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Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk

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Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk ORIGINAL RESEARCH published: 23 June 2021 doi: 10.3389/fgene.2021.639246 Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma Wenjing Song 1, Xin He 1, Pengju Gong 1, Yan Yang 1, Sirui Huang 1, Yifan Zeng 1, Lei Wei 2 and Jingwei Zhang 1* 1 Department of Breast and Thyroid Surgery, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Zhongnan Hospital, Wuhan University, Wuhan, China, 2 Department of Pathology and Pathophysiology, School of Basic Medical Sciences, Wuhan University, Wuhan, China Objective: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Although progress has been made in the treatment of PDAC, its prognosis remains unsatisfactory. This study aimed to develop novel prognostic genes related to glycolysis in PDAC and to apply these genes to new risk stratification. Methods: In this study, based on the Cancer Genome Atlas (TCGA) PAAD cohort, the expression level of glycolysis-related gene at mRNA level in PAAD and its relationship with prognosis were analyzed. Non-negative matrix decomposition (NMF) clustering was used to cluster PDAC patients according to glycolytic genes. Prognostic glycolytic genes, Edited by: Alessandro Romanel, screened by univariate Cox analysis and LASSO regression analysis were established to University of Trento, Italy calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group Reviewed by: and the low-risk group were analyzed, and the signal pathway was further enriched Paolo Gandellini, to analyze the correlation between glycolysis genes. In addition, based on RNA-seq University of Milan, Italy Xiaojie Zhao, data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC Rosalind Franklin University of samples, and ESTIMATE was used to calculate the immune score of the samples. Medicine and Science, United States *Correspondence: Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples Jingwei Zhang were divided into two clusters by NMF cluster analysis. Survival analysis showed that [email protected] PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with < Specialty section: cluster 2 samples (P 0.001). A risk prediction model based on 11 glycolysis genes was This article was submitted to constructed, according to which patients were divided into two groups, with significantly Cancer Genetics and Oncogenomics, poorer prognosis in high-risk group than in low-risk group (P < 0.001). Both internal a section of the journal Frontiers in Genetics validation and external dataset validation demonstrate good predictive ability of the model Received: 14 February 2021 (AUC = 0.805, P < 0.001; AUC = 0.763, P < 0.001). Gene aggregation analysis showed Accepted: 25 May 2021 that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis Published: 23 June 2021 level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk Citation: group showed immunosuppressed status and infiltrated by relatively more macrophages Song W, He X, Gong P, Yang Y, Huang S, Zeng Y, Wei L and Zhang J and less CD8+T cell. (2021) Glycolysis-Related Gene Expression Profiling Screen for Conclusions: These findings suggested that the gene signature based on Prognostic Risk Signature of glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value Pancreatic Ductal Adenocarcinoma. for PDAC. Front. Genet. 12:639246. doi: 10.3389/fgene.2021.639246 Keywords: glycolysis, gene signature, overall survival, prognostic biomarker, pancreatic ductal adenocarcinoma Frontiers in Genetics | www.frontiersin.org 1 June 2021 | Volume 12 | Article 639246 Song et al. Glycolysis-Related Gene Signature in PDAC INTRODUCTION dysfunction, abnormal expression of oncogenic genes, specific transcription factors, hypoxic tumor microenvironment, and Pancreatic ductal adenocarcinoma (PDAC) is the most common tumor-associated macrophage (Cheng et al., 2019; Karasinska type of pancreatic cancer, accounting for about 90% of pancreatic et al., 2020; Yang et al., 2020). This energy metabolic pathway not cancer cases. PDAC is a highly fatal cancer with a 5-year overall only provides energy to cancer cells, but also produces metabolic survival rate of 6% (2–9%) (Ilic and Ilic, 2016). By 2030, PDAC is intermediates that promote cell proliferation, invasion and drug projected to be the second leading cause of cancer-related deaths resistance of cancer (Deberardinis et al., 2008). Reprogramming (Rahib et al., 2014). Surgical resection is considered to be the only of metabolism in cancer cells is regulated by multiple factors and treatment for PDAC, which can significantly prolong the survival signaling pathways, such as hypoxia inducible factor (HIF-1), time. However, due to the hidden onset, rapid progression, Myc, p53, and the PI3K/Akt/mTOR pathway (Pelicano et al., early metastasis and lack of sensitive screening methods (Singhi 2006; Dang et al., 2009; Liao et al., 2009; Masui et al., 2013). et al., 2019), most PDAC are confirmed in its advanced stage Some preclinical and clinical studies have shown that drugs and lost the thus become unresectable. Therefore, about 80% of targeting these factors and signaling pathways and the use of people diagnosed with PDAC will die within a year and thus anti-glycolysis agents to deprive the basic metabolic needs of early diagnosis of PDAC is particularly important (Zhang et al., cancer cells and interfere with cancer growth are effective as 2017). For diagnostic methods, the specificity and sensitivity therapies to inhibit cancer progression (Abdel-Wahab et al., of tumor biomarkers for predicting PDAC are not satisfactory 2019; Jagust et al., 2019). In addition, it is gratifying to note that (Zhou et al., 2015), and it is difficult to find small lesions by anti-glycolysis agents have been found to have the potential to computed tomography (CT) (Fox et al., 2016; Zhang et al., increase the sensitivity of cancer cells and improve treatment 2017). Although pathological examination is the gold standard resistance. Therefore, it is of great significance to search for for the diagnosis of PDAC, the deep location of pancreas limits molecules related to glycolysis and explore their expression its application. Therefore, it is urgent to develop a method and characteristics and functional involvement in PDAC. strategy for early detection of PDAC, which will be beneficial In the present study, 11 prognostic glycolysis-related genes to the diagnosis and treatment of PDAC patients. In addition, were selected based on high-throughput sequencing results and radiotherapy, chemotherapy, targeted molecular therapy and clinicopathological features of the PDAC data sets, and the immunotherapy have also been shown to be effective in the prognostic gene signature was proposed, which was used to treatment of PDAC (Kamisawa et al., 2016). The diversity of describe the glycolytic level in PDAC samples. Multivariate treatment options requires more individualized management of Cox regression confirmed that prognostic gene signature was PDAC patients, which can improve both the treatment of cancer independent prognostic influential factor of PDAC, and was and the quality of life. Tumor progression and drug response in validated using GEO data sets. In addition, we described the PDAC patients are closely related to the molecular characteristics, gene expression at the protein level, and investigated in depth phenotypic differences and tumor microenvironment (TME). the correlation between the signal pathways involved in the According to these characteristics, different classification systems gene signature and biochemical processes and tumor immunity. for PDAC subtypes could be established to predict the prognosis These results may be helpful for the study of glycolysis-related of patients and select therapeutic drugs and therapies (Collisson molecules in PDAC. et al., 2011; Moffitt et al., 2015; Bailey et al., 2016; Puleo et al., 2018). Advances in tumor molecular biology have led to the development of new predictive tools based on prognostic genes. METHODS These prognostic markers reflecting tumor progression at the Patient Data Acquisition molecular level may contribute to more accurate personalized The Cancer genome atlas (TCGA) (https://portal.gdc.cancer. survival prediction. gov/) is a cancer research project established by the national Microenvironment is the cellular environment where cancer institute and the national human genome research cells grow, proliferate and invade, and tumor cells are no institute jointly, covering 33 kinds of cancer. Download RNA-seq exception (Wang et al., 2018). Most solid tumors rely heavily on and corresponding clinical data of PDAC patients (survival time, aerobic glycolysis for energy production due to the metabolic survival status, diagnostic age, gender, history of smoking, history reprogramming of tumor cells to facilitate the aerobic glycolysis of alcohol, history of diabetes, history of chronic pancreatitis, process to adapt to their heterogeneous microenvironment. tumor site, histological grade, pathological T, N, M, stage, residual In addition to the tumor cells, the TME also includes the tumor and radiation therapy). After data cleaning, a total of surrounding immune cells, fibroblasts and immune cells. Because 173 patients with PDAC had complete survival data, and 73 of the dense connective tissue and vascular microenvironment
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