Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer

Zhang, Enchong; He, Jieqian; Zhang, Hui; Shan, Liping; Wu, Hongliang; Zhang, Mo; Song, Yi

doi:10.3389/fgene.2020.595657

Cited by 15 publications

(12 citation statements)

References 67 publications

(72 reference statements)

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“…A certain number of existing prognostic models for PC patients were proposed ( Zhang et al, 2020 ; Xiaoli et al, 2015 ). These predictive models were developed by lncRNAs, miRNAs, or immune-related genes ( Zhang et al, 2020 ; Xiaoli et al, 2015 ). Nevertheless, none of them involved circadian clock genes in.…”

Section: Discussionmentioning

confidence: 99%

Prognostic values of the core components of the mammalian circadian clock in prostate cancer

Yue

Wang

et al. 2021

PeerJ

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Background Prostate cancer (PC) is one of the most common malignancies in males. Extensive and complex connections between circadian rhythm and cancer were found. Nonetheless, in PC, the potential role of the core components of the mammalian circadian clock (CCMCCs) in prognosis prediction has not been fully clarified. Methods We firstly collected 605 patients with PC from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Survival analysis was carried out for each CCMCC. Then, we investigated the prognostic ability of CCMCCs by Cox regression analysis. Independent prognostic signatures were extracted for the establishment of the circadian clock-based risk score model. We explored the predictive performance of the risk score model in the TCGA training cohort and the independent GEO dataset. Finally, the relationships between risk score and clinicopathological parameters, biological processes, and signaling pathways were evaluated. Results The expression levels of CCMCCs were widely correlated with age, tumor status, lymph node status, disease-free survival (DFS), progression-free survival (PFS), and overall survival (OS). Nine circadian clock genes, including CSNK1D, BTRC, CLOCK, CSNK1E, FBXL3, PRKAA2, DBP, NR1D2, and RORB, were identified as vital prognostic factors in PC and were used to construct the circadian clock-based risk score model. For DFS, the area under the 3-year or 5-year receiver operating characteristic curves ranged from 0.728 to 0.821, suggesting better predictive performance. When compared with T3-4N1 stage, PC patients at T2N0 stage might be benefited more from the circadian clock-based risk score model. Furthermore, a high circadian clock-based risk score indicated shorter DFS (p < 0.0001), early progression (p < 0.0001), and higher 5-year death rate (p = 0.007) in PC. The risk score was related to tumor status (p < 0.001), lymph node status (p < 0.001), and ribosome-related biogenesis and pathways. Conclusions The vital roles of circadian clock genes in clinical outcomes were fully depicted. The circadian clock-based risk score model could reflect and predict the prognosis of patients with PC.

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Section: Discussionmentioning

confidence: 99%

Prognostic values of the core components of the mammalian circadian clock in prostate cancer

Yue

Wang

et al. 2021

PeerJ

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“…Finally, the clinical diagnostic value of the signature was compared with that of clinical features (Gleason score and PSA) by a decision curve analysis (DCA) ( Van Calster et al, 2018 ). DCA is used to compare prediction models that incorporate clinical outcomes; it requires only the dataset on which the models are tested and can be applied to models with either continuous or dichotomous results ( Zhang et al, 2020a ).…”

Section: Methodsmentioning

confidence: 99%

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Zhang

Shiori

Mu³

et al. 2021

Front. Cell Dev. Biol.

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Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.

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“…Prostate cancer (PRAD) is the most common carcinoma in men ( 1 ) and a significant global health concern ( 2 ). The main treatment for PRAD is radical prostatectomy ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer

Liu

Pan

et al. 2022

Front. Oncol.

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This study aimed to identify critical cell cycle-related genes (CCRGs) in prostate cancer (PRAD) and to evaluate the clinical prognostic value of the gene panel selected. Gene set variation analysis (GSVA) of dysregulated genes between PRAD and normal tissues demonstrated that the cell cycle-related pathways played vital roles in PRAD. Patients were classified into four clusters, which were associated with recurrence-free survival (RFS). Moreover, 200 prognostic-related genes were selected using the Kaplan–Meier (KM) survival analysis and univariable Cox regression. The prognostic CCRG risk score was constructed using random forest survival and multivariate regression Cox methods, and their efficiency was validated in Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70770. We identified nine survival-related genes: CCNL2, CDCA5, KAT2A, CHTF18, SPC24, EME2, CDK5RAP3, CDC20, and PTTG1. Based on the median risk score, the patients were divided into two groups. Then the functional enrichment analyses, mutational profiles, immune components, estimated half-maximal inhibitory concentration (IC50), and candidate drugs were screened of these two groups. In addition, the characteristics of nine hub CCRGs were explored in Oncomine, cBioPortal, and the Human Protein Atlas (HPA) datasets. Finally, the expression profiles of these hub CCRGs were validated in RWPE-1 and three PRAD cell lines (PC-3, C4-2, and DU-145). In conclusion, our study systematically explored the role of CCRGs in PRAD and constructed a risk model that can predict the clinical prognosis and immunotherapeutic benefits.

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Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer

Cited by 15 publications

References 67 publications

Prognostic values of the core components of the mammalian circadian clock in prostate cancer

Prognostic values of the core components of the mammalian circadian clock in prostate cancer

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer

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