Long non-coding RNAs modulate tumor microenvironment to promote metastasis: novel avenue for therapeutic intervention

Cancer is a devastating disease and the primary cause of morbidity and mortality worldwide, with cancer metastasis responsible for 90% of cancer-related deaths. Cancer metastasis is a multistep process characterized by spreading of cancer cells from the primary tumor and acquiring molecular and phenotypic changes that enable them to expand and colonize in distant organs. Despite recent advancements, the underlying molecular mechanism(s) of cancer metastasis is limited and requires further exploration. In addition to genetic alterations, epigenetic changes have been demonstrated to play an important role in the development of cancer metastasis. Long non-coding RNAs (lncRNAs) are considered one of the most critical epigenetic regulators. By regulating signaling pathways and acting as decoys, guides, and scaffolds, they modulate key molecules in every step of cancer metastasis such as dissemination of carcinoma cells, intravascular transit, and metastatic colonization. Gaining a good knowledge of the detailed molecular basis underlying lncRNAs regulating cancer metastasis may provide previously unknown therapeutic and diagnostic lncRNAs for patients with metastatic disease. In this review, we concentrate on the molecular mechanisms underlying lncRNAs in the regulation of cancer metastasis, the cross-talk with metabolic reprogramming, modulating cancer cell anoikis resistance, influencing metastatic microenvironment, and the interaction with pre-metastatic niche formation. In addition, we also discuss the clinical utility and therapeutic potential of lncRNAs for cancer treatment. Finally, we also represent areas for future research in this rapidly developing field

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer

RNase L Is Involved in Liposaccharide-Induced Lung Inflammation

RNase L mediates interferon (IFN) function during viral infection and cell proliferation. Furthermore, the role of RNase L in the regulation of gene expression, cell apoptosis, autophagy, and innate immunity has been well established in the last decade. Tissue distribution reveals that RNase L is highly expressed in the lung and other organs. However, the physiological roles of RNase L in the lung are largely unknown. In this study, we found that polysaccharide (LPS)-induced acute lung injury (ALI) was remarkably intensified in mice deficient in RNase L compared to wild type mice under the same condition. Furthermore, we found that RNase L mediated the TLR4 signaling pathway, and regulated the expression of various pro- and anti-inflammatory genes in the lung tissue and blood. Most importantly, RNase L function in macrophages during LPS stimulation may be independent of the 2-5A system. These findings demonstrate a novel role of RNase L in the immune response via an atypical molecular mechanism

Differential Expression of Serum Proinflammatory Cytokine TNF-α and Genetic Determinants of TNF-α, CYP2C19*17, miR-423 Genes and Their Effect on Coronary Artery Disease Predisposition and Progression

Coronary artery disease (CAD) is the leading cause of death and hospitalization worldwide and represents a problem for public health systems everywhere. In Saudi Arabia, the prevalence of CAD is estimated to be 5.5%. Risk factors for CAD include older age, male gender, obesity, high blood pressure, smoking, diabetes, hyperlipidemia, and genetic factors. Reducing the risk factors in susceptible individuals will decrease the prevalence of CAD. Genome wide association studies have helped to reveal the association of many loci with diseases like CAD. In this study, we examined the link between single nucleotide variations (SNVs) of TNF-α-rs1800629 G>A, CYP2C19*17 (rs12248560) C>T, and miR-423 rs6505162 C>A and the expression of TNF-α with CAD. We used the mutation specific PCR, ARMS-PCR, and ELISA. The results showed that the A allele of the TNF-α rs1800629 G>A SNP is linked to CAD with odd ratio (OR) (95% CI) = 2.10, p-value = 0.0013. The T allele of the CYP2C19*17 (rs12248560) C>T is linked to CAD with OR (95% CI) = 2.02, p-value = 0.003. In addition, the A allele of the miR-423 rs6505162 C>A SNV is linked to CAD with OR (95% CI) = 1.49, p-value = 0.036. The ELISA results indicated that the TNF-α serum levels are significantly increased in CAD patients compared to healthy controls. We conclude the TNF-α rs1800629 G>A, CYP2C19*17, and miR-423 rs6505162 C>A are potential genetic loci for CAD in the Saudi population. These findings require further verification in future studies. After being verified, our results might be utilized in genetic testing to identify individuals that are susceptible to CAD and, therefore, for whom reducing modifiable risk factors (e.g., poor diet, diabetes, obesity, and smoking) would result in prevention or delay of CAD

Nutritional Composition and Anti-Type 2 Diabetes Mellitus Potential of Femur Bone Extracts from Bovine, Chicken, Sheep, and Goat: Phytochemical and In Vivo Studies

Nutritional deficits in one’s diet have been established as the key risk factor for T2DM in recent years. Nutritional therapy has been demonstrated to be useful in treating T2DM. The current study was carried out to assess the nutritional composition of bovine (12 months), chicken (4 months), sheep (13 months), and goat (9 months) femur bone extracts, as well as their potential therapeutic effects on T2DM regression in a Wistar albino rat model (500 mg/kg b.wt.). The proximate composition of the different extracts, their fatty acid composition, their amino acids, and their mineral contents were identified. In vivo data indicated considerably improved T2DM rats, as seen by lower serum levels of TL, TG, TC, ALT, AST, ALP, bilirubin, creatinine, urea, IL-6, TNF-α, sICAM-1, sVCAM-1, and MDA. Low levels of HDL-C, GSH, and total proteins were restored during this study. Histological investigations of liver and pancreatic tissue revealed that the distribution of collagen fibers was nearly normal. The bovine extract, on the other hand, was the most active, followed by the sheep, goat, and finally chicken extract. This research could result in the creation of a simple, noninvasive, low-cost, and reliable method for T2DM control, paving the way for potential early therapeutic applications in T2DM control

Nutraceutical Effect of Resveratrol on the Mammary Gland: Focusing on the NF-κb /Nrf2 Signaling Pathways

The aim of this study is to evaluate the defensive role of resveratrol, which is antagonistic to the oxidative stress and inflammation that is prompted by LPS in mammary tissue of female mice. Thirty adult mice were distributed into three groups (n = 10) control (CON), lipopolysaccharides at 2.5 mg/kg (LPS), and lipopolysaccharides at 2.5 mg/kg with 2 mg/kg of resveratrol (RES + LPS). The treatments were applied for 15 consecutive days. Spectrophotometry was used to quantify ROS in the blood, and proinflammatory cytokines concentrations were determined through radioimmunoassay. NF-κB, Jnk, IL-1β, Erk, IL-6, Nrf2 and TNF-α were quantified by RT-qPCR, and Western blots were used to quantifyP65 and pP65 protein intensities. MDA production was considerably increased, and the activity of T-AOC declined in the LPS treatment in comparison with the CON group but was significantly reversed in the RES + LPS group. Proinflammatory cytokines production and the genes responsible for inflammation and oxidative stress also showed higher mRNA and pP65 protein intensity in the LPS group, while Nrf2 showed a remarkable decline in mRNA expression in the LPS versus the CON group. All these mRNA intensities were reversed in the RES + LPS group. There were no remarkable changes in P65 protein intensity observed between the CON, LPS, and RES + LPS groups. In conclusion, resveratrol acts as a protective agent to modulate cellular inflammation and oxidative stress caused by LPS in mammary tissue of female mice

New cytotoxic dammarane type saponins from Ziziphus spina-christi

Abstract Cancer is the world's second-leading cause of death. Drug development efforts frequently focus on medicinal plants since they are a valuable source of anticancer medications. A phytochemical investigation of the edible Ziziphus spina-christi (F. Rhamnaceae) leaf extract afforded two new dammarane type saponins identified as christinin E and F (1, 2), along with the known compound christinin A (3). Different cancer cell lines, such as lung cancer (A549), glioblastoma (U87), breast cancer (MDA-MB-231), and colorectal carcinoma (CT-26) cell lines, were used to investigate the extracted compounds' cytotoxic properties. Our findings showed significant effects on all the tested cell lines at varying concentrations (1, 5, 10, and 20 µg/mL). The three compounds exhibited potent activity at low concentrations (< 10 μg/mL), as evidenced by their low IC50 values. To further investigate the complex relationships between these identified cancer-relevant biological targets and to identify critical targets in the pathogenesis of the disease, we turned to network pharmacology and in silico-based investigations. Following this, in silico-based analysis (e.g., inverse docking, ΔG calculation, and molecular dynamics simulation) was performed on the structures of the isolated compounds to identify additional potential targets for these compounds and their likely interactions with various signalling pathways relevant to this disease. Based on our findings, Z. spina-christi's compounds showed promise as potential anti-cancer therapeutic leads in the future

Scabicidal Potential of Coconut Seed Extract in Rabbits via Downregulating Inflammatory/Immune Cross Talk: A Comprehensive Phytochemical/GC-MS and In Silico Proof

Scabies is an invasive skin condition caused by Sarcoptes scabiei mites. The present study investigates the antiscabies potential of coconut seed extract (CSE) in rabbits. GC-MS analysis of the seed oil identified 17 known compounds, while CSE phytochemical investigation afforded 4 known ones. The topical application of seed extract improved all signs of infection, and the improvement started 3 days post application. However, in vitro application of the extract caused 99% mortality of mites 1 day post application. Histopathological examination revealed the absence of inflammatory infiltration and hyperkeratosis of the epidermis, compared with ivermectin-treated groups which revealed less improvement. The mRNA gene expression results revealed a suppression of IL-1β, IL-6, IL-10, MMP-9, VEGF, and MCP-1, and an upregulation of I-CAM-1, KGF as well as TIMP-1. The docking analysis emphasized a strong binding of gondoic acid with IL-1β, IL-6, and VEGF with high binding scores of −5.817, −5.291, and −8.362 kcal/mol, respectively, and a high binding affinity of 3″(1‴-O-β-D-glucopyranosyl)-sucrose with GST with −7.24 kcal/mol. Accordingly, and for the first time, our results highlighted the scabicidal potential of coconut seed extract, which opens the gate for an efficient, cost-effective as well as herbal-based alternative for the control of scabies in rabbits

<i>Abelmoschus eculentus</i> Seed Extract Exhibits In Vitro and In Vivo Anti-Alzheimer’s Potential Supported by Metabolomic and Computational Investigation

Abelmoschus esculentus Linn. (okra, F. Malvaceae) is a fruit widely consumed all over the world. In our study, the anti-Alzheimer’s potential of A. esculentus was evaluated. An in vitro DPPH free radical assay on A. esculentus seed’s total extract and AChE inhibition potential screening indicated a significant anti-Alzheimer’s activity of the extract, which was confirmed through an in vivo study in an aluminum-intoxicated rat model. Additionally, in vivo results demonstrated significant improvement in Alzheimer’s rats, which was confirmed by improving T-maze, beam balance tests, lower serum levels of AChE, norepinephrine, glycated end products, IL-6, and MDA. The levels of dopamine, BDNF, GSH, and TAC returned to normal values during the study. Moreover, histological investigations of brain tissue revealed that the destruction in collagen fiber nearly returns back to the normal pattern. Metabolomic analysis of the ethanolic extract of A. esculentus seeds via LC–HR-ESI-MS dereplicated ten compounds. A network pharmacology study displayed the relation between identified compounds and 136 genes, among which 84 genes related to Alzheimer’s disorders, and focused on AChE, APP, BACE1, MAPT and TNF genes with interactions to all Alzheimer’s disorders. Consequently, the results revealed in our study grant potential dietary elements for the management of Alzheimer’s disorders