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

Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer

Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.

The roles and clinical applications of interleukins in endometrial carcinoma

As a common malignant tumor of the female reproductive system, endometrial carcinoma (EC) seriously endangers women's health with an increasing incidence. The oncogenesis and progression of cancer are closely linked with immune microenvironment, of which interleukins are the important components. In order to illustrate the roles and clinical applications of interleukins in EC, literature of interleukins and EC were reviewed. Based on the present studies, interleukins play crucial roles in the oncogenesis and development of EC via regulating the proliferation, migration, invasion, angiogenesis, apoptosis, pyroptosis and autophagy of EC as well as the immune function against EC. And some of the interleukins seems to have prospective clinical applications in EC, such as evaluating the risk of tumorigenesis, discriminating the malignancy from benign disorders or normal condition, indicating cancer aggressiveness, predicting the prognosis of patients and serving as the novel therapy. However, there is still a long way to go before the clinical applications of interleukins in EC come into reality. Nevertheless, it is certain that the exploration of interleukins will definitely be of great benefit to the screening, diagnosis and treatment of EC in the future.

A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification

Background

Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN).

Methods

Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model.

Results

The methylation analysis tool (ChAMP) considered DECs that were up-regulated (n =121) and down-regulated (n =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model.

Conclusion

Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment.