Identification and validation of hub genes predicting prognosis of hepatocellular carcinoma

2021 ◽  
Author(s):  
Ziyan Chen ◽  
Haitao Yu ◽  
Lijun Wu ◽  
Sina Zhang ◽  
Zhihui Lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Model ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Histological Grade ◽  
High Risk Group ◽  
Low Risk ◽  
Hub Genes ◽  
Cox Regression Analysis

Introduction: Selecting the hub genes associated with hepatocellular carcinoma (HCC) to construct a COX regression model for predicting prognosis in HCC patients. Methods: Using HCC patient data from the ICGC and TCGA databases, screened for 40 core genes highly correlated with histological grade of HCC. Univariate and multivariate COX regression analysis were performed on the genes highly associated with HCC prognosis and the model was established. The expression of those genes was measured by immunohistochemistry in 110 HCC patients who underwent the surgery in The First Affiliated Hospital of Wenzhou Medical University. The survival of HCC patients was analyzed by the Kaplan-Meier method. Results: Eight genes (CDC45, CENPA, MCM10, MELK, CDC20, ASF1B, FANCD2 and NCAPH) were correlated with prognosis, and the same result was observed in 110 HCC patients. Using the regression model, the HCC patients in the training set were classified as high- and low-risk groups. The overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group, the same results were obtained in verification set. Conclusion: This study found that the risk model according to these eight genes can be used as a predictor of prognosis in HCC. These genes may become alternative biomarkers and therapeutic targets and provide new therapeutic strategies for HCC.

scholarly journals Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

2020 ◽  
Author(s):  
Li Liu ◽  
She Tian ◽  
Zhu Li ◽  
Yongjun Gong ◽  
Hao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Principal Component ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

scholarly journals Construction and Identification of a Novel 5-Gene Signature for Predicting the Prognosis in Breast Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Lingling Guo ◽  
Yu Jing
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
M2 Macrophage ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Breast cancer is one of the most common malignancies in women worldwide. The purpose of this study was to identify the hub genes and construct prognostic signature that could predict the survival of patients with breast cancer (BC).Methods: We identified differentially expressed genes between the responder group and non-responder group based on the GEO cohort. Drug-resistance hub genes were identified by weighted gene co-expression network analysis, and a multigene risk model was constructed by univariate and multivariate Cox regression analysis based on the TCGA cohort. Immune cell infiltration and mutation characteristics were analyzed.Results: A 5-gene signature (GP6, MAK, DCTN2, TMEM156, and FKBP14) was constructed as a prognostic risk model. The 5-gene signature demonstrated favorable prediction performance in different cohorts, and it has been confirmed that the signature was an independent risk indicater. The nomogram comprising 5-gene signature showed better performance compared with other clinical features, Further, in the high-risk group, high M2 macrophage scores were related with bad prognosis, and the frequency of TP53 mutations was greater in the high-risk group than in the low-risk group. In the low-risk group, high CD8+ T cell scores were associated with a good prognosis, and the frequency of CDH1 mutations was greater in the low-risk group than that in the high-risk group. At the same time, patients in the low risk group have a good response to immunotherapy in terms of immunotherapy. The results of immunohistochemistry showed that MAK, GP6, and TEMEM156 were significantly highly expressed in tumor tissues, and DCTN2 was highly expressed in normal tissues.Conclusions: Our study may find potential new targets against breast cancer, and provide new insight into the underlying mechanisms.

scholarly journals A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Moxuan Zhang ◽  
Yanhao Cheng ◽  
Zhengchun Xue ◽  
Qiang Sun ◽  
Jian Zhang
Keyword(s):  
High Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Genome Atlas

Abstract Background Glioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear. Methods In this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort. Results In this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated. Conclusion We established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma.

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

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.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

2021 ◽  
Author(s):  
Menglin He ◽  
Cheng Hu ◽  
Jian Deng ◽  
Hui Ji ◽  
Weiqian Tian
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals Survival-associated 11-AS Events Interact with the Immune Microenvironment in Ovarian Cancer

2021 ◽  
Author(s):  
Congbo Yue ◽  
Tianyi Zhao ◽  
Shoucai Zhang ◽  
Yingjie Liu ◽  
Guixi Zheng ◽  
...  
Keyword(s):  
T Cell ◽  
Natural Killer ◽  
Immune Cells ◽  
Cox Regression ◽  
Cytotoxic T Lymphocyte ◽  
Risk Group ◽  
Killer Cell ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Abstract Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of cancer. Transcriptome data and Percent Spliced In (PSI) values of OV patients were downloaded from TCGA database and TCGA SpliceSeq. Totally we identified 1,472 AS events that were associated with survival of OV and exon skipping (ES) was the most important type. Univariate and multivariate Cox regression analysis were performed to identify survival-associated AS events and develop the prognostic model based on 11-AS events. The immune cells and different response to cytotoxic T lymphocyte associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1) blockers in low-risk and high-risk group of OV patients were analyzed. Ten kinds of immune cells were found up-regulated in low-risk group. Activated B cell, natural killer T cell, natural killer cell and regulatory T cell were associated with survival of OV. The patients in low-risk group had good response to CTLA-4 and PD-1 blockers treatment. Moreover, a regulatory network was established according to the correlation between AS events and splicing factors (SFs). The present study provided valuable insights into the underlying mechanisms of OV. AS events that were correlated with the immune system might be potential therapeutic targets.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

scholarly journals An autophagy-related long non-coding RNA signature for patients with colorectal cancer

2021 ◽  
Author(s):  
Dongyan Zhao ◽  
Xizhen Sun ◽  
Sidan Long ◽  
Shukun Yao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

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