TNFα signalling predicts poor prognosis of patients with endometrial cancer

2020 ◽  
Vol 41 (8) ◽  
pp. 1065-1073
Author(s):  
Verena Wieser ◽  
Samira Abdel Azim ◽  
Susanne Sprung ◽  
Katharina Knoll ◽  
Johanna Kögl ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Clinical Outcome ◽  
The Cancer Genome Atlas ◽  
Western World ◽  
Low Grade ◽  
Necrosis Factor Alpha ◽  
Cancer Genome Atlas ◽  
Factor Alpha ◽  
The Impact

Abstract Endometrial cancer (EC) is the most common gynaecologic tumour in the Western world. Previous studies have implicated an imbalance of oestrogens and progestogens in the development of most ECs, while the role of low-grade tissue inflammation remains largely unexplored. We investigated the impact of tumour necrosis factor alpha (TNFα), a central mediator of inflammation and spermatogenesis-associated protein 2 (SPATA2), a regulator of TNF receptor signalling, on clinical outcomes in EC. We evaluated TNFA and SPATA2 transcript levels in 239 EC patients and 25 non-malignant control tissues. Findings were validated in a cohort of 332 EC patients from The Cancer Genome Atlas (TCGA). Expression of TNFA and SPATA2 was increased in EC when compared with control tissues (P < 0.001). TNFA expression correlated with SPATA2 expression in non-malignant (P = 0.003, rS = 0.568) and EC tissue (P = 0.005, rS = 0.179). High TNFA and SPATA2 expression were associated with poor recurrence-free survival (RFS; P = 0.049 and P = 0.018) and disease-specific (P = 0.034 and P = 0.002) survival. Increased SPATA2 expression was also associated with decreased overall survival (OS; P = 0.013). In multivariate analysis, both TNFA and SPATA2 were predictors of clinical outcome. The impact of SPATA2 on RFS and OS could be validated in the TCGA cohort. Our study demonstrates that ECs exhibit a TNF signature which predicts clinical outcome. These findings indicate that TNF signalling modulates the course of EC, which could be therapeutically utilized in the future.

Regulation of microRNAs in Inflammation-Associated Colorectal Cancer: A Mechanistic Approach

2020 ◽  
Vol 20 ◽  
Author(s):  
Sridhar Muthusami ◽  
Ilangovan Ramachandran ◽  
Sneha Krishnamoorthy ◽  
Yuvaraj Sambandam ◽  
Satish Ramalingam ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Mechanisms ◽  
Colorectal Carcinogenesis ◽  
Model Systems ◽  
Necrosis Factor Alpha ◽  
Multi Stage ◽  
Mechanistic Approach ◽  
Tnf Α ◽  
Factor Alpha

: The development of colorectal cancer (CRC) is a multi-stage process. The inflammation of the colon as in inflammatory bowel disease (IBD) such as ulcerative colitis (UC) or Crohn’s disease (CD) is often regarded as the initial trigger for the development of CRC. Many cytokines such as tumor necrosis factor alpha (TNF-α) and several interleukins (ILs) are known to exert proinflammatory actions, and inflammation initiates or promotes tumorigenesis of various cancers, including CRC through differential regulation of microRNAs (miRNAs/miRs). miRNAs can be oncogenic miRNAs (oncomiRs) or anti-oncomiRs/tumor suppressor miRNAs, and they play key roles during colorectal carcinogenesis. However, the functions and molecular mechanisms of regulation of miRNAs involved in inflammation-associated CRC are still anecdotal and largely unknown. Consolidating the published results and offering perspective solutions to circumvent CRC, the current review is focused on the role of miRNAs and their regulation in the development of CRC. We have also discussed the model systems adapted by researchers to delineate the role of miRNAs in inflammation-associated CRC.

scholarly journals Mannoproteins from Cryptococcus neoformans Promote Dendritic Cell Maturation and Activation

2005 ◽  
Vol 73 (2) ◽  
pp. 820-827 ◽  
Author(s):  
Donatella Pietrella ◽  
Cristina Corbucci ◽  
Stefano Perito ◽  
Giovanni Bistoni ◽  
Anna Vecchiarelli
Keyword(s):  
Cryptococcus Neoformans ◽  
Nuclear Factor Κb ◽  
Dendritic Cell Maturation ◽  
Interleukin 12 ◽  
Necrosis Factor Alpha ◽  
Histocompatibility Complex ◽  
Factor Alpha ◽  
Human Dendritic Cells ◽  
Mannose Receptors

ABSTRACT Our previous data show that mannoproteins (MPs) from Cryptococcus neoformans are able to induce protective responses against both C. neoformans and Candida albicans. Here we provide evidence that MPs foster maturation and activation of human dendritic cells (DCs). Maturation was evaluated by the ability of MPs to facilitate expression of costimulatory molecules such as CD40, CD86, CD83, and major histocompatibility complex classes I and II and to inhibit receptors such as CD14, CD16, and CD32. Activation of DCs was measured by the capacity of MPs to promote interleukin-12 and tumor necrosis factor alpha secretion. DC-induced maturation and interleukin-12 induction are largely mediated by engagement of mannose receptors and presume MP internalization and degradation. DC activation leads to IκBα phosphorylation, which is necessary for nuclear factor κB transmigration into the nucleus. MP-loaded DCs are efficient stimulators of T cells and show a remarkable capacity to promote CD4 and CD8 proliferation. In conclusion, we have evidenced a novel regulatory role of MPs that promotes their candidacy as a vaccine against fungi.

scholarly journals A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 576
Author(s):  
Maurizio Polano ◽  
Emanuele Fabbiani ◽  
Eva Adreuzzi ◽  
Federica Di Cintio ◽  
Luca Bedon ◽  
...  
Keyword(s):  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Low Grade ◽  
Machine Learning Classification ◽  
Immune State ◽  
Out Of Sample ◽  
Cancer Genome Atlas ◽  
The Central Nervous System ◽  
Neural Network Classifiers ◽  
Definition Of

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state.

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