MétaCan
Menu
← Back to cohort

Genome-wide 5-hydroxymethylation mapping and epigenetic pathways in multiple myeloma.

2024· article· en· W4399480972 on OpenAlexaboutno aff
Zhou Zhang, Bei Wang, Krissana Kowitwanich, Xiaolong Cui, Parveen Bhatti, Chuan He, Brian C.‐H. Chiu, Wei Zhang

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsEpigeneticsMedicineGenomeMultiple myelomaComputational biologyDNA methylationGeneticsEpigenomicsEpigenomeCancer researchGeneBiologyGene expressionImmunology

Abstract

fetched live from OpenAlex

7566 Background: Multiple myeloma (MM), a B-cell neoplasm characterized by bone marrow infiltration of malignant plasma cells, is the second most common blood malignancy, with an estimated number of ~35,000 new patients annually in the United States. The molecular pathogenesis of MM is complex and involves various genetic and epigenetic alterations. Key molecular mechanisms, including chromosomal abnormalities such as translocations involving the immunoglobulin heavy chain locus and various oncogenes (e.g., MMSET, FGFR3, CCND1, MAF), alterations in epigenetic regulators (e.g., EZH2, DNMT3A), dysregulation of cell cycle control, and aberrant activation of signaling pathways (e.g., NF-kB, PI3K/AKT, and JAK/STAT), play critical roles in MM pathogenesis. However, epigenetic pathways implicated in MM has not been comprehensively investigated, partly due to technical limitations that cannot distinguish major cytosine modification types. Methods: Using the 5hmC-Seal, a highly sensitive chemical labeling technique, we profiled genome-wide 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA) from a population-based case-control study of MM (cases, n = 313; controls, n = 317) conducted in Canada. Results: The 5hmC modification levels were summarized for various genomic features, showing an enrichment in gene bodies and enhancer markers, consistent with the putative role of gene regulation for 5hmC modification. A genome-wide scan of gene bodies identified 771 differential features between cases and controls, adjusting for age, sex, smoking status, education, and first two principal components, at a permutation-based empirical p-value cutoff of 10-4. For instance, IL1RAP, a component of the interleukin-1 signaling cascade, may impact tumor progression and immune system evasion. Furthermore, functional analysis indicated canonical pathways associated with MM pathology and treatment, such as calcium signaling, and synthesis and secretion of cortisol/aldosterone, were enriched in the differential 5hmC features between cases and controls. Notably, the calcium signaling pathway, integral to Ca2+ transport and involved in various physiological and pathological processes, plays a critical role in MM pathogenesis. Within this pathway, the CAMK1D gene, which has been identified as a crucial regulator of tumor-intrinsic immune resistance, showed differential 5hmC level between cases and controls, highlighting a vital connection between epigenetic modifications and immune evasion mechanisms in MM. Conclusions: Leveraging a state-of-the-art technique, we identified novel epigenetic modifications and pathways in the risk of MM. This approach establishes a solid foundation for further investigating etiology of MM, deepening our understanding of the disease, and advancing the discovery of biomarkers, which could potentially guide preventive strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.380
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicEpigenetics and DNA Methylation→French-language works237,207→