Evidence map›Paper›PMID 42488648›Full record

ArticleFrontiers in immunology2026

Identification and validation of parthanatos-related genes in lung adenocarcinoma and construction of a prognostic risk model.

Yanna Yang, Jie Liu, Bin Shang, Shujuan Jiang

Abstract read
In one paragraph

Article in Frontiers in immunology, 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

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

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

4 authors.

Yanna YangDepartment of Pulmonary and Critical Care Medicine, Jinan Central Hospital, Shandong University, Jinan, China.
Jie LiuDepartment of Oncology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China.
Bin ShangDepartment of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Shujuan JiangDepartment of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital, Shandong University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As a major cause of cancer-related death, lung adenocarcinoma (LUAD) remains a significant health challenge. Parthanatos plays a crucial role in tumor progression, influencing cancer cell survival and therapy resistance. This study constructed a parthanatos-based prognostic model and explored its associated biological processes. Methods: Data on LUAD transcriptomics and parthanatos-related genes were collected from publicly databases and related literature. Differential expression analysis and weighted gene co-expression network analysis were employed to identify candidate genes. Univariate Cox regression analysis and machine learning algorithms were employed to screen prognostic genes and construct the risk model. Gene expression patterns and intercellular communication within distinct cell types were explored by single-cell sequencing. Lastly, prognostic gene expression in tissue samples was verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting. Results: Conclusion: This study constructed and validated a risk model for LUAD associated with parthanatos, providing new insights into the pathological mechanisms of LUAD and highlighting potential therapeutic targets.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorLung NeoplasmsGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisTranscriptomeBiomarkers, Tumorlung adenocarcinomamachine learningparthanatosprognostic risk modelsingle-cell RNA sequencing

Identifiers

PMID42488648
PMCPMC13388751

What Socratic holds

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LicenceCC BY
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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 Socratic graph.