A Prognostic Model of 15 Immune-Related Gene Pairs Associated With Tumor Mutation Burden for Hepatocellular Carcinoma

Huo, Junyu; Wu, Liqun; Zang, Yunjin

doi:10.3389/fmolb.2020.581354

“…Several prior analyses have also shown that certain gene signatures may predict patient survival (20)(21)(22)(23)(24)(25)(26); however, our study has some differences and/or advantages compared with similar analyses. First, the gene signatures built in previous studies require many genes (20)(21)(22)(23), which possibly leads to some difficulties in real-world practice. Our novel signature requires only 4 genes, and the predictive ability of our signature is acceptable, which increases the feasibility of the use of our signature in real-world practice.…”

Section: Discussionmentioning

confidence: 80%

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Zhu

¹

,

Ru

²

,

Ma

³

2021

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PurposeHepatocellular carcinoma (HCC) is a common solid-tumor malignancy with high heterogeneity, and accurate prognostic prediction in HCC remains difficult. This analysis was performed to find a novel prognostic multigene signature.MethodsThe TCGA-LIHC dataset was analyzed for differentially coexpressed genes through weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. A protein-protein interaction (PPI) network and univariate Cox regression analysis of overall survival (OS) were utilized to identify their prognostic value. Next, we used least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic module. Subsequently, the ICGC-LIRI-JP dataset was applied for further validation. Based on this module, HCC cases were stratified into high-risk and low-risk groups, and differentially expressed genes (DEGs) were identified. Functional enrichment analyses of these DEGs were conducted. Finally, single-sample gene set enrichment analysis (ssGSEA) was performed to explore the correlation between the prognostic signature and immune status.ResultsA total of 393 differentially coexpressed genes were obtained. Forty differentially coexpressed hub genes were identified using the CytoHubba plugin, and 38 of them were closely correlated with OS. Afterward, we established the four-gene prognostic signature with an acceptable accuracy (area under the curve [AUC] of 1-year survival: 0.739). The ICGC-LIRI-JP dataset also supported the acceptable accuracy (AUC of 1-year survival:0.752). Compared with low-risk cohort, HCC cases in the high-risk cohort had shorter OS, higher tumor grades, and higher T stages. The risk scores of this signature still act as independent predictors of OS (P<0.001). Functional enrichment analyses suggest that it was mainly organelle fission and nuclear division that were enriched. Finally, ssGSEA revealed that this signature is strongly associated with the immune status of HCC patients.ConclusionsThe proposed prognostic signature of four differentially coexpressed hub genes has satisfactory prognostic ability, providing important insight into the prediction of HCC prognosis.

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“…This method has been reported to have reliable results in many studies, including molecular classi cation of cancer. [12][13][14] In the present study, we identi ed an immune-related gene pair signature to predict overall survival for melanoma patients. The prognostic signature consists of 25 immune-related gene pairs (IRGPs) containing 41 unique IRGs.…”

Section: Discussionmentioning

confidence: 99%

“…[11] In recent years, a new method based on relative ranking of gene expression level has been proposed to eliminate the shortcomings of data normalization and scaling in gene expression data processing, and reliable results have been obtained in various researches. [12][13][14] There is growing evidence that the immune system plays a key role in the development and progression of melanoma. [15] In 2014, nivolumab and pembrolizumab, two checkpoint inhibitors binding to PD-1, were approved for the treatment of metastatic melanoma.…”

Section: Introductionmentioning

confidence: 99%

An Immune-Related Gene Pairs Signature Predicts Overall Survival in Melanoma

Pan¹,

Ou²,

Wu³

et al. 2021

Preprint

0

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Background:The incidence rate and mortality rate of melanoma have been increasing in recent decades. Increasing evidence has depicted the correlation between melanoma prognosis and immune signature. Therefore, the aim of this study is to develop a robust prognostic immune-related gene pairs (IRGPs) signature for estimating overall survival (OS) of melanoma.Methods:Gene expression profiling and clinical information of melanoma patients were derived from two public data sets, divided into training and validation cohorts. Immune genes significantly associated with prognosis were selected. Results:Among 1,646 immune genes, a 25 IRGPs signature was built which was significantly associated with OS in the training cohort (P=1.80×10−22; hazard ratio [HR] =9.50 [6.04, 14.93]). In the validation datasets, the IRGPs signature significantly divided patients into high- vs low- risk groups considering their prognosis (P=2.47×10−4; HR =2.99 [1.66, 5.38]) and was prognostic in multivariate analysis. Functional analysis showed that several biological processes, including keratinization and pigment phenotype-related pathways, enriched in the high-risk group. Macrophages M0, NK cells resting and T cells gamma delta were significantly higher in the high-risk group compared with the low-risk group. Conclusions:We successfully constructed a robust IRGPs signature with prognostic values for melanoma, providing new insights into post-operational treatment strategies.

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“…The LASSO regression analysis was performed with 3 Journal of Immunology Research the "glmnet" R package, where the time-dependent ROC curve was plotted with "survivalROC" and the Kaplan-Meier survival curve was generated with "survminer." This analysis facilitated the evaluation of the risk score's performance [14]. In addition, with the "rms" R package, the prognostic signature was visualized in the form of a nomogram, and…”

Section: Development and Validation Of A Prognostic Signaturementioning

confidence: 99%

“…A growing number of reports have revealed that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers [11][12][13][14]. However, the prognostic value of IRGs for GC has not been fully elucidated.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Huo

¹

,

Wu

²

,

Zang

³

2021

Journal of Immunology Research

Self Cite

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Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

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A Prognostic Model of 15 Immune-Related Gene Pairs Associated With Tumor Mutation Burden for Hepatocellular Carcinoma

Cited by 32 publications

References 42 publications

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

An Immune-Related Gene Pairs Signature Predicts Overall Survival in Melanoma

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

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