Transcriptomics-based validation of the relatedness of heterogeneous nuclear ribonucleoproteins to chronic lymphocytic leukemia as potential biomarkers of the disease aggressiveness

Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis

High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules, the interpretability of DL-extracted features can be challenging. To address these challenges, we propose an interpretable cancer prognosis model called Cox-Sage. Specifically, we first propose an algorithm to construct a patient similarity graph from heterogeneous clinical data, and then extract protein-coding genes from the patient's gene expression data to embed them as features into the graph nodes. We utilize multilayer graph convolution to model proportional hazards pattern and introduce a mathematical method to clearly explain the meaning of our model's parameters. Based on this approach, we propose two metrics for measuring gene importance from different perspectives: mean hazard ratio and reciprocal of the mean hazard ratio. These metrics can be used to discover two types of important genes: genes whose low expression levels are associated with high cancer prognosis risk, and genes whose high expression levels are associated with high cancer prognosis risk. We conducted experiments on seven datasets from TCGA, and our model achieved superior prognostic performance compared with some state-of-the-art methods. As a primary research, we performed prognostic biomarker discovery on the LIHC (Liver Hepatocellular Carcinoma) dataset. Our code and dataset can be found at https://github.com/beeeginner/Cox-sage.

Evaluation of Bruton's Tyrosine Kinase (BTK) inhibition with alternative doses of ibrutinib in subjects with Chronic Lymphocytic Leukemia (CLL)

PURPOSE

To evaluate alternative ibrutinib dosing regimens that maintain Bruton's tyrosine kinase (BTK) receptor occupancy over the entire dosing interval for CLL patients using a model-based approach.

METHODS

Ibrutinib inhibits B-cell proliferation via irreversible binding of BTK. As IC50 is not an appropriate parameter to describe the potency of the inhibition in the presence of a covalent binding inhibitor. A BTK covalent binding model was developed using kinact/KI as key parameter to account for covalent binding. The ibrutinib-BTK covalent binding model was used to describe the effect of daily doses of 140, 280, 420 and 560 mg on the proportion of subjects with more than 90% BTK inhibition at steady state trough concentrations. Predictive performance of the model was assessed using the available ibrutinib BTK inhibition data following QD dosing. Model-based predictions were used to identify the minimum ibrutinib QD dose that provides more than 90% inhibition in more than 90% of the subjects.

RESULTS

The covalent binding model was able to describe the data and predicted that ibrutinib QD dose reduced from 420 mg to 280 mg or 140 mg may inhibit de novo synthetized BTK efficiently in a CLL population.

CONCLUSION

Using a model-based approach showed that reducing the ibrutinib dosing regimen to 280 mg QD or even 140 mg in case of adverse events could maintain BTK inhibition over the entire dosing interval.

Multiple omics levels of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is a lymphoproliferative malignancy characterized by the proliferation of functionally mature but incompetent B cells. It is the most prevalent type of leukemia in Western populations, accounting for approximately 25% of new leukemia cases. While recent advances, such as ibrutinib and venetoclax treatment have improved patient outlook, aggressive forms of CLL such as Richter transformation still pose a significant challenge. This discrepancy may be due to the heterogeneity of factors contributing to CLL development at multiple -omics levels. However, information on the omics of CLL is fragmented, hindering multi-omics-based research into potential treatment options. To address this, we aggregated and presented a selection of important aspects of various omics levels of the disease in this review. The purpose of the present literature analysis is to portray examples of CLL studies from different omics levels, including genomics, epigenomics, transcriptomics, epitranscriptomics, proteomics, epiproteomics, metabolomics, glycomics and lipidomics, as well as those identified by multi-omics approaches. The review includes the list of 102 CLL-associated genes with relevant genomics information. While single-omics studies yield substantial and useful data, they omit a significant level of complex biological interplay present in the disease. As multi-omics studies integrate several different layers of data, they may be better suited for complex diseases such as CLL and have thus far yielded promising results. Future multi-omics studies may assist clinicians in improved treatment choices based on CLL subtypes as well as allow the identification of novel biomarkers and targets for treatments.

In silico investigations identified Butyl Xanalterate to competently target CK2α (CSNK2A1) for therapy of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is an incurable malignancy of B-cells. In this study, bioinformatics analyses were conducted to identify possible pathogenic roles of CK2α, which is a protein encoded by CSNK2A1, in the progression and aggressiveness of CLL. Furthermore, various computational tools were used to search for a competent inhibitor of CK2α from fungal metabolites that could be proposed for CLL therapy. In CLL patients, high-expression of CSNK2A1 was associated with early need for therapy (n = 130, p < 0.0001) and short overall survival (OS; n = 107, p = 0.005). Consistently, bioinformatics analyses showed CSNK2A1 to associate with/play roles in CLL proliferation and survival-dependent pathways. Furthermore, PPI network analysis identified interaction partners of CK2α (PPI enrichment p value = 1 × 10^-16) that associated with early need for therapy (n = 130, p < 0.003) and have been known to heavily impact on the progression of CLL. These findings constructed a rational for targeting CK2α for CLL therapy. Consequently, computational analyses reported 35 fungal metabolites out of 5820 (filtered from 19,967 metabolites) to have lower binding energy (ΔG: - 10.9 to - 11.7 kcal/mol) and better binding affinity (Kd: 9.77 × 10⁷ M^-1 to 3.77 × 10⁸ M^-1) compared with the native ligand (ΔG: - 10.8, Kd: 8.3 × 10⁷ M-^-1). Furthermore, molecular dynamics simulation study established that Butyl Xanalterate-CK2α complex continuously remained stable throughout the simulation time (100 ns). Moreover, Butyl Xanalterate interacted with most of the catalytic residues, where complex was stabilized by more than 65% hydrogen bond interactions, and a significant hydrophobic interaction with residue Phe113. Here, high-expression of CSNK2A1 was implicated in the progression and poor prognosis of CLL, making it a potential therapeutic target in the disease. Butyl Xanalterate showed stable and strong interactions with CK2α, thus we propose it as a competitive inhibitor of CK2α for CLL therapy.

Investigating the impact of RNA integrity variation on the transcriptome of human leukemic cells

High RNA integrity is essential for good quality of transcriptomics profiling. Nevertheless, in some cases samples with low RNA integrity is the only available material to study. This work was set to investigate the impact of thermal-induced RNA degradation on the transcriptomic profiles of human leukemic cells. DNA microarray-based transcriptomics was conducted on two groups of samples; high RNA integrity samples (n = 4) and low RNA integrity samples (n = 5). RNA degradation caused limited but noticeable changes in the transcriptomes. Only 1945 (6.7%) of 29,230 genes showed altered quantitation (fold change ≥ two-fold, p value ≤ 0.03, corrected p value ≤ 0.05). RNA degradation had the most impact on short transcripts and those with short distance between their 5’end and the probe binding position. Overall, the present work identified the genes whose relative quantification is sensitive to RNA degradation. Therefore, altered expression of these genes should be interpreted with caution when studied in low integrity RNA samples.

Transcriptomics-Based Investigation of Molecular Mechanisms Underlying Apoptosis Induced by ZnO Nanoparticles in Human Diffuse Large B-Cell Lymphoma

Introduction

Zinc oxide nanoparticles (ZnO NPs) show anti-cancer activity. Diffuse Large B-cell Lymphoma (DLBCL) is a type of B-cell malignancies with unsatisfying treatment outcomes. This study was set to assess the potential of ZnO NPs to selectively induce apoptosis in human DLBCL cells (OCI-LY3), and to describe possible molecular mechanisms of action.

Methods

The impact of ZnO NPs on DLBCL cells and normal peripheral blood mononuclear cells (PBMCs) was studied using cytotoxicity assay and flow-cytometry. Transcriptomics analysis was conducted to identify ZnO NPs-dependent changes in the transcriptomic profiles of DLBCL cells.

Results

ZnO NPs selectively induced apoptosis in DLBCL cells, and caused changes in their transcriptomes. Deferential gene expression (DGE) with fold change (FC) ≥3 and p ≤ 0.008 with corrected p ≤ 0.05 was identified for 528 genes; 125 genes were over-expressed and 403 genes were under-expressed in ZnO NPs-treated DLBCL cells. The over-expressed genes involved in biological processes and pathways like stress response to metal ion, cellular response to zinc ion, metallothioneins bind metals, oxidative stress, and negative regulation of growth. In contrast, the under-expressed genes were implicated in DNA packaging complex, signaling by NOTCH, negative regulation of gene expression by epigenetic, signaling by WNT, M phase of cell cycle, and telomere maintenance. Setting the FC to ≥1.5 with p ≤ 0.05 and corrected p ≤ 0.1 showed ZnO NPs to induce over-expression of anti-oxidant genes and under-expression of oncogenes; target B-cell receptor (BCR) signaling pathway and NF-κB pathway; and promote apoptosis by intrinsic and extrinsic pathways.

Discussion

Overall, ZnO NPs selectively induced apoptosis in DLBCL cells, and possible molecular mechanisms of action were described.

Mechanosensitive pathways are regulated by mechanosensitive miRNA clusters in endothelial cells

Shear stress is known to affect many processes in (patho-) physiology through a complex, multi-molecular mechanism, termed mechanotransduction. The sheer complexity of the process has raised questions how mechanotransduction is regulated. Here, we comprehensively evaluate the literature about the role of small non-coding miRNA in the regulation of mechanotransduction. Regulation of mRNA by miRNA is rather complex, depending not only on the concentration of mRNA to miRNA, but also on the amount of mRNA competing for a single mRNA. The only mechanism to counteract the latter factor is through overarching structures of miRNA. Indeed, two overarching structures are present miRNA families and miRNA clusters, and both will be discussed in details, regarding the latest literature and a previous conducted study focussed on mechanotransduction. Both the literature and our own data support a new hypothesis that miRNA-clusters predominantly regulate mechanotransduction, affecting 65% of signalling pathways. In conclusion, a new and important mode of regulation of mechanotransduction is proposed, based on miRNA clusters. This finding implicates new avenues for treatment of mechanotransduction and atherosclerosis.

The HNRNPA2B1-MST1R-Akt axis contributes to epithelial-to-mesenchymal transition in head and neck cancer

The deregulation of splicing factors and alternative splicing are increasingly viewed as major contributory factors in tumorigenesis. In this study, we report overexpression of a key splicing factor, heterogeneous nuclear ribonucleoprotein A2B1 (HNRNPA2B1), and thereby misregulation of alternative splicing, which is associated with the poor prognosis of head and neck cancer (HNC). The role of HNRNPA2B1 in HNC tumorigenesis via deregulation of alternative splicing is not well understood. Here, we found that the CRISPR/Cas9-mediated knockout of HNRNPA2B1 results in inhibition of HNC cells growth via the misregulation of alternative splicing of MST1R, WWOX, and CFLAR. We investigated the mechanism of HNRNPA2B1-mediated HNC cells growth and found that HNRNPA2B1 plays an important role in the alternative splicing of a proto-oncogene, macrophage stimulating 1 receptor (MST1R), which encodes for the recepteur d'origine nantais (RON), a receptor tyrosine kinase. Our results indicate that HNRNPA2B1 mediates the exclusion of cassette exon 11 from MST1R, resulting in the generation of RON∆165 isoform, which was found to be associated with the activation of Akt/PKB signaling in HNC cells. Using the MST1R-minigene model, we validated the role of HNRNPA2B1 in the generation of RON∆165 isoform. The depletion of HNRNPA2B1 results in the inclusion of exon 11, thereby reduction of RON∆165 isoform. The decrease of RON∆165 isoform causes inhibition of Akt/PKB signaling, which results in the upregulation of E-cadherin and downregulation of vimentin leading to the reduced epithelial-to-mesenchymal transition. The overexpression of HNRNPA2B1 in HNRNPA2B1 knockout cells rescues the expression of the RON∆165 isoform and leads to activation of Akt/PKB signaling and induces epithelial-to-mesenchymal transition in HNC cells. In summary, our study identifies HNRNPA2B1 as a putative oncogene in HNC that promotes Akt/PKB signaling via upregulation of RON∆165 isoform and promotes epithelial to mesenchymal transition in head and neck cancer cells.

Pan-cancer analysis of alternative splicing regulator heterogeneous nuclear ribonucleoproteins (hnRNPs) family and their prognostic potential

As the most critical alternative splicing regulator, heterogeneous nuclear ribonucleoproteins (hnRNPs) have been reported to be implicated in various aspects of cancer. However, the comprehensive understanding of hnRNPs in cancer is still lacking. The molecular alterations and clinical relevance of hnRNP genes were systematically analysed in 33 cancer types based on next-generation sequence data. The expression, mutation, copy number variation, functional pathways, immune cell correlations and prognostic value of hnRNPs were investigated across different cancer types. HNRNPA1 and HNRNPAB were highly expressed in most tumours. HNRNPM, HNRNPUL1, and HNRNPL showed high mutation frequencies, and most hnRNP genes were frequently mutated in uterine corpus endometrial carcinoma (UCEC). HNRNPA2B1 showed widespread copy number amplification across various cancer types. HNRNPs participated in cancer-related pathways including protein secretion, mitotic spindle, G2/M checkpoint, DNA repair, IL6/JAK/STAT3 signal and coagulation, of which hnRNP genes of HNRNPF, HNRNPH2, HNRNPU and HNRNPUL1 are more likely to be implicated. Significant correlation of hnRNP genes with T help cells, NK cells, CD8 positive T cells and neutrophils was identified. Most hnRNPs were associated with worse survival of adrenocortical carcinoma (ACC), liver hepatocellular carcinoma (LIHC) and lung adenocarcinoma (LUAD), whereas hnRNPs predicted better prognosis in kidney renal clear cell carcinoma (KIRC) and thymoma (THYM). The prognosis analysis of KIRC suggested that hnRNPs gene cluster was significantly associated with overall survival (HR = 0.5, 95% CI = 0.35-0.73, P = 0.003). These findings provide novel evidence for further investigation of hnRNPs in the development and therapy of cancer in the future.

Omics-based insights into therapy failure of pediatric B-lineage acute lymphoblastic leukemia

B-lineage acute lymphoblastic leukemia (B-ALL) is the most common type of cancer seen in children and is characterized by a variable clinical course. Although there have been remarkable improvements in the therapy outcomes of pediatric B-ALL, treatment failure remains the leading-cause of death in 18% of the afflicted patients during the first 5 years after diagnosis. Molecular heterogeneities of pediatric B-ALL play important roles as determinants of the therapy response. Therefore, many of these molecular abnormalities have an established prognostic value in the disease. The present review discusses the omics-based revelations from epigenomics, genomics, transcriptomics and proteomics about treatment failure in pediatric B-ALL. Next it highlights the promise of the molecular aberration-targeted therapy to improve the treatment outcomes.