Immune‐related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Zhang, Yaqiong; Zhang, Liming; Xu, Yong; Wu, Xiaoyu; Zhou, Yong; Mo, Jinggang

doi:10.1002/jcp.29730

Cited by 88 publications

(90 citation statements)

References 57 publications

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“…The value of immune-related lncRNA has been shown in many cancers such as hepatocellular carcinoma (HCC), anaplastic glioma ( 37 ), glioblastoma multiforme ( 38 ), diffuse large B cell lymphoma ( 39 ), and breast cancer. In the research of HCC, Zhang et al identified that the immune-related lncRNA signature not only had a significant effect on survival prognosis but also had the possible role of evaluating the response to ICB (immune checkpoint blockade) immunotherapy ( 40 ). Shen et al discovered the 11-lncRNA signature of breast cancer and analyzed the correlation between the lncRNA prognostic signature and the infiltration of immune cell subtypes ( 41 ).…”

Section: Discussionmentioning

confidence: 99%

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Liu

et al. 2020

Front. Oncol.

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Background Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD). Methods RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method. Results A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance ( P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis. Conclusions We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

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Section: Discussionmentioning

confidence: 99%

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Liu

et al. 2020

Front. Oncol.

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“…Currently, the main treatments for HCC include radiofrequency ablation, surgical resection, immunotherapy, and liver implantation, among others. However, the clinical efficacy of treatments for specific patients is not satisfactory due to poor therapeutic targets, tumor immune escape and complications, leading to a low 5-year survival rate [7,14,28]. In past decades, studies focused on immunotherapy for various cancers have obtained meaningful breakthroughs, especially in melanoma, non-small cell lung cancer, and others.…”

Section: Discussionmentioning

confidence: 99%

“…Immune-related cells infiltration into the tumor microenvironment (TME) is a key reason leading to immune responses at primary and secondary tumor sites, which is tightly regulated by various mediators, such as chemokines. Several studies have indicated that a variety of different immune cells, including CD4 + and CD8 + T -cells, dendritic cells, and tumor-associated macrophages (TAMs), have been identified in different cancers, such as prostate cancer, HCC and others [13,14]. Chunying et al also demonstrated that CD4 + and CD8 + T-cells could be recruited into the TME after CXCR4 inhibition in sorafenib-treated HCC in a mouse model [15].…”

Section: Introductionmentioning

confidence: 99%

Identification of Prognostic Biomarkers and Correlation with Immune Infiltrates in Hepatocellular Carcinoma Based on a Competing Endogenous RNA Network

Zhu

Wang

et al. 2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC. Methods: RNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds. Results: The predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8 + T cells, CD4 + T cells, monocytes, macrophages, neutrophils, and dendritic cells. Conclusions: The findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.

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“…Compared to a certain number of studies carried out to explore IRL signatures in several human malignancies [48][49][50][51][52][53][54], this was the rst study based on a comprehensive analysis that focused on IRL signature in CC. However, our study has some limitations.…”

Section: Discussionmentioning

confidence: 99%

Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

Zheng

Tong

Cao

et al. 2020

Preprint

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Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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Immune‐related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Cited by 88 publications

References 57 publications

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Identification of Prognostic Biomarkers and Correlation with Immune Infiltrates in Hepatocellular Carcinoma Based on a Competing Endogenous RNA Network

Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

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