Evidence map›Paper›PMID 39496775›Full record

ArticleScientific reports2024

Effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer.

Tingjie Wang, Lingxi Tian, Bing Wei, Jun Li, Cuiyun Zhang, Ruitao Long, Xiaofei Zhu, Yougai Zhang, Bo Wang, Guangbo Tang and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Targeting RAD52 overcomes PARP inhibitor resistance in preclinicalbioRxiv : the preprint server for biology · 2025
    Article
  6. Article
  7. Review
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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

12 authors.

Tingjie Wang *Department of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.
Lingxi Tian *MOE Key Laboratory of Intelligent Biomanufacturing, School of Bioengineering, Dalian University of Technology, Dalian, 116024, People's Republic of China.
Bing WeiDepartment of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.
Jun LiDepartment of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.
Cuiyun ZhangDepartment of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.
Ruitao LongSchool of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.
Xiaofei ZhuDepartment of Clinical Laboratory, The Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, People's Republic of China.
Yougai ZhangDepartment of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.
Bo WangDepartment of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China.
Guangbo TangKey Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China.
Jun YangMOE Key Laboratory of Intelligent Biomanufacturing, School of Bioengineering, Dalian University of Technology, Dalian, 116024, People's Republic of China. junyang@dlut.edu.cn.
Yongjun GuoDepartment of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, People's Republic of China. yongjunguo@hotmail.com.

Funding

Dalian Science and Technology Innovation Fund 2022JJ12SN049Henan Province and Ministry of Health of Medical Science and Technology Program SBGJ202101009Henan Province and Ministry of Health of Medical Science and Technology Program SBGJ202302028
6 · The paper itself

Abstract

Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we used tumor evolution analysis to determine the intra- and intertumoral heterogeneity of high-grade serous ovarian cancer (HGSOC) and analyze the correlation between tumor heterogeneity and prognosis, as well as chemotherapy response, through single-cell and spatial transcriptomic analysis. We collected and curated 28 HGSOC patients' single-cell transcriptomic data from five datasets. Then, we developed a novel text-mining-based machine-learning approach to deconstruct the evolutionary patterns of tumor cell functions. We then identified key tumor-related genes within different evolutionary branches, characterized the microenvironmental cell compositions that various functional tumor cells depend on, and analyzed the intra- and intertumoral heterogeneity as well as the tumor microenvironments. These analyses were conducted in relation to the prognosis and chemotherapy response in HGSOC patients. We validated our findings in two spatial and seven bulk transcriptomic datasets (total: 1,030 patients). Using transcriptomic clusters as proxies for functional clonality, we identified a significant increase in tumor cell state heterogeneity that was strongly correlated with patient prognosis and treatment response. Furthermore, increased intra- and intertumoral functional clonality was associated with the characteristics of cancer-associated fibroblasts (CAFs). The spatial proximity between CXCL12-positive CAFs and tumor cells, mediated through the CXCL12/CXCR4 interaction, was highly positively correlated with poor prognosis and chemotherapy resistance in HGSOC. Finally, we constructed a panel of 24 genes through statistical modeling that correlate with CXCL12-positive fibroblasts and can predict both prognosis and the response to chemotherapy in HGSOC patients. Our study offers insights into the collective behavior of tumor cell communities in HGSOC, as well as potential drivers of tumor evolution in response to therapy. There was a strong association between CXCL12-positive fibroblasts and tumor progression, as well as treatment outcomes.

Indexed as

Drug Resistance, NeoplasmOvarian NeoplasmsTumor MicroenvironmentCancer-Associated FibroblastsChemokine CXCL12Cystadenocarcinoma, SerousFemaleFibroblastsGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisSingle-Cell AnalysisTranscriptomeChemokine CXCL12CXCL12 protein, humancancer-associated fibroblastsCXCL12functional clonalityHGSOCmicroenvironmentspatiotemporal transcriptomeTumor cell statetumor evolutiontumor transcriptomic heterogeneity

Identifiers

PMID39496775
PMCPMC11535537

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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.