Dataset Information

Comprehensive analysis to identify DLEU2L/TAOK1 axis as a prognostic biomarker in hepatocellular carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors that are harmful to human health. Increasing evidence has underscored the critical role of the competitive endogenous RNA (ceRNA) regulatory networks among various human cancers. However, the complexity and behavior characteristics of the ceRNA network in HCC were still unclear. In this study, we aimed to clarify a phosphatase and tensin homolog (PTEN)-related ceRNA regulatory network and identify potential prognostic markers associated with HCC. The expression profiles of three RNAs (long non-coding RNAs [lncRNAs], microRNAs [miRNAs], and mRNAs) were extracted from The Cancer Genome Atlas (TCGA) database. The DLEU2L-hsa-miR-100-5p/ hsa-miR-99a-5p-TAOK1 ceRNA network related to the prognosis of HCC was obtained by performing bioinformatics analysis. Importantly, we identified the DLEU2L/TAOK1 axis in the ceRNA by using correlation analysis, and it appeared to become a clinical prognostic model by Cox regression analysis. Furthermore, methylation analyses suggested that the abnormal upregulation of the DLEU2L/TAOK1 axis likely resulted from hypomethylation, and immune infiltration analysis showed that the DLEU2L/TAOK1 axis may have an impact on the changes in the tumor immune microenvironment and the development of HCC. In summary, the current study constructing a ceRNA-based DLEU2L/TAOK1 axis might be a novel important prognostic factor associated with the diagnosis and prognosis of HCC.

SUBMITTER: Shi Y

PROVIDER: S-EPMC7851426 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Comprehensive analysis to identify DLEU2L/TAOK1 axis as a prognostic biomarker in hepatocellular carcinoma.

Shi Yi Y Zhang Dan-Dan DD Liu Ji-Bin JB Yang Xiao-Li XL Xin Rui R Jia Cheng-You CY Wang Hui-Min HM Lu Gai-Xia GX Wang Pei-Yao PY Liu Yu Y Li Zi-Jin ZJ Deng Jing J Lin Qin-Lu QL Ma Liang L Feng Shan-Shan SS Chen Xiao-Qi XQ Zheng Xiang-Min XM Zhou Ya-Fu YF Hu Yong-Jun YJ Yin Hua-Qun HQ Tian Lin-Lin LL Gu Li-Peng LP Lv Zhong-Wei ZW Yu Fei F Li Wen W Ma Yu-Shui YS Da Fu F

Molecular therapy. Nucleic acids 20210101

Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors that are harmful to human health. Increasing evidence has underscored the critical role of the competitive endogenous RNA (ceRNA) regulatory networks among various human cancers. However, the complexity and behavior characteristics of the ceRNA network in HCC were still unclear. In this study, we aimed to clarify a phosphatase and tensin homolog (PTEN)-related ceRNA regulatory network and identify potential prognostic marker ...[more]

PMID: 33575116

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Project description:Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with the high rates of the morbidity and mortality due to the lack of the effective prognostic model for prediction. Aim: To construct a risk model composed of the epithelial-mesenchymal transition (EMT)-related immune genes for the assessment of the prognosis, immune infiltration status, and chemosensitivity. Methods: We obtained the transcriptome and clinical data of the HCC samples from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) databases. The Pearson correlation analysis was applied to identify the differentially expressed EMT-related immune genes (DE-EMTri-genes). Subsequently, the univariate Cox regression was introduced to screen out the prognostic gene sets and a risk model was constructed based on the least absolute shrinkage and selection operator-penalized Cox regression. Additionally, the receiver operating characteristic (ROC) curves were plotted to compare the prognostic value of the newly established model compared with the previous model. Furthermore, the correlation between the risk model and survival probability, immune characteristic, and efficacy of the chemotherapeutics were analyzed by the bioinformatics methods. Results: Six DE-EMTri-genes were ultimately selected to construct the prognostic model. The area under the curve (AUC) values for 1-, 2-, and 3- year were 0.773, 0.721, and 0.673, respectively. Stratified survival analysis suggested that the prognosis of the low-score group was superior to the high-score group. Moreover, the univariate and multivariate analysis indicated that risk score [hazard ratio (HR) 5.071, 95% CI 3.050, 8.432; HR 4.396, 95% CI 2.624, 7.366; p < 0.001] and stage (HR 2.500, 95% CI 1.721, 3.632; HR 2.111, 95% CI 1.443, 3.089; p < 0.001) served as an independent predictive factors in HCC. In addition, the macrophages, natural killer (NK) cells, and regulatory T (Treg) cells were significantly enriched in the high-risk group. Finally, the patients with the high-risk score might be more sensitive to cisplatin, doxorubicin, etoposide, gemcitabine, and mitomycin C. Conclusion: We established a reliable EMTri-genes-based prognostic signature, which may hold promise for the clinical prediction.

Project description:Background: SPP1, secreted phosphoprotein 1, is a member of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. Previous studies have proven SPP1 overexpressed in a variety of cancers and can be identified as a prognostic factor, while no study has explored the function and carcinogenic mechanism of SPP1 in cervical cancer. Methods: We aimed to demonstrate the relationship between SPP1 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated SPP1 expression of cervical cancer in the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of SPP1 as a differentiating factor by the area under curve (AUC) score. Cox regression and logistic regression were performed to evaluate factors associated with prognosis. The SPP1-binding protein network was built by the STRING tool. Enrichment analysis by the R package clusterProfiler was used to explore potential function of SPP1. The single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER database were used to investigate the association between the immune infiltration level and SPP1 expression in cervical cancer. Results: Pan-cancer data analysis showed that SPP1 expression was higher in most cancer types, including cervical cancer, and we got the same result in the GEO database. The ROC curve suggested that SPP1 could be a potential diagnostic biomarker (AUC = 0.877). High SPP1 expression was associated with poorer overall survival (OS) (P = 0.032). Further enrichment and immune infiltration analysis revealed that high SPP1 expression was correlated with regulating the infiltration level of neutrophil cells and some immune cell types, including macrophage and DC. Conclusion: SPP1 expression was higher in cervical cancer tissues than in normal cervical epithelial tissues. It was significantly associated with poor prognosis and immune cell infiltration. Thus, SPP1 may become a promising prognostic biomarker for cervical cancer patients.

Project description:BackgroundAccumulating studies have demonstrated the abnormal expressions and prognostic values of certain members of the tripartite motif (TRIM) family in diverse cancers. However, comprehensive prognostic values of the TRIM family in hepatocellular carcinoma (HCC) are yet to be clearly defined.MethodsThe prognostic values of the TRIM family were evaluated by survival analysis and univariate Cox regression analysis based on gene expression data and clinical data of HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The expression profiles, protein-protein interaction among the TRIM family, prediction of transcription factors (TFs) or miRNAs, genetic alterations, correlations with the hallmarks of cancer and immune infiltrates, and pathway enrichment analysis were explored by multiple public databases. Further, a TRIM family gene-based signature for predicting overall survival (OS) in HCC was built by using the least absolute shrinkage and selection operator (LASSO) regression. TCGA-Liver Hepatocellular Carcinoma (LIHC) cohort was used as the training set, and GSE76427 was used for external validation. Time-dependent receiver operating characteristic (ROC) and survival analysis were used to estimate the signature. Finally, a nomogram combining the TRIM family risk score and clinical parameters was established.ResultsHigh expressions of TRIM family members including TRIM3, TRIM5, MID1, TRIM21, TRIM27, TRIM32, TRIM44, TRIM47, and TRIM72 were significantly associated with HCC patients' poor OS. A novel TRIM family gene-based signature (including TRIM5, MID1, TRIM21, TRIM32, TRIM44, and TRIM47) was built for OS prediction in HCC. ROC curves suggested the signature's good performance in OS prediction. HCC patients in the high-risk group had poorer OS than the low-risk patients based on the signature. A nomogram integrating the TRIM family risk score, age, and TNM stage was established. The ROC curves suggested that the signature presented better discrimination than the similar model without the TRIM family risk score.ConclusionOur study identified the potential application values of the TRIM family for outcome prediction in HCC.

Dataset Information

Comprehensive analysis to identify DLEU2L/TAOK1 axis as a prognostic biomarker in hepatocellular carcinoma.

Publications

Comprehensive analysis to identify DLEU2L/TAOK1 axis as a prognostic biomarker in hepatocellular carcinoma.

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