Evidence map›Paper›PMID 41649637›Full record

ArticleDiscover oncology2026

Immune heterogeneity at diagnosis influences treatment response and survival in multiple myeloma.

Yue Wang, Tianwei Lan, Shiyang Gu, Yian Zhang, Peng Liu

Abstract read
In one paragraph

Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yue WangDepartment of Hematology, Zhongshan Hospital, Fudan University, Shanghai, China.
Tianwei LanDepartment of Hematology, Zhongshan Hospital, Fudan University, Shanghai, China.
Shiyang GuDepartment of Hematology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yian ZhangDepartment of Hematology, Zhongshan Hospital, Fudan University, Shanghai, China.
Peng LiuDepartment of Hematology, Zhongshan Hospital, Fudan University, Shanghai, China. liu.peng@zs-hospital.sh.cn.

Funding

Natural Science Foundation of Shanghai Municipality 22ZR1411400Science and Technology Innovation Action Plan of Shanghai 21YF1406300
6 · The paper itself

Abstract

The clinical heterogeneity of multiple myeloma (MM) remains incompletely captured by existing staging systems. To determine whether baseline immune profiles could refine prognostication, we conducted a large-scale analysis of 703 newly diagnosed MM patients. Peripheral blood immune subsets and serum cytokines were quantified before treatment via flow cytometry and multiplex immunoassays. Time-dependent ROC analysis identified optimal prognostic thresholds for each parameter. Univariate analysis associated inferior overall survival (OS) with low CD19⁺ B-cell counts, a low CD4⁺/CD8⁺ ratio, high NK cell percentage, elevated levels of IL-1β, sIL-2R, IL-6, IL-8, IL-10, and TNF, and low complement C3. A multivariate Cox model integrated the most robust predictors into an immune risk score (IM): IM = − 0.107 × (CD4⁺/CD8⁺) + 0.001 × sIL-2R + 0.003 × IL-6 + 0.006 × IL-8 − 1.238 × C3. Using the optimal cut-off (0.394), patients were stratified into high-risk (n = 231) and low-risk (n = 472) groups. The low-risk group exhibited significantly longer median OS (64.5 months vs. 32.2 months; p < 0.0001), and the IM score remained an independent prognostic factor after adjusting for clinical variables. Subgroup analysis confirmed its predictive value across treatment backgrounds. These results establish the pre-treatment systemic immune state as a powerful prognostic determinant and provide a clinically applicable immune-based scoring system for improved risk stratification in MM.

Indexed as

CytokinesImmune microenvironmentMultiple myelomaPrognostic modelSurvival analysis

Identifiers

PMID41649637
PMCPMC12976244

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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.