Construction of a Prognosis Model of the Pyroptosis-Related Gene in Multiple Myeloma and Screening of Core Genes

Li, Can; Liang, Hongzheng; Bian, Sicheng; Hou, Xiaoxu; Ma, Yanping

doi:10.1021/acsomega.2c04212

Cited by 4 publications

(9 citation statements)

References 48 publications

(72 reference statements)

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“…The immune infiltration analysis revealed that the infiltration degrees of many immune cells were different in cluster 1 and cluster 2, which indicates the composition of the immunological microenvironment may affect the prognosis of myeloma. Li et al [ 13 ] also performed a clustering analysis based on the TCGA-MMRF database and identified two clusters using the R package “ConsensusClusterPlus”. Similar to the findings of our study, their study showed that the levels of immune activation were different in two clusters, indicating that pyroptosis could be involved in defining the immunological microenvironment of MM [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…Li et al [ 13 ] also performed a clustering analysis based on the TCGA-MMRF database and identified two clusters using the R package “ConsensusClusterPlus”. Similar to the findings of our study, their study showed that the levels of immune activation were different in two clusters, indicating that pyroptosis could be involved in defining the immunological microenvironment of MM [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…The transcriptome sequencing (RNA-seq) data and the whole exome sequencing (WES) data of CD 138 + myeloma cells within bone marrow from 764 patients, along with corresponding clinical characteristics and follow-up data, were obtained from the MMRF-COMPASS project in the TCGA database ( https://portal.gdc.cancer.gov/projects/MMRF-COMMPASS , accession number: MMRF-COMMPASS, accessed date: 1st June 2022). The gene expression microarray results and the annotation files of the GSE2658 dataset (microarray platform: GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array) [ 13 ] and the GSE39754 dataset (microarray platform: GPL5175 [HuEx-1_0-st] Affymetrix Human Exon 1.0 ST Array) [ 14 ] were downloaded from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2658 , accession number: GSE2658, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39754 , accession number: GSE39754, accessed date: 1st June 2022). The GSE2658 dataset [ 14 ], which contained transcriptomic data of CD138 + myeloma cells from 559 patients and matched prognostic information, was used as a validation set for this study.…”

Section: Methodsmentioning

confidence: 99%

“…At present, there are few studies on pyroptosis in MM [ 10 – 13 ]. Xia et al [ 10 ] found that PRMT5 regulates pyroptosis in MM.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [ 12 ] constructed a prognostic model including a risk score with 11 pyroptosis-related genes (PRGs) from the GSE136324 dataset, but the small sample size of the verification cohorts might affect the reliability of the results. Li et al [ 13 ] constructed a prognostic gene model based on 6 PRGs from the GSE24080 dataset, but they didn’t assess the mutation characteristics of PRGs. Moreover, there is a high false-positive rate in the single dataset analysis.…”

Section: Introductionmentioning

confidence: 99%

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A novel prognostic model based on pyroptosis-related genes for multiple myeloma

Zhang

Chen

2023

BMC Med Genomics

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Background Multiple myeloma (MM) is an incurable and relapse-prone disease with apparently prognostic heterogeneity. At present, the risk stratification of myeloma is still incomplete. Pyroptosis, a type of programmed cell death, has been shown to regulate tumor growth and may have potential prognostic value. However, the role of pyroptosis-related genes (PRGs) in MM remains undetermined. The aims of this study were to identify potential prognostic biomarkers and to construct a predictive model related to PRGs. Methods Sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Non-negative matrix factorization (NMF) was performed to identify molecular subtype screening. LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed. The Maxstat package was utilized to calculate the optimal cutoff value, according to which patients were divided into a high-risk group and a low-risk group, and the survival curves were plotted using the Kaplan-Meier (K-M) method. Nomograms and calibration curves were established using the rms package. Results A total of 33 PRGs were extracted from the TCGA database underlying which 4 MM molecular subtypes were defined. Patients in cluster 1 had poorer survival than those in cluster 2 (p = 0.035). A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658). Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset. The nomogram constructed by gender, age, International Staging System (ISS) stage, and risk score had the best prognostic predictive performance with a c-index of 0.721. Conclusion Our model could enhance the predictive ability of ISS staging and give a reference for clinical decision-making. The new, prognostic, and pyroptosis-related markers screened out by us may facilitate the development of novel risk stratification for MM. Clinical trial registration Not applicable.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…At present, there are few studies on pyroptosis in MM [ 10 – 13 ]. Xia et al [ 10 ] found that PRMT5 regulates pyroptosis in MM.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel prognostic model based on pyroptosis-related genes for multiple myeloma

Zhang

Chen

2023

BMC Med Genomics

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A novel prognostic model based on pyroptosis signature in AML

Zhang,

Zhu,

Sheng

et al. 2024

Heliyon

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Development and validation of an immune-related gene prognostic index for lung adenocarcinoma

Liu,

Lei,

Shen

et al. 2023

J Thorac Dis

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Background Lung cancer is the most common malignant tumor in the world, and its prognosis is still not optimistic. The aim of this study was to establish an immune-related gene (IRG) prognostic index (IRGPI) for lung adenocarcinoma (LUAD) based on IRGs, and to explore the prognosis, molecular and immune features, and response to immune checkpoint inhibitor (ICI) therapy in IRGPI-classified different subgroups of LUAD. Methods Based on the LUAD transcriptome RNA-sequencing data in TCGA database, the differentially expressed genes (DEGs) were selected. Subsequently, DEGs were intersected with IRGs to obtain differentially expressed immune-related genes (DEIRGs). Weighted gene co-expression network analysis (WGCNA) identified hub genes in DEIRGs. Finally, univariate and multivariate Cox regression analyses were used to build an IRGPI model. Subsequently, TCGA patients were divided into high- and low-risk groups, and the survival of patients in different groups was further analyzed. Besides, we validated the molecular and immune characteristics, relationship with immune checkpoints, angiogenesis-related genes, and immune subtypes distribution in different subgroups. Meanwhile, we further validated the response to ICI therapy in different subgroups. Results The IRGPI was constructed based on 13 DEIRGs. Compared with the low-risk group, overall survival (OS) was lower in the high-risk group, and the high-risk score was independently associated with poorer OS. Besides, the high-risk score was associated with cell cycle pathway, high mutation rate of TP53 and KRAS , high infiltration of M0 macrophages, and immunosuppressive state, and these patients had poorer prognosis but the TIDE score of the high-risk group was lower than that of the other group, which means that the high-risk group could benefit more from ICI treatment. In contrast, the low-risk score was related to low mutation rate of TP53 and KRAS , high infiltration of plasma cells, and immunoactive state, and these patients had better prognosis but the low-risk group less benefit from ICI treatment based on the results of TIDE score. Conclusions IRGPI is a prospective biomarker based on IRGs that can distinguish high- and low-risk groups to predict patient prognosis, help characterize the tumor immune microenvironment, and evaluate the benefit of ICI therapy in LUAD.

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Construction of a Prognosis Model of the Pyroptosis-Related Gene in Multiple Myeloma and Screening of Core Genes

Cited by 4 publications

References 48 publications

A novel prognostic model based on pyroptosis-related genes for multiple myeloma

A novel prognostic model based on pyroptosis-related genes for multiple myeloma

A novel prognostic model based on pyroptosis signature in AML

Development and validation of an immune-related gene prognostic index for lung adenocarcinoma

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