Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA

Abstract

(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model’s predictive ability was evaluated by Kaplan–Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.

Identification of a Prognostic Signature for Ovarian Cancer Based on Ubiquitin-Related Genes Suggesting a Potential Role for FBXO9

“…Remarkably, this signature with only two genes demonstrated good predictive power as revealed by the ROC curve (Figure 4). Its predictive capability, estimated by the AUC value of ROC curve, is comparable with that of most OV prognostic signatures consisting of more genes, i.e., an aging-related gene signature with 8 genes [39] and is even better than a signature of ferroptosis-related long noncoding RNAs (lncRNAs) with 18 lncRNAs [40]. In addition, univariate and multivariate Cox regression analyses identified both age and risk score as independent prognostic factors for OV patients.…”

Signatures of tumor-associated macrophages correlate with treatment response in ovarian cancer patients

“…Patients characterized by higher RS demonstrated heightened sensitivity to immunotherapeutic interventions, suggesting that TAMGs may serve as prospective predictive biomarkers for the assessment of immunotherapeutic outcomes. While prognostic models in the context of OC have received considerable research attention, the development of predictive models for characterizing TAMs in OC remains a notably unresolved aspect [41][42][43][44]. This article offers a valuable source of information that can potentially enhance the understanding of TAMs in OC and provide a foundation for further study in this area.…”

Screening of Prognostic Molecular Markers and Establishment of Prognostic Model for G-protein Coupled Receptor-Related Genes in Epithelial Ovarian Serous Cancer Based on Machine Learning Method

“…Other studies [55] have identi ed a signature that includes 18 Ferroptosis-Related lncRNAs and constructed a risk-scoring model and the KM survival analysis indicated that there was a signi cant difference (p < 0.0001) in survival rates between the high-and low-risk groups.Perhaps in the future, GPCRRGs and non-coding genes can be combined to predict the prognosis of ovarian cancer might have a higher predictive value.The RGS17, RGS16, RGS1 and RGS8 of the RGS (regulator of G protein signaling) gene family have been screened and identi ed as diagnostic biomarkers for the immunologic subtypes of ovarian cancer [56].…”