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BackgroundBladder cancer lacks useful and robust prognostic markers to stratify patients at risk. Our study is to identify a robust prognostic marker for bladder cancer.MethodsThe transcriptome and clinical data of bladder cancer were downloaded from multiple databases. We searched for genes with robust prognosis by Kaplan-Meier analysis of the whole genome. CIBERSORT and TIMER algorithm was used to calculate the degree of immune cell infiltration.ResultsWe identified OLFML2B as a robust prognostic marker for bladder cancer in five cohorts. Kaplan-Meier analysis showed that patients with a high level of OLFML2B expression had a poor prognosis. The expression of OLFML2B increased with the increase of stage and grade. We found that patients with high expression of OLFML2B still had a poor prognosis in two small bladder cancer cohorts. OLFML2B also has the prognostic ability in ten other tumors, and the prognosis is poor in high expression. The correlation analysis between OLFML2B and immune cells showed that it was positively correlated with the degree of macrophage infiltration and highly co-expressed with tumor-associated macrophage markers. Finally, the Wound-healing assay and Colony formation assay results showed that the migration and proliferation ability of bladder cancer cell lines decreased after the knockdown of OLFML2B.ConclusionsIn summary, OLFML2B is a robust risk prognostic marker, and it can help patients with bladder cancer improve individualized treatment.

PurposeTherapeutic targets of tumor-associated macrophages have been discovered and used clinically as immunotherapy. M2 macrophages are tumor-associated macrophages that promote cancer progression. This article explores the related factors and the effects of type M2 macrophages.MethodWe obtained bladder cancer (BC) sequencing data from TCGA and GSE31189. We used the CIBERSORT algorithm calculate M2 macrophage proportions among 22 type immune cells. The Estimate package was used to measure BC purity. M2 macrophage-related genes were selected using WGCNA. Receiver operating characteristic curves and Kaplan–Meier analyses were performed to determine the risk score, conducted for M2 macrophage-related factors. The Pearson test was used to determine the correlation among M2 macrophage-related genes, clinical phenotype, immune phenotype and tumor mutation burden (TMB). The TIMER database was used to calculate correlations among M2 macrophages and other cancers.ResultsExpression of four M2 macrophages co-expressed genes (CD163, CD209, CSF1, MMD) positively correlated with infiltration of M2 macrophages, which were enriched in the negative regulation of immune system process and the positive regulation of tumor necrosis factor production. M2 macrophage-related factors are robust biomarkers for predicting the BC and immune phenotypes. The Cox regression model built on these four co-expression factors showed a close correlation with outcome (AUC = 0.64). The four co-expression factors negatively correlated outcome and TMB.ConclusionFour co-expressed genes promote high levels of infiltration of type M2 macrophages in the negative regulation of immune system processes and the positive regulation of tumor necrosis factor production processes. These co-expressed genes and the biological process they involve might suggest new strategies for regulation of chemotaxis in M2 macrophages.

BackgroundHigh-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.MethodThe study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.ResultsA prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p < 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, the immune-related pathways were enriched in the low-risk group.ConclusionThe prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

PurposeTo identify immune-related co-expressed genes that promote CD8+ T cell infiltration in bladder cancer, and to explore the interactions among relevant genes in the tumor microenvironment.MethodWe obtained bladder cancer gene matrix and clinical information data from TCGA, GSE32894 and GSE48075. The “estimate” package was used to calculate tumor purity and immune score. The CIBERSORT algorithm was used to assess CD8+ T cell proportions. Weighted gene co-expression network analysis was used to identify the co-expression modules with CD8+ T cell proportions and bladder tumor purity. Subsequently, we performed correlation analysis among angiogenesis factors, angiogenesis inhibitors, immune inflammatory responses, and CD8+ T cell related genes in tumor microenvironment.ResultsA CD8+ T cell related co-expression network was identified. Eight co-expressed genes (PSMB8, PSMB9, PSMB10, PSME2, TAP1, IRF1, FBOX6, ETV7) were identified as CD8+ T cell-related genes that promoted infiltration of CD8+ T cells, and were enriched in the MHC class I tumor antigen presentation process. The proteins level encoded by these genes (PSMB10, PSMB9, PSMB8, TAP1, IRF1, and FBXO6) were lower in the high clinical grade patients, which suggested the clinical phenotype correlation both in mRNA and protein levels. These factors negatively correlated with angiogenesis factors and positively correlated with angiogenesis inhibitors. PD-1 and PD-L1 positively correlated with these genes which suggested PD-1 expression level positively correlated with the biological process composed by these co-expression genes. In the high expression group of these genes, inflammation and immune response were more intense, and the tumor purity was lower, suggesting that these genes were immune protective factors that improved the prognosis in patients with bladder cancer.ConclusionThese co-expressed genes promote high levels of infiltration of CD8+ T cells in an immunoproteasome process involved in MHC class I molecules. The mechanism might provide new pathways for treatment of patients who are insensitive to PD-1 immunotherapy due to low degrees of CD8+ T cell infiltration.