Prognostic signature of ovarian cancer based on 14 tumor microenvironment-related genes

Nie, Xiazi; Song, Lina; Li, Xiaohua; Wang, Yirong; Qu, Bo

doi:10.1097/md.0000000000026574

Cited by 8 publications

(5 citation statements)

References 51 publications

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“…In addition, Mirco Friedrich showed that memory CD8 T cells may pass through a certain signal transduction that drives the differentiation of immune B cells to CD4 T cells [ 32 ], which may explain the decrease of immune B cells and the increase of memory CD8 T cells in the high-risk group. Experiments by Daniele Fanale et al showed that the low expression of PD-1, PD-L1, BTN3A1, pan-BTN3As, BTN2A1, and BTLA can be used as markers for the diagnosis of advanced high-grade serous OV [ 33 ], and Xiazi Nie et al also proved that immune checkpoints, CTLA4, IDO1, and LAG3 are all associated with poor prognosis in OV [ 34 ], in addition, CXCL10 and CD27 infiltration is associated with anti-tumor immunity and treatment response in OV subtypes [ 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

Sun,

Feng,

et al. 2024

J Ovarian Res

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Background Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis. Results Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples. Conclusion A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

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Section: Discussionmentioning

confidence: 99%

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

Sun,

Feng,

et al. 2024

J Ovarian Res

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“…Diagnosis is difficult in the early stages of OV [30]. Among the gynecological malignancies, the high mortality rate associated with OV poses a severe threat to the lives of women [31].…”

Section: Discussionmentioning

confidence: 99%

Construction of an Immunity and Ferroptosis-Related Risk Score Model to Predict Ovarian Cancer Clinical Outcomes and Immune Microenvironment

Wei¹,

Zhao²,

Gao³

et al. 2023

Front. Biosci. (Landmark Ed)

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Background: Ovarian cancer (OV) is a severe and common gynecological disease. Ferroptosis can regulate the progression and invasion of tumors. The immune system is a decisive factor in cancer. The present study aimed to use gene expression data to establish an immunity and ferroptosis-related risk score model as a prognostic biomarker to predict clinical outcomes and the immune microenvironment of OV. Methods: Common gene expression data were searched from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Immunity-related genes and ferroptosis-related genes were searched and downloaded from the ImmPort and FerrDb databases, followed by the analysis of the overall survival of patients with OV and the identification of genes. Subsequently, the status of the infiltration of immune cells and the association between immune checkpoints and risk score were assessed. Results: A total of 10 prognostic genes (C5AR1, GZMB, IGF2R, ISG20, PPP3CA, STAT1, TRIM27, TSHR, RB1, and EGFR) were included in the immunity and ferroptosisrelated risk score model. The high-risk group had a higher infiltration of immune cells. The risk score, an independent prognostic feature of OV was negatively associated with each immune checkpoint. The risk score may thus help to predict the response to immunotherapy. Conclusions: The immunity and ferroptosis-related risk score model is an independent prognostic factor for OV. The established risk score may help to predict the response of patients to immunotherapy.

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“…In addition, Mirco Friedrich showed that memory CD8 T cells may pass through A certain signal transduction that drives the differentiation of immune B cells to CD4 T cells [25], which may explain the decrease of immune B cells and the increase of memory CD8 T cells in the high-risk group. Experiments by Daniele Fanale et al showed that the low expression of PD-1, PD-L1, BTN3A1, pan-BTN3As, BTN2A1, and BTLA can be used as a marker for the diagnosis of advanced high-grade serous OV [26], and Xiazi Nie et al also proved that immunological examination Points CTLA4, IDO1, LAG3 are all associated with poor prognosis in OV [27], in addition, CXCL10, CD27 in ltration is associated with anti-tumor immunity and treatment response in OV subtypes [28,29].…”

Section: Discussionmentioning

confidence: 99%

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

Sun

Liu

et al. 2023

Preprint

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Background Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis.Results Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples.Conclusion A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

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Prognostic signature of ovarian cancer based on 14 tumor microenvironment-related genes

Cited by 8 publications

References 51 publications

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

Construction of an Immunity and Ferroptosis-Related Risk Score Model to Predict Ovarian Cancer Clinical Outcomes and Immune Microenvironment

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

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