Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

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The prognostic value of lncRNA SNHG4 and its potential mechanism in liver cancer

Bioscience Reports ◽

10.1042/bsr20190729 ◽

2020 ◽

Vol 40 (1) ◽

Cited By ~ 4

Author(s):

Yan Jiao ◽

Yanqing Li ◽

Baoxing Jia ◽

Qingmin Chen ◽

Guoqiang Pan ◽

...

Keyword(s):

Liver Cancer ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Rank Test ◽

P Value ◽

Expression Data ◽

Potential Mechanism ◽

Cox Regression Analysis ◽

Cerna Network ◽

Log Rank Test

Abstract Background and object: Emerging evidence shows that non-coding RNA functions as new gene regulators and prognostic markers in several cancers, including liver cancer. Here, we focused on the small nucleolar RNA host gene 4 (SNHG4) in liver cancer prognosis based on The Cancer Genome Atlas (TCGA) data. Methods: The expression data and clinical information were downloaded from TCGA. Chi-square tests evaluated the correlation between SNHG4 expression and clinical parameters. Differences in survival between high and low expression groups (optic cutoff value determined by ROC) from Cox regression analysis were compared, and P-value was calculated by a log-rank test. Kaplan–Meier curves were compared with the log-rank test. GSEA and ceRNA network were conducted to explore the potential mechanism. Results: Data mining of lncRNA expression data for 371 patients with primary tumor revealed overexpression of SNHG4 in liver cancer. High SNHG4 expression was correlated with histological type (P = 0.01), histologic grade (P = 0.001), stage (P = 0.01), T classification (P = 0.004) and survival status (P = 0.013). Patients with high SNHG4 expression had poor overall survival and relapse-free survival compared with those with low SNHG4 expression. Multivariate analysis identified SNHG4 as an independent prognostic factor of poor survival in liver cancer. GSEA revealed related signaling pathway and ceRNA network explored the further mechanism. Conclusion: High SNHG4 expression is an independent predictor of poor prognosis in liver cancer.

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Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5970085 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Honglan Guo ◽

Qinqiao Fan

Keyword(s):

Hepatocellular Carcinoma ◽

Roc Curve ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

Cancer Cell International ◽

10.1186/s12935-019-1001-0 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 12

Author(s):

Zi-Hao Wang ◽

Yun-Zheng Zhang ◽

Yu-Shan Wang ◽

Xiao-Xin Ma

Keyword(s):

Overall Survival ◽

Endometrial Cancer ◽

Mrna Expression ◽

Cox Regression ◽

Single Gene ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Rank Test ◽

Cox Regression Analysis

Abstract Background Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC. Methods mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log‐rank test were used to validate the significance of risk parameters for prognosis prediction. Result Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors. Conclusion A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

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Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

10.21203/rs.3.rs-46796/v1 ◽

2020 ◽

Author(s):

Wenjing Zhu ◽

Qiliang Zhang ◽

Min Liu ◽

Meixing Yan ◽

Xiao Chu ◽

...

Keyword(s):

Dna Repair ◽

Liver Cancer ◽

Prediction Model ◽

Risk Model ◽

Cox Regression ◽

Clinical Specimen ◽

Normal Liver ◽

Gene Set Enrichment Analysis ◽

P Value ◽

Cox Regression Analysis

Abstract Background: Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment. Material/Methods: 422 LC patients with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan–Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens. Results: The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4, ZWINT, UPF3B, NCBP2, ADA, SF3A3, and GTF2H1. On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7% to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P < 0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 (95%CI [confidence interval]:1.523-3.132) and log-rank P-value<0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted. Conclusions: Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

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Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Bioscience Reports ◽

10.1042/bsr20201427 ◽

2020 ◽

Vol 40 (8) ◽

Author(s):

Sihan Chen ◽

Guodong Cao ◽

Wei Wu ◽

Yida Lu ◽

Xiaobo He ◽

...

Keyword(s):

Cox Regression ◽

Mechanistic Model ◽

Single Gene ◽

Colon Adenocarcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Gene Markers ◽

Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

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Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

Technology in Cancer Research & Treatment ◽

10.1177/1533033820963578 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096357

Author(s):

Xiaoyong Gong ◽

Bobin Ning

Keyword(s):

Prostate Cancer ◽

Network Analysis ◽

Cox Regression ◽

Characteristic Curve ◽

Interaction Network ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Normal Prostate ◽

Cox Regression Analysis ◽

Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

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PDK2: a novel diagnostic and prognostic biomarker for liver cancer

10.21203/rs.2.24570/v1 ◽

2020 ◽

Author(s):

Zhi-Cheng Liu ◽

Yan-Qing Li ◽

Yan Jiao ◽

Yue-Chen Zhao

Keyword(s):

Liver Cancer ◽

Pyruvate Dehydrogenase ◽

Cox Regression ◽

Area Under The Curve ◽

Normal Liver ◽

The Cancer Genome Atlas ◽

High Morbidity ◽

Cox Regression Analysis ◽

Diagnosis And Prognosis ◽

Potential Biomarker

Abstract Background: Liver cancer (LC) is a common malignancy with very high morbidity. Pyruvate dehydrogenase kinases (PDKs) are regulators of mitochondrial pyruvate dehydrogenase complexes (PDCs) and play an important role in regulating cellular energy metabolism. In this study, The Cancer Genome Atlas (TCGA) database was used to analyze the expression of PDK2 mRNA in LC, and to explore the value of PDK2 in the diagnosis and prognosis of LC.Methods: The TCGA database, containing the clinical data of 373 LC patients, includes information on PDK2 expression values. The receiver operating characteristic (ROC) curve of PDK2 was drawn to evaluate its diagnostic ability. Patients were divided into PDK2 high- and low-expressing groups by threshold levels. The Chi-square test was used to evaluate the correlation between PDK2 levels and clinicopathological characteristics. The Kaplan-Meier estimator and Cox regression analysis were performed to assess the effect of PDK2 levels on survival outcomes.Results: PDK2 expression in LC tissue was lower than that in normal liver tissues. According to the area under the curve (AUC) value calculated by ROC, PDK2 has a considerable diagnostic value for LC prognosis. The decreased expression of PDK2 is associated with clinical parameters, such as histologic grade ( P =0.0001), radiation therapy ( P =0.0490), vital status ( P =0.0240), and overall survival (OS) ( P =0.0222). Multivariate analysis shows that decreased PDK2 level is an independent risk factor for predicting poor prognosis in LC.Conclusions: PDK2 has a significant impact on the prognosis of LC and is a potential biomarker for the diagnosis and prognosis of LC.

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

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.611626 ◽

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.

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Upregulation of GNPNAT1 Predicts Poor Prognosis and Correlates With Immune Infiltration in Lung Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.605754 ◽

2021 ◽

Vol 8 ◽

Author(s):

Wenting Liu ◽

Kaiting Jiang ◽

Jingya Wang ◽

Ting Mei ◽

Min Zhao ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Poor Prognosis ◽

Cox Regression ◽

Clinical Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Value ◽

Cox Regression Analysis ◽

Normal Tissues

BackgroundGlucosamine 6-phosphate N-acetyltransferase (GNPNAT1) is a key enzyme in the hexosamine biosynthetic pathway (HBP), which functions as promoting proliferation in some tumors, yet its potential biological function and mechanism in lung adenocarcinoma (LUAD) have not been explored.MethodsThe mRNA differential expression of GNPNAT1 in LUAD and normal tissues was analyzed using the Cancer Genome Atlas (TCGA) database and validated by real-time PCR. The clinical value of GNPNAT1 in LUAD was investigated based on the data from the TCGA database. Then, immunohistochemistry (IHC) of GNPNAT1 was applied to verify the expression and clinical significance in LUAD from the protein level. The relationship between GNPNAT1 and epigenetics was explored using the cBioPortal database, and the miRNAs regulating GNPNAT1 were found using the miRNA database. The association between GNPNAT1 expression and tumor-infiltrating immune cells in LUAD was observed through the Tumor IMmune Estimation Resource (TIMER). Finally, Gene set enrichment analysis (GSEA) was used to explore the biological signaling pathways involved in GNPNAT1 in LUAD.ResultsGNPNAT1 was upregulated in LUAD compared with normal tissues, which was verified through qRT-PCR in different cell lines (P < 0.05), and associated with patients’ clinical stage, tumor size, and lymphatic metastasis status (all P < 0.01). Kaplan–Meier (KM) analysis suggested that patients with upregulated GNPNAT1 had a relatively poor prognosis (P < 0.0001). Furthermore, multivariate Cox regression analysis indicated that GNPNAT1 was an independent prognostic factor for LUAD (OS, TCGA dataset: HR = 1.028, 95% CI: 1.013–1.044, P < 0.001; OS, validation set: HR = 1.313, 95% CI: 1.130–1.526, P < 0.001). GNPNAT1 overexpression was correlated with DNA copy amplification (P < 0.0001), low DNA methylation (R = −0.52, P < 0.0001), and downregulation of hsa-miR-30d-3p (R = −0.17, P < 0.001). GNPNAT1 expression was linked to B cells (R = −0.304, P < 0.0001), CD4+T cells (R = −0.218, P < 0.0001), and dendritic cells (R = −0.137, P = 0.002). Eventually, GSEA showed that the signaling pathways of the cell cycle, ubiquitin-mediated proteolysis, mismatch repair and p53 were enriched in the GNPNAT1 overexpression group.ConclusionGNPNAT1 may be a potential prognostic biomarker and novel target for intervention in LUAD.

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PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022009 ◽

2021 ◽

Vol 19 (1) ◽

pp. 169-190

Author(s):

Peiyuan Li ◽

◽

Gangjie Qiao ◽

Jian Lu ◽

Wenbin Ji ◽

...

Keyword(s):

Urothelial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Gene Set Enrichment ◽

Cox Regression Analysis ◽

Gene Set ◽

Bladder Urothelial Carcinoma

<abstract> <p>Plasmacytoma variant translocation 1 (PVT1) is involved in multiple signaling pathways and plays an important regulatory role in a variety of malignant tumors. However, its role in the prognosis and immune invasion of bladder urothelial carcinoma (BLCA) remains unclear. This study investigated the expression of PVT1 in tumor tissue and its relationship with immune invasion, and determined its prognostic role in patients with BLCA. Patients were identified from the cancer genome atlas (TCGA). The enrichment pathway and function of PVT1 were explained by gene ontology (GO) term analysis, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA), and the degree of immune cell infiltration was quantified. Kaplan–Meier analysis and Cox regression were used to analyze the correlation between PVT1 and survival rate. PVT1-high BLCA patients had a lower 10-year disease-specific survival (DSS P < 0.05) and overall survival (OS P < 0.05). Multivariate Cox regression analysis showed that PVT1 (high vs. low) (P = 0.004) was an independent prognostic factor. A nomogram was used to predict the effect of PVT1 on the prognosis. PVT1 plays an important role in the progression and prognosis of BLCA and can be used as a medium biomarker to predict survival after cystectomy.</p> </abstract>

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