Evidence map›Paper›PMID 38956290›Full record

ArticleScientific reports2024

A prognostic model for anoikis-related genes in pancreatic cancer.

Wenbin Song, Haiyang Hu, Zhengbo Yuan, Hao Yao

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Wenbin Song *Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.
Haiyang Hu *Department of Cardiac Critical Care Medicine, Affiliated Hospital of Jining Medical University, Jining, 272007, People's Republic of China.
Zhengbo YuanSchool of Medicine, Xiamen University, No.4221 Xiangan South Road, Xiangan District, Xiamen, 361102, People's Republic of China. yzbvans@outlook.com.
Hao YaoDepartment of Hepatological Surgery, The Second Hospital of Tianjin Medical University, No.23 Pingjiang Road, Hexi District, Tianjin, 300211, People's Republic of China. yaohao0417@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

Indexed as

AnoikisPancreatic NeoplasmsBiomarkers, TumorCarcinoma, Pancreatic DuctalFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMutationNomogramsPrognosisSurvival AnalysisBiomarkers, TumorAnoikisICGC-PACAPancreatic cancerPrognostic modelTCGA-PAAD

Identifiers

PMID38956290
PMCPMC11220081

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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 OpenQuestion graph.