Clinical implications and mechanism of CST1 in gastric carcinoma based on database screening.

Gastric cancer (GC) remains one of the most common malignant tumours worldwide, with extremely high morbidity and mortality rates. An in-depth understanding of the pathogenesis of GC is key to the future diagnosis and treatment of GC. In this study, we analysed the differentially expressed genes (DEGs) in gastric carcinoma (GC) through GEO database and their clinical implications, with the aim of providing clinical reference and guidance. We selected the GSE118916 dataset for bioinformatics analysis and identified a total of 3231 DEGs. Keywords, including extracellular region, vesicle, protein digestion and absorption, ECM-receptor interaction, etc., of DEGs can be seen by the GO and KEGG enrichment analysis. The online database determined up-regulated CST1 in GC and some other tumors, as well as a close connection between CST1 with patient prognosis. Subsequently, we collected a number of GC clinical cases and examined the expression of CST1, which was seen to be highly expressed in GC, with a favorable diagnostic effect on the occurrence of GC (P<0.05) and a strong correlation with TNM stage, tumor invasion, tumor diameter and differentiation (P<0.05). In other words, CST1 is closely related to the occurrence and development of GC, and has the potential to be a breakthrough in the diagnosis and treatment of GC in the future.

Prediction of EGFR-TP53 genes co-mutations in patients with lung adenocarcinoma (LUAD) by 18F-FDG PET/CT radiomics.

PURPOSE: This retrospective study was undertaken to assess the predictive efficacy of 18F-FDG PET/CT -derived radiomic features concerning the co-mutation status of epidermal growth factor receptor (EGFR) and TP53 in LUAD. METHODS: A cohort of 150 LUAD patients underwent pretreatment 18F-FDG PET/CT scans with known mutation status of EGFR and TP53 were collected. The feature extraction based on their PET/CT images utilized the Pyradiomics package based on the 3D Slicer. The optimal radiomic features were selected through correlation analysis and the Gradient Boosting Decision Tree (GBDT) algorithm, followed by the construction of the radiomic model. The clinical model incorporated meaningful clinical variables, whereas the complex model integrated both the radiomic and clinical models. The area under the receiver operating characteristic curve (AUC) facilitated the comparison of prediction performance across the three models. The DCA gauged the clinical utility of these models. RESULTS: The patient cohort was randomly allocated into a training set (n = 105) and a validation set (n = 45) in a 7:3 ratio. Eleven PET and eleven CT optimal radiomic features were selected to construct the radiomic model. The model showed a good ability to discriminate the co-occurrence of EGFR and TP53, with AUC equal to 0.850 in the training set, and 0.748 in the validation set, compared with 0.750 and 0.626 for the clinical model. The complex model exhibited the highest AUC values, with 0.880 and 0.794 in both sets, but there were no significant differences compared to the radiomic model. The DCA revealed favorable clinical value.

Molecular map of disulfidptosis-related genes in lung adenocarcinoma: the perspective toward immune microenvironment and prognosis.

BACKGROUND: Disulfidptosis is a recently discovered form of programmed cell death that could impact cancer development. Nevertheless, the prognostic significance of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD) requires further clarification. METHODS: This study systematically explores the genetic and transcriptional variability, prognostic relevance, and expression profiles of DRGs. Clusters related to disulfidptosis were identified through consensus clustering. We used single-sample gene set enrichment analysis and ESTIMATE to assess the tumor microenvironment (TME) in different subgroups. We conducted a functional analysis of differentially expressed genes between subgroups, which involved gene ontology, the Kyoto encyclopedia of genes and genomes, and gene set variation analysis, in order to elucidate their functional status. Prognostic risk models were developed using univariate Cox regression and the least absolute shrinkage and selection operator regression. Additionally, single-cell clustering and cell communication analysis were conducted to enhance the understanding of the importance of signature genes. Lastly, qRT-PCR was employed to validate the prognostic model. RESULTS: Two clearly defined DRG clusters were identified through a consensus-based, unsupervised clustering analysis. Observations were made concerning the correlation between changes in multilayer DRG and various clinical characteristics, prognosis, and the infiltration of TME cells. A well-executed risk assessment model, known as the DRG score, was developed to predict the prognosis of LUAD patients. A high DRG score indicates increased TME cell infiltration, a higher mutation burden, elevated TME scores, and a poorer prognosis. Additionally, the DRG score showed a significant correlation with the tumor mutation burden score and the tumor immune dysfunction and exclusion score. Subsequently, a nomogram was established for facilitating the clinical application of the DRG score, showing good predictive ability and calibration. Additionally, crucial DRGs were further validated by single-cell sequencing data. Finally, crucial DRGs were further validated by qRT-PCR and immunohistochemistry. CONCLUSION: Our new DRG signature risk score can predict the immune landscape and prognosis of LUAD. It also serves as a reference for LUAD's immunotherapy and chemotherapy.