Evidence map›Paper›PMID 34025929›Full record

ArticleComputational and structural biotechnology journal2021

Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system.

Zhiqiao Zhang, Tingshan He, Liwen Huang, Jing Li, Peng Wang

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Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

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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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

5 authors.

Zhiqiao ZhangDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.
Tingshan HeDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.
Liwen HuangDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.
Jing LiDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.
Peng WangDepartment of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer. Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system. Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score. The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

Indexed as

AJCC, the American Joint Committee on CancerCI, confidence intervalDCA, decision curve analysisDFS, disease free survivalDisease free survivalGastric cancerGC, gastric cancerGEO, the Gene Expression OmnibusHR, hazard ratioImmune genePrognostic signatureROC, receiver operating characteristicSD, standard deviationTCGA, The Cancer Genome AtlasTranscription factor

Identifiers

PMID34025929
PMCPMC8111455

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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.