Evidence map›Paper›PMID 36524971›Full record

ArticleJournal of clinical laboratory analysis2023

Development of 14-gene signature prognostic model based on metastasis for colorectal cancer.

Tong Li, Qian Yu, Te Liu, Wenjing Yang, Wei Chen, Anli Jin, Hao Wang, Lin Ding, Chunyan Zhang, Baishen Pan and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  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

12 authors at 5 institutions in 1 country.

Tong LiDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Qian YuDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Te LiuDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Wenjing YangDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Wei ChenDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Anli JinDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Hao WangDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Lin DingDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Chunyan ZhangDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Baishen PanDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Beili WangDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Wei GuoDepartment of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Sun Yat-sen University · CNZhongshan Hospital · CNFudan University · CNShanghai University of Traditional Chinese Medicine · CNZhongshan Hospital of Xiamen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMetastasis is the main cause of death of colorectal tumors, in our study a prognosis model was built by analyzing the differentially expressed genes between metastatic and non-metastatic colorectal cancer (CRC). We used this feature to predict CRC patient prognosis and explore the causes of colorectal tumor metastasis by characterizing the immune status alteration.

methodsCRC patient data were obtained from TCGA and GEO databases. We constructed a risk prognostic model by using Cox regression and the least absolute shrinkage and selection operator (LASSO) based on CRC metastasis-related genes. We also obtained a nomogram to predict the prognosis of CRC patients. Finally, we explored the underlying mechanism of these metastasis-related genes and CRC prognosis using immune infiltration analysis and experimental verification.

resultsAccording to our prognostic model, in TCGA, the area under the curve (AUC) values of the training and test sets were 0.72 and 0.76, respectively, and 0.68 for the GEO external data set. This suggested that the treatment and prognosis of patients could be effectively determined. At the same time, we found that the B and T cells in both tissues and peripheral blood of high MR-risk score patients were mostly in immune static or inactivated states compared with those of low MR-risk score patients.

conclusionsMR-risk score has a direct correlation with CRC patient prognosis. It is useful for predicting the prognosis and patient immune status for these patients.

Indexed as

Colorectal NeoplasmsNomogramsArea Under CurveDatabases, FactualHumansPrognosisGEOimmune infiltrationlasso regression analysisprognostic signatureTCGA

Identifiers

PMID36524971
PMCPMC9833974
OpenAlexW4311677624

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.