Identifying new targets in leukemogenesis using computational approaches

Copy number variant analysis for syndromic congenital heart disease in the Chinese population

Background

Syndromic congenital heart disease (CHD) is among the most severe conditions in the pediatric population. Copy number variant (CNV) is an important cause of syndromic CHD, but few studies focused on CNVs related to these patients in China. The present study aimed to identify pathogenic CNVs associated with syndromic CHD in the Chinese population.

Methods

A total of 109 sporadic patients with syndromic CHD were applied chromosomal microarray analysis (CMA). Phenotype spectrum of pathogenic or likely pathogenic CNVs was analyzed. CHD-related genes were prioritized from genes within pathogenic or likely pathogenic CNVs by VarElect, OVA, AMELIE, and ToppGene.

Results

Using CMA, we identified 43 candidate CNVs in 37/109 patients. After filtering CNVs present in the general population, 29 pathogenic/likely pathogenic CNVs in 24 patients were identified. The diagnostic yield of CMA for pathogenic/likely pathogenic CNVs was 23.1% (24/104), excluding 5 cases with aneuploidies or gross chromosomal aberrations. The overlapping analysis of CHD-related gene lists from different prioritization tools highlighted 16 CHD candidate genes.

Conclusion

As the first study focused on CNVs in syndromic CHD from the Chinese population, this study reveals the importance of CMA in exploring the genetic etiology of syndromic CHD and expands our understanding of these complex diseases. The bioinformatic analysis of candidate genes suggests several CHD-related genes for further functional research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00426-8.

Methylation-Driven Gene PLAU as a Potential Prognostic Marker for Differential Thyroid Carcinoma

Purpose: Aberrant DNA methylation plays a crucial role in the tumorigenesis of differentiated thyroid cancer (DTC); nevertheless, the factors leading to the local and regional recurrence of DTC are not well understood. This study aimed to establish the connection between DNA methylation-driven genes and the recurrence of DTC.

Methods: RNA sequencing profiles and DNA methylation profiles of DTC were downloaded from The Cancer Genome Atlas (TCGA) database. Combined application of the methylmix R package and univariate Cox regression analyses were used to screen and distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses were utilized to identify the target genes that were closely associated with the recurrence of DTC. Then, correlations between the expression levels of the target genes and the clinicopathological features were verified, as well as their potential biological functions.

Results: A total of 168 Methylation-driven genes were differentially expressed in thyroid cancer, among which 10 genes (GSTO2, GSTM5, GSTM1, GPX7, FGF2, LIF, PLAU, BCL10, SHARPIN and TNFRSF1A) were identified as Hub genes. We selected PLAU for further analysis because PLAU was most strongly correlated with DTC recurrence and the DNA methylation levels of PLAU were closely associated with multiple clinicopathological features of DTC. PLAU was significantly upregulated in DTC, and patients with a high expression level of PLAU had a higher risk of recurrence (p < 0.05). Functional predictions suggested that PLAU-related genes were mainly involved in the regulation of immune-related signaling pathways. Moreover, the mRNA level of PLAU was found to be positively correlated with the cell markers of neutrophils and dendritic cells. In addition, we found that two DNA methylation sites (cg06829584, cg19399285) were associated with abnormal expression of PLAU in DTC.

Conclusion: The methylation-driven gene PLAU is an independent risk factor for the recurrence of DTC and it functions as an oncogene through the regulation of immune-related signaling pathways, which offers new insight into the molecular mechanisms of thyroid cancer and provides new possibilities for individualized treatment of thyroid cancer patients.

Protein-biomolecule interactions play a major role in shaping corona proteome: studies on milk interacted dietary particles

Despite the significance of surface absorbed proteins in determining the biological identity of nanoparticles (NPs) entering the human body, little is known about the surface corona and factors that shape their formation on dietary particles used as food additives. In this study, food grade NPs of silica and titania and their food additive counterparts (E551 and E171) were interacted with milk proteins or with skimmed milk and the levels of protein adsorption were quantified. Characteristics of proteins correlating with their level of adsorption to NPs were determined using partial least squares regression analysis. Results from individual protein-particle interactions revealed the significance of factors such as zeta potential, hydrophobicity and hydrodynamic size of particles, and protein characteristics such as the number of beta strands, isoelectric points, the number of amino acid units (Ile, Tyr, Ala, Gly, Pro, Asp, and Arg), and phosphorylation sites on their adsorption to particles. Similar regression analysis was performed to identify the characteristics of twenty abundant and enriched proteins (identified using LC-MS/MS analysis) for their association with the surface corona of milk-interacted particles. Contrary to individual protein-particle interactions, protein characteristics such as helices, turns, protein structures, disulfide bonds, the number of amino acid units (Cys, Met, Leu, and Trp), and Fe binding sites were significant for their association with the surface corona of milk interacted particles. This difference in factors identified from individual proteins and milk interacted particles suggested possible interactions of proteins with surface adsorbed biomolecules as revealed by scanning transmission X-ray microscopy and other biochemical assays.

CDK9 Inhibitor Induces the Apoptosis of B-Cell Acute Lymphocytic Leukemia by Inhibiting c-Myc-Mediated Glycolytic Metabolism

B-cell acute lymphocytic leukemia (B-ALL), a common blood cancer in children, leads to high mortality. Cyclin-dependent kinase 9 inhibitor (CDK9i) effectively attenuates acute myeloid leukemia and chronic lymphoblastic leukemia by inducing apoptosis and inhibiting cell proliferation. However, the effect of CDK9i on B-ALL cells and the underlying mechanisms remain unclear. In this study, we showed that CDK9i induced the apoptosis of B-ALL cells in vitro by activating the apoptotic pathways. In addition, CDK9i restrained the glycolytic metabolism of B-ALL cells, and CDK9i-induced apoptosis was enhanced by co-treatment with glycolysis inhibitors. Furthermore, CDK9i restained the glycolysis of B-ALL cell lines by markedly downregulating the expression of glucose transporter type 1 (GLUT1) and the key rate-limiting enzymes of glycolysis, such as hexokinase 2 (HK2) and lactate dehydrogenase A (LDHA). Moreover, cell apoptosis was rescued in B-ALL cells with over-expressed c-Myc after treatment with CDK9i, which is involved in the enhancement of glycolytic metabolism. In summary, our findings suggest that CDK9 inhibitors induce the apoptosis of B-ALL cells by inhibiting c-Myc-mediated glycolytic metabolism, thus providing a new strategy for the treatment of B-ALL.

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside

Background

Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD).

Methods

The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets—GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram.

Results

In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873).

Conclusions

We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes.