Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis

Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide and its incidence continues to increase year by year. Endoplasmic reticulum stress (ERS) caused by protein misfolding within the secretory pathway in cells and has an extensive and deep impact on cancer cell progression and survival. Growing evidence suggests that the genes related to ERS are closely associated with the occurrence and progression of HCC. This study aimed to identify an ERS-related signature for the prospective evaluation of prognosis in HCC patients. RNA sequencing data and clinical data of patients from HCC patients were obtained from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC). Using data from TCGA as a training cohort (n=424) and data from ICGC as an independent external testing cohort (n=243), ERS-related genes were extracted to identify three common pathways IRE1, PEKR, and ATF6 using the GSEA database. Through univariate and multivariate Cox regression analysis, 5 gene signals in the training cohort were found to be related to ERS and closely correlated with the prognosis in patients of HCC. A novel 5-gene signature (including HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker. The prognosis of patients with high-risk HCC was worse than that of patients with low-risk HCC. Multivariate Cox regression analysis confirmed that the signature was an independent prognostic biomarker for HCC. The results were further validated in an independent external testing cohort (ICGC). Also, GSEA indicated a series of significantly enriched oncological signatures and different metabolic processes that may enable a better understanding of the potential molecular mechanism mediating the progression of HCC. The 5-gene biomarker has a high potential for clinical applications in the risk stratification and overall survival prediction of HCC patients. In addition, the abnormal expression of these genes may be affected by copy number variation, methylation variation, and post-transcriptional regulation. Together, this study indicated that the genes may have potential as prognostic biomarkers in HCC and may provide new evidence supporting targeted therapies in HCC.

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Exploration and Verification of a Six-RNA Binding Proteins-based Prognosis Evaluating Model for Hepatocellular Carcinoma

10.21203/rs.3.rs-117829/v1 ◽

2020 ◽

Author(s):

Miao Chen ◽

Shujie Li ◽

Jiakang Zhang ◽

Jianbo Han ◽

Lili Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Survival Analysis ◽

Binding Proteins ◽

Cancer Genome ◽

Functional Enrichment ◽

Operating Characteristics ◽

Training Cohort ◽

Cox Regression Analysis ◽

Receiver Operating

Abstract BackgroundRNA binding protein (RBP) plays a crucial role in tumorigenesis at post-transcriptional level in various cancer types. Nevertheless, the role of RBPs in liver hepatocellular carcinoma (LIHC) remains obscure. We attempted to uncover the association between RBPs and the prognosis of LIHC patients. MethodsWe analyzed the transcriptome and corresponding clinical data of LIHC patients from the cancer genome atlas (TCGA) (training cohort) and international cancer genome consortium (ICGC) (validating cohort) database with a series of bioinformatics methods. Differently expressed RNA-binding proteins (DERBPs) were screened and subjected to functional enrichment analysis and co-expression network establishment. Overall survival (OS) related DERBPs and our prognosis risk model were confirmed by univariate, LASSO and multivariate regression analysis in training cohort. Survival analysis, Receiver operating characteristic curve (ROC) and nomogram were conducted in both training and validating groups to confirm the performance of our model. Human protein atlas (HPA) database and Kaplan-Meier plotter were used to verify the expression and prognostic significance of the hub RBPs respectively.Results There were 330 RBPs were found significantly different in TCGA. Functional analysis indicated most of the DERBPs were majored in RNA processing, alternative splicing and metabolism, etc. 6 RBPs (UPF3B, MRPL54, ZC3H13, DHX58, PPARGC1A, EIF2AK4) were recognized as OS related and enrolled into our prognostic model. Survival analysis showed the risk signature was negatively correlated with the OS of LIHC patients in both training (p = 5.808e-06) and validating (p = 3.38e-03) groups. The area under curves (AUC) of the receiver operating characteristics (ROC) curve in training and validating cohorts was 0.756, 0.781 respectively which indicating the good performance of our model. The risk signature was an independent hazardous factor in multivariate COX regression analysis either in TCGA (HR = 1.626;95% CI 1.394 -1.897, p < 0.001) or ICGC (HR = 1.939;95% CI 1.324 -2.838, p < 0.001). Nomogram and calibration curve indicated our model had best performance in predicting 3-year survival rate.ConclusionsWe constructed a six-RBPs based risk signature model which had moderate efficiency in LIHC patients’ prognosis forecasting which may assist practitioners to make better decision in the management of LIHC.

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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v1 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

BioMed Research International ◽

10.1155/2020/8579651 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Xiaoqing Yu ◽

Jingsong Zhang ◽

Rui Yang ◽

Chun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Single Sample ◽

Survival Prediction ◽

Prediction Ability ◽

Cox Regression Analysis ◽

Score Model ◽

Function Enrichment

Objective. Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need. Methods. The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape. Results. Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions. Conclusion. This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

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Identification and Validation of a Novel Metabolism‑Related Prognostic Signature in Hepatocellular Carcinoma

10.21203/rs.3.rs-50552/v1 ◽

2020 ◽

Author(s):

Zhihao Wang ◽

Kidane Siele Embaye ◽

Qing Yang ◽

Lingzhi Qin ◽

Chao Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Cancer Genome ◽

Regression Analyses ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Tcga Dataset

Abstract Background: Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes(MRGs) for hepatocellular carcinoma (HCC) diagnosis and treatment. Methods: The metabolism-related genes sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium(ICGC) and The Cancer Genome Atlas (TCGA). The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate Cox regression analysis were performed to identify metabolism-related DEGs that related to overall survival(OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses . Furthermore, the signature was validated in the TCGA dataset. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in HCC. Results: A total of 178 differentially expressed MRGs were detected between the ICGA dataset and the TCGA dataset. We found that 17 MRGs were most significantly associated with OS by using the univariate Cox proportional hazards regression analysis in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes, and were further associated with tumor TNM stage, gender, age, and pathological stage. Finally, the signature was found to be associated with various clinicopathological features. Conclusions: In summary, our data provided evidence that the metabolism-based signature could serve as a reliable prognostic and predictive tool for overall survival in patients with HCC.

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Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/5564040 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Fanbo Qin ◽

Junyong Zhang ◽

Jianping Gong ◽

Wenfeng Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Value ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

Clinical Parameters ◽

Cox Regression Analysis ◽

Model Based

Background. Accumulating 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. Methods. Differentially 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. Results. A 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. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

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Prognostic Power of a Chaperonin Containing TCP-1 Subunit Genes Panel for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.668871 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wenli Li ◽

Jun Liu ◽

Hetong Zhao

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Cox Regression ◽

Gene Signature ◽

Cancer Genome ◽

Medical Decision ◽

Survival Prediction ◽

Cox Regression Analysis ◽

T Complex

Chaperonin containing TCP-1 (T-complex protein 1) (CCT) is a large molecular weight complex that contains nine subunits (TCP1, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT6B, CCT7, CCT8). This study aimed to reveal key genes which encode CCT subunits for prognosis and establish prognostic gene signatures based on CCT subunit genes. The data was downloaded from The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus. CCT subunit gene expression levels between tumor and normal tissues were compared. Corresponding Kaplan-Meier analysis displayed a distinct separation in the overall survival of CCT subunit genes. Correlation analysis, protein-protein interaction network, Gene Ontology analysis, immune cells infiltration analysis, and transcription factor network were performed. A nomogram was constructed for the prediction of prognosis. Based on multivariate Cox regression analysis and shrinkage and selection method for linear regression model, a three-gene signature comprising CCT4, CCT6A, and CCT6B was constructed in the training set and significantly associated with prognosis as an independent prognostic factor. The prognostic value of the signature was then validated in the validation and testing set. Nomogram including the signature showed some clinical benefit for overall survival prediction. In all, we built a novel three-gene signature and nomogram from CCT subunit genes to predict the prognosis of hepatocellular carcinoma, which may support the medical decision for HCC therapy.

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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v2 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS.Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients. Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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Development and validation of a novel pseudogene pair-based prognostic signature for prediction of overall survival in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-020-07391-2 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yajuan Du ◽

Ying Gao

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Pearson Correlation ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

The Novel ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background There is growing evidence that pseudogenes may serve as prognostic biomarkers in several cancers. The present study was designed to develop and validate an accurate and robust pseudogene pairs-based signature for the prognosis of hepatocellular carcinoma (HCC). Methods RNA-sequencing data from 374 HCC patients with clinical follow-up information were obtained from the Cancer Genome Atlas (TCGA) database and used in this study. Survival-related pseudogene pairs were identified, and a signature model was constructed by Cox regression analysis (univariate and least absolute shrinkage and selection operator). All individuals were classified into high- and low-risk groups based on the optimal cutoff. Subgroups analysis of the novel signature was conducted and validated in an independent cohort. Pearson correlation analyses were carried out between the included pseudogenes and the protein-coding genes based on their expression levels. Enrichment analysis was performed to predict the possible role of the pseudogenes identified in the signature. Results A 19-pseudogene pair signature, which included 21 pseudogenes, was established. Patients in high-risk group demonstrated an increased the risk of adverse prognosis in the TCGA cohort and the external cohort (all P < 0.001). The novel pseudogene signature was independent of other conventional clinical variables used for survival prediction in HCC patients in the two cohorts revealed by the multivariate Cox regression analysis (all P < 0.001). Subgroup analysis further demonstrated the diagnostic value of the signature across different stages, grades, sexes, and age groups. The C-index of the prognostic signature was 0.761, which was not only higher than that of several previous risk models but was also much higher than that of a single age, sex, grade, and stage risk model. Furthermore, functional analysis revealed that the potential biological mechanisms mediated by these pseudogenes are primarily involved in cytokine receptor activity, T cell receptor signaling, chemokine signaling, NF-κB signaling, PD-L1 expression, and the PD-1 checkpoint pathway in cancer. Conclusion The novel proposed and validated pseudogene pair-based signature may serve as a valuable independent prognostic predictor for predicting survival of patients with HCC.

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Idenfication of a novel signature based on mTORC1 pathway for hepatocellular carcinoma.

10.21203/rs.2.23745/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shaoju Luo ◽

Zhirui Cao ◽

Hao Hu ◽

Ling Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Prognostic Factor ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Signal Pathway ◽

Cancer Genome ◽

Cox Regression Analysis

Abstract Background mTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature. Methods HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, a nomogram was established and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the six genes ( ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP ). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Conclusion The mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.

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A Novel Signature Based On Mtorc1 Pathway In Hepatocellular Carcinoma

10.21203/rs.3.rs-18272/v1 ◽

2020 ◽

Author(s):

Zhuomao Mo ◽

Shuqiao Zhang ◽

Shijun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

T Cells ◽

Regression Analysis ◽

Prognostic Factor ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Signal Pathway ◽

Cancer Genome ◽

Cox Regression Analysis

Abstract BackgroundmTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature.MethodsHCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot.ResultsThe signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found significantly associated with the infiltration of B cells, CD4+T-cells, CD8+T-cells, dendritic cells, macrophages and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Finally, the nomogram involved age, gender, stage and signature have been established.ConclusionThe mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC. *These authors contributed equally.

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