Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn's disease based on three machine learning methods.

Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.

Integrated analysis of multiple microarray studies to establish differential diagnostic models of Crohn's disease and ulcerative colitis based on a metalloproteinase-associated module.

Background: The ulcerative colitis (UC) and Crohn's disease (CD) subtypes of inflammatory bowel disease (IBD) are autoimmune diseases influenced by multiple complex factors. The clinical treatment strategies for UC and CD often differ, indicating the importance of improving their discrimination. Methods: Two methods, robust rank aggregation (RRA) analysis and merging and intersection, were applied to integrate data from multiple IBD cohorts, and the identified differentially expressed genes (DEGs) were used to establish a protein-protein interaction (PPI) network. Molecular complex detection (MCODE) was used to identify important gene sets. Two differential diagnostic models to distinguish CD and UC were established via a least absolute shrinkage and selection operator (LASSO) logistic regression, and model evaluation was performed in both the training and testing groups, including receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA). The potential value of MMP-associated genes was further verified using different IBD cohorts and clinical samples. Results: Four datasets (GSE75214, GSE10616, GSE36807, and GSE9686) were included in the analysis. Both data integration methods indicated that the activation of the MMP-associated module was significantly elevated in UC. Two LASSO models based on continuous variable (Model_1) and binary variable (Model_2) MMP-associated genes were established to discriminate CD and UC. The results showed that Model_1 exhibited good discrimination in the training and testing groups. The calibration analysis and DCA showed that Model_1 exhibited good performance in the training group but failed in the testing group. Model_2 exhibited good discrimination, calibration and DCA results in the training and testing groups and exhibited greater diagnostic value. The effects of Model_1 and Model_2 were further verified in a new IBD cohort of GSE179285. The MMP genes exhibited high value as biomarkers for the discrimination of IBD patients using published cohort and immunohistochemistry (IHC) staining data. The MMP-associated gene levels were statistically significantly positively correlated with the levels of the differentially expressed cell types, indicating their potential value in differential diagnosis. The single-cell analysis confirmed that the expression of ANXA1 in UC was higher than that in CD. Conclusion: MMP-associated modules are the main differential gene sets between CD and UC. The established Model_2 overcomes batch differences and has good clinical applicability. Subsequent in-depth research investigating how MMPs are involved in the development of different IBD subtypes is necessary.

Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma.

BACKGROUND: Ferroptosis is a novel form of regulated cell death involved in tumor progression. The role of ferroptosis-related lncRNAs in hepatocellular carcinoma (HCC) remains unclear. METHODS: RNA-seq and clinical data for HCC patients were downloaded from The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal. Bioinformatics methods, including weighted gene coexpression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) analysis, were used to identify signature markers for diagnosis/prognosis. The tumor microenvironment, immune infiltration and functional enrichment were compared between the low-risk and high-risk groups. Subsequently, small molecule drugs targeting ferroptosis-related signature components were predicted via the L1000FWD and PubChem databases. RESULTS: The prognostic model consisted of 2 ferroptosis-related mRNAs (SLC1A5 and SLC7A11) and 8 ferroptosis-related lncRNAs (AC245297.3, MYLK-AS1, NRAV, SREBF2-AS1, AL031985.3, ZFPM2-AS1, AC015908.3, MSC-AS1). The areas under the curves (AUCs) were 0.830 and 0.806 in the training and test groups, respectively. Decision curve analysis (DCA) revealed that the ferroptosis-related signature performed better than all pathological characteristics. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor. The survival probability of low- and high-risk patients could be clearly distinguished by the principal component analysis (PCA) plot. The risk score divided HCC patients into two distinct groups in terms of immune status, especially checkpoint gene expression, which was further supported by the Gene Ontology (GO) biological process, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, several small molecule drugs (SIB-1893, geldanamycin and PD-184352, etc) targeting ferroptosis-related signature components were identified for future reference. CONCLUSION: We constructed a new ferroptosis-related mRNA/lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis.

Background: Ulcerative colitis (UC) is a chronic, complicated, inflammatory disease with an increasing incidence and prevalence worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of UC have not yet been fully elucidated. Methods: All UC datasets published in the GEO database were analyzed and summarized. Subsequently, the robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between UC patients and controls. Gene functional annotation and PPI network analysis were performed to illustrate the potential functions of the DEGs. Some important functional modules from the protein-protein interaction (PPI) network were identified by molecular complex detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and analyses were performed. The results of CytoHubba, a plug for integrated algorithm for biomolecular interaction networks combined with RRA analysis, were used to identify the hub genes. Finally, a mouse model of UC was established by dextran sulfate sodium salt (DSS) solution to verify the expression of hub genes. Results: A total of 6 datasets met the inclusion criteria (GSE38713, GSE59071, GSE73661, GSE75214, GSE87466, GSE92415). The RRA integrated analysis revealed 208 significant DEGs (132 upregulated genes and 76 downregulated genes). After constructing the PPI network by MCODE plug, modules with the top three scores were listed. The CytoHubba app and RRA identified six hub genes: LCN2, CXCL1, MMP3, IDO1, MMP1, and S100A8. We found through enrichment analysis that these functional modules and hub genes were mainly related to cytokine secretion, immune response, and cancer progression. With the mouse model, we found that the expression of all six hub genes in the UC group was higher than that in the control group (P < 0.05). Conclusion: The hub genes analyzed by the RRA method are highly reliable. These findings improve the understanding of the molecular mechanisms in UC pathogenesis.

[18F]FP-(+)-DTBZ PET study in a lactacystin-treated rat model of Parkinson disease.

OBJECTIVE: Lactacystin has been used to establish rodent models of Parkinson disease (PD), with cerebral α-synuclein inclusions. This study evaluated the uptake of [18F]9-fluoropropyl-(+)-dihydrotetrabenazine ([18F]FP-(+)-DTBZ), a vesicular monoamine transporter type 2 (VMAT2)-targeting radiotracer, through positron emission tomography (PET) in lactacystin-treated rat brains. METHODS: Adult male Sprague-Dawley rats were randomly treated with a single intracranial dose of lactacystin (2 or 5 µg) or saline (served as the sham control) into the left medial forebrain bundle. A 30-min static [18F]FP-(+)-DTBZ brain PET scan was performed following an intravenous [18F]FP-(+)-DTBZ dose (approximately 22 MBq) in each animal at 2 and 3 weeks after lactacystin treatment. Upon completing the last PET scans, the animals were killed, and their brains were dissected for ex vivo autoradiography (ARG) and immunohistochemical (IHC) staining of tyrosine hydroxylase (TH) as well as VMAT2. RESULTS: Both the 2- and 5-µg lactacystin-treated groups exhibited significantly decreased specific [18F]FP-(+)-DTBZ uptake in the ipsilateral striata (I-ST) at 2 weeks (1.51 and 1.16, respectively) and 3 weeks (1.36 and 1.00, respectively) after lactacystin treatment, compared with the uptake in the corresponding contralateral striata (C-ST) (3.48 and 3.08 for the 2- and 5-µg lactacystin-treated groups, respectively, at 2 weeks; 3.36 and 3.11 for the 2- and 5-µg lactacystin-treated groups, respectively, at 3 weeks) and the sham controls (3.34-3.53). Lactacystin-induced decline in I-ST [18F]FP-(+)-DTBZ uptake was also demonstrated through ex vivo ARG, and the corresponding dopaminergic neuron damage was confirmed by the results of TH- and VMAT2-IHC studies. CONCLUSIONS: In this PD model, lactacystin-induced dopaminergic terminal damage in the ipsilateral striatum could be clearly visualized through in vivo [18F]FP-(+)-DTBZ PET imaging. This may serve as a useful approach for evaluating the effectiveness of new treatments for PD.

Quantitative analysis of the therapeutic effect of magnolol on MPTP-induced mouse model of Parkinson's disease using in vivo 18F-9-fluoropropyl-(+)-dihydrotetrabenazine PET imaging.

18F-9-Fluoropropyl-(+)-dihydrotetrabenazine [18F-FP-(+)-DTBZ] positron emission tomography (PET) has been shown to detect dopaminergic neuron loss associated with Parkinson's disease (PD) in human and neurotoxin-induced animal models. A polyphenol compound, magnolol, was recently proposed as having a potentially restorative effect in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)- or 6-hydroxydopamine-treated animal models. In this study, 18F-FP-(+)-DTBZ PET was used to determine the therapeutic efficacy of magnolol in an MPTP-PD mouse model that was prepared by giving an intraperitoneally (i.p.) daily dose of 25 mg/kg MPTP to male C57BL/6 mice for 5 consecutive days. Twenty-minute static 18F-FP-(+)-DTBZ PET scans were performed before MPTP treatment and 5 days after the termination of MPTP treatment to set up the baseline control. Half of the MPTP-treated mice then received a daily dose of magnolol (10 mg/kg dissolved in corn oil, i.p.) for 6 days. 18F-FP-(+)-DTBZ PET imaging was performed the day after the final treatment. All 18F-FP-(+)-DTBZ PET images were analysed and the specific uptake ratio (SUr) was calculated. Ex vivo autoradiography (ARG) and corresponding immunohistochemistry (IHC) studies were conducted to confirm the distribution of dopaminergic terminals in the striatum. The striatal SUr ratios of 18F-FP-(+)-DTBZ PET images for the Sham, the MPTP, and the MPTP + Magnolol-treated groups were 1.25 ± 0.05, 0.75 ± 0.06, and 1.00 ± 0.11, respectively (n = 4 for each group). The ex vivo 18F-FP-(+)-DTBZ ARG and IHC results correlated favourably with the PET imaging results. 18F-FP-(+)-DTBZ PET imaging suggested that magnolol post-treatment may reverse the neuronal damage in the MPTP-lesioned PD mice. In vivo imaging of the striatal vesicular monoamine transporter type 2 (VMAT2) distribution using 18F-FP-(+)-DTBZ animal PET is a useful method to evaluate the efficacy of therapeutic drugs i.e., magnolol, for the management of PD.