A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

Ye, Zhimin; Zou, Shengmei; Niu, Zhiyuan; Xu, Zhijie; Hu, Yongbin

doi:10.3389/fcell.2021.691676

Cited by 20 publications

(19 citation statements)

References 63 publications

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“…Compared to cluster 2, the prognosis of the patients in cluster 1 was poor, accompanied by a high abundance of immunosuppressive cells and a general increase in the expression of immune checkpoints, indicating an immunosuppressive microenvironment. This is consistent with findings regarding other malignancies [ 6 , 13 , 14 , 21 ]. As the consensus clustering was based on a MAG expression matrix, we inferred that the expression of MAGs was related to the prognosis and immunosuppressive microenvironment of DLBCL patients.…”

Section: Discussionsupporting

confidence: 93%

“…It is necessary for us to develop new strategies to identify risk among DLBCL patients more reliably, so as to personalize treatment strategies. Recent studies have shown that risk models based on multi-gene expression are a reliable choice [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, recent studies have focused on the relationship between metabolism and survival: pan-cancer studies have indicated that tumor subtypes with different MAG expression patterns lead to significantly different survival [ 10 , 11 ]. Moreover, several risk models based on MAGs have been proposed for breast cancer [ 6 ], colorectal cancer [ 12 ], gastric cancer [ 13 ] and osteosarcoma [ 14 ]. However, the value of MAGs in DLBCL subtype identification and prognostic prediction remains unclear.…”

Section: Introductionmentioning

confidence: 99%

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Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Chen

Xue

et al. 2022

J Transl Med

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Background Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment. Methods Gene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC). Results Consensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses. Conclusions The molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Chen

Xue

et al. 2022

J Transl Med

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“…Recently, PLA2G2D was identified as one of the high-risk genes for colorectal and rectum adenocarcinoma that were negatively correlated with patient survival. However, PLA2G2D expression was also found to be positively correlated with immune infiltration and better prognosis in head and neck squamous cell carcinoma and breast cancer in human (49,50), which is consistent with our current findings in CSCC. The opposite effects of PLA2G2D in different cancer types may result from the distinct microenvironments and different downstream lipid mediators presented in each cancer type, which in turn result in either an elevated or dampened inflammatory responses, thereby leading to the distinct clinical outcomes reported in these investigations (48).…”

Section: Discussionsupporting

confidence: 92%

Metabolic Molecule PLA2G2D Is a Potential Prognostic Biomarker Correlating With Immune Cell Infiltration and the Expression of Immune Checkpoint Genes in Cervical Squamous Cell Carcinoma

Liu

Gao

et al. 2021

Front. Oncol.

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Cervical squamous cell carcinoma (CSCC) is the major pathological type of cervical cancer (CC), the second most prevalent reproductive system malignant tumor threatening the health of women worldwide. The prognosis of CSCC patients is largely affected by the tumor immune microenvironment (TIME); however, the biomarker landscape related to the immune microenvironment of CSCC and patient prognosis is less characterized. Here, we analyzed RNA-seq data of CSCC patients from The Cancer Genome Atlas (TCGA) database by dividing it into high- and low-immune infiltration groups with the MCP-counter and ESTIMATE R packages. After combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis, we found that PLA2G2D, a metabolism-associated gene, is the top gene positively associated with immune infiltration and patient survival. This finding was validated using data from The Cancer Genome Characterization Initiative (CGCI) database and further confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, multiplex immunohistochemistry (mIHC) was performed to confirm the differential infiltration of immune cells between PLA2G2D-high and PLA2G2D-low tumors at the protein level. Our results demonstrated that PLA2G2D expression was significantly correlated with the infiltration of immune cells, especially T cells and macrophages. More importantly, PLA2G2D-high tumors also exhibited higher infiltration of CD8+ T cells inside the tumor region than PLA2G2D-low tumors. In addition, PLA2G2D expression was found to be positively correlated with the expression of multiple immune checkpoint genes (ICPs). Moreover, based on other immunotherapy cohort data, PLA2G2D high expression is correlated with increased cytotoxicity and favorable response to immune checkpoint blockade (ICB) therapy. Hence, PLA2G2D could be a novel potential biomarker for immune cell infiltration, patient survival, and the response to ICB therapy in CSCC and may represent a promising target for the treatment of CSCC patients.

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“…demonstrated that piRNA-30473, which promotes DLBCL progression by regulating m6A RNA methylation in DLBCL, can improve the prognostic stratification and therapeutic approach ( 31 ). With the development of next-generation sequencing, many prognostic cancer models have been established in recent years ( 32 – 34 ) based on public transcriptomic databases such as the GEO and TCGA datasets.…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma

et al. 2022

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BackgroundDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, the survival of DLBCL patients remains low, emphasizing the need to develop novel prognostic biomarkers for early risk stratification and treatment optimization.MethodIn this study, we screened genes related to the overall survival (OS) of DLBCL patients in datasets GSE117556, GSE10846, and GSE31312 using univariate Cox analysis. Survival-related genes among the three datasets were screened according to the criteria: hazard ratio (HR) >1 or <1 and p-value <0.01. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to optimize and establish the final gene risk prediction model. The TCGA-NCICCR datasets and our clinical cohort were used to validate the performance of the prediction model. CIBERSORT and ssGSEA algorithms were used to estimate immune scores in the high- and low-risk groups.ResultsWe constructed an eight-gene prognostic signature that could reliably predict the clinical outcome in training, testing, and validation cohorts. Our prognostic signature also performed distinguished areas under the ROC curve in each dataset, respectively. After stratification based on clinical characteristics such as cell-of-origin (COO), age, eastern cooperative oncology group (ECOG) performance status, international prognostic index (IPI), stage, and MYC/BCL2 expression, the difference in OS between the high- and low-risk groups was statistically significant. Next, univariate and multivariate analyses revealed that the risk score model had a significant prediction value. Finally, a nomogram was established to visualize the prediction model. Of note, we found that the low-risk group was enriched with immune cells.ConclusionIn summary, we identified an eight-gene prognostic prediction model that can effectively predict survival outcomes of patients with DLBCL and built a nomogram to visualize the perdition model. We also explored immune alterations between high- and low-risk groups.

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A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer

Cited by 20 publications

References 63 publications

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Metabolic Molecule PLA2G2D Is a Potential Prognostic Biomarker Correlating With Immune Cell Infiltration and the Expression of Immune Checkpoint Genes in Cervical Squamous Cell Carcinoma

Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma

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