scholarly journals Prolactin and its receptor as therapeutic targets in glioblastoma multiforme

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Antonela Sofía Asad ◽  
Alejandro Javier Nicola Candia ◽  
Nazareno Gonzalez ◽  
Camila Florencia Zuccato ◽  
Araceli Abt ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Patient Survival ◽  
Meta Analysis ◽  
Transcriptomic Data ◽  
Biological Sex ◽  
Grade Ii ◽  
Male Patients ◽  
And Migration ◽  
Plasmid Transfection ◽  
Metalloproteinase Activity

AbstractAlthough prolactin (PRL) and its receptor (PRLR) have been detected in glioblastoma multiforme (GBM), their role in its pathogenesis remains unclear. Our aim was to explore their contribution in GBM pathogenesis. We detected PRL and PRLR in all GBM cell lines tested. PRLR activation or overexpression using plasmid transfection increased proliferation, viability, clonogenicity, chemoresistance and matrix metalloproteinase activity in GBM cells, while PRLR antagonist ∆1–9-G129R-hPRL reduced their proliferation, viability, chemoresistance and migration. Meta-analysis of transcriptomic data indicated that PRLR was expressed in all grade II-III glioma (GII-III) and GBM samples. PRL was upregulated in GBM biopsies when compared to GII-III. While in the general population tumour PRL/PRLR expression did not correlate with patient survival, biological sex-stratified analyses revealed that male patients with PRL+/PRLRHIGH GBM performed worse than PRL+/PRLRLOW GBM. In contrast, all male PRL+/PRLRHIGH GII-III patients were alive whereas only 30% of PRL+/PRLRLOW GII-III patients survived after 100 months. Our study suggests that PRLR may be involved in GBM pathogenesis and could constitute a therapeutic target for its treatment. Our findings also support the notion that sexual dimorphism should be taken into account to improve the care of GBM patients.

Bioinformatic profiling of tumor immunity from patient biopsies to predict survival and response to immunotherapy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15198-e15198
Author(s):  
Yamil Damian Mahmoud ◽  
Florencia Veigas ◽  
Joaquin Merlo ◽  
Monica Balzarini ◽  
Dario Rocha ◽  
...  
Keyword(s):  
Immune Cells ◽  
Web Application ◽  
Immune Cell ◽  
Patient Survival ◽  
Meta Analysis ◽  
Response To Therapy ◽  
Tumor Escape ◽  
M2 Macrophages ◽  
Transcriptomic Data ◽  
Melanoma Patients

e15198 Background: Immunotherapies have revolutionized cancer treatment, but responses are not universal and patients who initially respond to therapy develop resistance. The accurate quantification of tumor-infiltrating immune cells holds the promise to reveal the role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. We present MIXTURE, a new algorithm for tumor immune cell-type proportions deconvolution from transcriptomic data that overcomes competitive methods and revealed novel associations of immune cell types with patient survival and immunotherapy response. Methods: We applied MIXTURE to transcriptomic data from BRCA (n = 1095), LUAD (n = 506), SKCM (n = 472), HNSC (n = 499) and COAD (n = 521) cohorts from TCGA and five published datasets of melanoma patients treated with anti-PD-1/anti-CLTA-4. Results: The analysis of TCGA breast cancer biopsies showed high proportions of M2-macrophages associated with poor patient survival (p < 0.001). In contrast, we found that proportions of follicular T helper cells were associated with better outcome (p < 0.001). We observed a differential immune composition in biopsies of lung adenocarcinoma patients with mutations in TP53 (WT; n = 247; Mut; n = 259) and EGFR (WT, n = 438; Mut, n = 68) related with their different response to immunotherapy (p < 0.05). We found correlations between immune cells proportions and current biomarkers of response to immunotherapy such as TMB, intratumoral heterogeneity, MSI and PD-L1 expression in the TCGA cohorts (p < 0.05). The meta-analysis of melanoma patients treated with immunotherapy showed a distinct immune infiltrate in patients who responded to anti-PD-1 with an increase of immune effector cells such as CD8, CD4 memory activated and gamma-delta T cells, and a decrease in immunosuppressive M2-macrophages (p < 0.05; R = 81; NR = 107). According with the latest findings, we observed higher presence of B cells in responders to anti-PD-1 on-treatment (p = 0.033; R = 31; NR = 23) and in baseline of responders to anti-CTLA-4 (p = 0.028; R = 14; NR = 26). Interestingly, patients that previously progressed to anti-CTLA-4 showed a differential immune profile that was associated with response to anti-PD-1 (p < 0.05; Ipi-Prog = 59; Ipi-Naïve = 102). Conclusions: We demonstrated the potential of MIXTURE to understand the tumor immune microenvironment and its relationship with patient survival and response to immunotherapies. MIXTURE is available for the wider scientific community as web application and as packages for R and Python.

scholarly journals A meta-analysis of the literature on climate change and migration

2021 ◽  
pp. 1-52
Author(s):  
Michel Beine ◽  
Lionel Jeusette
Keyword(s):  
Climate Change ◽  
Climatic Factors ◽  
Human Mobility ◽  
Meta Analysis ◽  
Empirical Studies ◽  
Displacement Effect ◽  
Methodological Choices ◽  
Econometric Methods ◽  
And Migration

Abstract Recent surveys of the literature on climate change and migration emphasize the important diversity of outcomes and approaches of the empirical studies. In this paper, we conduct a meta-analysis in order to investigate the role of the methodological choices of these empirical studies in finding some particular results concerning the role of climatic factors as drivers of human mobility. We code 51 papers representative of the literature in terms of methodological approaches. This results in the coding of more than 85 variables capturing the methodology of the main dimensions of the analysis at the regression level. These dimensions include authors' reputation, type of mobility, measures of mobility, type of data, context of the study, econometric methods, and last but not least measures of the climatic factors. We look at the influence of these characteristics on the probability of finding any effect of climate change, a displacement effect, an increase in immobility, and evidence in favor of a direct vs. an indirect effect. Our results highlight the role of some important methodological choices, such as the frequency of the data on mobility, the level of development, the measures of human mobility and of the climatic factors as well as the econometric methodology.

Transcription factor 3 controls cell proliferation and migration in glioblastoma multiforme cell lines

2016 ◽  
Vol 94 (3) ◽  
pp. 247-255 ◽  
Author(s):  
Ruiting Li ◽  
Yinghui Li ◽  
Xin Hu ◽  
Haiwei Lian ◽  
Lei Wang ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Glioblastoma Multiforme ◽  
Cell Lines ◽  
Normal Brain ◽  
Phosphatidylinositol 3 Kinase ◽  
T Cell Factor ◽  
And Migration ◽  
Induced Apoptosis ◽  
Proliferation And Migration ◽  
Transcription Factor 3

Transcription factor 3 (TCF3) is a member of the T-cell factor/lymphoid enhancer factor (TCF/LEF) transcription factor family. Recent studies have demonstrated its potential carcinogenic properties. Here we show that TCF3 was upregulated in glioma tissues compared with normal brain tissues. This upregulation of the TCF3 gene probably has functional significance in brain-tumor progression. Our studies on glioblastoma multiforme (GBM) cell lines show that knock-down of TCF3 induced apoptosis and inhibited cell migration. Further analysis revealed that down-regulation of TCF3 gene expression inhibits Akt and Erk1/2 activation, suggesting that the carcinogenic properties of TCF3 in GBM are partially mediated by the phosphatidylinositol 3-kinase–Akt and MAPK–Erk signaling pathways. Considered together, the results of this study demonstrate that high levels of TCF3 in gliomas potentially promote glioma development through the Akt and Erk pathways.

Smoking in male patients with schizophrenia in China: A meta-analysis

2016 ◽  
Vol 162 ◽  
pp. 146-153 ◽  
Author(s):  
Yan Li ◽  
Xiao−Lan Cao ◽  
Bao-Liang Zhong ◽  
Gabor S. Ungvari ◽  
Helen F.K. Chiu ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Male Patients
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