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Metabolic reprogramming is one of the core characteristics of cancer
.
The liver is the most important metabolic organ in the human body
.
Liver cancer has undergone significant changes in various metabolic pathways such as glucose and lipid metabolism, oxidative metabolism and other processes
.
The diversity and heterogeneity of liver cancer metabolism makes it difficult for people to gain a comprehensive understanding of the metabolic landscape of liver cancer
.
At present, many studies on the heterogeneity of liver cancer metabolism focus on the study of the differential changes in gene expression in metabolic enzymes and metabolic pathways, and the study of metabolites and metabolite-protein interactions that play an important role in cancer metabolic reprogramming Relatively lack [1,2]
.
On July 11, 2021, Hailong Park’s research group from Dalian Institute of Chemical Physics, Chinese Academy of Sciences published an article titled Identification and characterization of robust hepatocellular carcinoma prognostic subtypes based on an integrative metabolite-protein interaction network in Advanced Science.
Based on metabolite-protein interaction network (MPI) and transcriptomics data, redefine liver cancer subtypes with significant prognosis, combined with cancer multi-omics data and metabolite-protein interactions to predict and reveal poor prognosis The specific biological characteristics of the liver cancer subtypes in metabolism, immunity, tumor microenvironment, methylation regulation, etc.
, and the above results have been carried out through multiple independent liver cancer cohorts and multi-omics experiments based on liver cancer cell linesVerification
.
Researchers have carried out the identification of key cancer-related biological processes based on protein-protein interactions including protein ubiquitinating enzyme-substrate and deubiquitinating enzyme-substrate interactions [3,4]
.
In this study, the protein-protein interaction was further extended to protein-metabolite interactions, and a human global metabolite-protein interaction network was constructed through the integration of multiple pathway databases
.
Based on the transcriptomics data of 13 cancers in the TCGA database, the transcript expression profile of the core protein (protein node degree> 4) in the global metabolite-protein interaction network was used for PCA dimensionality reduction analysis, and the results showed that the liver cancer samples were obviously independent Compared with other cancer types, liver cancer has significantly specific metabolic regulation characteristics compared with other cancer types
.
The researchers further carried out a cluster analysis of tissue samples for liver cancer based on the expression profiles of the above core proteins.
As a result, they identified two liver cancer subtypes with significant differences in metabolism, and the two subtypes have extremely significant differences in prognosis.
Sex (P=1×10-5)
.
The researchers also used three other liver cancer sample collections from different sources for analysis, and obtained consistent subtype classification and prognostic differences
.
Interestingly, compared with subtypes with good prognosis, subtypes with poor prognosis have a significant increase in hypoxia scores, and most metabolic pathways show a significant downward trend, while a large number of immune system-related pathways show a significant upward-regulated trend
.
The down-regulation of metabolic pathways is mainly related to the hypermethylation of metabolic enzymes
.
In order to further reveal the potential relationship between the down-regulation of metabolic pathways and the up-regulation of immune pathways, the researchers proposed a method for estimating changes in the expression of metabolic enzymes based on a directional metabolite-protein interaction network and a metabolite-protein interaction based on machine learning.
The function prediction algorithm predicts that a variety of fatty acids in the subtypes with poor prognosis will show a relative accumulation trend, and have potential interactions with a variety of immune regulatory proteins, and may participate in the up-regulation of immune pathways
.
In addition, the researchers estimated the proportion of various cell types in the tumor microenvironment of each liver cancer sample based on transcriptomics data, and found that the two subtypes have significant differences in multiple immune cell components, and the subtype with a poor prognosis The type can be further subdivided into two sub-subtypes that are significantly different in prognosis and metabolism
.
In order to verify the prediction results of the above calculations, the researchers further used liver cancer cell lines cultured under hypoxic and normoxic conditions to approximate the liver cancer subtypes with poor prognosis and good prognosis, and obtained their metabolomics and transcriptomics data
.
Results Hypoxic cell lines also showed significant down-regulation and up-regulation of metabolic and immune pathways at the transcription level, and a large amount of fatty acids showed a tendency to accumulate in hypoxic cell lines, which was consistent with the predicted results
.
This work provides new ideas for in-depth exploration of the metabolic heterogeneity of liver cancer and the interaction between metabolic reprogramming and the immune system.
It also reveals that the metabolic heterogeneity of liver cancer is of great significance for clinical classification and immunotherapy
.
The above research work was completed by Piao Hailong's research group at Dalian Institute of Chemical Physics, Chinese Academy of Sciences.
The research team's associate researcher Chen Di, current graduate student Zhang Yiran, and Ph.
D.
graduate Wang Wen are co-first authors, and the corresponding author is Researcher Piao Hailong
.
Park Hailong’s research group is mainly engaged in cancer metabolism, the integration and mining of biomedical big data [3,4], and the construction of cancer metabolic molecular network and the functional analysis of metabolic molecules [5,6], dedicated to the prediction of the molecular mechanism of cancer metabolism And research
.
Researcher Park Hailong and his team have published many research articles on cancer metabolism and molecular mechanisms in internationally renowned journals such as Nature, Cell Metabolism, Nature Cell Biology, Nature Genetics, Advanced Science, etc.
in recent years
.
Welcome more outstanding doctors with backgrounds in computer, bioinformatics, and cancer biology to join the research group to carry out post-doctoral research
.
Interested parties please contact Researcher Park Hailong
.
Resume delivery (if interested, please send your resume and other materials to): https://jinshuju.
net/f/ZqXwZt or scan the QR code to deliver the original resume link: http://doi.
org/10.
1002/advs.
202100311 People: Eleven References 1.
Chaisaingmongkol J, Budhu A, Dang H, Rabibhadana S, Pupacdi B, et al.
Common Molecular Subtypes Among Asian Hepatocellular Carcinoma and Cholangiocarcinoma.
Cancer Cell.
2017, 32(1):57-70.
e3.
2.
Björnson E, Mukhopadhyay B, Asplund A, Pristovsek N, Cinar R, et ak.
Stratification of Hepatocellular Carcinoma Patients Based on Acetate Utilization.
Cell Rep.
2015, 13(9):2014-26.
3.
Chen D, Liu X, Xia T, Tekcham DS, Wang W, et al.
A Multidimensional Characterization of E3 Ubiquitin Ligase and Substrate Interaction Network.
iScience.
2019,16:177-191.
4.
Chen D, Ning Z, Chen H, Lu C, Liu X , et al.
An integrative pan-cancer analysis of biological and clinical impacts underlying ubiquitin-specific-processing proteases.
Oncogene.
2020, 39(3):587–602.
5.
Yan M, Qi H, Xia T, Zhao X, Wang W, et al.
Metabolomics profiling of metformin-mediated metabolic reprogramming by passing AMPKα.
Metabolism.
2019,91: 18-29.
6.
Zhang L, Zhu Z, Yan H, Wang W, Wu Z, et al.
Creatine promotes cancer metastasis through activation of Smad2/3.
Cell Metab.
2021:S1550-4131(21)00116-9.
Note for reprint [Non-original article] The copyright of this article belongs to the author of the article All, personal forwarding and sharing are welcome, reprinting is prohibited without permission, the author has all legal rights, offenders must be investigated
.
.
The liver is the most important metabolic organ in the human body
.
Liver cancer has undergone significant changes in various metabolic pathways such as glucose and lipid metabolism, oxidative metabolism and other processes
.
The diversity and heterogeneity of liver cancer metabolism makes it difficult for people to gain a comprehensive understanding of the metabolic landscape of liver cancer
.
At present, many studies on the heterogeneity of liver cancer metabolism focus on the study of the differential changes in gene expression in metabolic enzymes and metabolic pathways, and the study of metabolites and metabolite-protein interactions that play an important role in cancer metabolic reprogramming Relatively lack [1,2]
.
On July 11, 2021, Hailong Park’s research group from Dalian Institute of Chemical Physics, Chinese Academy of Sciences published an article titled Identification and characterization of robust hepatocellular carcinoma prognostic subtypes based on an integrative metabolite-protein interaction network in Advanced Science.
Based on metabolite-protein interaction network (MPI) and transcriptomics data, redefine liver cancer subtypes with significant prognosis, combined with cancer multi-omics data and metabolite-protein interactions to predict and reveal poor prognosis The specific biological characteristics of the liver cancer subtypes in metabolism, immunity, tumor microenvironment, methylation regulation, etc.
, and the above results have been carried out through multiple independent liver cancer cohorts and multi-omics experiments based on liver cancer cell linesVerification
.
Researchers have carried out the identification of key cancer-related biological processes based on protein-protein interactions including protein ubiquitinating enzyme-substrate and deubiquitinating enzyme-substrate interactions [3,4]
.
In this study, the protein-protein interaction was further extended to protein-metabolite interactions, and a human global metabolite-protein interaction network was constructed through the integration of multiple pathway databases
.
Based on the transcriptomics data of 13 cancers in the TCGA database, the transcript expression profile of the core protein (protein node degree> 4) in the global metabolite-protein interaction network was used for PCA dimensionality reduction analysis, and the results showed that the liver cancer samples were obviously independent Compared with other cancer types, liver cancer has significantly specific metabolic regulation characteristics compared with other cancer types
.
The researchers further carried out a cluster analysis of tissue samples for liver cancer based on the expression profiles of the above core proteins.
As a result, they identified two liver cancer subtypes with significant differences in metabolism, and the two subtypes have extremely significant differences in prognosis.
Sex (P=1×10-5)
.
The researchers also used three other liver cancer sample collections from different sources for analysis, and obtained consistent subtype classification and prognostic differences
.
Interestingly, compared with subtypes with good prognosis, subtypes with poor prognosis have a significant increase in hypoxia scores, and most metabolic pathways show a significant downward trend, while a large number of immune system-related pathways show a significant upward-regulated trend
.
The down-regulation of metabolic pathways is mainly related to the hypermethylation of metabolic enzymes
.
In order to further reveal the potential relationship between the down-regulation of metabolic pathways and the up-regulation of immune pathways, the researchers proposed a method for estimating changes in the expression of metabolic enzymes based on a directional metabolite-protein interaction network and a metabolite-protein interaction based on machine learning.
The function prediction algorithm predicts that a variety of fatty acids in the subtypes with poor prognosis will show a relative accumulation trend, and have potential interactions with a variety of immune regulatory proteins, and may participate in the up-regulation of immune pathways
.
In addition, the researchers estimated the proportion of various cell types in the tumor microenvironment of each liver cancer sample based on transcriptomics data, and found that the two subtypes have significant differences in multiple immune cell components, and the subtype with a poor prognosis The type can be further subdivided into two sub-subtypes that are significantly different in prognosis and metabolism
.
In order to verify the prediction results of the above calculations, the researchers further used liver cancer cell lines cultured under hypoxic and normoxic conditions to approximate the liver cancer subtypes with poor prognosis and good prognosis, and obtained their metabolomics and transcriptomics data
.
Results Hypoxic cell lines also showed significant down-regulation and up-regulation of metabolic and immune pathways at the transcription level, and a large amount of fatty acids showed a tendency to accumulate in hypoxic cell lines, which was consistent with the predicted results
.
This work provides new ideas for in-depth exploration of the metabolic heterogeneity of liver cancer and the interaction between metabolic reprogramming and the immune system.
It also reveals that the metabolic heterogeneity of liver cancer is of great significance for clinical classification and immunotherapy
.
The above research work was completed by Piao Hailong's research group at Dalian Institute of Chemical Physics, Chinese Academy of Sciences.
The research team's associate researcher Chen Di, current graduate student Zhang Yiran, and Ph.
D.
graduate Wang Wen are co-first authors, and the corresponding author is Researcher Piao Hailong
.
Park Hailong’s research group is mainly engaged in cancer metabolism, the integration and mining of biomedical big data [3,4], and the construction of cancer metabolic molecular network and the functional analysis of metabolic molecules [5,6], dedicated to the prediction of the molecular mechanism of cancer metabolism And research
.
Researcher Park Hailong and his team have published many research articles on cancer metabolism and molecular mechanisms in internationally renowned journals such as Nature, Cell Metabolism, Nature Cell Biology, Nature Genetics, Advanced Science, etc.
in recent years
.
Welcome more outstanding doctors with backgrounds in computer, bioinformatics, and cancer biology to join the research group to carry out post-doctoral research
.
Interested parties please contact Researcher Park Hailong
.
Resume delivery (if interested, please send your resume and other materials to): https://jinshuju.
net/f/ZqXwZt or scan the QR code to deliver the original resume link: http://doi.
org/10.
1002/advs.
202100311 People: Eleven References 1.
Chaisaingmongkol J, Budhu A, Dang H, Rabibhadana S, Pupacdi B, et al.
Common Molecular Subtypes Among Asian Hepatocellular Carcinoma and Cholangiocarcinoma.
Cancer Cell.
2017, 32(1):57-70.
e3.
2.
Björnson E, Mukhopadhyay B, Asplund A, Pristovsek N, Cinar R, et ak.
Stratification of Hepatocellular Carcinoma Patients Based on Acetate Utilization.
Cell Rep.
2015, 13(9):2014-26.
3.
Chen D, Liu X, Xia T, Tekcham DS, Wang W, et al.
A Multidimensional Characterization of E3 Ubiquitin Ligase and Substrate Interaction Network.
iScience.
2019,16:177-191.
4.
Chen D, Ning Z, Chen H, Lu C, Liu X , et al.
An integrative pan-cancer analysis of biological and clinical impacts underlying ubiquitin-specific-processing proteases.
Oncogene.
2020, 39(3):587–602.
5.
Yan M, Qi H, Xia T, Zhao X, Wang W, et al.
Metabolomics profiling of metformin-mediated metabolic reprogramming by passing AMPKα.
Metabolism.
2019,91: 18-29.
6.
Zhang L, Zhu Z, Yan H, Wang W, Wu Z, et al.
Creatine promotes cancer metastasis through activation of Smad2/3.
Cell Metab.
2021:S1550-4131(21)00116-9.
Note for reprint [Non-original article] The copyright of this article belongs to the author of the article All, personal forwarding and sharing are welcome, reprinting is prohibited without permission, the author has all legal rights, offenders must be investigated
.
