A Cholesterol Homeostasis-Related Gene Signature Predicts Prognosis of Endometrial Cancer and Correlates With Immune Infiltration

Diabetes mellitus complications associated with recurrence of stage I endometrioid endometrial cancer: A single-center retrospective study

OBJECTIVE

Identifying clinical features that are associated with recurrence of endometrioid endometrial carcinoma (EEC) in patients with diabetes mellitus (DM).

METHODS

A single-center retrospective cohort study was performed on patients with a diagnosis of both DM and Stage I EEC. Clinical and pathologic features were analyzed in relation to 5-year progression free survival (PFS). Kaplan-Meier Curves and Cox proportional hazard ratios were utilized to assess effect on 5-year PFS.

RESULTS

A total of 539 patients were included, with biopsy proven recurrence in 86 (18 %), and 456 (82 %) with no evidence of recurrence. Age, BMI, HgbA1c, metformin use, number of antihyperglycemic medications, use of adjuvant radiation, and surgical approach were not associated with differences in PFS. Presence of end-organ complications associated with diabetes was correlated with worse PFS (HR 1.78, 95 % CI 1.1-2.9, P = 0.02), and specifically diabetic neuropathy was associated with higher rates of recurrence (HR 3.6, 95 % CI 2.1-6.2, P < 0.01). In this cohort, PFS was independently associated with extent of myoinvasion (HR 2.33, 95 % CI 1.4-3.7, P < 0.01) as well as both microsatellite instability (HR 3.43, 95 % CI 1.8-6.6, P < 0.01), and no specific molecular profile (HR 0.3, 95 % CI 0.2-0.6, P < 0.01) molecular subtypes.

CONCLUSIONS

In patients with DM and EEC, extent of myoinvasion and TCGA molecular subtype correlated with worse PFS. Control of DM as evidenced by HgbA1c, BMI, and use of antihyperglycemic medications did not correlate with PFS in our cohort of patients with Stage I EEC, while the presence of diabetic neuropathy was associated with a higher risk of recurrence. These results highlight importance of evaluating diabetes severity and molecular subtype in endometrial cancer patients.

Lipid metabolism reprogramming in endometrial cancer: biological functions and therapeutic implications

BACKGROUND

Endometrial cancer is one of the major gynecological cancers, with increasing incidence and mortality in the past decades. Emerging preclinical and clinical data have indicated its close association with obesity and dyslipidemia. Metabolism reprogramming has been considered as the hallmark of cancer, to satisfy the extensive need of nutrients and energy for survival and growth. Particularly, lipid metabolism reprogramming has aroused the researchers' interest in the field of cancer, including tumorigenesis, invasiveness, metastasis, therapeutic resistance and immunity modulation, etc. But the roles of lipid metabolism reprogramming in endometrial cancer have not been fully understood. This review has summarized how lipid metabolism reprogramming induces oncogenesis and progression of endometrial cancer, including the biological functions of aberrant lipid metabolism pathway and altered transcription regulation of lipid metabolism pathway. Besides, we proposed novel therapeutic strategies of targeting lipid metabolism pathway and concentrated on its potential of sensitizing immunotherapy and hormonal therapy, to further optimize the existing treatment modalities of patients with advanced/metastatic endometrial cancer. Moreover, we expect that targeting lipid metabolism plus hormone therapy may block the endometrial malignant transformation and enrich the preventative approaches of endometrial cancer.

CONCLUSION

Lipid metabolism reprogramming plays an important role in tumor initiation and cancer progression of endometrial cancer. Targeting the core enzymes and transcriptional factors of lipid metabolism pathway alone or in combination with immunotherapy/hormone treatment is expected to decrease the tumor burden and provide promising treatment opportunity for patients with advanced/metastatic endometrial cancer.

The constitutively active form of a key cholesterol synthesis enzyme is lipid droplet-localized and upregulated in endometrial cancer tissues

Cholesterol is essential for both normal cell viability and cancer cell proliferation. Aberrant activity of squalene monooxygenase (SM, also known as squalene epoxidase), the rate-limiting enzyme of the committed cholesterol synthesis pathway, is accordingly implicated in a growing list of cancers. We previously reported that hypoxia triggers the truncation of SM to a constitutively active form, thus preserving sterol synthesis during oxygen shortfalls. Here, we show SM truncation is upregulated and correlates with the magnitude of hypoxia in endometrial cancer tissues, supporting the in vivo relevance of our earlier work. To further investigate the pathophysiological consequences of SM truncation, we examined its lipid droplet-localized pool using complementary immunofluorescence and cell fractionation approaches and found that it exclusively comprises the truncated enzyme. This partitioning is facilitated by the loss of an endoplasmic reticulum-embedded region at the SM N terminus, whereas the catalytic domain containing membrane-associated C-terminal helices is spared. Moreover, we determined multiple amphipathic helices contribute to the lipid droplet localization of truncated SM. Taken together, our results expand on the striking differences between the two forms of SM and suggest upregulated truncation may contribute to SM-related oncogenesis.

Serum cholesterol level as a predictive biomarker for prognosis of Neuroblastoma

BACKGROUND

Neuroblastoma (NB), a type of solid tumor in children, has a poor prognosis. Few blood biomarkers can accurately predict the prognosis, including recurrence and survival, in children with NB. In this study, we found that the serum total cholesterol (Tchol) level was associated with the prognosis of patients through a retrospective study.

METHODS

Multivariate Cox regression model was used to identify the independent risk factors in the children with NB. Kaplan-Meier method was used to analyze the correlation between the common biomarkers, including the serum Tchol level, and the prognosis of the patients. ROC curves were used to predict the accuracy of the International Neuroblastoma Staging System (INSS) stage and Children's Oncology Group (COG) risk stratification after adding the serum Tchol level.

RESULTS

Compared with the other patients, serum Tchol level was significantly increased in the relapsed and died patients (P < 0.05). Subsequently, serum Tchol level was found as an independent risk factor to affect the outcome of patients (P < 0.05). Finally, we added serum Tchol level into traditional stage and risk classification system to form the new INSS stage and COG risk classification system. It was found that the areas under the ROC curve (AUC) of recurrence-free survival in the new INSS stage and COG risk classification system were increased to 0.691 (95%CI: 0.535-0.847) and 0.748 (95%CI: 0.622-0.874), respectively. Moreover, the AUC areas of overall survival in the new INSS stage and COG risk classification system were increased to 0.722 (95%CI: 0.561-0.883) and 0.668 (95%CI: 0.496-0.819), respectively.

CONCLUSION

We found that serum Tchol level, a clinical biomarker, is a risk factor for recurrence and death among the children with NB. The serum Tchol level could significantly increase the accuracy of the prediction for NB prognosis.

Identification of cuproptosis-related subtypes, establishment of a prognostic model and tumor immune landscape in endometrial carcinoma

Cuproptosis, the mechanism of copper-dependent cell death, is distinct from all other known forms of regulated cell death and dependents on mitochondrial respiration. Cuproptosis promises to be a novel treatment, especially for tumors resistant to conventional therapies. We investigated the changes in cuproptosis-related genes (CRGs) in endometrial cancer (EC) cohorts from the merged Gene Expression Omnibus and the Cancer Genome Atlas databases, which could be divided into three distinct CRGclusters. Patients in CRGcluster C would have higher survival probability (P = 0.007), and higher levels of tumor microenvironment (TME) cell infiltration than other CRGclusters. CRG score was calculated via the results of univariate, multivariate cox analysis and least absolute shrinkage and selection operator regression analysis. Patients were divided into two risk subgroups according to the median risk score. Low-risk patients exhibited a more favorable prognosis, higher immunogenicity, and greater immunotherapy efficacy. Besides, CRG scores were strongly correlated to copy number variation, immunophenoscore, tumor mutation load, cancer stem cell index, microsatellite instability, and chemosensitivity. The c-index of our model is 0.702, which is higher than other four published model. The results proved that our model can distinguish EC patients with high-risk and low-risk and accurately predict the prognosis of EC patients. It will provide new ideas for clinical prognosis and precise treatments.

Assessing Metabolic Risk Factors for LVSI in Endometrial Cancer: A Cross-Sectional Study

Objective

This study analyzed metabolic factors associated with lymphovascular space invasion (LVSI) and compared the difference between type 1 and type 2 endometrial cancer (EC).

Methods

Four hundred patients primarily diagnosed with EC who underwent hysterectomy with pathological results at Fujian Medical University Cancer Hospital from January 2019 to January 2021 were included. Demographic variable data were collected as well as pathological results. Laboratory evaluations included fasting blood glucose (FBG), serum cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A (Apo A) and apolipoprotein B (Apo B). Characterization of binary logistic regression models was used to test the odds ratios (ORs) between LVSI and its metabolic parameters with different subtypes of EC.

Results

The results indicated that CA125, ROMA, Ki67 score, FBG and TC were higher in EC patients with LVSI (all p<0.05). Negative ER and PR expression was positively associated with LVSI (P<0.05). CA125, ROMA, FBG, TC and ER were found to be independent risk factors for LVSI. CA125, ROMA and FBG were significantly elevated in type 1 EC patients with LVSI compared with those without LVSI (all p<0.05). TC and Ki67 scores were much higher in type 2 EC patients with vs without LVSI (all p<0.05). Negative PR expression was positively related to both type 1 and type 2 EC patients with LVSI. Consequently, CA125, ROMA, FBG and Apo B were found to be independent risk factors for LVSI in type 1 EC, and TC was found to be an independent risk factor for LVSI in type 2 EC.

Conclusion

FBG and TC were both independent risk factors for LVSI in EC. FBG and Apo B were independent risk factors for LVSI in type 1 EC. TC was an independent risk factor for LVSI in type 2 EC.

A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

Over the past decades, the incidence and mortality rates of breast cancer (BC) have increased rapidly; however, molecular biomarkers that can reliably detect BC are yet to be discovered. Our study aimed to identify a novel signature that can predict the prognosis of patients with BC. Data from the TCGA-BRCA cohort were analyzed using univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis was performed to build a stable prognostic model. Subsequently, Kaplan–Meier (K–M) and receiver operating characteristic (ROC) analyses were performed to demonstrate the predictive power of our gene signature. Each patient was assigned to either a low- or high-risk group. Patients with high-risk BC had poorer survival than those with low-risk BC. Cox regression analysis suggested that our signature was an independent prognostic factor. Additionally, decision curve analysis and calibration accurately predicted the capacity of our nomogram. Thus, based on the differentially expressed genes (DEGs) of mitophagy-related tumor classification, we established a 13-gene signature and robust nomogram for predicting BC prognosis, which can be beneficial for the diagnosis and treatment of BC.

Crosstalk of Redox-Related Subtypes, Establishment of a Prognostic Model and Immune Responses in Endometrial Carcinoma

Simple Summary

In order to explore the role of redox as a prognostic indicator in endometrial carcinoma (EC), we detected the expression patterns of 55 redox-related genes (RRGs) in EC cohorts from public databases. Performing consensus cluster algorithm, we determined four molecular subclusters based on RRGs which had significant differences in overall survival (OS) and immune activities of EC patients. Furthermore, we developed a prognostic risk model on the basis of the redox-related subtype by stepwise Cox regression analyses. All EC patients were divided into high-risk and low-risk groups according to the median value of risk score. Our proposed model could accurately assess the clinical outcome and had favorable independent ability in EC cases. Moreover, our signature can serve as a predictor for immune status and chemotherapy sensitivity.

Abstract

Redox plays a central part in the pathogeneses and development of tumors. We comprehensively determined the expression patterns of redox-related genes (RRGs) in endometrial carcinoma (EC) cohorts from public databases and identified four different RRG-related clusters. The prognosis and the characteristics of TME cell infiltration of RRGcluster C patients were worse than those of other RRG clusters. When it comes to the gene cluster, there were great differences in clinicopathology traits and immunocyte infiltration. The RRG score was calculated by Cox analyses, and an RRG-based signature was developed. The risk score performed well in the EC cohort. Samples were separated into two risk subgroups with the standard of the value of the median risk score. Low-risk patients had a better prognosis and higher immunogenicity. In addition, RRG score was closely associated with immunophenoscore, microsatellite instability, tumor mutation burden, tumor stem cell index, copy number variation and chemotherapy sensitivity. The nomogram accurately predicted the prognosis of patients, and our model showed better performance than other published models. In conclusion, we built a prognostic model of RRGs which can help to evaluate clinical outcomes and guide more effective treatment.

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.