MicroRNAs play an important role in regulating gene expression, but their target identification is a difficult task due to their short length and imperfect complementarity. Burge and coworkers developed a program called TargetScan that allowed imperfect complementarity and established a procedure favoring targets with multiple binding sites conserved in multiple organisms. We improved their algorithm in two major aspects - (i) using well-defined UTR (untranslated region) database, (ii) examining the extent of conservation inside the 3' UTR specifically. Average length in our UTR database, based on the ECgene annotation, is more than twice longer than the Ensembl. Then, TargetScan was used to identify putative binding sites. The extent of conservation varies significantly inside the 3' UTR. We used the "tight" tracks in the UCSC genome browser to select the conserved binding sites in multiple species. By combining the longer 3' UTR data, TargetScan, and tightly conserved blocks of genomic DNA, we identified 107 putative target genes with multiple binding sites conserved in multiple species, of which 85 putative targets are novel.
3' Untranslated Regions* ; Binding Sites ; DNA ; Gene Expression ; Genome ; Genomics* ; Methods ; MicroRNAs*

Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.
Classification ; Dataset ; Diagnosis ; Gene Expression ; Humans ; Learning ; Neuroblastoma

Background: Although the prevalence of diabetic kidney disease (DKD) is increasing, reliable biomarkers for its early detection are scarce. This study aimed to evaluate the association of adenosine and succinate levels and their related pathways, including hyaluronic acid (HA) synthesis, with DKD. Methods: We examined 235 participants and categorized them into three groups: healthy controls; those with diabetes but without DKD; and those with DKD, which was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. We compared the concentrations of urinary adenosine, succinate, and HA and the serum levels of cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation, among the groups with DKD or albuminuria. In addition, we performed multiple logistic regression analysis to evaluate the independent association of DKD or albuminuria with the metabolites after adjusting for risk factors. We also showed the association of these metabolites with eGFR measured several years before enrollment. This study was registered with the Clinical Research Information Service (https://cris.nih.go.kr; Registration number: KCT0003573). Results: Urinary succinate and serum CD39 levels were higher in the DKD group than in the control and non-DKD groups. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR, albuminuria, and ΔeGFR, which was calculated retrospectively. However, among the various metabolites studied, only urinary succinate was identified as an independent indicator of DKD and albuminuria. Conclusion Among several potential metabolites, only urinary succinate was independently associated with DKD. These findings hold promise for clinical application in the management of DKD.

Background: Although the prevalence of diabetic kidney disease (DKD) is increasing, reliable biomarkers for its early detection are scarce. This study aimed to evaluate the association of adenosine and succinate levels and their related pathways, including hyaluronic acid (HA) synthesis, with DKD. Methods: We examined 235 participants and categorized them into three groups: healthy controls; those with diabetes but without DKD; and those with DKD, which was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. We compared the concentrations of urinary adenosine, succinate, and HA and the serum levels of cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation, among the groups with DKD or albuminuria. In addition, we performed multiple logistic regression analysis to evaluate the independent association of DKD or albuminuria with the metabolites after adjusting for risk factors. We also showed the association of these metabolites with eGFR measured several years before enrollment. This study was registered with the Clinical Research Information Service (https://cris.nih.go.kr; Registration number: KCT0003573). Results: Urinary succinate and serum CD39 levels were higher in the DKD group than in the control and non-DKD groups. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR, albuminuria, and ΔeGFR, which was calculated retrospectively. However, among the various metabolites studied, only urinary succinate was identified as an independent indicator of DKD and albuminuria. Conclusion Among several potential metabolites, only urinary succinate was independently associated with DKD. These findings hold promise for clinical application in the management of DKD.

Background: Although the prevalence of diabetic kidney disease (DKD) is increasing, reliable biomarkers for its early detection are scarce. This study aimed to evaluate the association of adenosine and succinate levels and their related pathways, including hyaluronic acid (HA) synthesis, with DKD. Methods: We examined 235 participants and categorized them into three groups: healthy controls; those with diabetes but without DKD; and those with DKD, which was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. We compared the concentrations of urinary adenosine, succinate, and HA and the serum levels of cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation, among the groups with DKD or albuminuria. In addition, we performed multiple logistic regression analysis to evaluate the independent association of DKD or albuminuria with the metabolites after adjusting for risk factors. We also showed the association of these metabolites with eGFR measured several years before enrollment. This study was registered with the Clinical Research Information Service (https://cris.nih.go.kr; Registration number: KCT0003573). Results: Urinary succinate and serum CD39 levels were higher in the DKD group than in the control and non-DKD groups. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR, albuminuria, and ΔeGFR, which was calculated retrospectively. However, among the various metabolites studied, only urinary succinate was identified as an independent indicator of DKD and albuminuria. Conclusion Among several potential metabolites, only urinary succinate was independently associated with DKD. These findings hold promise for clinical application in the management of DKD.

Background: Although the prevalence of diabetic kidney disease (DKD) is increasing, reliable biomarkers for its early detection are scarce. This study aimed to evaluate the association of adenosine and succinate levels and their related pathways, including hyaluronic acid (HA) synthesis, with DKD. Methods: We examined 235 participants and categorized them into three groups: healthy controls; those with diabetes but without DKD; and those with DKD, which was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. We compared the concentrations of urinary adenosine, succinate, and HA and the serum levels of cluster of differentiation 39 (CD39) and CD73, which are involved in adenosine generation, among the groups with DKD or albuminuria. In addition, we performed multiple logistic regression analysis to evaluate the independent association of DKD or albuminuria with the metabolites after adjusting for risk factors. We also showed the association of these metabolites with eGFR measured several years before enrollment. This study was registered with the Clinical Research Information Service (https://cris.nih.go.kr; Registration number: KCT0003573). Results: Urinary succinate and serum CD39 levels were higher in the DKD group than in the control and non-DKD groups. Correlation analysis consistently linked urinary succinate and serum CD39 concentrations with eGFR, albuminuria, and ΔeGFR, which was calculated retrospectively. However, among the various metabolites studied, only urinary succinate was identified as an independent indicator of DKD and albuminuria. Conclusion Among several potential metabolites, only urinary succinate was independently associated with DKD. These findings hold promise for clinical application in the management of DKD.