ArticlePeerJ2024
Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma.
Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Acyl-CoA thioesterases and their derived lipid mediators: linking Acyl-CoA metabolic reprogramming to the tumor microenvironment.Frontiers in immunology · 2026Review
- TIPE3 in Cancer: A Multifaceted Regulator of Tumorigenesis, Therapeutic Resistance, and Clinical Outcomes.Technology in cancer research & treatmentReview
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: A novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset. Methods: This prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells Results: We constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells Conclusions: The analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene
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