Evidence map›Paper›PMID 40546417›Full record

ArticleBlood and lymphatic cancer : targets and therapy2025

A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights.

Linzhi Xie, Meng Gao, Shiming Tan, Yi Zhou, Jing Liu, Liwen Wang, Xin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Blood and lymphatic cancer : targets and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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

7 authors.

Linzhi Xie *Department of Hematology, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
Meng Gao *Department of Blood Transfusion, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
Shiming TanDepartment of Hematology, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
Yi ZhouDepartment of Hematology, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
Jing LiuDepartment of Hematology, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
Liwen WangDepartment of Hematology, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
Xin LiDepartment of Hematology, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Multiple myeloma (MM) is a haematological malignancy, driven by complex interactions between tumor and immune cells. Nevertheless, the overall pattern of immune cells and MM pathogenesis within the bone marrow tumor microenvironment (BM-TME) remains underexplored. Methods and Results: Firstly, we performed Mendelian Randomization analysis for 731 immunocyte phenotypes and MM, identifying 21 immune traits significantly associated with increased MM risk (OR>1, P Conclusion: Our findings reveal a complex interplay between immune cells and MM. Through multi-omics analyses and machine learning algorithms, we established a robust monocyte-related prognostic signature. By identifying high-risk patients, MGPS may help refine treatment strategies, such as intensifying immunomodulatory therapies, potentially improving survival and immunotherapy outcomes for MM patients.

Indexed as

immunophenotypemachine learningMendelian randomizationmonocytemulti-omicsmultiple myeloma

Identifiers

PMID40546417
PMCPMC12180464

What Socratic 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 Socratic graph.