Evidence map›Paper›PMID 39849454›Full record

ArticleBMC cancer2025

Utilizing sc-linker to integrate single-cell RNA sequencing and human genetics to identify cell types and driver genes associated with non-small cell lung cancer.

Yangfan Zhou, Liang Zhao, Meimei Cai, Doudou Luo, Yizhen Pang, Jianhao Chen, Qicong Luo, Qin Lin

Abstract readDataset
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

8 authors.

Yangfan Zhou *Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Liang Zhao *Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Meimei Cai *Department of Rheumatology and Clinical Immunology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Doudou LuoDepartment of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Yizhen PangDepartment of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Jianhao ChenDepartment of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Qicong LuoDepartment of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China. qcluo@xmu.edu.cn.
Qin LinDepartment of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China. linqin05@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenome-wide association studies (GWAS) provide a powerful method for identifying the loci and genes that contribute to disease. However, in many cases, the specific cell types and states that confer disease risk through these genes remain unknown. Determining this relationship is crucial for identifying pathogenic processes and developing therapeutic strategies.

methodsIn this study, we utilized the sc-linker framework developed by Jagadeesh, which is an integrated framework that combines single-cell RNA sequencing (scRNA-seq), epigenomic single nucleotide polymorphism (SNP)-to-gene mapping, and GWAS summary statistics to infer potential cell types and diseases affected by genetic variations.

resultsUsing normal cell type programs in the sc-linker, we identified type 2 alveolar cells in normal lung tissues that are closely associated with non-small cell lung cancer (NSCLC). Additionally, we identified cancer-associated fibroblasts (CAFs) associated with lung cancer using disease-dependent programs. By integrating extensive single-cell data from NSCLC, we discerned heterogeneity among CAFs subgroups. Finally, using MAGMA, we identified RAB31 as a driver gene in disease-related fibroblasts. Proteins from the RAB family are involved in the dynamic regulation of cell membrane compartments and are dysregulated in various tumor types, potentially altering biological properties such as the proliferation, migration, and invasion of cancer cells. We found that the Ras-related protein Rab-31 (RAB31) was significantly overexpressed in tumor-associated fibroblasts compared to that in normal fibroblasts and was closely associated with poor prognosis in patients with NSCLC.

conclusionsBy integrating scRNA-seq, epigenomic, and GWAS datasets, we found that ACT2 and CAFs have specific disease heritability in lung cancer and identified the driver gene RAB31 as a potential therapeutic target.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsSingle-Cell Gene Expression AnalysisCancer-Associated FibroblastsDisease ProgressionGenome-Wide Association StudyHumansrab GTP-Binding ProteinsRAB31 protein, humanrab GTP-Binding ProteinsCAFsNSCLCRAB31Sc-linkerSingle cell RNA seq

Identifiers

PMID39849454
PMCPMC11755902

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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.