https://orcid.org/0000-0002-4007-7868
Figures
Abstract
Background
The SARS-CoV-2 virus’s frequent mutations have made disease control with vaccines and antiviral drugs difficult; as a result, there is a need for more effective coronavirus drugs. Therefore, detecting the expression of various diagnostic biomarkers, including ncRNA in SARS-CoV2, implies new therapeutic strategies for the disease.
Aim
Our study aimed to measure NEAT-1, miR-374b-5p, and IL6 in the serum of COVID-19 patients, demonstrating the correlation between target genes to explore the possible relationship between them. Also, the association between target genes and patients’ clinical findings and radiological severity indices will be explored.
Patients and methods
The current study included 48 COVID-19-infected individuals and 40 controls. Quantitative real-time PCR (qPCR) was performed to detect lncRNA NEAT-1 and miRNA374b-5p fold change (FC) in the participants’ sera. Enzyme-Linked Immune Sorbent Assay (ELISA) is used to detect IL6.
Results
Our results showed statistical significance with lower levels of (NEAT-1) [ median (range) = 0.08 (0.001-0.602)], and (miR374b-5p) [ median (range) = 0.14 (.01-7.16)] while higher IL-6 levels [ median (range) = 41.3 (7.2-654) pg/ml] when compared to controls with p-value <0.001. Serum level of NEAT-1 correlates negatively with IL-6 level (r = -.317, P = .008). ROC curve analysis revealed that sensitivity and specificity tests for NEAT-1 and IL-6 levels in the diagnosis of cases illustrated a sensitivity of (100% and 97.9%) and a specificity of (85% and 100%) at cut-off values (0.985 and 12.55), respectively. In comparison, miR374b-5p showed sensitivity and specificity of around 85% in distinguishing COVID-19 patients from controls. No significant association was detected between target genes and radiological severity indices.
Citation: Ali MA, Shaker OG, Ezzat EM, Ali ESG, Aboelor MI, Ahmed MI, et al. (2024) Peripheral lncRNA NEAT-1, miR374b-5p, and IL6 panel to guide in COVID-19 patients’ diagnosis and prognosis. PLoS ONE 19(12): e0313042. https://doi.org/10.1371/journal.pone.0313042
Editor: Vinay Kumar, Pennsylvania State University Hershey Medical Center, UNITED STATES OF AMERICA
Received: June 17, 2024; Accepted: October 16, 2024; Published: December 27, 2024
Copyright: © 2024 Ali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The COVID-19 pandemic, caused by the SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2), presents an ongoing challenge to the medical and research community. Over 775 million confirmed cases and over seven million deaths have been reported globally by June 2024 [1].
Several studies reported the role of ncRNAs in coronavirus infection. Among these ncRNAs are lncRNAs that are nearly 200 nucleotides long with no translational activity; instead, these lncRNAs can interact with various molecules, such as DNA, proteins, and other RNAs. Additionally, they may modify targeted mRNA expression by sponging microRNAs (types of ncRNA molecules with 21-25 nt in length) for the same binding sites, as proposed by competing endogenous RNA (ceRNA hypothesis) [2].
One of the lncRNAs, nuclear paraspeckle assembly transcript 1 (NEAT-1), has been shown to play a role in the inflammatory activities of immune cells, including macrophages and monocytes [3]. Previous literature showed that NEAT-1 is overexpressed in the peripheral blood of CORVID-19 patients [4], while others showed that its expression is exclusive to tissues [5]. Furthermore, some articles showed that NEAT-1 is differentially expressed between different stages of disease severity [5,6]. However, others showed that there are no significant differences in NEAT-1 expression between moderate and severe stages of the disease [4,7] or between the acute and post-acute phases of COVID-19 infection [8]. Meanwhile, Meydan et al., 2020 article showed that NEAT-1 is down-regulated in lung cells infected with SARS-CoV2 compared to normal lung cells or lung cells infected with other respiratory viral infections such as the influenza virus (H1N1) [6]. These controversial results reflect the various NEAT-1 functions through targeting different noncoding RNA and different protein-coding RNA genes in different stages and severity of COVID-19 infection [3]
Amongst this noncoding RNA that is targeted by NEAT-1 is miR374b-5p, which is in 2023 recent research demonstrating that NEAT-1 may serve as the ceRNA of miR-374b-5p in the development of osteoarthritis [9] Huang et al. 2023 reported that silencing of NEAT-1 increases miR374b-5p expression [9]. Furthermore, in another 2023 bioinformatic analysis paper, miR374b can inhibit the RNA-dependent RNA polymerase enzyme (RdRp), which is crucial for the life cycle of the SARS-CoV2 virus that leads to decreased inflammation in COVID-19 infection and maybe a new therapeutic approach to coronavirus control [10].
Among the protein-coding RNAs targeted by NEAT-1 is IL6 [10]. IL6 is an essential cytokine that increases in patients with COVID-19 at all stages of the disease and at all severity levels [11] that ends with cytokine storm syndrome [12–14], and fatal acute respiratory distress syndrome and other organ failure [13]. IL-6 is also a key cytokine target for therapy in COVID-19 [14,15].
Recently, it was found that IL6 is a target for miR-374 family members; Sanchez et al., 2022 demonstrated that by in vivo and invitro evidence, miR-374a-5p expression was inversely correlated with some of its targets, including IL6 and other inflammatory genes in inflammatory bowel disease [16]. Additionally, Zhao et al., 2022 reported that the loss of miR-374c-5p negatively regulates IL6 in unexplained recurrent spontaneous abortion [17]. Furthermore, Huang et al., 2023 found that the loss of NEAT-1 or the upregulation of miR-374b-5p dramatically accelerated apoptosis, led to the arrest of G1 / S, and promoted the secretion of inflammatory cytokines (TNF-α, IL-1β, and IL-6) in lipopolysaccharide (LPS) induced chondrocytes [9].
Our study aimed to measure NEAT-1, miR-374b-5p, and IL6 in the serum of COVID-19 patients, demonstrating a correlation between target genes to explore the possible relationship between them. Also, to explore the association between target genes and clinical findings of the patients, as well as radiological severity indices.
Materials and methods
Ethical approval and sample collection
EL-Fayoum Ethical Committee no (R 239, Session No 96) approved the current case-control study. After all participants were informed of the aim and procedure of the study, they assigned written consent. This study followed the ethical rules of Helsinki. COVID-19 patients were enrolled from the Chest, Internal Medicine, Tropical Medicine, and Critical Care departments of Fayoum University Hospital, El Fayoum, Egypt. All blood samples were collected from Jul. 15, 2023, to 25 th Sep 2023. All practical experiments were done in the Medical Biochemistry, Microbiology, and Clinical Pathology departments at Fayoum University, Al Fayoum, Egypt.
Participants
The current study included 48 COVID-19-infected individuals and 40 healthy subjects. Nasopharyngeal swabs were collected from all patients and used for reverse transcriptase PCR (RT-PCR) for detection of the virus; the initial RT-PCR test revealed that 38 patients were positive while 10 were negative RT-PCR but, by repeating RT-PCR, all patients were positive. Exclusion criteria include co-infected with another viral infection like HBV, HIV, HCV, or CMV or with TB or other superimposed bacterial chest infections detected by sputum discoloration and confirmed by culture were excluded from the study. Also, pregnant females were excluded.
History taking, routine physical examination, arterial blood gases, and laboratory tests were performed for all patients. CT and chest radiographs were done for all patients. Diagnosis of COVID-19 by considering a combination of factors, including a physical examination and review of symptoms by a healthcare professional, imaging tests such as X-rays or CT scans, CO-RADs (COVID-19 Reporting and Data System) or CT-RSNA, and /or positive reverse transcriptase real-time (RT-PCR) [18,19]. Forty healthy controls were involved in this study without any acute infection or chronic disease. Negative RT-PCR of nasopharyngeal swabs confirmed the eligibility of healthy individuals.
Radiological scoring system of COVID-19 Severity
CT-TSS (CT total severity score), a quantitative clinical evaluation, will grade each lobe of the five lung lobes from 1 to 4 according to the affected portion in the following way: 0 = (0%), 1 = (1-25%), 2 = (26-50%), 3 = (51-75%), or 4 = (76-100%). The total severity scores (TSS) were calculated by summing up the grade from each lobe, and the TSS score ranged from 0 to 20. The patients were divided into four groups: none (0), mild (1–5), moderate (6–10), and severe (11–20).
Mild and Moderate patients were admitted to inpatient rooms, while severe cases were admitted to the Critical Care department of Fayoum University Hospital and were put on mechanical ventilation.
The Chest X-Ray Scoring System (CXR score 18) used in this study involved dividing the lungs into six regions using two lines. Each region was assigned a score of 0 to 3 based on the extent of lung lesions observed. A score of 0 indicated normal lung appearance, 1 indicated interstitial infiltrates, 2 indicated a combination of interstitial and alveolar infiltrates (with interstitial dominance), and 3 indicated a combination of alveolar and interstitial infiltrates (with alveolar dominance). The scores for the six lung zones were added, resulting in a total score of 0 to 18. The patients were then classified into four groups based on their overall CXR score: normal (score of 0), mild (score of 1-6), moderate (score of 7-12), and severe (score of 13-18). This new scoring system allows for the determination of disease severity in COVID-19 patients [20].
Preparation of serum samples
A total of five milliliters of blood were collected from each participant. Out of the five milliliters, two milliliters were collected in EDTA tubes. The remaining three milliliters were centrifuged to separate the serum and then used to assess the relative expression of miR-374b-5p and NEAT-1 through RT-PCR. Additionally, the serum protein level of IL6 was measured using ELISA. Various other tests, including CBC, serum creatinine, urea, alanine aminotransferase (ALT) and aspartate aminotransferase (AST), D-dimer, lactate dehydrogenase (LDH), albumin, serum Na and K and C-reactive protein (CRP) levels, were also examined.
Total RNA isolation and complementary DNA (cDNA) synthesis
The Qiagen kit from Valencia, CA, USA, was used to extract RNA from the serum. In summary, 200 μL of the serum sample was mixed with 1 mL of QIAzol lysis reagent and incubated at room temperature for 5 minutes. The mixture was then subjected to chloroform phase separation. The upper aqueous phase was combined with 100% ethanol and transferred to RNeasy Mini spin columns placed in collection tubes. The columns were centrifuged at room temperature at a speed of 8000 xg for 15 seconds. Subsequently, the RNA was eluted from the columns. The eluted RNA was then quantified, and its purity was assessed using the NanoDrop® (ND)-1000 spectrophotometer from NanoDrop Technologies, Inc. in Wilmington, USA. The extracted RNA was subjected to reverse transcription using the miScript II RT kit from Qiagen, located in Valencia, CA, USA. The reverse transcription reaction was performed in a final volume of 20 μL.
Quantitative real-time PCR (qPCR) for the detection of lncRNA and miRNA
Serum expression levels of the studied lncRNA NEAT-1 and miR374b-5p were evaluated using GAPDH and SNORD 68 as internal controls, respectively. Following the manufacturer’s instructions, we used primers for NEAT-1, miR374b-5p, GAPDH, SNORD 68 (Table 1), and the Maxima SYBR Green PCR kit (Thermo, USA). For a qPCR reaction, a 20-μl mixture was prepared (10 μl master mix + 1 μl forward primer + 1 μl reverse primer + 2.5 μl cDNA + 5.5 μl RNAase-free water). The experiment was executed by the Rotor-Gene Q System (Qiagen) with the following cycling conditions: 95°C for 10 min, followed by 45 cycles at 95°C for 15 s and 60°C for 60 s.
Gene expression analysis using real-time PCR
Fold changes (FC) of target genes (NEAT-1 and miR374b-5p) were measured using the 2-ΔΔCt equation. First, we subtract the Ct values of SNORD 68 from the Ct values of the miR374b-5p and the Ct values of GAPDH from the Ct of NEAT-1. Second, the ΔΔCt was determined by subtracting the ΔCt of controls from the ΔCt of patients [21].
Human interleukin 6 (IL-6) ELISA Kit
We used an Enzyme-Linked Immune Sorbent Assay (ELISA) kit from Bioassay Technology to detect human interleukin 6 based on Biotin double antibody sandwich technology. Incubate wells already pre-coated with the interleukin-6 (IL-6) monoclonal antibody. Next, biotin-labeled anti-IL-6 antibodies with streptavidin-HRP are combined to form an immune complex. After incubation and washing, remove any enzymes that remain unbound. Combine substrates A and B. The solution will then turn blue and yellow because of the acidic effect. The solution shades and the concentration of human interleukin 6 (IL-6) correlate positively.
Statistical analysis
The collected data were organized and coded to facilitate data manipulation. It was then double-entered into Microsoft Access, a database management system. Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) software version 22, running on Windows 7 (SPSS Inc., Chicago, IL, USA). A simple descriptive analysis was conducted for qualitative data, presenting the data as numbers and percentages. For quantitative parametric data, central tendency was measured using arithmetic means, which provide an average value. The dispersion of the data was assessed using standard deviations. A comparison between quantitative parametric data was performed by using an independent samples t-test between two groups and a one-way ANOVA test if there were more than two groups. For quantitative non-parametric data, the Kruskal-Wallis test was performed to compare more than two independent groups, and the Mann-Whitney test was used for more than two independent groups. For qualitative data, the Chi-square test is used. Bivariate Spearman Correlation test to explore the possible correlation between quantitative non-parametric variables. ROC curve "Receiver Operating Characteristic" was executed. The P-value< 0.05 is a significant value.
Sample size
The sample size was calculated using the G-Power version 3.1.7 (Institute of Experimental Psychology, Heinrich Heine University, Dusseldorf, Germany). The minimum sample size of the patients was 47. Effect size: 0.60, based on previous research findings. The two-sided (two tails) type I error is 0.05, with 80% power.
Results
Raw data supporting the results were shown in (S2 Text).
Demographic basic data of studied subjects
The detailed description of basic medical history, full examination laboratory findings and treatment regimens are shown in Tables A-E in S1 Text. Table 2 showed insignificant differences between cases and controls regarding age and sex.
Expression profile of fold change (NEAT-1), fold change miR374b-5p, and IL-6 in cases and controls
Cases showed statistical significance with lower levels of FC (NEAT-1) and FC (miR374b-5p) while higher IL-6 levels when compared to controls with P-value <0.001 (Table 3) (Figs 1–3).
Correlation between NEAT-1, miR374b-5p and IL6 levels
Fold change of NEAT-1 correlates negatively with IL-6 level (r = -.317, P = .008) (Table 4) (Fig 4).
Comparisons of FC (NEAT-1), IL-6, and miR374b-5p levels in different demographic and medical, CT findings, and clinical characteristics between COVID-19 patients
No significant differences were detected between NEAT-1, IL-6 or miR374b-5p levels and sex, comorbidities, and treatment types. (Table 5A). There was a statistically significant lower median of NEAT-1 level among cases with GGO (median (range) was 0.06 (0.001-0.11) versus 0.09 (0.01-0.60) in cases with no GGO), and higher IL-6 level among cases with grade 4 CORADS degree (62.9 versus nearly 40 for grade 3 and 5) with P-value <0.05, but no difference in other CT findings as regards levels of NEAT-1 and IL-6 (Table 5B).
B. Comparisons of FC (NEAT--1), IL-6, and miR374b-5p levels in different CT findings among cases.
No statistically significant difference was reported between patients regarding FC (NEAT-1), FC (miR374b-5p), and IL-6 levels in different clinical data (Table 6).
Correlation between FC (NEAT-1), IL-6, FC (miR374b-5p) levels & clinical and laboratory data among cases
There was no statistically significant correlation among cases between FC (NEAT-1), FC (miR374b-5p), and IL6 levels & vital data (Table 7).
There was a statistically significant positive correlation between FC level (NEAT-1) and level of RBG and a statistically significant negative correlation with WBCS, ALT, AST and creatinine. The IL-6 level showed a statistically significant positive correlation with ALT, AST, and creatinine (p-value <0.05). MiR374b-5p showed a significant positive correlation with serum Na while a significant negative correlation with HB, MCV, MCH, and MCHC (Table 8).
Sensitivity and specificity of NEAT-1, and IL-6, miR374b-5p level in diagnosing COVID-19 cases
Sensitivity and specificity tests for NEAT-1 and IL6 levels in the diagnosis of cases illustrated a sensitivity of (100% and 97.9%) and a specificity of (85% and 100%) at cut-off values (of 0.985 and 12.55) respectively. At the same time, miR374b-5p showed sensitivity and specificity of around 85%in distinguishing COVID-19 patients from controls (Table 9, Fig 5).
7. Relationship between radiological severity score (CXR and CT-TSS) of SARS-CoV2 and three target genes (NEAT-1, miR374b-5p, and IL6)
No significant association was detected between target genes and radiological severity indices (CXR and CT-TSS) (Table 10A and 10B).
Discussion
The SARS-CoV-2 pandemic poses an enormous threat to global public health due to its high morbidity and mortality. Disruption of cytokine regulation caused by COVID-19 is known as a cytokine storm and culminates in multiple organ failure and acute respiratory distress syndrome. Therefore, developing therapeutic strategies with minimal side effects is imperative in the fight against this virus. Preventing patient deterioration and preserving life requires efficient suppression of cytokine storms [7].
Mammalian cells express noncoding RNAs (ncRNAs) extensively, and these molecules serve as important RNA regulators in a range of cellular processes, including the activation of inflammatory signaling pathways. Infections caused by RNA or DNA viruses have been found to regulate the expression of numerous ncRNAs. Hence, viral regulation of ncRNAs, in turn, affects the expression of pro-inflammatory molecules [7].
The SARS-CoV-2 virus’s frequent mutations have made disease control with vaccines and antiviral drugs difficult as newer variants emerge regularly. As a result, there is a need for more effective coronavirus drugs. Hence, detecting the expression of various ncRNA in SARS-CoV2 implies therapeutic strategies for the disease [6].
In our study, we focus on measuring the expression levels of two ncRNAs (NEAT-1 and miR-374b-5p) due to the following causes: both NEAT-1 and miR374b-5p are pro-inflammatory molecules that affect innate immune responses, which is the first line of defense of the host against viral infection [6,9]. Furthermore, NEAT-1 has been reported to function as a competing endogenous RNA (ceRNA) by competitive binding to miR-374b-5p [9]. Finally, Morenikeji et al. 2022 bioinformatic analysis conveyed that miR374b is a promising antiviral drug that inhibits enzymes crucial for the virus life cycle [22]. Also, we aimed to measure serum IL6 and explore its correlation to NEAT-1 and miR374b-5p. IL6 is a crucial player in the cytokine story [14] and a target for both NEAT-1 and miR374b-5p[10].
Our findings revealed for the first time a significant decrease in NEAT-1-fold change in the sera from COVID-19 patients. Furthermore, it was the first study to measure serum miR374b-5p and found a lower level of miR374b-5p, while we found a significant increase in IL6 in the blood of COVID-19 patients compared to controls.
Regarding NEAT-1, these finding agrees with Meydan et al., 2020 who demonstrated that bioinformatic analysis of differentially expressed ncRNAs in the RNA-seq datasets from lung, brain, and blood of COVID-19 inflammation-prone individuals revealed downregulation of NEAT-1 and DANCER in infected lung cell with COVID19 in contrary to lung cell infected with H1N1 pandemic (influenza virus) which doesn’t affect the expression level of NEAT-1 and DANCER [6]. In contrast to our results, many researchers reported that NEAT-1 was highly expressed in various samples from COVID-19 patients, such as saliva, serum, whole blood, lung cells [4,8,7,23].
Previous studies reported that NEAT-1 was among the most differentially expressed lncRNAs in SARS-CoV2 and played a crucial role in cytokine storm formation through different mechanisms by inducing pro-inflammatory cytokines such as IL6, IL8, and TNF-α, which are essential players in the innate immune response to SARS-CoV-2 infection [6]. Also, NEAT-1 can regulate inflammatory-related genes (HIF1a, CCR7, and TLR4) in SARS-CoV2 patients, and their transcript levels correlate positively with the disease’s severity[6]. NEAT-1 activates caspase-1 by binding to pro-caspase-1 and activating NLRP3, NLRC4, and AIM2 inflammasomes, increasing cytokine production and pyroptosis. Furthermore, NEAT-1 has been shown to act as "sponges" in mediating inflammation by blocking the activity of "sponged" miRNAs, e.g., miR342-3p [24], miR124, and mir129[25]. MiR-3076-3p is absorbed by NEAT-1 and reduced, which controls the expression of NLRP3. Upregulated NEAT-1 competes with Let-7a to release TLR4, activating it and stimulating downstream signaling. NEAT-1 competitively binds miR-1246 and releases NAKP, which mediates TNF-α and IL-1-induced NF-κB activation and severe inflammatory response [24,25]Click or tap here to enter text.
In COVID-19, IL-6 is frequently described as a pro-inflammatory cytokine that plays an important role in pathogen resistance and tissue homeostasis. IL-6 concentrations in normal human serum are typically low (1-5 pg/ml). At the pandemic’s beginning, elevated cytokine levels (notably IL-6, GM-CSF, TNF, interferons, and IL-18) were commonly reported in severely ill patients with COVID-19, contributing to the cytokine storm [11].
The down-regulated miR374b-5p reported in this study agrees with bioinformatic analysis that reveals that this down-regulation is determined in all stages of the disease [26]. MiR374b was one of 27 miRNAs discovered useful as broad-spectrum antiviral drugs against coronaviruses by inhibiting RNA-dependent RNA polymerase (RdRp), an enzyme required for the coronavirus life cycle. Inhibiting the expression of the RdRp enzyme via noncoding RNA is novel and of great therapeutic importance in controlling coronavirus replication. It could serve as a broad-spectrum antiviral drug. [27].
In the current study, the expression level of NEAT-1 showed a significant negative correlation with the expression of IL6, which showed inconsistency with previous studies [10]. The explanation of these findings may refer to the presence of multiple factors, including several ncRNAs that affect the IL6 level in COVID-19 patients [28].
However, in this study, the correlation between miR374b-5p and NEAT-1 or IL6 was insignificant, which needs further functional large-scale studies to be proven. Estimated expression levels of target genes and proteins in the blood of patients with COVID-19 in this study (NEAT-1, miR374b-5p, and IL6), together with the previously reported role of NEAT-1 and IL6 in the development of cytokine storms, and the recently reported function of miR374b-5p in inhibiting viral replication, may set the foundations for new therapeutic regimens.
Our study explored the relationship between studies genes and the radiological severity score of coronavirus patients and revealed non-significant results. However, there was a statistically significant lower median of NEAT-1 level among cases with GGO (median (range) was 0.06 (0.001-0.11) versus 0.09 (0.01-0.60) in cases with no GGO (P = 0.01), and higher IL-6 level among cases with grade 4 CO-RADs degree (62.9 versus nearly 40 for grade 3 and 5) with P-value = 0.04 needed further studies.
The strengths of our study include that it is the first time to detect decreased NEAT-1 in the serum of COVID-19 patients and the first study to measure serum miR374b-5p in COVID-19 patients and to correlate its level with NEAT-1, IL6, and other laboratory findings. Furthermore, the NEAT-1, miR374b, and IL6 axis and the relationship between three genes, radiological findings, and radiological severity scores were validated. There are several limitations, like relatively small patient numbers collected from the same geographical area, which elicit the need for further large-scale functional studies to reveal the mechanistic basis of linking these molecules to the pathogenesis of the disease.
Conclusions
Our study is the first to detect decreased NEAT-1 and miR374b-5p expression in COVID-19 patients’ serums. There was also an increase in IL6 levels. There is a negative correlation between NEAT-1 and IL6 in COVID-19 patients.
Supporting information
S1 Text. The supplementary file details the basic medical history, full examination, laboratory findings, and treatment regimens for COVID-19 patients.
https://doi.org/10.1371/journal.pone.0313042.s001
(XLSX)
S2 Text. The supplementary file showed the Excel sheet of the raw data of COVID-19 patients and controls.
https://doi.org/10.1371/journal.pone.0313042.s002
(DOCX)
Acknowledgments
The authors thank all study participants and clinicians who helped recruit patients and collect clinical data.
References
- 1.
“Coronavirus disease (ILK) Epidemiological Updates and Monthly Operational Updates”.
- 2. Statello L., Guo C. J., Chen L. L., and Huarte M., "Gene regulation by long noncoding RNAs and its biological functions," Feb. 01, 2021, Nature Research. pmid:33353982
- 3. Pan Y. et al., "Novel Insights into the Emerging Role of Neat1 and Its Effects Downstream in the Regulation of Inflammation," 2022, Dove Medical Press Ltd. pmid:35115805
- 4. Tayel S. I., El-Masry E. A., Abdelaal G. A., Shehab-Eldeen S., Essa A., and Muharram N. M., "Interplay of LncRNAs NEAT1 and TUG1 in Incidence of Cytokine Storm in Appraisal of COVID-19 Infection," Int J Biol Sci, vol. 18, no. 13, pp. 4901–4913, 2022, pmid:35982898
- 5. Huang K., Wang C., Vagts C., Raguveer V., Finn P. W., and Perkins D. L., "Long noncoding RNAs (lncRNAs) NEAT1 and MALAT1 are differentially expressed in severe COVID-19 patients: An integrated single-cell analysis," PLoS One, vol. 17, no. 1 January, Jan. 2022, pmid:35007307
- 6. Meydan C., Madrer N., and Soreq H., "The Neat Dance of COVID-19: NEAT1, DANCR, and Co-Modulated Cholinergic RNAs Link to Inflammation," Front Immunol, vol. 11, Oct. 2020, pmid:33163005
- 7. Rahni Z. et al., "Long noncoding RNAs ANRIL, THRIL, and NEAT1 as potential circulating biomarkers of SARS-CoV-2 infection and disease severity," Virus Res, vol. 336, Oct. 2023, pmid:37657511
- 8. Abbasi-Kolli M. et al., "The expression patterns of MALAT-1, MIR, THRIL, and miR-155-5p in the acute to the post-acute phase of COVID-19 disease," Brazilian Journal of Infectious Diseases, vol. 26, no. 3, May 2022, pmid:35500644
- 9. Huang F., Su Z., Yang J., Zhao X., and Xu Y., "Downregulation of lncRNA NEAT1 interacts with miR-374b-5p/PGAP1 axis to aggravate the development of osteoarthritis," J Orthop Surg Res, vol. 18, no. 1, Dec. 2023, pmid:37691099
- 10. Xiang M. et al., "LncRNA NEAT1 promotes IL-6 secretion in monocyte-derived dendritic cells via sponging miR-365a-3p in systemic lupus erythematosus," Epigenetics, vol. 18, no. 1, 2023, pmid:37343193
- 11. Broman N. et al., "IL-6 and other biomarkers as predictors of severity in COVID-19," Ann Med, vol. 53, no. 1, pp. 410–412, 2021, pmid:33305624
- 12. Chen L. Y. C., Hoiland R. L., Stukas S., Wellington C. L., and Sekhon M. S., "Confronting the controversy: Interleukin-6 and the COVID-19 cytokine storm syndrome," Oct. 01, 2020, European Respiratory Society. pmid:32883678
- 13. Santa Cruz A. et al., "Interleukin-6 Is a Biomarker for the Development of Fatal Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia," Front Immunol, vol. 12, Feb. 2021, pmid:33679753
- 14. Jones S. A. and Hunter C. A., "Is IL-6 a key cytokine target for therapy in COVID-19?," Jun. 01, 2021, Nature Research. pmid:33850327
- 15. Vatansever H. S. and Becer E., "Relationship between IL-6 and COVID-19: To be considered during treatment," Dec. 01, 2020, Future Medicine Ltd.
- 16. Perez-Sanchez C. et al., "miR-374a-5p regulates inflammatory genes and monocyte function in patients with inflammatory bowel disease," Journal of Experimental Medicine, vol. 219, no. 5, May 2022, pmid:35363256
- 17. Zhao L. et al., "Diminished miR-374c-5p negatively regulates IL (interleukin)-6 in unexplained recurrent spontaneous abortion," J Mol Med, vol. 100, no. 7, pp. 1043–1056, Jul. 2022, pmid:35689099
- 18. Prokop M. et al., "CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation," Radiology, vol. 296, no. 2, pp. E97–E104, Aug. 2020, pmid:32339082
- 19. de Jaegere T. M. H., Krdzalic J., Fasen B. A. C. M., and Kwee R. M., "Radiological Society of North America chest ct classification system for reporting covid-19 pneumonia: Interobserver variability and correlation with reverse-transcription polymerase chain reaction," Radiol Cardiothorac Imaging, vol. 2, no. 3, Jun. 2020, pmid:33778589
- 20. Wasilewski P. G., Mruk B., Mazur S., Półtorak-Szymczak G., Sklinda K., and Walecki J., "COVID-19 severity scoring systems in radiological imaging – A review," 2020, Medical Science International. pmid:32817769
- 21. Ali M. A. et al., "Expression profile of serum LncRNAs MALAT-1 and CCAT-1 and their correlation with Mayo severity score in ulcerative colitis patients can diagnose and predict the prognosis of the disease," Noncoding RNA Res, vol. 9, no. 2, pp. 318–329, Jun. 2024, pmid:38505308
- 22. Morenikeji O. B. et al., "Deciphering inhibitory mechanism of coronavirus replication through host miRNAs-RNA-dependent RNA polymerase interactome," Front Genet, vol. 13, Aug. 2022, pmid:36092931
- 23. Rodrigues A. C. et al., "NEAT1 and MALAT1 are highly expressed in saliva and nasopharyngeal swab samples of COVID-19 patients," Mol Oral Microbiol, vol. 36, no. 6, pp. 291–294, Dec. 2021, pmid:34463043
- 24. Lin Y., Sun Q., Zhang B., Zhao W., and Shen C., "The regulation of lncRNAs and miRNAs in SARS-CoV-2 infection," 2023, Frontiers Media SA. pmid:37576600
- 25. Pan Y. et al., "Novel Insights into the Emerging Role of Neat1 and Its Effects Downstream in the Regulation of Inflammation," 2022, Dove Medical Press Ltd. pmid:35115805
- 26. Srivastava S. et al., "Evaluation of altered miRNA expression pattern to predict COVID-19 severity," Heliyon, vol. 9, no. 2, Feb. 2023, pmid:36743852
- 27. Morenikeji O. B. et al., "Deciphering inhibitory mechanism of coronavirus replication through host miRNAs-RNA-dependent RNA polymerase interactome," Front Genet, vol. 13, Aug. 2022, pmid:36092931
- 28. Zhang J. and Chu M., "Targeting of IL-6-Relevant Long Noncoding RNA Profiles in Inflammatory and Tumorous Disease," Inflammation, vol. 42, no. 4, pp. 1139–1146, Aug. 2019, pmid:30825076