Signature of prognostic epithelial–mesenchymal transition related long noncoding RNAs (ERLs) in hepatocellular carcinoma

Xu, Banghao; Jiang, Jing‐Hang; Luo, Tao; Zao, Jiang; Liu, Xinyu; Li, Le‐Qun

doi:10.1097/md.0000000000026762

Cited by 10 publications

(10 citation statements)

References 44 publications

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“…Besides, a nomogram, established basing on age, gender, grade, clinical stage, and risk score, provided better clinical practicality than the traditional tumor grade or clinical stage system. In comparison with the recently published studies that explored the EMT-related lncRNAs or other prognostic predictors in HCC ( 31 ), Our study demonstrates certain strengths. First, 343 HCC samples obtained from TCGA database were randomly assigned to training and testing group, the 12 EMT-related lncRNAs signature was built based on training cohort, and we tested the performance of the model with independent testing cohort.…”

Section: Discussionmentioning

confidence: 72%

“…Next, we compared the predictive performance of the risk signature built in this study with other five EMT-related prognostic models for HCC, including the EMT-related five-lncRNA signature ( 31 ), EMT-related teen-gene signature ( 32 ), EMT-related five-gene signature ( 33 ), the EMT-related six-gene signature ( 34 ). We mainly compared the AUCs of ROC curves to evaluate the accuracy of these risk model.…”

Section: Resultsmentioning

confidence: 99%

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Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients

Huang

Zhuang

et al. 2022

Front. Med.

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IntroductionHepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Epithelial–mesenchymal transition (EMT) is crucial for cancer progression and metastasis. Thus, we aimed to construct an EMT-related lncRNA signature for predicting the prognosis of HCC patients.MethodsCox regression analysis and LASSO regression method were used to build an EMT-related lncRNAs risk signature based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. ROC curves and Cox proportional-hazards analysis were performed to evaluate the performance of the risk signature. RT-qPCR was conducted in HCC cell lines and tissue samples to detect the expression of some lncRNAs in this risk model. Furthermore, a nomogram involving the risk score and clinicopathological features was built and validated with calibration curves and ROC curves. In addition, we explored the association between risk signature and tumor immunity, somatic mutations status, and drugs sensitivity.ResultsTwelve EMT-related lncRNAs were obtained to construct the prognostic risk signature for patients with HCC. The Kaplan-Meier curve analysis revealed that patients in the high-risk group had worse overall survival (OS) than those in low-risk group. ROC curves and Cox regression analysis suggested the risk signature could predict HCC survival exactly and independently. The prognostic value of the risk model was confirmed in the testing and entire groups. We also found AC099850.3 and AC092171.2 were highly expressed in HCC cells and HCC tissues. The nomogram could accurately predict survival probability of HCC patients. Gene set enrichment analysis (GSEA) and gene ontology (GO) analysis showed that cancer-related pathways and cell division activity were enriched in high-risk group. The SNPs showed that the prevalence of TP53 mutations was significantly different between high- and low-risk groups; the TP53 mutations and the high TMB were both associated with a worse prognosis in patients with HCC. We also observed widely associations between risk signature and drugs sensitivity in HCC.ConclusionA novel EMT-related lncRNAs risk signature, including 12 lncRNAs, was established and identified in patients with HCC, which can accurately predict the prognosis of HCC patients and may be used to guide individualized treatment in the clinical practice.

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Section: Discussionmentioning

confidence: 72%

Section: Resultsmentioning

confidence: 99%

Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients

Huang

Zhuang

et al. 2022

Front. Med.

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“…In the study by Zou et al (2014) , TRAF3IP2-AS1 expression was altered in gastric cancer cells after 125 I irradiation, providing a target for future drug development. AC099850.3 has been repeatedly extracted as a prognosis-related gene in the bioinformatics analysis of cancer, including squamous cell carcinoma of the tongue, hepatocellular carcinoma, and non-small cell lung cancer ( Hao Wu et al, 2020 ; Jia et al, 2020 ; Zheng et al, 2021a ; Junliang Zhou et al, 2021 ; Jiang et al, 2021 ; Wu et al, 2021 ; Xu et al, 2021 ; Zhang et al, 2021 ). The MIR193BHG motif can fine-tune cellular sterol/steroid biosynthesis by producing lincNORS to repress the expression of multiple pathway components ( Xue Wu et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Prognostication of Pancreatic Cancer Using The Cancer Genome Atlas Based Ferroptosis-Related Long Non-Coding RNAs

Zhang

Tao

et al. 2022

Front. Genet.

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Background: Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical.Methods: In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed.Results: Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selection operator and the multivariate Cox analysis were used to assess the prognostic value of these ferroptosis-related lncRNAs. A prognostic risk model, of six lncRNAs, including LINC01705, AC068620.2, TRAF3IP2-AS1, AC092171.2, AC099850.3, and MIR193BHG was constructed. The Kaplan Meier (KM) and time-related receiver operating characteristic (ROC) curve analysis were performed to calculate overall survival and compare high- and low-risk groups. There was also a significant difference in survival time between the high-risk and low-risk groups for the testing cohort and the entire cohort, with AUCs of .723, .753, respectively. Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for pancreatic adenocarcinoma through univariate and multivariate Cox regression. Moreover, a nomogram showed good prediction.Conclusion: The signature of six FRLs had significant prognostic value for pancreatic adenocarcinoma. They may be a promising therapeutic target in clinical practice.

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“… 31 Pan et al 32 have also proved that LINC00324 promoted the proliferation and metastasis of LUAD tissues and cells by the miR‐615‐5p/AKT1 pathway. However, in the remaining orlncRNAs of 11 orlncRNAs (e.g., AC010980.2, 33 , 34 , 35 , 36 AL365203.2, 37 , 38 , 39 AL606489.1, 34 , 40 AC004687.1, 41 , 42 , 43 HLA‐DQB1‐AS1, 44 , 45 , 46 , 47 and AL590226.1, 48 although there are few experiments to explore their relationship with the occurrence and development of malignant tumors, plenty of prognostic signatures have included them to predict the OS of patients with various malignant tumors.…”

Section: Discussionmentioning

confidence: 99%

Construction of an algorithm based on oncosis‐related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma

Chen

Zhou

et al. 2022

Clinical Laboratory Analysis

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Background As an important non‐apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few research reported the relationship between oncosis and lung cancer. Methods In this study, we established an oncosis‐based algorithm comprised of cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with lung adenocarcinomas (LUAD). We selected 11 oncosis‐related lncRNAs associated with the prognosis (CARD8‐AS1, LINC00941, LINC01137, LINC01116, AC010980.2, LINC00324, AL365203.2, AL606489.1, AC004687.1, HLA‐DQB1‐AS1, and AL590226.1) to divide the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low‐risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low‐risk patients tended to have a higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD‐1, CTLA‐4, HAVCR2, and were more sensitive to immune checkpoint inhibitors (ICIs), including PD‐1/CTLA‐4 inhibitors. The risk score had a significantly positive correlation with tumor mutation burden (TMB). The survival curve of the novel oncosis‐based algorithm suggested that low‐risk patients in cluster2 have the most obvious survival advantage. Conclusion The novel oncosis‐based algorithm investigated the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.

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Signature of prognostic epithelial–mesenchymal transition related long noncoding RNAs (ERLs) in hepatocellular carcinoma

Abstract: Supplemental Digital Content is available in the text

Cited by 10 publications

References 44 publications

Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients

Identification of an Epithelial-Mesenchymal Transition-Related Long Non-coding RNA Prognostic Signature to Determine the Prognosis and Drug Treatment of Hepatocellular Carcinoma Patients

Prognostication of Pancreatic Cancer Using The Cancer Genome Atlas Based Ferroptosis-Related Long Non-Coding RNAs

Construction of an algorithm based on oncosis‐related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma

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