Evidence map›Paper›PMID 32355515›Full record

ArticleInternational journal of clinical and experimental pathology2020

Identification of key lncRNAs as prognostic prediction models for colorectal cancer based on LASSO.

Xiao Huang, Wei Cai, Wenliang Yuan, Sihua Peng

Abstract read
In one paragraph

Article in International journal of clinical and experimental pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 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.

Xiao HuangSchool of Big Data and Artificial Intelligence, Chizhou University Anhui, China.
Wei CaiKey Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Shanghai Ocean University), Ministry of Education Shanghai, China.
Wenliang YuanCollege of Mathematics and Information Engineering, Jiaxing University Zhejiang, China.
Sihua PengKey Laboratory of Exploration and Utilization of Aquatic Genetic Resources (Shanghai Ocean University), Ministry of Education Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer (CRC) is one of the most common malignancies, with varying prognoses and a high mortality. There is an urgent need to establish a new prediction model to predict the survival risk of CRC patients. The long non-coding RNAs (lncRNAs) expression profiles and corresponding clinical information of CRC patients were obtained from The Cancer Genome Atlas, TCGA. We identified a total of 1,176 lncRNAs differentially expressed between 480 CRC and 41 normal tissues. In the training test, we combined these differentially expressed lncRNAs with overall survival of CRC patients. Six lncRNAs (AL356270.1, LINC02257, AC020891.2, LINC01485, AC083967.1 and RBAKDN) were finally screened out by using LASSO regression mode to establish a novel prediction model as a prognostic indicator for CRC patients. The area under the curve (AUC) of 3- and 5-year ROC analysis in CRC were 0.6923 and 0.7328 for training set, and were 0.6803 and 0.7035 for testing set, respectively. K-M analysis revealed a significant difference between high risk and low risk in the training set (

Indexed as

CRCLASSO regressionlncRNAsoverall survival

Identifiers

PMID32355515
PMCPMC7191144

What OpenQuestion holds

Textmetadata
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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.