A competing risk-based nomogram to predict cancer-specific survival in patients with retroperitoneal leiomyosarcoma.

Considering the rarity and aggressive nature of retroperitoneal leiomyosarcoma (RLMS), several prognostic factors might contribute to the cancer-specific mortality of these patients. This study aimed to construct a competing risk-based nomogram to predict cancer-specific survival (CSS) for patients with RLMS. In total, 788 cases from the Surveillance, Epidemiology, and End Results (SEER) database (2000-2015) were included. Based on the Fine & Gray's method, independent predictors were screened to develop a nomogram for predicting 1-, 3-, and 5-year CSS. After multivariate analysis, CSS was found significantly associated with tumor characteristics (tumor grade, size, range), as well as surgery status. The nomogram showed solid prediction power and was well calibrated. Through decision curve analysis (DCA), a favorable clinical utility of the nomogram was demonstrated. Additionally, a risk stratification system was developed and distinctive survival between risk groups was observed. In summary, this nomogram showed a better performance than the AJCC 8th staging system and can assist in the clinical management of RLMS.

A SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer.

Immune checkpoint inhibitors (ICIs) therapy has been successfully utilized in the treatment of multiple tumors, but only a fraction of patients with gastric cancer (GC) could greatly benefit from it. A recent study has shown that the tumor microenvironment (TME) can greatly affect the effect of immunotherapy in GC. In this study, we established a novel immune risk signature (IRS) for prognosis and predicting response to ICIs in GC based on the TCGA-STAD dataset. Characterization of the TME was explored and further validated to reveal the underlying survival mechanisms and the potential therapeutic targets of GC. The GC patients were stratified into high- and low-risk groups based on the IRS. Patients in the high-risk group, associated with poorer outcomes, were characterized by significantly higher immune function. Further analysis showed higher T cell immune dysfunction and probability of potential immune escape. In vivo, we detected the expressions of SERPINE1 by the quantitative real-time polymerase chain reaction (qPCR)in tumor tissues and adjacent normal tissues. In vitro, knockdown of SERPINE1 significantly attenuated malignant biological behaviors of tumor cells in GC. Our signature can effectively predict the prognosis and response to immunotherapy in patients with GC.