Lysine-specific histone demethylase 1A (LSD1) in cervical cancer

microRNA-17 is a tumor suppressor in oral squamous cell carcinoma and is repressed by LSD1

OBJECTIVE

The effects of epigenetic modifiers have been uncovered on cellular reprogramming and, specifically, on sustaining characteristics of cancer stem cells. We here aim to investigate whether lysine-specific demethylase 1 (LSD1) affects the development of oral squamous cell carcinoma (OSCC) by sustaining the cancer stem cells from OSCC (OSCSCs).

METHODS

RT-qPCR detection was firstly conducted to screen out research gene by determining differential expression of histone demethylases and methylases in identified OSCSCs. Then, microarray analysis was carried out in cells with poor expression of LSD1.

RESULTS

OSCSCs expressed high levels of LSD1, and LSD1 inhibition reduced cell viability, migration, invasion, and sphere formation of OSCSCs. Later mechanistic studies suggested that LSD1 inhibited microRNA (miR)-17 expression through histone demethylation. miR-17 bound to KPNA2, and LSD1 downstream genes were mainly enriched in the PI3K/AKT pathway. Importantly, miR-17 inhibitor reversed the inhibitory effect of si-LSD1 on cell activity, while si-KPNA2 abolished the promotive effect of miR-17 inhibitor on cell activity both in vitro and in vivo.

CONCLUSION

Overall, LSD1 functions as a cancer stem cell supporter in OSCC by catalyzing demethylation of miR-17 and activating the downstream KPNA2/PI3K/AKT pathway, which contributes to understanding of the mechanisms associated with epigenetic regulation in OSCC.

Comparative G-Protein-Coupled Estrogen Receptor (GPER) Systems in Diabetic and Cancer Conditions: A Review

For many patients, diabetes Mellitus and Malignancy are frequently encountered comorbidities. Diabetes affects approximately 10.5% of the global population, while malignancy accounts for 29.4 million cases each year. These troubling statistics indicate that current treatment approaches for these diseases are insufficient. Alternative therapeutic strategies that consider unique signaling pathways in diabetic and malignancy patients could provide improved therapeutic outcomes. The G-protein-coupled estrogen receptor (GPER) is receiving attention for its role in disease pathogenesis and treatment outcomes. This review aims to critically examine GPER' s comparative role in diabetes mellitus and malignancy, identify research gaps that need to be filled, and highlight GPER's potential as a therapeutic target for diabetes and malignancy management. There is a scarcity of data on GPER expression patterns in diabetic models; however, for diabetes mellitus, altered expression of transport and signaling proteins has been linked to GPER signaling. In contrast, GPER expression in various malignancy types appears to be complex and debatable at the moment. Current data show inconclusive patterns of GPER expression in various malignancies, with some indicating upregulation and others demonstrating downregulation. Further research should be conducted to investigate GPER expression patterns and their relationship with signaling pathways in diabetes mellitus and various malignancies. We conclude that GPER has therapeutic potential for chronic diseases such as diabetes mellitus and malignancy.

Design Two Novel Tetrahydroquinoline Derivatives against Anticancer Target LSD1 with 3D-QSAR Model and Molecular Simulation

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure-activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q² = 0.778, Rpred2 = 0.709)) and CoMSIA (Comparative Molecular Similarity Index Analysis (q² = 0.764, Rpred2 = 0.713)) yielded good statistical and predictive properties. Based on the corresponding contour maps, seven novel tetrahydroquinoline derivatives were designed. For more information, three of the compounds (D1, D4, and Z17) and the template molecule 18x were explored with molecular dynamics simulations, binding free energy calculations by MM/PBSA method as well as the ADME (absorption, distribution, metabolism, and excretion) prediction. The results suggested that D1, D4, and Z17 performed better than template molecule 18x due to the introduction of the amino and hydrophobic groups, especially for the D1 and D4, which will provide guidance for the design of LSD1 inhibitors.

The CBL-LSD1-CXCL8 axis regulates methionine metabolism in glioma

Gliomas are the most frequent type of brain tumors, with a high mortality rate and a lack of efficient targeted therapy. Methionine is an essential amino acid, and restriction of methionine in the diet has been found to prevent metabolic diseases and aging, inhibit cancer growth and improve cancer treatment. However, mechanisms of action by which methionine metabolism affects gliomas remain largely unclear. The present study found that methionine starvation of glioma cells significantly increased the expression of CXCL8. Mechanistically, E3 ubiquitin ligase was found to mediate the ubiquitinated degradation of the histone demethylase LSD1 via CBL, reducing LSD1 protein stability and, enhancing H3K4me1 modification of the CXCL8 gene. CXCL8 was found to be involved in regulating the reprogramming of glycerophospholipid metabolism, enabling it to respond to a methionine-deprived environment. CXCL8 expression was significantly higher in glioma than in normal brain tissue samples, with elevated CXCL8 being associated with poor prognosis. In summary, CBL-mediated degradation of LSD1 acts as an anti-braking system and serves as a quick adaptive mechanism for re-remodeling epigenetic modifications. This, in turn, promotes cell proliferation, even in a methionine-restricted environment. Taken together, these findings indicate that the CBL/LSD1/CXCL8 axis is a novel mechanistic connection linking between methionine metabolism, histone methylation and glycerophospholipid reprogramming in the tumor microenvironment.

Systematic Review and Meta-Analysis of Lysine-Specific Demethylase 1 Expression as a Prognostic Biomarker of Cancer Survival and Disease Progression

Background

Numerous studies on the prognostic significance of lysine-specific demethylase 1 (LSD1) up-regulation in tumors have different outcomes. The inconsistency originated from various studies looking into the association between LSD1 and tumor cells has prompted the decision of this quantitative systematic review to decipher how up-regulated LSD1 and overall survival (OS) or recurrence-free survival (RFS) or disease-free survival (DFS) are linked in tumor patients.

Methods

Articles were searched from online databases such as Embase, Web of Science Core, PubMed, Google Scholar, and Scopus. The extraction of the hazard ratios (HR) with their 95% confidence intervals (CIs) was attained and survival data of 3151 tumor patients from 17 pieces of related research were used for this meta-analysis.

Results

To shed light on the link between LSD1 up-regulation and the prognosis of diverse tumors, the pooled hazard ratios (HRs) with their 95% confidence intervals (CIs) were determined. In this meta-analysis, it was observed that LSD1 up-regulation is linked with poor OS (HR = 2.08, 95% CI: 1.66–2.61, P < .01) and RFS (HR = 3.09, 95% CI: 1.81–5.26, P < .01) in tumor patients. However, LSD1 up-regulation was not linked to DFS (HR = 1.49, 95% CI: .83–2.69, P = .18) in tumor patients. The subcategory examination grouped by tumor type and ethnicity showed that LSD1 up-regulation was linked with a poor outcome in the esophageal tumor and hepatocellular carcinoma and Asian patients, respectively. For clinical-pathological factors, up-regulated LSD1 was significantly linked with Lymph node status.

Conclusion

Despite the shortfall of the present work, this meta-analysis proposes that LSD1 up-regulation may be a prognostic biomarker for patients with tumors including esophageal tumors and hepatocellular carcinoma. We propose that large-scale studies are vital to substantiate these outcomes.

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.