dbLGL: an online leukemia gene and literature database for the retrospective comparison of adult and childhood leukemia genetics with literature evidence

Mutations in Genes Producing Nitric Oxide and Hydrogen Sulfide and Their Connection With Apoptotic Genes in Chronic Myeloid Leukemia

Background Despite advances in chronic myeloid leukemia (CML) genetics, the role of nitric oxide (NO) and hydrogen sulfide (H2S) gene mutations and their relationship to apoptotic genes is unclear. Therefore, this study investigated NO- and H2S-producing genes' mutations and their interactions with apoptotic genes using Sanger sequencing and next-generation sequencing (NGS). Methodology A complete blood count (CBC) was carried out to measure the total number of white blood cells, while IL-6 levels were assessed in both control and CML patients using an ELISA technique. Sanger sequencing was used to analyze mutations in the CTH and NOS3 genes, whereas NGS was applied to examine mutations on all chromosomes. Results White blood cell (WBC) and granulocyte counts were significantly higher in CML patients compared to controls (p<0.0001), and monocyte counts were similarly higher (p<0.05). Interleukin-6 (IL-6) levels were significantly elevated in CML patients than controls (p<0.0001), indicating a possible link to CML etiology or progression. Multiple mutations have been identified in both genes, notably in CTH exon 12 and the NOS3 genes VNTR, T786C, and G894T. This study also measured IL-6 concentrations using IL-6 assays, identifying its potential as a CML prognostic diagnostic. WBC counts, granulocyte counts, and mid-range absolute counts, or MID counts, were significantly higher in CML patients than in normal control individuals. NGS identified 1643 somatic and sex chromosomal abnormalities and 439 actively expressed genes in CML patients. The findings imply a genomic landscape beyond the BCR-ABL1 mutation in CML development compared to other databases. Conclusion In conclusion, this study advances the understanding of the genetic characteristics of CML by identifying mutations in the NO- and H2S-producing genes and their complex connections with genes involved in apoptosis. The comprehensive genetic profile obtained by Sanger sequencing and NGS provides possibilities for identifying novel targets for therapy and personalized treatments for CML, therefore contributing to developments in hematological diseases.

SRSF2 plays an unexpected role as reader of m5C on mRNA, linking epitranscriptomics to cancer

A common mRNA modification is 5-methylcytosine (m5C), whose role in gene-transcript processing and cancer remains unclear. Here, we identify serine/arginine-rich splicing factor 2 (SRSF2) as a reader of m5C and impaired SRSF2 m5C binding as a potential contributor to leukemogenesis. Structurally, we identify residues involved in m5C recognition and the impact of the prevalent leukemia-associated mutation SRSF2P95H. We show that SRSF2 binding and m5C colocalize within transcripts. Furthermore, knocking down the m5C writer NSUN2 decreases mRNA m5C, reduces SRSF2 binding, and alters RNA splicing. We also show that the SRSF2P95H mutation impairs the ability of the protein to read m5C-marked mRNA, notably reducing its binding to key leukemia-related transcripts in leukemic cells. In leukemia patients, low NSUN2 expression leads to mRNA m5C hypomethylation and, combined with SRSF2P95H, predicts poor outcomes. Altogether, we highlight an unrecognized mechanistic link between epitranscriptomics and a key oncogenesis driver.

Identification of consistent post-translational regulatory triplets related to oncogenic and tumour suppressive modulators in childhood acute lymphoblastic leukemia

Background

Acute lymphoblastic leukemia (ALL) is the most common type of childhood cancer. It can be caused by mutations that turn on oncogenes or turn off tumour suppressor genes. For instance, changes in certain genes including Rb and p53 are common in ALL cells. Oncogenes and TSGs may serve as a modulator gene to regulate the gene expression level via their respective target genes. To investigate the regulatory relationship between oncogenes, tumour suppressor genes and transcription factors at the post translational level in childhood ALL, we performed an integrative network analysis on the gene regulation in the post-translational level for childhood ALL based on many publicly available cancer gene expression data including TARGET and GEO database.

Methods

We collected 259 childhood ALL-related genes from the latest online leukemia database, Leukemia Gene Literature Database. These 259 genes were selected from a comprehensive systematic literature with experimental evidences. The identified and curated genes were also associated with patient survival cases and we incorporated this pediatric ALL-related gene list into our analysis. We extracted the known human TFs from the TRRUST database. Among 259 childhood ALL-related genes, 101 unique regulators were mapped to the list of oncogene and tumour suppressor genes (TSGs) from the ONGene and the TSGene databases, and these included 74 TSGs, 62 oncogenes and 46 TF genes.

Results

The resulted regulation was presented as a hierarchical regulatory network with transcription factors (TFs) as intermediate regulators connecting the top modulators (oncogene and TSGs) to the common target genes. Cross-validation was applied to the results from the TARGET dataset by identifying the consistent regulatory motifs based on three independent ALL expression datasets. A three-layer regulatory network of consistent positive modulators in childhood ALL was constructed in which 74 modulators (40 oncogenes, 34 TSGs) are considered as the most important regulators. The middle layer and the bottom layer contain 34 TFs and 176 target genes, respectively. Oncogenes mostly participated in positive regulation of gene expression and the transcription process of RNA II polymerase, while TSGs were mainly involved in the negative regulation of gene expression. In addition, the oncogene-specific targets were enriched with regulators of the MAPK cascade while tumour suppressor-specific targets were associated with cell death.

Conclusion

The results revealed that oncogenes and TSGs possess a different functional regulatory pattern with regard to not only their biological functions but also their specific target genes in childhood ALL cancer progression. Taken together, our findings could contribute to a better understanding of the important regulatory mechanisms and this method could be used to analyse the targeted genes at the post-translational level in childhood ALL through integrative network analysis.

Online database for brain cancer-implicated genes: exploring the subtype-specific mechanisms of brain cancer

Background

Brain cancer is one of the eight most common cancers occurring in people aged 40+ and is the fifth-leading cause of cancer-related deaths for males aged 40–59. Accurate subtype identification is crucial for precise therapeutic treatment, which largely depends on understanding the biological pathways and regulatory mechanisms associated with different brain cancer subtypes. Unfortunately, the subtype-implicated genes that have been identified are scattered in thousands of published studies. So, systematic literature curation and cross-validation could provide a solid base for comparative genetic studies about major subtypes.

Results

Here, we constructed a literature-based brain cancer gene database (BCGene). In the current release, we have a collection of 1421 unique human genes gathered through an extensive manual examination of over 6000 PubMed abstracts. We comprehensively annotated those curated genes to facilitate biological pathway identification, cancer genomic comparison, and differential expression analysis in various anatomical brain regions. By curating cancer subtypes from the literature, our database provides a basis for exploring the common and unique genetic mechanisms among 40 brain cancer subtypes. By further prioritizing the relative importance of those curated genes in the development of brain cancer, we identified 33 top-ranked genes with evidence mentioned only once in the literature, which were significantly associated with survival rates in a combined dataset of 2997 brain cancer cases.

Conclusion

BCGene provides a useful tool for exploring the genetic mechanisms of and gene priorities in brain cancer. BCGene is freely available to academic users at http://soft.bioinfo-minzhao.org/bcgene/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07793-x.