Construction and verification of an endoplasmic reticulum stress-related prognostic model for endometrial cancer based on WGCNA and machine learning algorithms.

Background: Endoplasmic reticulum (ER) stress arises from the accumulation of misfolded or unfolded proteins within the cell and is intricately linked to the initiation and progression of various tumors and their therapeutic strategies. However, the precise role of ER stress in uterine corpus endometrial cancer (UCEC) remains unclear. Methods: Data on patients with UCEC and control subjects were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), we identified pivotal differentially expressed ER stress-related genes (DEERGs). Further validation of the significance of these genes in UCEC was achieved through consensus clustering and bioinformatic analyses. Using Cox regression analysis and several machine learning algorithms (least absolute shrinkage and selection operator [LASSO], eXtreme Gradient Boosting [XGBoost], support vector machine recursive feature elimination [SVM-RFE], and Random Forest), hub DEERGs associated with patient prognosis were effectively identified. Based on the four identified hub genes, a prognostic model and nomogram were constructed. Additionally, a drug sensitivity analysis and in vitro validation experiments were performed. Results: A total of 94 DEERGs were identified in patients with UCEC and healthy controls. Consensus clustering analysis revealed significant differences in prognosis, typical immune checkpoints, and tumor microenvironments between the subtypes. Using Cox regression analysis and machine learning, four hub DEERGs, MYBL2, RADX, RUSC2, and CYP46A1, were identified to construct a prognostic model. The reliability of the model was validated using receiver operating characteristic (ROC) curves. Decision curve analysis (DCA) demonstrated the superior predictive ability of the nomogram in terms of 3- and 5-year survival, compared with that of other clinical indicators. Drug sensitivity analysis revealed increased sensitivity to dactinomycin, docetaxel, selumetinib, and trametinib in the low-risk group. The expressions of RADX, RUSC2, and CYP46A1 were downregulated, whereas that of MYBL2 was upregulated in UCEC tissues, as demonstrated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence assays. Conclusion: This study developed a stable and accurate prognostic model based on multiple bioinformatics analyses, which can be used to assess the prognosis of UCEC. This model may contribute to future research on the risk stratification of patients with UCEC and the formulation of novel treatment strategies.

Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.

Background: Recurrent pregnancy loss defined as the occurrence of two or more pregnancy losses before 20-24 weeks of gestation, is a prevalent and significant pathological condition that impacts human reproductive health. However, the underlying mechanism of RPL remains unclear. This study aimed to investigate the biomarkers and molecular mechanisms associated with RPL and explore novel treatment strategies for clinical applications. Methods: The GEO database was utilized to retrieve the RPL gene expression profile GSE165004. This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. ANN model were constructed to assess the performance of hub genes in the dataset. The expression of hub genes in both the RPL and control group samples was validated using RT-qPCR. The immune cell infiltration level of RPL was assessed using CIBERSORT. Additionally, pan-cancer analysis was conducted using Sangerbox, and small-molecule drug screening was performed using CMap. Results: A total of 352 DEGs were identified, including 198 up-regulated genes and 154 down-regulated genes. Enrichment analysis indicated that the DEGs were primarily associated with Fc gamma R-mediated phagocytosis, the Fc epsilon RI signaling pathway, and various metabolism-related pathways. The turquoise module, which showed the highest relevance to clinical symptoms based on WGCNA results, contained 104 DEGs. Three hub genes, WBP11, ACTR2, and NCSTN, were identified using machine learning algorithms. ROC curves demonstrated a strong diagnostic value when the three hub genes were combined. RT-qPCR confirmed the low expression of WBP11 and ACTR2 in RPL, whereas NCSTN exhibited high expression. The immune cell infiltration analysis results indicated an imbalance of macrophages in RPL. Meanwhile, these three hub genes exhibited aberrant expression in multiple malignancies and were associated with a poor prognosis. Furthermore, we identified several small-molecule drugs. Conclusion: This study identifies and validates hub genes in RPL, which may lead to significant advancements in understanding the molecular mechanisms and treatment strategies for this condition.

Upregulation of miRNA-26a Enhances the Apoptosis of Cerebral Neurons by Targeting EphA2 and Inhibiting the MAPK Pathway.

Neural tube defects (NTDs) constitute the second most common congenital malformation of the central nervous system. The pathogenesis of NTDs is not entirely clear. In recent years, microRNAs (miRNAs) have become a hot spot in genetic and developmental biology research. The present study aimed to explore the potential role of miRNA-26a in NTDs and the underlying pathogenesis thereof. First, we found significantly increased miRNA-26a expression in fetuses with NTDs (p < 0.0001), which significantly downregulated EphA2 and ERK1 mRNA and protein expression levels in fetuses with NTDs compared to normal controls (p < 0.01). In addition, a dual-luciferase reporter assay showed that miR-26a negatively regulated EphA2 by directly binding with the 3'-untranslated region of EphA2. Second, the upregulation of miRNA-26a expression increased caspase 3 and 9 protein expression levels (p < 0.01) and decreased EphA2 mRNA and protein expression levels (p < 0.01), as well as ERK1 and SRF protein expression levels (p < 0.01) in mouse neural stem cells (NE-4C) and human astroblastoma cells (U87MG). Furthermore, the upregulation of miRNA-26a inhibited cell proliferation and enhanced apoptosis of NE-4C and U87MG cells (p < 0.05). Similar results were observed with the MAPK inhibitor PD98059 (p < 0.01). These results suggest that miR-26a targets EphA2, modulates phosphorylation of the MAPK/ERK (MEK) pathway, regulates SRF, and participates in regulating nervous cell proliferation and apoptosis. Dysregulation of the aforementioned mechanism may be involved in the pathogenesis of NTDs.

Placental transcriptome sequencing combined with bioinformatics predicts potential genes and circular RNAs associated with hemoglobin Bart's hydrops fetalis syndrome.

AIM: Hemoglobin Bart's hydrops fetalis syndrome (BHFS) is the most severe form of α-thalassemia. Histological alternations can be observed in placenta, but placental transcriptome profile and circular RNAs have not been studied in this disease. The aim of this study was to define the placental transcriptional changes and find relevant circular RNAs in BHFS. METHODS: We performed high-throughput RNA sequencing to detect placental samples from fetuses affected by BHFS (n = 5) and normal fetuses (NF, n = 5), quantitative reverse transcription polymerase chain reaction (RT-qPCR), and Sanger sequencing to validate the differentially expressed circRNAs and their potentially related miRNAs (BHFS, n = 22; NF, n = 11). Bioinformatics methods were performed for further analysis. RESULTS: Our results showed 152 differentially expressed genes (DEGs), 112 circRNAs, and 45 microRNAs that were differentially expressed. DEGs were found to be involved in Gene Ontology terms related to gas transport, cell adhesion, oxidative stress, organ development, hemopoiesis, and others. RT-qPCR results showed that hsa_circ_0003961 and hsa_circ_0006687 were upregulated (p < 0.05). The competing endogenous RNA and co-expression networks showed that hsa_circ_0003961 and hsa_circ_0006687 were connected with 3 miRNAs and some DEGs, including cell adhesion genes (e.g., CLDN19), hemoglobin related genes (e.g., SOX6 and HBZ) and angiogenesis related genes (e.g., EPHB2). Downregulations of hsa-miR-1299 and hsa-miR-625-5p in ceRNA network were also validated by RT-qPCR. Gene set enrichment analysis results for the two circRNAs showed that some gene sets associated with cell adhesion, hematopoietic system and apoptosis were significantly enriched. CONCLUSIONS: Our study characterized the placental transcriptome of BHFS. The circRNAs hsa_circ_0003961 and hsa_circ_0006687 in placenta may be relevant to BHFS.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis.

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382-0.912], p = 0.017; 0.605 [0.371-0.986], p = 0.044; 1.678 [1.132-2.488], p = 0.01; 2.428 [1.372-4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes' coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.