A bioinformatics analysis and an experimental validation of the hypoxia-related prognostic model

Lei Zhou; Weigang Zhang; Haoxiang Ni; Jin Liu; Hui Sun; Zhanwen Liang; Ruoqin Wang; Xiaofeng Xue; Kai Chen; Wei Li

doi:10.21037/jgo-23-301

A bioinformatics analysis and an experimental validation of the hypoxia-related prognostic model

J Gastrointest Oncol. 2023 Jun 30;14(3):1504-1524. doi: 10.21037/jgo-23-301. Epub 2023 Jun 19.

Authors

Lei Zhou^#¹, Weigang Zhang^#², Haoxiang Ni^#^{3

4}, Jin Liu^#¹, Hui Sun¹, Zhanwen Liang¹, Ruoqin Wang¹, Xiaofeng Xue², Kai Chen¹, Wei Li¹

Affiliations

¹ Department of Oncology, the First Affiliated Hospital of Soochow University, Suzhou, China.
² Department of General Surgery, the First Affiliated Hospital of Soochow University, Suzhou, China.
³ The Second Clinical Medical College of Xuzhou Medical University, Xuzhou, China.
⁴ Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, China.

^# Contributed equally.

Abstract

Background: Hypoxia plays an important role in the development of pancreatic cancer (PCA). However, there is few research on the application of hypoxia molecules in predicting the prognosis of PCA. We aimed to establish a prognostic model based on hypoxia-related genes (HRGs) for PCA to discover new biomarkers, and to reveal the potential of this prognostic model for evaluating the tumor microenvironment (TME).

Methods: Univariate Cox regression analysis was used to identify HRGs associated with overall survival (OS) of PCA samples. A hypoxia-related prognostic model was established based on least absolute shrinkage and selection operator (LASSO) regression analysis in The Cancer Genome Atlas (TCGA) cohort. The model was validated in the Gene Expression Omnibus (GEO) datasets. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the infiltration of immune cells. A wound healing assay and transwell invasion assay were used to explore the biological functions of target genes in PCA.

Results: A total of 18 HRGs were differentially expressed between the tumor and normal pancreatic tissue, 4 (BHLHE40, ENO1, SDC4, and TGM2) of which were selected to construct a prognostic model. According to this model, patients in the high-risk group had a less favorable prognosis. Furthermore, the proportion of M0 macrophages was significantly higher in high-risk tissue-type patients, whereas naïve B cells, plasma cells, CD8⁺ T cells, and activated CD4⁺ memory T cells were significantly lower. The expression of BHLHE40 in PCA cells was significantly up-regulated under hypoxic conditions. Moreover, BHLHE40 was shown to regulate the transcription and expression of the downstream target gene TLR3. The wound healing assay and transwell invasion assay indicated that BHLHE40 mediated PCA cell migration and invasion by targeting the downstream gene TLR3.

Conclusions: The hypoxia-related prognostic model established by the expression pattern of 4 HRGs can be used to predict the prognosis and assess the TME of PCA patients. Mechanically, activation of the BHLHE40/TLR3 axis is responsible for the promoted invasion and migration of PCA cells in a hypoxic environment.

Keywords: Hypoxia; invasion; migration; pancreatic cancer; tumor microenvironment.