Evidence map›Paper›PMID 41522114›Full record

ArticleJournal of thoracic disease2025

Tumor prognostic risk stratification based on pseudo-time analysis of single-cell sequencing for patients with lung adenocarcinoma.

Huanle Jin, Huandi Jin, Ting Wu, Keling Huang, Jiangnan Lin, Linyu Wu, Chen Gao

Abstract read
In one paragraph

Article in Journal of thoracic disease, 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. 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

7 authors.

Huanle Jin *Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Huandi Jin *Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Ting WuDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Keling HuangDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Jiangnan LinDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.ORCID https://orcid.org/0000-0003-4800-9373
Linyu WuDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.ORCID https://orcid.org/0000-0001-7695-0385
Chen GaoDepartment of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.ORCID https://orcid.org/0000-0001-9372-8014

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accurate prognostic risk stratification for lung adenocarcinoma (LUAD) remains a critical challenge. This study employs single-cell pseudo-time series analysis to identify pseudo-time differential genes (PTDGs) associated with LUAD development and constructs tumor progression-related prognostic risk stratification for patients with LUAD. Methods: This study conducted quality control for single-cell RNA sequencing (RNA-seq) data firstly. Then, malignant tumor cells of the epithelium were identified using cell-cluster markers and InferCNV. Trajectory analysis with Monocle identified PTDGs, further refined to 2,597 candidate genes. The prognostic signature of PTDGs was constructed by using Cox, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses. In addition, multicenter datasets and immunohistochemistry analysis were used to evaluate the expression of the identified PTDGs between the two groups. Results: The developmental trajectory of the LUAD epithelium was plotted, and 13 PTDGs were identified to be highly associated with the prognosis of LUAD. Based on the prognostic risk score, The Cancer Genome Atlas (TCGA)-LUAD patients were well stratified into low- and high-risk groups. The PTDGs-based risk model demonstrated good performance in the training, internal and external validation datasets, as well as being compared with existing risk score formulas. In addition, multi-center datasets and immunohistochemistry revealed a significant up-regulation of DDIT4, FURIN, PTTG1 and RIPK2 at transcriptional and protein expression levels in LUAD tissues compared to normal tissues. Conclusions: PTDGs are potential biological markers for prognostic risk stratification of patients with LUAD.

Indexed as

lung adenocarcinoma (LUAD)Pseudo-time analysisrisk stratificationsingle-cell RNA sequencing (single-cell RNA-seq)

Identifiers

PMID41522114
PMCPMC12780389

What Socratic holds

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LicenceCC BY-NC-ND
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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.