Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization

The SARS-CoV-2 (COVID-19) pandemic has caused millions of deaths worldwide. Early risk assessment of COVID-19 cases can help direct early treatment measures that have been shown to improve the prognosis of severe cases. Currently, circulating miRNAs have not been evaluated as canonical COVID-19 biomarkers, and identifying biomarkers that have a causal relationship with COVID-19 is imperative. To bridge these gaps, we aim to examine the causal effects of miRNAs on COVID-19 severity in this study using two-sample Mendelian randomization approaches. Multiple studies with available GWAS summary statistics data were retrieved. Using circulating miRNA expression data as exposure, and severe COVID-19 cases as outcomes, we identified ten unique miRNAs that showed causality across three phenotype groups of COVID-19. Using expression data from an independent study, we validated and identified two high-confidence miRNAs, namely, hsa-miR-30a-3p and hsa-miR-139-5p, which have putative causal effects on developing cases of severe COVID-19. Using existing literature and publicly available databases, the potential causative roles of these miRNAs were investigated. This study provides a novel way of utilizing miRNA eQTL data to help us identify potential miRNA biomarkers to make better and early diagnoses and risk assessments of severe COVID-19 cases.

CircularLRRC7 is a Potential Tumor Suppressor Associated With miR-1281 and PDXP Expression in Glioblastoma

Circular RNAs (circRNAs) are usually enriched in neural tissues, yet about 80% circRNAs have lower expression in gliomas relative to normal brains, highlighting the importance of circRNAs as tumor suppressors. However, the clinical impact as well as the pathways regulated by the tumor-suppressive circRNAs remain largely unknown in glioblastoma (GBM). Through bioinformatic analysis followed by experimental validation, we found that hsa_circ_0114014 (circLRRC7) was dramatically down-regulated in GBM when compared with normal brain tissues (p < 0.0001). GBM patients with a lower circLRRC7 expression had poorer progression-free survival (PFS, p < 0.05) and overall survival (OS, p < 0.05). Analyses of the predicted target miRNAs of circLRRC7 in CSCD and CRI databases, in combination with the miRNA expression data in GBMs and normal brains from GSE database, revealed miR-1281 as a potential downstream target of circLRRC7. Subsequently, the target genes of hsa-mir-1281 were predicted by TargetScan, miRDB and miRNATAR databases. Intersection analysis and correlation test indicated that PDXP was a potential target of miR-1281. In summary, circLRRC7 may be a tumor suppressor that associated with miR-1281 and PDXP expression in GBM, which may provide novel therapeutic targets for GBM treatment.

Dimensionality reduction by sparse orthogonal projection with applications to miRNA expression analysis and cancer prediction

Background: High dimensionality, i.e. p>n, is an inherent feature of machine learning. Fitting a classification model directly to p-dimensional data risks overfitting and a reduction in accuracy. Thus, dimensionality reduction is necessary to address overfitting and high dimensionality. Results: We present a novel dimensionality reduction method which uses sparse, orthogonal projections to discover linear separations in reduced dimension space. The technique is applied to miRNA expression analysis and cancer prediction. We use least squares fitting and orthogonality constraints to find a set of orthogonal directions which are highly correlated to the class labels. We also enforce L^1 norm sparsity penalties, to prevent overfitting and remove the uninformative features from the model. Our method is shown to offer a highly competitive classification performance on synthetic examples and real miRNA expression data when compared to similar methods from the literature which use sparsity ideas and orthogonal projections. %Specifically, our method offers a more consistent performance in terms of sensitivity and AUC, particularly in the case $p>n$, and when the training samples are weighted towards one class. Discussion: A novel technique is introduced here, which uses sparse, orthogonal projections for dimensionality reduction. The approach is shown to be highly effective in reducing the dimension of miRNA expression data. The application of focus in this article is miRNA expression analysis and cancer prediction. The technique may be generalizable, however, to other high dimensionality datasets.

Fast and robust imputation for miRNA expression data using constrained least squares

High dimensional transcriptome profiling, whether through next generation sequencing techniques or high-throughput arrays, may result in scattered variables with missing data. Data imputation is a common strategy to maximize the inclusion of samples by using statistical techniques to fill in missing values. However, many data imputation methods are cumbersome and risk introduction of systematic bias. Here we present a new data imputation method using constrained least squares and algorithms from the inverse problems literature and present applications for this technique in miRNA expression analysis. The proposed technique is shown to offer an imputation orders of magnitude faster, with greater than or equal accuracy when compared to similar methods from the literature.

A Novel TCGA-Validated, MiRNA-Based Signature for Prediction of Breast Cancer Prognosis and Survival

Breast cancer (BC) is the most common cancer affecting women and the leading cause of cancer-related deaths worldwide. Compelling evidence indicates that microRNAs (miRNAs) are inextricably involved in the development of cancer. Here, we constructed a novel model, based on miRNA-seq and clinical data downloaded from The Cancer Genome Atlas (TCGA). Data from a total of 962 patients were included in this study, and the relationships among their clinicopathological features, survival, and miRNA-seq expression levels were analyzed. Hsa-miR-186 and hsa-miR-361 were identified as internal reference miRNAs and used to normalize miRNA expression data. A five-miRNA signature, constructed using univariate and multivariate Cox regression, was significantly associated with disease-specific survival (DSS) of patients with BC. Kaplan–Meier (KM) and receiver operating characteristic (ROC) analyses were conducted to confirm the clinical significance of the five-miRNA signature. Finally, a nomogram was constructed based on the five-miRNA signature to evaluate its clinical value. Cox regression analysis revealed that a five-miRNA signature was significantly associated with DSS of patients with BC. KM analysis demonstrated that the signature could efficiently distinguish high- and low-risk patients. Moreover, ROC analysis showed that the five-miRNA signature exhibited high sensitivity and specificity in predicting the prognosis of patients with BC. Patients in the high-risk subgroup who received adjuvant chemotherapy had a significantly lower incidence of mortality than those who did not. A nomogram constructed based on the five-miRNA signature was effective in predicting 5-year DSS. This study presents a novel five-miRNA signature as a reliable prognostic tool to predict DSS and provide theoretical reference significance for individualized clinical decisions for patients with BC.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Background Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. Results In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. Conclusions The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers

Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer.

Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data

Multiple myeloma (MM) is a plasma cell malignancy with diverse clinical phenotypes and molecular heterogeneity not completely understood. Differentially expressed genes (DEGs) and miRNAs (DEMs) in MM may influence disease pathogenesis, clinical presentation / drug sensitivities. But these signatures overlap meagrely plausibly due to complexity of myeloma genome, diversity in primary cells studied, molecular technologies/ analytical tools utilized. This warrants further investigations since DEGs/DEMs can impact clinical outcomes and guide personalized therapy. We have conducted genome-wide meta-analysis of DEGs/DEMs in MM versus Normal Plasma Cells (NPCs) and derived unified putative signatures for MM. 100 DEMs and 1,362 DEGs were found deranged between MM and NPCs. Signatures of 37 DEMs (‘Union 37’) and 154 DEGs (‘Union 154’) were deduced that shared 17 DEMs and 22 DEGs with published prognostic signatures, respectively. Two miRs (miR-16–2-3p, 30d-2-3p) correlated with survival outcomes. PPI analysis identified 5 topmost functionally connected hub genes (UBC, ITGA4, HSP90AB1, VCAM1, VCP). Transcription factor regulatory networks were determined for five seed DEGs with ≥ 4 biomarker applications (CDKN1A, CDKN2A, MMP9, IGF1, MKI67) and three topmost up/ down regulated DEMs (miR-23b, 195, let7b/ miR-20a, 155, 92a). Further studies are warranted to establish and translate prognostic potential of these signatures for MM.

Genetic and tumor microenvironment differences between cell cycle progression pathway altered/non-altered patients with lung adenocarcinoma

Background Lung cancer is the leading cause of cancer-related death worldwide, among which lung adenocarcinoma (LUAD) is the most common type. Identified as a hallmark of cancer, the dysregulated cell cycle progression plays an important role in the promotion and progression of LUAD. This article aims to elucidate the heterogeneity between CDKN2A-CDK/cyclin-RB1 cell cycle progression pathway altered /non-altered patients with LUAD, thus helping us have a better understanding of the effect of the aberrant cell cycle. Material and Methods The data of this study were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/) and UCSC Xena Browser (http://xena.ucsc.edu/), including simple nucleotide variation data, RNA-seq gene expression data, survival data, clinical data, and miRNA expression data. After matching the RNA-seq gene expression data, simple nucleotide variation data, miRNA expression data, and survival data with clinical data, 510 gene and long non-coding RNA expression data, 506 simple nucleotide variation data, 440 microRNA expression data, and 497 survival data were included in this study for further analysis. R software (version 4.0.3) was used for analysis. Results After dividing the patients into mutation (n = 57) and wild (n = 453) groups according to the cell cycle progression pathway status, we found no significant difference in survivorship between them. The mutation group had a higher mutational load and mutational rates of various genes such as tumor protein P53 (TP53) compared to the wild group. Subsequently, we analyzed the differentially expressed genes (DEGs) between the two groups. Among the 58387 genes analyzed, 302 were upregulated, and 354 were downregulated in the mutation group. Enrichment analysis indicated that these DEGs were closely related to metabolism items and cell cycle-related events. After performing immune cell infiltration analysis, we found the two groups have different patterns of immune cell profiling. Albeit the immune and stromal scores were higher in the wild group, we failed to find any significant difference between the two groups. Finally, we build a computational model to predict the cell cycle progression pathway-related gene mutation by LASSO-binary logistic regression analysis, the predictive accuracy of which is 0.88. Conclusion In summary, our study compared the genetic and microenvironment differences between cell cycle progression pathway altered /non-altered patients with LUAD by analyzing the data from TCGA datasets. We hope our findings could improve our understanding of the heterogeneity between the two kinds of patients, thus providing new insight into LUAD patients' treatments.

Evaluation of the severity of nonalcoholic fatty liver disease by the analysis of serum exosomal miRNA expression

Noninvasive modalities exhibit limited diagnostic performance for evaluating the severity of nonalcoholic fatty liver disease (NAFLD). MicroRNAs (miRNAs) serve as useful biomarkers for diagnosing and monitoring disease progression and treatment response. Here, we evaluated whether serum exosomal miRNAs can be used to clinically predict the severity of NAFLD. Exosomal miRNAs were isolated from the sera of 41 NAFLD patients (diagnosed using biopsy) for array profiling. To compare differentially expressed miRNAs, the degree of severity was determined using inflammation, steatosis, ballooning, and NAFLD activity score (NAS). The correlation of miRNAs with clinical and biochemical parameters was analyzed. The correlation between miRNA and mRNA expression was analyzed by comparing our miRNA expression data with publicly available mRNA expression data. Twenty-five, eleven, thirteen, and fourteen miRNAs correlated with inflammation score, steatosis score, ballooning score, and NAS, respectively. Thirty-three significant correlations were observed between twenty-seven miRNAs and six clinical variables (age, ALP, AST, ALT, GGT, and NAS). In fibrosis, 52 and 30 interactions corresponding to high miRNA-low mRNA and low miRNA-high mRNA expression, respectively, were observed. We demonstrated that serum exosomal miRNAs can be used to evaluate NAFLD severity and also delineate potential targets for NAFLD treatment.