Evidence map›Paper›PMID 40248742›Full record

ArticleTranslational lung cancer research2025

Single cell RNA-seq and bulk RNA-seq analysis identifies MUC1 as a key gene for lung adenocarcinoma to neuroendocrine transformation.

Hongxia Li, Tiantian Yang, Yu Chen, Zhiqin Xie

Abstract read
In one paragraph

Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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

4 authors.

Hongxia Li *Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0001-5940-2264
Tiantian Yang *Department of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Yu ChenDepartment of Pathology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Zhiqin XieDepartment of Hepatobiliary and Pancreatic Surgery, Medical Center of Digestive Disease, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tyrosine kinase inhibitors (TKIs) have demonstrated significant effectiveness in treating advanced non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor ( Methods: Bulk RNA-sequencing and Mendelian randomization (MR) analysis were utilized to identify candidate genes associated with the progression from LUAD to NEC. Expression quantitative trait locus data from publicly available databases were leveraged to pinpoint key genes in relevant tissues. Furthermore, the immune microenvironment was explored using weighted gene co-expression network analysis (WGCNA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) databases. Single-cell RNA sequencing data from 16,765 cells across six tissue biopsy samples of LUAD and NEC were scrutinized to investigate cell interaction networks during histological transformation. The molecular pathways involved in dynamic cellular processes were elucidated through the analysis of cellular communication and pseudotime trajectory. Results: Through the use of RNA-sequencing and MR analysis, it was determined that mucin-1 ( Conclusions: In conclusion, our study provides insights into the molecular landscape governing the LUAD-to-NEC transition, highlighting

Indexed as

Bulk RNA sequencinghistological transformationlung adenocarcinoma (LUAD)neuroendocrine carcinoma (NEC)single-cell RNA sequencing

Identifiers

PMID40248742
PMCPMC12000963

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.