Development and validation of an immune gene-set based Prognostic signature in ovarian cancer

Shen, Sipeng; Wang, Guanrong; Zhang, Ruyang; Zhao, Yang; Yu, Hao; Wei, Yongyue; Chen, Feng

doi:10.1016/j.ebiom.2018.12.054

Cited by 213 publications

(179 citation statements)

References 38 publications

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“…Human immune-related genes were obtained from the ImmPort project 53 . These gene sets were widely used in immune-related studies 54,55 . All these genes are mapped to Ensembl IDs.…”

Section: Methodsmentioning

confidence: 99%

Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers

et al. 2020

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Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets.

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“…Human immune-related genes were obtained from the ImmPort project 53 . These gene sets were widely used in immune-related studies 54,55 . All these genes are mapped to Ensembl IDs.…”

Section: Methodsmentioning

confidence: 99%

Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers

et al. 2020

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“…Tumor immune microenvironment (TIME) is an important variable relating to the progression of CC. 11,12 Additionally, several immune prognostic signatures have been reported to predict the prognosis of patients with cancer, such as lung cancer, 13 ovarian cancer, 14 colorectal cancer, 15 and hepatocellular carcinoma. 16 These studies indicate that IRGs or TIME can serve as promising biomarkers for estimating survival in corresponding cancer.…”

Section: Introductionmentioning

confidence: 99%

Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors

et al. 2019

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2019) Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors, OncoImmunology, 8:12, e1659094, ABSTRACT Cervical cancer (CC) is a leading cause of cancer-related death in women. Limited studies have investigated whether immune-related genes (IRGs) or tumor immune microenvironment (TIME) could be indicators for CC prognoses. The aim of this study was to develop an improved prognostic signature for CC based on IRGs or TIME to predict survival and response to immune checkpoint inhibitors (ICIs). A prognostic signature was constructed using bioinformatics method and its predictive capability was validated. The mechanisms underlying the signature's predictive capability were explored with CIBERSORT algorithm and mutation analysis. Immunophenoscore (IPS) is validated for ICIs response, and was therefore explored in relation to the signature. A prognostic signature based on 11 IRGs was developed. A multivariate analysis revealed that the 11-IRG signature was an independent prognostic factor for overall survival (OS) and progression-free interval in CC patients. In the 11-IRG signature highrisk group, CD8 T cells and resting mast cells, which are found to associate with better OS in our study, were lower; activated mast cells, associated with poorer OS, were higher, compared with the low-risk group. An IPS analysis suggested that the 11-IRG signature low-risk group, which possessed a higher IPS, represented a more immunogenic phenotype that was more inclined to respond to ICIs. In short, an 11-IRG prognostic signature for predicting CC patients' survival and response to ICIs was firmly established. The predictive capability of this model in CC requires further testing with the goal of better prognostic stratification and treatment management. ARTICLE HISTORY

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“…For instance, the prognostic signature of seven immune genes was identified in clear cell renal cell carcinoma [15]. An immune gene-set can predict the prognosis of patients with ovarian cancer [16]. Guo and colleagues established immune signature to predict survival of lung adenocarcinoma [17].…”

Section: Introductionmentioning

confidence: 99%

Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

Wang

Qian

Zhang

et al. 2020

Preprint

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Background Esophageal cancer (ESCA) is one of the deadliest solid malignancies with worse survival in the world. The poor prognosis of ESCA is not only related to malignant cells, but also affected by the microenvironment. We aimed to establish prognostic signature consisting of immune genes to predict the survival outcome of patients and estimate the prognosis value of infiltrating immune cells in tumor microenvironment (TME). Methods Based on integrated analysis of gene expression profiling and immune gene database, differentially immune-related genes were filtered out. Then, stepwise Cox regression analysis was applied to identify survival related immune genes and construct prognosis signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognosis signature and infiltrating immune cells by Tumor Immune Estimation Resource (TIMER) and CIBERSORT. Results Finally, we identified 303 differentially immune genes as candidate and constructed immune prognosis signature composed of six immune genes. Furthermore, we observed that the prognosis signature was enriched in cytokine-mediated signaling pathway, lymphocyte activation, immune effector process, cancer pathway, NF-kappa B signaling pathway. K-M survival curves showed that the prognosis signature indeed have good predictive ability in entire ESCA set ( P =0.003), validation set 1 ( P =0.008) and validation set 2 ( P =0.036). The area under the curve (AUC) of ROC curves validated the predictive accuracy of immune signature in three cohorts (AUC=0.757, 0.800 and 0.701), respectively. In addition, we identified the prognosis value of infiltrating-immune cells including activated memory CD4 T cells, T cells follicular helper cells and monocytes and provided a landscape of TME. Conclusions The results indicated that immune prognosis signature can be a novel biomarker to predict survival outcome, which can provide new targets for immunotherapy and individualized therapies in ESCA and open up a new prospect for improving the prognosis of ESCA patients in the era of immunotherapy.

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Development and validation of an immune gene-set based Prognostic signature in ovarian cancer

Cited by 213 publications

References 38 publications

Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers

Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers

Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors

Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

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