Evidence map›Paper›PMID 33197892›Full record

ArticleAging2020

Establishing and validating a pathway prognostic signature in pancreatic cancer based on miRNA and mRNA sets using GSVA.

Junfeng Zhang, Jianyou Gu, Shixiang Guo, Wenjie Huang, Yao Zheng, Xianxing Wang, Tao Zhang, Weibo Zhao, Bing Ni, Yingfang Fan and 1 more

Abstract read
In one paragraph

Article in Aging, 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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  8. Analysis of mScientific reports · 2022
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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

11 authors.

Junfeng ZhangInstitute of Hepatopancreatobiliary Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 401120, P R China.
Jianyou GuDepartment of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong Province, P R China.
Shixiang GuoInstitute of Hepatopancreatobiliary Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 401120, P R China.
Wenjie HuangDepartment of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong Province, P R China.
Yao ZhengInstitute of Hepatopancreatobiliary Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 401120, P R China.
Xianxing WangInstitute of Hepatopancreatobiliary Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 401120, P R China.
Tao ZhangInstitute of Hepatopancreatobiliary Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 401120, P R China.
Weibo ZhaoPLA Strategic Support Force Characteristic Medical Center (The 306th Hospital of PLA), Beijing 100101, P R China.
Bing NiDepartment of Pathophysiology, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P R China.
Yingfang FanDepartment of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong Province, P R China.
Huaizhi WangInstitute of Hepatopancreatobiliary Surgery, Chongqing General Hospital, University of Chinese Academy of Sciences, Chongqing 401120, P R China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic cancer (PC) is a severe disease with the highest mortality rate among various cancers. It is urgent to find an effective and accurate way to predict the survival of PC patients. Gene set variation analysis (GSVA) was used to establish and validate a miRNA set-based pathway prognostic signature for PC (miPPSPC) and a mRNA set-based pathway prognostic signature for PC (mPPSPC) in independent datasets. An optimized miPPSPC was constructed by combining clinical parameters. The miPPSPC, optimized miPPSPC and mPPSPC were established and validated to predict the survival of PC patients and showed excellent predictive ability. Four metabolic pathways and one oxidative stress pathway were identified in the miPPSPC, whereas linoleic acid metabolism and the pentose phosphate pathway were identified in the mPPSPC. Key factors of the pentose phosphate pathway and linoleic acid metabolism, G6PD and CYP2C8/9/18/19, respectively, are related to the survival of PC patients according to our tissue microarray. Thus, the miPPSPC, optimized miPPSPC and mPPSPC can predict the survival of PC patients efficiently and precisely. The metabolic and oxidative stress pathways may participate in PC progression.

Indexed as

Gene Expression Regulation, NeoplasticBiomarkers, TumorGene Expression ProfilingHumansKaplan-Meier EstimateMicroRNAsPancreatic NeoplasmsPrognosisRNA, MessengerROC CurveBiomarkers, TumorMicroRNAsRNA, MessengerGSVAmetabolic pathwaysmiRNA setspancreatic cancerprognostic signature

Identifiers

PMID33197892
PMCPMC7746356

What OpenQuestion holds

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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.