Evidence map›Paper›PMID 40767620›Full record

ArticleThoracic cancer2025

Integrating Single-Cell Transcriptomics and Machine Learning to Define an ac4C Gene Signature in Lung Adenocarcinoma.

Yuan Wang, Wei Su, Guangyao Zhou, Yijie Wang, Chunnuan Wu, Pengpeng Zhang, Lianmin Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Thoracic cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

7 authors.

Yuan WangKey Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Wei SuDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Guangyao ZhouDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.ORCID https://orcid.org/0009-0009-0736-5727
Yijie WangDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Chunnuan WuKey Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Pengpeng ZhangDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Lianmin ZhangDepartment of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.ORCID https://orcid.org/0000-0002-7419-9628

Funding

the Tianjin Natural Science Foundation 21JCYBJC01020
6 · The paper itself

Abstract

introductionLung adenocarcinoma, the most common subtype of non-small cell lung cancer, faces challenges such as drug resistance and tumor heterogeneity. N4-acetylcytidine (ac4C) is an important RNA modification involved in cancer progression, but its role in lung adenocarcinoma remains unclear.

methodsThis study analyzed transcriptomic and single-cell RNA sequencing data from public databases to investigate the expression and clinical significance of ac4C-related genes in lung adenocarcinoma. Ten machine learning algorithms were applied to develop and validate an ac4C-related gene signature (ARGSig) for prognosis prediction across multiple independent cohorts.

resultsCells with high ac4C activity showed increased intercellular communication and activation of tumor-associated pathways. The ARGSig model effectively stratified patients by survival outcomes and predicted sensitivity to immune checkpoint inhibitors and chemotherapy agents.

conclusionac4C modification and its related genes play a critical role in lung adenocarcinoma development. The ARGSig model provides a promising molecular tool for prognosis evaluation and personalized treatment guidance in lung adenocarcinoma patients.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorLung NeoplasmsMachine LearningSingle-Cell AnalysisTranscriptomeFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMalePrognosisBiomarkers, Tumorlung cancermachine learningN4‐acetylcytidineprognostic signatureRNA modification

Identifiers

PMID40767620
PMCPMC12326626

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.