Genomewide identification of a novel six‐LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma

Wu, Ying; Wang, Pengshuo; Wang, Ben-gang; Lu, Xu; Fang, Wanxia; Che, Xiaofang; Qu, Xiujuan; Liu, Yunpeng; Li, Zhi

doi:10.1002/cam4.1854

Cited by 32 publications

(32 citation statements)

References 37 publications

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“…To our knowledge, little is known concerning the lncRNA signature for HCC patients. Gu and colleagues constructed a six-lncRNA signature to predict HCC recurrence-free survival, while Wu et al performed analysis in a specified resectable HCC population (9,10). Different from previous publications, this study was performed in HCC patients to predict survival using an lncRNA signature.…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma

Chen

Huang

et al. 2020

Front. Oncol.

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Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P-value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238-5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both Li et al. Prognostic lncRNA Signature for HCC OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.

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Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma

Chen

Huang

et al. 2020

Front. Oncol.

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“…Several previous studies constructed risk scoring systems to predict the prognosis of HCC patients (Gu et al, 2018;Liao et al, 2018;Ma et al, 2018;Shi et al, 2018;Sui et al, 2018;Wu et al, 2018b;Zhao et al, 2018;Bai et al, 2019;Yan et al, 2019;Ye et al, 2019;Zhang et al, 2019). However, all these risk scoring systems are based on lncRNAs differentially expressed between HCC and normal samples, while our systems are based on lncRNAs differentially expressed between HCC patients with or without cirrhosis.…”

Section: Discussionmentioning

confidence: 99%

Risk Scoring System based on lncRNA Expression for Predicting Survival in Hepatocellular Carcinoma with Cirrhosis

Wei

et al. 2020

Asian Pac J Cancer Prev

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Objective: This study aims to explore the roles of long non-coding RNAs (lncRNAs) for predicting survival in hepatocellular carcinoma (HCC) patients with cirrhosis. Methods: A set of lncRNAs differentially expressed between HCC patients with or without cirrhosis was identified using expression profiles of The Cancer Genome Atlas database, and these lncRNAs were screened for their risk scoring system to predict recurrence-free survival (RFS) or overall survival (OS). Predictive ability of risk scoring systems was confirmed using uni- and multivariate Cox analyses while adjusting for clinical features. Predictive lncRNAs were analyzed by functional enrichment analysis. Results: Our screen identified 22 lncRNAs that were upregulated in the presence of cirrhosis and 59 that were downregulated. To predict OS of HCC patients with cirrhosis, a risk scoring system was developed with four lncRNAs (LINC02086, LINC00880, LINC01549 and AC136475.3); to predict RFS in these patients, the risk scoring system contained five lncRNAs (SH3RF3-AS1, AC104117.3, AC136475.3, LINC00239 and MRPL23-AS1). All risk scoring systems were associated with an area under the receiver operating characteristic curve > 0.7. Based on uni- and multivariate Cox analyses, the risk scoring system could serve as a significant independent predictor for OS in HCC patients with cirrhosis. Functional enrichment analysis suggested that the lncRNAs in the risk scoring systems are involved primarily in the pathway of Wnt signal and cytokine-cytokine receptor interaction. Conclusion: Risk scoring systems based on lncRNAs can effectively predict OS of HCC patients with cirrhosis. The system should be further developed and validated in larger, preferably multi-site patient populations.

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“…Three literature studies that provided overall survival data of MIR22HG were enrolled for comprehensive analysis of the prognostic significance of MIR22HG for HCC. 15,16,26 Survival data extracted from the three included literature studies are listed in Table 6. Pooled HRs incorporating overall survival data from mRNA-seq, two microarrays, and three literature studies implicated MIR22HG as a protective prognostic factor for HCC (HR = 0.75, 95% CI = 0.64-0.89, I 2 = 64%, P<0.01) ( Figure 9A).…”

Section: Dovepressmentioning

confidence: 99%

<p>MIR22HG As A Tumor Suppressive lncRNA In HCC: A Comprehensive Analysis Integrating RT-qPCR, mRNA-Seq, And Microarrays</p>

Xiong

et al. 2019

OTT

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Introduction: MIR22HG has a reported involvement in the tumorigenesis of a variety of cancers, including hepatocellular carcinoma (HCC). However, the exact molecular mechanism of MIR22HG in HCC has not been clarified. Methods: In the present study, we integrated data from in-house RT-qPCR, RNA-sequencing, microarray, and literature studies to conduct a comprehensive evaluation of the clinicopathological and prognostic significance of MIR22HG in an extremely large group of HCC samples. We also explored the potential mechanism of MIR22HG in HCC by analyzing the alteration profiles of MIR22HG in HCC to predict transcription factors (TFs) that may interact with MIR22HG and to annotate the biological functions of genes co-expressed with MIR22HG. MIR22HG expression was also compared in HCC nude mice xenografts before and after a treatment with nitidine chloride. Results: We found that MIR22HG was downregulated in HCC and that this downregulation correlated with the malignant phenotype of HCC. Comprehensive analysis of the prognostic impact of MIR22HG in HCC revealed a beneficial effect of MIR22HG on the survival outcome of HCC patients. Seven cases of MIR22HG deep deletion occurred in 360 of the cancer genome atlas (TCGA) provisional HCC samples. A total of 22 MIR22HG-TF-mRNA triplets in HCC were predicted by the lncRNAmap. Co-expressed genes of MIR22HG, identified by weighted correlation network analysis (WGCNA), mainly participated in the pathways involving osteoclast differentiation, chemokine signaling pathways, and hematopoietic cell lineage. In vivo experiments demonstrated that nitidine chloride could stimulate MIR22HG expression in HCC xenografts. Conclusion: In summary, MIR22HG may play a tumor-suppressive role in HCC by coordinating with predicted TFs and co-expressed genes, such as NLRP3, CSF1R, SIGLEC10, and ZEB2, or by being controlled by nitidine chloride.

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Genomewide identification of a novel six‐LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma

Cited by 32 publications

References 37 publications

Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma

Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma

Risk Scoring System based on lncRNA Expression for Predicting Survival in Hepatocellular Carcinoma with Cirrhosis

<p>MIR22HG As A Tumor Suppressive lncRNA In HCC: A Comprehensive Analysis Integrating RT-qPCR, mRNA-Seq, And Microarrays</p>

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