Immune profiling reveals prognostic genes in high-grade serous ovarian cancer

Wu, Yong; Xia, Lingfang; Zhao, Ping; Deng, Yu; Guo, Qinhao; Jun, Zhu; Chen, Xiaojun; Ju, Xingzhu; Wu, Xiaohua

doi:10.18632/aging.103199

Cited by 15 publications

(14 citation statements)

References 41 publications

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“…Mounting evidence demonstrated that IRGs is closely related to hepatocellular carcinoma [ 22 ], gastric cancer [ 23 ], and ovarian cancer [ 24 ]. However, there is still a lack of comprehensive analyses of IRGs in psychiatric disorders.…”

Section: Discussionmentioning

confidence: 99%

A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder

et al. 2022

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Background Immune response is prevalently related with major depressive disorder (MDD) pathophysiology. However, the study on the relationship between immune-related genes (IRGs) and immune infiltrates of MDD remains scarce. Methods We extracted expression data of 148 MDD patients from 2 cohorts, and systematically characterized differentially expressed IRGs by using limma package in R software. Then, the LASSO and multivariate logistic regression analysis was used to identify the most powerful IRGs. Next, we analyzed the relationship between IRGs and immune infiltrates of MDD. Finally, GSE76826 was used to to verificate of IRGs as a diagnostic markers in MDD. Results 203 different IRGs s in MDD has been identified (P < 0.05). GSEA revealed that the different IRGs was more likely to be enriched in immune-specific pathways. Then, a 9 IRGs was successfully established to predict MDD based on LASSO. Next, 4 IRGs was obtained by multivariate logistic regression analysis, and AUC for CD1C, SPP1, CD3D, CAMKK2, and IRGs model was 0.733, 0.767, 0.816, 0.800, and 0.861, suggesting that they have a good diagnostic performance. Furthermore, the proportion of T cells CD8, T cells γδ, macrophages M0, and NK cells resting in MDD group was lower than that in the healthy controls, suggesting that the immune system in MDD group is impaired. Simultaneously, CD3D was validated a reliable marker in MDD, and was positively correlated with T cells CD8. GSEA revealed high expression CD3D was more likely to be enriched in immune-specific pathways, and low expression CD3D was more likely to be enriched in glucose metabolism metabolism-specific pathways. Conclusions We applied bioinformatics approaches to suggest that a 4 IRGs could serve as diagnostic markers to provide a novel direction to explore the pathogenesis of MDD.

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Section: Discussionmentioning

confidence: 99%

A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder

et al. 2022

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“…In ovarian cancer, studies have begun to explore the relationship between immune composition, immune-related genes, and clinical outcomes. Several studies have used computational analysis of tumors with publicly available gene expression data to report relationships between immune cell subsets or gene expression and prognosis (10)(11)(12)(13)). Yet other studies have employed immunohistochemical approaches to establish a relationship between intra-epithelial T cells and other immune cell subsets and survival (14,15).…”

Section: Introductionmentioning

confidence: 99%

Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

et al. 2021

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Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy worldwide, as patients are typically diagnosed at a late stage and eventually develop chemoresistant disease following front-line platinum-taxane based therapy. Only modest results have been achieved with PD-1 based immunotherapy in ovarian cancer patients, despite the fact that immunological responses are observed in EOC patients. Therefore, the goal of this present study was to identify novel immune response genes and cell subsets significantly associated with improved high grade serous ovarian cancer (HGSOC) patient prognosis. A transcriptomic-based immune modeling analysis was employed to determine levels of 8 immune cell subsets, 10 immune escape genes, and 22 co-inhibitory/co-stimulatory molecules in 26 HGSOC tumors. Multidimensional immune profiling analysis revealed CTLA-4, LAG-3, and Tregs as predictive for improved progression-free survival (PFS). Furthermore, the co-stimulatory receptor ICOS was also found to be significantly increased in patients with a longer PFS and positively correlated with levels of CTLA-4, PD-1, and infiltration of immune cell subsets. Both ICOS and LAG-3 were found to be significantly associated with improved overall survival in The Cancer Genome Atlas (TCGA) ovarian cancer cohort. Finally, PVRL2 was identified as the most highly expressed transcript in our analysis, with immunohistochemistry results confirming its overexpression in HGSOC samples compared to normal/benign. Results were corroborated by parallel analyses of TCGA data. Overall, this multidimensional immune modeling analysis uncovers important prognostic immune factors that improve our understanding of the unique immune microenvironment of ovarian cancer.

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“…The role of tumor microenvironment in predicting clinical outcome and efficacy was gradually recognized. Researchers were trying to find reliable immune-related prognostic genes in ovarian cancer, including TAP1 (Liu et al, 2020;Wu et al, 2020;Huo et al, 2021) and CXCL13 (Liu et al, 2020;Wu et al, 2020;Li et al, 2021). The results were based on screening for differentially expressed genes between high and low abundance immune subtypes (Li et al, 2021), ovarian cancer and normal tissues (Liu et al, 2020), high and low Tumor Mutational Burden (TMB) samples (Huo et al, 2021), high and low tumor microenvironment scores (Wu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers were trying to find reliable immune-related prognostic genes in ovarian cancer, including TAP1 (Liu et al, 2020;Wu et al, 2020;Huo et al, 2021) and CXCL13 (Liu et al, 2020;Wu et al, 2020;Li et al, 2021). The results were based on screening for differentially expressed genes between high and low abundance immune subtypes (Li et al, 2021), ovarian cancer and normal tissues (Liu et al, 2020), high and low Tumor Mutational Burden (TMB) samples (Huo et al, 2021), high and low tumor microenvironment scores (Wu et al, 2020). However, TAP1 and CXCL13 as main prognostic markers were primarily carried out using a univariate Cox model (Wu et al, 2020), without considering the influence of other genes.…”

Section: Introductionmentioning

confidence: 99%

“…The results were based on screening for differentially expressed genes between high and low abundance immune subtypes (Li et al, 2021), ovarian cancer and normal tissues (Liu et al, 2020), high and low Tumor Mutational Burden (TMB) samples (Huo et al, 2021), high and low tumor microenvironment scores (Wu et al, 2020). However, TAP1 and CXCL13 as main prognostic markers were primarily carried out using a univariate Cox model (Wu et al, 2020), without considering the influence of other genes. In addition, some study included more genes in the prognostic model, which may increase the difficulty of application (Liu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma

Feng

Liu

et al. 2021

Front. Mol. Biosci.

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Serous ovarian cancer is the most common and primary death type in ovarian cancer. In recent studies, tumor microenvironment and tumor immune infiltration significantly affect the prognosis of ovarian cancer. This study analyzed the four gene expression types of ovarian cancer in TCGA database to extract differentially expressed genes and verify the prognostic significance. Meanwhile, functional enrichment and protein interaction network analysis exposed that these genes were related to immune response and immune infiltration. Subsequently, we proved these prognostic genes in an independent data set from the GEO database. Finally, multivariate cox regression analysis revealed the prognostic significance of TAP1 and CXCL13. The genetic alteration and interaction network of these two genes were shown. Then, we established a nomogram model related to the two genes and clinical risk factors. This model performed well in Calibration plot and Decision Curve Analysis. In conclusion, we have obtained a list of genes related to the immune microenvironment with a better prognosis for serous ovarian cancer, and based on this, we have tried to establish a clinical prognosis model.

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Immune profiling reveals prognostic genes in high-grade serous ovarian cancer

Cited by 15 publications

References 41 publications

A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder

A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder

Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma

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