Discovery and Validation of an Epithelial-Mesenchymal Transition-Based Signature in Gastric Cancer by Genomics and Prognosis Analysis.

OBJECTIVE: Epithelial-mesenchymal transition (EMT) exerts a key function in cancer initiation and progression. Herein, we aimed to develop an EMT-based prognostic signature in gastric cancer. METHODS: The gene expression profiles of gastric cancer were obtained from TCGA dataset as a training set and GSE66229 and GSE84437 datasets as validation sets. By LASSO regression and Cox regression analyses, key prognostic EMT-related genes were screened for developing a risk score (RS) model. Potential small molecular compounds were predicted by the CMap database based on the RS model. GSEA was employed to explore signaling pathways associated with the RS. ESTIMATE and seven algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC) were applied to assess the RS and immune microenvironment. RESULTS: This study developed an EMT-related gene signature comprised of SERPINE1, PCOLCE2, MATN3, and DKK1. High-RS patients displayed poorer survival outcomes than those with low RS. ROC curves demonstrated the robustness of the model in predicting the prognosis. After external validation, the RS model was an independent risk factor for gastric cancer. Several compounds were predicted for gastric cancer treatment based on the RS model. ECM receptor interaction, focal adhesion, pathway in cancer, TGF-beta, and WNT pathways were distinctly activated in high-RS samples. Also, high RS was significantly associated with increased stromal and immune scores and increased infiltration of CD4+ T cell, CD8+ T cell, cancer-associated fibroblast, and macrophage in gastric cancer tissues. CONCLUSION: Our findings suggested that the EMT-related gene model may robustly predict gastric cancer prognosis, which could improve the efficacy of personalized therapy.

A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer.

BACKGROUND We attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs). MATERIAL AND METHODS RNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen survival-related RBPs, followed by least absolute shrinkage and selection operator Cox modeling. Overall and stratified survival analysis was carried out between high and low risk score groups, followed by receiver operator characteristic curve construction. Univariate and multivariate survival analysis was applied to assess its independent prognostic potential. A nomogram was constructed by combining age and the risk score, which was verified by calibration curves and decision curve analyses for 1-, 3-, and 5-year survival. Molecular subtypes were identified using nonnegative matrix factorization method. Clinical features of the identified subtypes were characterized on prognosis, drug sensitivity, and immune infiltration. An external Gene Expression Omnibus dataset was used to verify the above findings. RESULTS On the basis of 44 survival-related RBPs, a robust prognostic 15-RBP signature was constructed. Patients with high risk score had a poorer prognosis than those with low risk score. The risk score had good performance in predicting clinical outcomes for 1-, 3-, and 5-year survival. The signature was effectively independent of other clinical features. The nomogram model combining age and the 15-RBP prognostic model exhibited better practicality and reliability for prognosis. RBP expression data were utilized to define 2 distinct molecular subtypes obviously related to survival outcomes, chemotherapeutic drug sensitivity, and immune infiltration. CONCLUSIONS Our study provides a nomogram model that consists of age and a 15-RBP signature and identifies 2 molecular subtypes for gastric cancer that possess potential value for preclinical, clinical, and translational research on gastric cancer.

Prevalence, outcome and prognostic factors of neuropsychiatric systemic lupus erythematosus: A real world single center study.

Objectives: To investigate the prevalence, outcome and prognostic factors of neuropsychiatric systemic lupus erythematosus (NPSLE).Methods: SLE inpatients from 2005 to 2016 were included. Information on survival duration and causes of death was collected. Data were analyzed using Kaplan-Meier curves, log-rank tests and Cox proportional hazards modeling.Results: Among 1589 SLE patients, 101 (6.4%) were diagnosed with NPSLE. The overall survival rates of the NPSLE patients were 89%, 85% and 84% at 1, 3 and 5 years, respectively. The standardized mortality ratio of NPSLE patients was 11.14. The most common cause of death was NPSLE related conditions (7, 47%), including intracranial hypertension syndrome, cerebrovascular disease and motor neuron disease. The following variables were associated with death: cardiac involvement, renal involvement, diffuse NPSLE, acute confusional state, more than one NPSLE manifestation, low lymphocyte count, elevated C-reactive protein, abnormal cerebrospinal fluid (CSF) and high systemic lupus erythematosus disease activity index. Acute confusional state (p = .001), elevated intracranial pressure (p = .010) and C-reactive protein (CRP) (p = .032) were independently predictive factors of death.Conclusion: Our study demonstrates an 11.14-fold increased mortality of NPSLE patients compared with general population. NPSLE related disorders are main causes of death. Acute confusional state is the most significant predictive factor for poor prognosis.