Stat5-/- CD4+ T cells elicit anti-melanoma effect by CD4+ T cell remolding and Notch1 activation

STAT5 phosphorylation plus minimal residual disease defines a novel risk classification in adult B-cell acute lymphoblastic leukaemia

The dysregulation of the Janus family tyrosine kinase-signal transducer and activator of transcription (JAK-STAT) is closely related to acute lymphoblastic leukaemia (ALL), whereas the clinical value of phosphorylated STAT5 (pSTAT5) remains elusive. Herein we performed a prospective study on clinical significance of flow cytometry-based pSTAT5 in adult B-ALL patients. A total of 184 patients were enrolled in the Precision-Classification-Directed-Target-Total-Therapy (PDT)-ALL-2016 cohort between January 2018 and December 2021, and STAT5 phosphorylation was detected by flow cytometry at diagnosis. Based on flow-pSTAT5, the population was classified into pSTAT5low (113/184, 61.1%) and pSTAT5high (71/184, 38.9%). Overall survival (OS) and event-free survival (EFS) were inferior in pSTAT5high patients than in those with pSTAT5low (OS, 44.8% vs. 65.2%, p = 0.004; EFS, 23.5% vs. 52.1%, p < 0.001), which was further confirmed in an external validation cohort. Furthermore, pSTAT5 plus flow-based minimal residual disease (MRD) postinduction defines a novel risk classification as being high risk (HR, pSTAT5high + MRD+), standard risk (SR, pSTAT5low + MRD-) and others as moderate-risk group. Three identified patient subgroups are distinguishable with disparate survival curves (3-year OS rates, 36.5%, 56.7% and 76.3%, p < 0.001), which was confirmed on multivariate analysis (hazard ratio 3.53, p = 0.003). Collectively, our study proposed a novel, simple and flow-based risk classification by integrating pSTAT5 and MRD in favour of risk-guided treatment for B-ALL.

Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value

Background

We explored the characteristics of single-cell differentiation data in glioblastoma and established prognostic markers based on CRYAB to predict the prognosis of glioblastoma patients. Aberrant expression of CRYAB is associated with invasive behavior in various tumors, including glioblastoma. However, the specific role and mechanisms of CRYAB in glioblastoma are still unclear.

Methods

We assessed RNA-seq and microarray data from TCGA and GEO databases, combined with scRNA-seq data on glioma patients from GEO. Utilizing the Seurat R package, we identified distinct survival-related gene clusters in the scRNA-seq data. Prognostic pivotal genes were discovered through single-factor Cox analysis, and a prognostic model was established using LASSO and stepwise regression algorithms. Moreover, we investigated the predictive potential of these genes in the immune microenvironment and their applicability in immunotherapy. Finally, in vitro experiments confirmed the functional significance of the high-risk gene CRYAB.

Results

By analyzing the ScRNA-seq data, we identified 28 cell clusters representing seven cell types. After dimensionality reduction and clustering analysis, we obtained four subpopulations within the oligodendrocyte lineage based on their differentiation trajectory. Using CRYAB as a marker gene for the terminal-stage subpopulation, we found that its expression was associated with poor prognosis. In vitro experiments demonstrated that knocking out CRYAB in U87 and LN229 cells reduced cell viability, proliferation, and invasiveness.

Conclusion

The risk model based on CRYAB holds promise in accurately predicting glioblastoma. A comprehensive study of the specific mechanisms of CRYAB in glioblastoma would contribute to understanding its response to immunotherapy. Targeting the CRYAB gene may be beneficial for glioblastoma patients.

Mapping knowledge landscapes and emerging trends of the biomarkers in melanoma: a bibliometric analysis from 2004 to 2022

Background

Melanoma is a skin tumor with a high mortality rate, and early diagnosis and effective treatment are the key to reduce its mortality rate. Therefore, more and more attention has been paid for biomarker identification for early diagnosis, prognosis prediction and prognosis evaluation of melanoma. However, there is still a lack of a report that comprehensively and objectively evaluates the research status of melanoma biomarkers. Therefore, this study aims to intuitively analyze the research status and trend of melanoma biomarkers through the methods of bibliometrics and knowledge graph.

Objective

This study uses bibliometrics to analyze research in biomarkers in melanoma, summarize the field's history and current status of research, and predict future research directions.

Method

Articles and Reviews related to melanoma biomarkers were retrieved by using Web of Science core collection subject search. Bibliometric analysis was performed in Excel 365, CiteSpace, VOSviewer and Bibliometrix (R-Tool of R-Studio).

Result

A total of 5584 documents from 2004 to 2022 were included in the bibliometric analysis. The results show that the number of publications and the frequency of citations in this field are increasing year by year, and the frequency of citations has increased rapidly after 2018. The United States is the most productive and influential country in this field, with the largest number of publications and institutions with high citation frequency. Caroline Robert, F. Stephen Hodi, Suzanne L. Topalian and others are authoritative authors in this field, and The New England Journal of Medicine, Journal of Clinical Oncology and Clinical Cancer Research are the most authoritative journals in this field. Biomarkers related to the diagnosis, treatment and prognosis of melanoma are hot topics and cutting-edge hotspots in this field.

Conclusion

For the first time, this study used the bibliometric method to visualize the research in the field of melanoma biomarkers, revealing the trends and frontiers of melanoma biomarkers research, which provides a useful reference for scholars to find key research issues and partners.