Identification of the Prognostic Signature Associated With Tumor Immune Microenvironment of Uterine Corpus Endometrial Carcinoma Based on Ferroptosis-Related Genes

Identification of a disulfidptosis-related prognostic signature for prediction of the effect of treatment in patients with endometrial carcinoma

Background

Disulfide, an essential compounds family, has diverse biological activity and can affect the dynamic balance between physiological and pathological states. A recently published study found that aberrant accumulation of disulfide had a lethal effect on cells. This mechanism of cell death, named disulfidptosis, differs from other known cell death mechanisms, including cuproptosis, apoptosis, necroptosis, and pyroptosis. The relationship between disulfidptosis and development of cancer, in particular endometrial carcinoma, remains unclear.

Methods

To address this knowledge gap, we performed a preliminary analysis of samples from The Cancer Genome Atlas database. The samples were divided equally into a training group and a test group. A total of 2308 differentially expressed genes were extracted, and 11 were used to construct a prognostic model.

Results

Based on the risk score calculated using the prognostic model, the samples were divided into a high-risk group and a low-risk group. Survival time, tumor mutation burden, and microsatellite instability scores differed significantly between the two groups. Furthermore, a between-group difference in treatment effect was predicted. Comparison with other models in the literature indicated that this prognostic model had better predictive anility.

Conclusion

The results of this study provide a general framework for understanding the relationship between disulfidptosis and endometrial cancer that could be used for clinical evaluation and selection of appropriate personalized treatment strategies.

Cell-Cycle-related Protein Centromere Protein F Deficiency Inhibits Cervical Cancer Cell Growth by Inducing Ferroptosis Via Nrf2 Inactivation

Cervical cancer (CC) is one of the severe cancers that pose a threat to women's health and result in death. CENPF, the centromere protein F, plays a crucial role in mitosis by regulating numerous cellular processes, such as chromosome segregation during mitosis. According to bioinformatics research, CENPF serves as a master regulator that is upregulated and activated in cervical cancer. Nevertheless, the precise biological mechanism that CENPF operates in CC remains unclear. The aim of this study was to analyze the function of CENPF on cervical cancer and its mechanism. We conducted immunohistochemistry and western blot analysis to examine the expression levels of CENPF in both cervical cancer tissues and cells. To explore the hidden biological function of CENPF in cell lines derived from CC, we applied lentivirus transfection to reduce CENPF manifestation. CENPF's main role is to regulate ferroptosis which was assessed by analyzing Reactive Oxygen Species (ROS), malonaldehyde (MDA), etc. The vitro findings were further validated through a subcutaneous tumorigenic nude mouse model. Our research finding indicates that there is an apparent upregulation of CENPF in not merely tumor tissues but also cell lines in the carcinomas of the cervix. In vitro and vivo experimental investigations have demonstrated that the suppression of CENPF can impede cellular multiplication, migration, and invasion while inducing ferroptosis. The ferroptosis induced by CENPF inhibition in cervical cancer cell lines is likely mediated through the Nrf2/HO-1 pathway. The data herein come up with the opinion that CENPF may have a crucial role in influencing anti-cervical cancer effects by inducing ferroptosis via the triggering of the Nrf2/HO-1 signaling pathway.

Integrating ferroptosis-related genes (FRGs) and prognostic models to enhance UCEC outcome prediction and therapeutic insights

Ferroptosis is closely associated with uterine corpus endometrial carcinoma (UCEC) development. This project aimed to identify new potential biomarkers to predict the prognosis of UCEC. In this work, UCEC transcriptome data along with clinical information was retrieved from the TCGA database including a total of 382 FRGs. We performed univariate Cox regression analysis to evaluate ferroptosis-related genes (FRGs) for prognostic significance. The genes with prognostic significance were then analyzed using LASSO-Cox to construct a prognosis model. The model genes were further characterized through various proteomic analyses and expression detection in clinical samples. A multivariate Cox regression model was constructed containing four FRGs (CDKN1A, CDKN2A, CEBPG, NOS2). Among four FRGs, higher expressions of CDKN2A, CEBPG, and NOS2 were associated with poorer overall survival probability, while higher expression of CDKN1A was associated with better overall survival probability. The area under the receiver operating characteristic curve of the risk model was 0.617, 0.688, and 0.693 for 1 year, 3 years, and 5 years, respectively. Moreover, proteomic analysis showed that the protein expression of CDKN1A, CDKN2A, and CEBPG was higher in tumor tissues than that in normal tissues. Higher protein expression of CDKN1A and CDKN2A predicted poorer survival probability. Besides, CDKN1A protein had an interaction relationship with CDKN2A protein or NOS2 protein. In clinical samples, all four FRGs were upregulated in UCEC tissues, regardless of gene expression or protein expression. Our four FRGs risk model provides new insights for predicting the prognosis of UCEC patients.

Coordination of Single-cell and Bulk RNA Sequencing to Construct a Cuproptosis-related Gene Prognostic Index for Endometrial Cancer Prognosis, Immune Microenvironment Infiltration, and Immunotherapy Treatment Options

Background: This study aims to identify molecular subtypes and develop a cuproptosis-related gene prognostic index (CRGPI) for endometrial cancer (EC), in addition to outlining the immune features and the efficacy of immune checkpoint inhibitor (ICI) therapy in CRGPI-defined groups of EC. Methods: Between malignant and normal cells distinguished from single-cell RNA sequencing data GSE154763 dataset, the difference in KEGG pathways and cuproptosis-related gene (CRG) scores was intensively investigated. On the basis of TCGA dataset (n=492), CRGs were used to identify two distinct molecular subtypes. Using the Cox regression method, a CRGPI was constructed and externally validated with the IMvigor210 dataset (n=348) and GSE78220. Then, the molecular and immune characteristics and the efficacy of ICI therapy in subgroups defined by CRGPI were investigated. Results: Compared to normal cells, the expression of the TCA cycle and CRGs genes was significantly higher in malignant cells. The CRGPI was established on the premise of ATF5, FBXO46, P2RX4, SMARCD3, DAPK3, and C1orf53. Comprehensive results demonstrated a correlation between a low CRGPI score and better overall survival, younger age, early stage, immune cells and functions activation, high tumor mutation burden and high microsatellite instability, as well as better benefit from ICI therapy, and its significance for forecasting immunotherapeutic effects was verified as well (IMvigor210 cohort: HR, 1.358; 95% CI, 1.047, 1.761; p=0.02; GSE78220 cohort: HR, HR = 3.857, 95% CI, 1.009, 14.74; p=0.034). CRGPI anticipated the immunotherapy medication. Conclusions: CRGPI is a prospective biomarker to estimate the prognosis, immune and molecular characteristics, and treatment benefit of immunotherapy in EC.

Crosstalk between ferroptosis and steroid hormone signaling in gynecologic cancers

Ferroptosis is a novel types of regulated cell death and is widely studied in cancers and many other diseases in recent years. It is characterized by iron accumulation and intense lipid peroxidation that ultimately inducing oxidative damage. So far, signaling pathways related to ferroptosis are involved in all aspects of determining cell fate, including oxidative phosphorylation, metal-ion transport, energy metabolism and cholesterol synthesis progress, et al. Recently, accumulated studies have demonstrated that ferroptosis is associated with gynecological oncology related to steroid hormone signaling. This review trends to summarize the mechanisms and applications of ferroptosis in cancers related to estrogen and progesterone, which is expected to provide a theoretical basis for the prevention and treatment of gynecologic cancers.

The dual role of ferroptosis in anthracycline-based chemotherapy includes reducing resistance and increasing toxicity

In conjunction with previous studies, we have noted that ferroptosis, as an emerging mode of regulated cell death (RCD), is intimately related to anthracycline pharmacotherapy. Not only does ferroptosis significantly modulate tumour resistance and drug toxicity, which are core links of the relevant chemotherapeutic process, but it also appears to play a conflicting role that has yet to be appreciated. By targeting the dual role of ferroptosis in anthracycline-based chemotherapy, this review aims to focus on the latest findings at this stage, identify the potential associations and provide novel perspectives for subsequent research directions and therapeutic strategies.

Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism- and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma

(1) Background: The effect of tumor immunotherapy is influenced by the immune microenvironment, and it is unclear how lipid metabolism and ferroptosis regulate the immune microenvironment of uterine corpus endometrial carcinoma (UCEC). (2) Methods: Genes associated with lipid metabolism and ferroptosis (LMRGs-FARs) were extracted from the MSigDB and FerrDb databases, respectively. Five hundred and forty-four UCEC samples were obtained from the TCGA database. The risk prognostic signature was constructed by consensus clustering, univariate cox, and LASSO analyses. The accuracy of the risk modes was assessed through receiver operating characteristic (ROC) curve, nomogram, calibration,, and C-index analyses. The relationship between the risk signature and immune microenvironment was detected by the ESTIMATE, EPIC, TIMER, xCELL, quan-TIseq, and TCIA databases. The function of a potential gene, PSAT1, was measured by in vitro experiments. (3) Results: A six-gene (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) risk signature based on MRGs-FARs was constructed and evaluated with high accuracy in UCEC. The signature was identified as an independent prognostic parameter and it divided the samples into high- and low-risk groups. The low-risk group was positively associated with good prognosis, high mutational status, upregulated immune infiltration status, high expression of CTLA4, GZMA and PDCD1, anti-PD-1 treatment sensitivity, and chemoresistance. (4) Conclusions: We constructed a risk prognostic model based on both lipid metabolism and ferroptosis and evaluated the relationship between the risk score and tumor immune microenvironment in UCEC. Our study has provided new ideas and potential targets for UCEC individualized diagnosis and immunotherapy.

Comprehensive analysis of the prognostic signature and tumor microenvironment infiltration characteristics of cuproptosis-related lncRNAs for patients with colon adenocarcinoma

Background

Cuproptosis, a newly described method of regulatory cell death (RCD), may be a viable new therapy option for cancers. Long noncoding RNAs (lncRNAs) have been confirmed to be correlated with epigenetic controllers and regulate histone protein modification or DNA methylation during gene transcription. The roles of cuproptosis-related lncRNAs (CRLs) in Colon adenocarcinoma (COAD), however, remain unknown.

Methods

COAD transcriptome data was obtained from the TCGA database. Thirteen genes associated to cuproptosis were identified in published papers. Following that, correlation analysis was used to identify CRLs. The cuproptosis associated prognostic signature was built and evaluated using Lasso regression and COX regression analysis. A prognostic signature comprising six CRLs was established and the expression patterns of these CRLs were analyzed by qRT-PCR. To assess the clinical utility of prognostic signature, we performed tumor microenvironment (TME) analysis, mutation analysis, nomogram generation, and medication sensitivity analysis.

Results

We identified 49 prognosis-related CRLs in COAD and constructed a prognostic signature consisting of six CRLs. Each patient can be calculated for a risk score and the calculation formula is: Risk score =TNFRSF10A-AS1 * (-0.2449) + AC006449.3 * 1.407 + AC093382.1 *1.812 + AC099850.3 * (-0.0899) + ZEB1-AS1 * 0.4332 + NIFK-AS1 * 0.3956. Six CRLs expressions were investigated by qRT-PCR in three colorectal cancer cell lines. In three cohorts, COAD patients were identified with different risk groups, with the high-risk group having a worse prognosis than the low-risk group. Furthermore, there were differences in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups. We also identified certain drugs that were more sensitive to the high-risk group: Paclitaxel, Vinblastine, Sunitinib and Elescloml.

Conclusions

Our findings may be used to further investigate RCD, comprehension of the prognosis and tumor microenvironment infiltration characteristics in COAD.

Identification of an integrated kinase-related prognostic gene signature associated with tumor immune microenvironment in human uterine corpus endometrial carcinoma

In the worldwide, uterine corpus endometrial carcinoma (UCEC) is the sixth most common malignancy in women, and the number of women diagnosed is increasing. Kinase plays an important role in the occurrence and development of malignant tumors. However, the research about kinase in endometrial cancer is still unclear. Here, we first downloaded the gene expression data of 552 UCEC patients and 23 healthy endometrial tissues from The Cancer Genome Atlas (TCGA), obtained 538 kinase-related genes from the previous literature, and calculated 67 differentially expressed kinases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were referenced to identify multiple important biological functions and signaling pathways related to 67 differentially expressed kinases. Using univariate Cox regression and Least absolute shrinkage and selection operator (LASSO), seven kinases (ALPK2, CAMKV, TTK, PTK6, MAST1, CIT, and FAM198B) were identified to establish a prognostic model of endometrial cancer. Then, patients were divided into high- and low-risk groups based on risk scores. Receiver operating characteristic (ROC) curves were plotted to evaluate that the model had a favorable predictive ability. Kaplan–Meier survival analysis suggested that high-risk groups experienced worse overall survival than low-risk groups. qRT-PCR and ISH assays confirmed the consistency between predicted candidate genes and real sample contents. CIBERSORT algorithm and ssGSEA were adopted to investigate the relationship between this signature and tumor immune microenvironment, and revealed that in low- and high-risk groups, the types of tumor-infiltrating immune cells and the immune cell-related functions were significantly different. In summary, a seven-gene signature risk model has been constructed, and could accurately predict the prognosis of UCEC, which may offer ideas and breakthrough points to the kinase-associated development of UCEC.

Prediction of Prognosis in Patients With Endometrial Carcinoma and Immune Microenvironment Estimation Based on Ferroptosis-Related Genes

Background: Ferroptosis, a form of non-apoptotic cell death, has aroused worldwide interest in cancer researchers. However, the current study about the correlation between ferroptosis-related genes (FRGs) and endometrial cancer (EC) remains limited.

Methods: First, the transcriptome profiling and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) program as the training group and testing group, respectively. FRGs were acquired through literature mining. Then, we used R 4.1.1 software to screen the differently expressed FRGs from TCGA, which was also connected with the prognosis of EC patients. Subsequently, the risk score of each tumor sample was identified by LASSO regression analysis, and we classified these samples into the high- and low-risk groups in the light of the median risk score. Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier analysis were performed to assess the accuracy of this signature. Significantly, the data from CPTAC was used to validate the prediction model externally. Furthermore, we evaluated the immune microenvironment in this model via single-sample gene set enrichment analysis (ssGSEA).

Results: Among the 150 FRGs, 6 differentially expressed genes (DEGs) based on TCGA had a relationship with the prognosis of EC patients, namely, TP53, AIFM2, ATG7, TLR4, PANX1 and MDM2. The survival curve indicated a higher survival probability in the low-risk group. Moreover, the FRGs-based signature acted well in the prediction of overall survival (OS). The results of external verification confirmed the prediction model we established. Finally, ssGSEA revealed significant differences in the abundance of 16 immune cells infiltration and the activity of 13 immune functions between different risk groups.

Conclusion: We identified a novel ferroptosis-related gene signature which could concisely predict the prognosis and immunotherapy in EC patients.

Ferroptosis: Opportunities and Challenges in Treating Endometrial Cancer

Ferroptosis, a new way of cell death, is involved in many cancers. A growing number of studies have focused on the unique role of ferroptosis on endometrial cancer. In this study, we made a comprehensive review of the relevant articles published to get deep insights in the association of ferroptosis with endometrial cancer and to present a summary of the roles of different ferroptosis-associated genes. Accordingly, we made an evaluation of the relationships between the ferroptosis-associated genes and TNM stage, tumor grade, histological type, primary therapy outcome, invasion and recurrence of tumor, and accessing the different prognosis molecular typing based on ferroptosis-associated genes. In addition, we presented an introduction of the common drugs, which targeted ferroptosis in endometrial cancer. In so doing, we clarified the opportunities and challenges of ferroptosis activator application in treating endometrial cancer, with a view to provide a novel approach to the disease.

Establishing a Prognostic Signature Based on Epithelial-Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

Backgrounds

Epithelial-mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).

Methods

EMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.

Results

A total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N ⁶-methyladenosine (m⁶A) RNA expression, microsatellite instability level, and stemness indices.

Conclusion

We constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.