Evidence map›Paper›PMID 35870987›Full record

ArticleClinical epigenetics2022

Identification of clinical implications and potential prognostic models of chromatin regulator mutations in multiple myeloma.

Lina Zhang, Run Zhang, Jing Wang, Ying Chen, Chun Qiao, Qinglin Shi, Yuanyuan Jin, Xuxing Shen, Jianyong Li, Lijuan Chen

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.4field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Lina ZhangDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.ORCID 0000-0002-3176-4922
Run ZhangDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Jing WangDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Ying ChenDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Chun QiaoDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Qinglin ShiDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Yuanyuan JinDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Xuxing ShenDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Jianyong LiDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
Lijuan ChenDepartment of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China. chenljb@126.com.ORCID 0000-0002-6497-1194
Jiangsu Province Hospital · CNJiangsu Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the rapid development of next-generation sequencing (NGS) technologies, researchers are making efforts to reveal the genomic landscape of multiple myeloma (MM). However, the clinical significance of many mutations remains poorly defined due to the genetic heterogeneity of MM. To systematically explore the clinical implications of gene mutations and build practical prognostic models, we performed DNA sequencing in newly diagnosed MM patients.

methodsMM cells were purified from bone marrow aspirates using CD138 microbeads and subjected to sequencing with a 387-gene Panel. Nomogram was developed using Cox's proportional hazards model, and candidate variables were screened by stepwise regression. Internal validation was carried out by the bootstrap method.

resultsBetween July 2016 and December 2020, a total of 147 patients were included in our study. We found patients with a higher mutational load had a significantly shorter progress-free survival (PFS) (19.0 vs. 32.0 months, P = 0.0098) and overall survival (OS) (3-year OS rates were 66.1% and 80.0%, P = 0.0290). Mutations in chromatin regulators (CRs) including KMT2C (14.3%), KMT2D (14.3%), EP300 (11.6%) and ARID gene family (31.3%) were highly frequent in newly diagnosed MM patients. Interestingly, proteins encoded by these genes could form a complex called KMT2C/D COMPASS (K

conclusionOur findings emphasized the importance of CRs mutations in newly diagnosed MM patients and indicated the mutations affecting K

Indexed as

Multiple MyelomaChromatinDNA MethylationHumansMutationPrognosisChromatinChromatin regulatorEpigeneticMultiple myelomaNext-generation sequencingNomogram

Identifiers

PMID35870987
PMCPMC9308335
OpenAlexW4286715799

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.