Evidence map›Paper›PMID 39840054›Full record

ArticleFrontiers in immunology2024

Single-cell RNA sequencing and immune microenvironment analysis reveal PLOD2-driven malignant transformation in cervical cancer.

Zhiheng Lin, Fengxin Wang, Renwu Yin, Shengnan Li, Yuquan Bai, Baofang Zhang, Chenlin Sui, Hengjie Cao, Dune Su, Lianwei Xu and 1 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 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

11 authors.

Zhiheng Lin *Department of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Fengxin Wang *The Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Renwu Yin *Department of Urology, Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Shengnan LiDepartment of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yuquan BaiDepartment of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Baofang ZhangDepartment of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Chenlin SuiDepartment of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Hengjie CaoDepartment of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Dune SuThe Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Lianwei XuDepartment of Gynecology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Honghong WangThe Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cervical cancer is the fourth most common cancer in women globally, and the main cause of the disease has been found to be ongoing HPV infection. Cervical cancer remains the primary cause of cancer-related death despite major improvements in screening and treatment approaches, especially in low- and middle-income nations. Therefore, it is crucial to investigate the tumor microenvironment in advanced cervical cancer in order to identify possible treatment targets. Materials and methods: In order to better understand malignant cervical cancer epithelial cells (EPCs), this study used bulk RNA-seq data from UCSC in conjunction with single-cell RNA sequencing data from the ArrayExpress database. After putting quality control procedures into place, cell type identification and clustering analysis using the Seurat software were carried out. To clarify functional pathways, enrichment analysis and differential gene expression were carried out. The CIBERSORT and ESTIMATE R packages were used to evaluate the immune microenvironment characteristics, and univariate and multivariate Cox regression analyses were used to extract prognostic features. Furthermore, assessments of drug sensitivity and functional enrichment were carried out. Results: Eight cell types were identified, with EPCs showing high proliferative and stemness features. Five EPC subpopulations were defined, with C1 NNMT+ CAEPCs driving tumor differentiation. A NNMT CAEPCs Risk Score (NCRS) model was developed, revealing a correlation between elevated NCRS scores and adverse patient outcomes characterized by immune evasion. Conclusion: This investigation delineated eight cell types and five subpopulations of malignant EPCs in cervical cancer, establishing the C1 NNMT+ CAEPCs as a crucial therapeutic target. The NCRS model demonstrated its prognostic capability, indicating that higher scores are associated with poorer clinical outcomes. The validation of PLOD2 as a prognostic gene highlights its therapeutic potential, underscoring the critical need for integrating immunotherapy and targeted treatment strategies to enhance diagnostic and therapeutic approaches in cervical cancer.

Indexed as

Cell Transformation, NeoplasticProcollagen-Lysine, 2-Oxoglutarate 5-DioxygenaseTumor MicroenvironmentUterine Cervical NeoplasmsBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansPrognosisSequence Analysis, RNASingle-Cell AnalysisBiomarkers, TumorProcollagen-Lysine, 2-Oxoglutarate 5-Dioxygenasecervical cancerimmune evasionprognostic modelsingle-cell RNA sequencingtherapeutic targetstumor microenvironment

Identifiers

PMID39840054
PMCPMC11747275

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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.