scholarly journals Development and validation of a novel ten-gene signature predicting prognosis in Hepatocellular carcinoma

2020 ◽  
Author(s):  
Guanbao Zhou ◽  
Genjie Lu ◽  
Liang Yang ◽  
Yangfang Lu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hierarchical Clustering ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Molecular Biomarkers ◽  
The Cancer Genome Atlas ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer with relatively poor prognosis. Thus, we aimed to identify novel molecular biomarkers to effectively predict the prognosis of HCC patients and eventually guide treatment. Methods: Prognosis-associated genes were determined by Kaplan-Meier and multivariate Cox regression analyses using the expression and clinical data of 373 HCC patients from The Cancer Genome Atlas (TCGA) database and validated in an independent Gene Expression Omnibus (GEO) dataset. The classification of AML was performed by unsupervised hierarchical clustering of ten gene expression levels. A prognostic risk score was established based on a linear combination of ten gene expression levels using the regression coefficients derived from the multivariate Cox regression models. Results: A total of 183 genes were significantly associated with prognosis in HCC. SLC25A15, RAB8A, GOT2, SORBS2, IL18RAP were top five protective genes, while FHL3, AMD1, DCAF13, UBE2E1, PTDSS2 were top five risk genes in HCC. SLC25A15, GOT2, IL18RAP were significantly down-regulated and DCAF13, PTDSS2 and SORBS2 were significantly up-regulated in the HCC samples and these genes exhibited high accuracy in differentiating HCC tissues from normal liver tissues. Hierarchical clustering analysis of the ten genes discovered three clusters of HCC patients. HCC tumors of cluster1 and 2 were significantly associated with more favourable OS than those of cluster3, cluster2 tumors showed higher pathologic stage than cluster3 tumors. The risk score was predictive of increased mortality rate in HCC patients. Conclusions: The ten-gene signature and the risk score may turn out to be novel molecular biomarkers and stratification of HCC patients to considerably ameliorate the prognostic prediction.

scholarly journals A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Hong ◽  
Yu Zhou ◽  
Xiangbang Xie ◽  
Wanrui Wu ◽  
Changsheng Shi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Prognostic Significance ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cumulative Distribution ◽  
Gene Module ◽  
Expression Levels

Abstract Background Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. Methods Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. Results We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. Conclusions Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC.

scholarly journals Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

scholarly journals Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

2021 ◽  
Author(s):  
Xiaohan Zhou ◽  
Chengdong Liu ◽  
Hanyi Zeng ◽  
Dehua Wu ◽  
Li Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
R Package ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Pathway Enrichment Analysis ◽  
Score Model ◽  
Predictive Efficiency

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency. Methods: lasso regression analysis followed by univariate cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package “clusterProfiler” was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by ssGSEA of R package “GSVA”. Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition. Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.

scholarly journals Upregulated LINC01667 Expression Is Correlated With Poor Prognosis in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kainan Zhang ◽  
Hui Liu ◽  
Mengsi Yu ◽  
Hui Zhao ◽  
Ning Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cancer Progression ◽  
The Cancer Genome Atlas ◽  
Migration And Invasion ◽  
Poor Overall Survival ◽  
Expression Levels ◽  
Huh7 Cells ◽  
Gene Expression Levels

The development of hepatocellular carcinoma (HCC) is a complex pathological process. Long intergenic non–protein-coding RNA 1667 (LINC01667, also known as MGC38584) plays an oncogenic role in several human cancers; however, its functional role in HCC tumorigenesis remains unknown. Here, we first evaluated the gene expression levels of LINC01667 in HCC using data from The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We then elucidated the association between LINC01667 gene expression levels and the survival rates of patients with HCC. We detected the effect of LINC01667 on the malignant phenotypes (cell proliferation, migration, invasion and apoptosis etc.) and the MAPK and PI3K/AKT/mTOR signaling pathways of HepG2, SMMC-7721 and HUH7 cells. We also analyzed the sensitivity of HepG2, SMMC-7721 and HUH7 with different expression levels of LINC01667 to anti-HCC drugs in vitro. Based on data from the aforementioned databases and our experiments in vitro, we found that LINC01667 was overexpressed in HCC, and that patients with high LINC01667 levels had a remarkably poor overall survival rate. In addition, inhibition of LINC01667 expression suppressed the proliferation, migration and invasion of HepG2 and SMMC-7721 cells and promoted their apoptosis in vitro. In contrast, overexpression of LINC01667 promoted the proliferation, migration and invasion of HUH7 cells and suppressed their apoptosis in vitro. ChIRP-seq (chromatin isolation by RNA purification) showed that LINC01667 bound to MEG3, and downregulated the expression of MEG3. In addition, western blotting showed that LINC01667 could activate the NF-κB pathway to promote cancer progression. In conclusion, we report that LINC01667 is an important oncogene in HCC and may be used as a potential diagnostic and prognostic biomarker of HCC.

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.

scholarly journals Developing ZNF Gene Signatures Predicting Radiosensitivity of Patients with Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Derui Yan ◽  
Mingjing Shen ◽  
Zixuan Du ◽  
Jianping Cao ◽  
Ye Tian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Cox Regression ◽  
Molecular Taxonomy ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Expression Levels ◽  
The Relationship

Adjuvant radiotherapy is one of the main treatment methods for breast cancer, but its clinical benefit depends largely on the characteristics of the patient. This study aimed to explore the relationship between the expression of zinc finger (ZNF) gene family proteins and the radiosensitivity of breast cancer patients. Clinical and gene expression data on a total of 976 breast cancer samples were obtained from The Cancer Genome Atlas (TCGA) database. ZNF gene expression was dichotomized into groups with a higher or lower level than the median level of expression. Univariate and multivariate Cox regression analyses were used to evaluate the relationship between ZNF gene expression levels and radiosensitivity. The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database was used for validation. The results revealed that 4 ZNF genes were possible radiosensitivity markers. High expression of ZNF644 and low expression levels of the other 3 genes (ZNF341, ZNF541, and ZNF653) were related to the radiosensitivity of breast cancer. Hierarchical cluster, Cox, and CoxBoost analysis based on these 4 ZNF genes indicated that patients with a favorable 4-gene signature had better overall survival on radiotherapy. Thus, this 4-gene signature may have value for selecting those patients most likely to benefit from radiotherapy. ZNF gene clusters could act as radiosensitivity signatures for breast cancer patients and may be involved in determining the radiosensitivity of cancer.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

scholarly journals A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5837
Author(s):  
Changwu Wu ◽  
Siming Gong ◽  
Georg Osterhoff ◽  
Nikolas Schopow
Keyword(s):  
Soft Tissue ◽  
Risk Score ◽  
Cox Regression ◽  
Soft Tissue Sarcomas ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Malignant Tumours ◽  
Free Survival

Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

scholarly journals Integrated Analysis of Methylation-driven Genes and Pretreatment Prognostic Factors in Patients with Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Dongsheng He ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
Mengxing You ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background: The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients.Methods: The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than -0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test.Results: In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage.Conclusion: We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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