Evidence map›Paper›PMID 33607990›Full record

ArticleCancer cell international2021

Identification of glycolysis related pathways in pancreatic adenocarcinoma and liver hepatocellular carcinoma based on TCGA and GEO datasets.

Ji Li, Chen Zhu, Peipei Yue, Tianyu Zheng, Yan Li, Biao Wang, Xin Meng, Yao Zhang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer cell international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

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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

8 authors at 4 institutions in 1 country.

Ji Li *Department of Radiation Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, Liaoning, China.
Chen Zhu *Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Peipei Yue *Department of Biochemistry and Molecular Biology, College of Life Science, China Medical University, Shenyang, Liaoning, China.
Tianyu ZhengThe VIP Department, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang, Liaoning, China.
Yan LiDepartment of General Surgery, The Fourth Affiliated Hospital of China Medical University, Chongshan East Street, Shenyang, Liaoning, China.
Biao WangDepartment of Biochemistry and Molecular Biology, College of Life Science, China Medical University, Shenyang, Liaoning, China.
Xin MengDepartment of Biochemistry and Molecular Biology, College of Life Science, China Medical University, Shenyang, Liaoning, China.
Yao ZhangDepartment of gynaecology, Shengjing Hospital of China Medical University, NO.36 Sanhao street, Heping district, 110000, Shenyang, Liaoning, China. zhangy7@sj-hospital.org.ORCID http://orcid.org/0000-0002-6509-0946
China Medical University · CNFourth Affiliated Hospital of China Medical University · CNFirst Hospital of China Medical University · CNLiaoning Cancer Hospital & Institute · CN

Funding

National Natural Science Foundation of China 81572831
6 · The paper itself

Abstract

backgroundAbnormal energy metabolism is one of the characteristics of tumor cells, and it is also a research hotspot in recent years. Due to the complexity of digestive system structure, the frequency of tumor is relatively high. We aim to clarify the prognostic significance of energy metabolism in digestive system tumors and the underlying mechanisms.

methodsGene set variance analysis (GSVA) R package was used to establish the metabolic score, and the score was used to represent the metabolic level. The relationship between the metabolism and prognosis of digestive system tumors was explored using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Volcano plots and gene ontology (GO) analyze were used to show different genes and different functions enriched between different glycolysis levels, and GSEA was used to analyze the pathway enrichment. Nomogram was constructed by R package based on gene characteristics and clinical parameters. qPCR and Western Blot were applied to analyze gene expression. All statistical analyses were conducted using SPSS, GraphPad Prism 7, and R software. All validated experiments were performed three times independently.

resultsHigh glycolysis metabolism score was significantly associated with poor prognosis in pancreatic adenocarcinoma (PAAD) and liver hepatocellular carcinoma (LIHC). The STAT3 (signal transducer and activator of transcription 3) and YAP1 (Yes1-associated transcriptional regulator) pathways were the most critical signaling pathways in glycolysis modulation in PAAD and LIHC, respectively. Interestingly, elevated glycolysis levels could also enhance STAT3 and YAP1 activity in PAAD and LIHC cells, respectively, forming a positive feedback loop.

conclusionsOur results may provide new insights into the indispensable role of glycolysis metabolism in digestive system tumors and guide the direction of future metabolism-signaling target combined therapy.

Indexed as

Digestive system tumorsGlycolysisPrognosisSignaling pathway

Identifiers

PMID33607990
PMCPMC7893943
OpenAlexW3133362725

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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.