scholarly journals The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis–Trans Isomerase Gene Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Stepwise Regression ◽  
Gene Signature ◽  
Training Set ◽  
Model Based ◽  
Kaplan Meier ◽  
Validation Set

Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis–trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma.Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis–trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis–trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis–trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan–Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score.Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan–Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal.Conclusion: A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.

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

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.

scholarly journals A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xun Liu ◽  
Bobo Chen ◽  
Jiahui Chen ◽  
Shaolong Sun
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Roc Curves ◽  
Gene Signature ◽  
Survival Prediction ◽  
Training Set ◽  
Kaplan Meier ◽  
Validation Set

Abstract Background Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer. Methods Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores. Results TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) – (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823. Conclusions The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals The Landscape of Glycosyltransferase Gene Signature for Overall Survival Prediction in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Qiang Cai ◽  
Shizhe Yu ◽  
Jian Zhao ◽  
Duo Ma ◽  
Long Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Regression Analyses ◽  
Risk Patients

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals FAM225B Is a Prognostic lncRNA for Patients with Recurrent Glioblastoma

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Junsheng Li ◽  
Qian Zhang ◽  
Peicong Ge ◽  
Chaofan Zeng ◽  
Fa Lin ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Value ◽  
Cox Regression ◽  
Recurrent Glioblastoma ◽  
Regulation Of Transcription ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Validation Set ◽  
Pathway Analyses

Objective. The overall survival of patients with recurrent glioblastoma (rGBM) is quite different, so clinical outcome prediction is necessary to guide personalized clinical treatment for patients with rGBM. The expression level of lncRNA FAM225B was analyzed to determine its prognostic value in rGBMs. Methods. We collected 109 samples of Chinese Glioma Genome Atlas (CGGA) RNA sequencing dataset and divided into training set and validation set. Then, we analyzed the expression of FAM225B, clinical characteristics, and overall survival (OS) information. Kaplan-Meier survival analysis was used to estimate the OS distributions. The prognostic value of FAM225B in rGBMs was tested by univariate and multivariate Cox regression analyses. Moreover, we analyzed the biological processes and signaling pathways of FAM225B. Results. We found that FAM225B was upregulated in rGBMs ( P = 0.0009 ). The expression of FAM225B increased with the grades of gliomas ( P < 0.0001 ). The OS of rGBMs in the low-expression group was significantly longer than that in the high-expression group ( P = 0.0041 ). Similar result was found in the training set ( P = 0.0340 ) and verified in the validation set ( P = 0.0292 ). In multivariate Cox regression analysis, FAM225B was identified to be an independent prognostic factor for rGBMs ( P = 0.003 ). Biological process and KEGG pathway analyses implied FAM225B mainly played a functional role on transcription, regulation of transcription, cell migration, focal adhesion, etc. Conclusions. FAM225B is expected to be as a new prognostic biomarker for the identification of rGBM patients with poor outcome. And our study provided a potential therapeutic target for rGBMs.

Circulating Exosomal microRNAs Are Prognostic Markers in Multiple Myeloma

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1770-1770
Author(s):  
Salomon Manier ◽  
Herve Avet-Loiseau ◽  
Federico Campigotto ◽  
Karma Salem ◽  
Daisy Huynh ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Significance ◽  
Newly Diagnosed ◽  
Mirna Signature ◽  
Training Set ◽  
Advisory Committees ◽  
Exosomal Mirnas ◽  
Validation Set

Abstract Background Exosomes are secreted by several cell types including cancer cells and can be isolated from peripheral blood. They contain proteins and nucleic acids and promote tumorigenesis in many types of cancer. We aimed to establish the prognostic significance of circulating exosomal microRNAs (miRNAs) in multiple myeloma (MM). Methods We first analyzed the miRNAs content of circulating exosomes in MM by small RNA sequencing of 10 samples from MM patients and 5 healthy controls. We then analyzed 156 serum samples from newly diagnosed patients with MM, uniformly treated with a Bortezomib and Dexamethasone based regimen. Using a quantitative RT-PCR array for 23 miRNAs, we assessed the associations between exosomal miRNAs and progression-free survival. Findings By next generation sequencing, we identified 158 differentially expressed miRNAs in MM compared to normal healthy controls, notably including let-7 family members, miR-17/92 or miR-99b/125a clusters. We further identified a three-miRNA signature based on 156 MM samples (combining miR-106b, miR-18a and let-7e) and calculated a risk score to classify patients as high risk or low risk. Compared to low risk score, patients with a high risk score had a shorter PFS in the training set (hazard ratio [HR] 1·8, 95% CI 1·0-3·0; p=0·0375) and the validation set (HR 2·6, 1·5-4·4; p=0·0005). To further validate this signature, we generated 500 randomly computed re-sampling of the data sets. The three-miRNA signature was consistently significant with a p-value < 0·05 in more than 78% and < 0·10 in 86% of the 500 randomizations. The circulating exosomal miRNA signature was an independent prognostic marker after adjusting for cytogenetics and ISS. In a receiver operating characteristic (ROC) analysis, a combination of this signature together with International Staging System (ISS) and cytogenetics had a better prognostic value than ISS and cytogenetics alone in the training set (2 years area under the ROC curve 0·64 [95% CI 0·56-0·72] vs. 0·60 [95% CI 0·52-0·69]) and the validation set (0·67 [0·59-0·75] vs. 0·58 [0·50-0·66]). Interpretation This study demonstrates unprecedented evidence of the prognostic significance of exosomal miRNAs in patients with MM. We identified a three-miRNA signature in circulating exosomes that adds prognostic value to ISS and cytogenetic status and helps improve prognostic identification of newly diagnosed MM patients. Disclosures Avet-Loiseau: jansen: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; celgene: Membership on an entity's Board of Directors or advisory committees; onyx: Membership on an entity's Board of Directors or advisory committees; onyx: Membership on an entity's Board of Directors or advisory committees; jansen: Membership on an entity's Board of Directors or advisory committees; millenium: Membership on an entity's Board of Directors or advisory committees; millenium: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Moreau:Celgene, Janssen, Takeda, Novartis, Amgen: Membership on an entity's Board of Directors or advisory committees. Facon:Onyx: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Pierre Fabre: Membership on an entity's Board of Directors or advisory committees.

A gene signature of chemo-immunization to predict outcome in patients with triple negative breast cancer treated with neoadjuvant chemotherapy.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 575-575 ◽  
Author(s):  
Mohamed-Amine Bayar ◽  
Carmen Criscitiello ◽  
Giuseppe Curigliano ◽  
William Fraser Symmans ◽  
Christine Desmedt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Triple Negative Breast Cancer ◽  
Prognostic Value ◽  
Triple Negative ◽  
Residual Disease ◽  
Gene Signature ◽  
Training Set ◽  
Pre Treatment ◽  
Validation Set

575 Background: In patients with triple-negative breast cancer (TNBC), the extent of tumor-infiltrating lymphocytes (TILs) in the residual disease after anthracycline-based neoadjuvant chemotherapy (NACT) is associated with a better prognosis. We aimed to develop a genomic signature from pre-treatment samples to predict the extent of TILs after NACT, and then to test its prognostic value on survival. Methods: Using 99 pre-treatment samples (training set), we generated a four-gene signature that predicts post-NACT TILs using the LASSO technique. Prognostic value of the signature on survival was assessed on the training set (n=99) and then evaluated on an independent validation set including 185 patients with TNBC treated with NACT. Results: A four-gene signature, assessed on pre-treatment samples and combining the expression levels of HLF, CXCL13, SULT1E1, and GBP1 predicted the extent of lymphocytic infiltration after NACT. In a multivariate analysis performed on the training set, a one-unit increase in the signature value was associated with distant-relapse free survival (DRFS) (HR: 0.28, 95%CI: 0.13-0.63, p=0.002). For the validation set, the four-gene signature was significantly associated with DRFS in the entire set (HR: 0.26, 95%CI: 0.11-0.59, p=0.001) and in the subset of patients with residual disease (HR: 0.23, 95%CI: 0.10-0.55, p< 0·001). Conclusions: We developed a four-gene signature of chemotherapy-induced immune-activation, which predicts outcome in patients with TNBC treated with NACT. [Table: see text]

scholarly journals Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

2021 ◽  
Vol 22 (4) ◽  
pp. 1632
Author(s):  
Eskezeia Yihunie Dessie ◽  
Siang-Jyun Tu ◽  
Hui-Shan Chiang ◽  
Jeffrey J.P. Tsai ◽  
Ya-Sian Chang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Gene Signature ◽  
Adaptive Lasso ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

scholarly journals Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenjie Wang ◽  
Chen Zhang ◽  
Qihong Yu ◽  
Xichuan Zheng ◽  
Chuanzheng Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Lipid Metabolism ◽  
Liver Cancer ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Patient Prognosis

Abstract Background Liver cancer is one of the most common malignancies worldwide. HCC (hepatocellular carcinoma) is the predominant pathological type of liver cancer, accounting for approximately 75–85 % of all liver cancers. Lipid metabolic reprogramming has emerged as an important feature of HCC. However, the influence of lipid metabolism-related gene expression in HCC patient prognosis remains unknown. In this study, we performed a comprehensive analysis of HCC gene expression data from TCGA (The Cancer Genome Atlas) to acquire further insight into the role of lipid metabolism-related genes in HCC patient prognosis. Methods We analyzed the mRNA expression profiles of 424 HCC patients from the TCGA database. GSEA(Gene Set Enrichment Analysis) was performed to identify lipid metabolism-related gene sets associated with HCC. We performed univariate Cox regression and LASSO(least absolute shrinkage and selection operator) regression analyses to identify genes with prognostic value and develop a prognostic model, which was tested in a validation cohort. We performed Kaplan-Meier survival and ROC (receiver operating characteristic) analyses to evaluate the performance of the model. Results We identified three lipid metabolism-related genes (ME1, MED10, MED22) with prognostic value in HCC and used them to calculate a risk score for each HCC patient. High-risk HCC patients exhibited a significantly lower survival rate than low-risk patients. Multivariate Cox regression analysis revealed that the 3-gene signature was an independent prognostic factor in HCC. Furthermore, the signature provided a highly accurate prediction of HCC patient prognosis. Conclusions We identified three lipid-metabolism-related genes that are upregulated in HCC tissues and established a 3-gene signature-based risk model that can accurately predict HCC patient prognosis. Our findings support the strong links between lipid metabolism and HCC and may facilitate the development of new metabolism-targeted treatment approaches for HCC.

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