Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers

Abstract Background Asthma is a chronic respiratory disease affecting millions of people worldwide, but early detection can be challenging due to the time-consuming nature of the traditional technique. Machine learning has shown great potential in the prompt prediction of asthma. However, because of the inherent complexity of asthma-related patterns, current models often fail to capture the correlation between data samples, limiting their accuracy. Our objective was to use our novel model to address the above problem via an Affinity Graph Enhanced Classifier (AGEC) to improve predictive accuracy. Methods The clinical dataset used in this study consisted of 152 samples, where 24 routine blood markers were extracted as features to participate in the classification due to their ease of sourcing and relevance to asthma. Specifically, our model begins by constructing a projection matrix to reduce the dimensionality of the feature space while preserving the most discriminative features. Simultaneously, an affinity graph is learned through the resulting subspace to capture the internal relationship between samples better. Leveraging domain knowledge from the affinity graph, a new classifier (AGEC) is introduced for asthma prediction. AGEC’s performance was compared with five state-of-the-art predictive models. Results Experimental findings reveal the superior predictive capabilities of AGEC in asthma prediction. AGEC achieved an accuracy of 72.50%, surpassing FWAdaBoost (61.02%), MLFE (60.98%), SVR (64.01%), SVM (69.80%) and ERM (68.40%). These results provide evidence that capturing the correlation between samples can enhance the accuracy of asthma prediction. Moreover, the obtained $$p$$ p values also suggest that the differences between our model and other models are statistically significant, and the effect of our model does not exist by chance. Conclusion As observed from the experimental results, advanced statistical machine learning approaches such as AGEC can enable accurate diagnosis of asthma. This finding holds promising implications for improving asthma management

MiR-144 Inhibits Uveal Melanoma Cell Proliferation and Invasion by Regulating c-Met Expression

<div><p>MicroRNAs (miRNAs) are a group endogenous small non-coding RNAs that inhibit protein translation through binding to specific target mRNAs. Recent studies have demonstrated that miRNAs are implicated in the development of cancer. However, the role of miR-144 in uveal melanoma metastasis remains largely unknown. MiR-144 was downregulated in both uveal melanoma cells and tissues. Transfection of miR-144 mimic into uveal melanoma cells led to a decrease in cell growth and invasion. After identification of two putative miR-144 binding sites within the 3' UTR of the human c-Met mRNA, miR-144 was proved to inhibit the luciferase activity inMUM-2B cells with a luciferase reporter construct containing the binding sites. In addition, the expression of c-Met protein was inhibited by miR-144. Furthermore, c-Met-mediated cell proliferation and invasion were inhibited by restoration of miR-144 in uveal melanoma cells. In conclusion, miR-144 acts as a tumor suppressor in uveal melanoma, through inhibiting cell proliferation and migration. miR-144 might serve as a potential therapeutic target in uveal melanoma patients.</p></div

Inhibition of c-Met inhibits uveal melanoma cell proliferation and invasion (A) Western blotting analysis was performed to examine the effects of siRNA-c-Met on the expression of c-Met.

<p>GAPDH was also detected as a loading control. (B) The cell growth in MUM-2B cells co-transfected with either siRNA-c-Met, siRNA-c-Met and miR-144 inhibitor or control using CCK-8 proliferation assay. (C) The cell invasive in MUM-2B cells co-transfected with either siRNA-c-Met, siRNA-c-Met and miR-144 inhibitor or control using invasion assay. *p<0.05, ** p<0.01, and ***p<0.001.</p

The expression ofmiR-144 was downregulated in uveal melanoma cells and tissues (A) qRT–PCR analysis of miR-144expression in uveal melanoma cell lines (MUM-2B, C918, MUM-2C and OCM-1A) and one human melanocyte cell line (D78).

<p>The level of miR-144 expression was normalized to U6. (B) qRT–PCR analysis of miR-144expression in 5human uveal melanoma tissues and 5 normal uvea tissues. The level of miR-144 expression was normalized to U6.***p<0.001.</p

Overexpression of miR-144inhibited proliferation and invasion of uveal melanoma cells (A) qRT–PCR analysis of miR-144 expression in MUM-2B cells which was transfected miR-144 mimics, inhibitors, scramble or control.

<p>(B) The CCK-8 proliferation assay showed that miR-144 mimics can inhibit the proliferation of the MUM-2B cells. Meanwhile, miR-144 inhibitor increased the proliferation of the MUM-2B cells. (C) Invasion analysis of MUM-2B cells after treatment withmiR-144 mimics, inhibitors or scramble or control; the relative ratio of invasive cells per field is shown below, *p<0.05, ** p<0.01, and ***p<0.001.</p

c-Met is a critical downstream target of miR-144 (A) Targetscan analysis using available algorithms indicated that c-Met is a theoretical target gene of miR-144.

<p>(B) Luciferase reporter gene assays showed that ectopic of miR-144 remarkably reduced luciferase activity in the c-Met wild-type reporter gene but not the mutant c-Met 3’UTR.(C) qRT-PCR analysis of c-Met expression in the MUM-2B cells which was transected miR-144 mimics, inhibitors, scramble or control. GAPDH was used as internal control. (D) Western blot analysis has shown that miR-144 mimic inhibited the protein expression of c-Met in MUM-2B cells. GAPDH was also detected as a loading control. ***p<0.001.</p

Grouping Annotations on the Subcellular Layered Interactome Demonstrates Enhanced Autophagy Activity in a Recurrent Experimental Autoimmune Uveitis T Cell Line

<div><p>Human uveitis is a type of T cell-mediated autoimmune disease that often shows relapse–remitting courses affecting multiple biological processes. As a cytoplasmic process, autophagy has been seen as an adaptive response to cell death and survival, yet the link between autophagy and T cell-mediated autoimmunity is not certain. In this study, based on the differentially expressed genes (GSE19652) between the recurrent versus monophasic T cell lines, whose adoptive transfer to susceptible animals may result in respective recurrent or monophasic uveitis, we proposed grouping annotations on a subcellular layered interactome framework to analyze the specific bioprocesses that are linked to the recurrence of T cell autoimmunity. That is, the subcellular layered interactome was established by the Cytoscape and Cerebral plugin based on differential expression, global interactome, and subcellular localization information. Then, the layered interactomes were grouping annotated by the ClueGO plugin based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. The analysis showed that significant bioprocesses with autophagy were orchestrated in the cytoplasmic layered interactome and that mTOR may have a regulatory role in it. Furthermore, by setting up recurrent and monophasic uveitis in Lewis rats, we confirmed by transmission electron microscopy that, in comparison to the monophasic disease, recurrent uveitis <i>in vivo</i> showed significantly increased autophagy activity and extended lymphocyte infiltration to the affected retina. In summary, our framework methodology is a useful tool to disclose specific bioprocesses and molecular targets that can be attributed to a certain disease. Our results indicated that targeted inhibition of autophagy pathways may perturb the recurrence of uveitis.</p></div