[Exploration of key ferroptosis-related genes as therapeutic targets for sepsis based on bioinformatics and the depiction of their immune profiles characterization].

Objective: To explore the characteristics of key ferroptosis-related genes as therapeutic targets for sepsis based on bioinformatics analysis, and describe their immune characteristics.

Methods: The transcriptome datasets GSE57065, GSE9960, GSE28750, and GSE137340 were downloaded from the Gene Expression Omnibus (GEO) database, immune-related gene (IRG) were obtained from ImmPort and InnateDB databases, and ferroptosis-related gene (FRG) were downloaded from the FerrDb database. The datasets GSE57065, GSE9960, and GSE28750 were integrated into an analysis dataset by the surrogate variable analysis (SVA) package and analyzed this analysis dataset by using the "limma" package to obtain differentially expressed gene (DEG), then the intersection set of DEG, FRG, and IRG were considered as ferroptosis and immune-related DEG (FImDEG).

Early Warning Models Using Machine Learning to Predict Sepsis-Associated Chronic Critical Illness: A Study Based on the Medical Information Mart for Intensive Care Database.

Background Patients with chronic critical illness (CCI) experience poor prognoses and incur high medical costs. However, there is currently limited clinical awareness of sepsis-associated CCI, resulting in insufficient vigilance. Therefore, it is necessary to build a machine learning model that can predict whether sepsis patients will develop CCI.

Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis.

Background: Cuproptosis is a copper-dependent cell death that is connected to the development and immune response of multiple diseases. However, the function of cuproptosis in the immune characteristics of sepsis remains unclear.

Method: We obtained two sepsis datasets (GSE9960 and GSE134347) from the GEO database and classified the raw data with R packages.