Hao1 Is Not a Pathogenic Factor for Ectopic Ossifications but Functions to Regulate the TCA Cycle In Vivo

Analysis of the Protective Effects of Rosa roxburghii-Fermented Juice on Lipopolysaccharide-Induced Acute Lung Injury in Mice through Network Pharmacology and Metabolomics

Acute lung injury, a fatal condition characterized by a high mortality rate, necessitates urgent exploration of treatment modalities. Utilizing UHPLS-Q-Exactive Orbitrap/MS, our study scrutinized the active constituents present in Rosa roxburghii-fermented juice (RRFJ) while also assessing its protective efficacy against LPS-induced ALI in mice through lung histopathological analysis, cytokine profiling, and oxidative stress assessment. The protective mechanism of RRFJ against ALI in mice was elucidated utilizing metabolomics, network pharmacology, and molecular docking methodologies. Our experimental findings demonstrate that RRFJ markedly ameliorates pathological injuries in ALI-afflicted mice, mitigates systemic inflammation and oxidative stress, enhances energy metabolism, and restores dysregulated amino acid and arachidonic acid metabolic pathways. This study indicates that RRFJ can serve as a functional food for adjuvant treatment of ALI.

Screening of potential biomarkers in propofol-induced neurotoxicity via bioinformatics prediction and experimental verification

OBJECTIVES

To identify hub genes and biological processes of propofol-induced neurotoxicity and promote the development of pediatric anesthesiology.

METHODS

We downloaded the GSE106799 dataset from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened, then Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and Gene Set Enrichment analyses were performed on all DEGs. We identified potential ferroptosis genes in the pathogenesis of propofol-induced neurotoxicity. A key module was obtained after performing weighted gene co-expression network analysis (WGCNA) on the GSE106799 dataset. Hub genes were identified after the least absolute shrinkage and selection operator (LASSO) regression analysis of the intersection of DEGs and genes from the key module. We established a competing endogenous RNA network and predicted potential drugs according to the hub genes. Total RNA and proteins were extracted for real-time quantitative polymerase chain reaction and Western blotting, respectively.

RESULTS

A total of 112 DEGs, including 76 upregulated and 36 downregulated ones were screened out. Propofol-induced neurotoxicity involved processes such as nervous system development, activation of JAK/STAT and MAPK signaling pathways, vascular regeneration, and oxidative stress. The results of WGCNA suggested that the tan module was the most strongly associated with propofol-induced neurotoxicity. We identified 4 hub genes (EGR4, HAO1, ITK and GM14446) after LASSO regression analysis. Animal experiments demonstrated that propofol caused overexpression of the protein levels of HAO1, ITK and inflammatory factors in the brain, as well as the mRNA levels of HAO1, ITK and GM14446. Propofol inhibited expression of EGR4 at mRNA and protein levels.

CONCLUSIONS

Previous studies have demonstrated that EGR4, HAO1, ITK and GM14446 play a role in intellectual development, neuroinflammation and neuronal differentiation. These hub genes may help us to find new preventive and therapeutic targets for propofol-induced neurotoxicity.

Establishment and validation of a novel peroxisome-related gene prognostic risk model in kidney clear cell carcinoma

BACKGROUND

Kidney clear cell carcinoma (KIRC) is the most common subtype of renal cell carcinoma. Peroxisomes play a role in the regulation of tumorigenesis and cancer progression, yet the prognostic significance of peroxisome-related genes (PRGs) remains rarely studied. The study aimed to establish a novel prognostic risk model and identify potential biomarkers in KIRC.

METHODS

The significant prognostic PRGs were screened through differential and Cox regression analyses, and LASSO Cox regression analysis was performed to establish a prognostic risk model in the training cohort, which was validated internally in the testing and entire cohorts, and further assessed in the GSE22541 cohort. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function and pathway differences between the high-risk and low-risk groups. The relationship between risk score and immune cell infiltration levels was evaluated in the CIBERSORT, ESTIMATE and TIMER databases. Finally, potential biomarkers were identified and validated from model genes, using immunohistochemistry.

RESULTS

Fourteen significant prognostic PRGs were identified using multiple analyses, and 9 genes (ABCD1, ACAD11, ACAT1, AGXT, DAO, EPHX2, FNDC5, HAO1, and HNGCLL1) were obtained to establish a prognostic model via LASSO Cox regression analysis. Combining the risk score with clinical factors to construct a nomogram, which provided support for personalized treatment protocols for KIRC patients. GO and KEGG analyses highlighted associations with substance metabolism, transport, and the PPAR signaling pathways. Tumor immune infiltration indicated immune suppression in the high-risk group, accompanied by higher tumor purity and the expression of 9 model genes was positively correlated with the level of immune cell infiltration. ACAT1 has superior prognostic capabilities in predicting the outcomes of KIRC patients.

CONCLUSIONS

The peroxisome-related prognostic risk model could better predict prognosis in KIRC patients.