miRNA and mRNA Integration Network Construction Reveals Novel Key Regulators in Left-Sided and Right-Sided Colon Adenocarcinoma

Discovery of Novel miRNAs in Colorectal Cancer: Potential Biological Roles and Clinical Utility

Deregulated miRNAs are associated with colorectal cancer (CRC), with alterations depending on the tumor location. Novel tissue-specific miRNAs have been identified in different tumors and are associated with cancer. We used miRMaster to identify novel miRNAs in CRC from the TCGA and GEO data (discovery and validation groups). We used TCGA data from five tissues to analyze miRNA tissue specificity. miRDB was used to predict miRNA targets, and the UCSC Xena Browser was used to evaluate target expression. After successive analyses, we identified 15 novel miRNAs with the same expression patterns in CRC in both the discovery and validation groups. Four molecules (nov-miR-13844-5p, nov-miR-7154-5p, nov-miR-5035-3p, and nov-miR-590-5p) were differentially expressed in proximal and distal CRC. The nov-miR-3345-5p and nov-miR-13172-3p, which are upregulated in tumors, are only expressed in colorectal tissues. These molecules have been linked to a worse prognosis in right-sided colon and rectal carcinomas. An analysis revealed an association between eight novel miRNAs and 81 targets, mostly cancer-related genes, with varying expression based on tumor location. These findings provide new miRNAs with potential biological relevance, molecular biomarkers, and therapeutic targets for CRC treatment.

Fat storage-inducing transmembrane proteins: beyond mediating lipid droplet formation

Fat storage-inducing transmembrane proteins (FITMs) were initially identified in 2007 as members of a conserved endoplasmic reticulum (ER) resident transmembrane protein gene family, and were found to be involved in lipid droplet (LD) formation. Recently, several studies have further demonstrated that the ability of FITMs to directly bind to triglyceride and diacylglycerol, and the diphosphatase activity of hydrolyzing fatty acyl-CoA, might enable FITMs to maintain the formation of lipid droplets, engage in lipid metabolism, and protect against cellular stress. Based on the distribution of FITMs in tissues and their important roles in lipid droplet biology and lipid metabolism, it was discovered that FITMs were closely related to muscle development, adipocyte differentiation, and energy metabolism. Accordingly, the abnormal expression of FITMs was not only associated with type 2 diabetes and lipodystrophy, but also with cardiac disease and several types of cancer. This study reviews the structure, distribution, expression regulation, and functionality of FITMs and their potential relationships with various metabolic diseases, hoping to provide inspiration for fruitful research directions and applications of FITM proteins. Moreover, this review will provide an important theoretical basis for the application of FITMs in the diagnosis and treatment of related diseases.

Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer

The study was conducted to construct a cellular senescence-related risk score signature to predict prognosis and immunotherapy response in colon cancer. Colon cancer data were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. And cellular senescence-related genes were obtained from the CellAge database. The colon cancer data were classified into different clusters based on cellular senescence-related gene expression. Next, prognostic differential genes among clusters were identified with survival analysis. A cellular senescence-related risk score signature was developed by performing the LASSO regression analysis. Finally, PCA analysis, t-SNE analysis, Kaplan-Meier survival analysis, ROC analysis, univariate Cox regression analysis, multivariate Cox regression analysis, C-index analysis, meta-analysis, immune infiltration analysis, and IPS score analysis were used to evaluate the significance of the risk signature for predicting prognosis and immunotherapy response in colon cancer. The colon cancer data were classified into three clusters. The patients in cluster A and cluster B had longer survival. A cellular senescence-related risk score signature was developed. Patients in the low-risk score group showed a better prognosis. The risk score signature could predict colon cancer patients’ prognosis independently of other clinical characteristics. The risk score signature predicted the prognosis of colon cancer patients more accurately than other signatures. Patients in the low-risk score group showed a better response to immunotherapy. The opposite was true for the high-risk score group. In conclusion, the cellular senescence-related risk score signature could be used for the prediction of prognosis and immunotherapy response in colon cancer.

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/

NIR-to-Vis Handheld Platforms for Detecting miRNA Level and Mutation Based on Sub-10 nm Sulfide Nanodots and HCR Amplification

Sub-10 nm monodisperse alkaline-earth sulfide nanodots (ASNDs) with bright near-infrared (NIR)-excitation fluorescence and adjustable emission wavelength were prepared by a thermal decomposition method for the first time. The ASNDs exhibited high NIR-to-vis conversion efficiency and served as multicolor fluorescent labels in the proposed miR-224 assay. Targeted detection of the miR-224 level and single-nucleotide variation in miR-224 was carried out on a smartphone-based platform using a hybridization chain reaction (HCR) amplification strategy. In the presence of miR-224, the ASND-labeled HCR probes self-assembled on the surface of the diagnosis kits, generating strong fluorescent signals linearly proportional to miR-224 contents in the range of 10-2000 fM. Significantly, mutations in miR-224 led to the variation in the fluorescence intensity ratio in RGB channels. Simultaneously, evident changes of fluorescent brightness and color were easily visualized by the naked eye, which enabled on-site discrimination of miR-224 with different mutant loci. This work provides a novel preparation approach for ultrasmall NIR excitation sulfide nanodots and reveals the potential of the as-synthesized ASNDs in point-of-care (POC) nucleic acid testing. Further, it may provide a handheld platform for miRNA single-nucleotide polymorphism analysis.

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

Integrative Transcriptomic and Small RNA Sequencing Reveals Immune-Related miRNA-mRNA Regulation Network for Soybean Meal-Induced Enteritis in Hybrid Grouper, Epinephelus fuscoguttatus♀ × Epinephelus lanceolatus♂

A 10-week feeding experiment was conducted to reveal the immune mechanism for soybean meal-induced enteritis (SBMIE) in hybrid grouper, Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂. Four isonitrogenous and isolipidic diets were formulated by replacing 0, 10, 30, and 50% fish meal protein with soybean meal (namely FM, SBM10, SBM30, and SBM50, respectively). The weight gain rate of the SBM50 group was significantly lower than those of the other groups. Plica height, muscular layer thickness, and goblet cells of the distal intestine in the SBM50 group were much lower than those in the FM group. The intestinal transcriptomic data, including the transcriptome and miRNAome, showed that a total of 6,390 differentially expressed genes (DEGs) and 92 DEmiRNAs were identified in the SBM50 and FM groups. DEmiRNAs (10 known and 1 novel miRNAs) and their DE target genes were involved in immune-related phagosome, natural killer cell-mediated cytotoxicity, Fc gamma R-mediated phagocytosis, and the intestinal immune network for IgA production pathways. Our study is the first to offer transcriptomic and small RNA profiling for SBMIE in hybrid grouper. Our findings offer important insights for the understanding of the RNA profile and further elucidation of the underlying molecular immune mechanism for SBMIE in carnivorous fish.

Epidemiological characteristics of bacillary dysentery from 2009 to 2016 and its incidence prediction model based on meteorological factors

Background

This study aimed to analyse the epidemiological characteristics of bacillary dysentery (BD) caused by Shigella in Chongqing, China, and to establish incidence prediction models based on the correlation between meteorological factors and BD, thus providing a scientific basis for the prevention and control of BD.

Methods

In this study, descriptive methods were employed to investigate the epidemiological distribution of BD. The Boruta algorithm was used to estimate the correlation between meteorological factors and BD incidence. The genetic algorithm (GA) combined with support vector regression (SVR) was used to establish the prediction models for BD incidence.

Results

In total, 68,855 cases of BD were included. The incidence declined from 36.312/100,000 to 23.613/100,000, with an obvious seasonal peak from May to October. Males were more predisposed to the infection than females (the ratio was 1.118:1). Children < 5 years old comprised the highest incidence (295.892/100,000) among all age categories, and pre-education children comprised the highest proportion (34,658 cases, 50.335%) among all occupational categories. Eight important meteorological factors, including the highest temperature, average temperature, average air pressure, precipitation and sunshine, were correlated with the monthly incidence of BD. The obtained mean absolute percent error (MAPE), mean squared error (MSE) and squared correlation coefficient (R²) of GA_SVR_MONTH values were 0.087, 0.101 and 0.922, respectively.

Conclusion

From 2009 to 2016, BD incidence in Chongqing was still high, especially in the main urban areas and among the male and pre-education children populations. Eight meteorological factors, including temperature, air pressure, precipitation and sunshine, were the most important correlative feature sets of BD incidence. Moreover, BD incidence prediction models based on meteorological factors had better prediction accuracies. The findings in this study could provide a panorama of BD in Chongqing and offer a useful approach for predicting the incidence of infectious disease. Furthermore, this information could be used to improve current interventions and public health planning.

Regulation of MicroRNA-155 and Its Related Genes Expression by Inositol Hexaphosphate in Colon Cancer Cells

Inositol hexaphosphate (IP6), a natural dietary component, has been found as an antitumor agent by stimulating apoptosis and inhibiting cancer cell proliferation, their migration, and metastasis in diverse cancers including colon cancer. However, molecular mechanisms of its action have not been well understood. In recent years, microRNAs (miRNAs) have been reported to play important roles in a broad range of biologic processes, such as cell growth, proliferation, apoptosis, or autophagy. These small noncoding molecules regulate post-transcriptional expression of targets genes via degradation of transcript or inhibition of protein synthesis. Aberrant expression and/or dysregulation of miRNAs have been characterized during tumor development and progression, thus, they are potential molecular targets for cancer prevention. The aim of this study was to investigate the effect of IP6 on the miRNAs expression profile in Caco-2 colon cancer cells. 84 miRNAs were analyzed in Caco-2 cells treated with 2.5 mM and 5 mM IP6 by the use of PCR (Polymerase Chain Reaction) array. The effect of 5 mM IP6 on selected potential miR-155 targets was determined by real-time (RT)-qPCR and ELISA (quantitative Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay )method. The results indicated alteration in the specific 10 miRNA expression in human colon cancer cells following their treatment with 5 mM IP6. It down-regulated 8 miRNAs (miR-155, miR-210, miR-144, miR-194, miR-26b, miR-126, miR-302c, and miR-29a) and up-regulated 2 miRNAs (miR-223 and miR-196b). In silico analysis revealed that FOXO3a, HIF-1α, and ELK3 mRNAs are those of predicted targets of miR-155. IP6 at the concentration of 5 mM markedly induced FOXO3a and HIF-1a genes’ expression at both mRNA and protein level and decreased the amount of ELK3 mRNA as well as protein concentration in comparison to the control. In conclusion, the present study indicates that one of the mechanisms of antitumor potential of IP6 is down-regulation of the miR-155 expression in human colon cancer cells. Moreover, the expression of genes that are targeted by miRNA are also modulated by IP6.

Identification of Transcriptional Markers and microRNA-mRNA Regulatory Networks in Colon Cancer by Integrative Analysis of mRNA and microRNA Expression Profiles in Colon Tumor Stroma

The aberrant expression of microRNAs (miRNAs) and genes in tumor microenvironment (TME) has been associated with the pathogenesis of colon cancer. An integrative exploration of transcriptional markers (gene signatures) and miRNA–mRNA regulatory networks in colon tumor stroma (CTS) remains lacking. Using two datasets of mRNA and miRNA expression profiling in CTS, we identified differentially expressed miRNAs (DEmiRs) and differentially expressed genes (DEGs) between CTS and normal stroma. Furthermore, we identified the transcriptional markers which were both gene targets of DEmiRs and hub genes in the protein–protein interaction (PPI) network of DEGs. Moreover, we investigated the associations between the transcriptional markers and tumor immunity in colon cancer. We identified 17 upregulated and seven downregulated DEmiRs in CTS relative to normal stroma based on a miRNA expression profiling dataset. Pathway analysis revealed that the downregulated DEmiRs were significantly involved in 25 KEGG pathways (such as TGF-β, Wnt, cell adhesion molecules, and cytokine–cytokine receptor interaction), and the upregulated DEmiRs were involved in 10 pathways (such as extracellular matrix (ECM)-receptor interaction and proteoglycans in cancer). Moreover, we identified 460 DEGs in CTS versus normal stroma by a meta-analysis of two gene expression profiling datasets. Among them, eight upregulated DEGs were both hub genes in the PPI network of DEGs and target genes of the downregulated DEmiRs. We found that three of the eight DEGs were negative prognostic factors consistently in two colon cancer cohorts, including COL5A2, EDNRA, and OLR1. The identification of transcriptional markers and miRNA–mRNA regulatory networks in CTS may provide insights into the mechanism of tumor immune microenvironment regulation in colon cancer.