Natural dietary ingredients like flavonoids are important for body improvement against diseases. The flavonol rutin is widely found in fruits and vegetables and shows significant anticancer properties. However, the underlined signaling pathways have not been elucidated yet. In this study, the impacts of various doses of rutin (400–700 mM/ml) have been examined on human colon cancer SW480 cells metabolism, cell cycle, and apoptosis. The transcriptome was analyzed by bioinformatics tools and the interactions between rutin modulated microRNAs (miRNAs), long noncoding RNAs (lncRNAs), messenger RNAs (mRNAs), and transcription factors (TFs) were built, filtered and enriched. A dose of 600 mM of rutin significantly decreased cells metabolic activity, halved the population and arrested the cell cycle at the sub‐G1 phase. The enrichment analysis of miRNAs‐lncRNAs‐mRNAs‐TFs network showed that these effects were mediated through alteration of glucose, lipid, and protein metabolism, modulating endoplasmic reticulum stress responses, negative regulation of cell cycle process, and inducing the extrinsic and intrinsic apoptotic signaling pathways. Additionally, the key parent nodes of each annotation were illustrated. These findings create a detailed image of rutin underlying intracellular signaling pathways in CRC and also help us to better understand the role of dietary natural compounds in cancer treatment.

Background The feces of colorectal cancer (CRC) patients contain tumor colonocytes, which constantly shed into the lumen area. Therefore, stool evaluation can be considered as a rapid and low‐risk way to directly determine the colon and rectum status. As long non‐coding RNAs (lncRNAs) alterations are important in cancer cells fate regulation, we aimed to assess the level of a panel of cancer‐related lncRNAs in fecal colonocytes. Methods The population study consisted of 150 subjects, including a training set, a validation set, and a group of 30 colon polyps. The expression levels of lncRNAs were evaluated by quantitative real‐time PCR (qRT‐PCR). The NPInetr and EnrichR tools were used to identify the interactions and functions of lncRNAs. Results A total of 10 significantly dysregulated lncRNAs, including CCAT1, CCAT2, H19, HOTAIR, HULC, MALAT1, PCAT1, MEG3, PTENP1, and TUSC7, were chosen for designing a predictive panel. The diagnostic performance of the panel in distinguishing CRCs from the healthy group was AUC: 0.8554 in the training set and 0.8465 in the validation set. The AUC for early CRCs (I‐II TNM stages) was 0.8554 in the training set and 0.8465 in the validation set, and for advanced CRCs (III‐IV TNM stages) were 0.9281 in the training set and 0.9236 in the validation set. The corresponding AUC for CRCs vs polyps were 0.9228 (I‐IV TNM stages), 0.9042 (I‐II TNM stages), and 0.9362 (III‐IV TNM stages). Conclusions These data represented the application of analysis of fecal colonocytes lncRNAs in early detection of CRC.

Microglial activation can release free radicals and various pro-inflammatory cytokines, which implicates the progress of a neurodegenerative disease. Therefore suppression of microglial activation can be an appropriate strategy for combating neurodegenerative diseases. Betanin is a red food dye that acts as free radical scavenger and can be a promising candidate for this purpose. In this study, purification of betanin from red beetroots was carried out by normal phase colum chromatography, yielding 500 mg of betanin from 100 g of red beetroot. The purified betanin was evaluated by TLC, UV-visible, HPLC, ESI-MASS, FT-IR spectroscopy. Investigation on the inhibitory effect of betanin on activated microglia was performed using primary microglial culture. The results showed that betanin significantly inhibited lipopolysaccharide induced microglial function including the production of nitric oxide free radicals, reactive oxygen species, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and interleukin-1 beta (IL-1β). Moreover, betanin modulated mitochondrial membrane potential, lysosomal membrane permeabilization and adenosine triphosphate. We further investigated the interaction of betanin with TNF-α, IL-6 and Nitric oxide synthase (iNOS or NOS2) using in silico molecular docking analysis. The docking results demonstrated that betanin have significant negative binding energy against active sites of TNF-α, IL-6 and iNOS.

Metastasis is known as a key step in cancer recurrence and could be stimulated by multiple factors. Calumenin (CALU) is one of these factors which has a direct impact on cancer metastasis and yet, its underlined mechanisms have not been completely elucidated. The current study was aimed to identify CALU co-expressed genes, their signaling pathways, and expression status within the human cancers. To this point, CALU associated genes were visualized using the Cytoscape plugin BisoGenet and annotated with the Enrichr webbased application. The list of CALU related diseases was retrieved using the DisGenNet, and cancer datasets were downloaded from The Cancer Genome Atlas (TCGA) and analyzed with the Cufflink software. ROC curve analysis was used to estimate the diagnostic accuracy of DEGs in each cancer, and the Kaplan-Meier survival analysis was performed to plot the overall survival of patients. The protein level of the signature biomarkers was measured in 40 biopsy specimens and matched adjacent normal tissues collected from CRC and lung cancer patients. Analysis of CALU co-expressed genes network in TCGA datasets indicated that the network is markedly altered in human colon (COAD) and lung (LUAD) cancers. Diagnostic accuracy estimation of differentially expressed genes showed that a gene panel consisted of CALU, AURKA, and MCM2 was able to successfully distinguish cancer tumors from healthy samples. Cancer cases with abnormal expression of the signature genes had a significantly lower survival rate than other patients. Additionally, comparison of CALU, AURKA, and MCM2 proteins between healthy samples, early and advanced tumors showed that the level of these proteins was increased through normalcarcinoma transition in both types of cancers. These data indicate that the interactions between CALU, AURKA, and MCM2 has a pivotal role in cancer development, and thereby needs to be explored in the future.

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Parkinson's disease (PD) is one of the most prevalent neurodegenerative disorders with no precise etiology. Multiple lines of evidence support that environmental factors, either neurotoxins or neuroinflammation, can induce Parkinsonism. In this study, we purified an active compound, neobaicalein (Skullcapflavone II), from the roots of Scutellaria pinnatifida (S. pinnatifida). Neobaicalein not only had protective impacts on rotenone-induced neurotoxicity but in glial cultures, it dampened the inflammatory response when stimulated with lipopolysaccharide (LPS). Neobaicalein had high antioxidant activity without any obvious toxicity. In addition, it could raise the cell viability, decrease early apoptosis, reduce the generation of reactive oxygen species (ROS), and keep the neurite's length normal in the treated SH-SY5Y cells. Pathway enrichment analysis (PEA) and target prediction provided insights into the PD related genes, protein-protein interaction (PPI) network, and the key proteins enriched in the signaling pathways. Furthermore, docking simulation (DS) on the proteins of the PD-PPI network revealed that neobaicalein might interact with the key proteins involved in PD pathology, including MAPK14, MAPK8, and CASP3. It also blocks the destructive processes, such as cell death, inflammation, and oxidative stress pathways. Our results demonstrate that neobaicalein alleviates pathological effects of factors related to PD, and may provide new insight into PD therapy.

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Nearly 16% of people with breast cancer (BC) have Diabetes Mellitus type 2 (DM2) and are at a higher risk of death worldwide. Their common regulatory factors and functional mechanisms can be targeted applying multi-target drugs including Metformin (MTFN) and Curcumin (CURC). In this study, we used in-silico approaches to study the potential underlying mechanisms of this co-treatment strategy on BC and DM2 in order to introduce novel therapeutic targets.The total number of 48 shared differentially expressed genes (17 up-regulated and 31 down-regulated) were identified through establishing diseases' protein-protein network and BC RNA-sequencing expression data. The integration of functional clustering and pathway analyses revealed that the most involved cellular pathways and processes are regard to cells' proliferation, death, migration, and response to external stimulus. Afterwards, the MTFN/CURC correlation and co-treatment optimization was probed through response surface methodology (RSM) based on MCF7 cell line and confirmed by MDA-MB-231. Combination index calculation by MTT viability assay proved supportive effects on both cell lines. The superior apoptotic potential of co-treatment compared to single treatments was shown on inhibition of MCF7 proliferation and induction of cell death demonstrated by cell body co-staining and flow cytometry as well as gene expression analysis via RT-PCR. Furthermore, wound-healing scratch assay showed that this co-treatment has a slightly higher effect on migration inhibition compared to single treatments.In conclusion, our study used in-silico and in-vitro approaches and introduced a potential regulatory panel between BC and DM2. We also provided a linear model and equation that show the positive relation of drugs' co-treatment. The proposed co-treatment strategy successfully controlled the biological processes under investigation. METHODS Identification and validation of overlapped DEGs between BC and DM2The STRING disease network importer of Cytoscape 3.6 was used to establish the PPI networks of BC and DM2 with ultimate number of proteins (2000 nodes) and 0.7 as the minimum interaction score. The networks were merged together to find the overlapped factors and subsequently, filtered by BC differentially expressed genes (DEGs) of The Cancer Genome Atlas (TCGA) database. TCGA database contains the RNA-sequencing (RNA-seq) expression data of 33 different types of human cancers. In this study, BC RNA-seq raw data of 224 samples (112 cancerous tissues and 112 adjacent normal ones) was downloaded and normalized using TCGAbiolinks package of R v3.5.2 software. To identify the (DEGs) the criteria of FDR < 0.05 and |logFC| > 1 were applied. Functional Annotation Clustering and Pathway AnalysisTo understand the biological meaning behind the obtained DEGs in groups, Functional Annotation Clustering tool of David 6.7 database, based on Kappa statistics and fuzzy heuristic clustering algorithms, was used to make the ontology report easier to follow [23]. Each cluster contains Gene Ont...