Identification of long noncoding RNAs biomarkers in patients with hepatitis B virus-associated hepatocellular carcinoma

Mou, Yi; Wang, Dongguo; Xing, Renwei; Nie, Hanqiu; Mou, Yiping; Zhang, Yang; Zhou, Xianfei

doi:10.3233/cbm-181424

Cited by 23 publications

(23 citation statements)

References 27 publications

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“…In addition to macrophages, the functions of Tregs and fibroblasts can also be regulated by lncRNAs from tumor cells in TME. The tumor suppressor lncRNA FOXF1 Adjacent Non-Coding Developmental Regulatory RNA (FENDRR) is reported to be a favorable diagnostic biomarker for HCC (92). Microarray analysis suggested that the expression of FENDRR lncRNA in HCC samples was higher than that in the normal samples.…”

Section: Effect Of Lncrna On Immune Cells Within Tmementioning

confidence: 99%

Long Non-coding RNAs: Emerging Roles in the Immunosuppressive Tumor Microenvironment

Luo

Yang

et al. 2020

Front. Oncol.

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Section: Effect Of Lncrna On Immune Cells Within Tmementioning

confidence: 99%

Long Non-coding RNAs: Emerging Roles in the Immunosuppressive Tumor Microenvironment

Luo

Yang

et al. 2020

Front. Oncol.

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“…Some of these lncRNAs have been recognized as being associated with cancer. For example, C17orf82 was reported to be significantly overexpressed in HBV-related HCC [ 35 ]. LncRNA LINC01446 promotes glioblastoma progression by modulating miR-489-3p/TPT1 axis [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

A Four-Methylated lncRNAs-Based Prognostic Signature for Hepatocellular Carcinoma

Liao

Zheng

2020

Genes

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Currently, an increasing number of studies suggest that long non-coding RNAs (lncRNAs) and methylation-regulated lncRNAs play a critical role in the pathogenesis of various cancers including hepatocellular carcinoma (HCC). Therefore, methylated differentially expressed lncRNAs (MDELs) may be critical biomarkers of HCC. In this study, 63 MDELs were identified by screening The Cancer Genome Atlas (TCGA) HCC lncRNAs expression data set and lncRNAs methylation data set. Based on univariate and multivariate survival analysis, four MDELs (AC025016.1, LINC01164, LINC01183 and LINC01269) were selected to construct the survival prognosis prediction model. Through the PI formula, the study indicates that our new prediction model performed well and is superior to the traditional staging method. At the same time, compared with the previous prediction models reported in the literature, the results of time-dependent receiver operating characteristic (ROC) curve analysis show that our 4-MDELs model predicted overall survival (OS) stability and provided better prognosis. In addition, we also applied the prognostic model to Cancer Cell Line Encyclopedia (CCLE) cell lines and classified different hepatoma cell lines through the model to evaluate the sensitivity of different hepatoma cell lines to different drugs. In conclusion, we have established a new risk scoring system to predict the prognosis, which may have a very important guiding significance for the individualized treatment of HCC patients.

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“…[29][30][31][32] However, the specific molecular mechanism between the five lncRNAs and potential pathways remained unclear. Then, we identified the central genes relevant to BLCA disease.…”

Section: Discussionmentioning

confidence: 99%

Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

Zhang

et al. 2019

J of Cellular Biochemistry

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Nowadays, an increasing number of studies illustrated that bladder urothelial cancer (BLCA) may act as the most common subtype of urological malignancies with a high rate of recurrence and metastasis. In this study, we attempted to establish a prognostic model and identify the possible pathway crosstalk. Long noncoding RNAs (lncRNAs) and mRNA expression and corresponding clinical information of patients with BLCA were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes analysis, univariate Cox analysis, the least absolute shrinkage, and selection operator Cox (LASSO Cox) regression model were then applied to identify five crucial lncRNAs (AC092725.1, AC104071.1, AL023584.1, AL132642.1, and AL137804.1). The multivariate cox analysis was utilized to calculate the regression coefficients (βi). The risk‐score model was subsequently constructed as follows: (0.13541AC092725.1) + (0.20968AC104071.1) + (0.1525AL023584.1) − (0.14768AL132642.1) + (0.14387AL137804.1). Nomogram and assessment of overall survival (OS) prediction were verificated by the receiver operating characteristic curve in the testing group. As to 3‐, 5‐year OS prediction, the area under curve (AUC) for the nomogram of training data set was 0.83 and 0.86. Besides, the AUC (0.883 and 0.879) presented excellent predictive power in the testing group. In addition, the calibration plots validated the predictive performance of the nomogram. Weighted correlation network analysis (WGCNA) coupled with functional enrichment analysis contributed to explore the potential pathways, including PI3K‐Akt, HIF‐1, and Jak‐STAT signaling pathways. Construction of the risk‐score model and data analysis were both derived from multiple packages on the basis of the R platform chiefly.

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Identification of long noncoding RNAs biomarkers in patients with hepatitis B virus-associated hepatocellular carcinoma

Abstract: This study identified five potential lncRNAs biomarkers for HBV-associated HCC with great diagnostic and prognostic value and provided clues for their functions in HBV-associated HCC.

Cited by 23 publications

References 27 publications

Long Non-coding RNAs: Emerging Roles in the Immunosuppressive Tumor Microenvironment

Long Non-coding RNAs: Emerging Roles in the Immunosuppressive Tumor Microenvironment

A Four-Methylated lncRNAs-Based Prognostic Signature for Hepatocellular Carcinoma

Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

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