Systematic Construction and Validation of a Novel Ferroptosis-Related Gene Model for Predicting Prognosis in Cervical Cancer

Abstract

Background and Purpose. Ferroptosis, a mechanism of cell death that is iron-dependent, participates in various pathologies of cancer (CC). Nevertheless, the specific function that ferroptosis plays in the onset and progression of cervical cancer (CC) is yet uncertain. This research sought to examine the value of ferroptosis-related genes (FRGs) in the progression and prognosis of CC. Methods. Datasets containing RNA sequencing and corresponding clinical data of cervical cancer patients were obtained from searching publicly accessible databases. The “NMF” R package was conducted to calculate the matrix of the screened prognosis gene expression. Ferroptosis-related differential genes in cervical cancer were detected using the “limma” R function and WGCNA. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were conducted to develop a novel prognostic signature. The prediction model was verified by the nomogram integrating clinical characteristics; the GSE44001 dataset was used as an external verification. Then, the immune status and tumor mutation load were explored. Finally, immunohistochemistry as well as quantitative polymerase chain reaction (RT-qPCR) was utilized to ascertain the expression of FRGs. Results. Two molecular subgroups (cluster 1 and cluster 2) with different FRG expression patterns were recognized. A ferroptosis-related model based on 4 genes (VEGFA, CA9, DERL3, and RNF130) was developed through TCGA database to identify the unfavorable prognosis cases. Patients in cluster 1 showed significantly decreased overall survival in contrast with those in cluster 2 ( P < 0.05 ). The LASSO technique and Cox regression analysis were both utilized to establish the independence of the prognostic model. The validity of nomogram prognostic predictions has been well demonstrated for 3- and 5-year survival in both internal and external data validation cohorts. These two subgroups showed striking differences in tumor-infiltrating leukocytes and tumor mutation burden. The low-risk subgroup showed a longer overall survival time with a higher immune cell score and higher tumor mutation rate. Gene functional enrichment analyses revealed predominant enrichment in various tumor-associated signaling pathways. Finally, the expression of each gene was confirmed by immunohistochemistry and RT-qPCR. Conclusion. A novel and comprehensive ferroptosis-related gene model was proposed for cervical cancer which was capable of distinguishing the patients independently with high risk for poor survival, and targeting ferroptosis may represent a promising approach for the treatment of CC.

Identification of an inflammatory response-related gene prognostic signature and immune microenvironment for cervical cancer

“…Nevertheless, the lack of reported evidence indicates that the inflammatory response-related gene signature has not been established as a prognostic marker for CC. Previous studies have suggested that the m6A-related gene signature, ferroptosis-related gene signature, immune-related gene signature and hypoxia-related gene signature predict 3-year OS for CC patients with AUCs of 0.702, 0.734, 0.785 and 0.716, respectively ( Yang et al, 2021 ; Lu et al, 2022 ; Qin et al, 2022 ; Wang et al, 2022 ), which are similar to our findings. Apart from achieving high accuracy in predicting CC prognosis, the inflammatory response-related gene signature developed in our study possesses additional benefits in comparison to the aforementioned gene signatures.…”

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

“…Over time, nomogram is no longer limited to the study of oncology, but more applied to the study of diagnosis and prediction of non-tumor-related diseases. For example, nomograms were used to analyze the correlation between factors in genomics or proteomics and the development and progression of diseases ( 32 – 34 ); Nomograms were used to predict adverse outcomes in obstetrics or reproductive medicine ( 35 – 38 ); it can also be combined with radiomics to analyze the relationship between different image features or image segmentation factors and disease diagnosis ( 39 , 40 ), predict chronic disease incidence in orthopedics ( 41 – 43 ), or analyze and detect the incidence of chronic diseases combined with metabolomics ( 37 ). In addition, the display modes of nomogram are gradually diversified.…”

DERL2 (derlin 2) stabilizes BAG6 (BAG cochaperone 6) in chemotherapy resistance of cholangiocarcinoma

“…Recent studies have suggested amplification of the DERL1 protein is involved in cell behavior and functionality of breast cancer [ 25 ], colon cancer [ 26 ], and bladder tumors [ 27 ]. DERL3 silence confers the cell’s unlimited proliferation potency and drives the progression of breast cancer [ 28 ], lung adenocarcinoma [ 29 ], and cervical cancer [ 30 ]. DERL2, an ER membrane-associated and luminal protein characterized by three predicted loops, has been shown to cause perinatal lethality in whole-body DERL2 deletion mice, with the surviving mice developing skeletal dysplasia due to abnormal accumulation of collagen matrix proteins within the ER lumen [ 31 ].…”