A Prognostic Model for Patients with Hepatocellular Carcinoma Based on Hypoxia-Related Long Noncoding RNAs

Jiang, Lan; Li, Huacheng; Zhao, Deqing

doi:https://doi.org/10.1155/2022/4642477

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Volume 2022 | Article ID 4642477 | https://doi.org/10.1155/2022/4642477

A Prognostic Model for Patients with Hepatocellular Carcinoma Based on Hypoxia-Related Long Noncoding RNAs

Lan Jiang,¹Huacheng Li,²and Deqing Zhao¹

Academic Editor: Weiguo Li

Received23 Feb 2022

Accepted22 Mar 2022

Published01 Apr 2022

Abstract

Objective. The prognosis of patients with hepatocellular carcinoma (HCC) varies greatly due to the hypoxic environment and multiple factors driving metastasis progress. In this study, we aimed to correlate the expression of hypoxia-related long noncoding RNAs (lncRNAs) with the survival of patients with HCC to develop a prognostic model for HCC. Methods. The Pearson correlation analysis was used to screen hypoxia-related lncRNAs between differentially expressed lncRNAs by analyzing lncRNA expression profiles in The Cancer Genome Atlas (TCGA) database and 200 hypoxia genes downloaded from the Molecular Signatures Database (M5891). The univariate and multivariate Cox regression analyses were used to identify significantly predictive hypoxia-related lncRNAs, and a prognostic model based on these factors was constructed to predict the survival of HCC. The Kaplan-Meier (K-M) survival analysis and receiver operating characteristic curve (ROC) were performed to evaluate the performance of the model. Results. A total of 490 hypoxia-related lncRNAs were screened out. A prognostic model comprising 10 significantly predictive hypoxia-related lncRNAs was constructed by the multivariate Cox regression analysis. The hypoxia-related risk scores were calculated and were divided into high-risk and low-risk groups. The K-M survival analysis showed a lower overall survival rate of patients in the high-risk group (). ROC analysis showed that the AUC value of hypoxia-related risk score was 0.799, demonstrating that the hypoxia-related risk score was an independent prognosis factor of HCC. Conclusion. Our study indicates that identified 10 key hypoxia-related lncRNAs have potential prognostic values for HCC patients and may provide new targets for the treatment of HCC.

1. Introduction

Hepatocellular carcinoma (HCC) is the main type of liver cancer, accounting for 85%-90% of the primary liver cancers, and the global disease burden caused by HCC is also increasing [1]. Although some new advances have been made in the treatment of HCC in recent years, the rate of 5-year overall survival in patients with HCC is still no more than 20% [2]. Nowadays, molecular mechanism research based on bioinformatics analysis has become one of the important tools in cancer research [3, 4]. Therefore, using bioinformatics technique to explore new molecular markers for predicting the survival of HCC is of great significance for individualized and precision therapy of HCC.

Hypoxia, which manifested as a decrease in the level of available oxygen in the tissues, often occurs in patients with acute and chronic vascular diseases, lung diseases, and cancer [5]. Due to the imbalance between tumor cell proliferation speed and vascular nutrient supply, hypoxia is a common phenomenon in solid tumors [6]. Many studies had recognized the important role of hypoxia in tumor angiogenesis, cell proliferation, cell differentiation, and apoptosis [7, 8]. The liver is one of the three most vulnerable organs to hypoxia, and HCC is a hypermetabolic tumor that consumes more oxygen than its surrounding normal tissues. Previous studies have found that hypoxia was related to HCC metastasis, poor prognosis, and treatment resistance [9, 10]. A recent study found that an increased expression of hypoxia-inducible factor-1α (HIF-1α) in HCC tissues may contribute to the invasion and metastasis of HCC and poor prognosis [11]. Besides, HIF-1α is also the main reason for the resistance of HCC to sorafenib [12].

Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length of more than 200 nucleotides [13]. Recently, lncRNAs are gaining increasing attention from researchers because they are involved in several key molecular and biological processes of the body, such as they affect the occurrence and development of tumors [14]. Besides, studies have shown that various lncRNAs responding to hypoxia environment played an important role in the occurrence and development of tumors [15]. Based on this, we speculate that hypoxia-related lncRNAs may be served as prognostic markers for HCC patients. Therefore, this study was aimed at exploring prognostic markers of HCC based on hypoxia-related lncRNAs by a bioinformatics approach and constructing a prognostic model for predicting the survival outcomes in HCC.

2. Materials and Methods

2.1. Data Acquisition and Processing

The gene expression profiles and clinical data of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository) on August 10, 2021. We excluded those with an overall survival period of ≤30 days (because these patients may die from unpredictable factors such as infection or bleeding) and those with unknown survival time and survival status. A total of 350 HCC patients (cancerous liver tissues and adjacent noncancerous liver tissues) were included in this study.

2.2. Screening Hypoxia-Related lncRNAs

The transcriptome data of 350 HCC patients were annotated into mRNAs and lncRNAs. Differentially expressed lncRNAs between cancerous liver tissues and adjacent noncancerous liver tissues were screened by analyzing raw data of gene expression profiles using the Limma package from the R/Bioconductor software in accordance with and adjusted . A total of 200 iconic hypoxia genes on the hypoxia-related pathway (HALLMARK-HYPOXIA) were downloaded from the Molecular Signatures Database V7.4 (http://www.gsea http://msigdb.org/gsea/msigdb/index.jsp). Then Pearson correlation analysis was used to analyze the correlation between the hypoxia score and the expression of lncRNAs, and the hypoxia-related lncRNAs were screened out according to the criteria of correlation coefficient and .

2.3. Identifying Key Hypoxia-Related lncRNAs Associated with the Prognosis of HCC

The univariable Cox regression was used to identify the key hypoxia-related lncRNAs associated with the prognosis of HCC. Hypoxia-related lncRNAs with significant prognostic value () were defined as key hypoxia-related lncRNAs and were screened out for subsequent research.

2.4. Constructing the Hypoxia-Related Prognostic Model

The multivariate Cox regression analysis was used to construct the hypoxia-related prognostic model, and the risk score of each sample was calculated by the following algorithm: Risk score = Expression_lncRNA1 × Coefficient_lncRNA1 + Expression_lncRNA2 × Coefficient_lncRNA2+⋯+Expression_lncRNAn × Coefficient_lncRNAn (Expression _lncRNA refers to the expression level of hypoxia-related lncRNA; Coefficient _lncRNA is the regression coefficient of lncRNA obtained by multivariate Cox regression analysis). Then, patients were divided into low-risk group and high-risk group according to their median value of the risk score.

2.5. Bioinformatics Analysis

Receiver operating characteristic (ROC) curve was used to evaluate the performance of the prognostic model. Principal component analysis (PCA) and gene set enrichment analysis (GSEA) (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) were performed to determine whether the hypoxia-related lncRNA set is statistically significantly enriched in some distribution pattern and some functional pathways between high-risk group and low-risk group.

2.6. Statistical Analysis

The Pearson correlation analysis was used to identify the hypoxia-related lncRNAs. The Kaplan-Meier (K-M) analysis was used to draw survival curve, and log-rank test was used to compare the survival rate of patients in high-risk and low-risk groups. The univariate and multivariate Cox regression analyses were used to determine the prognostic hypoxia-related lncRNAs of HCC. All statistical analysis was performed by R software (Version 4.0.2; https://mirror.lzu.edu.cn/CRAN/). indicated that the difference is statistically significant.

3. Results

3.1. Identification of Hypoxia-Related lncRNAs

After downloading the transcriptome data and clinical data of HCC samples from the TACG database, converting the ID of the data into the gene name, we annotated the transcriptome data into lncRNAs and mRNAs. The iconic hypoxia genes including 200 genes were downloaded from the Molecular Signatures Database. By constructing a hypoxia lncRNA-mRNA coexpression network, a total of 490 hypoxia-related lncRNAs were screened out following the selection criteria of and .

3.2. Construction of the Hypoxia-Related Prognostic Model

Among the 490 hypoxia-related lncRNAs screened out above, a total of 37 hypoxia-related lncRNAs were associated with the prognosis of HCC by the univariate Cox regression analysis (Figure 1). The multivariate Cox regression identified 10 key hypoxia-related lncRNAs to construct the prognostic model, including AL365203.2, AC015908.3, MSC-AS1, AC145207.5, AL117336.3, TMEM220-AS1, AL031985.3, AC009005.1, THUMPD3-AS1, and PRRT3-AS1 (Table 1). Based on the expression levels of 10 hypoxia-related lncRNAs and their coefficients from the multivariable Cox regression, we then calculated the risk score of each patient by using the following algorithm: . The details of the 10 hypoxia-related lncRNAs are shown in Table 1.

3.3. Clinical Characteristics of HCC Patients in High-Risk and Low-Risk Groups

Based on the above risk score calculation formula, 350 HCC patients were divided into high-risk and low-risk groups according to the median value of risk score. The survival status, risk score distribution, and key gene expression are shown in Figure 2, from which we can see that the low-risk group had significantly lower hypoxia-related risk score (Figure 2(a)) and higher survival rate (Figure 2(b)). Furthermore, as the hypoxia-related risk score increased, the expression levels of AL365203.2, MSC-AS1, AC145207.5, AL117336.3, AL031985.3, AC009005.1, THUMPD3-AS1, and PRRT3-AS1 were increased, while the expression levels of AC015908.3 and TMEM220-AS1 were decreased (Figure 2(c)). In addition, the K-M survival analysis showed that the overall survival rate of the high-risk group was significantly lower than that of the low-risk group () (Figure 2(d)).

(a)

(b)

(c)

(d)

3.4. Correlation between Hypoxia-Related Risk Scores and Clinical Characteristics

We analyzed the correlation between the hypoxia-related risk scores and clinical and demographic characteristics such as age, gender, grade, T stage, N stage, and M stage. The results showed that C009005.1, AC015908.3, AC145207.5, AL031985.3, THUMPD3-AS1, and TMEM220-AS1 were correlated with T stage (all ). AC145207.5, AL031985.3, AC009005.1, and THUMPD3-AS1 have low expression in stage T1 and high expression in stages T2-T4, while TMEM220-AS1 and AC015908.3 have low expression in stages T2 and T3 but high expression in stages T1 and T4 (Figure 3). In addition, both univariate and multivariate Cox regression analysis results showed that only hypoxia-related risk scores were significantly related to the prognosis of HCC patients (all ) (Table 2). ROC analysis showed that the AUC values of hypoxia-related risk score, age, gender, classification, staging, T stage, N stage, and M stage were 0.799, 0.454, 0.506, 0.475, 0.743, 0.752, 0.508, and 0.508, respectively (Figure 4), demonstrating that the hypoxia-related risk score was an independent prognosis factor of HCC.

3.5. Analysis of Hypoxia Characteristics of Patients in High-Risk and Low-Risk Groups

Based on the key hypoxia-related lncRNAs for constructing the prognostic model and all hypoxia-related lncRNAs, we used PCA to explore the different distribution patterns between the high-risk and low-risk groups. The results showed that the distribution of hypoxia-related lncRNAs in the high-risk and low-risk groups was significantly different, showing two separate parts (Figures 5(a) and 5(b)). The GSEA analysis further indicated that hypoxia-related phenotypes were significantly enriched in the high-risk group (Figure 6).

(a)

(b)

4. Discussion

HCC is a highly invasive malignant tumor of the digestive system [16, 17]. Despite some progress has been made in the diagnosis and treatment of HCC in recent years, the prognosis of HCC is still poor [18]. Therefore, identifying new prognostic markers to optimize treatment is a great challenge in the field of tumor research. Studies have shown that the occurrence and development of HCC is a complex process affected by many factors [19, 20].

Hypoxia is an important feature of the microenvironment of most malignant tumors, especially HCC, and it is closely related to the poor prognosis of patients [21]. Several studies have shown that hypoxia is associated with the aggressive development of HCC [5, 8]. Recently, lncRNA-related signatures have received more and more attention due to their higher prediction accuracy compared with standard benchmarks [22–24]. Previous studies have also demonstrated that lncRNAs are involved in the occurrence, development, and metastasis of HCC [25, 26]. Hypoxia can affect the expression of some lncRNAs [27]. In view of the role of lncRNAs in the biological process of cancer and their correlation with hypoxia, it is of great clinical significance to explore the predictive value of hypoxia-related lncRNAs in the prognosis of HCC. However, prognostic markers based on the hypoxia-related lncRNA expression profile have not been studied in HCC.

In this study, we focused on hypoxia-related lncRNA signatures with the prognosis value of HCC. We analyzed the transcriptome information and clinical data of HCC patients in TCGA database, and found a group of hypoxia-related lncRNAs by coexpression analysis. A total of 37 hypoxia-related lncRNAs were found to be associated with the prognosis of HCC by the univariate Cox regression analysis, indicating that these hypoxia-related lncRNAs were involved in the development of HCC. In further analysis of the data by the multivariate Cox regression, we identified 10 key hypoxia-related lncRNAs (AL365203.2, AC015908.3, MSC-AS1, AC145207.5, AL117336.3, TMEM220-AS1, AL031985.3, AC009005.1, THUMPD3-AS1, and PRRT3-AS1) which were associated with the prognosis of HCC. A prognostic model of HCC was constructed based on these10 key hypoxia-related lncRNAs. The prognosis of patients in the high-risk group was worse than those in the low-risk group. Besides, we also found that the hypoxia-related risk score was significantly correlated with T stage. Therefore, these results further confirmed that these key hypoxia-related lncRNAs were associated with the progress of HCC. ROC analysis indicated that the 10 key hypoxia-related lncRNA signatures can be a good indicator for the prognosis of HCC.

Among the 10 key hypoxia-related lncRNAs found in HCC, AL365203.2 has been reported in previous studies as autophagy-related lncRNA, epithelial-mesenchymal transition-related lncRNA, and immune-related lncRNA to participate in the progress of HCC [28–30]. It was reported that AC015908.3 was also involved in the regulation of HCC cells’ stemness, which was closely related to drug resistance and poor prognosis of HCC [28–30]. MSC-AS1 can promote HCC oncogenesis via inducing the expression of phosphoglycerate kinase1 [31]. AC145207.5, AL031985.3, and PRRT3-AS1 participated in the infiltration of immune cells, which was associated with immunotherapy response in patients with HCC [30, 32]. AL117336.3 and AC009005.1 can also regulate the process of autophagy and participate in the occurrence and development of HCC [28, 32]. TMEM220-AS1 has been demonstrated to suppress HCC by regulating the miR-484/MAGI1 axis as a competing endogenous RNA [28, 33]. THUMPD3-AS1 has been reported to be associated with the prognosis of non-small-cell lung cancer and HBV-related HCC [34, 35].

In addition, we also used PCA to study the different distribution patterns between the high-risk and low-risk groups based on the expression sets of key hypoxia-related lncRNAs for constructing the risk scoring model and all hypoxia-related lncRNAs. According to the gene sets of the two hypoxia-related lncRNAs, patients in the high-risk and low-risk groups were clearly divided into two parts, and the hypoxia risk score of the low-risk group was lower than that of the high-risk group. GSEA showed that hypoxia-related phenotypes were abundant in high-risk patients. These results suggest that the prognostic model based on the 10 key hypoxia-related lncRNAs can help identify high-risk patients from patients with the same clinical or molecular characteristics, make accurate judgments on the prognosis of patients, and thus achieve individualized treatment.

However, the current study has the following limitations that need to be acknowledged. Firstly, we failed to find an available independent lncRNA dataset to verify the usefulness of the prognostic model. Secondly, the samples in this study mainly come from international databases, lacking our own samples to verify the model. Thirdly, we just screened out 10 hypoxia-related lncRNAs that were associated with the prognosis of HCC by a series of bioinformatics method; further in vivo and in vitro experiments are needed to explore their potential mechanisms affecting the prognosis of HCC.

In conclusion, this study identified 10 key hypoxia-related lncRNAs that were associated with the prognosis of HCC by a series of bioinformatics methods based on the TCGA database, indicating that hypoxia-related lncRNAs have potential prognostic value for HCC patients and it may provide a new target for the treatment of HCC.

Data Availability

The data used to support the findings of this study are included in the article.

Conflicts of Interest

No potential conflicts of interest need to be declared.

Authors’ Contributions

Lan Jiang and Huacheng Li equally contributed to the work.

References

J. M. Llovet, F. Castet, M. Heikenwalder et al., “Immunotherapies for hepatocellular carcinoma,” Nature Reviews. Clinical Oncology, vol. 19, no. 3, pp. 151–172, 2022.
View at: Publisher Site | Google Scholar
J. C. Nault and A. Villanueva, “Biomarkers for hepatobiliary cancers,” Hepatology, vol. 73, Suppl 1, pp. 115–127, 2021.
View at: Google Scholar
I. D. Kyrochristos, D. E. Ziogas, and D. H. Roukos, “Dynamic genome and transcriptional network-based biomarkers and drugs: precision in breast cancer therapy,” Medicinal Research Reviews, vol. 39, no. 3, pp. 1205–1227, 2019.
View at: Publisher Site | Google Scholar
F. Yin, L. Shu, X. Liu et al., “Microarray-based identification of genes associated with cancer progression and prognosis in hepatocellular carcinoma,” Journal of Experimental & Clinical Cancer Research, vol. 35, no. 1, p. 127, 2016.
View at: Google Scholar
X. X. Xiong, X. Y. Qiu, D. X. Hu, and X. Q. Chen, “Advances in hypoxia-mediated mechanisms in hepatocellular carcinoma,” Molecular Pharmacology, vol. 92, no. 3, pp. 246–255, 2017.
View at: Publisher Site | Google Scholar
L. H. Gray, A. D. Conger, M. Ebert, S. Hornsey, and O. C. Scott, “The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy,” The British Journal of Radiology, vol. 26, no. 312, pp. 638–648, 1953.
View at: Publisher Site | Google Scholar
N. Nishida and M. Kudo, “Oxidative stress and epigenetic instability in human hepatocarcinogenesis,” Digestive Diseases, vol. 31, no. 5-6, pp. 447–453, 2013.
View at: Publisher Site | Google Scholar
X. Z. Wu, G. R. Xie, and D. Chen, “Hypoxia and hepatocellular carcinoma: the therapeutic target for hepatocellular carcinoma,” Journal of Gastroenterology and Hepatology, vol. 22, no. 8, pp. 1178–1182, 2007.
View at: Publisher Site | Google Scholar
K. Graham and E. Unger, “Overcoming tumor hypoxia as a barrier to radiotherapy, chemotherapy and immunotherapy in cancer treatment,” International Journal of Nanomedicine, vol. Volume 13, pp. 6049–6058, 2018.
View at: Publisher Site | Google Scholar
J. T. Erler and A. J. Giaccia, “Lysyl oxidase mediates hypoxic control of metastasis,” Cancer Research, vol. 66, no. 21, pp. 10238–10241, 2006.
View at: Publisher Site | Google Scholar
S. S. Zheng, X. H. Chen, X. Yin, and B. H. Zhang, “Prognostic significance of HIF-1alpha expression in hepatocellular carcinoma: a meta-analysis,” PLoS One, vol. 8, no. 6, article e65753, 2013.
View at: Google Scholar
C. Mendez-Blanco, F. Fondevila, A. Garcia-Palomo, J. Gonzalez-Gallego, and J. L. Mauriz, “Sorafenib resistance in hepatocarcinoma: role of hypoxia-inducible factors,” Experimental & Molecular Medicine, vol. 50, no. 10, pp. 1–9, 2018.
View at: Publisher Site | Google Scholar
A. Fatica and I. Bozzoni, “Long non-coding RNAs: new players in cell differentiation and development,” Nature Reviews. Genetics, vol. 15, no. 1, pp. 7–21, 2014.
View at: Publisher Site | Google Scholar
A. Bhan, M. Soleimani, and S. S. Mandal, “Long noncoding RNA and cancer: a new paradigm,” Cancer Research, vol. 77, no. 15, pp. 3965–3981, 2017.
View at: Publisher Site | Google Scholar
Y. N. Chang, K. Zhang, Z. M. Hu et al., “Hypoxia-regulated lncRNAs in cancer,” Gene, vol. 575, no. 1, pp. 1–8, 2016.
View at: Publisher Site | Google Scholar
M. Huang, H. Wang, X. Hu, and X. Cao, “lncRNA MALAT1 binds chromatin remodeling subunit BRG1 to epigenetically promote inflammation-related hepatocellular carcinoma progression,” Oncoimmunology, vol. 8, no. 1, article e1518628, 2019.
View at: Google Scholar
J. Ahodantin, M. Bou-Nader, C. Cordier et al., “Hepatitis B virus X protein promotes DNA damage propagation through disruption of liver polyploidization and enhances hepatocellular carcinoma initiation,” Oncogene, vol. 38, no. 14, pp. 2645–2657, 2019.
View at: Publisher Site | Google Scholar
J. U. Marquardt, P. R. Galle, and A. Teufel, “Molecular diagnosis and therapy of hepatocellular carcinoma (HCC): an emerging field for advanced technologies,” Journal of Hepatology, vol. 56, no. 1, pp. 267–275, 2012.
View at: Publisher Site | Google Scholar
H. B. El-Serag and K. L. Rudolph, “Hepatocellular carcinoma: epidemiology and molecular carcinogenesis,” Gastroenterology, vol. 132, no. 7, pp. 2557–2576, 2007.
View at: Publisher Site | Google Scholar
T. Couri and A. Pillai, “Goals and targets for personalized therapy for HCC,” Hepatology International, vol. 13, no. 2, pp. 125–137, 2019.
View at: Publisher Site | Google Scholar
W. R. Wilson and M. P. Hay, “Targeting hypoxia in cancer therapy,” Nature Reviews. Cancer, vol. 11, no. 6, pp. 393–410, 2011.
View at: Publisher Site | Google Scholar
R. Miao, C. Ge, X. Zhang et al., “Combined eight-long noncoding RNA signature: a new risk score predicting prognosis in elderly non-small cell lung cancer patients,” Aging (Albany NY), vol. 11, no. 2, pp. 467–479, 2019.
View at: Publisher Site | Google Scholar
Y. Liu, L. Wang, H. Liu, C. Li, and J. He, “The prognostic significance of metabolic syndrome and a related six-lncRNA signature in esophageal squamous cell carcinoma,” Frontiers in Oncology, vol. 10, p. 61, 2020.
View at: Publisher Site | Google Scholar
Y. Shen, X. Peng, and C. Shen, “Identification and validation of immune-related lncRNA prognostic signature for breast cancer,” Genomics, vol. 112, no. 3, pp. 2640–2646, 2020.
View at: Publisher Site | Google Scholar
Z. Huang, J. K. Zhou, Y. Peng, W. He, and C. Huang, “The role of long noncoding RNAs in hepatocellular carcinoma,” Mol Cancer, vol. 19, no. 1, p. 77, 2020.
View at: Google Scholar
L. J. Lim, S. Y. S. Wong, F. Huang et al., “Roles and regulation of long noncoding RNAs in hepatocellular carcinoma,” Cancer Research, vol. 79, no. 20, pp. 5131–5139, 2019.
View at: Publisher Site | Google Scholar
H. Choudhry, A. L. Harris, and A. McIntyre, “The tumour hypoxia induced non-coding transcriptome,” Molecular Aspects of Medicine, vol. 47-48, pp. 35–53, 2016.
View at: Publisher Site | Google Scholar
Y. Jia, Y. Chen, and J. Liu, “Prognosis-predictive signature and nomogram based on autophagy-related long non-coding RNAs for hepatocellular carcinoma,” Frontiers in Genetics, vol. 11, p. 608668, 2020.
View at: Publisher Site | Google Scholar
B. H. Xu, J. H. Jiang, T. Luo, Z. J. Jiang, X. Y. Liu, and L. Q. Li, “Signature of prognostic epithelial-mesenchymal transition related long noncoding RNAs (ERLs) in hepatocellular carcinoma,” Medicine Baltimore, vol. 100, no. 30, article e26762, 2021.
View at: Google Scholar
P. Zhou, Y. Lu, Y. Zhang, and L. Wang, “Construction of an immune-related six-lncRNA signature to predict the outcomes, immune cell infiltration, and immunotherapy response in patients with hepatocellular carcinoma,” Frontiers in Oncology, vol. 11, p. 661758, 2021.
View at: Publisher Site | Google Scholar
C. Cao, Q. Zhong, L. Lu et al., “Long noncoding RNA MSC-AS1 promotes hepatocellular carcinoma oncogenesis via inducing the expression of phosphoglycerate kinase 1,” Cancer Medicine, vol. 9, no. 14, pp. 5174–5184, 2020.
View at: Publisher Site | Google Scholar
W. Kong, X. Wang, X. Zuo, Z. Mao, Y. Cheng, and W. Chen, “Development and validation of an immune-related lncRNA signature for predicting the prognosis of hepatocellular carcinoma,” Frontiers in Genetics, vol. 11, p. 1037, 2020.
View at: Publisher Site | Google Scholar
H. Wu, T. Liu, J. Qi, C. Qin, and Q. Zhu, “Four autophagy-related lncRNAs predict the prognosis of HCC through coexpression and ceRNA mechanism,” BioMed Research International, vol. 2020, Article ID 3801748, 2020.
View at: Google Scholar
X. Zhao, Z. Bai, C. Li, C. Sheng, and H. Li, “Identification of a novel eight-lncRNA prognostic signature for HBV-HCC and analysis of their functions based on coexpression and ceRNA networks,” BioMed Research International, vol. 2020, Article ID 8765461, 2020.
View at: Google Scholar
J. Hu, Y. Chen, X. Li et al., “THUMPD3-AS1 is correlated with non-small cell lung cancer and regulates self-renewal through miR-543 and ONECUT2,” Oncotargets and Therapy, vol. 12, pp. 9849–9860, 2019.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Lan Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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