Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

Mendelian randomization reveals immune cell composition as a key determinant of cervical cancer prognosis

BACKGROUND

Cervical cancer is still a major public health problem, and understanding the complex interplays in the tumor microenvironment is essential to implementing better clinical outcome measures. In this study, we used a Mendelian Randomization-based strategy to assess the causal roles of immune cell composition and disease prognosis in cervical cancer.

METHODS

The authors employed genetic natural experiments to measure the relationship between monocyte absolute counts, CD4-CD8-T cell ratios, and CD24 levels on memory B cells and indicators of cervical cancer prognosis.

RESULTS

Mendelian Randomization analysis showed that higher monocyte absolute counts were associated with a better prognosis, implying a protective role of these cells. In contrast, compared to individuals with good prognosis, high CD4-CD8-T cell ratios and low CD24 expression on memory B cells associated with bad prognosis suggest a risk-promoting role of these immune cell subsets. These observations were also further supported by additional analyses, such as scatter plots and linear regression models, which revealed further nuances in the intricate relationship between the immune microenvironment and the disease.

CONCLUSION

Our study utilizing the Mendelian Randomization approach provides novel insights into the causal relationships between the immune microenvironment of cervical cancer and its prognosis. The identified immune cell markers (monocyte counts, T cell ratios) have the potential to serve as prognostic biomarkers and to inform the design of targeted immunotherapeutic approaches in cervical cancer.

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

HMOX1 as a therapeutic target associated with diabetic foot ulcers based on single-cell analysis and machine learning

Diabetic foot ulcers (DFUs) are a serious chronic complication of diabetes mellitus and a leading cause of disability and death in diabetic patients. However, current treatments remain unsatisfactory. Although macrophages are associated with DFU, their exact role in this disease remains uncertain. This study sought to detect macrophage-related genes in DFU and identify possible therapeutic targets. Single-cell datasets (GSE223964) and RNA-seq datasets (GSM68183, GSE80178, GSE134431 and GSE147890) associated with DFU were retrieved from the gene expression omnibus (GEO) database for this study. Analysis of the provided single-cell data revealed the distribution of macrophage subpopulations in the DFU. Four independent RNA-seq datasets were merged into a single DFU cohort and further analysed using bioinformatics. This included differential expression (DEG) analysis, multiple machine learning algorithms to identify biomarkers and enrichment analysis. Finally, key results were validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western bolt. Finally, the findings were validated using RT-qPCR and western blot. We obtained 802 macrophage-related genes in single-cell analysis. Differential expression analysis yielded 743 DEGs. Thirty-seven macrophage-associated DEGs were identified by cross-analysis of marker genes with macrophage-associated DEGs. Thirty-seven intersections were screened and cross-analysed using four machine learning algorithms. Finally, HMOX1 was identified as a potentially valuable biomarker. HMOX1 was significantly associated with biological pathways such as the insulin signalling pathway. The results showed that HMOX1 was significantly overexpressed in DFU samples. In conclusion, the analytical results of this study identified HMOX1 as a potentially valuable biomarker associated with macrophages in DFU. The results of our analysis improve our understanding of the mechanism of macrophage action in this disease and may be useful in developing targeted therapies for DFU.

Down-regulation of lncRNA EMX2OS Is Associated with the Prognosis of Patients with Cervical Squamous Cell Carcinoma and Regulates Tumour Cell Progression via miR-574-5p

Cervical squamous cell carcinoma (CSCC) represents a malignant subtype of cervical cancer. Identification of novel biomarkers for CSCC development could enhance therapeutic efficiency and improve the patients' outcomes. This study focused on lncRNA EMX2OS, evaluating its expression and significance in the progression of CSCC while exploring its potential as a therapeutic target. A cohort of 135 patients with CSCC were enrolled, and tissue samples were collected for analysis. The expression of EMX2OS in the tissues was quantified by PCR, with its correlation to the clinicopathological features, and prognosis was evaluated by χ2, Kaplan-Meier and Cox regression analyses. The regulatory effects of EMX2OS on CSCC cells were investigated by CCK8 and Transwell assays, while the underlying molecular mechanisms were elucidated by luciferase reporter assays. Significant down-regulation of EMX2OS was observed in CSCC, correlating with advanced FIGO stages, poor differentiation and adverse prognosis of patients. Over-expression of EMX2OS significantly suppressed cell growth and metastasis in CSCC. Negative regulation of miR-574-5p by EMX2OS was observed, and over-expression of miR-574-5p alleviated the inhibition of CSCC cells by EMX2OS. Down-regulated EMX2OS indicates severe disease progression and poor prognosis in CSCC. Over-expression of EMX2OS could inhibit CSCC cell growth and metastasis by negatively modulating miR-574-5p.