DAPK1 as an independent prognostic marker in liver cancer

et al. 2018

BMC Cancer

BackgroundHepatocellular carcinoma (HCC) is a malignant tumor that threatens global human health. High PKM2 expression is widely reported in multiple cancers, especially in HCC. This study aimed to explore the effects of PKM2 on global gene expression, metabolic damages, patient prognosis, and multiple transcriptional regulation relationships, as well as to identify several key metabolic genes and screen some small-molecule drugs.MethodsTranscriptome and clinical HCC data were downloaded from the NIH-GDC repository. Information regarding the metabolic genes and subsystems was collected from the Recon 2 human metabolic model. Drug-protein interaction data were obtained from the DrugBank and UniProt databases. We defined patients with PKM2 expression levels ≥11.25 as the high-PKM2 group, and those with low PKM2 expression (< 11.25) were defined as the low-PKM2 group.ResultsThe results showed that the global metabolic gene expression levels were obviously divided into the high- or low-PKM2 groups. In addition, a greater number of affected metabolic subsystems were observed in the high-PKM2 group. Furthermore, we identified 98 PKM2-correlated deregulated metabolic genes that were associated with poor overall patient survival. Together, these findings suggest more comprehensive influences of PKM2 on HCC. In addition, we screened several small-molecule drugs that target these metabolic enzymes, some of which have been used in antitumor clinical studies.ConclusionsHCC patients with high PKM2 expression showed more severe metabolic damage, transcriptional regulation imbalance and poor prognosis than low-PKM2 individuals. We believe that our study provides valuable information for pathology research and drug development for HCC.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-5023-0) contains supplementary material, which is available to authorized users.

Section: Discussionmentioning

confidence: 54%

Effects of PKM2 on global metabolic changes and prognosis in hepatocellular carcinoma: from gene expression to drug discovery

et al. 2018

BMC Cancer

“…As the DrugBank indicated, pivots such as Fibrinolysin and Pasireotide have already been explored in the study of pancreatic cancer (52,53), Amcinonide came to the horizon. Amcinonide as a corticosteroid has been reported to effectively reverse the expression of the oncogene DAPK1 (death-associated protein kinase 1) in liver carcinoma (54). While the effect of Amcinonide on pancreatic cancer have not been investigated yet, the present study offered a good insight into the further study of pancreatic cancer.…”

Section: Discussionmentioning

confidence: 70%

Identification of Prognosis-Related Genes and Construction of Multi-regulatory Networks in Pancreatic Cancer Microenvironment by Bioinformatics Analysis

Liao³

et al. 2020

Preprint

Background: As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma. Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis and improve treatment.Methods: We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan-Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein-protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank.Results: In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 42 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated.Conclusion: The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.

“…As the DrugBank indicated, pivots such as Fibrinolysin and Pasireotide have already been explored in the study of pancreatic cancer (57,58), Amcinonide came to the horizon. Amcinonide as a corticosteroid has been reported to effectively reverse the expression of the oncogene DAPK1 (death-associated protein kinase 1) in liver carcinoma (59). While the effect of Amcinonide on pancreatic cancer have not been investigated yet, the present study offered a good insight into the further study of pancreatic cancer.…”

Section: Discussionmentioning

confidence: 70%

Identification of Prognosis-Related Genes and Construction of Multi-regulatory Networks in Pancreatic Cancer Microenvironment by Bioinformatics Analysis

Liao

et al. 2020

Preprint

Abstract Background: As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma . Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis andimprove treatment. Methods: We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan-Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein-protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank. Results: In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 38 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated. Conclusion: The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.