patients with high-risk scores was significantly shorter than that of patients with low-risk scores. The nomogram demonstrated satisfactory discrimination and calibration. The calibration curves validated a fine concordance between the nomogram prediction and actual observation. The efficacy of the necroptosis-related signature was also validated by an independent dataset and immunohistochemistry experiments. TIDE analysis revealed that patients in the high-risk group were possibly more susceptible to immunotherapy. Furthermore, high-risk patients were found to be more sensitive to conventional chemotherapeutic medicines such as bleomycin, bortezomib, and imatinib.ConclusionWe identified 4 necroptosis-related genes and established a prognostic risk model that could potentially predict prognosis and response to chemotherapy and immunotherapy in HCC patients in the future.
| S-EPMC9934426 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is the sixth most common cancer, which is characterized by complicated etiology, excessive heterogeneity, and poor prognosis. Necroptosis is a new kind of programmed cell death, which is intently associated with the occurrence and development of tumors. Although researchers have had a deep understanding of necroptosis in recent years, the expression level of necroptosis-related genes in HCC and its relationship with the survival time of HCC patients are not clear.MethodsAccording to the expression of necroptosis-related genes and the survival of HCC patients, HCC patients in the TCGA database were divided into 2 groups that were relatively independent of each other. The genes related to the survival time of HCC patients were screened from the 2 groups of differentially expressed genes. By using the Least Absolute Shrinkage and Selection Operator Cox regression analysis, the optimal λ value was obtained, and the 10-gene signature model was established.ResultsAccording to the median risk score of the TCGA cohort, HCC patients were averagely divided into high- and low-risk groups. Compared with the low-risk group, the death toll of the high-risk group was relatively higher and the survival time was relatively shorter. Principal component analysis and t-distributed stochastic neighbor embedding analysis showed that there was a significant separation between high- and low-risk groups. Through Kaplan-Meier analysis, it was found that the survival time of HCC patients in the high-risk group was significantly shorter than that in the low-risk group. Through receiver operating characteristic analysis, it was found that the sensitivity and specificity of the model were good. We also make a comprehensive analysis of the international cancer genome consortium database as a verification queue and prove the reliability of the 10-gene signature model. Gene Ontolog, Kyoto Encyclopedia of Genes and Genomes, and single-sample gene set enrichment analysis showed that many biological processes and pathways related to immunity had been enriched, and the antitumor immune function was weakened in the high-risk population.ConclusionThe risk score can be considered as an independent prognostic factor to predict the prognosis of patients with HCC, and necroptosis-related genes are also closely related to tumor immune function.
| S-EPMC10980426 | biostudies-literature
Project description:Background: Hepatocellular carcinoma (HCC) is among malignancies with the highest fatality toll globally and minimal therapeutic options. Necroptosis is a programmed form of necrosis or inflammatory cell death, which can affect prognosis and microenvironmental status of HCC. Therefore, we aimed to explore the prognostic value of necroptosis-related lncRNAs (NRLs) in HCC and the role of the tumor microenvironment (TME) in immunotherapy. Methods: The RNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). NRLs were identified by Pearson correlation analysis. The signature was constructed using the LASSO–Cox regression analysis and evaluated using the receiver operating characteristic curve (ROC) and the area under the Kaplan–Meier curve. The nomogram was built based on clinical information and risk score. Gene set enrichment analysis (GSEA), immunoassay, half-maximum inhibitory concentration (IC50) analysis of the risk group, and the HCC subtype identification based on NRLs were also carried out. Finally, we detected the expression of lncRNAs in HCC tissues and cell lines in vitro. Results: A total of 508 NRLs were screened out, and seven NRLs were constructed as a risk stratification system to classify patients into distinct low- and high-risk groups. Patients in the high-risk group had a significantly lower overall survival (OS) than those in the low-risk group. Using multivariate Cox regression analysis, we found that the risk score was an independent predictor of OS. Functional analysis showed that the immune status of different patients was different. The IC50 analysis of chemotherapy demonstrated that patients in the high-risk group were more sensitive to commonly prescribed drugs. qRT-PCR showed that three high-risk lncRNAs were upregulated in drug-resistant cells, and the expression in HCC tissues was higher than that in adjacent tissues. Conclusion: The prediction signature developed in this study can be used to assess the prognosis and microenvironment of HCC patients, and serve as a new benchmark for HCC treatment selection.
| S-EPMC9214229 | biostudies-literature
Project description:Background: Immunotherapy has achieved remarkable success in treating advanced liver cancer. Current evidence shows that most of the available immune checkpoint inhibitor (ICB) treatments are suboptimal, and specific markers are needed for patients regarded as good candidates for immunotherapy. Necroptosis, a type of programmed cell death, plays an important role in hepatocellular carcinoma (HCC) progression and outcome. However, studies on the necroptosis-related lncRNA in HCC are scarce. In this view, the present study investigates the link among necroptosis-related lncRNA, prognosis, immune microenvironment, and immunotherapy response. Methods: Gene transcriptome and clinical data were retrieved from The Cancer Genome Atlas database. Pearson correlation analysis of necroptosis-related genes was performed to identify necroptosis-related lncRNAs. The Wilcoxon method was used to detect differentially expressed genes, and prognostic relevant lncRNAs were obtained by univariate Cox regression analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were utilized to perform functional enrichment analysis. Lasso-Cox stepwise regression analysis was employed to calculate risk score, which was involved in analyzing immune cells infiltration, immune checkpoints expression, and predicting immunotherapeutic efficacy. Quantitative RT-PCR (qRT-PCR) was performed to detect the expression pattern of lncRNA in cell lines. Results: The 10 lncRNAs generated in this study were used to create a prognostic risk model for HCC and group patients into groups based on risk. High-risk patients with HCC have a significantly lower OS rate than low-risk patients. Multivariate Cox regression analysis showed that risk score is an independent risk factor for HCC with high accuracy. Patients in the high-risk group exhibited a weaker immune surveillance and higher expression level of immune checkpoint molecules. In terms of drug resistance, patients in the low-risk group were more sensitive to sorafenib. The OS-related nomogram was constructed to verify the accuracy of our model. Finally, quantitative RT-PCR experiments were used to verify the expression patterns of candidate genes. Conclusion: The lncRNA signature established herein, encompassing 10 necroptosis-related lncRNAs, is valuable for survival prediction and holds promise as prognostic markers for HCC.
| S-EPMC9468751 | biostudies-literature
Project description:Necroptosis is a newly identified programmed cell death associated with the biological process of various cancers, including esophageal carcinoma (ESCA). Meanwhile, the dysregulation of long non-coding RNAs (lncRNAs) is greatly implicated in ESCA progression and necroptosis regulation. However, the lncRNAs involved in regulating necroptosis in ESCA are still unclear. In this study, we aim to explore the expression profile of necroptosis-related lncRNAs (NRLs), and evaluate their roles in ESCA prognosis and treatment. In the present study, 198 differentially expressed NRLs were identified between the ESCA and adjacent normal tissues through screening the data extracted from the Cancer Genome Atlas (TCGA) database. And, a prognostic panel consisting of 6 NRLs was constructed using the LASSO algorithm and multivariate Cox regression analysis. The ESCA patients with high risks had a markedly reduced survival time and higher mortality prevalence. Moreover, C-index of 6 NRLs-panel was superior to 48 published prognostic models based on lncRNAs or mRNAs for ESCA. There were significant differences between the high-risk and low-risk groups in tumor-related pathways, genetic mutations, and drug sensitivity responses. In vitro analysis revealed that inhibition of PVT1 impeded the proliferation, migration, and colony formation of ESCA cells, increased the expressions of p-RIP1 and p-MLKL and promoted necroptosis. By contrast, PVT1 overexpression resulted in a decrease in necroptotic cell death events, thus promoting tumor progression. Collectively, the established 6-NRLs panel was a promising biomarker for the prognostic prediction of ESCA. Moreover, our current findings provided potential targets for individualized therapy for ESCA patients.
| S-EPMC10292891 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.
| S-EPMC8721125 | biostudies-literature
Project description:BackgroundAccumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients.MethodsDifferentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database.ResultsA total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients.ConclusionThe prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.
| S-EPMC7979286 | biostudies-literature
Project description:MVI has significant clinical value for treatment selection and prognosis evaluation in hepatocellular carcinoma (HCC). We aimed to construct a model based on MVI-Related Genes (MVIRGs) for risk assessment and prognosis prediction in patients with HCC. This study utilized various statistical analysis methods for prognostic model construction and validation in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts, respectively. In addition, immunohistochemistry and qRT-PCR were used to analyze and identify the value of the model in our cohort. After the analyses, 153 differentially expressed MVIRGs were identified, and three key genes were selected to construct a prognostic model. The high-risk group showed significantly lower overall survival (OS), and this trend was observed in all subgroups: different age groups, genders, stages, and grades. Risk score was a risk factor independent of age, gender, stage, and grade. Moreover, the ICGC cohort validated the prognostic value of the model corresponding to the TCGA. In our cohort, qRT-PCR and immunohistochemistry showed that all three genes had higher expression levels in HCC samples than in normal controls. High expression levels of genes and high-risk scores showed significantly lower recurrence-free survival (RFS) and OS, especially in MVI-positive HCC samples. Therefore, the prognostic model constructed by three MVIRGs can reliably predict the RFS and OS of patients with HCC and is valuable for guiding clinical treatment selection and prognostic assessment of HCC.
| S-EPMC8692135 | biostudies-literature
Project description:Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors with a poor prognosis. Recently, necroptosis has been reported to participate in the progression of multiple tumors. However, few studies have revealed the relationship between necroptosis and PDAC, and the role of necroptosis in PDAC has not yet been clarified. Methods: The mRNA expression data and corresponding clinical information of PDAC patients were downloaded from the TCGA and GEO databases. The necroptosis-related genes (NRGs) were obtained from the CUSABIO website. Consensus clustering was performed to divide PDAC patients into two clusters. Univariate and LASSO Cox regression analyses were applied to screen the NRGs related to prognosis to construct the prognostic model. The predictive value of the prognostic model was evaluated by Kaplan-Meier survival analysis and ROC curve. Univariate and multivariate Cox regression analyses were used to evaluate whether the risk score could be used as an independent predictor of PDAC prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA) were used for functional enrichment analysis. Finally, using qRT-PCR examined NRGs mRNA expression in vitro. Results: Based on the TCGA database, a total of 22 differential expressed NRGs were identified, among which eight NRGs (CAPN2, CHMP4C, PLA2G4F, PYGB, BCL2, JAK3, PLA2G4C and STAT4) that may be related to prognosis were screened by univariate Cox regression analysis. And CAPN2, CHMP4C, PLA2G4C and STAT4 were further selected to construct the prognostic model. Kaplan-Meier survival analysis and ROC curve showed that there was a significant correlation between the risk model and prognosis. Univariate and multivariate Cox regression analyses showed that the risk score of the prognostic model could be used as an independent predictor. The model efficacy was further demonstrated in the GEO cohort. Functional analysis revealed that there were significant differences in immune status between high and low-risk groups. Finally, the qRT-PCR results revealed a similar dysregulation of NRGs in PDAC cell lines. Conclusion: This study successfully constructed and verified a prognostic model based on NRGs, which has a good predictive value for the prognosis of PDAC patients.
| S-EPMC9243220 | biostudies-literature