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

Alterations in genes involved in glycolysis and hypoxia affect the prognosis of pancreatic cancer

Purpose

To construct a prognostic model for pancreatic cancer based on glycolytic and hypoxic metabolic subtypes. To analyze the biological characteristics of these subtypes and explore potential therapeutic options.

Methods

We obtained mRNA, simple nucleotide variation (SNP), and clinical data for pancreatic cancer from The Cancer Genome Atlas (TCGA). Patients were classified into four metabolic subtypes. We focused on glycolysis and hypoxia subtypes. Single-sample gene set enrichment analysis (ssGSEA) assessed immune cell infiltration. We evaluated the effects of immunotherapy and chemotherapy on these subtypes. Cox regression and random survival forest algorithms were used to build a prognostic model. Validation was performed using data from the International Cancer Genome Consortium (ICGC) and ArrayExpress database.

Results

We identified four subtypes. Kaplan-Meier survival analysis showed the glycolytic subtype had the longest survival, while the hypoxic subtype had the shortest. The glycolytic subtype exhibited higher immune cell infiltration. Immunotherapy and chemotherapy appeared more beneficial for the glycolytic subtype. KRAS mutations were more frequent in the hypoxic subtype. Our prognostic model indicated a worse prognosis for high-risk groups, validated by external data.

Conclusion

The glycolytic metabolic subtype of pancreatic cancer is associated with longer survival and better response to chemotherapy and immunotherapy compared to the hypoxic subtype.

Targeting YAP1 to improve the efficacy of immune checkpoint inhibitors in liver cancer: mechanism and strategy

Liver cancer is the third leading of tumor death, including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Immune checkpoint inhibitors (ICIs) are yielding much for sufferers to hope for patients, but only some patients with advanced liver tumor respond. Recent research showed that tumor microenvironment (TME) is critical for the effectiveness of ICIs in advanced liver tumor. Meanwhile, metabolic reprogramming of liver tumor leads to immunosuppression in TME. These suggest that regulating the abnormal metabolism of liver tumor cells and firing up TME to turn "cold tumor" into "hot tumor" are potential strategies to improve the therapeutic effect of ICIs in liver tumor. Previous studies have found that YAP1 is a potential target to improve the efficacy of anti-PD-1 in HCC. Here, we review that YAP1 promotes immunosuppression of TME, mainly due to the overstimulation of cytokines in TME by YAP1. Subsequently, we studied the effects of YAP1 on metabolic reprogramming in liver tumor cells, including glycolysis, gluconeogenesis, lipid metabolism, arachidonic acid metabolism, and amino acid metabolism. Lastly, we summarized the existing drugs targeting YAP1 in the treatment of liver tumor, including some medicines from natural sources, which have the potential to improve the efficacy of ICIs in the treatment of liver tumor. This review contributed to the application of targeted YAP1 for combined therapy with ICIs in liver tumor patients.

Role of Up-Regulated Transmembrane Channel-Like Protein 5 in Pancreatic Adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is a malignant tumor responsible for a heavy disease burden. Previously, only one pan-cancer study of Transmembrane channel-like protein 5 (TMC5) showed that TMC5 was highly expressed in PAAD, but the results lacked comprehensive verification, and the mechanism of TMC5 in PAAD was still unclear.

METHODS

For exploring the expression and clinical value of TMC5 in PAAD better, we adopted a comprehensive evaluation method, using internal immunohistochemistry (IHC) data combined with microarray and RNA-sequencing data collected from public databases. The single cell RNA-sequencing (scRNA-seq) data were exploited to explore the TMC5 expression in cell populations and intercellular communication. The potential mechanism of TMC5 in PAAD was analyzed from the aspects of immune infiltration, transcriptional regulation, function and pathway enrichment.

RESULTS

Our IHC data includes 148 PAAD samples and 19 non-PAAD samples, along with the available microarray and RNA-sequencing data (1166 PAAD samples, 704 non-PAAD samples). The comprehensive evaluation results showed that TMC5 was evidently up-regulated in PAAD (SMD = 1.17). Further analysis showed that TMC5 was over-expressed in cancerous epithelial cells. Furthermore, TMC5 was up-regulated in more advanced tumor T and N stages. Interestingly, we found that STAT3 as an immune marker of Th17 cells was not only positively correlated with TMC5 and up-regulated in PAAD tissues, but also the major predicted TMC5 transcription regulator. Moreover, STAT3 was involved in cancer pathway of PAAD.

CONCLUSION

Up-regulated TMC5 indicates advanced tumor stage in PAAD patients, and its role in promoting PAAD development may be regulated by STAT3.

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

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, screened by univariate Cox analysis and LASSO regression analysis were established to calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group and the low-risk group were analyzed, and the signal pathway was further enriched to analyze the correlation between glycolysis genes. In addition, based on RNA-seq data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC samples, and ESTIMATE was used to calculate the immune score of the samples.

Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples were divided into two clusters by NMF cluster analysis. Survival analysis showed that PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with cluster 2 samples (P < 0.001). A risk prediction model based on 11 glycolysis genes was constructed, according to which patients were divided into two groups, with significantly poorer prognosis in high-risk group than in low-risk group (P < 0.001). Both internal validation and external dataset validation demonstrate good predictive ability of the model (AUC = 0.805, P < 0.001; AUC = 0.763, P < 0.001). Gene aggregation analysis showed that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk group showed immunosuppressed status and infiltrated by relatively more macrophages and less CD8+T cell.

Conclusions: These findings suggested that the gene signature based on glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value for PDAC.