Evidence map›Paper›PMID 42234055›Full record

ArticleDiscover oncology2026

Single-cell data analysis of lung cancer brain metastasis reveals SEC61G serves as a potential risk indicator.

Yunjing Li, Yuanyuan Zhang, Wenxin Tian, Zhixun Zhao, Lei He

Abstract read
In one paragraph

Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yunjing LiDepartment of Thoracic Surgery, The First Medical Center of PLA General Hospital, Beijing, 100853, China. tgzy6668@163.com.
Yuanyuan ZhangDepartment of Thoracic Surgery, The First Medical Center of PLA General Hospital, Beijing, 100853, China.
Wenxin TianDepartment of Thoracic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Zhixun ZhaoDepartment of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. xunnna@qq.com.
Lei HeDepartment of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China. helei4361@bjhmoh.cn.

Funding

National High Level Hospital Clinical Research Funding LC2024A22
6 · The paper itself

Abstract

Lung adenocarcinoma is a highly malignant and life-threatening disease, with brain metastases representing a major cause of mortality in patients with lung cancer. Although extensive research has focused on interpreting single-cell data from lung cancer brain metastases, most studies emphasize the immune microenvironment, leaving the molecular mechanisms and pathways involving epithelial cells under explored. In this study, we investigated epithelial cells using single-cell data derived from lung cancer brain metastases (comprising six lung cancer and six lung-to-brain metastasis samples) and identified a significant enrichment of the protein-folding pathway in brain metastases, with SEC61G emerging as a particularly prominent contributor. Subsequent in silico replication in clinical samples and public cohort confirmed that SEC61G can serve as a biomarker for brain metastases in lung cancer. These results provide novel insights into the molecular mechanisms driving brain metastases in lung cancer and underscore the potential of SEC61G as both a therapeutic target and a diagnostic marker. Our findings advance understanding of the metastatic process in lung adenocarcinoma and provide a foundation for developing targeted therapies aimed at improving patient outcomes.

Indexed as

Brain metastasisLung cancerRisk indicatorscRNA-seqSEC61G

Identifiers

PMID42234055
PMCPMC13447619

What OpenQuestion 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 OpenQuestion graph.