Identification of a Potential PPAR-Related Multigene Signature Predicting Prognosis of Patients with Hepatocellular Carcinoma

Peroxisome proliferator-activated receptors (PPARs) and part of their target genes have been reported to be related to the progression of hepatocellular carcinoma (HCC). The prognosis of HCC is not optimistic, and more accurate prognostic markers are needed. This study focused on discovering potential prognostic markers from the PPAR-related gene set. The mRNA data and clinical information of HCC were collected from TCGA and GEO platforms. Univariate Cox and lasso Cox regression analyses were used to screen prognostic genes of HCC. Three genes (MMP1, HMGCS2, and SLC27A5) involved in the PPAR signaling pathway were selected as the prognostic signature of HCC. A formula was established based on the expression values and multivariate Cox regression coefficients of selected genes, that was, risk score = 0.1488 ∗ expression value of M M P 1 + − 0.0393 ∗ expression value of H M G C S 2 + − 0.0479 ∗ expression value of S L C 27 A 5 . The prognostic ability of the three-gene signature was assessed in the TCGA HCC dataset and verified in three GEO sets (GSE14520, GSE36376, and GSE76427). The results showed that the risk score based on our signature was a risk factor with a HR (hazard ratio) of 2.72 ( 95 % CI Confidence Interval = 1.87 ~ 3.95 , p < 0.001 ) for HCC survival. The signature could significantly ( p < 0.0001 ) distinguish high-risk and low-risk patients with poor prognosis for HCC. In addition, we further explored the independence and applicability of the signature with other clinical indicators through multivariate Cox analysis ( p < 0.001 ) and nomogram analysis ( C ‐ index = 0.709 ). The above results indicate that the combination of MMP1, HMGCS2, and SLC27A5 selected from the PPAR signaling pathway could effectively, independently, and applicatively predict the prognosis of HCC. Our research provided new insights to the prognosis of HCC.

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Identification of ferroptosis-related genes for overall survival prediction in hepatocellular carcinoma

10.21203/rs.3.rs-523396/v1 ◽

2021 ◽

Author(s):

Lianxiang Luo ◽

Xinyue Yao ◽

Fangfang Huang ◽

Hui Luo

Keyword(s):

Hepatocellular Carcinoma ◽

Target Genes ◽

Cox Regression ◽

Clinical Information ◽

Gene Signature ◽

High Risk Group ◽

Differentially Expressed ◽

Survival Prediction ◽

Potential Index ◽

Tumor Tissues

Abstract Ferroptosis is a novel type of cell death depending on iron, which has been confirmed strongly related with the development of tumor. Hepatocellular carcinoma (HCC) is a malignancy with high incidence. Despite some reports demonstrated the relation between ferroptosis-related genes and HCC, more details have not been excavated. In present study, we analyzed ferroptosis-related genes with their clinical information from TCGA-LIHC project to find out prognostic genes in HCC. And four genes (GPX2, MT3, PRDX1 and SRXN1) were established as a prognostic model after differentially expressed analysis, Cox regression analyses and LASSO approach. High-risk group separating by cutoff value with poor prognosis was proved, and risk score regarded as an independent prognostic factor. Subsequently, enrichment analyses were processed to the differentially expressed genes, we found that genes generally enriched in ferroptosis-related functions and pathways, also in some immune cells and functions with different immune status. Eventually, we constructed ferroptosis potential index (FPI) to reveal the functional roles of ferroptosis in tumor tissues. The immunohistochemistry performed prognostic genes expression in normal and tumor tissues. In conclusion, these results demonstrated the four-gene signature can be a biomarker for predicting HCC prognosis and its members can be drug target genes for HCC treatment.

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A Novel Ferroptosis-Related 4-Gene Prognostic Signature for Cholangiocarcinoma and Photodynamic Therapy

Frontiers in Oncology ◽

10.3389/fonc.2021.747445 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zi-jian Zhang ◽

Yun-peng Huang ◽

Xiao-xue Li ◽

Zhong-tao Liu ◽

Kai Liu ◽

...

Keyword(s):

Photodynamic Therapy ◽

Target Genes ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinical Information ◽

Gene Signature ◽

Cox Regression Analysis ◽

Public Data ◽

Study Gene Expression

Cholangiocarcinoma is the second most common malignant tumor in the hepatobiliary system. Compared with data on hepatocellular carcinoma, fewer public data and prognostic-related studies on cholangiocarcinoma are available, and effective prognostic prediction methods for cholangiocarcinoma are lacking. In recent years, ferroptosis has become an important subject of tumor research. Some studies have indicated that ferroptosis plays an important role in hepatobiliary cancers. However, the prediction of the prognostic effect of ferroptosis in patients with cholangiocarcinoma has not been reported. In addition, many reports have described the ability of photodynamic therapy (PDT), a potential therapy for cholangiocarcinoma, to regulate ferroptosis by generating reactive oxygen species (ROS). By constructing ferroptosis scores, the prognoses of patients with cholangiocarcinoma can be effectively predicted, and potential gene targets can be discovered to further enhance the efficacy of PDT. In this study, gene expression profiles and clinical information (TCGA, E-MTAB-6389, and GSE107943) of patients with cholangiocarcinoma were collected and divided into training sets and validation sets. Then, a model of the ferroptosis gene signature was constructed using least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis. Furthermore, through the analysis of RNA-seq data after PDT treatment of cholangiocarcinoma, PDT-sensitive genes were obtained and verified by immunohistochemistry staining and Western blot. The results of this study provide new insight for predicting the prognosis of cholangiocarcinoma and screening target genes that enhance the efficacy of PDT.

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

BMC Cancer ◽

10.1186/s12885-021-08351-0 ◽

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.

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Constructing the Logical Regression Model to Predict the Target of Jianpi Jiedu Decoction in the Treatment of Hepatocellular Carcinoma

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/8859558 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Rongjie Zhang ◽

Yan Chen ◽

Ge Zhou ◽

Baoguo Sun ◽

Yue Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Survival Analysis ◽

Regression Model ◽

Target Genes ◽

Cox Regression ◽

Risk Group ◽

Cox Regression Analysis ◽

Expression Levels ◽

Prognostic Analysis ◽

Significant Difference

Objectives. The purpose of this study was to identify the molecular mechanism and prognosis-related genes of Jianpi Jiedu decoction in the treatment of hepatocellular carcinoma. Methods. The gene expression data of hepatocellular carcinoma samples and normal tissue samples were downloaded from TCGA database, and the potential targets of drug composition of Jianpi Jiedu decoction were obtained from TCMSP database. The genes were screened out in order to obtain the expression of these target genes in patients with hepatocellular carcinoma. The differential expression of target genes was analyzed by R software, and the genes related to prognosis were screened by univariate Cox regression analysis. Then, the LASSO model was constructed for risk assessment and survival analysis between different risk groups. At the same time, independent prognostic analysis, GSEA analysis, and prognostic analysis of single gene in patients with hepatocellular carcinoma were performed. Results. 174 compounds of traditional Chinese medicine were screened by TCMSP database, corresponding to 122 potential targets. 39 upregulated genes and 9 downregulated genes were screened out. A total of 20 candidate prognostic related genes were screened out by univariate Cox analysis, of which 12 prognostic genes were involved in the construction of the LASSO regression model. There was a significant difference in survival time between the high-risk group and low-risk group ( p < 0.05 ). Among the genes related to prognosis, the expression levels of CCNB1, NQO1, NUF2, and CHEK1 were high in tumor tissues ( p < 0.05 ). Survival analysis showed that the high expression levels of these four genes were significantly correlated with poor prognosis of HCC ( p < 0.05 ). GSEA analysis showed that the main KEGG enrichment pathways were lysine degradation, folate carbon pool, citrate cycle, and transcription factors. Conclusions. In the study, we found that therapy target genes of Jianpi Jiedu decoction were mainly involved in metabolism and apoptosis in hepatocellular carcinoma, and there was a close relationship between the prognosis of hepatocellular carcinoma and the genes of CCNB1, NQO1, NUF2, and CHEK1.

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Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.11273 ◽

2021 ◽

Vol 9 ◽

pp. e11273

Author(s):

Lei Yang ◽

Weilong Yin ◽

Xuechen Liu ◽

Fangcun Li ◽

Li Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Overall Survival ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Enrichment Score ◽

Hub Genes ◽

Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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Establishment and Validation of an MTORC1 Signaling-Related Gene Signature to Predict Overall Survival in Patients with Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/6299472 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Zheng Yao ◽

Song Wen ◽

Jun Luo ◽

Weiyuan Hao ◽

Weiren Liang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Decision Tree ◽

Survival Data ◽

Cox Regression ◽

Gene Signature ◽

Related Gene ◽

Cox Regression Analysis ◽

Cancer Hallmarks ◽

Mtorc1 Signaling

Background. Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients. Methods. The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient. Results. Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals. Conclusions. The present study may contribute to the improvement of current prognostic systems for patients with HCC.

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Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

Future Oncology ◽

10.2217/fon-2020-1112 ◽

2021 ◽

Author(s):

Q Shi ◽

Z Meng ◽

XX Tian ◽

YF Wang ◽

WH Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Prognostic Index ◽

Interaction Network ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Cox Regression Analysis ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

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Dysregulation of tRNA-derived small RNAs and their potential roles in lupus nephritis

Lupus ◽

10.1177/09612033211061482 ◽

2021 ◽

pp. 096120332110614

Author(s):

Yan Liang ◽

Ji Zhang ◽

Wenxian Qiu ◽

Bo Chen ◽

Ying Zhou ◽

...

Keyword(s):

Lupus Nephritis ◽

Signaling Pathway ◽

Small Rnas ◽

High Throughput Sequencing ◽

Target Genes ◽

Expression Profiles ◽

Mapk Signaling ◽

Pcr Analysis ◽

Clinical Indicators ◽

Qrt Pcr

Objective Lupus nephritis (LN) is a major end-organ complication of systemic lupus erythematosus (SLE), and the molecular mechanism of LN is not completely clear. Accumulating pieces of evidence indicate the potential vital role of tRNA-derived small RNAs (tsRNAs) in human diseases. Current study aimed to investigate the potential roles of tsRNAs in LN. Methods We herein employed high‐throughput sequencing to screen the expression profiles of tsRNAs in renal tissues of the LN and control groups. To validate the sequencing data, we performed quantitative real-time PCR (qRT-PCR) analysis. Correlational analysis of verified tsRNAs expression and clinical indicators was conducted using linear regression. The potential target genes were also predicted. The biological functions of tsRNAs were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Results Our findings revealed that the expression profiles of tsRNAs were significantly altered in the kidney tissues from LN patients compared with control. Overall, 160 tsRNAs were significantly dysregulated in the LN group, of which 79 were upregulated, whereas 81 were downregulated. Subsequent qRT-PCR results confirmed the different expression of candidate tsRNAs. Correlation analysis results found that expression of verified tsRNAs were correlated to clinical indicators. The target prediction results revealed that verified tsRNAs might act on 712 target genes. Further bioinformatics analysis uncovered tsRNAs might participate in the pathogenesis of LN through several associated pathways, including cell adhesion molecules, MAPK signaling pathway, PI3K-Akt signaling pathway and B cell receptor signaling pathway. Conclusion This study provides a novel insight for studying the mechanism of LN.

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