Evidence map›Paper›PMID 39279964›Full record

ArticleJournal of gastrointestinal oncology2024

Liquid-liquid phase separation-related features of

Xiaofeng Li, Ranran Yu, Baochang Shi, Akhil Chawla, Xianguang Feng, Kai Zhang, Li Liang

Abstract read
In one paragraph

Article in Journal of gastrointestinal oncology, 2024. 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

7 authors.

Xiaofeng Li *Department of Hepatobiliary Surgery, Shandong Provincial Third Hospital, Shandong University, Jinan, China.
Ranran Yu *Department of Pathology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Baochang ShiDepartment of Hepatobiliary Surgery, Shandong Provincial Third Hospital, Shandong University, Jinan, China.
Akhil ChawlaDepartment of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Xianguang FengDepartment of Hepatobiliary Surgery, Shandong Provincial Third Hospital, Shandong University, Jinan, China.
Kai ZhangDepartment of Hepatobiliary Surgery, Shandong Provincial Third Hospital, Shandong University, Jinan, China.
Li LiangDepartment of Hepatobiliary Surgery, Shandong Provincial Third Hospital, Shandong University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The growth and metastasis of pancreatic cancer (PC) has been found to be closely associated with liquid-liquid phase separation (LLPS). This study sought to identify LLPS-related biomarkers in PC to construct a robust prognostic model. Methods: Transcriptomic data and clinical information related to PC were retrieved from publicly accessible databases. The PC-related data set was subjected to differential expression, Mendelian randomization (MR), univariate Cox, and least absolute selection and shrinkage operator analyses to identify biomarkers. Using the biomarkers, we subsequently constructed a risk model, identified the independent prognostic factors of PC, established a nomogram, and conducted an immune analysis. Results: The study identified four genes linked with an increased risk of PC; that is, Conclusions: Six genes were identified as having potential causal relationships with PC. These genes were integrated into a prognostic risk model, thereby serving as unique prognostic signatures. Our findings provide novel insights into predicting the prognosis of PC patients.

Indexed as

biomarkersliquid-liquid phase separation (LLPS)Mendelian randomization (MR)Pancreatic cancer (PC)prognosis

Identifiers

PMID39279964
PMCPMC11399862

What OpenQuestion holds

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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 OpenQuestion graph.