Mammaglobin B may be a prognostic biomarker of uterine corpus endometrial cancer

Metabolism-associated molecular classification of uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies. Currently, for UCEC cancer, molecular classification based on metabolic gene characteristics is rarely established. Here, we describe the molecular subtype features of UCEC by classifying metabolism-related gene profiles. Therefore, integrative analysis was performed on UCEC patients from the TCGA public database. Consensus clustering of RNA expression data on 2,752 previously reported metabolic genes identified two metabolic subtypes, namely, C1 and C2 subtypes. Two metabolic subtypes for prognostic characteristics, immune infiltration, genetic alteration, and responses to immunotherapy existed with distinct differences. Then, differentially expressed genes (DEGs) among the two metabolic subtypes were also clustered into two subclusters, and the aforementioned features were similar to the metabolic subtypes, supporting that the metabolism-relevant molecular classification is reliable. The results showed that the C1 subtype has high metabolic activity, high immunogenicity, high gene mutation, and a good prognosis. The C2 subtype has some features with low metabolic activity, low immunogenicity, high copy number variation (CNV) alteration, and poor prognosis. Finally, a model was identified, with three gene metabolism-related signatures, which can predict the prognosis. These findings of this study demonstrate a new classification in UCEC based on the metabolic pattern, thereby providing valuable information for understanding UCEC's molecular characteristics.

COVID-19–associated lncRNAs as predictors of survival in uterine corpus endometrial carcinoma: A prognostic model

Background: Patients with uterine corpus endometrial carcinoma (UCEC) may be susceptible to the coronavirus disease-2019 (COVID-19). Long non–coding RNAs take on a critical significance in UCEC occurrence, development, and prognosis. Accordingly, this study aimed to develop a novel model related to COVID-19–related lncRNAs for optimizing the prognosis of endometrial carcinoma.

Methods: The samples of endometrial carcinoma patients and the relevant clinical data were acquired in the Carcinoma Genome Atlas (TCGA) database. COVID-19–related lncRNAs were analyzed and obtained by coexpression. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed to establish a COVID-19–related lncRNA risk model. Kaplan–Meier analysis, principal component analysis (PCA), and functional enrichment annotation were used to analyze the risk model. Finally, the potential immunotherapeutic signatures and drug sensitivity prediction targeting this model were also discussed.

Results: The risk model comprising 10 COVID-19–associated lncRNAs was identified as a predictive ability for overall survival (OS) in UCEC patients. PCA analysis confirmed a reliable clustering ability of the risk model. By regrouping the patients with this model, different clinic-pathological characteristics, immunotherapeutic response, and chemotherapeutics sensitivity were also observed in different groups.

Conclusion: This risk model was developed based on COVID-19–associated lncRNAs which would be conducive to the precise treatment of patients with UCEC.

Novel DNA Damage-Related Subtypes Characterization Identifies Uterine Corpus Endometrial Carcinoma (UCEC) Based on Machine Learning

Objective. Accumulating evidence suggests that DNA damage is associated with numerous gynecological illnesses, particularly advanced uterine corpus endometrial carcinoma (UCEC), illustrating the involvement of the DNA damage pathway in the advancement of UCEC. This research aimed to discover a robust subtype with the potential to contribute to the scientific treatment of UCEC. Methods. In this work, the expression patterns of prognostic DNA damage-related genes were curated, and consensus clustering analyses were undertaken to determine DNA damage subtypes in patients with UCEC in the TCGA cohort. Two DNA damage-related subtypes were identified for further investigation. Differentially expressed genes (DEGs) analysis, gene ontology analysis, mutation analysis, and immune cell infraction analysis were performed to find the molecular mechanism behind it. Finally, the polymerase chain reaction (PCR) was conducted to verify the correlation of the hub genes. Results. In total, 545 patients with UCEC were tested for two distinct DNA damage subtypes. The clinical prognosis was poorer among patients with DNA damage subtype 2 than those in subtype 1. The DEGs analysis and PPI analysis showed that ASMP, BUB1, CENPF, MAD2L1, NCAPG, SGO2, and TOP2A were expressed higher in UCEC tissues than in the normal tissues. Immune cell infraction analysis showed that hub genes were associated with the tumor microenvironment (TME). Conclusion. Altogether, our research identified two distinct DNA damage subtypes that are complicated and heterogeneous. A better knowledge of the characteristics of the TME may be gained by quantitative measurement of DNA damage subtypes in individual patients, which can also lead to the development of more successful treatment regimens.

Shotgun Proteomics for the Identification and Profiling of the Tear Proteome of Keratoconus Patients

Purpose

The qualitative approach followed in this study aims to obtain an extensive view of the keratoconus (KC) tear proteome, which could highlight proteins previously undetected and enlarge our knowledge of the disease's pathophysiology.

Methods

Twenty-five patients diagnosed with KC and 25 control subjects were studied in a prospective, cross-sectional study. KC screening examinations, including clinical and tomographic examinations, were performed on all participants. Tear samples were collected using Schirmer strips and analyzed by liquid chromatography-tandem mass spectrometry in a data-dependent workflow. A spectral count was used as a semiquantification tool. The tear proteomes of both groups were identified and profiled, and the functional interactions and biological characterization of differential proteins were analyzed using in silico tools.

Results

We identified a total of 232 proteins, of whom 133 were expressed in both groups’ samples; 41 were observed only in control samples and 58 were identified just in tears of patients with KC. A semiquantitative analysis showed the dysregulation of 17 proteins in the KC samples. An in silico analysis linked proteins only expressed in KC samples to oxidative stress, skin development, and apoptosis. The dysregulation of proteins involved in iron transport, inflammation, oxidative stress, and protease inhibition was observed in the semiquantitative results.

Conclusions

A shotgun analysis showed that the tear proteome of patients with KC differed from controls by more than one-third of the total proteins identified, highlighting the relationship of the proteins only expressed in KC tears with processes of cell death, oxidative damage, and inflammation. The underexpression of proteins involved in iron pathways might support the iron imbalance as a contributing factor to cellular damage and death in KC disease.

Immune Infiltrates of m5C RNA Methylation-Related LncRNAs in Uterine Corpus Endometrial Carcinoma

Aberrant 5-methylcytidine (m5C) modification plays an essential role in the progression of different cancers. More and more researchers are focusing on developing a lncRNA-based risk model to assess the clinical prognosis of cancer patients. However, the impact of m5C-related lncRNAs on the prognosis of patients with uterine corpus endometrial carcinoma (UCEC), as well as the immune microenvironment of UCEC, remains unclear. Here, we comprehensively analyzed the predictive value of m5C-associated lncRNAs in UCEC and their association with the tumor immune microenvironment, according to the information extracted from the TCGA-UCEC dataset. We identified a total of 32 m5C-associated lncRNAs that were significantly correlated with the prognosis of UCEC patients. Two molecular subtypes were determined by consensus clustering analysis of these 32 m5C-associated prognostic lncRNAs. Further data showed that cluster 1 was associated with poor clinical prognosis, advanced tumor grade, higher PD-L1 expression levels, higher ESTIMATEScore, and higher immuneScore, as well as the immune cell infiltration. Then, 17 m5C-associated lncRNAs with prognostic values were obtained using LASSO regression analysis. And a risk model was constructed based on these 17 lncRNAs. It was revealed that the risk model could be used as an independent factor for UCEC prognosis. In addition, patients with UCEC in the high-risk group had higher tumor grades and immune scores. The risk model based on m5C-related lncRNAs was also closely associated with infiltrating immune cells. In conclusion, our study elucidated the crucial roles of the identified m5C-related lncRNAs in the UCEC patients' prognoses, as well as in the immune microenvironment in UCEC. The results suggest that the components of risk models based on the m5C-related lncRNAs may serve as important mediators of the immune microenvironment in UCEC.

Secretoglobins in the big picture of immunoregulation in airway diseases

The proteins of the secretoglobin (SCGB) family are expressed by secretory tissues of barrier organs. They are embedded in immunoregulatory and anti-inflammatory processes of airway diseases. This review particularly illustrates the immune regulation of SCGBs by cytokines and their implication in the pathophysiology of airway diseases. The biology of SCGBs is a complex topic of increasing importance, as they are highly abundant in the respiratory tract and can also be detected in malignant tissues and as elements of immune control. In addition, SCGBs react to cytokines, they are embedded in Th1 and Th2 immune responses, and they are expressed in a manner dependent on cell maturation. The big picture of the SCGB family identifies these factors as critical elements of innate immune control at the epithelial barriers and highlights their potential for diagnostic assessment of epithelial activity. Some members of the SCGB family have so far only been superficially examined, but have high potential for translational research.

Regulatory patterns analysis of transcription factor binding site clustered regions and identification of key genes in endometrial cancer

Endometrial cancer (EC) is one of the three fatal tumors of the female reproductive system. Epigenetic alterations have been reported to be important in tumorigenesis, especially the chromatin accessibility changes and transcription factor binding differences. However, the regulatory mechanism underlying epigenetic alterations in EC development remains unclear. Here, we identified and characterized transcription factor binding site clustered regions (TFCRs) by integrating chromatin accessibility and transcription factor binding information. We totally identified 78,820 TFCRs and explored the relationship between TFCRs and regulatory elements, gene expression and mutation. Finally, we constructed a bioinformatic framework to identify candidate oncogenes and screened 13 candidate key genes, which may serve as potential diagnostic markers or therapeutic targets of EC.

A radiogenomics application for prognostic profiling of endometrial cancer

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.

ALDH2 promotes uterine corpus endometrial carcinoma proliferation and construction of clinical survival prognostic model

UCEC is one of the three common malignant tumors of the female reproductive tract. According to reports, the cure rate of early UCEC can reach 95%. Therefore, the development of prognostic markers will help UCEC patients to find the disease earlier and develop treatment earlier. The ALDH family was first discovered to be the essential gene of the ethanol metabolism pathway in the body. Recent studies have shown that ALDH can participate in the regulation of cancer. In our research, we explored the expression of the ALDH family in 33 cancers. It was found that ALDH2 was abnormally expressed in UCEC. Besides, in vivo and in vitro experiments were conducted to explore the effect of ALDH2 expression on the proliferation of UCEC cell lines. Meanwhile, the change of its expression is not due to gene mutations, but is regulated by miR-135-3p. At the same time, the impact of ALDH2 changes on the survival of UCEC patients is deeply discussed. Finally, a nomogram for predicting survival was constructed, with a C-index of 0.798 and AUC of 0.764. This study suggests that ALDH2 may play a crucial role in UCEC progression and has the potential as a prognostic biomarker of UCEC.

Immune and Metabolic Dysregulated Coding and Non-coding RNAs Reveal Survival Association in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies, but only a few biomarkers have been proven to be effective in clinical practice. Previous studies have demonstrated the important roles of non-coding RNAs (ncRNAs) in diagnosis, prognosis, and therapy selection in UCEC and suggested the significance of integrating molecules at different levels for interpreting the underlying molecular mechanism. In this study, we collected transcriptome data, including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs), of 570 samples, which were comprised of 537 UCEC samples and 33 normal samples. First, differentially expressed lncRNAs, miRNAs, and mRNAs, which distinguished invasive carcinoma samples from normal samples, were identified, and further analysis showed that cancer- and metabolism-related functions were enriched by these RNAs. Next, an integrated, dysregulated, and scale-free biological network consisting of differentially expressed lncRNAs, miRNAs, and mRNAs was constructed. Protein-coding and ncRNA genes in this network showed potential immune and metabolic functions. A further analysis revealed two clinic-related modules that showed a close correlation with metabolic and immune functions. RNAs in the two modules were functionally validated to be associated with UCEC. The findings of this study demonstrate an important clinical application for improving outcome prediction for UCEC.