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Background: A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the gastric cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to develop a stromal-immune score-based gene signature in gastric cancer. Methods: Stromal and immune scores were estimated from transcriptomic profiles of a gastric cancer cohort from TCGA using the ESTIMATE algorithm. A robust partial likelihood-based Cox proportional hazard regression model was applied to select prognostic genes and to construct a stromal-immune score-based gene signature. Two independent datasets from GEO were used for external validation. Results: Favorable overall survivals were found in patients with high stromal score (p = 0.014) and immune score (p = 0.045). Forty-five stromal-immune score-related differentially expressed genes were identified. Using a robust partial likelihood-based Cox proportional hazard regression model, a gene signature containing SOX9, LRRC32, CECR1, and MS4A4A was identified to develop a risk stratification model. Multivariate analysis revealed that the stromal-immune risk score was an independent prognostic factor (p = 0.018). Based on the risk stratification model, the cohort was classified into three groups yielding incremental survival outcomes (log-rank test p = 0.0004). A nomogram integrating the risk stratification model and clinicopathologic factors was developed. Calibration and decision curves showed a better performance and net benefits for the nomogram. Similar findings were validated in two independent cohorts. Conclusion: The stromal-immune score-based gene signature represents a prognosis stratification tool in gastric cancer.

The JMAAV study was an open-labeled prospective clinical trial, which proposed severity-based treatment protocols for patients with microscopic polyangiitis (MPA). The results suggest that the proposed protocols are useful (remission rate: 89.4%), but are also indicative of relapse or patient demise regardless of the treatment (recurrence rate: 19.0%; mortality rate: 10.6%). The aim of this study is to develop the method to predict response to the treatment in patients with MPA. In the present study, transcriptome analysis was performed using peripheral blood from patients enrolled in the JMAAV study before and 1-week after the beginning of treatment. The gene expression profile before treatment was not directly related to the response to the treatment. However, when the samples from 9 patients with good response (persistent remission for 18 months) were examined, the expression of 88 genes was significantly altered by the treatment. Thirty statistically reliable genes were selected, and then the alteration of expression by the treatment was examined among 22 patients, including 17 with good response, which was defined as persistent remission for 18 months and 5 with poor response, which was defined as relapse after remission or no remission. Discrimination analysis between the alteration of expression of the 30 genes by the treatment and the response identified a combination of 16 genes as the most valuable gene set to predict the response to the treatment. This preliminary study identified IRF7, IFIT1, IFIT5, OASL, CLC, GBP-1, PSMB9, HERC5, CCR1, CD36, MS4A4A, BIRC4BP, PLSCR1, DEFA1/DEFA3, DEFA4, and COL9A2 as the important genes that can predict the response to the treatment in patients with MPA at an early point during the therapy.