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

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11458
Author(s):  
Malik Yousef ◽  
Gokhan Goy ◽  
Ramkrishna Mitra ◽  
Christine M. Eischen ◽  
Amhar Jabeer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mrna Expression ◽  
Mirna Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mrna Gene

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.

scholarly journals In Silico Discovery of Candidate Drugs against Covid-19

Viruses ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 404 ◽  
Author(s):  
Claudia Cava ◽  
Gloria Bertoli ◽  
Isabella Castiglioni
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Basic Mechanism ◽  
Cell Receptor ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

scholarly journals Comprehensive analysis of ceRNA networks reveals prognostic lncRNAs related to immune infiltration in colorectal cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jingyi Chen ◽  
Yuxuan Song ◽  
Mei Li ◽  
Yu Zhang ◽  
Tingru Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Target Genes ◽  
Immune Cell ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cerna Network

Abstract Background Competing endogenous RNA (ceRNA) represents a class of RNAs (e.g., long noncoding RNAs [lncRNAs]) with microRNA (miRNA) binding sites, which can competitively bind miRNA and inhibit its regulation of target genes. Increasing evidence has underscored the involvement of dysregulated ceRNA networks in the occurrence and progression of colorectal cancer (CRC). The purpose of this study was to construct a ceRNA network related to the prognosis of CRC and further explore the potential mechanisms that affect this prognosis. Methods RNA-Seq and miRNA-Seq data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs), and a prognosis-related ceRNA network was constructed based on DElncRNA survival analysis. Subsequently, pathway enrichment, Pearson correlation, and Gene Set Enrichment Analysis (GSEA) were performed to determine the function of the genes in the ceRNA network. Gene Expression Profiling Interactive Analysis (GEPIA) and immunohistochemistry (IHC) were also used to validate differential gene expression. Finally, the correlation between lncRNA and immune cell infiltration in the tumor microenvironment was evaluated based on the CIBERSORT algorithm. Results A prognostic ceRNA network was constructed with eleven key survival-related DElncRNAs (MIR4435-2HG, NKILA, AFAP1-AS1, ELFN1-AS1, AC005520.2, AC245884.8, AL354836.1, AL355987.4, AL591845.1, LINC02038, and AC104823.1), 54 DEmiRNAs, and 308 DEmRNAs. The MIR4435-2HG- and ELFN1-AS1-associated ceRNA subnetworks affected and regulated the expression of the COL5A2, LOX, OSBPL3, PLAU, VCAN, SRM, and E2F1 target genes and were found to be related to prognosis and tumor-infiltrating immune cell types. Conclusions MIR4435-2HG and ELFN1-AS1 are associated with prognosis and tumor-infiltrating immune cell types and could represent potential prognostic biomarkers or therapeutic targets in colorectal carcinoma.

scholarly journals Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Qingyu Liang ◽  
Gefei Guan ◽  
Xue Li ◽  
Chunmi Wei ◽  
Jianqi Wu ◽  
...  
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Treatment Strategies ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Risky Behaviors ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Transition ◽  
Genome Atlas

Abstract Background Molecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlighted that long non-coding RNAs (lncRNAs) are key mediators in cancer mesenchymal transition. However, the relationship between lncRNAs and PMT in glioma has not been systematically investigated. Methods Gene expression profiles from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, and Rembrandt with available clinical and genomic information were used for analyses. Bioinformatics methods such as weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), Cox analysis, and least absolute shrinkage and selection operator (LASSO) analysis were performed. Results According to PMT scores, we confirmed that PMT status was positively associated with risky behaviors and poor prognosis in glioma. The 149 PMT-related lncRNAs were identified by WGCNA analysis, among which 10 (LINC01057, TP73-AS1, AP000695.4, LINC01503, CRNDE, OSMR-AS1, SNHG18, AC145343.2, RP11-25K21.6, RP11-38L15.2) with significant prognostic value were further screened to construct a PMT-related lncRNA risk signature, which could divide cases into two groups with distinct prognoses. Multivariate Cox regression analyses indicated that the signature was an independent prognostic factor for high-grade glioma. High-risk cases were more likely to be classified as the mesenchymal subtype, which confers enhanced immunosuppressive status by recruiting macrophages, neutrophils, and regulatory T cells. Moreover, six lncRNAs of the signature could act as competing endogenous RNAs to promote PMT in glioblastoma. Conclusions We profiled PMT status in glioma and established a PMT-related 10-lncRNA signature for glioma that could independently predict glioma survival and trigger PMT, which enhanced immunosuppression.

scholarly journals miRNA activity inferred from single cell mRNA expression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Morten Muhlig Nielsen ◽  
Jakob Skou Pedersen
Keyword(s):  
Mrna Expression ◽  
Single Cell ◽  
Target Genes ◽  
Expression Profiles ◽  
Single Cells ◽  
Enrichment Analysis ◽  
Sequence Motifs ◽  
Data Set ◽  
Motif Enrichment ◽  
Activity Measures

AbstractHigh throughput single-cell RNA sequencing (scRNAseq) can provide mRNA expression profiles for thousands of cells. However, miRNAs cannot currently be studied at the same scale. By exploiting that miRNAs bind well-defined sequence motifs and typically down-regulate target genes, we show that motif enrichment analysis can be used to derive miRNA activity estimates from scRNAseq data. Motif enrichment analyses have traditionally been used to derive binding motifs for regulatory factors, such as miRNAs or transcription factors, that have an effect on gene expression. Here we reverse its use. By starting from the miRNA seed site, we derive a measure of activity for miRNAs in single cells. We first establish the approach on a comprehensive set of bulk TCGA cancer samples (n = 9679), with paired mRNA and miRNA expression profiles, where many miRNAs show a strong correlation with measured expression. By downsampling we show that the method can be used to estimate miRNA activity in sparse data comparable to scRNAseq experiments. We then analyze a human and a mouse scRNAseq data set, and show that for several miRNA candidates, including liver specific miR-122 and muscle specific miR-1 and miR-133a, we obtain activity measures supported by the literature. The methods are implemented and made available in the miReact software. Our results demonstrate that miRNA activities can be estimated at the single cell level. This allows insights into the dynamics of miRNA activity across a range of fields where scRNAseq is applied.

scholarly journals Phenotypic and Functional Analyses of B7S1 in Ovarian Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Dongli Cai ◽  
Fang Wang ◽  
Changgang Wang ◽  
Liping Jin
Keyword(s):  
Mrna Expression ◽  
Signaling Pathways ◽  
Human Cancer ◽  
Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Antitumor Effects

Background: Although programmed death (PD) ligand 1 (PD-L1)/PD-1 inhibitors show potent and durable antitumor effects in a variety of tumors, their efficacy in patients with OvCa is modest. Thus, additional immunosuppressive mechanisms beyond PD-L1/PD-1 need to be identified.Methods: The mRNA expression profiles of OvCa patients were obtained from The Cancer Genome Atlas (TCGA) database. The expression and clinical characteristics of VTCN1 (encoding B7S1) in OvCa were analyzed. The molecular interaction network, Gene Ontology (GO) analysis and Gene set enrichment analysis (GSEA) were used to functionally annotate and predict signaling pathways of VTCN1 in OvCa. Moreover, 32 treatment-naïve patients with OvCa were recruited to assess B7S1 expression. The cytotoxic immune phenotypes in distinct subgroups were analyzed.Results: B7S1 expression was increased in tumor sections compared with that in normal tissues from OvCa patients at both the mRNA and protein levels. VTCN1 expression was significantly correlated with the mRNA expression levels of several other co-inhibitory immune checkpoints. B7S1 protein was found to be highly expressed in CD45+CD68+ myeloid cells, whereas its putative receptor was expressed in CD8+ tumor-infiltrating lymphocytes (TILs). Furthermore, expression of B7S1 in antigen-presenting cells (APCs) was significantly correlated with the cytolytic function of CD8+ TILs. Functional annotations indicated that VTCN1 was involved in regulating T cell-mediated immune responses and participated in the activation of a variety of classic signaling pathways related to the progression of human cancer.Conclusion: In OvCa, B7S1 was highly expressed and may initiate dysfunction of CD8+ TILs, which could be targeted for cancer immunotherapy.

scholarly journals Joint analysis of lncRNA m6A methylome and lncRNA/mRNA expression profiles in gastric cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhi Lv ◽  
Liping Sun ◽  
Qian Xu ◽  
Chengzhong Xing ◽  
Yuan Yuan
Keyword(s):  
Gastric Cancer ◽  
Mrna Expression ◽  
Expression Analysis ◽  
Target Genes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Joint Analysis ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment

Abstract Background N6-methyladenosine (m6A) modification might be closely associated with the genesis and development of gastric cancer (GC). Currently, the evidence established by high-throughput assay for GC-related m6A patterns based on long non-coding RNAs (lncRNAs) remains limited. Here, a joint analysis of lncRNA m6A methylome and lncRNA/mRNA expression profiles in GC was performed to explore the regulatory roles of m6A modification in lncRNAs. Methods Three subjects with primary GC were enrolled in our study and paired sample was randomly selected from GC tissue and adjacent normal tissue for each case. Methylated RNA Immunoprecipitation NextGeneration Sequencing (MeRIP-Seq) and Microarray Gene Expression Profiling was subsequently performed. Then co-expression analysis and gene enrichment analysis were successively conducted. Results After data analysis, we identified 191 differentially m6A-methylated lncRNAs, 240 differentially expressed lncRNAs and 229 differentially expressed mRNAs in GC. Furthermore, four differentially m6A-methylated and expressed lncRNAs (dme-lncRNAs) were discovered including RASAL2-AS1, LINC00910, SNHG7 and LINC01105. Their potential target genes were explored by co-expression analysis. And gene enrichment analysis suggested that they might influence the cellular processes and biological behaviors involved in mitosis and cell cycle. The potential impacts of these targets on GC cells were further validated by CCLE database and literature review. Conclusions Four novel dme-lncRNAs were identified in GC, which might exert regulatory roles on GC cell proliferation. The present study would provide clues for the lncRNA m6A methylation-based research on GC epigenetic etiology and pathogenesis.

scholarly journals In Silico Evaluation of Natural Compounds for an Acidic Extracellular Environment in Human Breast Cancer

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2673
Author(s):  
YoungJoon Park ◽  
Jaekwang Jeong ◽  
Shin Seong ◽  
Wonnam Kim
Keyword(s):  
Breast Cancer ◽  
Target Genes ◽  
Information Gain ◽  
Therapeutic Potential ◽  
Expression Profiles ◽  
Survival Rates ◽  
Gene Expression Profiles ◽  
Natural Compounds ◽  
The Cancer Genome Atlas ◽  
Potential Candidate

The survival rates for breast cancer (BC) have improved in recent years, but resistance, metastasis, and recurrence still remain major therapeutic challenges for BC. The acidic tumor microenvironment (TME) has attracted attention because of its association with tumorigenesis, metastasis, drug resistance, and immune surveillance. In this study, we evaluated natural compounds from traditional herbal medicine used to treat cancer that selectively target genes regulated by extracellular acidosis. We integrated four transcriptomic data including BC prognostic data from The Cancer Genome Atlas database, gene expression profiles of MCF-7 cells treated with 102 natural compounds, patterns of gene profiles by acidic condition, and single-cell RNA-sequencing from BC patient samples. Bruceine D (BD) was predicted as having the highest therapeutic potential, having an information gain (IG) score of 0.24, to regulate reprogrammed genes driven by acidosis affecting the survival of BC patients. BD showed the highest IG on EMT (IG score: 0.11) and invasion (IG score: 0.1) compared to the other phenotypes with the CancerSEA database. BD also demonstrated therapeutic potential by interfering with the tumor cell–TME interactions by reducing the amyloid beta precursor protein and CD44 expression. Therefore, BD is a potential candidate to target the acidic TME induced metastatic process in BC.

scholarly journals Identification and characterization of mRNAs and lncRNAs in the uterus of polytocous and monotocous Small Tail Han sheep (Ovis aries)

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6938 ◽  
Author(s):  
Yongfu La ◽  
Jishun Tang ◽  
Xiaoyun He ◽  
Ran Di ◽  
Xiangyu Wang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Ovis Aries ◽  
Differentially Expressed ◽  
Retinol Metabolism ◽  
Two Phases

Background Long non-coding RNAs (lncRNAs) regulate endometrial secretion and uterine volume. However, there is little research on the role of lncRNAs in the uterus of Small Tail Han sheep (FecB++). Herein, RNA-seq was used to comparatively analyze gene expression profiles of uterine tissue between polytocous and monotocous sheep (FecB++) in follicular and luteal phases. Methods To identify lncRNA and mRNA expressed in the uterus, the expression of lncRNA and mRNA in the uterus of Small Tail Han sheep (FecB++) from the polytocous group (n = 6) and the monotocous group (n = 6) using RNA-sequencing and real-time polymerase chain reaction (RT-PCR). Identification of differentially expressed lncRNAs and mRNAs were performed between the two groups and two phases . Gene ontology (GO) and pathway enrichment analyses were performed to analyze the biological functions and pathways for the differentially expressed mRNAs. LncRNA-mRNA co-expression network was constructed to further analyses the function of related genes. Results In the follicular phase, 473 lncRNAs and 166 mRNAs were differentially expressed in polytocous and monotocous sheep; in the luteal phase, 967 lncRNAs and 505 mRNAs were differentially expressed in polytocous and monotocous sheep. GO and KEGG enrichment analysis showed that the differentially expressed lncRNAs and their target genes are mainly involved in ovarian steroidogenesis, retinol metabolism, the oxytocin signaling pathway, steroid hormone biosynthesis, and the Foxo signaling pathway. Key lncRNAs may regulate reproduction by regulating genes involved in these signaling pathways and biological processes. Specifically, UGT1A1, LHB, TGFB1, TAB1, and RHOA, which are targeted by MSTRG.134747, MSTRG.82376, MSTRG.134749, MSTRG.134751, and MSTRG.134746, may play key regulatory roles. These results offer insight into molecular mechanisms underlying sheep prolificacy.

scholarly journals Understanding the Modus Operandi of MicroRNA Regulatory Clusters

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1103 ◽  
Author(s):  
Arthur C. Oliveira ◽  
Luiz A. Bovolenta ◽  
Lucas Alves ◽  
Lucas Figueiredo ◽  
Amanda O. Ribeiro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Modus Operandi ◽  
Wide Range

MicroRNAs (miRNAs) are non-coding RNAs that regulate a wide range of biological pathways by post-transcriptionally modulating gene expression levels. Given that even a single miRNA may simultaneously control several genes enrolled in multiple biological functions, one would expect that these tiny RNAs have the ability to properly sort among distinctive cellular processes to drive protein production. To test this hypothesis, we scrutinized previously published microarray datasets and clustered protein-coding gene expression profiles according to the intensity of fold-change levels caused by the exogenous transfection of 10 miRNAs (miR-1, miR-7, miR-9, miR-124, miR-128a, miR-132, miR-133a, miR-142, miR-148b, miR-181a) in a human cell line. Through an in silico functional enrichment analysis, we discovered non-randomic regulatory patterns, proper of each cluster identified. We demonstrated that miRNAs are capable of equivalently modulate the expression signatures of target genes in regulatory clusters according to the biological function they are assigned to. Moreover, target prediction analysis applied to ten vertebrate species, suggest that such miRNA regulatory modus operandi is evolutionarily conserved within vertebrates. Overall, we discovered a complex regulatory cluster-module strategy driven by miRNAs, which relies on the controlled intensity of the repression over distinct targets under specific biological contexts. Our discovery helps to clarify the mechanisms underlying the functional activity of miRNAs and makes it easier to take the fastest and most accurate path in the search for the functions of miRNAs in any distinct biological process of interest.

scholarly journals miRNA Expression and Interaction with Genes Involved in Susceptibility to Pristane-Induced Arthritis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jussara Gonçalves Fernandes ◽  
Andrea Borrego ◽  
José Ricardo Jensen ◽  
Wafa Hanna Koury Cabrera ◽  
Mara Adriana Correa ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Bone Destruction ◽  
Human Rheumatoid Arthritis ◽  
Rna Molecules ◽  
Peritoneal Cells ◽  
Mirna Expression Profiles ◽  
Chronic Autoimmune Disease

Pristane-induced arthritis (PIA) in mice is an experimental model that resembles human rheumatoid arthritis, a chronic autoimmune disease that affects joints and is characterized by synovial inflammation and articular cartilage and bone destruction. AIRmax and AIRmin mouse lines differ in their susceptibility to PIA, and linkage analysis in this model mapped arthritis severity QTLs in chromosomes 5 and 8. miRNAs are a class of small RNA molecules that have been extensively studied in the development of arthritis. We analyzed miRNA and gene expression profiles in peritoneal cells of AIRmax and AIRmin lines, in order to evaluate the genetic architecture in this model. Susceptible AIRmax mice showed higher gene (2025 vs 1043) and miRNA (240 vs 59) modulation than resistant AIRmin mice at the onset of disease symptoms. miR-132-3p/212-3p, miR-106-5p, miR-27b-3p, and miR-25-3p were among the miRNAs with the highest expression in susceptible animals, showing a negative correlation with the expression of predicted target genes (Il10, Cd69, and Sp1r1). Our study showed that global gene and miRNA expression profiles in peritoneal cells of susceptible AIRmax and resistant AIRmin lines during pristane-induced arthritis are distinct, evidencing interesting targets for further validation.

Sign in / Sign up

Export Citation Format

Share Document