Relapse Rate and Complete Recovery Rate in Control Subjects by the rs1800541 Polymorphism.

<p>Note: POCC, primary osteosarcoma cell culture. ^a Fisher’s exact <i>P</i> value for GG vs. TT; ^b Fisher’s exact <i>P</i> value for TG vs. TT.</p

Image_1_A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer.pdf

BackgroundTo explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model.MethodsThe RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods.ResultsWe identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters via consistent clustering analysis. Patients in cluster B had higher tumor purity, higher T stage, and worse prognoses compared to those in cluster A. The latter generally showed higher immune infiltration. A prognostic model was constructed using 11 genes (GZMA, ASNS, GLS, PRKAA2, VLDLR, PRDX6, PSAT1, CDKN2A, SIRT3, TNFRSF1A, LRPPRC), and exhibited good diagnostic performance. Patients with high-risk scores were older, and had higher stage and grade tumors, along with worse prognoses. The frequency of mutations in PCD-related genes was correlated with the risk score. LRPPRC, an adverse prognostic gene in EC, was strongly correlated with proliferation-related genes and multiple PCD-related genes. LRPPRC expression was higher in patients with higher clinical staging and in the deceased patients. In addition, a positive correlation was observed between LRPPRC and infiltration of multiple immune cell types.ConclusionWe identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.</p

DataSheet_1_A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer.csv

BackgroundTo explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model.MethodsThe RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods.ResultsWe identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters via consistent clustering analysis. Patients in cluster B had higher tumor purity, higher T stage, and worse prognoses compared to those in cluster A. The latter generally showed higher immune infiltration. A prognostic model was constructed using 11 genes (GZMA, ASNS, GLS, PRKAA2, VLDLR, PRDX6, PSAT1, CDKN2A, SIRT3, TNFRSF1A, LRPPRC), and exhibited good diagnostic performance. Patients with high-risk scores were older, and had higher stage and grade tumors, along with worse prognoses. The frequency of mutations in PCD-related genes was correlated with the risk score. LRPPRC, an adverse prognostic gene in EC, was strongly correlated with proliferation-related genes and multiple PCD-related genes. LRPPRC expression was higher in patients with higher clinical staging and in the deceased patients. In addition, a positive correlation was observed between LRPPRC and infiltration of multiple immune cell types.ConclusionWe identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.</p

Data_Sheet_1_Transcriptome analysis reveals molecular mechanisms underlying salt tolerance in halophyte Sesuvium portulacastrum.xlsx

Soil salinity is an important environmental problem that seriously affects plant growth and crop productivity. Phytoremediation is a cost-effective solution for reducing soil salinity and potentially converting the soils for crop production. Sesuvium portulacastrum is a typical halophyte which can grow at high salt concentrations. In order to explore the salt tolerance mechanism of S. portulacastrum, rooted cuttings were grown in a hydroponic culture containing ½ Hoagland solution with or without addition of 400 mM Na for 21 days. Root and leaf samples were taken 1 h and 21 days after Na treatment, and RNA-Seq was used to analyze transcript differences in roots and leaves of the Na-treated and control plants. A large number of differentially expressed genes (DEGs) were identified in the roots and leaves of plants grown under salt stress. Several key pathways related to salt tolerance were identified through KEGG analysis. Combined with physiological data and expression analysis, it appeared that cyclic nucleotide gated channels (CNGCs) were implicated in Na uptake and Na+/H+ exchangers (NHXs) were responsible for the extrusion and sequestration of Na, which facilitated a balance between Na+ and K+ in S. portulacastrum under salt stress. Soluble sugar and proline were identified as important osmoprotectant in salt-stressed S. portulacastrum plants. Glutathione metabolism played an important role in scavenging reactive oxygen species. Results from this study show that S. portulacastrum as a halophytic species possesses a suite of mechanisms for accumulating and tolerating a high level of Na; thus, it could be a valuable plant species used for phytoremediation of saline soils.</p

Table_1_Gut microbiome-based noninvasive diagnostic model to predict acute coronary syndromes.xlsx

BackgroundPrevious studies have shown that alterations in the gut microbiota are closely associated with Acute Coronary Syndrome (ACS) development. However, the value of gut microbiota for early diagnosis of ACS remains understudied.MethodsWe recruited 66 volunteers, including 29 patients with a first diagnosis of ACS and 37 healthy volunteers during the same period, collected their fecal samples, and sequenced the V4 region of the 16S rRNA gene. Functional prediction of the microbiota was performed using PICRUSt2. Subsequently, we constructed a nomogram and corresponding webpage based on microbial markers to assist in the diagnosis of ACS. The diagnostic performance and usefulness of the model were analyzed using boostrap internal validation, calibration curves, and decision curve analysis (DCA).ResultsCompared to that of healthy controls, the diversity and composition of microbial community of patients with ACS was markedly abnormal. Potentially pathogenic genera such as Streptococcus and Acinetobacter were significantly increased in the ACS group, whereas certain SCFA-producing genera such as Blautia and Agathobacter were depleted. In addition, in the correlation analysis with clinical indicators, the microbiota was observed to be associated with the level of inflammation and severity of coronary atherosclerosis. Finally, a diagnostic model for ACS based on gut microbiota and clinical variables was developed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.963 (95% CI: 0.925–1) and an AUC value of 0.948 (95% CI: 0.549–0.641) for bootstrap internal validation. The calibration curves of the model show good consistency between the actual and predicted probabilities. The DCA showed that the model had a high net clinical benefit for clinical applications.ConclusionOur study is the first to characterize the composition and function of the gut microbiota in patients with ACS and healthy populations in Southwest China and demonstrates the potential effect of the microbiota as a non-invasive marker for the early diagnosis of ACS.</p