Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus.

Background: Effectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE. Methods: The GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis. Results: We identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01). Conclusions: Our preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.

Identification of Candidate Genes in Breast Cancer Induced by Estrogen Plus Progestogens Using Bioinformatic Analysis.

Menopausal hormone therapy (MHT) was widely used to treat menopause-related symptoms in menopausal women. However, MHT therapies were controversial with the increased risk of breast cancer because of different estrogen and progestogen combinations, and the molecular basis behind this phenomenon is currently not understood. To address this issue, we identified differentially expressed genes (DEGs) between the estrogen plus progestogens treatment (EPT) and estrogen treatment (ET) using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data. As a result, a total of 96 upregulated DEGs were first identified. Seven DEGs related to the cell cycle (CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3) were validated by RT-qPCR. Specifically, these seven DEGs were increased in EPT compared to ET (p < 0.05) and had higher expression levels in breast cancer than adjacent normal tissues (p < 0.05). Next, we found that estrogen receptor (ER)-positive breast cancer patients with a higher CNNE2 expression have a shorter overall survival time (p < 0.05), while this effect was not observed in the other six DEGs (p > 0.05). Interestingly, the molecular docking results showed that CCNE2 might bind to 17ß-estradiol (−6.791 kcal/mol), progesterone (−6.847 kcal/mol), and medroxyprogesterone acetate (−6.314 kcal/mol) with a relatively strong binding affinity, respectively. Importantly, CNNE2 protein level could be upregulated with EPT and attenuated by estrogen receptor antagonist, acolbifene and had interactions with cancer driver genes (AKT1 and KRAS) and high mutation frequency gene (TP53 and PTEN) in breast cancer patients. In conclusion, the current study showed that CCNE2, CDCA5, RAD51, TCF19, KNTC1, MCM10, and NEIL3 might contribute to EPT-related tumorigenesis in breast cancer, with CCNE2 might be a sensitive risk indicator of breast cancer risk in women using MHT.

MicroRNA-Disease Network Analysis Repurposes Methotrexate for the Treatment of Abdominal Aortic Aneurysm in Mice.

Abdominal aortic aneurysm (AAA) is a permanent dilatation of the abdominal aorta and is highly lethal. The main purpose of the current study is to search for noninvasive medical therapies for AAA, for which there is currently no effective drug therapy. Network medicine represents a cutting-edge technology, as analysis and modeling of disease networks can provide critical clues regarding the etiology of specific diseases and which therapeutics may be effective. Here, we proposed a novel algorithm to quantify disease relations based on a large accumulated microRNA-disease association dataset and then built a disease network covering 15 disease classes and 304 diseases. Analysis revealed some patterns for these diseases. For instance, diseases tended to be clustered and coherent in the network. Surprisingly, we found that AAA showed the strongest similarity with rheumatoid arthritis and systemic lupus erythematosus, both of which are autoimmune diseases, suggesting that AAA could be one type of autoimmune disease in etiology. Based on this observation, we further hypothesized that drugs for autoimmune disease could be repurposed for the prevention and therapy of AAA. Finally, animal experiments confirmed that methotrexate, a drug for autoimmune disease, was able to alleviated the formation and development of AAA.

Erratum: MicroRNA Profiling in Paired Left and Right Eyes, Lungs, and Testes of Normal Mice.

[This corrects the article DOI: 10.1016/j.omtn.2020.07.006.].

SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec.

BACKGROUND: Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy. RESULTS: In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually. CONCLUSIONS: We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.

Y/X-Chromosome-Bearing Sperm Shows Elevated Ratio in the Left but Not the Right Testes in Healthy Mice.

The sex chromosomes play central roles in determining the sex of almost all of the multicellular organisms. It is well known that meiosis in mammalian spermatogenesis produces ~50% Y- and ~50% X-chromosome-bearing sperm, a 1:1 ratio. Here we first reveal that the X-chromosome-encoded miRNAs show lower expression levels in the left testis than in the right testis in healthy mice using bioinformatics modeling of miRNA-sequencing data, suggesting that the Y:X ratio could be unbalanced between the left testis and the right testis. We further reveal that the Y:X ratio is significantly elevated in the left testis but balanced in the right testis using flow cytometry. This study represents the first time the biased Y:X ratio in the left testis but not in the right testis is revealed.

MicroRNA Profiling in Paired Left and Right Eyes, Lungs, and Testes of Normal Mice.

Physiological and pathophysiological differences widely exist in paired organ systems. However, the molecular basis for these differences remains largely unknown. We previously reported that there exist differentially expressed miRNAs (DEMs) in the left and right kidneys of normal mice. Here, we identified the DEMs in the left and right eyes, lungs, and testes of normal mice via RNA sequencing. As a result, we identified 26 DEMs in eyes, with 23 higher and 3 lower in the left eyes compared with right eyes; 21 DEMs in lungs, with 15 higher and 6 lower in the left lungs compared with right lungs; and 54 DEMs in testes, with 6 higher and 48 lower in the left testes compared with right testes. Ten microRNAs (miRNAs) were further examined by quantitative PCR assays, and seven of these were confirmed. In addition, correlation analysis was performed between paired organ miRNA expressions and diverse body fluid miRNA expressions. Finally, we explored the functions and networks of DEMs and performed biological process and pathway enrichment analysis of target genes for DEMs, providing insights into the physiological and pathophysiological differences between the two entities of paired organs.

sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers.

MicroRNAs (miRNAs) are an important class of small noncoding RNA molecules that serve as excellent biomarkers of various diseases. However, current miRNA biomarkers, including those comprised of multiple miRNAs, work at a single-miRNA level but not at a miRNA-set level, which is defined as a group of miRNAs sharing common biological characteristics. Given the rapidly accumulating miRNA omics data, we believe that the miRNA-set level analysis could be an important supplement to the single-miRNA level analysis. Therefore, we present sTAM (http://mir.rnanut.net/stam), a computational tool for single-sample miRNA-set enrichment analysis. Moreover, we demonstrate the utility of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conduct a pan-cancer analysis of the sTAM scores of the "tumor suppressor miRNA set" on 15 types of cancers from The Cancer Genome Atlas (TCGA) and 14 from Gene Expression Omnibus (GEO), results of which indicated a good performance in distinguishing cancers from controls. Moreover, we reveal that the sTAM scores of the "brain development miRNA set" can effectively predict cerebrovascular disorder (CVD). Finally, we believe that sTAM can be used to discover disease-related biomarkers at a miRNA-set level.

Annotation and curation of the causality information in LncRNADisease.

Disease causative non-coding RNAs (ncRNAs) are of great importance in understanding a disease, for they directly contribute to the development or progress of a disease. Identifying the causative ncRNAs can provide vital implications for biomedical researches. In this work, we updated the long non-coding RNA disease database (LncRNADisease) with long non-coding RNA (lncRNA) causality information with manual annotations of the causal associations between lncRNAs/circular RNAs (circRNAs) and diseases by reviewing related publications. Of the total 11 568 experimental associations, 2297 out of 10 564 lncRNA-disease associations and 198 out of 1004 circRNA-disease associations were identified to be causal, whereas 635 lncRNAs and 126 circRNAs were identified to be causative for the development or progress of at least one disease. The updated information and functions of the database can offer great help to future researches involving lncRNA/circRNA-disease relationship. The latest LncRNADisease database is available at http://www.rnanut.net/lncrnadisease.

The value of plasma fibrillin-1 level in patients with spontaneous coronary artery dissection.

BACKGROUND: Spontaneous coronary artery dissection (SCAD) has emerged as an important etiology of myocardial infarction and sudden death, especially in young women. Early diagnosis is essential for appropriate management. OBJECTIVES: To explore the value of plasma fibrillin-1 (FBN1) levels in patients with SCAD. METHODS: 70 patients with non-atherosclerotic SCAD between January 2014 and September 2018 were age and sex matched with 70 patients with non-SCAD acute coronary syndrome (ACS) and 70 healthy controls. The plasma FBN1 level was measured and compared among three groups. The value of FBN1 for prognosis and treatment decision making was further explored. RESULTS: The plasma FBN1 level of SCAD group (58.44 ± 7.06 ng/mL) was higher than that of non-SCAD ACS group (52.39 ± 6.92 ng/mL, P < 0.001) or healthy controls (50.56 ± 4.48 ng/mL, P < 0.001). Compared with controls, significantly higher percentages of patients with SCAD were found in the highest compared with lowest quartile of FBN1 concentration. The area under the curve (AUC) for plasma FBN1 level to discriminate patients with SCAD from non-SCAD ACS was 0.81 (95% CI 0.74-0.88, P < 0.001). A cut-off value of 54.64 ng/mL was determined to differentiate SCAD from non-SCAD ACS with a sensitivity of 0.77 (95%CI: 0.66-0.86) and specificity of 0.76 (95%CI: 0.64-0.85). After a median follow-up of 28.35 (14.07 ± 44.69) months, 11 (15.7%) cases suffered from major adverse cardiac events (MACE). Higher FBN1 level was detected in patients with MACE (63.71 ± 7.49 vs. 57.45 ± 6.58 ng/mL) (P = 0.006). A cut-point of 58.14 was determined for SCAD patients to identify MACE. At this point, FBN1 might also have potential use for decision making in SCAD patients. CONCLUSION: Plasma FBN1 is a promising biomarker for aiding the diagnosis of SCAD and have potential value in prognosis prediction.

Benchmark of computational methods for predicting microRNA-disease associations.

BACKGROUND: A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness. RESULTS: Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations. CONCLUSION: Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.

A Computational Model to Predict the Causal miRNAs for Diseases.

MicroRNAs (miRNAs) are one class of important noncoding RNA molecules, and their dysfunction is associated with a number of diseases. Currently, a series of databases and algorithms have been developed for dissecting human miRNA-disease associations. However, these tools only presented the associations between miRNAs and disease but did not address whether the associations are causal or not, a key biomedical issue that is critical for understanding the roles of candidate miRNAs in the mechanisms of specific diseases. Here we first manually curated causal miRNA-disease association information and updated the human miRNA disease database (HMDD) accordingly. Then we built a computational model, MDCAP (MiRNA-Disease Causal Association Predictor), to predict novel causal miRNA-disease associations. As a result, we collected 6,667 causal miRNA-disease associations between 616 miRNAs and 440 diseases, which accounts for ∼20% of the total data in HMDD. The MDCAP model achieved an area under the receiver operating characteristic (ROC) curve of 0.928 for ROC analysis by independent test and an area under the ROC curve of 0.925 for ROC analysis by 10-fold cross-validation. Finally, case studies conducted on myocardial infarction and hsa-mir-498 further suggested the biomedical significance of the predictions.

MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations.

MicroRNAs (miRNAs) are one class of important small non-coding RNA molecules and play critical roles in health and disease. Therefore, it is important and necessary to evaluate the functional relationship of miRNAs and then predict novel miRNA-disease associations. For this purpose, here we developed the updated web server MISIM (miRNA similarity) v2.0. Besides a 3-fold increase in data content compared with MISIM v1.0, MISIM v2.0 improved the original MISIM algorithm by implementing both positive and negative miRNA-disease associations. That is, the MISIM v2.0 scores could be positive or negative, whereas MISIM v1.0 only produced positive scores. Moreover, MISIM v2.0 achieved an algorithm for novel miRNA-disease prediction based on MISIM v2.0 scores. Finally, MISIM v2.0 provided network visualization and functional enrichment analysis for functionally paired miRNAs. The MISIM v2.0 web server is freely accessible at http://www.lirmed.com/misim/.

HMDD v3.0: a database for experimentally supported human microRNA-disease associations.

Comprehensive databases of microRNA-disease associations are continuously demanded in biomedical researches. The recently launched version 3.0 of Human MicroRNA Disease Database (HMDD v3.0) manually collects a significant number of miRNA-disease association entries from literature. Comparing to HMDD v2.0, this new version contains 2-fold more entries. Besides, the associations have been more accurately classified based on literature-derived evidence code, which results in six generalized categories (genetics, epigenetics, target, circulation, tissue and other) covering 20 types of detailed evidence code. Furthermore, we added new functionalities like network visualization on the web interface. To exemplify the utility of the database, we compared the disease spectrum width of miRNAs (DSW) and the miRNA spectrum width of human diseases (MSW) between version 3.0 and 2.0 of HMDD. HMDD is freely accessible at http://www.cuilab.cn/hmdd. With accumulating evidence of miRNA-disease associations, HMDD database will keep on growing in the future.

Identification and Analysis of Human Sex-biased MicroRNAs.

Sex differences are widely observed under various circumstances ranging from physiological processes to therapeutic responses, and a myriad of sex-biased genes have been identified. In recent years, transcriptomic datasets of microRNAs (miRNAs), an important class of non-coding RNAs, become increasingly accessible. However, comprehensive analysis of sex difference in miRNA expression has not been performed. Here, we identified the differentially-expressed miRNAs between males and females by examining the transcriptomic datasets available in public databases and conducted a systemic analysis of their biological characteristics. Consequently, we identified 73 female-biased miRNAs (FmiRs) and 163 male-biased miRNAs (MmiRs) across four tissues including brain, colorectal mucosa, peripheral blood, and cord blood. Our results suggest that compared to FmiRs, MmiRs tend to be clustered in the human genome and exhibit higher evolutionary rate, higher expression tissue specificity, and lower disease spectrum width. In addition, functional enrichment analysis of miRNAs show that FmiR genes are significantly associated with metabolism process and cell cycle process, whereas MmiR genes tend to be enriched for functions like histone modification and circadian rhythm. In all, the identification and analysis of sex-biased miRNAs together could provide new insights into the biological differences between females and males and facilitate the exploration of sex-biased disease susceptibility and therapy.

A potential role of microRNAs in protein accumulation in cellular senescence analyzed by bioinformatics.

Cellular senescence is an important protective mechanism against cell proliferation and has critical roles in aging and aging-related disease. Recently, one interesting observation is that the protein abundance is higher in senescent cells than that in young cells. So far, some factors were presented to interpret this observation, such as active protein synthesis linked with autophagy, mTOR, and oxidative stress. Here, applying bioinformatic analysis of microRNA profiles in young cells and aging cells, we revealed that globally senescent cells show lower miRNA abundance than that in young cells, suggesting that the repression of protein synthesis by miRNA in senescent cells could be largely attenuated. This finding provides clues that protein accumulation in cellular senescence could be associated with lower miRNA abundance in aging cells.