Evidence map›Paper›PMID 41969615›Full record

ArticleHuman mutation2026

Panomics Integration via Machine Learning Prioritizes TAF1D as a Therapeutic Vulnerability in Lung Adenocarcinoma.

Lan Ding, Qingmei Xu, Dongdong Liu, Jingyu Wu, Xufan Cai, Feiqi Xu, Shuhan Ma, Haitao Wang, Yanyan Shi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human mutation, 2026. 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

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

1 citing paper in PubMed.

  1. 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

9 authors.

Lan DingDepartment of Thoracic Surgery, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China, hznu.edu.cn.
Qingmei XuGeriatric Ward, The 903rd Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Hangzhou, Zhejiang, China.
Dongdong LiuDepartment of Thoracic Surgery, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China, hznu.edu.cn.
Jingyu WuDepartment of General Surgery, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China, zchospital.com.
Xufan CaiDepartment of Thoracic Surgery, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China, hznu.edu.cn.
Feiqi XuDepartment of Thoracic Surgery, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China, hznu.edu.cn.
Shuhan MaDepartment of Graduate, Hangzhou Normal University, Hangzhou, Zhejiang, China, hznu.edu.cn.
Haitao WangDepartment of Thoracic Surgery, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China, hznu.edu.cn.ORCID https://orcid.org/0000-0003-3813-9772
Yanyan ShiDepartment of Thoracic Surgery, Cancer Center, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China, hznu.edu.cn.ORCID https://orcid.org/0009-0008-9298-4698

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung adenocarcinoma (LUAD) is a leading cause of cancer mortality, necessitating the identification of robust biomarkers and a deeper understanding of its molecular underpinnings. This study is aimed at screening for potential LUAD biomarkers and characterizing their biological functions. Using an integrative computational framework, we combined multitranscriptomic data analysis with three machine learning algorithms (LASSO, SVM-RFE, and random forest) to identify a consensus seven-gene signature (TTC13, TAF1D, ZNF587, PRPF3, LINC01355, TARBP1, and CCNL2). A classifier based on this signature achieved exceptional diagnostic accuracy (AUC = 0.972), with TAF1D identified as the most influential predictor via SHAP analysis. TAF1D was significantly upregulated in tumors, correlated with an immunosuppressive microenvironment, and promoted cancer cell proliferation by regulating cell cycle and immune-related pathways. Critically, TAF1D exhibited significant spatial heterogeneity in expression across different samples and tissue regions, suggesting it may exert region-specific biological functions within the tumor. In conclusion, our work defines a validated gene signature for LUAD, nominating TAF1D as a key oncogenic driver and promising candidate for diagnostic and therapeutic development.

Indexed as

Adenocarcinoma of LungBiomarkers, TumorLung NeoplasmsMachine LearningTATA-Binding Protein Associated FactorsTranscription Factor TFIIDComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticHistone AcetyltransferasesHumansTumor MicroenvironmentBiomarkers, TumorHistone AcetyltransferasesTATA-binding protein associated factor 250 kDaTATA-Binding Protein Associated FactorsTranscription Factor TFIIDcell proliferationimmune cell infiltrationlung adenocarcinomaSHAP analysissingle-cell RNA sequencingsomatic mutationTAF1D

Identifiers

PMID41969615
PMCPMC13069365

What OpenQuestion holds

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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 OpenQuestion graph.