Only SETBP1 hotspot mutations are associated with refractory disease in myeloid malignancies

The Role of SETBP1 in Gastric Cancer: Friend or Foe

Background

Gastric cancer (GC) remains a common disease with a poor prognosis worldwide. The SET binding protein 1 (SETBP1) has been implicated in the pathogenesis of several cancers and plays a dual role as an oncogene and a tumor suppressor gene. However, the role and underlying mechanism of SETBP1 in GC remain unclear.

Materials and Methods

We used next-generation RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) to explore the correlation between SETBP1 expression and tumor progression. We then quantified SETBP1 expression in GC cells with real-time quantitative polymerase chain reactions (RT-qPCR). The chi-square test and logistic regression were used to assess the correlation between SETBP1 expression and clinicopathological features. Kaplan-Meier survival analysis and Cox proportional hazards regression model were used to assess the relationship between SETBP1 expression and survival. Finally, gene set enrichment analyses (GSEA) were used to examine GC-related signaling pathways in low and high SETBP1 expressing samples.

Results

We found SETBP1 expression levels in GC tissues to be significantly lower than in adjacent non-tumor tissues in the TCGA database. In addition, SETBP1 expression differed significantly between groups classified by tumor differentiation. Furthermore, SETBP1 expression in diffuse-type GC was significantly higher than in intestinal-type GC. However, it did not differ significantly across pathological- or T-stage groups. RT-qPCR and comprehensive meta-analysis showed that SETBP1 expression is downregulated in GC cells and tissues. Interestingly, SETBP1 expression in poorly- or un-differentiated GC cells was higher than in well-differentiated GC cells. Moreover, the chi-square test and logistic regression analyses showed that SETBP1 expression correlates significantly with tumor differentiation. Kaplan–Meier curves indicated that patients with relatively high SETBP1 expression had a poor prognosis. Multivariate analyses indicated that SETBP1 expression might be an important predictor of poor overall survival in GC patients. GSEA indicated that 20 signaling pathways were significantly enriched in samples with high and low SETBP1 expression.

Conclusion

SETBP1 may play a dual role in GC progression.

Acute Mast Cell Leukemia Preceded by Malignant Mediastinal Germ Cell Tumor: A Case Report and Literature Review

Background

Case Presentation

Conclusion

A 7-gene signature predicts the prognosis of patients with bladder cancer

The biomarkers have an important guiding role in prognosis and treatment of patients with bladder cancer (BC). The aim of the present study was to identify and evaluate a prognostic gene signature in BC patients. The gene expression profiles of BC samples and the corresponding clinicopathological data were downloaded from GEO and TCGA. The differentially expressed genes (DEGs) were identified by R software. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct the prognostic score model. A nomogram was established with the identified prognostic factors to predict the overall survival rates of BC patients. The discriminatory and predictive capacity of the nomogram was evaluated based on the concordance index (C‐index), calibration curves and decision curve analysis (DCA). A 7-gene signature (KLRB1, PLAC9, SETBP1, NR2F1, GRHL2, ANXA1 and APOL1) was identified from 285 DEGs by univariate and LASSO Cox regression analyses. Univariate and multivariate Cox regression analyses showed that age, lymphovascular invasion, lymphatic metastasis, metastasis and the 7-gene signature risk score was an independent predictor of BC patient prognosis. A nomogram that integrated these independent prognostic factors was constructed. The C-index (0.73, CI 95%, 0.693–0.767) and calibration curve demonstrated the good performance of the nomogram. DCA of the nomogram further showed that this model exhibited good net benefit. The combined 7-gene signature could serve as a biomarker for predicting BC prognosis. The nomogram built by risk score and other clinical factors could be an effective tool for predicting the prognosis of patients with BC.

Epigenomic and Transcriptomic Characterization of Secondary Breast Cancers

BACKGROUND

Molecular alterations impact tumor prognosis and response to treatment. This study was designed to identify transcriptomic and epigenomic signatures of breast cancer (BC) tumors from patients with any prior malignancy.

METHODS

RNA-sequencing and genome-wide DNA methylation profiles from BCs were generated in the Cancer Genome Atlas project. Patients with secondary breast cancer (SBC) were separated by histological subtype and matched to primary breast cancer controls to create two independent cohorts of invasive ductal (IDC, n = 36) and invasive lobular (ILC, n = 40) carcinoma. Differentially expressed genes, as well as differentially methylated genomic regions, were integrated to identify epigenetically regulated abnormal gene pathways in SBCs.

RESULTS

Differentially expressed genes were identified in IDC SBCs (n = 727) and in ILC SBCs (n = 261; Wilcoxon's test; P < 0.05). In IDC SBCs, 105 genes were upregulated and hypomethylated, including an estrogen receptor gene, and 73 genes were downregulated and hypermethylated, including genes involved in antigen presentation and interferon response pathways (HLA-E, IRF8, and RELA). In ILC SBCs, however, only 17 genes were synchronously hypomethylated and upregulated, whereas 46 genes hypermethylated and downregulated. Interestingly, the SBC gene expression signatures closely corresponded with each histological subtype with only 1.51% of genes overlapping between the two histological subtypes.

CONCLUSIONS

Differential gene expression and DNA methylation signatures are seen in both IDC and ILC SBCs, including genes that are relevant to tumor growth and proliferation. Differences in gene expression signatures corresponding with each histological subtype emphasize the importance of disease subtype-specific evaluations of molecular alterations.