Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Guo, Ya; Wang, Ya Li; Wang, Su; Peng, Yang; Chen, Jing; Luo, Heng

doi:10.3389/fgene.2020.00990

Cited by 10 publications

(12 citation statements)

References 65 publications

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“…In our study, the AUCs of the signatures at 3, and 5 years were 0.709, 0.762, respectively, which were significantly higher than those of most hallmark predictive models. Table 4 shows that the AUCs of the other three prognostic signatures, namely, the 21 immune‐related gene signature, 35 17 immune‐related gene signature (0.754 at 3 years, 0.824 at 5 years) 36 and 17 transcription factor ‐related gene signature (0.803 at 5 years), 37 were comparable to the predictive capabilities of our predictive model and distinctly higher than those of other signatures, such as the epithelial‐mesenchymal transition related gene signature, 38 TME‐related gene signature, 39,40 RNA‐binding protein‐related gene signature, 41 energy metabolism‐related gene signature, 42 autophagy‐related gene signature, 43,44 ferroptosis‐related gene signature, 45 protein‐coding gene signature, 46 and DNA methylated gene signatures 47 . The larger the AUC value of a biomarker, the better was the predictive ability of the signature, indicating that our gene signature outperformed most of the other signatures in predicting OV prognosis.…”

Section: Resultsmentioning

confidence: 67%

“…The included studies 11,35–47,54–61 used models built based on the TCGA cohort and involved all types of breast cancer. The final results showed that our signature and three other prognostic signatures, namely, a 21 immune‐related gene signature, 35 17 immune‐related gene signature 36 and 17 TF‐related gene signature 37 performed better than the other hallmark signatures in the prediction of OS in patients with OV 11,35,38–47,54–61 . Additionally, Yu et al has constructed a five GRG signature (ANGPTL4, PYGB, ISG20, SEH1L and IRS2) for patients with OV 62 .…”

Section: Discussionmentioning

confidence: 99%

“…To further explore the predictive ability of our signature, a comparison was performed among several significant molecular signatures that were employed for predicting OS in patients with OV. The included studies 11 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 used models built based on the TCGA cohort and involved all types of breast cancer. The final results showed that our signature and three other prognostic signatures, namely, a 21 immune‐related gene signature, 35 17 immune‐related gene signature 36 and 17 TF‐related gene signature 37 performed better than the other hallmark signatures in the prediction of OS in patients with OV.…”

Section: Discussionmentioning

confidence: 99%

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Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

Zhang

Yang

et al. 2021

Cancer Medicine

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Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

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Section: Resultsmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

Zhang

Yang

et al. 2021

Cancer Medicine

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“…MMP12 plays a pivotal role in inflammatory diseases, such as colitis ( Nighot et al, 2021 ) and chronic obstructive pulmonary disease ( Baggio et al, 2020 ). In addition, MMP12 has been regarded as a potential prognostic biomarker for ovarian cancer ( Guo et al, 2020 ) and gallbladder cancer ( Zhao et al, 2019 ). Our results initially identified MMP12 as a negative prognostic biomarker for UM.…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Novel Immunosignature for Overall Survival in Uveal Melanoma

Wang

et al. 2021

Front. Cell Dev. Biol.

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ObjectivesUveal melanoma (UM) is the most common primary intraocular malignancy in adults, and immune infiltration plays a crucial role in the prognosis of UM. This study aimed to generate an immunological marker-based predictive signature for the overall survival (OS) of UM patients.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) was used to profile immune cell infiltration in 79 patients with UM from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate least absolute shrinkage and selection operator (LASSO) Cox regressions were used to determine the prognostic factors for UM and construct the predictive immunosignature. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the clinical ability and accuracy of the model. In addition, the predictive accuracy was compared between the immunosignature and the Tumor, Node, Metastasis (TNM) staging system of American Joint Committee on Cancer (AJCC). We further analyzed the differences in clinical characteristics, immune infiltrates, immune checkpoints, and therapy sensitivity between high- and low-risk groups characterized by the prognostic model.ResultsHigher levels of immune cell infiltration in UM were related to a lower survival rate. Matrix metallopeptidase 12 (MMP12), TCDD inducible poly (ADP-ribose) polymerase (TIPARP), and leucine rich repeat neuronal 3 (LRRN3) were identified as prognostic signatures, and an immunological marker-based prognostic signature was constructed with good clinical ability and accuracy. The immunosignature was developed with a concordance index (C-index) of 0.881, which is significantly better than that of the TNM staging system (p < 0.001). We further identified 1,762 genes with upregulated expression and 798 genes with downregulated expression in the high-risk group, and the differences between the high- and low-risk groups were mainly in immune-related processes. In addition, the expression of most of the immune checkpoint-relevant and immune activity-relevant genes was significantly higher in the high-risk group, which was more sensitive to therapy.ConclusionWe developed a novel immunosignature constructed by MMP12, TIPARP, and LRRN3 that could effectively predict the OS of UM.

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“…But it's controversial whether the expression of PGR serves as the prognostic marker in endometrial cancer. GYPC (glycophorins), is de ned as a negative prognostic biomarker in ovarian cancer [31]. But there has no report about the prognostic value of GYPC in endometrial cancer.…”

Section: Discussionmentioning

confidence: 99%

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

Dai

Deng

Tan

et al. 2021

Preprint

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Background: Endometrial cancer greatly threatens the health of female. Emerging evidences demonstrated that the DNA methylation and immune infiltration involve in the occurrence and development of endometrial cancer. However, the mechanism and prognosis biomarker of endometrial cancer is unclear. In this study, we attempt to assess the DNA methylation and immune infiltration via bioinformatic analysis. Methods: The latest RNA-Seq, DNA methylation data and clinical data of endometrial cancer were download from the UCSC Xena dataset. The methylated driven genes were selected out, and then the risk score was obtained using the “MethylMix” and “corrplot” package. The correction of methylated genes and prognosis values, and the expression of above driven genes were identified by via survminer package and beeswarm package, respectively. Finally, the role of VTCN1 gene in immune infiltration was analyzed via CIBERSORT package. Results: In this study, 179 up-regulated genes, and 311 down-regulated genes are identified, which are related to the extracellular matrix organization, cell-cell junction, and cell adhesion molecular binding. Above them, 13 methylated driven genes were selected out. Meanwhile, it’s obvious that the expression of PGR and VTCN1 are lower in tumor group than that of in normal group. Conversely, VTCN1 gene expresses the opposite. high methylation & low expression of PGR and GYPC are positive correction with the prognosis, while the VTCN1 has the inverse relationship with the prognosis of endometrial cancer. More importantly, the hypomethylation of VTCN1 promoter leads to its high expression, which can cause tumor development by inhibiting CD8+T cell infiltration. Conclusions: In total, our study firstly reveals the mechanism of endometrial cancer combining DNA methylation and immune cell infiltration via integrated bioinformatic analysis. Besides, we found the pivotal prognosis biomarker for this disease. Our study may provide the potential targets for the diagnosis and prognosis of endometrial cancer.

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Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Cited by 10 publications

References 65 publications

Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

Construction and Validation of a Novel Immunosignature for Overall Survival in Uveal Melanoma

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

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