Glutathione Reductase change the tumor immune microenvironment and prognosis of colon cancer：A study based on proteomic analysis

doi:10.21203/rs.3.rs-3095319/v1

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Glutathione Reductase change the tumor immune microenvironment and prognosis of colon cancer：A study based on proteomic analysis

https://doi.org/10.21203/rs.3.rs-3095319/v1

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Background

Colonic malignant tumor cells can cause metabolic changes in the harsh microenvironment. Glutathione reductase (GSR) plays a key role in anti-oxidative stress in tumor cells, but its specific mechanism remains unclear. Therefore, the objective of this study was to investigate the prognostic value of GSR in patients with colon cancer and its effect on tumor microenvironment.

Methods

Differential proteins of colon cancer and normal intestinal tissues were obtained by proteomic differential analysis, and key proteins in metabolic pathways were identified by enrichment analysis and protein-protein interaction network (PPI) analysis. Survival analysis, immune infiltration analysis and clinical information correlation analysis were performed.

Results

A total of 1209 differentially expressed proteins were identified in 10 colon cancer samples. GO and KEGG enrichment analysis showed that differential proteins were significantly enriched in metabolic pathways, with 144 enriched genes (p < 0.05). PPI analysis showed that ADH1A, ADH1B, ADH1C, ADH5, ALDH18A1, ALDH3A1, GSTA1, GSTA2, HPGDS and GSR were the key proteins in the differential proteins metabolic pathway of colon cancer. GEPIA2 survival analysis showed that GSR significantly affected overall survival of colon cancer patients (p = 0.0017, HR = 0.45). Timer and TISIDB database analysis showed that the expression of GSR in colon cancer can significantly affect the level of immune cell infiltration. UALCAN database analysis showed that GSR expression predicted better N stage, overall stage and lower TP53 mutation rate of colon cancer (p < 0.05). The results of HPA database analysis suggested that GSR was mainly expressed in the cytoplasm and nuclear membrane of colonic malignant tumor cells.

Conclusions

Our study found that GSR plays an important role in the metabolic pathway of colon cancer and affects the prognosis of patients with statistical significance. GSR significantly affects the tumor immune microenvironment, clinical stage and TP53 mutation rate of colonic malignancies. This study provides some basis for the mechanism research and treatment of colon malignancy.

GSR1

Colon cacner2

Metabolism3

Prognosis4

Tumor immune microenvironment5

Colon cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer-related death worldwide, and its incidence and mortality have been increasing in recent years (1). In 2018, colon cancer ranked the third in incidence and the second in mortality among malignant tumors in China, with 376,000 and 191,000 new cases and deaths, respectively (2). Genetic, living environment, diet and other factors are all risk factors for colon cancer (3) (4). At present, with the development of medical diagnosis and treatment technology, tumor molecular targets and biological therapy have gradually become powerful clinical treatment methods.

Like other types of cancer, colon cancer is characterized by increased proliferative signaling, evasion of growth inhibition, increased cell viability and invasion, evasion of immune surveillance, achieving replication immortalization, resistance to apoptosis, and altered cell metabolism (5) (6). In the past few decades, targeting cancer metabolism has received much attention because tumor metabolism plays a key role in tumor initiation and progression, including glucose metabolism, nucleic acid metabolism, enzyme metabolism, protein metabolism, and others (7) (8) (9) (10). Studies have shown that tumor metabolism has a significant correlation with tumor prognosis. Most cancers reprogram their metabolism, such as increased aerobic glycolysis and pentose phosphate pathways, increased de novo lipogenesis, and nucleotide biosynthesis, to support their biomass and bioenergetic requirements (11) (12). The discovery of small molecules that can inhibit the activity of key enzymes in the above-mentioned metabolic pathways has paved the way for new anticancer drugs (13) (14) (15).

Numerous studies have shown that tumor-infiltrating immune cells, as an important component of the TME (Tumor microenvironment), play an important role in promoting tumor progression (16) (17). Among these tumor-infiltrating immune cells, regulatory T cells (Tregs), for example, promote tumor proliferation and development by inhibiting antitumor responses, thereby inducing angiogenesis and metastasis (18). Notably, tumor cells have been detected to release metabolic substances to block the antitumor activity of cytotoxic T cells (19). Stromal cells in the TME, such as tumor-associated adipocytes and cancer-associated fibroblasts (CAFs), may exacerbate cancer-stroma interactions during cancer progression and subsequently affect infiltrating immune cell function (20) (21). In the past decade, with the advent of immune checkpoint inhibitors, including those targeting programmed cell death-1 (PD-1), programmed cell death ligand-1 (PD-L1), and cytotoxic t lymphocyte antigen-4 (CTLA-4), great progress has been made in clinical immunotherapy for tumors (22). However, comprehensive studies on the relationship between tumor metabolism and tumor immune microenvironment (TIME) are still limited.

The gene GSR encodes glutathione (GSH) reductase, located at 8p12, and plays a key role in REDOX homeostasis and cellular resistance to oxidative stress (23). Glutathione is a major intracellular antioxidant that plays a crucial role in maintaining intracellular REDOX homeostasis, signal transduction, xenobiotic metabolism, and thiol balance. Since GSH plays a key role in cancer progression and response to therapy (24) (25), deficiency of GSR may affect clinical outcomes in cancer patients. However, little is known about alterations in this gene in primary human cancers.

In this study, proteomic differential analysis found that the differentially expressed proteins in colon cancer were mainly enriched in metabolic pathways. Further survival analysis found that GSR, as a protease that plays an important role in REDOX homeostasis and antioxidant response in tumor cells, significantly affected the prognosis of colon cancer patients. Further analysis showed that GSR had significant effects on colon cancer immune microenvironment and clinical information.

Clinical cohort and differential protein dataset

Ten clinical samples were collected, including 5 colon cancer tissues and 5 adjacent colon cancer tissues. Five patients with colon cancer were confirmed and diagnosed by preoperative clinical data, intraoperative and postoperative pathology, and none of them had metastasis. All patients underwent radical resection of colon cancer.

The results of differential proteins were analyzed by DIA (Data independent acquisition) mass spectrometry. The results were good and all passed the quality control.

Differences and cluster analysis

Based on the trusted protein, two standards were selected to calculate the difference between samples. Foldchange was used to evaluate the fold change of the expression level of a protein between samples. The p-value calculated by T-test test shows the significance of the difference between samples. Difference filter condition for | log2 (FC) | = 2 and p - value < 0.05. Unsupervised hierarchical clustering of data and samples was performed based on R language.

GO and KEGG enrichment analysis

After get the differentially expressed proteins in the database of DAVID (https://david.ncifcrf.gov/) to GO/KEGG enrichment analysis of differences in protein, its function is described. Methods of GO/KEGG functional enrichment analysis: The species protein was used as the background list, and the differential protein list was used as the candidate list screened from the background list. The p value representing whether the functional set was significantly enriched in the differential protein list was calculated by hypergeometric distribution test, and then the FDR was obtained after Benjamini& Hochberg multiple test correction.

PPI protein interaction and correlation analysis

Protein-protein interaction network (PPI) analysis was performed using the String analysis tool (https://cn.string-db.org/), and the cytoHubba plugin was used to screen out the top 10 proteins with the most connected nodes. Correlation analysis using the Timer web analytics tool (https://cistrome.shinyapps.io/timer/) is analyzed.

Analysis of survival

Survival analysis was performed using the web analysis tool GEPIA2 (http://gepia2.cancer-pku.cn/#index) (the integrated database was TCGA cancer database).

Analysis of immune infiltration

Gene expression and immunity infiltration amount of correlation analysis and cell immunity infiltration between copy number changes way group level difference analysis using the Timer and TISIDB (http://cis.hku.hk/TISIDB/index.php) to evaluate web analytics tool.

Clinical correlation analysis and immunohistochemistry

Clinical correlation analysis using UALCAN (http://ualcan.path.uab.edu/analysis.html) web analytics tool. All of these analysis tools are accessible online. Immunohistochemical results access HPA database (https://www.proteinatlas.org/).

Statistical Analysis

Student's T test was used to compare the differential proteins between groups. All correlation analyses were performed using Spearman's correlation analysis method. Kaplan-Meier survival analysis was performed using the logrank test. TIMER web analysis tool was used to evaluate the difference between gene somatic copy number changes and immune cell infiltration. two-sided Wilcoxon rank-sum test was used. A p value of less than 0.05 was considered to indicate statistical significance. Database derivatives were used for all statistical tests.

KEGG pathway enrichment of differential proteins was most significantly enriched in metabolic pathways

Firstly, we performed differential proteomic analysis of 10 samples of submitted tissues (5 samples of colon cancer tissues and 5 samples of adjacent tissues). Figure 1.A shows the results of differential analysis.A total of 1536 differential proteins were obtained, of which 977 were significantly up-regulated and 559 were significantly down-regulated. Figure 1. The results of cluster analysis can distinguish cancer tissues and adjacent tissues well. A total of 1209 differentially expressed proteins were identified (p < 0.05,log2(FC) > 1or<-1) after excluding proteins that were not detected in cancer tissues or adjacent tissues.

GO and KEGG enrichment analysis were performed on the 1209 differentially expressed proteins screened out in the DAVID database. Figure 1.b shows the enrichment results. In GO_BP enrichment analysis, 1209 differentially expressed proteins were mainly enriched in innate immune response, rRNA processing, RNA splicing, mRNA processing, and mRNA splicing. via spliceosome, defense response to bacterium, DNA replication, complement activation, classical pathway, translation, phagocytosis, recognition and other biological processes; GO_CC enrichment results suggested that the differential proteins were mainly located in cytosol, nucleus, nucleoplasm, extracellular exosome, membrane and extracellular region, extracellular space, nucleolus and endoplasmic reticulum; GO_MF enrichment results showed that the differential proteins were mainly enriched in protein binding, RNA binding, identical protein binding, antigen binding, nucleic acid binding and ATPase activity, mRNA binding, structural constituent of ribosome, immunoglobulin receptor binding, extracellular matrix structural constituent and other molecular functions. The 1209 differentially expressed proteins were mainly enriched in Metabolic pathways, Coronavirus disease-COVID-19, Ribosome biogenesis in KEGG enrichment analysis eukaryotes, Ribosome, Spliceosome, DNA replication, Nucleocytoplasmic transport, Drug metabolism-Cytochrome P450, Metabolism xenobiotics by cytochrome P450, Complement and coagulation cascades. Interestingly, we found that these 1209 differential proteins were significantly enriched (FDR < 0.05) in metabolic pathways, with the most enriched differential proteins (144). Therefore, these 144 differential proteins were selected for further analysis.

PPI network analysis identified the top 10 most important proteins in protein-protein interactions

To further explore the interaction between these 144 differentially expressed proteins enriched in metabolic pathways, we performed protein-protein interaction network analysis by Cytoscape software (Fig. 2.a), counted the number of connections between nodes by CytoHubba plug-in, and selected the top 10 proteins with the most connections: ADH1A, ADH1B, ADH1C, ADH5, ALDH18A1, ALDH3A1, GSTA1, GSTA2, HPGDS, GSR. Figure 2.a shows the interactions among these 10 proteins. In order to further explore the correlation between these 10 proteins that play key roles in metabolic pathways, we conducted correlation analysis (Spearman correlation analysis) through the Timer database and found that there were obvious correlations between some proteins (either positive or negative correlation). Figure 2.b specifically shows the results of their correlation analysis.

Colon cancer patients with high expression of GSR have a higher overall survival rate

Survival analysis using the GEPIA2 database showed that only GSR significantly affected the overall survival of patients with colon cancer, and the expression of GSR was significantly positively correlated with the overall survival of patients with colon cancer (p = 0.0017, HR = 0.45). It has been reported that GSR plays a key role in tumor metabolism (26). Through the previous correlation analysis, it was found that ADH5 had the strongest correlation with GSR (Cor = 0.32, P < 0.05). Although the effect of ADH5 on overall survival was not statistically significant, it seemed to have the same trend as GSR from the KM curve. In addition, it has been reported that ADH5 plays an important role in tumorigenesis and development (27).

GSR has a significant impact on the immune microenvironment and molecular subtypes of colon cancer

We wanted to further investigate whether GSR, while affecting colon cancer metabolism, could affect immune effects in the tumor microenvironment. Using Timer database, we analyzed the correlation between GSR and immune cell infiltration. In colon cancer, the expression of GSR was correlated with dendritic cells (Cor = 0.277, P < 0.05), central granulocytes (Cor = 0.266, P < 0.05), CD8 + cells (Cor = 0.282, P < 0.05), and the expression of GSR was correlated with immune cell infiltration. P < 0.05) and B cell infiltration (Cor = 0.177, P < 0.05). Through the analysis of different somatic copy number changes of GSR gene and immune cell infiltration levels, it was found that in colon cancer, arm deletion and arm amplification of GSR gene mutations can significantly reduce the level of B cell infiltration, and deep deletion, arm deletion and high amplification can significantly reduce the level of CD8 + T cell infiltration. Deep deletion and arm deletion significantly reduced the infiltration level of neutrophils, while deep deletion, arm deletion and arm amplification significantly reduced the infiltration level of dendritic cells.

We used the TISIDB database for further analysis. In terms of the correlation between tumor infiltrating lymphocytes (TILs) and GSR gene expression in colon cancer, there was a significant positive correlation between GSR and iDC, Act_DC, Th2 and Monocyte infiltration. There was a significant positive correlation between GSR and CD244 expression (Cor = 0.246, P < 0.05). For the correlation between immune stimulator and GSR gene expression in colon cancer, GSR was associated with TNFSF13 (Cor = 0.26, P < 0.05), TNFSF9 (Cor = 0.323, P < 0.05), RAET1E (Cor = 0.208, P < 0.05), and TNFSF9 (cor = 0.323, P < 0.05). P < 0.05) and KLRC1 (Cor = 0.256, P < 0.05). There was a significant positive correlation between the expression of GSR and B2M (Cor = 0.266, P < 0.05). There was a significant positive correlation between GSR and CXCL3 (Cor = 0.202, P < 0.05) and CXCL8 (Cor = 0.211, P < 0.05).

Correlation of GSR gene expression with clinical information in colon cancer patients

Using the UALCAN database, we analyzed the correlation between the expression of GSR and clinical information of colon cancer patients. Firstly, we found that the expression of GSR was significantly decreased in colon cancer tissues compared with normal tissues (p < 0.05). Previously, we found that colon cancer patients with high expression of GSR had better prognosis, suggesting that GSR may play a tumor suppressor role in colon cancer. The expression of GSR was the highest in Stage1 colon cancer. With the decrease of GSR expression, the stage of colon cancer was worse, and the expression of GSR was the lowest in Stage4 colon cancer. For N stage, we found that the expression of GSR in N0 was significantly higher than that in N1 and N2 (p < 0.05), indicating that colon cancer with low expression of GSR was more likely to have lymph node metastasis, leading to a later pathological overall stage. Finally, we found that GSR expression was significantly down-regulated in TP53-mutated colon cancers compared with TP53-unmutated colon cancers (p < 0.05). It is well known that TP53 gene acts as the "guardian" of malignant transformation, and its mutation predicts poor tumor staging and prognosis (28) (29) (30) (31). GSR and TP53 may play a synergistic role or have an upstream and downstream regulatory relationship in the development of colon cancer. In conclusion, our results suggest that GSR expression predicts better N stage, overall stage, and lower mutation rate of TP53 in colon cancer.

Immunohistochemical analysis of GSR in colon cancer

Finally, the results of immunohistochemical analysis of the HPA database showed that the degree of immunohistochemical staining of GSR in colon cancer suggested high staining, and the degree of intensity suggested strong intensity. GSR was mainly expressed in the cytoplasm and nuclear membrane of colon cancer cells.

Colon cancer is still a difficult problem in the medical community and the main cause of cancer-related death. With the continuous development of tumor molecular targets and biological therapy, especially the deepening understanding of tumor metabolism (8) (9) and immune microenvironment (16) (17), it provides new confidence and direction for the treatment of colon cancer. The gene GSR encodes the enzyme glutathione (GSH) reductase, which plays a crucial role in cancer progression and response to therapy (24) (25). Our proteomics results first found that 144 of the 1209 differentially expressed proteins in colon cancer were significantly enriched in KEGG metabolic pathways, which aroused our great interest. We have learned that metabolic reprogramming is a hallmark of colon cancer. Colon cancer cells are prone to rapid proliferation in a relatively harsh environment and require constant supply of nutrients and removal of cell-generated waste. Therefore, the metabolism of colon cancer cells is a key link that we have to pay attention to for the occurrence and development of tumors (32). It has been found that STAT proteins, as signal converters and transcriptional activators, affect tumor progression and drug resistance by regulating the dynamic metabolism of cancer cells, immune cells, and adipocytes, providing new targets for the treatment of metabolic reprogramming of cancer (33). The ADH family, ALDH family, GSTA family, GSR and HPGDS proteins were found to play critical roles in the metabolic pathways by protein-protein interaction network and correlation analysis. Previous studies have shown that total alcohol dehydrogenase (ADH) activity is significantly higher in cancer tissues than in healthy organs, such as liver, stomach, esophagus, colon, etc. This significant difference may lead to increased ability of ethanol to produce acetaldehyde and metabolic disorders of some biologically important substances (such as retinoic acid) to aggravate carcinogenesis (34). Earlier studies showed that ALDH knockdown was associated with proliferation and viability changes in lung cancer cells, demonstrating the importance of the ALDH enzyme in cell homeostasis (35). GSTA and HPGDS have also been found to play important roles in tumorigenesis and development (36) (37). We were most concerned about GSR, because GSR was the only one of these 10 genes that significantly affected the prognosis of colon cancer patients by survival analysis. GSR is the central enzyme in cellular antioxidant defense, reducing oxidized glutathione disulfide (GSSG) to the sulfhydryl form of GSH, an important cellular antioxidant. In a 2019 study, GSR-deficient lung cancer was identified as a possible target for thioredoxin/thioredoxin reductase (TXNRD) inhibitors. GSH/GSR and TXNRD are two major compensatory thiol-dependent antioxidant pathways that maintain the protein dithiol/disulfide balance (38). Also in a 2019 study, DEN-induced malignant progression of liver cancer under chronic low oxidative stress (TrxR1 deletion, standard care) was compared with elevated oxidative stress (TrxR1/GSR deficient livers, TrxR1 inactivation livers with standard care or phenobarbital exposure). Elevated oxidative stress is associated with a significant increase in HCC malignancy. Thioredoxin reductase-1 (TrxR1) and glutathione reductase (GSR) together with the Nrf2 transcription factor driven antioxidant system form an integrated network that can both combat potential oncogenic oxidative damage and protect cancer cells from oxidative death. Thus TrxR1, GSR, Nrf2, and oxidative stress are important factors in HCC development and progression in a complex, counterbalancing manner (26). Our correlation analysis and survival analysis found that GSR had the strongest correlation with ADH5, and the survival analysis of ADH had the same trend with GSR, especially the long-term survival rate, although it was not statistically significant. It has been shown that GSR, together with ADH5, is involved in the L-glutamine-attenuated 4-hydroxy-2-nonenal (4-HNE) -induced apoptosis pathway in intestinal epithelial cells, in which ADH5 is the gene involved in 4-HNE metabolism (39). Therefore, GSR and ADH5 may be involved in the important steps of cell metabolism in colon cancer cells, which needs further experiments to prove.

In recent years, studies have found that tumor cells can affect the infiltration of immune cells through metabolism, which in turn affects the efficacy and drug resistance of immunotherapy, which has aroused great interest (19). For example, many malignant tumor cells produce increased lactate, which promotes the development of myeloid-derived suppressor cells (MDSCs). MDSCs, lactate, and low pH in the tumor microenvironment inhibit the function of natural killer (NK) cells and T lymphocytes, leading to disease progression. A ketogenic diet can deplet MDSC and regulatory T cells in tumor-bearing animals, thereby improving their immune status (40). Our results show that GSR, as a metabolism-related protein that can significantly affect the prognosis of colon cancer patients, also has an important relationship with some immune-related cells in the correlation analysis of colon cancer immune infiltration. For example, specific immunity, CD8 + T cells have a central role in anti-tumor immunity, but their activity is often inhibited in the tumor microenvironment. Studies have found that regulating cholesterol metabolism can enhance the anti-tumor response of CD8 + T cells in mice. This proves that metabolic processes in the tumor microenvironment not only affect the tumor itself, but also affect the immune cells in the microenvironment, thereby affecting their activity (41). Our results also found a significant positive correlation between GSR gene expression and CD8 + T cell infiltration. Other immune cells such as dendritic cells, neutrophils, monocytes and other adaptive immune cells also show a significant positive correlation with GSR gene expression, which was also demonstrated in a 2020 study of hepatocellular carcinoma (42). Our results also demonstrate a positive correlation between GSR and some immunosuppressive agents and immunostimulants. It has been shown that targeting immunosuppressive targets or using immunostimulants can improve the therapeutic effect of tumors. Whether GSR can affect these targets and thus the tumor immune microenvironment may need further experiments to prove (43) (44) (45). Recent studies have found that tumors evade immune eradication by impairing antigen processing and peptide presentation. Lack or down-regulation of MHC-I on the surface of tumor cells leads to insufficient antigen presentation, which hinders the anti-tumor activity of CD8 + T cells and further affects the efficacy of tumor immunotherapy. It has been found in recent years that up to 30% of MSI (microsatellite instability) colorectal cancers display mutations in the B2M gene, leading to sequential loss of HLA class I antigens, which in turn prevents cytotoxic T cells from recognizing and killing B2M mutant cells (46) (47) (48) (49). Our results show that GSR is positively correlated with B2M expression, and we speculate that GSR affects the mutation of B2M in tumor cells by participating in the metabolism of glutathione, which leads to tumor escape from the anti-tumor activity of immune cells, which needs to be further proved by subsequent experiments. CXCL is a small soluble chemotactic cytokine involved in cell migration, adhesion and chemotaxis, and it has been suggested that these proteins may promote tumor progression (50) (51). We found a significant positive correlation between GSR expression and CXCL-3 and CXCL-8, and it has been experimentally demonstrated that CXCL-8 (chemokine − 8) has greater diagnostic value for colon cancer compared with the classical tumor marker CEA (52). Whether GSR can regulate the expression of tumor chemokines through affecting the metabolism of colon cancer cells, which leads to cell migration, adhesion and chemotaxis needs to be confirmed by further experiments. The analysis of the effects of different mutation patterns of GSR on immune cell infiltration showed that from the genomic level, the mutation pattern of GSR can also affect the infiltration of immune cells. At present, there are few studies on this aspect, which also aroused our interest in studying the specific mechanism of the effect of GSR mutation on immune infiltration from the genomic level. Furthermore, it can improve the immunosuppressive microenvironment of colon cancer.

Survival analysis showed that GSR as a tumor suppressor factor in colon cancer significantly improved the prognosis of patients. We also found that GSR was significantly correlated with N stage, overall stage and TP53 mutation in colon cancer patients. According to previous studies, GSR is mostly involved in the glutathione metabolism process of anti-oxidative stress response in colon malignant tumors, which provides energy for tumor cells in harsh environment (high lactic acid, high ROS, etc.) (53) (54), but this is not consistent with our results. We found that high expression of GSR was associated with better N stage, overall stage, and lower probability of TP53 mutation in colon cancer with high expression of GSR. Previous studies have found that GSR and TP53 may be involved in the treatment of some anti-tumor drugs (55) (56), but there is no study on whether there is a mechanism between GSR and TP53 that affects the occurrence and development of colonic malignant tumors. We plan to design experiments to explore the mechanism in the future. We verified the protein expression of GSR in colonic malignant tumors through HPA database, which also provided a certain basis for our subsequent experimental design.

Our study has certain limitations: (1) Our study lacks cell experiments and animal experiments to verify, we will further design experiments to study its mechanism; (2) The number of samples submitted for proteomics analysis is relatively small, and more samples may need to be submitted in the future to obtain more accurate results. The innovation of our study lies in the following aspects: (1) we analyzed the functions of genes from proteomics to transcriptomics to achieve multi-dimensional analysis; (2) We comprehensively analyzed the effects of genes on colon cancer microenvironment from the perspectives of tumor metabolism and tumor immunity.

In conclusion, our study identified differentially expressed proteins between colon cancer and normal tissues by differential analysis, and further functional enrichment analysis and survival analysis revealed that GSR plays a significant role in the metabolic pathways of colon cancer and significantly affects the prognosis of patients. Further bioinformatics analysis showed that GSR significantly affected the immune infiltration, clinical stage and TP53 mutation rate of colon cancer. This study provides a certain basis for the subsequent mechanism research and the treatment of colonic malignant tumors.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author contributions

Yuyao Wang wrote the first draft of the manuscript. Ju Wu and Xi Chen revised the manuscript and was in charge of language editing. Jiajun Yin and Zhequn Nie reviewed and edited the final manuscript. All authors contributed to the article and approved the submitted version.

Funding

A project admitted in the Dalian Deng Feng Program: key medical specialties in construction funded by the People's Government of Dalian Municipality (No. 243, 2021).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 2021;71(3):209–49.
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries - Bray - 2018 - CA: A Cancer Journal for Clinicians - Wiley Online Library [Internet]. [cited 2023 Jan 6]. Available from: https://acsjournals.onlinelibrary.wiley.com/doi/full/10.3322/caac.21492
Connell LC, Mota JM, Braghiroli MI, Hoff PM. The Rising Incidence of Younger Patients With Colorectal Cancer: Questions About Screening, Biology, and Treatment. Curr Treat Options in Oncol. 2017 Apr 8;18(4):23.
A Promotor-Led Pilot Study to Increase Colorectal Cancer Screening in Latinos: The Juntos Contra El Cáncer Program - Elva M. Arredondo, Jill Dumbauld, Maria Milla, Hala Madanat, Gloria D. Coronado, Jessica Haughton, Felipe Garcia-Bigley, Christian Ramers, Jesse Nodora, Balambal Bharti, Gabriel Lopez, Mirna Diaz, Jessica Marquez, Samir Gupta, 2021 [Internet]. [cited 2023 Jan 6]. Available from: https://journals.sagepub.com/doi/10.1177/1524839920912240
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011 Mar 4;144(5):646–74.
Pavlova NN, Zhu J, Thompson CB. The hallmarks of cancer metabolism: Still emerging. Cell Metab. 2022 Mar 1;34(3):355–77.
Kuo CY, Ann DK. When fats commit crimes: fatty acid metabolism, cancer stemness and therapeutic resistance. Cancer Commun (Lond). 2018 Jul 11;38(1):47.
Alfarouk KO. Tumor metabolism, cancer cell transporters, and microenvironmental resistance. J Enzyme Inhib Med Chem. 2016 Dec;31(6):859–66.
Alfarouk KO, Verduzco D, Rauch C, Muddathir AK, Bashir AHH, Elhassan GO, et al. Erratum: Glycolysis, tumor metabolism, cancer growth and dissemination. A new pH-based etiopathogenic perspective and therapeutic approach to an old cancer question. Oncoscience. 2015;2(4):317.
Lee WR, Ishikawa T, Umetani M. The interaction between metabolism, cancer and cardiovascular disease, connected by 27-hydroxycholesterol. Clin Lipidol. 2014;9(6):617–24.
Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell. 2012 Mar 20;21(3):297–308.
DeBerardinis RJ, Chandel NS. Fundamentals of cancer metabolism. Sci Adv. 2016 May;2(5):e1600200.
Vander Heiden MG. Targeting cancer metabolism: a therapeutic window opens. Nat Rev Drug Discov. 2011 Aug 31;10(9):671–84.
Luengo A, Gui DY, Vander Heiden MG. Targeting Metabolism for Cancer Therapy. Cell Chem Biol. 2017 Sep 21;24(9):1161–80.
Stine ZE, Schug ZT, Salvino JM, Dang CV. Targeting cancer metabolism in the era of precision oncology. Nat Rev Drug Discov. 2022 Feb;21(2):141–62.
Milo I, Bedora-Faure M, Garcia Z, Thibaut R, Périé L, Shakhar G, et al. The immune system profoundly restricts intratumor genetic heterogeneity. Sci Immunol. 2018 Nov 23;3(29):eaat1435.
Li X, Wenes M, Romero P, Huang SCC, Fendt SM, Ho PC. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat Rev Clin Oncol. 2019 Jul;16(7):425–41.
Lan HR, Du WL, Liu Y, Mao CS, Jin KT, Yang X. Role of immune regulatory cells in breast cancer: Foe or friend? Int Immunopharmacol. 2021 Jul;96:107627.
Kleinfeld AM, Okada C. Free fatty acid release from human breast cancer tissue inhibits cytotoxic T-lymphocyte-mediated killing. J Lipid Res. 2005 Sep;46(9):1983–90.
Li Z, Sun C, Qin Z. Metabolic reprogramming of cancer-associated fibroblasts and its effect on cancer cell reprogramming. Theranostics. 2021;11(17):8322–36.
Man K, Kallies A, Vasanthakumar A. Resident and migratory adipose immune cells control systemic metabolism and thermogenesis. Cell Mol Immunol. 2022 Mar;19(3):421–31.
Gaynor N, Crown J, Collins DM. Immune checkpoint inhibitors: Key trials and an emerging role in breast cancer. Semin Cancer Biol. 2022 Feb;79:44–57.
Couto N, Wood J, Barber J. The role of glutathione reductase and related enzymes on cellular redox homoeostasis network. Free Radic Biol Med. 2016 Jun;95:27–42.
Benhar M, Shytaj IL, Stamler JS, Savarino A. Dual targeting of the thioredoxin and glutathione systems in cancer and HIV. J Clin Invest. 2016 May 2;126(5):1630–9.
Bansal A, Simon MC. Glutathione metabolism in cancer progression and treatment resistance. J Cell Biol. 2018 Jul 2;217(7):2291–8.
McLoughlin MR, Orlicky DJ, Prigge JR, Krishna P, Talago EA, Cavigli IR, et al. TrxR1, Gsr, and oxidative stress determine hepatocellular carcinoma malignancy. Proc Natl Acad Sci U S A. 2019 Jun 4;116(23):11408–17.
Ladeira C, Viegas S, Carolino E, Gomes MC, Brito M. The influence of genetic polymorphisms in XRCC3 and ADH5 genes on the frequency of genotoxicity biomarkers in workers exposed to formaldehyde. Environ Mol Mutagen. 2013 Apr;54(3):213–21.
Guerra E, Alberti S. TP53 Gene Indels Impact on Cancer Outcomes. J Clin Oncol. 2023 Jan 1;41(1):145–7.
Pan M, Jiang C, Tse P, Achacoso N, Alexeeff S, Solorzano AV, et al. TP53 Gain-of-Function and Non-Gain-of-Function Mutations Are Differentially Associated With Sidedness-Dependent Prognosis in Metastatic Colorectal Cancer. J Clin Oncol. 2022 Jan 10;40(2):171–9.
Liu Y, Zhang X, Han C, Wan G, Huang X, Ivan C, et al. TP53 loss creates therapeutic vulnerability in colorectal cancer. Nature. 2015 Apr 30;520(7549):697–701.
Errico A. Colorectal cancer: POLR2A deletion with TP53 opens a window of opportunity for therapy. Nat Rev Clin Oncol. 2015 Jul;12(7):374.
Sedlak JC, Yilmaz ÖH, Roper J. Metabolism and Colorectal Cancer. Annu Rev Pathol. 2022 Nov 2;
Li YJ, Zhang C, Martincuks A, Herrmann A, Yu H. STAT proteins in cancer: orchestration of metabolism. Nat Rev Cancer. 2023 Jan 3;
Jelski W, Szmitkowski M. Alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) in the cancer diseases. Clin Chim Acta. 2008 Sep;395(1–2):1–5.
Moreb JS, Baker HV, Chang LJ, Amaya M, Lopez MC, Ostmark B, et al. ALDH isozymes downregulation affects cell growth, cell motility and gene expression in lung cancer cells. Mol Cancer. 2008 Nov 24;7:87.
Terrazzino S, La Mattina P, Gambaro G, Masini L, Franco P, Canonico PL, et al. Common variants of GSTP1, GSTA1, and TGFβ1 are associated with the risk of radiation-induced fibrosis in breast cancer patients. Int J Radiat Oncol Biol Phys. 2012 Jun 1;83(2):504–11.
Tippin BL, Levine AJ, Materi AM, Song WL, Keku TO, Goodman JE, et al. Hematopoietic prostaglandin D synthase (HPGDS): a high stability, Val187Ile isoenzyme common among African Americans and its relationship to risk for colorectal cancer. Prostaglandins Other Lipid Mediat. 2012 Jan;97(1–2):22–8.
Yan X, Zhang X, Wang L, Zhang R, Pu X, Wu S, et al. Inhibition of Thioredoxin/Thioredoxin Reductase Induces Synthetic Lethality in Lung Cancers with Compromised Glutathione Homeostasis. Cancer Res. 2019 Jan 1;79(1):125–32.
Liu N, Ma X, Luo X, Zhang Y, He Y, Dai Z, et al. l-Glutamine Attenuates Apoptosis in Porcine Enterocytes by Regulating Glutathione-Related Redox Homeostasis. J Nutr. 2018 Apr 1;148(4):526–34.
Husain Z, Seth P, Sukhatme VP. Tumor-derived lactate and myeloid-derived suppressor cells: Linking metabolism to cancer immunology. Oncoimmunology. 2013 Nov 1;2(11):e26383.
Yang W, Bai Y, Xiong Y, Zhang J, Chen S, Zheng X, et al. Potentiating the antitumour response of CD8(+) T cells by modulating cholesterol metabolism. Nature. 2016 Mar 31;531(7596):651–5.
Mo Z, Zhang S, Zhang S. A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma. J Oncol. 2020;2020:8291036.
Malaer JD, Marrufo AM, Mathew PA. 2B4 (CD244, SLAMF4) and CS1 (CD319, SLAMF7) in systemic lupus erythematosus and cancer. Clin Immunol. 2019 Jul;204:50–6.
Li L, Yang M, Yu J, Cheng S, Ahmad M, Wu C, et al. A Novel L-Phenylalanine Dipeptide Inhibits the Growth and Metastasis of Prostate Cancer Cells via Targeting DUSP1 and TNFSF9. Int J Mol Sci. 2022 Sep 18;23(18):10916.
McGilvray RW, Eagle RA, Rolland P, Jafferji I, Trowsdale J, Durrant LG. ULBP2 and RAET1E NKG2D ligands are independent predictors of poor prognosis in ovarian cancer patients. Int J Cancer. 2010 Sep 1;127(6):1412–20.
Bicknell DC, Kaklamanis L, Hampson R, Bodmer WF, Karran P. Selection for beta 2-microglobulin mutation in mismatch repair-defective colorectal carcinomas. Curr Biol. 1996 Dec 1;6(12):1695–7.
Bernal M, Ruiz-Cabello F, Concha A, Paschen A, Garrido F. Implication of the β2-microglobulin gene in the generation of tumor escape phenotypes. Cancer Immunol Immunother. 2012 Sep;61(9):1359–71.
Chang CC, Ferrone S. Immune selective pressure and HLA class I antigen defects in malignant lesions. Cancer Immunol Immunother. 2007 Feb;56(2):227–36.
Kloor M, Michel S, Buckowitz B, Rüschoff J, Büttner R, Holinski-Feder E, et al. Beta2-microglobulin mutations in microsatellite unstable colorectal tumors. Int J Cancer. 2007 Jul 15;121(2):454–8.
Fearon ER, Vogelstein B. A genetic model for colorectal tumorigenesis. Cell. 1990 Jun 1;61(5):759–67.
Heras SC de las, Martínez-Balibrea E. CXC family of chemokines as prognostic or predictive biomarkers and possible drug targets in colorectal cancer. World Journal of Gastroenterology. 2018 Nov 14;24(42):4738–49.
Pączek S, Łukaszewicz-Zając M, Gryko M, Mroczko P, Kulczyńska-Przybik A, Mroczko B. CXCL-8 in Preoperative Colorectal Cancer Patients: Significance for Diagnosis and Cancer Progression. Int J Mol Sci. 2020 Mar 17;21(6):2040.
Zhao M, Liu Q, Gong Y, Xu X, Zhang C, Liu X, et al. GSH-dependent antioxidant defense contributes to the acclimation of colon cancer cells to acidic microenvironment. Cell Cycle. 2016;15(8):1125–33.
Wang YN, Lu YX, Liu J, Jin Y, Bi HC, Zhao Q, et al. AMPKα1 confers survival advantage of colorectal cancer cells under metabolic stress by promoting redox balance through the regulation of glutathione reductase phosphorylation. Oncogene. 2020 Jan;39(3):637–50.
Mucaki EJ, Zhao JZL, Lizotte DJ, Rogan PK. Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning. Signal Transduct Target Ther. 2019;4:1.
Wang L, Li C, Tian J, Liu J, Zhao Y, Yi Y, et al. Genome-wide transcriptional analysis of Aristolochia manshuriensis induced gastric carcinoma. Pharm Biol. 2020 Dec;58(1):98–106.

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Glutathione Reductase change the tumor immune microenvironment and prognosis of colon cancer：A study based on proteomic analysis

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and methods

Clinical cohort and differential protein dataset

Differences and cluster analysis

GO and KEGG enrichment analysis

PPI protein interaction and correlation analysis

Analysis of survival

Analysis of immune infiltration

Clinical correlation analysis and immunohistochemistry

Statistical Analysis

Results

KEGG pathway enrichment of differential proteins was most significantly enriched in metabolic pathways

PPI network analysis identified the top 10 most important proteins in protein-protein interactions

Colon cancer patients with high expression of GSR have a higher overall survival rate

GSR has a significant impact on the immune microenvironment and molecular subtypes of colon cancer

Correlation of GSR gene expression with clinical information in colon cancer patients

Immunohistochemical analysis of GSR in colon cancer

Discussion

Conclusion

Declarations

Data availability statement

Author contributions

Funding

Conflict of interest

Publisher’s note

References

Additional Declarations

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