A Novel Nine-lncRNA Risk Signature Correlates With Immunotherapy in Hepatocellular Carcinoma

Nie, Ye; Li, Jianhui; Wu, Wenlong; Guo, Dongnan; Lei, Xinjun; Zhang, Tianchen; Wang, Yanfang; Mao, Zhenzhen; Zhang, Xuan; Song, Wenjie

doi:10.3389/fonc.2021.706915

Cited by 8 publications

(8 citation statements)

References 51 publications

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“…Statistical analysis showed that this risk model has good robustness and predictive power and that it can independently predict OS in patients with HCC. Furthermore, the predictive accuracy of our signature surpassed that of the previously reported prediction signature of HCC dependent on FR-lncRNAs or FRGs (Liang et al, 2020;Chen et al, 2021;Liang Wang et al, 2021;Liang et al, 2021;Nie et al, 2021;Wan et al, 2021;Xu et al, 2021). The nomogram consisting of age, stage, and six FR-related lncRNA risk scores could be used to visually predict the OS of individual patients with HCC at 1, 3, and 5 years.…”

Section: Discussionmentioning

confidence: 64%

Construction and Validation of a Ferroptosis-Related lncRNA Signature as a Novel Biomarker for Prognosis, Immunotherapy and Targeted Therapy in Hepatocellular Carcinoma

Zhang

Wan

et al. 2022

Front. Cell Dev. Biol.

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Recently, immunotherapy combined with targeted therapy has significantly prolonged the survival time and improved the quality of life of patients with hepatocellular carcinoma (HCC). However, HCC treatment remains challenging due to the high heterogeneity of this malignancy. Sorafenib, the first-line drug for the treatment of HCC, can inhibit the progression of HCC by inducing ferroptosis. Ferroptosis is associated with the formation of an immunosuppressive microenvironment in tumours. Moreover, long non-coding RNAs (lncRNAs) are strongly associated with ferroptosis and the progression of HCC. Discovery of ferroptosis-related lncRNAs (FR-lncRNAs) is critical for predicting prognosis and the effectiveness of immunotherapy and targeted therapies to improve the quality and duration of survival of HCC patients. Herein, all cases from The Cancer Genome Atlas (TCGA) database were divided into training and testing groups at a 6:4 ratio to construct and validate the lncRNA signatures. Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression analyses were used to screen the six FR-lncRNAs (including MKLN1-AS, LINC01224, LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1). Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curve analyses demonstrated the optimal predictive prognostic ability of the signature. Furthermore, a nomogram indicated favourable discrimination and consistency. For further validation, we used real-time quantitative polymerase chain reaction (qRT-PCR) to analyse the expression of LNCSRLR, LINC01063, PRRT3-AS1, and POLH-AS1 in HCC tissues. Moreover, we determined the ability of the signature to predict the effects of immunotherapy and targeted therapy in patients with HCC. Gene set enrichment analysis (GSEA) and somatic mutation analysis showed that ferroptosis-related pathways, immune-related pathways, and TP53 mutations may be strongly associated with the overall survival (OS) outcomes of HCC patients. Overall, our study suggests that a new risk model of six FR-lncRNAs has a significant prognostic value for HCC and that it could contribute to precise and individualised HCC treatment.

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Section: Discussionmentioning

confidence: 64%

Construction and Validation of a Ferroptosis-Related lncRNA Signature as a Novel Biomarker for Prognosis, Immunotherapy and Targeted Therapy in Hepatocellular Carcinoma

Zhang

Wan

et al. 2022

Front. Cell Dev. Biol.

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“…For instance, lncRNA-ROR, a stress-responsive lncRNA highly expressed in HCC cells and enriched in extracellular vesicles derived from tumor cells, could induce primary chemoresistance by activating the TGF-β pathway in HCC (Takahashi et al, 2014). Moreover, immune-related lncRNA signatures have been reported as prognostic biomarkers and therapeutic targets in several tumors, including lung adenocarcinoma, bladder cancer, esophageal cancer, gastric cancer, rectal cancer, cervical cancer, and HCC (Chen et al, 2021;Lai et al, 2021;Liu et al, 2021;Nie et al, 2021;Pang et al, 2021;Song et al, 2021;Xing et al, 2021;Ye et al, 2021), indicating that immune-related lncRNAs could effectively be applied to determine the survival, prognosis, and treatment response. However, all these studies focused on excerpting the immune-related lncRNA profiles from the obtained immune-related genes based on co-expression analysis while ignoring the important role of the stromal components in tumor progression; immune-related genes remain inconclusive in different research studies, and there are no uniform criteria for filtering immune-related lncRNAs, which may limit the generalization of the predictive model.…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Huang

Zhang

Lai

et al. 2021

Front. Mol. Biosci.

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Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

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“…It was reported that the immune checkpoint related genes can effectively reflect the response rate of immune checkpoint inhibitors (ICIs), and immunophenoscore (IPS) associated with the responds to ICIs ( 29 , 30 ). Thus, we further investigated the association of risk signature with immune-related genes and IPS.…”

Section: Methodsmentioning

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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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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A Novel Nine-lncRNA Risk Signature Correlates With Immunotherapy in Hepatocellular Carcinoma

Cited by 8 publications

References 51 publications

Construction and Validation of a Ferroptosis-Related lncRNA Signature as a Novel Biomarker for Prognosis, Immunotherapy and Targeted Therapy in Hepatocellular Carcinoma

Construction and Validation of a Ferroptosis-Related lncRNA Signature as a Novel Biomarker for Prognosis, Immunotherapy and Targeted Therapy in Hepatocellular Carcinoma

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy 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

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