Evidence map›Paper›PMID 39738404›Full record

ArticleScientific reports2024

Weighted gene co-expression network analysis reveals key stromal prognostic markers in pancreatic cancer.

G Mantini, A Agostini, M Tufo, S Rossi, M Kulesko, C Carbone, L Salvatore, G Tortora, G Scambia, L Giacò

Abstract read
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

10 authors.

G MantiniBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy. giulia.mantini@policlinicogemelli.it.
A AgostiniBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
M TufoBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
S RossiBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
M KuleskoBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
C CarboneMedical Oncology, Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
L SalvatoreMedical Oncology, Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
G TortoraMedical Oncology, Comprehensive Cancer Center, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
G ScambiaDepartment of Woman, Child and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
L GiacòBioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, it has been shown that stroma compartment can favor tumor proliferation and aggressiveness. Although extensive research with network analyses such as Weighted Gene Co-expression Network Analysis (WGCNA) has been conducted on pancreatic cancer and its stromal components, WGCNA has not previously been applied to isolate and identify genes associated with the abundance of stroma and survival outcome from bulk RNA data. We investigated the gene expression profile and clinical information of 140 pancreatic ductal adenocarcinoma patients from TCGA. Network analysis was performed using WGCNA and four modules were found to be associated to patients' clinical traits. Specifically, one module of 2459 genes, was associated to stromal sample content. Subsequently, those genes were further analyzed for survival association through log-rank test and Cox regression. HPGDS and ITGA9-AS1 emerged as significant indicators of favorable prognosis while KCMF1 and YARS1 were implicated in poorer prognostic outcomes. Importantly, HPGDS was found to be stromal-specific in the TMA cohort of Human Protein Atlas. Single sample GSEA showed that the stromal module is enriched for stromal signature of Moffitt and Puleo. These findings suggest that we uncovered a stromal specific signature through WGCNA and found putative prognostic markers.

Indexed as

Biomarkers, TumorGene Expression Regulation, NeoplasticGene Regulatory NetworksPancreatic NeoplasmsStromal CellsCarcinoma, Pancreatic DuctalFemaleGene Expression ProfilingHumansMaleMiddle AgedPrognosisTranscriptomeBiomarkers, TumorBiomarkersPDACStromaSurvivalWGCNA

Identifiers

PMID39738404
PMCPMC11685961

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.