Evidence map›Paper›PMID 38482222›Full record

ArticleJournal of gastrointestinal oncology2024

Establishing a carcinoembryonic antigen-associated competitive endogenous RNA network and forecasting an important regulatory axis in colon adenocarcinoma patients.

Fangfang Liang, Yansong Xu, Haiping Zheng, Weizhong Tang

Open access · diamondAbstract 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, top 98% of its field
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, 0 citations in OpenAlex.

  1. Review
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 at 2 institutions in 1 country.

Fangfang LiangDepartment of Medical Oncology, Guangxi Medical University First Affiliated Hospital, Nanning, China.
Yansong XuCollege of Oncology, Guangxi Medical University, Nanning, China.
Haiping ZhengDepartment of Medical Oncology, Guangxi Medical University First Affiliated Hospital, Nanning, China.
Weizhong TangCollege of Oncology, Guangxi Medical University, Nanning, China.
First Affiliated Hospital of GuangXi Medical University · CNGuangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colorectal cancer is one of the top five malignant tumors in the world in terms of morbidity and mortality. Numerous long non-coding RNAs (lncRNAs) are specifically expressed in tumours and can affect various types of human cancer by participating in competitive endogenous RNA (ceRNA) regulatory networks. However, the specific mechanisms and complex networks of ceRNA regulatory patterns in colon adenocarcinoma (COAD) remain unclear. Methods: Using The Cancer Genome Atlas (TCGA) database, we identified the differentially expressed lncRNA, microRNA (miRNA), and messenger RNA (mRNA) between colon cancer and normal tissues, as well as between groups with high and low CEACAM5 expression. Then, we constructed CEACAM5-related ceRNA networks, established the key lncRNA-miRNA-mRNA regulatory axis, and explored the biological mechanisms of this axis and its clinical significance in colon cancer from multiomic aspects. Results: We constructed a ceRNA network of 18 lncRNAs, 177 miRNAs, and 25 mRNAs associated with CEACAM5 and finally established the key Conclusions: The LCMT1-AS2/RPS6KA5 axis may be instrumental in tumor progression, potentially acting as a prognostic biomarker and therapeutic target.

Indexed as

CEACAM5Colon adenocarcinoma (COAD)competitive endogenous RNA (ceRNA)ribosomal protein S6 kinase A5 (RPS6KA5)

Identifiers

PMID38482222
PMCPMC10932660
OpenAlexW4392301543

What OpenQuestion holds

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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.