In Silico Identification and Clinical Validation of a Novel Long Non-Coding RNA/mRNA/miRNA Molecular Network for Potential Biomarkers for Discriminating SARS CoV-2 Infection Severity

Abstract

(1) Background: The coronavirus (COVID-19) pandemic is still a major global health problem, despite the development of several vaccines and diagnostic assays. Moreover, the broad symptoms, from none to severe pneumonia, and the various responses to vaccines and the assays, make infection control challenging. Therefore, there is an urgent need to develop non-invasive biomarkers to quickly determine the infection severity. Circulating RNAs have been proven to be potential biomarkers for a variety of diseases, including infectious ones. This study aimed to develop a genetic network related to cytokines, with clinical validation for early infection severity prediction. (2) Methods: Extensive analyses of in silico data have established a novel IL11RA molecular network (IL11RNA mRNA, LncRNAs RP11-773H22.4 and hsa-miR-4257). We used different databases to confirm its validity. The differential expression within the retrieved network was clinically validated using quantitative RT-PCR, along with routine assessment diagnostic markers (CRP, LDH, D-dimmer, procalcitonin, Ferritin), in100 infected subjects (mild and severe cases) and 100 healthy volunteers. (3) Results: IL11RNA mRNA and LncRNA RP11-773H22.4, and the IL11RA protein, were significantly upregulated, and there was concomitant downregulation of hsa-miR-4257, in infected patients, compared to the healthy controls, in concordance with the infection severity. (4) Conclusion: The in-silico data and clinical validation led to the identification of a potential RNA/protein signature network for novel predictive biomarkers, which is in agreement with ferritin and procalcitonin for determination of COVID-19 severity.

Host miRNAs as biomarkers of SARS-CoV-2 infection: a critical review

“…72 Hsa-miR-4257 was downregulated in COVID-19 patients versus controls and in severe versus mild COVID-19 as reported by Agwa and colleagues. 73 ROC analysis revealed that hsa-miR-4257 expression could be used as biomarker to distinguish COVID-19 patients from controls with an AUC of 0.911. 73 Differential expression of two miRNAs was found by using whole serum of patients with severe and non-severe COVID-19, whereas ten distinct differentially expressed miRNAs were identified when using extracellular vesicles isolated from sera (Table 1).…”

“…Hsa-miR-4257 was downregulated in COVID-19 patients versus controls and in severe versus mild COVID-19 as reported by Agwa and colleagues. 73 ROC analysis revealed that hsa-miR-4257 expression could be used as biomarker to distinguish COVID-19 patients from controls with an AUC of 0.911. 73…”

“…73 ROC analysis revealed that hsa-miR-4257 expression could be used as biomarker to distinguish COVID-19 patients from controls with an AUC of 0.911. 73…”

Cardiovascular signatures of COVID-19 predict mortality and identify barrier stabilizing therapies

“… 47 , 48 Circulating profiles of plasma microRNAs (miRNA) in particular have been shown to be tightly associated with disease and capable of providing not only detailed prognostic information but also mechanistic insight. 49 , 50 Indeed, circulating miRNAs are emerging as a potential biomarker for SARS-CoV-2 infection, 51 , 52 , 53 and may even be involved in regulating disease phenotypes, 54 and may be considered in novel treatments. 55 , 56 Whether miRNAs can be used as a prognosticator for disease severity and mortality in vulnerable groups such as patients with CVD remains largely unexplored.…”

Transcriptomics Meta-Analysis Predicts Two Robust Human Biomarkers for Severe Infection with SARS-CoV-2

“…Some studies have identified associations between acute infection and the host response [17,18]. Other studies have evaluated associations between disease severity and aspects of the adaptive immune system [19][20][21][22][23] and quantified viral RNA [24][25][26][27][28][29]. A recent study has used neural networks to predict patient survival outcomes with high accuracy [30], which can be useful when whole transcriptome data are available.…”