Evidence mapPaperPMID 39773329Full record

ArticleMolecular medicine (Cambridge, Mass.)2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

Lele Ye, Chunhao Long, Binbing Xu, Xuyang Yao, Jiaye Yu, Yunhui Luo, Yuan Xu, Zhuofeng Jiang, Zekai Nian, Yawen Zheng and 3 more

Abstract read
In one paragraph

Article in Molecular medicine (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

13 authors.

Lele Ye *Department of Gynecology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.ORCID http://orcid.org/0000-0002-6741-8626
Chunhao Long *School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Binbing Xu *First Clinical College, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Xuyang Yao *First Clinical College, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Jiaye YuWenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-Related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yunhui LuoWenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-Related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yuan XuWenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-Related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Zhuofeng JiangSchool of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Zekai NianSecond Clinical College, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yawen ZhengCancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education) of the Second Affiliated Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yaoyao CaiDepartment of Obstetrics, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Xiangyang XueWenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-Related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China. wzxxy001@163.com.ORCID http://orcid.org/0000-0002-8670-2016
Gangqiang GuoDepartment of Gynecology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China. gangqiangg@wmu.edu.cn.ORCID http://orcid.org/0000-0001-8031-7937

Funding

National Natural Science Foundation of China 32200476Wenzhou Municipal Science and Technology Bureau Y20220044Zhejiang Provincial Natural Science Foundation of China LQ23C060002
6 · The paper itself

Abstract

backgroundPredictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes.

methodsWe included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model.

resultsThe CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients.

conclusionsThe CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Indexed as

Cystadenocarcinoma, SerousGene Expression Regulation, NeoplasticMachine LearningOvarian NeoplasmsTranscriptomeApoptosisBiomarkers, TumorCohort StudiesComputational BiologyFemaleGene Expression ProfilingGenomicsHumansMiddle AgedMultiomicsPrecision MedicineBiomarkers, TumorCell death indexPredictive modelPredictive preventive and personalized medicine (PPPM/3PM)Programmed cell deathSerous ovarian carcinoma

Identifiers

PMID39773329
PMCPMC11707953

What Socratic holds

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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.