Evidence map›Paper›PMID 33714960›Full record

ArticleAging2021

An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

Gaoming Li, Qiuyue Song, Angsong Cai, Zeliang Wei, Rui Zhang, Dali Yi, Jia Chen, Fang Li, Yanqi Zhang, Ling Liu and 2 more

Abstract read
In one paragraph

Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Gaoming LiDepartment of Health Statistics, Army Medical University, Chongqing, China.
Qiuyue SongDepartment of Health Statistics, Army Medical University, Chongqing, China.
Angsong CaiDepartment of Health Statistics, Army Medical University, Chongqing, China.
Zeliang WeiDepartment of Health Statistics, Army Medical University, Chongqing, China.
Rui ZhangDepartment of Health Statistics, Army Medical University, Chongqing, China.
Dali YiDepartment of Journal Editorial, Army Medical University, Chongqing, China.
Jia ChenDepartment of Health Statistics, Army Medical University, Chongqing, China.
Fang LiDepartment of Health Statistics, Army Medical University, Chongqing, China.
Yanqi ZhangDepartment of Health Statistics, Army Medical University, Chongqing, China.
Ling LiuDepartment of Health Statistics, Army Medical University, Chongqing, China.
Yazhou WuDepartment of Health Statistics, Army Medical University, Chongqing, China.
Dong YiDepartment of Health Statistics, Army Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to construct and validate an immunoscore nomogram that may be used to predict the prognosis of oesophageal cancer. With the gene expression data of oesophageal cancer in a public database, we used CIBERSORT to estimate the fractions of 22 infiltrating immune cell types. We then built an immunoscore signature based on 12 types of infiltrating immune cells using the least absolute shrinkage and selection operator (LASSO) model. This immunoscore was used as an independent predictor in the prognostic model (training cohort: [hazard ratio (HR), 4.78; 95% confidence interval (CI), 2.64-8.67; P < 0.001], validation cohort: [HR, 2.15; 95% CI, 1.04-4.45; P = 0.040]). Subgroup analysis by clinical features showed that overall survival was significantly different between the high-immunoscore group and the low-immunoscore group. The predictors that constituted the individualized prediction nomogram were immunoscore, age, and tumour stage. The nomogram had good discrimination and calibration. Decision curve analysis showed that the immunoscore nomogram was clinically useful. Therefore, the novel immunoscore signature based on infiltrating immune cells can be used as a reliable predictor of the prognosis of oesophageal cancer, and the immunoscore nomogram is a convenient tool for predicting the survival of individual patients.

Indexed as

NomogramsAdultAgedEsophageal NeoplasmsFemaleHumansLymphocytes, Tumor-InfiltratingMaleMiddle AgedPrognosisCIBERSORTimmune cellnomogramoesophageal cancerprognosis

Identifiers

PMID33714960
PMCPMC8034926

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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.