KAT2

Nonspecific orbital inflammation (NSOI) is an idiopathic, persistent, and proliferative inflammatory condition affecting the orbit, characterized by polymorphous lymphoid infiltration. Its pathogenesis and progression have been linked to imbalances in tumor metabolic pathways, with glutamine (Gln) metabolism emerging as a critical aspect in cancer. Metabolic reprogramming is known to influence clinical outcomes in various malignancies. However, comprehensive research on glutamine metabolism's significance in NSOI is lacking.

This study conducted a bioinformatics analysis to identify and validate potential glutamine-related molecules (GlnMgs) associated with NSOI. The discovery of GlnMgs involved the intersection of differential expression analysis with a set of 42 candidate GlnMgs. The biological functions and pathways of the identified GlnMgs were analyzed using GSEA and GSVA. Lasso regression and SVM-RFE methods identified hub genes and assessed the diagnostic efficacy of fourteen GlnMgs in NSOI. The correlation between hub GlnMgs and clinical characteristics was also examined. The expression levels of the fourteen GlnMgs were validated using datasets GSE58331 and GSE105149.

Fourteen GlnMgs related to NSOI were identified, including FTCD, CPS1, CTPS1, NAGS, DDAH2, PHGDH, GGT1, GCLM, GLUD1, ART4, AADAT, ASNSD1, SLC38A1, and GFPT2. Biological function analysis indicated their involvement in responses to extracellular stimulus, mitochondrial matrix, and lipid transport. The diagnostic performance of these GlnMgs in distinguishing NSOI showed promising results.

This study successfully identified fourteen GlnMgs associated with NSOI, providing insights into potential novel biomarkers for NSOI and avenues for monitoring disease progression.

Lung cancer is the most common malignant tumour. More than 80% of all diagnosed cases are non-small cell carcinoma which can be effectively treated by radical resection. Despite significant progress in the field of diagnostic and therapeutic methods, the results of lung cancer treatment are still unsatisfactory. Lung cancer is detected relatively late, which leads to an unfavourable prognosis. Kynurenine aminotransferases are an important element of the kynurenine pathway of tryptophan metabolism, which has recently aroused great interest from the aspect of possible use as a target point of personalized therapies in malignant tumours.The aim of the study was to analyze the expression of the selected gene of kynurenine aminotransferases GOT 2 at the mRNA level in peripheral blood leukocytes of patients with lung cancer.

The mRNA expression of the GOT 2 gene was tested on blood samples from 50 patients treated surgically for non-small cell lung cancer.The control group consisted of 15 healthy individuals.The determination of mRNA expression of the GOT 2 gene was performed using the real-time PCR method.The GAPDH gene was used as the endogenous reference level.

The mRNA expression of the GOT2 gene on the 6th day after surgery was statistically significantly lower than before surgery (p = 0,05). In the study group, the average LogRQ mRNA expression of the GOT2 gene before the procedure was 0.192082±0.292174 in woman. This was statistically significantly higher than in men whose average LogRQ mRNA expression of the GOT2 gene before the procedure was 0.004210±0.235065 (p=0.0183).

Surgical resection of lung cancer results in inhibition of GOT2 mRNA expression in leukocytes. Further studies are expected to show whether it may be used as a target point for personalized therapies in lung cancer.

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.