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Advances in Rare Diseases Biomarkers
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Advances in Rare Diseases Biomarkers

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 9709

Special Issue Editor

Dr. Andrea Bernini

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Guest Editor
Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
Interests: structural biology; rare diseases; metabolomics; nuclear magnetic resonance; protein dynamics; protein core & surface; transient pockets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A rare disease is a health condition with a lower prevalence than common diseases. The World Health Organization defines a rare disease as one that strikes fewer than 65 per 100,000 people. However, their combined effect is significant: around 7000 rare diseases affect approximately 350 million people worldwide.

Biomarkers play a crucial role in diagnosing and monitoring rare diseases, which are often challenging to detect and understand due to their low prevalence and diverse clinical manifestations. Biomarkers serve as measurable indicators of biological processes or conditions in rare diseases, offering valuable insights into disease mechanisms and progression. These markers may include genetic mutations, protein levels, or other molecular signatures unique to a rare condition. The discovery and validation of such biomarkers contribute to early detection and the development of targeted therapies, allowing for more effective and personalised treatment approaches.

As technology advances, the integration of omics technologies, such as genomics, proteomics, and metabolomics, has further expanded the repertoire of potential biomarkers, fostering a deeper understanding of rare diseases and paving the way for innovative diagnostic and therapeutic strategies. Biomarkers enhance our ability to navigate diagnostic challenges and promise to improve patient care and foster breakthroughs in treatment modalities.

For the Special Issue, we continue looking for original research articles and state-of-the-art reviews on novel and established proteomic, metabolomic, or transcriptomic biomarkers that can help us better understand the underlying molecular mechanisms of rare diseases. Additionally, we are interested in biomarkers that can be used to diagnose and predict the prognosis of rare diseases and determine individual responses to therapies.

Prof. Dr. Andrea Bernini
Guest Editor

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Keywords

  • orphan diseases
  • rare diseases
  • inborn errors of metabolism
  • mitochondrial disorders
  • biomarker discovery

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Published Papers (10 papers)

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17 pages, 4481 KiB  
Article
Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis
by Seong Ji Choi, Hyuk Soon Choi, Hyunil Kim, Jae Min Lee, Seung Han Kim, Jai Hoon Yoon, Bora Keum, Hyo Jung Kim, Hoon Jai Chun and Youngja H. Park
Int. J. Mol. Sci. 2024, 25(17), 9509; https://doi.org/10.3390/ijms25179509 - 1 Sep 2024
Viewed by 826
Abstract
Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and [...] Read more.
Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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Figure 1

Figure 1
<p>Schematic overview of the biomarker identification criteria applied in this study. (<b>a</b>) The metabolic overview started with the extraction of metabolites from 37 gastric cancer (GC) and intestinal metaplasia (IM) tissues. (<b>b</b>) The scheme of untargeted metabolomics analysis. (1) Metabolites detection was done using Q-TOF-MS, generating mass spectra. (2) Data preprocessed using apLCMS version 6.3.8 and xMSanalyzer version 2.0.6.1. (3) Metabolic profiling with Manhattan plot with false discovery rate (FDR) analysis and sparse partial least square discriminant analysis (sPLS-DA). (4) Pathway analysis with Kyoto Encyclopedia of Genes and Genomes (KEGG). (5) Biomarker quantification using multiple reaction monitoring (MRM). apLCMS: adaptive processing of liquid chromatography-mass spectrometry. FDR, false discovery rate; GC, gastric cancer; IM, intestinal metaplasia; KEGG, Kyoto Encyclopedia of Genes and Genomes; MS, mass spectrometery; Q-TOF-MS, quadrupole time-of-flight mass spectrometry; sPLS-DA: sparse partial least square discriminant analysis.</p> Full article ">Figure 2
<p>Analytical performance evaluation by comparing pool and case samples. (<b>a</b>) PCA using 665 significant features (FDR q ≤ 0.05) from aqueous extraction data. (<b>b</b>) PCA using 278 significant features (FDR q ≤ 0.05) from organic extraction data. FDR, false discovery rate; GC, gastric cancer; IM, intestinal metaplasia; PCA, principal component analysis; PC1, principal component 1; PC2, principal component 2; PC3, principal component 3; QC, quality control.</p> Full article ">Figure 3
<p>Manhattan plot, PCA, and sPLS-DA between IM and GC. The Manhattan plot presents the significant features (FDR q ≤ 0.05) as colored dots, while their distribution is expressed in <span class="html-italic">m</span>/<span class="html-italic">z</span>. (<b>a</b>) Manhattan plot showing 665 significant features (FDR q ≤ 0.05) derived from the aqueous data. (<b>d</b>) Manhattan plot with 278 significant features (FDR q ≤ 0.05) derived from the organic data. PCA shows the separation of samples: (<b>b</b>) aqueous data and (<b>e</b>) organic data. sPLS-DA shows the separation of samples: (<b>c</b>) aqueous data and (<b>f</b>) organic data. PCA, principal component analysis; PC1, principal component 1; PC2, principal component 2, sPLS-DA, sparse partial least squares discriminant analysis.</p> Full article ">Figure 4
<p>Overview of the pathway analysis of the significant metabolites extracted from the combined aqueous and organic phases. (<b>a</b>) The bubble plot shows the pathways by impact (x-axis) and −log<sub>10</sub>(<span class="html-italic">p</span>) (y-axis). The color and size of each bubble represent the −log<sub>10</sub>(<span class="html-italic">p</span>) and impact, represented as color and size keys, respectively. (<b>b</b>) The top 16 pathways based on the −log<sub>10</sub>(<span class="html-italic">p</span>) are listed alongside their match status, indicating the hit metabolites to whole metabolites involved in each pathway, <span class="html-italic">p</span>-value, and impact.</p> Full article ">Figure 5
<p>Analysis of significantly altered metabolites between GC and IM in the steroid hormone biosynthesis pathway from the KEGG pathway. (<b>a</b>) Pathway of androgen steroid and (<b>b</b>) pathway of mineralocorticoid. All metabolites in the figure a and b were significantly altered (FDR, q ≤ 0.05). Boxplots illustrate the upper quartile, median (dashed bar), and lower quartile, with whiskers indicating the maximum and minimum values. FDR, false discovery rate; GC, gastric cancer; IM, intestinal metaplasia; KEGG, Kyoto Encyclopedia of Genes and Genomes.</p> Full article ">Figure 6
<p>Analysis of significantly altered metabolites between GC and IM in the tryptophan metabolism pathway from the KEGG pathway. All metabolites in the figure were significantly altered (FDR, q ≤ 0.05). Boxplots illustrate the upper quartile, median (dashed bar), and lower quartile, with whiskers indicating the maximum and minimum values. FDR, false discovery rate; GC, gastric cancer; IM, intestinal metaplasia; KEGG, Kyoto Encyclopedia of Genes and Genomes.</p> Full article ">Figure 7
<p>Analysis of significantly altered metabolites between GC and IM in the purine metabolism pathway from the KEGG pathway. All metabolites in the figure were significantly altered (FDR, q ≤ 0.05). Boxplots illustrate the upper quartile, median (dashed bar), and lower quartile, with whiskers indicating the maximum and minimum values. FDR, false discovery rate; GC, gastric cancer; GTP, guanosine triphosphate; IM, intestinal metaplasia; KEGG, Kyoto Encyclopedia of Genes and Genomes.</p> Full article ">Figure 8
<p>Analysis of significantly altered metabolites between GC and IM in the arginine and proline metabolism pathways from the KEGG pathway. All metabolites in the figure were significantly altered (FDR, q ≤ 0.05). Boxplots illustrate the upper quartile, median (dashed bar), and lower quartile, with whiskers indicating the maximum and minimum values. FDR, false discovery rate; GC, gastric cancer; IM, intestinal metaplasia; KEGG, Kyoto Encyclopedia of Genes and Genomes.</p> Full article ">Figure 9
<p>Relative metabolite intensities in tissues across various stages of GC at diagnosis. Boxplots illustrate the upper quartile, median (dashed bar), and lower quartile, with whiskers indicating the maximum and minimum values. Relative intensities of significant compounds correlated with four stages of GC. * <span class="html-italic">p</span> ≤ 0.05. GC, gastric cancer.</p> Full article ">
22 pages, 9707 KiB  
Article
TLR2 and TLR4 Are Expressed in Epiretinal Membranes: Possible Links with Vitreous Levels of Complement Fragments and DAMP-Related Proteins
by Lucia Dinice, Graziana Esposito, Andrea Cacciamani, Bijorn Omar Balzamino, Pamela Cosimi, Concetta Cafiero, Guido Ripandelli and Alessandra Micera
Int. J. Mol. Sci. 2024, 25(14), 7732; https://doi.org/10.3390/ijms25147732 - 15 Jul 2024
Viewed by 833
Abstract
Previous studies reported the expression of toll-like receptors (TLRs), merely TLR2 and TLR4, and complement fragments (C3a, C5b9) in vitreoretinal disorders. Other than pathogens, TLRs can recognize endogenous products of tissue remodeling as damage-associated molecular pattern (DAMPs). The aim of this study was [...] Read more.
Previous studies reported the expression of toll-like receptors (TLRs), merely TLR2 and TLR4, and complement fragments (C3a, C5b9) in vitreoretinal disorders. Other than pathogens, TLRs can recognize endogenous products of tissue remodeling as damage-associated molecular pattern (DAMPs). The aim of this study was to confirm the expression of TLR2 and TLR4 in the fibrocellular membranes and vitreal fluids (soluble TLRs) of patients suffering of epiretinal membranes (ERMs) and assess their association with disease severity, complement fragments and inflammatory profiles. Twenty (n = 20) ERMs and twelve (n = 12) vitreous samples were collected at the time of the vitrectomy. Different severity-staged ERMs were processed for: immunolocalization (IF), transcriptomic (RT-PCR) and proteomics (ELISA, IP/WB, Protein Chip Array) analysis. The investigation of targets included TLR2, TLR4, C3a, C5b9, a few selected inflammatory biomarkers (Eotaxin-2, Rantes, Vascular Endothelial Growth Factor (VEGFA), Vascular Endothelial Growth Factor receptor (VEGFR2), Interferon-γ (IFNγ), Interleukin (IL1β, IL12p40/p70)) and a restricted panel of matrix enzymes (Matrix metalloproteinases (MMPs)/Tissue Inhibitor of Metallo-Proteinases (TIMPs)). A reduced cellularity was observed as function of ERM severity. TLR2, TLR4 and myD88 transcripts/proteins were detected in membranes and decreased upon disease severity. The levels of soluble TLR2 and TLR4, as well as C3a, C5b9, Eotaxin-2, Rantes, VEGFA, VEGFR2, IFNγ, IL1β, IL12p40/p70, MMP7 and TIMP2 levels were changed in vitreal samples. Significant correlations were observed between TLRs and complement fragments and between TLRs and some inflammatory mediators. Our findings pointed at TLR2 and TLR4 over-expression at early stages of ERM formation, suggesting the participation of the local immune response in the severity of disease. These activations at the early-stage of ERM formation suggest a potential persistence of innate immune response in the early phases of fibrocellular membrane formation. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
►▼ Show Figures

Figure 1

Figure 1
<p>TLR2 and TLR4 protein expression in ERMs as a function of disease severity. A total of six membranes (N = 2 for stage 2, 1F/1M; N = 2 for stage 3, 1F/1M; N = 2 for stage 4, 1F/1M) were collected during pars plana vitrectomy and processed for Epifluorescent analysis (<b>A</b>–<b>C</b>), coupled to Integrated Density (IntDen; (<b>D</b>)) evaluation. Representative merged (main) and single (right side) panels of TLR2 (green) and TLR4 (red) in ERMs at stage 2 (<b>A</b>), stage 3 (<b>B</b>) and stage 4 (<b>C</b>). Membranes were counterstained with nuclear DAPI (blue) to better visualize the cells. Note the significant decrease in immunoreactivity (<b>D</b>) and cellularity (<b>E</b>), depending on severity (<span class="html-italic">p</span> ≤ 0.05). Data represent mean ± SEM and values of fluorescent intensity (IntDen; ImageJ) are expressed in arbitrary units. Magnifications ×400 (bar size = 50 µm). Significances are shown in the panels (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.005), as calculated using one-way ANOVA followed by a Tukey–Kramer post hoc test (mean ± SEM).</p> Full article ">Figure 2
<p>TLR2 and TLR4 immunoreactivity in myoFBs cells populating the ERM at stage 2 ((<b>A</b>,<b>B</b>) N = 2, 1F/1M), at stage 3 ((<b>C</b>,<b>D</b>) N = 2, 2F) and at stage 4 ((<b>E</b>,<b>F</b>) N = 2, 2F). Representative fluorescent panels of TLR2/green (<b>A</b>,<b>C</b>,<b>E</b>) or TLR4/green (<b>B</b>,<b>D</b>,<b>F</b>) and α-SMA/red from double-immunostained and nuclear counterstained (DAPI/blue) ERMs. Note the TLR2 and TLR4 immunoreactivity in, respectively, α-SMA positive myoFBs. Citofixed and whole-mounted membranes were used. Merged and single-channel panels are shown. Magnifications ×400 (bar size = 50 µm). Arrows indicate coexpression of targets.</p> Full article ">Figure 3
<p>TLR2 and TLR4 immunoreactivity in reactive microglia cells populating the ERM at stage 2 ((<b>A</b>,<b>B</b>) N = 2, 2F), at stage 3 ((<b>C</b>,<b>D</b>) N = 1, 1F) and at stage 4 ((<b>E</b>,<b>F</b>) N = 1, 1F). Representative fluorescent panels of TLR2/green (<b>A</b>,<b>C</b>,<b>E</b>) or TLR4/green (<b>B</b>,<b>D</b>,<b>F</b>) and Iba1/red from double-immunostained and nuclear counterstained (DAPI/blue) ERMs. Note the TLR2 and TLR4 immunoreactivity in, respectively, some Iba1 immunoreactive cells. Citofixed and whole-mounted membranes were used. Merged and single-channel panels are shown. Magnifications ×400 (bar size = 50 µm). Arrows indicate coexpression of targets.</p> Full article ">Figure 4
<p>TLR2 and TLR4 immunoreactivity in activated Müller cells populating the ERMs at stage 2 ((<b>A</b>,<b>B</b>) N = 2, 2F), at stage 3 ((<b>C</b>,<b>D</b>) N = 1, 1F) and at stage 4 ((<b>E</b>,<b>F</b>) N = 1, 1F). Representative fluorescent panels of TLR2/green (<b>A</b>,<b>C</b>,<b>E</b>) or TLR4/green (<b>B</b>,<b>D</b>,<b>F</b>) and GFAP/red and nuclear counterstained (DAPI/blue) ERMs. Note the TLR2 and TLR4 immunoreactivity in, respectively, some cellular compartments of GFAP-positive Müller cells. Citofixed and whole-mounted membranes were used. Merged and single-channel panels are shown. Magnifications ×400 (bar size = 50 µm). Arrows indicate coexpression of targets.</p> Full article ">Figure 5
<p>Transcripts’ expression as a function of disease severity and comorbidities. (<b>A</b>) Histogram showing the significant upregulation of <span class="html-italic">TLR2</span>mRNA in ERM at stage 3 with respect to stage 4, as well as compared to stage 2. The same trend was observed for <span class="html-italic">TLR4</span>mRNA. An inverted trend was observed for the adaptor molecule <span class="html-italic">myD88</span>mRNA, as downregulation was observed in ERM at stage 4 with respect to stage 3, and no significant changes were observed with respect to stage 2. Values are relative expression ratios (fold-changes in log2-scale; mean ± SEM), as generated by REST-analysis, comparing stage 3 or stage 4 with respect to stage 2. (<b>B</b>) Transcriptomic analysis highlighted a decreased <span class="html-italic">TLR2</span> and <span class="html-italic">TLR4</span> expression in the presence of the major comorbidities associated with hypertension (10/20; ID: 1, 2, 4, 8, 12, 13, 14, 15, 17, 19), while an increased expression was detected in the absence of hypertension (7/20; ID: 3, 5, 9, 10, 11, 16, 18). Values are relative expression ratios (fold-changes in log2-scale; mean ± SEM), as generated by REST-analysis, comparing clusters vs. normal controls (3/20; IDs: 3, 5, 10). The <span class="html-italic">p</span>-values were calculated according to the REST-ANOVA Tukey–Kramer coupled analysis. Red dot lines are referred to 2log-FC PCR biological significance. Significances are shown in the panels (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.005), as calculated using one-way ANOVA followed by a Tukey–Kramer post hoc test (mean ± SEM).</p> Full article ">Figure 6
<p>TLR2 and TLR4 levels in vitreous as a function of disease severity. A total of twelve specimens: N = 5 for stage 2 (ID: 2, 11, 13, 14, 19); N = 4 for stage 3 (ID: 8, 15, 16, 17); N = 3 for stage 4 (ID: 1, 4, 10) were processed for biochemical analysis. Briefly, the untouched vitreous samples were sonicated and cleared for Western blotting analysis. Representative vitreal levels of TLR2 and TLR4 (respectively, (<b>A</b>,<b>B</b>); IP/WB) are shown by gels, and densitometric IntDen analysis (<b>C</b>,<b>D</b>) was carried out considering all the samples. Note that TLR2 and TLR4 levels are comparable at early stages and greater than levels at late stages of the disease. Protein normalization was confirmed by actin expression. Data are mean ± SEM from optical density (OD) analysis (ImageJ) and <span class="html-italic">p</span>-values are shown by asterisks (see <a href="#sec4-ijms-25-07732" class="html-sec">Section 4</a>). Significant levels are shown in the panels (* <span class="html-italic">p</span> ≤ 0.05; *** <span class="html-italic">p</span> ≤ 0.005), as calculated using one-way ANOVA followed by a Tukey–Kramer post hoc test (mean ± SEM).</p> Full article ">Figure 7
<p>Inflammatory and remodeling profiles. (<b>A</b>,<b>B</b>) Inflammatory (<b>A</b>) and adhesion, proangiogenic and Th1/Th2 (<b>B</b>) patterns were analyzed, respectively, in ERMs and vitreous samples. (<b>A</b>) Note the <span class="html-italic">HLA-DR</span> and <span class="html-italic">p65NFkB</span> transcripts’ upregulation in ERMs at stage 3. (<b>B</b>) Adhesion molecules appear deregulated, <span class="html-italic">VEGFA</span> and <span class="html-italic">VEGFR2</span> were upregulated at stage 3, and only <span class="html-italic">IL1β</span> and <span class="html-italic">IL12p70</span> were increased at stage 4, all in vitreous samples. (<b>C</b>,<b>D</b>) Eotaxin-2 (<b>C</b>) and GFAP (<b>D</b>) expression in vitreal samples and coupled ERMs, as assayed by IP/WB analysis. Note that Eotaxin-2 and GFAP were increased in both membrane formation and coupled vitreous, in a manner related to disease severity. All diseases stages are shown. (<b>E</b>,<b>F</b>) Histogram depicting the relative transcript expression of <span class="html-italic">MMPs</span> (<b>E</b>) and <span class="html-italic">TIMPs</span> (<b>F</b>) in ERM extracts as function disease severity. Note the <span class="html-italic">MMP2</span> and <span class="html-italic">MMP9</span> transcript downregulation and the <span class="html-italic">MMP7</span> transcript upregulation as early as stage 3. Whenever required, fold changes were calculated with respect to stage 2, and comparisons were carried out between stages. Data in the bar graph are shown as mean ± SEM (2log fold-changes) and <span class="html-italic">p</span>-values are shown by asterisks (see <a href="#sec4-ijms-25-07732" class="html-sec">Section 4</a>). Red dot lines are referred to 2log-FC PCR biological significance and significant levels are shown in the panels (* <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.005), as calculated using one-way ANOVA followed by a Tukey–Kramer post hoc test (mean ± SEM).</p> Full article ">Figure 8
<p>Complement fragments in vitreal fluids as function of ERM severity. Vitreal levels of C3a and C5b9 (ELISA; (<b>A</b>)); note that C3a levels were higher than C5b9 levels. (<b>B</b>) Scatterplot displaying the positive relationship between C3a and C5b9 markers. Pearson correlation coefficient (r) and significance are shown in the panel. Data are mean ± SEM, as detected by ELISA assay. Significant levels are shown in the panels (* <span class="html-italic">p</span> ≤ 0.05), as calculated using one-way ANOVA followed by a Tukey–Kramer post hoc test (mean ± SEM).</p> Full article ">Figure 9
<p>Correlation analysis for <span class="html-italic">TLRs</span> (transcripts) and complement fragments (vitreal levels) depending on ERM severity. Scatterplot specific for <span class="html-italic">TLR2</span>/C3a at stage 3 (<b>A</b>) and stage 4 (<b>B</b>); <span class="html-italic">TLR2</span>/C5b9 at stage 3 (<b>C</b>) and stage 4 (<b>D</b>); for <span class="html-italic">TLR4</span>/C3a at stage 3 (<b>E</b>) and stage 4 (<b>F</b>); for <span class="html-italic">TLR4</span>/C5b9 at stage 3 (<b>G</b>) and stage 4 (<b>H</b>). Note the positive correlation between complement fragments and <span class="html-italic">TLR2</span> and <span class="html-italic">TLR4</span>. The result of Pearson correlation analysis is shown inside each panel (correlation coefficient, r, and significance).</p> Full article ">
8 pages, 1250 KiB  
Communication
A Plasma Pyrophosphate Cutoff Value for Diagnosing Pseudoxanthoma Elasticum
by Isabelle Rubera, Laetitia Clotaire, Audrey Laurain, Alexandre Destere, Ludovic Martin, Christophe Duranton and Georges Leftheriotis
Int. J. Mol. Sci. 2024, 25(12), 6502; https://doi.org/10.3390/ijms25126502 - 13 Jun 2024
Viewed by 726
Abstract
Pseudoxanthoma elasticum (PXE) is a rare inherited systemic disease responsible for a juvenile peripheral arterial calcification disease. The clinical diagnosis of PXE is only based on a complex multi-organ phenotypic score and/or genetical analysis. Reduced plasma inorganic pyrophosphate concentration [PPi]p has been linked [...] Read more.
Pseudoxanthoma elasticum (PXE) is a rare inherited systemic disease responsible for a juvenile peripheral arterial calcification disease. The clinical diagnosis of PXE is only based on a complex multi-organ phenotypic score and/or genetical analysis. Reduced plasma inorganic pyrophosphate concentration [PPi]p has been linked to PXE. In this study, we used a novel and accurate method to measure [PPi]p in one of the largest cohorts of PXE patients, and we reported the valuable contribution of a cutoff value to PXE diagnosis. Plasma samples and clinical records from two French reference centers for PXE (PXE Consultation Center, Angers, and FAVA-MULTI South Competent Center, Nice) were assessed. Plasma PPi were measured in 153 PXE and 46 non-PXE patients. The PPi concentrations in the plasma samples were determined by a new method combining enzymatic and ion chromatography approaches. The best match between the sensitivity and specificity (Youden index) for diagnosing PXE was determined by ROC analysis. [PPi]p were lower in PXE patients (0.92 ± 0.30 µmol/L) than in non-PXE patients (1.61 ± 0.33 µmol/L, p < 0.0001), corresponding to a mean reduction of 43 ± 19% (SD). The PPi cutoff value for diagnosing PXE in all patients was 1.2 µmol/L, with a sensitivity of 83.3% and a specificity of 91.1% (AUC = 0.93), without sex differences. In patients aged <50 years (i.e., the age period for PXE diagnosis), the cutoff PPi was 1.2 µmol/L (sensitivity, specificity, and AUC of 93%, 96%, and 0.97, respectively). The [PPi]p shows high accuracy for diagnosing PXE; thus, quantifying plasma PPi represents the first blood assay for diagnosing PXE. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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Figure 1

Figure 1
<p>Plasma PPi concentrations measured in non-PXE (n = 46) and PXE patients (n = 153). Solid bars are the median values, and whiskers show the interquartiles (5/95). **** <span class="html-italic">p</span> &lt; 0.0001.</p> Full article ">Figure 2
<p>Proposed model for accurate determination of the PPi cutoff for PXE diagnosis. The overall population (n = 199) was split into training (75%, n = 148) and test (25%, n = 51) datasets. Cutoff values were determined by ROC analysis of the “training” dataset and of the “simulated-training” dataset (n = 1000, using a bootstrap analysis of the “training” dataset). The same strategy was used to determine the cutoff value in patients aged &lt;50 y.o. Se: sensitivity; Spe: specificity.</p> Full article ">Figure 3
<p>Determination of the best cutoff value of plasma inorganic pyrophosphate (PPi) for diagnosing PXE using receiver operating characteristic (ROC) curves in all patients (<b>A</b>) and in patients &lt; 50 years old. (<b>B</b>). The black dots indicate the best compromise between sensitivity and specificity (Youden index). AUC: area under the curve.</p> Full article ">
12 pages, 1677 KiB  
Communication
Comparative Single Vesicle Analysis of Aqueous Humor Extracellular Vesicles before and after Radiation in Uveal Melanoma Eyes
by Shreya Sirivolu, Chen-Ching Peng, Paolo Neviani, Benjamin Y. Xu, Jesse L. Berry and Liya Xu
Int. J. Mol. Sci. 2024, 25(11), 6035; https://doi.org/10.3390/ijms25116035 - 30 May 2024
Viewed by 821
Abstract
Small extracellular vesicles (sEVs) have been shown to promote tumorigenesis, treatment resistance, and metastasis in multiple cancer types; however, sEVs in the aqueous humor (AH) of uveal melanoma (UM) patients have never previously been profiled. In this study, we used single particle analysis [...] Read more.
Small extracellular vesicles (sEVs) have been shown to promote tumorigenesis, treatment resistance, and metastasis in multiple cancer types; however, sEVs in the aqueous humor (AH) of uveal melanoma (UM) patients have never previously been profiled. In this study, we used single particle analysis to characterize sEV subpopulations in the AH of UM patients by quantifying their size, concentration, and phenotypes based on cell surface markers, specifically the tetraspanin co-expression patterns of CD9, CD63, and CD81. sEVs were analyzed from paired pre- and post-treatment (brachytherapy, a form of radiation) AH samples collected from 19 UM patients. In post-brachytherapy samples, two subpopulations, CD63/81+ and CD9/63/81+ sEVs, were significantly increased. These trends existed even when stratified by tumor location and GEP class 1 and class 2 (albeit not significant for GEP class 2). In this initial report of single vesicle profiling of sEVs in the AH of UM patients, we demonstrated that sEVs can be detected in the AH. We further identified two subpopulations that were increased post-brachytherapy, which may suggest radiation-induced release of these particles, potentially from tumor cells. Further study of the cargo carried by these sEV subpopulations may uncover important biomarkers and insights into tumorigenesis for UM. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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<p>Comparison of total sEV counts pre- and post-brachytherapy. (<b>A</b>) Paired sEV counts for each sample. (<b>B</b>) Box-and-whisker plot of pooled sEV counts.</p> Full article ">Figure 2
<p>sEV subpopulation dynamics. (<b>A</b>,<b>B</b>) Comparison of percent composition of sEV subpopulations pre- and post-brachytherapy. Percentages of CD63+ (<b>C</b>), CD63/81+ (<b>D</b>), and CD9/63/81+ (<b>E</b>). sEV subpopulations pre- and post-brachytherapy, shown as paired percentage for each sample and pooled percentage for all samples. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 3
<p>Comparison of percent composition of CD63+, CD63/81+, and CD9/63/81+ sEV subpopulations pre- and post-brachytherapy after GEP class stratification (<b>A</b>–<b>C</b>) and tumor location stratification (<b>D</b>–<b>F</b>).</p> Full article ">Figure A1
<p>Characterization of EVPs from unprocessed AH by Nanoparticle Tracking Analysis. (<b>A</b>–<b>C</b>) Average particle counts versus size distribution, average modal size, and average particle concentration in pre- and post-brachytherapy samples. (<b>D</b>,<b>E</b>) Average modal size and particle concentration in anterior and posterior tumors. (<b>F</b>,<b>G</b>) Average modal size and particle concentration in GEP class 1 and GEP class 2 tumors. Error bars represent standard deviations obtained from each study group. Due to AH availability, full experimental cohort was not included in this analysis.</p> Full article ">Figure A2
<p>Quantitative comparison of sEV tetraspanin subpopulation expression profiles in glaucoma (GLC) and UM pre-brachytherapy (UM_pre) AH samples. (<b>A</b>) Mean total sEV counts compared between GLC and UM_pre AH samples. (<b>B</b>) Mean sEV subpopulation percentages compared between GLC and UM_pre AH samples.</p> Full article ">Figure A3
<p>Comparison of pre-brachytherapy AH samples stratified into GEP class and tumor location. (<b>A</b>) Total EV counts in GEP class 1 and GEP class 2 tumors. (<b>B</b>) sEV tetraspanin expression profiles in GEP class 1 and GEP class 2 tumors. (<b>C</b>) Total EV counts in anterior and posterior tumors. (<b>D</b>) sEV tetraspanin expression profiles in anterior and posterior tumors.</p> Full article ">
17 pages, 3608 KiB  
Article
Exploratory Untargeted Metabolomics of Dried Blood Spot Samples from Newborns with Maple Syrup Urine Disease
by Abeer Z. Alotaibi, Reem H. AlMalki, Maha Al Mogren, Rajaa Sebaa, Mohammad Alanazi, Minnie Jacob, Ahamd Alodaib, Ahmad Alfares and Anas M. Abdel Rahman
Int. J. Mol. Sci. 2024, 25(11), 5720; https://doi.org/10.3390/ijms25115720 - 24 May 2024
Viewed by 964
Abstract
Currently, tandem mass spectrometry-based newborn screening (NBS), which examines targeted biomarkers, is the first approach used for the early detection of maple syrup urine disease (MSUD) in newborns, followed by confirmatory genetic mutation tests. However, these diagnostic approaches have limitations, demanding the development [...] Read more.
Currently, tandem mass spectrometry-based newborn screening (NBS), which examines targeted biomarkers, is the first approach used for the early detection of maple syrup urine disease (MSUD) in newborns, followed by confirmatory genetic mutation tests. However, these diagnostic approaches have limitations, demanding the development of additional tools for the diagnosis/screening of MUSD. Recently, untargeted metabolomics has been used to explore metabolic profiling and discover the potential biomarkers/pathways of inherited metabolic diseases. Thus, we aimed to discover a distinctive metabolic profile and biomarkers/pathways for MSUD newborns using untargeted metabolomics. Herein, untargeted metabolomics was used to analyze dried blood spot (DBS) samples from 22 MSUD and 22 healthy control newborns. Our data identified 210 altered endogenous metabolites in MSUD newborns and new potential MSUD biomarkers, particularly L-alloisoleucine, methionine, and lysoPI. In addition, the most impacted pathways in MSUD newborns were the ascorbate and aldarate pathways and pentose and glucuronate interconversions, suggesting that oxidative and detoxification events may occur in early life. Our approach leads to the identification of new potential biomarkers/pathways that could be used for the early diagnosis/screening of MSUD newborns but require further validation studies. Our untargeted metabolomics findings have undoubtedly added new insights to our understanding of the pathogenicity of MSUD, which helps us select the appropriate early treatments for better health outcomes. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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<p>Sample clustering and group separation based on a group of 20,568 features. (<b>A</b>) PLS-DA shows a clear separation between the two groups: MSUD newborn and healthy control. (<b>B</b>) OPLS-DA shows a clear separation between the two groups: MSUD newborns and healthy controls. The robustness of the created models was evaluated by the fitness of the model (R<sup>2</sup>Y: 0.963) and predictive ability (Q<sup>2</sup>: 0.353) values.</p> Full article ">Figure 2
<p>Volcano plot (moderated <span class="html-italic">t</span>-test, cut-off: <span class="html-italic">p</span> &lt; 0.05, FC 1.5) between two groups: MSUD newborns and healthy controls. The heatmap revealed 1040 significantly dysregulated metabolites, where 303 (red) and 737 (blue) were up- and downregulated, respectively.</p> Full article ">Figure 3
<p>Hierarchical clustering (HAC) and heatmap analyses demonstrating (<b>A</b>) Upregulated metabolites. (<b>B</b>) Downregulated metabolites in MSUD newborns compared with healthy controls. The color scaled bar, green referred to upregulated metabolites, and red referred to downregulated metabolites.</p> Full article ">Figure 4
<p>Metabolomics profiling and biomarker evaluation between MSUD newborn and healthy control groups. (<b>A</b>): A receiver operating characteristics (ROC) curve was created by the OPLS-DA model, with area under the curve (AUC) values calculated from the combination of 5, 10, 15, 25, 50, and 100 metabolites (<b>B</b>): The frequency plot shows the top 15 identified metabolites. (<b>C</b>,<b>D</b>): Examples of metabolites methionine sulfoxide and L-alloisoleucine were upregulated in MSUD newborn patients with (AUC:0.81) and (AUC: 0.926), respectively. (<b>E</b>): lysoPI downregulated in MSUD newborns compared to healthy control (AUC: 0.86).</p> Full article ">Figure 5
<p>Pathway analysis of the significant metabolites dysregulated in MSUD newborns. In total, 210 metabolites were finally identified as human endogenous metabolites; 51 were upregulated, and 159 were downregulated. The color variation (yellow to red) shows the different significance levels of metabolites in the data.</p> Full article ">Scheme 1
<p>The workflow of data analyses performed in this study.</p> Full article ">
11 pages, 1536 KiB  
Article
Serum Galectin-3 as a Non-Invasive Marker for Primary Sclerosing Cholangitis
by Ganimete Bajraktari, Tanja Elger, Muriel Huss, Johanna Loibl, Andreas Albert, Arne Kandulski, Martina Müller, Hauke Christian Tews and Christa Buechler
Int. J. Mol. Sci. 2024, 25(9), 4765; https://doi.org/10.3390/ijms25094765 - 27 Apr 2024
Viewed by 962
Abstract
Primary sclerosing cholangitis (PSC) is a serious liver disease associated with inflammatory bowel disease (IBD). Galectin-3, an inflammatory and fibrotic molecule, has elevated circulating levels in patients with chronic liver disease and inflammatory bowel disease (IBD). This study aims to clarify whether galectin-3 [...] Read more.
Primary sclerosing cholangitis (PSC) is a serious liver disease associated with inflammatory bowel disease (IBD). Galectin-3, an inflammatory and fibrotic molecule, has elevated circulating levels in patients with chronic liver disease and inflammatory bowel disease (IBD). This study aims to clarify whether galectin-3 can differentiate between patients with IBD, PSC, and PSC-IBD. Our study measured serum galectin-3 levels in 38 healthy controls, 55 patients with IBD, and 22 patients with PSC (11 patients had underlying IBD and 11 patients did not), alongside the urinary galectin-3 of these patients and 18 controls. Serum and urinary galectin-3 levels in IBD patients were comparable to those in controls. Among IBD patients, those with high fecal calprotectin, indicating severe disease, exhibited lower serum and elevated urinary galectin-3 levels compared to those with low calprotectin levels. Serum galectin-3 levels were inversely correlated with C-reactive protein levels. PSC patients displayed higher serum and urinary galectin-3 levels than IBD patients, with the highest serum levels observed in PSC patients with coexisting IBD. There was no correlation between serum and urinary galectin-3 levels and laboratory indicators of liver injury in both IBD and PSC patients. In conclusion, this study demonstrates that serum and urinary galectin-3 levels can distinguish IBD from PSC patients, and also reveals higher serum galectin-3 levels in PSC-IBD patients compared to those with isolated PSC. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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<p>Serum and urinary galectin-3 (Gal3) of healthy controls (HC) and patients with Crohn’s disease (CD) and ulcerative colitis (UC). (<b>A</b>) Serum Gal3; (<b>B</b>) urinary Gal3. Small circles in the figures are outliers.</p> Full article ">Figure 2
<p>Serum and urinary galectin-3 (Gal3) in relation to fecal calprotectin: (<b>A</b>) serum Gal3; (<b>B</b>) urinary Gal3. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. The small circle in the (<b>B</b>) is an outlier.</p> Full article ">Figure 3
<p>Serum and urinary galectin-3 (Gal3) of healthy controls (HC), patients with inflammatory bowel disease (IBD) and patients with primary sclerosing cholangitis (PSC). (<b>A</b>) Serum Gal3; (<b>B</b>) urinary Gal3; (<b>C</b>) serum Gal3 of PSC patients without IBD (PSC<sub>woIBD</sub>) and PSC patients with IBD (PSC-IBD). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. Small circles and asterisk in the figure are outliers.</p> Full article ">Figure 4
<p>Serum galectin-3 (Gal3) of healthy controls (HC), patients with inflammatory bowel disease (IBD), patients with primary sclerosing cholangitis without underlying IBD (PSC<sub>woIBD</sub>), and PSC-IBD patients: (<b>A</b>) Serum Gal3 of IBD, PSC<sub>woIBD</sub>, and PSC-IBD patients; (<b>B</b>) serum Gal3 of HC, PSC<sub>woIBD</sub>, and PSC-IBD patients. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. Small circle in the figure is an outlier.</p> Full article ">

Review

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15 pages, 707 KiB  
Review
Biomarkers Involved in the Pathogenesis of Hemophilic Arthropathy
by Oana Viola Badulescu, Dragos-Viorel Scripcariu, Minerva Codruta Badescu, Manuela Ciocoiu, Maria Cristina Vladeanu, Carmen Elena Plesoianu, Andrei Bojan, Dan Iliescu-Halitchi, Razvan Tudor, Bogdan Huzum, Otilia Elena Frasinariu and Iris Bararu-Bojan
Int. J. Mol. Sci. 2024, 25(18), 9897; https://doi.org/10.3390/ijms25189897 - 13 Sep 2024
Viewed by 537
Abstract
Hemophilia, which is a rare disease, results from congenital deficiencies of coagulation factors VIII and IX, respectively, leading to spontaneous bleeding into joints, resulting in hemophilic arthropathy (HA). HA involves complex processes, including synovial proliferation, angiogenesis, and tissue remodeling. Despite ongoing research, factors [...] Read more.
Hemophilia, which is a rare disease, results from congenital deficiencies of coagulation factors VIII and IX, respectively, leading to spontaneous bleeding into joints, resulting in hemophilic arthropathy (HA). HA involves complex processes, including synovial proliferation, angiogenesis, and tissue remodeling. Despite ongoing research, factors contributing to HA progression, especially in adults with severe HA experiencing joint pain, remain unclear. Blood markers, particularly collagen-related ones, have been explored to assess joint health in hemophilia. For example, markers like CTX-I and CTX-II reflect bone and cartilage turnover, respectively. Studies indicate elevated levels of certain markers post-bleeding episodes, suggesting joint health changes. However, longitudinal studies on collagen turnover and basement membrane or endothelial cell markers in relation to joint outcomes, particularly during painful episodes, are scarce. Given the role of the CX3CL1/CX3XR1 axis in arthritis, other studies investigate its involvement in HA. The importance of different inflammatory and bone damage biomarkers should be assessed, alongside articular cartilage and synovial membrane morphology, aiming to enhance understanding of hemophilic arthropathy progression. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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<p>Biomarkers in HA [<a href="#B38-ijms-25-09897" class="html-bibr">38</a>].</p> Full article ">
20 pages, 7218 KiB  
Review
Biomarker Landscape in RASopathies
by Noemi Ferrito, Juan Báez-Flores, Mario Rodríguez-Martín, Julián Sastre-Rodríguez, Alessio Coppola, María Isidoro-García, Pablo Prieto-Matos and Jesus Lacal
Int. J. Mol. Sci. 2024, 25(16), 8563; https://doi.org/10.3390/ijms25168563 - 6 Aug 2024
Viewed by 992
Abstract
RASopathies are a group of related genetic disorders caused by mutations in genes within the RAS/MAPK signaling pathway. This pathway is crucial for cell division, growth, and differentiation, and its disruption can lead to a variety of developmental and health issues. RASopathies present [...] Read more.
RASopathies are a group of related genetic disorders caused by mutations in genes within the RAS/MAPK signaling pathway. This pathway is crucial for cell division, growth, and differentiation, and its disruption can lead to a variety of developmental and health issues. RASopathies present diverse clinical features and pose significant diagnostic and therapeutic challenges. Studying the landscape of biomarkers in RASopathies has the potential to improve both clinical practices and the understanding of these disorders. This review provides an overview of recent discoveries in RASopathy molecular profiling, which extend beyond traditional gene mutation analysis. mRNAs, non-coding RNAs, protein expression patterns, and post-translational modifications characteristic of RASopathy patients within pivotal signaling pathways such as the RAS/MAPK, PI3K/AKT/mTOR, and Rho/ROCK/LIMK2/cofilin pathways are summarized. Additionally, the field of metabolomics holds potential for uncovering metabolic signatures associated with specific RASopathies, which are crucial for developing precision medicine. Beyond molecular markers, we also examine the role of histological characteristics and non-invasive physiological assessments in identifying potential biomarkers, as they provide evidence of the disease’s effects on various systems. Here, we synthesize key findings and illuminate promising avenues for future research in RASopathy biomarker discovery, underscoring rigorous validation and clinical translation. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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<p>Molecular biomarkers of RASopathies. (<b>A</b>) Each circle represents a specific RASopathy and includes the associated genes. (<b>B</b>) mRNA detection methods: Overview of the main mRNA detection methods used to study the RASopathies. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 3 August 2024).</p> Full article ">Figure 2
<p>Protein biomarkers of RASopathies. Each color represents a disease: NF1 (blue), LS (gray), CM-AVM (pink), NS (brown), CS (violet), NSML (orange), and CFC (light pink). Inhibitors are indicated in red. Yellow circles denote phosphorylation, and green circles denote ubiquitination. PTM indicates the presence of multiple types of posttranslational modifications on that protein. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 3 August 2024).</p> Full article ">Figure 3
<p>Histological and molecular characterization of RASopathies. Schematic representation of histological (<b>left panel</b>) and molecular (<b>right panel</b>) biomarkers commonly used in RASopathies for diagnosis, prognosis, and treatment response. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 3 August 2024).</p> Full article ">Figure 4
<p>Cardiac, bone, and embryonic RASopathy physiological biomarkers. Each color represents a specific RASopathy. NF1: blue; LS: gray; NS: brown; CS: violet; NSML: orange; CFC: pink. Biomarkers common to all RASopathies are indicated in white. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (accessed on 3 August 2024).</p> Full article ">
20 pages, 1047 KiB  
Review
Obesity-Related Ciliopathies: Focus on Advances of Biomarkers
by Qianwen Zhang, Yiguo Huang, Shiyang Gao, Yu Ding, Hao Zhang, Guoying Chang and Xiumin Wang
Int. J. Mol. Sci. 2024, 25(15), 8484; https://doi.org/10.3390/ijms25158484 - 3 Aug 2024
Viewed by 790
Abstract
Obesity-related ciliopathies, as a group of ciliopathies including Alström Syndrome and Bardet–Biedl Syndrome, exhibit distinct genetic and phenotypic variability. The understanding of these diseases is highly significant for understanding the functions of primary cilia in the human body, particularly regarding the relationship between [...] Read more.
Obesity-related ciliopathies, as a group of ciliopathies including Alström Syndrome and Bardet–Biedl Syndrome, exhibit distinct genetic and phenotypic variability. The understanding of these diseases is highly significant for understanding the functions of primary cilia in the human body, particularly regarding the relationship between obesity and primary cilia. The diagnosis of these diseases primarily relies on clinical presentation and genetic testing. However, there is a significant lack of research on biomarkers to elucidate the variability in clinical manifestations, disease progression, prognosis, and treatment responses. Through an extensive literature review, the paper focuses on obesity-related ciliopathies, reviewing the advancements in the field and highlighting the potential roles of biomarkers in the clinical presentation, diagnosis, and prognosis of these diseases. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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<p>The structure of primary cilia and the localization of proteins of obesity-related ciliopathies.</p> Full article ">Figure 2
<p>Mechanism of obesity. CCK, cholecystokinin; ARC, arcuate nucleus; PVN, paraventricular nucleus; POMC, pro-opiomelanocortin; AgRP, agouti-related protein; BBS, Bardet–Biedl syndrome; ALMS, Alström syndrome.</p> Full article ">
12 pages, 1629 KiB  
Review
The Role of MicroRNAs in Progressive Supranuclear Palsy—A Systematic Review
by Aleksandra Ćwiklińska, Grzegorz Procyk, Dariusz Koziorowski and Stanisław Szlufik
Int. J. Mol. Sci. 2024, 25(15), 8243; https://doi.org/10.3390/ijms25158243 - 28 Jul 2024
Viewed by 864
Abstract
Progressive supranuclear palsy (PSP) is a rare, neurodegenerative movement disorder. Together with multiple system atrophy (MSA), Dementia with Lewy bodies (DLB), and corticobasal degeneration (CBD), PSP forms a group of atypical parkinsonisms. The latest diagnostic criteria, published in 2017 by the Movement Disorders [...] Read more.
Progressive supranuclear palsy (PSP) is a rare, neurodegenerative movement disorder. Together with multiple system atrophy (MSA), Dementia with Lewy bodies (DLB), and corticobasal degeneration (CBD), PSP forms a group of atypical parkinsonisms. The latest diagnostic criteria, published in 2017 by the Movement Disorders Society, classify PSP diagnosis into defined, probable, and possible categories based on clinical examination. However, no single test is specific and sensitive for this disease. Microribonucleic acids (miRNAs) are promising molecules, particularly in the case of diseases that lack appropriate diagnostic and treatment tools, which supports exploring their role in PSP. We aimed to systematically review the current knowledge about the role of miRNAs in PSP. This study was registered in the Open Science Framework Registry, and the protocol is available online. Primary original studies, both clinical and preclinical, written in English and assessing miRNAs in PSP were included. Systematic reviews, meta-analyses, reviews, case reports, letters to editors, commentaries, conference abstracts, guidelines/statements, expert opinions, preprints, and book chapters were excluded. The following five databases were searched: Embase, Medline Ultimate, PubMed, Scopus, and Web of Science. Each database was last searched on 18 June 2024. Eventually, nine original studies relevant to the discussed area were included. The risk of bias was not assessed. The selected research suggests that miRNAs may be considered promising biomarkers in PSP. However, the exact involvement of miRNAs in the pathogenesis of PSP is still to be determined. Several microRNAs were found to be dysregulated in patients with PSP. This applies to both brain tissue and fluids like cerebrospinal fluid CSF or blood. Several miRNAs were found that could potentially be helpful in differentiating among PSP patients, PD patients, and healthy individuals. Although some correlations and alterations have already been found, this field requires much more research. MicroRNAs are exciting and promising small molecules, and their investigation into many diseases, including PSP, may lead to significant discoveries. Full article
(This article belongs to the Special Issue Advances in Rare Diseases Biomarkers)
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<p>The flowchart for the selection process; n—number of studies.</p> Full article ">Figure 2
<p>A graphical summary of the role of microRNAs in progressive supranuclear palsy. ↑—increased; ↓—decreased; HCs—healthy controls; miRNAs/miRs—micro-ribonucleic acids; PD—Parkinson’s Disease; PSP—progressive supranuclear palsy; ROC—receiver operating characteristic.</p> Full article ">
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