Identification and Validation of Immune-Related Gene Prognostic Signature for Hepatocellular Carcinoma

Chen, Wenbiao; Ou, Minglin; Tang, Donge; Dai, Yong; Du, Wenli

doi:10.1155/2020/5494858

Cited by 49 publications

(50 citation statements)

References 40 publications

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“…Yang et al revealed that a prediction immune model can forecast the survival outcome of cervical cancer ( 43 ). Chen et al reflected that a nine immune gene model has prognostic value for HCC ( 44 ). Nowadays, prognostic biomarkers related to tumor immunity in lung cancer are still lacking.…”

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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“…GAST, PDGFA and STC1. Other studies on IRG signatures in human cancers include breast cancer [12], colorectal cancer [27], hepatocellular cancer [28], lung cancer [29], and gliomas [30]. Therefore, based on TCGA and microarray methods, IRG signatures for predicting the prognosis of patients have increasingly become the focus of research.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of An Immune-Related Gene Pair Signature in Skin Cutaneous Melanoma

Xie¹,

Dong²,

Jiang

et al. 2020

Preprint

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Skin cutaneous melanoma (SKCM) is a common skin malignancy worldwide, and its metastasis and mortality rates are high. The molecular characteristics exhibited by tumor-immune interactions have drawn the attention from researchers. Therefore, increased knowledge and new strategies to identify effective immune-related biomarkers may improve the clinical management of SKCM by providing more accurate prognostic information. In this study, we established a prognostic immune-related gene pair (IRGP) signature for predicting the survival of SKCM patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided gene expression profiles together with clinical information, and the samples were randomly divided into three groups including the training, testing, and validation datasets. The regression model of least absolute shrinkage and selection operator (LASSO) helped to identify a 13-IRGP signature with a significant relation to the survival of SKCM patients. The training, TCGA, and independent sets have an average value of area under the curve of 0.79, 0.76, and 0.82, respectively. In addition, this 13-IRGP signature can noticeably divide SKCM patients into high-risk group and low-risk group with significantly different prognoses. Many biological activities such as gene family were enriched among the genes in our IRGP signature. While analyzing the risk signature and clinical characteristics, there was a large difference in the risk score between T stage and tumor stage grouping. Finally, we constructed a nomogram and forest plots of the risk score and clinical features. In summary, we developed a robust 13-IRGP prognostic signature in SKCM, which can identify and provide new insights into immunological biomarkers.

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“…The gene expression levels were compared pairwise in a given sample or profile to compute a score for each RBP-related gene pair (RRGP). In a pairwise comparison, if the expression value of the first RBP gene was higher than that of the second one, the output score of this RRGP was 1; otherwise, the output was 0, according to the proposed method [15,22]. The advantage of analyzing genes in a pairwise comparison is without the requirement for standardization process for individualized analysis.…”

Section: Prognostic Signature Constructionmentioning

confidence: 99%

“…With the rapid development of sequencing and precision medicine, increasing evidence indicated that gene signatures at mRNA level had promising potential in predicting HCC prognosis. Numerous studies have proposed various signatures for survival prediction in patients with HCC [14][15][16][17][18][19]. For example, Liu et al [20] have established a novel six-gene signature for HCC prognosis prediction, but only one external validation cohort was used to validate the performance of the predicted model without comparison of its performance with other existed biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a RNA binding protein gene pair-associated prognostic signature for prediction of overall survival in hepatocellular carcinoma

2020

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Background: Increasing evidence has demonstrated the correlation between hepatocellular carcinoma (HCC) prognosis and RNA binding proteins (RBPs) dysregulation. Thus, we aimed to develop and validate a reliable prognostic signature that can estimate the prognosis for HCC. Methods: Gene expression profiling and clinical information of 374 HCC patients were derived from the TCGA data portal. The survival-related RBP pairs were determined using univariate cox-regression analysis and the signature was built based on LASSO analysis. All patients were divided patients into high-and low-risk groups according to the optimal cut off of the signature score determined by time-dependent receiver operating characteristic (ROC) curve analysis. The predictive value of the signature was further validated in an independent cohort. Results: A 37-RBP pairs signature consisting of 61 unique genes was constructed which was significantly associated with the survival. The RBP-related signature accurately predicted the prognosis of HCC patients, and patients in high-risk groups showed poor survival in two cohorts. The novel signature was an independent prognostic factor of HCC in two cohorts (all P < 0.001). Furthermore, the C-index of the prognostic model was 0.799, which was higher than that of many established risk models. Pathway and process enrichment analysis showed that the 61 unique genes were mainly enriched in translation, ncRNA metabolic process, RNA splicing, RNA modification, and translational termination. Conclusion: The novel proposed RBP-related signature based on relative expression orderings could serve as a promising independent prognostic biomarker for patients with HCC, and could improve the individualized survival prediction in HCC.

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Identification and Validation of Immune-Related Gene Prognostic Signature for Hepatocellular Carcinoma

Cited by 49 publications

References 40 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

Development and Validation of An Immune-Related Gene Pair Signature in Skin Cutaneous Melanoma

Development and validation of a RNA binding protein gene pair-associated prognostic signature for prediction of overall survival in hepatocellular carcinoma

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