Multi-Omics Analyses Identify

Haiwei Quan, Zhiguang Xu, Lizhen Huo, Zhibin Wang

Abstract read

In one paragraph

Article in Genes, 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.

Haiwei QuanDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

Zhiguang XuDepartment of Biopharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Shenzhen University of Advanced Technology, Shenzhen 518107, China.

Lizhen HuoDepartment of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

Zhibin WangDepartment of Biopharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Shenzhen University of Advanced Technology, Shenzhen 518107, China.

Funding

National Key R&D Program of China 2023YFA0915700

6 · The paper itself

Abstract

backgroundLung cancer recurrence and metastasis are major causes of cancer-related mortality, but the molecular determinants underlying these processes remain incompletely understood. This study aimed to identify key regulators of lung cancer progression through integrative analyses of bulk and single-cell transcriptomic datasets.

methodsBulk transcriptomic and single-cell RNA sequencing data from multiple cohorts were integrated to identify genes associated with survival, recurrence, and metastasis. Tumor microenvironment features were further analyzed to prioritize core progression-related genes.

results

conclusions

Indexed as

Biomarkers, TumorCarcinoma, Non-Small-Cell LungLung NeoplasmsMicrofilament ProteinsNeoplasm Recurrence, LocalCell Line, TumorGene Expression Regulation, NeoplasticHumansMultiomicsNeoplasm MetastasisPrognosisTumor MicroenvironmentBiomarkers, TumorMicrofilament ProteinsANLNepithelial cellslung cancermetastasissingle cell

Identifiers

PMID42074579

DOI10.3390/genes17040461

PMCPMC13115928

What OpenQuestion 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 OpenQuestion graph.