scholarly journals High IKBIP expression as a biomarker for poor prognosis and correlated with immune infiltrates in hepatocellular carcinoma (HCC)

Author(s):  
Mingxin Lin ◽  
Jingping Liu ◽  
Xiaoli Shi ◽  
Jiajun Li ◽  
Huiming Ye

Abstract Background: I kappa B kinase interacting protein (IKBIP) is dysregulated and closely correlated with prognosis in tumor. However, the potential functions of IKBIP have not been utterly revealed in hepatocellular carcinoma (HCC). Thus, we explored the clinical values of IKBIP and its correlation with immune infiltrates in HCC.Methods: IKBIP expression in different tumor types was analyzed through the Tumor Immunity Evaluation Resource (TIMER) database. IKBIP expression between HCC and normal tissues were readily available for retrospective analyses from GEPIA, UALCAN and the Cancer Genome Atlas (TCGA) databases. The association between clinical features and IKBIP expression in HCC was analyzed by the Wilcoxon signed-rank test and logistic regression. Human Protein Atlas (HPA) was used to analyze the protein expression of IKBIP. A receiver operating characteristic (ROC) curve was performed to explore diagnostic potential of IKBIP. Kaplan-Meier (K-M) method and Cox regression were conducted to evaluate the influence of IKBIP on the prognosis of HCC patients. Protein-protein interaction (PPI) networks were established using STRING. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene functional enrichment analysis. The interrelation between IKBIP expression and immune infiltrates in HCC was analyzed through TIMER and tumor-immune system interaction database (TISIDB).Results: Our results revealed that IKBIP expression was abnormally upregulated in HCC, and ROC curve analysis confirmed the diagnostic power of IKBIP. High IKBIP expression was related to tumor sizes, pathologic stage and histologic grade. K-M plotter analysis showed that high IKBIP expression was correlated with poor prognosis of HCC patients. Moreover, multivariate Cox analysis further verified that high IKBIP expression was an independent risk factor in HCC patients. Correlation analysis found that IKBIP expression was associated with infiltrating immune cells.Conclusion: High IKBIP expression is associated with poor prognosis and immune infiltrates, which may serve as a prognostic biomarker and therapeutic target for HCC.

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Honglan Guo ◽  
Qinqiao Fan

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.


2020 ◽  
Author(s):  
Jun Hu ◽  
Fang Wang ◽  
Logen Liu ◽  
Wenfeng Ning

Abstract BACKGROUND: Mounting evidence has shown that long noncoding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) which participate in the initiation and progression of cancers. In the ceRNA network, lncRNAs, microRNAs (miRNAs) and mRNAs, communicate with and co-regulate each other. Rarely there is a systematic lncRNA-mediated ceRNA network and potential specific ceRNA pairs or triples of esophageal cancer (EC). In this study, we investigate the lncRNA-mediated ceRNA network in EC and screen the potential prognostic lncRNA biomarkers.METHODS: We obtained mRNA, miRNA, and lncRNA expression data and relevant clinical features on patients with EC from The Cancer Genome Atlas (TCGA), and used the edgR package to identify differentially expressed mRNAs, lncRNAs and miRNAs between EC samples and normal samples. The EC ceRNA network was constructed based on miRNA target prediction through the databases of miRcode, miRDB, miRTarBase and TargetScan. And then Pearson’s correlation analysis was adopted to identify co-expression mRNA-lncRNA pairs. Finally, the robust likelihood-based survival analysis and Cox regression models were used to identify prognosis-related lncRNAs, which was evaluated by Kaplan-Meier and receiver operating characteristic (ROC) curve analysis.RESULTS: A total of 3,200 mRNAs, 131 miRNAs and 1,338 lncRNAs were identified as significantly differentially expressed in EC, of which, 30 mRNAs, 15 lncRNAs, and 8 miRNAs were incorporated in the ceRNA network. According to the ceRNA network node degrees, lncRNA MAGI2-AS3, hsa-mir-93 and TGFBR2 were the key genes. Also, the ceRNA network revealed some important ceRNA pairs and triples, such as SNX29P2-TGFBR2 and MAGI2-AS-hsa-mir-143-COL1A1. Finally, we developed a six-lncRNA signature (ZNF341-AS1, AC130324.2, AC027271.1, AL591212.1, AL732314.4 and LOC105372352), with improved diagnostic potential for EC with the area under the ROC curve of 0.93.CONCLUSIONS: our present work sheds new light on the tumorigenesis roles of lncRNA-mediated ceRNA network in EC and identifies a six‐lncRNA model that could be used as candidate prognostic signature.


2019 ◽  
Author(s):  
Jun Hu ◽  
Fang Wang ◽  
Logen Liu ◽  
Ning Wenfeng

Abstract BACKGROUND: Mounting evidence has shown that long noncoding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) which participate in the initiation and progression of cancers. In the ceRNA network, lncRNAs, microRNAs (miRNAs) and mRNAs, communicate with and co-regulate each other. Rarely there is a systematic lncRNA-mediated ceRNA network and potential specific ceRNA pairs or triples of esophageal cancer (EC). In this study, we investigate the lncRNA-mediated ceRNA network in EC and screen the potential prognostic lncRNA biomarkers. METHODS: We obtained mRNA, miRNA, and lncRNA expression data and relevant clinical features on patients with EC from The Cancer Genome Atlas (TCGA), and used the edgR package to identify differentially expressed mRNAs, lncRNAs and miRNAs between EC samples and normal samples. The EC ceRNA network was constructed based on miRNA target prediction through the databases of miRcode, miRDB, miRTarBase and TargetScan. And then Pearson’s correlation analysis was adopted to identify co-expression mRNA-lncRNA pairs. Finally, the robust likelihood-based survival analysis and Cox regression models were used to identify prognosis-related lncRNAs, which was evaluated by Kaplan-Meier and receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 3,200 mRNAs, 131 miRNAs and 1,338 lncRNAs were identified as significantly differentially expressed in EC, of which, 30 mRNAs, 15 lncRNAs, and 8 miRNAs were incorporated in the ceRNA network. According to the ceRNA network node degrees, lncRNA MAGI2-AS3, hsa-mir-93 and TGFBR2 were the key genes. Also, the ceRNA network revealed some important ceRNA pairs and triples, such as SNX29P2-TGFBR2 and MAGI2-AS-hsa-mir-143-COL1A1. Finally, we developed a six-lncRNA signature (ZNF341-AS1, AC130324.2, AC027271.1, AL591212.1, AL732314.4 and LOC105372352), with improved diagnostic potential for EC with the area under the ROC curve of 0.93. CONCLUSIONS: our present work sheds new light on the tumorigenesis roles of lncRNA-mediated ceRNA network in EC and identifies a six‐lncRNA model that could be used as candidate prognostic signature.


2021 ◽  
pp. 1-10
Author(s):  
Lichao Xu ◽  
Shiqin Wang ◽  
Shengping Wang ◽  
Ying Wang ◽  
Wentao Li ◽  
...  

OBJECTIVES: To investigate whether the baseline apparent diffusion coefficient (ADC) can predict survival in the hepatocellular carcinoma (HCC) patients receiving chemoembolization. MATERIALS AND METHODS: Diffusion-weighted MR imaging of HCC patients is performed within 2 weeks before chemoembolization. The ADC of the largest index lesion is recorded. Responses are assessed by mRECIST after the start of the second course of chemoembolization. Receiver operating characteristic (ROC) curve analysis is performed to evaluate the diagnostic performance and determine optimal cut-off values. Cox regression and Kaplan–Meier survival analyses are used to explore the differences in overall survival (OS) between the responders and non-responders. RESULTS: The difference is statistically significant in the baseline ADC between the responders and non-responders (P <  0.001). ROC analyses indicate that the baseline ADC value is a good predictor of response to treatment with an area under the ROC curve (AUC) of 0.744 and the optimal cut-off value of 1.22×10–3 mm2/s. The Cox regression model shows that the baseline ADC is an independent predictor of OS, with a 57.2% reduction in risk. CONCLUSION: An optimal baseline ADC value is a functional imaging response biomarker that has higher discriminatory power to predict tumor response and prolonged survival following chemoembolization in HCC patients.


2019 ◽  
Vol 28 (4) ◽  
pp. 439-447 ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Bai Ji ◽  
Hongqiao Cai ◽  
Yahui Liu

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hao Guo ◽  
Jing Zhou ◽  
Yanjun Zhang ◽  
Zhi Wang ◽  
Likun Liu ◽  
...  

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.


2020 ◽  
Author(s):  
Hui Zhang ◽  
Senmiao Ni ◽  
Changxian Li ◽  
Haoming Zhou ◽  
Jianling Bai ◽  
...  

Abstract Background: Liver cancer is the fourth most common cause of cancer-related death and rank sixth in terms of incident cases. We aim to identify a set of miRNAs and a miRNA-based signature related to tumorigenesis and prognosis in patients with hepatocellular carcinoma (HCC). Methods: We analyzed the miRNA sequencing profiles of 373 HCC patients downloaded from The Cancer Genome Atlas LIHC program. The isoform quantification profiles were transformed into 5p and 3p mature miRNA names. Differentially expressed (DE) miRNAs between tumor and adjacent normal tissues were identified by Wald test based on the negative binomial distribution. Prognostic miRNAs associated with overall survival were confirmed by multivariate Cox proportional hazards models. The miRNA-based signatures were obtained from the linear predictors of cox regression, and the prognostic performance was compared by Harrel’s C-index and revealed by the restricted mean survival (RMS) curve. Results: The selected twelve DE miRNAs showed a good performance to classify tumor tissues from normal tissues. Meanwhile, a miRNA-based prognostic signature of eight mature miRNAs was constructed, which significantly stratified patients into high- vs low-risk groups in terms of overall survival (hazard ratio, 4.11; 95% CI, 2.71-6.24; P<0.001). When integrated with clinical information, the composite miRNA-clinical signature showed improved prognostic accuracy relative to the eight-miRNA signature alone. As we set the follow-up time at 5 years, the estimated RMST difference between low- and high-risk group stratified by miRNA index was 1.39 (95% CI: 0.95-1.83) months, which is lesser than the difference between miRNA-clinical risk groups (1.63, 95%CI: 1.20-2.06). Functional enrichment analysis indicated that the target mRNAs of selected miRNAs were mainly enriched in cancer-related pathways and vital cell biological processes. Conclusions: The proposed DE miRNAs and miRNA-clinical signature are promising biomarkers for discrimination and predicting overall survival respectively in HCC patients. These biomarkers may have significant relevance for development of new drug research and targeting therapies for HCC patients.


2021 ◽  
Vol 12 ◽  
Author(s):  
Guojun Lu ◽  
Ying Zhou ◽  
Chenxi Zhang ◽  
Yu Zhang

BackgroundProtein-coding gene LIM Domain Kinase 1 (LIMK1) is upregulated in various tumors and reported to promote tumor invasion and metastasis. However, the prognostic values of LIMK1 and correlation with immune infiltrates in lung adenocarcinoma are still not understood. Therefore, we evaluated the prognostic role of LIMK1 and its correlation with immune infiltrates in lung adenocarcinoma.MethodsTranscriptional expression profiles of LIMK1 between lung adenocarcinoma tissues and normal tissues were downloaded from the Cancer Genome Atlas (TCGA). The LIMK1 protein expression was assessed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas. Receiver operating characteristic (ROC) curve was used to differentiate lung adenocarcinoma from adjacent normal tissues. Kaplan-Meier method was conducted to assess the effect of LIMK1 on survival. Protein-protein interaction (PPI) networks were constructed by the STRING. Functional enrichment analyses were performed using the “ClusterProfiler” package. The relationship between LIMK1 mRNA expression and immune infiltrates was determined by tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB).ResultsThe expression of LIMK1 in lung adenocarcinoma tissues was significantly upregulated than those in adjacent normal tissues. Increased LIMK1 mRNA expression was associated with lymph node metastases and high TNM stage. The ROC curve analysis showed that with a cutoff level of 4.908, the accuracy, sensitivity, and specificity for LIMK1 differentiate lung adenocarcinoma from adjacent controls were 69.5, 93.2, and 71.9%, respectively. Kaplan-Meier survival analysis showed lung adenocarcinoma patients with high- LIMK1 had a worse prognosis than those with low- LIMK1 (43.1 vs. 55.1 months, P = 0.028). Correlation analysis indicated LIMK1 mRNA expression was correlated with tumor purity and immune infiltrates.ConclusionUpregulated LIMK1 is significantly correlated with poor survival and immune infiltrates in lung adenocarcinoma. Our study suggests that LIMK1 can be used as a biomarker of poor prognosis and potential immune therapy target in lung adenocarcinoma.


2020 ◽  
Author(s):  
Zhuomao Mo ◽  
Shaoju Luo ◽  
Hao Hu ◽  
Ling Yu ◽  
Zhirui Cao ◽  
...  

Abstract Background Many different signatures and models have been established for patients with hepatocellular carcinoma (HCC), but no signature based on m6A related genes was developed. The objective of this research was to establish the signature with m6A related genes in HCC. Methods Data from 377 HCC patients from The Cancer Genome Atlas (TCGA) database was downloaded. The included m6A related genes were selected by Cox regression analysis and the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, the nomogram was constructed and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the four m6A related genes (YTHDF2, YTHDF1, METTL3 and KIAA1429). Under the grouping from signature, patients in high risk group of showed the poor prognosis than those in low risk group. And significant difference was found in two kinds of immune cells (T cell gamma delta and NK cells activated) between two groups. The univariate and multivariate Cox regression analysis indicated that m6A related signature can be the potential independent prognosis factor in HCC. Finally, we developed a clinical risk model predicting the HCC prognosis and successfully verified it in C-index, calibration and ROC curve. Conclusion Our study identified the m6A related signature for predicting prognosis of HCC and provided the potential biomarker between m6A and immune therapy.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qiuyue Hu ◽  
Shen Shen ◽  
Jianhao Li ◽  
Liwen Liu ◽  
Xin Liu ◽  
...  

Hepatocellular carcinoma (HCC) is a malignant tumour associated with a high mortality rate and poor prognosis worldwide. Uridine diphosphate-glucose pyrophosphorylase 2 (UGP2), a key enzyme in glycogen biosynthesis, has been reported to be associated with the occurrence and development of various cancer types. However, its diagnostic value and prognostic value in HCC remain unclear. The present study observed that UGP2 expression was significantly downregulated at both the mRNA and protein levels in HCC tissues. Receiver operating characteristic (ROC) curve analysis revealed that UGP2 may be an indicator for the diagnosis of HCC. In addition, Kaplan-Meier and Cox regression multivariate analyses indicated that UGP2 is an independent prognostic factor of overall survival (OS) in patients with HCC. Furthermore, gene set enrichment analysis (GSEA) suggested that gene sets negatively correlated with the survival of HCC patients were enriched in the group with low UGP2 expression levels. More importantly, a significant correlation was identified between low UGP2 expression and fatty acid metabolism. In summary, the present study demonstrates that UGP2 may contribute to the progression of HCC, indicating a potential therapeutic target for HCC patients.


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