Microarray analysis of tRNA-derived small RNA (tsRNA) in LPS-challenged macrophages treated with metformin.

Metformin, a widely used anti-diabetic drug, has demonstrated its efficacy in addressing various inflammatory conditions. tRNA-derived small RNA (tsRNA), a novel type of small non-coding RNA, exhibits diverse regulatory functions and holds promise as both a diagnostic biomarker and a therapeutic target for various diseases. The purpose of this study is to investigate whether the abundance of tsRNAs changed in LPS versus LPS + metformin-treated cells, utilizing microarray technology. Firstly, we established an in vitro lipopolysaccharide (LPS)-induced inflammation model using RAW264.7 macrophages and assessed the protective effects of metformin against inflammatory damage. Subsequently, we extracted total RNA from both LPS-treated and metformin + LPS-treated cell samples for microarray analysis to identify differentially abundant tsRNAs (DA-tsRNAs). Furthermore, we conducted bioinformatics analysis to predict target genes for validated DA-tsRNAs and explore the biological functions and signaling pathways associated with DA-tsRNAs. Notably, metformin was found to inhibit the inflammatory response in RAW264.7 macrophages. The microarray results revealed a total of 247 DA-tsRNAs, with 58 upregulated and 189 downregulated tsRNAs in the Met + LPS group compared to the LPS group. The tsRNA-mRNA network was visualized, shedding light on potential interactions. The results of bioinformatics analysis suggested that these potential targets of specific tsRNAs were mainly related to inflammation and immunity. Our study provides compelling evidence that metformin exerts anti-inflammatory effects and modulates the abundance of tsRNAs in LPS-treated RAW264.7 macrophages. These findings establish a valuable foundation for using tsRNAs as potential biomarkers for metformin in the treatment of inflammatory conditions.

Identification of cuproptosis-related gene clusters and immune cell infiltration in major burns based on machine learning models and experimental validation.

Introduction: Burns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient's prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models. Methods: We analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR. Results: In both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury. Conclusion: This study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated.

Long noncoding RNA XIST promotes cell proliferation and migration in diabetic foot ulcers through the miR-126-3p/EGFR axis.

BACKGROUND: The prevalence of diabetic foot ulcers (DFUs) has caused serious harm to human health. To date, a highly effective treatment is lacking. Long noncoding RNA X-inactive specific transcript (lncRNA XIST) has been the subject of mounting research studies, all of which have found that it serves as a protective factor against certain diseases; however, its function in DFUs is not entirely understood. This study was performed to determine the importance of the lncRNA XIST in the pathogenesis and biological function of DFUs. METHODS: Diabetic ulcer skin from rats was analysed using haematoxylin-eosin (HE), Masson's trichrome, and immunohistochemistry (IHC) staining. The differences in the expression of genes and proteins were examined with real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Next, the interaction was verified with a dual luciferase gene reporter assay. In addition, CCK-8, Transwell, and wound healing assays were used to assess the proliferation and migration of HaCaT cells. RESULTS: The lncRNA XIST and epidermal growth factor receptor (EGFR) were downregulated, while microRNA-126-3p (miR-126-3p) was increased in diabetic ulcer rat skin tissues and high glucose-induced HaCaT cells. In addition, we found that the lncRNA XIST binds to miR-126-3p and that EGFR is directly targeted by miR­126­3p. Silencing XIST contributed to upregulated miR-126-3p expression, thus lowering EGFR levels and inhibiting the proliferative and migratory abilities of high glucose-treated HaCaT cells; however, the miR-126-3p inhibitor and overexpression of EGFR reversed this effect. CONCLUSION: Decreased lncRNA XIST expression inhibits the proliferative and migratory abilities of high glucose-induced HaCaT cells by modulating the miR-126-3p/EGFR axis, causing delayed wound healing.

Prognostic and risk factor analysis of cancer patients after unplanned ICU admission: a real-world multicenter study.

To investigate the occurrence and 90-day mortality of cancer patients following unplanned admission to the intensive care unit (ICU), as well as to develop a risk prediction model for their 90-day prognosis. We prospectively analyzed data from cancer patients who were admitted to the ICU without prior planning within the past 7 days, specifically between May 12, 2021, and July 12, 2021. The patients were grouped based on their 90-day survival status, and the aim was to identify the risk factors influencing their survival status. A total of 1488 cases were included in the study, with an average age of 63.2 ± 12.4 years. The most common reason for ICU admission was sepsis (n = 940, 63.2%). During their ICU stay, 29.7% of patients required vasoactive drug support (n = 442), 39.8% needed invasive mechanical ventilation support (n = 592), and 82 patients (5.5%) received renal replacement therapy. We conducted a multivariate COX proportional hazards model analysis, which revealed that BMI and a history of hypertension were protective factors. On the other hand, antitumor treatment within the 3 months prior to admission, transfer from the emergency department, general ward, or external hospital, high APACHE score, diagnosis of shock and respiratory failure, receiving invasive ventilation, and experiencing acute kidney injury (AKI) were identified as risk factors for poor prognosis within 90 days after ICU admission. The average length of stay in the ICU was 4 days, while the hospital stay duration was 18 days. A total of 415 patients died within 90 days after ICU admission, resulting in a mortality rate of 27.9%. We selected 8 indicators to construct the predictive model, which demonstrated good discrimination and calibration. The prognosis of cancer patients who are unplanned transferred to the ICU is generally poor. Assessing the risk factors and developing a risk prediction model for these patients can play a significant role in evaluating their prognosis.