A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma.
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ABSTRACT:
SUBMITTER: Wang J
PROVIDER: S-EPMC8481951 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AbstractAberrant immunity has been associated with the initiation and progression of cancers such as hepatocellular carcinoma (HCC). Here, we aim to develop a signature based on immune-related genes (IRGs) to predict the prognosis of HCC patients. The gene expression profiles of 891 HCC samples were derived from 4 publicly accessible datasets. A total of 1534 IRGs from Immunology Database and Analysis Portal website were obtained as candidate genes for prognostic assessment. Using least absolute shrinkage and selection operator (LASSO) regression analysis, 12 IRGs were selected as prognostic biomarkers and were then aggregated to generate an IRG score for each HCC sample. In the training dataset (n = 365), patients with high IRG scores showed a remarkably poorer overall survival than those with low IRG scores (log-rank P < .001). Similar results were documented in 3 independent testing datasets (n = 226, 221, 79, respectively). Multivariate Cox regression and stratified analyses indicated that the IRG score was an independent and robust signature to predict the overall survival in HCC patients. Patients with high IRG scores tended to be in advanced TNM stages, with increased risks of tumor recurrence and metastasis. More importantly, the IRG score was strongly associated with certain immune cell counts, gene expression of immune checkpoints, estimated immune score, and mutation of critical genes in HCC. In conclusion, the proposed IRG score can predict the prognosis and reflect the tumor immune microenvironment of HCC patients, which may facilitate the individualized treatment and provide potential immunotherapeutic targets.
| S-EPMC8376334 | biostudies-literature
Project description:BACKGROUND:Cellular senescence is a recognized barrier for progression of chronic liver diseases to hepatocellular carcinoma (HCC). The expression of a cluster of genes is altered in response to environmental factors during senescence. However, it is questionable whether these genes could serve as biomarkers for HCC patients. AIM:To develop a signature of senescence-associated genes (SAGs) that predicts patients' overall survival (OS) to improve prognosis prediction of HCC. METHODS:SAGs were identified using two senescent cell models. Univariate COX regression analysis was performed to screen the candidate genes significantly associated with OS of HCC in a discovery cohort (GSE14520) for the least absolute shrinkage and selection operator modelling. Prognostic value of this seven-gene signature was evaluated using two independent cohorts retrieved from the GEO (GSE14520) and the Cancer Genome Atlas datasets, respectively. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive accuracy of the seven-SAG signature and serum ?-fetoprotein (AFP). RESULTS:A total of 42 SAGs were screened and seven of them, including KIF18B, CEP55, CIT, MCM7, CDC45, EZH2, and MCM5, were used to construct a prognostic formula. All seven genes were significantly downregulated in senescent cells and upregulated in HCC tissues. Survival analysis indicated that our seven-SAG signature was strongly associated with OS, especially in Asian populations, both in discovery and validation cohorts. Moreover, time-dependent ROC curve analysis suggested the seven-gene signature had a better predictive accuracy than serum AFP in predicting HCC patients' 1-, 3-, and 5-year OS. CONCLUSION:We developed a seven-SAG signature, which could predict OS of Asian HCC patients. This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.
| S-EPMC6465944 | biostudies-literature
Project description:PurposeOur study is designed to develop and certify a promising prognostic signature for hepatocellular carcinoma (HCC). Materials and methods: We retrospectively analyzed mRNA expression profiles and clinicopathological data fetched from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. We formulated a prognostic seven-gene signature composed of differentially expressed mRNAs (DEmRNAs) between HCC and nonneoplastic tissues through univariate Cox regression analysis. The receiver operating characteristic (ROC) curve, survival analysis and multivariate Cox regression analysis as well as nomograms were utilized to assess the prognostic performance of the seven-gene signature. Results: The risk score based on a seven-gene signature categorized HCC subjects into a high- and low-risk group. There was significantly discrepant overall survival (OS) between patients in both groups and the corresponding ROC curve revealed a satisfactory predictive performance in HCC survival in both TCGA and GSE76427 cohort. Multivariate Cox regression analysis demonstrated that a seven-gene signature was an independently prognostic factor for HCC. Nomograms combining this prognostic signature with significant clinical characteristics conferred a crucial reference to predict the 1-,3- and 5 years OS. Conclusions: Our study defined a promising seven-gene signature and nomogram model to forecast the OS of HCC patients, which is instrumental in clinical decision and personalized therapy.
| S-EPMC7477431 | biostudies-literature
Project description:ObjectiveDysregulation of cell cycle progression (CCP) is one of the hallmarks of cancer. Here, our study is aimed at developing a CCP-derived gene signature for predicting high-risk population of hepatocellular carcinoma (HCC).MethodsOur study retrospectively analyzed the transcriptome profiling and clinical information of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects. Uni- and multivariate cox regression models were conducted for identifying which hallmarks of cancer were risk factors of HCC. CCP-derived gene signature was developed with LASSO method. The predictive efficacy was verified by ROC curves and subgroup analyses. A nomogram was then generated and validated by ROC, calibration, and decisive curves. Immune cell infiltration was estimated with ssGSEA method. Potential small molecular compounds were predicted via CTRP and CMap analyses. The response to chemotherapeutic agents was evaluated based on the GDSC project.ResultsAmong hallmarks of cancer, CCP was identified as a dominant risk factor for HCC prognosis. CCP-derived gene signature displayed the favorable predictive efficacy in HCC prognosis independent of other clinicopathological parameters. A nomogram was generated for optimizing risk stratification and quantifying risk evaluation. CCP-derived signature was in relation to immune cell infiltration, HLA, and immune checkpoint expression. Combining CTRP and CMap analyses, fluvastatin was identified as a promising therapeutic agent against HCC. Furthermore, CCP-derived signature might be applied for predicting the response to doxorubicin and gemcitabine.ConclusionCollectively, CCP-derived gene signature was a promising marker in prediction of survival outcomes and therapeutic responses for HCC patients.
| S-EPMC8553501 | biostudies-literature
Project description:Increased intracellular toxicity due to an imbalance in copper homeostasis caused by copper ion accumulation could regulate the rate of cancer cell growth and proliferation. The goal of this study was to create a novel Cuproptosis-related lncRNA signature that may be utilized to predict survival and immunotherapy in HCC patients. Cuproptosis-associated lncRNAs and differentially expressed lncRNAs between HCC tumor tissue and normal tissue were discovered first. By LASSO-Cox analysis, the overlapping lncRNAs were then utilized to build a Cuproptosis-associated lncRNA signature, which might be used to predict patient prognosis and responsiveness to immune checkpoint blockade (ICB) therapy. Differences in the infiltration of immune cell subpopulations between high and low-risk score subgroups were also analyzed. Moreover, a nomogram based on the Cuproptosis-associated lncRNA signature and clinical features was developed and demonstrated to have good predictive potential. Finally, qRT-PCR was performed in HerpG2 and MHCC-97H cell lines to explore whether these lncRNAs were indeed involved in the process of Cuproptosis. In summary, we created a prognostic lncRNA profile linked to Cuproptosis to forecast response to immunotherapy, which may provide a new potential non-apoptotic therapeutic perspective for HCC patients.
| S-EPMC9256635 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis‑related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC.MethodsBased on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glycolysis-related mRNAs were used to construct GRGPs. Cox regression was applied to establish a seventeen GRGPs signature in TCGA dataset, which was verified in two validation (European and American, and Asian) datasets.ResultsSeventeen prognostic GRGPs (HMMR_PFKFB1, CHST1_GYS2, MERTK_GYS2, GPC1_GYS2, LDHA_GOT2, IDUA_GNPDA1, IDUA_ME2, IDUA_G6PD, IDUA_GPC1, MPI_GPC1, SDC2_LDHA, PRPS1_PLOD2, GALK1_IER3, MET_PLOD2, GUSB_IGFBP3, IL13RA1_IGFBP3 and CYB5A_IGFBP3) were identified to be significantly progressive factors for the patients with HCC in the TCGA dataset, which constituted a GRGPs signature. The patients with HCC were classified into low-risk group and high-risk group based on the GRGPs signature. The GRGPs signature was a significantly independent prognostic indicator for the patients with HCC in TCGA (log-rank P = 2.898e-14). Consistent with the TCGA dataset, the patients in low-risk group had a longer OS in two validation datasets (European and American: P = 1.143e-02, and Asian: P = 6.342e-08). Additionally, the GRGPs signature was also validated as a significantly independent prognostic indicator in two validation datasets.ConclusionThe seventeen GRGPs and their signature might be molecular biomarkers and therapeutic targets for the patients with HCC.
| S-EPMC7531359 | 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:Background: Hepatocellular carcinoma (HCC) is characterized by a poor prognosis and accounts for the fourth common cause of cancer-related deaths. Recently, pyroptosis has been revealed to be involved in the progression of multiple cancers. However, the role of pyroptosis in the HCC prognosis remains elusive. Methods: The clinical information and RNA-seq data of the HCC patients were collected from the TCGA-LIHC datasets, and the differential pyroptosis-related genes (PRG) were firstly explored. The univariate Cox regression and consensus clustering were applied to recognize the HCC subtypes. The prognostic PRGs were then submitted to the LASSO regression analysis to build a prognostic model in the TCGA training cohort. We further evaluated the predictive model in the TCGA test cohort and ICGC validation cohort (LIRI-JP). The accuracy of prediction was validated using the Kaplan-Meier (K-M) and receiver operating characteristic (ROC) analyses. The single-sample gene set enrichment analysis (ssGSEA) was used to determine the differential immune cell infiltrations and related pathways. Finally, the expression of the prognostic genes was validated by qRT-PCR in vivo and in vitro. Results: We identified a total of 26 differential PRGs, among which three PRGs comprising GSDME, GPX4, and SCAF11 were subsequently chosen for constructing a prognostic model. This model significantly distinguished the HCC patients with different survival years in the TCGA training, test, and ICGC validation cohorts. The risk score of this model was confirmed as an independent prognostic factor. A nomogram was generated indicating the survival years for each HCC patient. The ssGSEA demonstrated several tumor-infiltrating immune cells to be remarkably associated with the risk scores. The qRT-PCR results also showed the apparent dysregulation of PRGs in HCC. Finally, the drug sensitivity was analyzed, indicating that Lenvatinib might impact the progression of HCC via targeting GSDME, which was also validated in human Huh7 cells. Conclusion: The PRG signature comprised of GSDME, GPX4, and SCAF11 can serve as an independent prognostic factor for HCC patients, which would provide further evidence for more clinical and functional studies.
| S-EPMC8606409 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. As part of the active cross-talk between the tumor and the host, inflammatory response in the tumor or its microenvironment could affect prognosis. However, the prognostic value of inflammatory response-related genes in HCC remains to be further elucidated.MethodsIn this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from the public database. The least absolute shrinkage and selection operator Cox analysis was utilized to construct a multigene prognostic signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Kaplan Meier analysis was used to compare the overall survival (OS) between high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors for OS. Single-sample gene set enrichment analysis was utilized to calculate the immune cell infiltration score and immune related pathway activity. Gene set enrichment analysis was implemented to conduct GO terms and KEGG pathways. The qRT-PCR and immunohistochemistry were utilized to perform the mRNA and protein expression of prognostic genes between HCC tissues and normal liver tissues respectively.ResultsAn inflammatory response-related gene signature model was constructed by LASSO Cox regression analysis. Compared with the low-risk group, patients in the high-risk group showed significantly reduced OS. Receiver operating characteristic curve analysis confirmed the predictive capacity of the prognostic gene signature. Multivariate Cox analysis revealed that the risk score was an independent predictor for OS. Functional analysis indicated that immune status was definitely different between two risk groups, and cancer-related pathways were enriched in high-risk group. The risk score was significantly correlated with tumor grade, tumor stage and immune infiltrate types. The expression levels of prognostic genes were significantly correlated with sensitivity of cancer cells to anti-tumor drugs. Furthermore, the expression of prognostic genes showed significant difference between HCC tissues and adjacent non-tumorous tissues in the separate sample cohort.ConclusionA novel signature constructed with eight inflammatory response-related genes can be used for prognostic prediction and impact the immune status in HCC. Moreover, inhibition of these genes may be a therapeutic alternative.
| S-EPMC8019928 | biostudies-literature
Project description:BackgroundTumor metastasis is one of the leading reasons of the dismal prognosis of hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) is closely associated with tumor metastasis including HCC. The purpose of this study is to construct and validate an EMT-related gene signature for predicting the prognosis of HCC patients.MethodsGene expression data of HCC patients was downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to found the EMT-related gene sets which were obviously distinct between normal samples and paired HCC samples. Cox regression analysis was used to develop an EMT-related prognostic signature, and the performance of the signature was evaluated by Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. A nomogram incorporating the independent predictors was established. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the hub genes in HCC cell lines, and the role of PDCD6 in the metastasis of HCC was determined by functional experiments.ResultsAn EMT-related 5-gene signature (PDCD6, TCOF1, TRIM28, EZH2 and FAM83D) was constructed using univariate and multivariate Cox regression analysis. Based on the signature, the HCC patients were classified into high- and low-risk groups, and patients in high-risk group had a poor prognosis. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC prognosis exactly and independently. The predictive capacity of the signature was also validated in two external cohorts. GSEA results showed that many cancer-related signaling pathways such as PI3K/Akt/mTOR pathway and TGF-?/SMAD pathway were enriched in high-risk group. The result of qRT-PCR revealed that PDCD6, TCOF1 and FAM83D were highly expressed in HCC cancer cells. Among them, PDCD6 were found to promote cell migration and invasion.ConclusionThe EMT-related 5-gene signature can serve as a promising prognostic biomarker for HCC patients and may provide a novel mechanism of HCC metastasis.
| S-EPMC7953549 | biostudies-literature