Evidence map›Paper›PMID 33240632›Full record

ArticlePeerJ2020

Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes.

Mingjun Yang, Boni Song, Juxiang Liu, Zhitong Bing, Yonggang Wang, Linmiao Yu

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2020. 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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–field-weighted citation impact, top 82% of its field
1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 4 institutions in 1 country.

Mingjun Yang *School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China.
Boni Song *School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China.
Juxiang LiuGansu Key Laboratory of Endocrine and metabolism, Department of Endocrinology, Gansu Provincial People's Hospital, Lanzhou, Gansu, China.
Zhitong BingInstitute of Modern Physics of Chinese Academy of Sciences, Lanzhou, China.
Yonggang WangSchool of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China.
Linmiao YuSchool of Life Science and Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China.
Lanzhou University of Technology · CNChinese Academy of Sciences · CNGansu Provincial Hospital · CNInstitute of Modern Physics · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer.

methodsBoth types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient's risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group.

resultsAn integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102,

conclusionsThe conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.

Indexed as

DiabetesLASSO Cox regressionPCPrognosis index

Identifiers

PMID33240632
PMCPMC7666560
OpenAlexW3103354836

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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.