Evidence map›Paper›PMID 41198835›Full record

ArticleScientific reports2025

Efferocytosis-related signatures identified via Single-cell analysis and machine learning predict TNBC outcomes and immunotherapy response.

Lan Wei, Siyang Wen, Tingting Dang, Tao Zeng, Shiyu Yang, Yiqing You, Jiafeng Tang, Haoli Sun, Liang Zhang, Qian Li and 6 more

Abstract read
In one paragraph

Article in Scientific reports, 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. Review
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

16 authors.

Lan Wei *Key Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Siyang Wen *Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, People's Republic of China.
Tingting DangKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Tao ZengKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Shiyu YangKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Yiqing YouKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Jiafeng TangKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Haoli SunKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Liang ZhangKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Qian LiKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Xiaolu LiKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Mengxin SunKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Xiran HeKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China.
Liuyang ZhaoKey Laboratory of Molecular Biology of Infectious Diseases (Chinese Ministry of Education), Chongqing Medical University, Chongqing, 400016, People's Republic of China. liuyangzhao@cqmu.edu.cn.
Xiaobing ZhuChongqing Blood Center, Chongqing, 400052, People's Republic of China. 32580063@qq.com.
Yan ZhangKey Laboratory of Medical Diagnostics of Ministry of Education, Department of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, People's Republic of China. yanzhang@cqmu.edu.cn.

Funding

China Postdoctoral Science Foundation NO.2024MD764037National Natural Science Foundation of China NO.81974449National Natural Science Foundation of China NO. 82103660Natural Science Foundation of Chongqing NO. CSTB2022NSCQ- MSX1076
6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) is characterized by poor prognosis and limited targeted treatment options. Efferocytosis, an essential immune mechanism for the clearance of apoptotic cells, is increasingly recognized as a key contributor to tumor immune evasion. This study aimed to identify key efferocytosis-related genes in TNBC, investigate their impact on the tumor microenvironment and immunotherapy responses, and construct a prognostic model to inform and optimize treatment strategies. RNA sequencing data and clinical information for patients with TNBC were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Machine learning models were employed to derive efferocytosis-related signatures to predict clinical outcomes and immunotherapy responses. Eight efferocytosis-related genes were identified, considered efferocytosis-related gene signatures herein: P2RX1, IFNG, IL1A, CD93, XKR8, SIAH2, F2RL1, and TLR4. Using the individual risk scores derived from this model, patients were stratified into high- and low-risk groups, revealing significant differences in immune infiltration and immuno-therapy response. Our study highlights the predictive significance of efferocytosis in assessing chemotherapy sensitivity, emphasizing the pivotal role of the immune microenvironment in mediating drug resistance. Moreover, we identified potential targets for immunotherapeutic strategies in the treatment of TNBC.

Indexed as

ImmunotherapyMachine LearningPhagocytosisSingle-Cell AnalysisTriple Negative Breast NeoplasmsEfferocytosisFemaleGene Expression Regulation, NeoplasticHumansPrognosisTumor MicroenvironmentEfferocytosisImmunotherapyMachine learningSingle-cell sequencingTNBC

Identifiers

PMID41198835
PMCPMC12592366

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.