Investigation of serum markers of esophageal squamous cell carcinoma based on machine learning methods

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract. The most effective method of reducing the disease burden in areas with a high incidence of esophageal cancer is to prevent the disease from developing into invasive cancer through screening. Endoscopic screening is key for the early diagnosis and treatment of ESCC. However, due to the uneven professional level of endoscopists, there are still many missed cases because of failure to recognize lesions. In recent years, along with remarkable progress in medical imaging and video evaluation technology based on deep machine learning, the development of artificial intelligence (AI) is expected to provide new auxiliary methods of endoscopic diagnosis and the treatment of early ESCC. The convolution neural network (CNN) in the deep learning model extracts the key features of the input image data using continuous convolution layers and then classifies images through full-layer connections. The CNN is widely used in medical image classification, and greatly improves the accuracy of endoscopic image classification. This review focuses on the AI-assisted diagnosis of early ESCC and prediction of early ESCC invasion depth under multiple imaging modalities. The excellent image recognition ability of AI is suitable for the detection and diagnosis of ESCC and can reduce missed diagnoses and help endoscopists better complete endoscopic examinations. However, the selective bias used in the training dataset of the AI system affects its general utility.

Comprehensive analysis of ferroptosis-related gene signatures as a potential therapeutic target for acute myeloid leukemia: A bioinformatics analysis and experimental verification

Background

Ferroptosis plays an important role in the development of acute myeloid leukemia (AML); however, the exact role of ferroptosis-related genes in the prognosis of AML patients is unclear.

Methods

RNA sequencing data and the clinicopathological characteristics of AML patients were obtained from The Cancer Genome Atlas database, and ferroptosis-related genes were obtained from the FerrDb database. Cox regression analysis and least absolute shrinkage and selection operator analysis were performed to identify ferroptosis-related gene signatures. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological functions of the ferroptosis-related genes. Finally, ferroptosis of AML cells was induced by erastin and sulfasalazine to detect the changes in the expression of relevant prognostic genes and explore the underlying mechanisms using quantitative real-time polymerase chain reaction (qRT-PCR).

Results

Seven ferroptosis-related gene signatures (SOCS1, ACSF2, MYB, EIF2AK4, AIFM2, SLC7A11, and GPX4) were identified in the training group. Kaplan-Meier and Cox regression analyses confirmed that risk score was an independent prognostic predictor of AML in the training and validation groups (P<0.05). Further, functional enrichment analysis revealed that seven ferroptosis-related genes were associated with many immune-related biological processes. Most importantly, erastin and sulfasalazine can induce the ferroptosis of AML cells. Overall, SLC7A11 and the SLC7A11/xCT-GSH-GPX4 pathway may be the respective key gene and potential regulatory pathway in erastin- and sulfasalazine-induced ferroptosis of AML cells.

Conclusions

A novel signature involving seven ferroptosis-related genes that could accurately predict AML prognosis was identified. Further, the Food and Drug Administration-approved drug, sulfasalazine, was demonstrated for the first time to induce the ferroptosis of AML cells. SLC7A11 and the SLC7A11/xCT-GSH-GPX4 pathway may be the respective key gene and underlying mechanism in this process, ultimately providing new insights into the strategies for the development of new AML therapies.