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

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

  • Cancer Cell Int. 2021 Feb 19;21(1):128. doi: 10.1186/s12935-021-01809-y.
Ji Li  # 1 Chen Zhu  # 2 Peipei Yue  # 3 4 Tianyu Zheng 5 Yan Li 6 Biao Wang 3 Xin Meng 3 Yao Zhang 7
Affiliations

Affiliations

  • 1 Department of Radiation Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang, Liaoning, China.
  • 2 Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
  • 3 Department of Biochemistry and Molecular Biology, College of Life Science, China Medical University, Shenyang, Liaoning, China.
  • 4 Department of Laboratory Medicine, The Fourth Affiliated Hospital of China Medical University, Chongshan East Street, Shenyang, Liaoning, China.
  • 5 The VIP Department, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Diseases, China Medical University, Shenyang, Liaoning, China.
  • 6 Department of General Surgery, The Fourth Affiliated Hospital of China Medical University, Chongshan East Street, Shenyang, Liaoning, China.
  • 7 Department of gynaecology, Shengjing Hospital of China Medical University, NO.36 Sanhao street, Heping district, 110000, Shenyang, Liaoning, China. zhangy7@sj-hospital.org.
  • # Contributed equally.
Abstract

Background: Abnormal 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.

Methods: Gene 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.

Results: High 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.

Conclusions: Our 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.

Keywords

Digestive system tumors; Glycolysis; Prognosis; Signaling pathway.

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