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

Yi Shi^{1

2

3}, Dan-Dan Zhang^{4

5}, Ji-Bin Liu², Xiao-Li Yang⁵, Rui Xin⁵, Cheng-You Jia⁶, Hui-Min Wang⁵, Gai-Xia Lu⁶, Pei-Yao Wang⁵, Yu Liu^{1

3}, Zi-Jin Li¹, Jing Deng¹, Qin-Lu Lin¹, Liang Ma³, Shan-Shan Feng³, Xiao-Qi Chen¹, Xiang-Min Zheng³, Ya-Fu Zhou⁷, Yong-Jun Hu⁷, Hua-Qun Yin⁸, Lin-Lin Tian⁵, Li-Peng Gu⁵, Zhong-Wei Lv⁶, Fei Yu⁶, Wen Li^{1

3}, Yu-Shui Ma^{6

9

10}, Fu Da^{1

5}

Affiliations

¹ National Engineering Laboratory for Deep Process of Rice and Byproducts, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China.
² Cancer Institute, Nantong Tumor Hospital, Nantong 226631, China.
³ College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
⁴ Department of Pathology, Shihezi University School of Medicine, Shihezi 832002, Xinjiang, China.
⁵ Central Laboratory for Medical Research, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
⁶ Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
⁷ Department of Cardiology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410005, Hunan, China.
⁸ School of Resource Processing and Bioengineering, Central South University, Changsha 410083, Hunan, China.
⁹ Department of Pancreatic and Hepatobiliary Surgery, Cancer Hospital, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
¹⁰ Pancreatic Cancer Institute, Fudan University, Shanghai 200032, China.

PMID: 33575116
PMCID: PMC7851426
DOI: 10.1016/j.omtn.2020.12.016

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

Yi Shi et al. Mol Ther Nucleic Acids. 2021.

. 2021 Jan 1:23:702-718.

doi: 10.1016/j.omtn.2020.12.016. eCollection 2021 Mar 5.

Authors

Affiliations

¹ National Engineering Laboratory for Deep Process of Rice and Byproducts, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China.
² Cancer Institute, Nantong Tumor Hospital, Nantong 226631, China.
³ College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
⁴ Department of Pathology, Shihezi University School of Medicine, Shihezi 832002, Xinjiang, China.
⁵ Central Laboratory for Medical Research, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
⁶ Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
⁷ Department of Cardiology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410005, Hunan, China.
⁸ School of Resource Processing and Bioengineering, Central South University, Changsha 410083, Hunan, China.
⁹ Department of Pancreatic and Hepatobiliary Surgery, Cancer Hospital, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
¹⁰ Pancreatic Cancer Institute, Fudan University, Shanghai 200032, China.

PMID: 33575116
PMCID: PMC7851426
DOI: 10.1016/j.omtn.2020.12.016

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.

Keywords: DLEU2L/TAOK1 axis; HCC; ceRNA network; prognosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
Flowchart of construction and analysis of ceRNA

**Figure 2**
The tumor suppressor role of PTEN in human hepatocellular carcinoma (HCC) (A) Expression distribution of PTEN in pan-cancer tissues. (B) Validation of the expression of PTEN on the translational level by the Human Protein Atlas database (immunohistochemistry). (C) The low (n = 185) and high expression (n = 184) of PTEN were compared using a Kaplan-Meier survival curve. (D) The distribution of PTEN genomic alterations in TCGA HCC is shown on a cBioPortal OncoPrint plot. (E and F) The association between PTEN copy number and mRNA expression are shown in the dot plot (E) and correlation plot (F) by cBioPortal.

**Figure 3**
Volcano plots and heatmap plots of DElncRNAs, DEmiRNAs, and DEmRNAs between the expression of PTEN^high and PTEN^low in HCC samples Red represents upregulated genes and blue indicates downregulated genes. (A–C) The volcano plots describe (A) 860DElncRNAs (|log₂fold change| > 0.5 and adjusted p value < 0.05), (B) 54DEmiRNAs (|log₂fold change| > 0.3 and adjusted p value < 0.05), and (C) 1871DEmRNAs (|log₂fold change| > 0.5 and adjusted p value < 0.05). (D–F) The horizontal axis of the heatmap indicates the samples, and the vertical axis of the heatmap indicates 15 significant DEGs.

**Figure 4**
Construction and functional enrichment analysis of the lnRNA-miRNA-mRNA triple regulatory network The ellipses denote miRNAs, diamonds denote mRNAs, and round rectangles denote lncRNAs. (A) The triple regulatory network in HCC. Red indicates upregulated, and blue represents downregulated. (B) Thirteen hub genes in this network with a score of >2. (C) Functional enrichment analysis (GO and KEGG) of the DEmRNAs in the network

**Figure 5**
The distribution of 13 hub-RNA expression patterns from the triple regulatory network in TCGA HCC dataset (A and B) The expression patterns of three hub-DElncRNAs, four hub-DEmiRNAs, and six hub-DEmRNAs in HCC samples with PTEN^high and PTEN^low expression groups (A), and in HCC and adjacent normal liver tissues (B).

**Figure 6**
Overall survival analysis for the RNAs in the hub triple regulatory network (A–C) The high- and low-expression values of three hub-lncRNAs (A), four hub-miRNAs (B), and six hub-mRNAs (C) were compared by a Kaplan-Meier survival curve for TCGA HCC patient cohort. The horizontal axis indicates the overall survival time in days, and the vertical axis represents the survival rate.

**Figure 7**
Construction and correlation analysis of the ceRNA network (A) The cellular localization for three hub-lncRNAs (DLEU2L, FAM99A, and ARRDC1-AS1) was predicted using lncLocator. (B) Schematic model of ceRNA. Blue indicates downregulated; red indicates upregulated. (C) Base pairing between miR-99a-5p and miR-100-5p and the target site in the DLEU2L and TAOK1 3′ UTR predicted by TarBase and TargetScan, respectively. (D) Correlation analysis between these four predictive RNAs and PTEN in HCC.

**Figure 8**
Methylation analysis of TAOK1 (A) Differential expression of three DNA methyltransferases (DNMT1, DNMT3A, and DNMT3B). (B) Methylation was evaluated using UALCAN. (C) Methylation was assessed using DiseaseMeth version 2.0. (D) The methylation site of TAOK1 DNA sequence association with gene expression was visualized using MEXPRESS. The expression of TAOK1 is illustrated by the blue line in the center of the plot. Pearson’s correlation coefficients and p values for methylation sites and query gene expression are shown on the right side.

**Figure 9**
Correlation analysis of TAOK1 expression and immune infiltration in HCC (A) Association between TAOK1 gene copy number and immune cell infiltration levels in HCC cohorts. (B) Correlation of TAOK1 expression with immune infiltration level in HCC. (C) Kaplan-Meier plots were used to analyze the immune infiltration and overall survival rate of HCC. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

See this image and copyright information in PMC

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

LinkOut - more resources

Full Text Sources

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Research Materials