IntroductionSignal transducer and activator of transcription 3 (STAT3) is over-activated or phosphorylated in breast cancers. The hyper-phosphorylation of STAT3 was attributed to either up-regulated phosphorylation by several tyrosine-kinases or down-regulated activity of phosphatases. Although several factors have been identified to phosphorylate STAT3, it remains unclear how STAT3 is dephosphorylated by PTPMeg2. The aim of this study was to determine the role of PTPMeg2 as a phosphatase in regulation of the activity of STAT3 in breast cancers.MethodsImmunoprecipitation assays were used to study the interaction of STAT3 with PTPMeg2. A series of biochemistry experiments were performed to evaluate the role of PTPMeg2 in the dephosphorylation of STAT3. Two breast cancer cell lines MCF7 (PTPMeg2 was depleted as it was endogenously high) and MDA-MB-231 (PTPMeg2 was overexpressed as it was endogenously low) were used to compare the level of phosphorylated STAT3 and the tumor growth ability in vitro and in vivo. Samples from breast carcinoma (n = 73) were subjected to a pair-wise Pearson correlation analysis for the correlation of levels of PTPMeg2 and phosphorylated STAT3.ResultsPTPMeg2 directly interacts with STAT3 and mediates its dephosphorylation in the cytoplasm. Over-expression of PTPMeg2 decreased tyrosine phosphorylation of STAT3 while depletion of PTPMeg2 increased its phosphorylation. The decreased tyrosine phosphorylation of STAT3 is coupled with suppression of STAT3 transcriptional activity and reduced tumor growth in vitro and in vivo. Levels of PTPMeg2 and phosphorylated STAT3 were inversely correlated in breast cancer tissues (P = 0.004).ConclusionsPTPMeg2 is an important phosphatase for the dephosphorylation of STAT3 and plays a critical role in breast cancer development.
Summary We developed Miscell, a self-supervised learning approach with deep neural network as latent feature encoder for mining information from single-cell transcriptomes. We demonstrated the capability of Miscell with canonical single-cell analysis tasks including delineation of single-cell clusters and identification of cluster-specific marker genes. We evaluated Miscell along with three state-of-the-art methods on three heterogeneous datasets. Miscell achieved at least comparable or better performance than the other methods by significant margin on a variety of clustering metrics such as adjusted rand index, normalized mutual information, and V -measure score. Miscell can identify cell-type specific markers by quantifying the influence of genes on cell clusters via deep learning approach.
Gastric cancer is the fifth most common type of human cancer and the third leading cause of cancer-related death. The purpose of this study is to investigate the immune infiltration signatures of gastric cancer and their relation to prognosis. We identified two distinct subtypes of gastric cancer (C1/C2) characterized by different immune infiltration signatures. C1 is featured by immune resting, epithelial–mesenchymal transition, and angiogenesis pathways, while C2 is featured by enrichment of the MYC target, oxidative phosphorylation, and E2F target pathways. The C2 subtype has a better prognosis than the C1 subtype (HR = 0.61, 95% CI: 0.44–0.85; log-rank test, p = 0.0029). The association of C1/C2 with prognosis remained statistically significant (HR = 0.62, 95% CI: 0.44–0.87; p = 0.006) after controlling for age, gender, and stage. The prognosis prediction of C1/C2 was verified in four independent cohorts (including an internal cohort). In summary, our study is helpful for better understanding of the association between immune infiltration and the prognosis of gastric cancer.
BackgroundBrain tumor ranks as the most devastating cancer type. The complex tumor immune microenvironment prevents brain tumor from receiving therapeutic benefits. The purpose of this study was to stratify brain tumors based on their distinct immune infiltration signatures to facilitate better clinical decision making and prognosis prediction.MethodsWe developed a deep learning model to characterize immune infiltration from transcriptome. The developed model was applied to distill expression signatures of transcriptome of brain tumor samples. We performed molecular subtyping with the extracted expression signatures to unveil brain tumor subtypes. Computational methods, including gene set enrichment analysis, Kaplan-Meier survival and multivariate Cox regression analyses, were employed.ResultsWe identified two distinctive subtypes (i.e. C1/2) of brain tumor featured by distinct immune infiltration signatures. The C1 subtype is characterized by protective immune infiltration signatures, including high infiltration of CD8+ T cells and activation of CX3CL1. The C2 subtype has an extensive infiltration of tumor-associated macrophages and microglia, and was enriched with immune suppressive, wound-healing, and angiogenic signatures. The C1 subtype had significantly better prognosis as compared with C2 (Log-rank test, HR: 2.5, 95% CI: 2.2 – 2.7; P = 8.2e-78). This difference remained statistically significant (multivariate Cox model, HR: 2.2, 95% CI: 1.7 – 2.9; P = 3.7e-10) by taking into account age, gender, recurrent/secondary status at sampling time, tumor grade, histology, radio-chemotherapy, IDH mutation, MGMT methylation, and co-deletion of 1p and 19q. This finding was validated in six datasets. The C2 subtype of glioblastoma patients with IDH mutation has poor survival analogous to those without IDH mutation (Log-rank test, adjusted P = 0.8), while C1 has favorable prognosis as compared with glioblastoma of C2 subtype with IDH mutation (Log-rank test, adjusted P = 1.2e-3) or without IDH mutation (Log-rank test, adjusted P = 1.3e-6).ConclusionsWe identified two distinctive subtypes of brain tumor with different immune infiltration signatures, which might be helpful as an independent prognosticator for brain tumor.
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