Hyperglycemia Enhances Immunosuppression and Aerobic Glycolysis of Pancreatic Cancer Through Upregulating Bmi1-UPF1-HK2 Pathway

Wu, Shihong; Zhang, Haoxiang; Gao, Chenggang; Chen, Jiaoshun; Li, Hehe; Meng, Zhaoqing; Bai, Jianwei; Shen, Qiang; Wu, Heshui; Yin, Tao

doi:10.1016/j.jcmgh.2022.07.008

Cited by 19 publications

(17 citation statements)

References 67 publications

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“…Recently, UPF1 has been found to be closely related to the tumor immune microenvironment [ 42 ]. We next examined the relationship between the UPF1 and immune-related characteristics in ccRCC, which indicated that UPF1 was positively correlated with a majority of immunomodulators, including chemokines, immunostimulators, MHC and receptors ( Figure 3 A).…”

Section: Resultsmentioning

confidence: 99%

Identification and Validation of UPF1 as a Novel Prognostic Biomarker in Renal Clear Cell Carcinoma

Chun

Chang

et al. 2022

Genes

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Background: Up frameshift protein 1 (UPF1) is a key component of nonsense-mediated mRNA decay (NMD) of mRNA containing premature termination codons (PTCs). The dysregulation of UPF1 has been reported in various cancers. However, the expression profile of UPF1 and its clinical significance in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods: In order to detect UPF1 expression in ccRCC and its relationship with the clinical features of ccRCC, bulk RNA sequencing data were analyzed from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and ArrayExpress databases. The impact of UPF1 on the immune microenvironment of ccRCC was evaluated by multiple immune scoring algorithms to identify the cell groups that typically express UPF1 using ccRCC single cell sequencing (scRNA) data. In addition, genes co-expressed with UPF1 were identified by the weighted gene correlation network analysis (WGCNA), followed by KEGG and Reactome enrichment analysis. A series of functional experiments were performed to assess the roles of UPF1 in renal cancer cells. Finally, pan-cancer analysis of UPF1 was also performed. Results: Compared with normal tissues, the expression levels of UPF1 mRNA and protein in tumor tissues of ccRCC patients decreased significantly. In addition, patients with low expression of UPF1 had a worse prognosis. Analysis of the immune microenvironment indicated that UPF1 immune cell infiltration was closely related and the ccRCC scRNA-seq data identified that UPF1 was mainly expressed in macrophages. WGCNA analysis suggested that the functions of co-expressed genes are mainly enriched in cell proliferation and cellular processes. Experimental tests showed that knockdown of UPF1 can promote the invasion, migration and proliferation of ccRCC cells. Lastly, pan-cancer analysis revealed that UPF1 disorders were closely associated with various cancer outcomes. Conclusions: UPF1 may play a tumor suppressive role in ccRCC and modulate the immune microenvironment. The loss of UPF1 can predict the prognosis of ccRCC, making it a promising biomarker and providing a new reference for prevention and treatment.

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Section: Resultsmentioning

confidence: 99%

Identification and Validation of UPF1 as a Novel Prognostic Biomarker in Renal Clear Cell Carcinoma

Chun

Chang

et al. 2022

Genes

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“…Hyperglycemia, characterised by excess -9 of 13 glucose, is considered a risk factor contributing to cancer cell proliferation and the rapid suppression of immune cell activity in tumour microenvironment. 26,27,36 Consequently, the inhibition of glucose uptake by SGLT-2is could potentially serve as an effective strategy to eliminate cancer cells, enhance immune cell functionality, and promote the healthy progression of normal cells. [37][38][39] SGLT-2is also have an impact on cancer cells by activating AMP-activated protein kinase, leading to an increase in acetyl-CoA carboxylase (ACC) phosphorylation and the inhibition of mammalian target of rapamycin (mTOR).…”

Section: Discussionmentioning

confidence: 99%

“…We used diabetes-related health care utilisation and the Diabetes Complications Severity Index (DCSI) to evaluate DM severity; the DM-related health care utilisation contained emergency department visits, admission, and disease duration; the DCSI was calculated on the basis of information on seven categories of DM complications (retinopathy, nephropathy, neuropathy, cerebrovascular complications, cardiovascular complications, peripheral vascular disease, and metabolic complications). 26,27 The comorbidities considered in the present study were hypertension (ICD-10-CM: I10-I15), hyperlipidaemia (ICD-10-CM: E78), chronic obstructive pulmonary disease (COPD, ICD-10-CM: J44), liver disease (ICD-10-CM: B15-B19 and K70-K77), and obesity (ICD-10-CM: E66). The following concomitant medications related to cancer risk were examined as potential confounders: insulins, metformin, sulfonylureas, meglitinides, α-glucosidase inhibitors, thiazolidinediones, glucagon-like peptide-1 receptor agonists, angiotensin converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), calcium channel blockers, α-blockers, β-blockers, thiazides, loop diuretics, mineralocorticoid receptor antagonists, statins, fibrates, aspirin, P2Y 12 antagonists, warfarin, nonsteroidal antiinflammatory drugs (NSAIDs), steroids, and hormone replacement therapy.…”

Section: Main Outcome and Covariatesmentioning

confidence: 99%

Comparison between sodium‐glucose cotransporter 2 inhibitors and dipeptidyl peptidase 4 inhibitors on the risk of incident cancer in patients with diabetes mellitus: A real‐world evidence study

Sung,

Hung,

Tung

et al. 2024

Diabetes Metabolism Res

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AimsSodium‐glucose cotransporter 2 inhibitors (SGLT‐2is) have been demonstrated to be associated with cancer cell mechanisms. However, whether they increase the risk of cancer remains unclear. Thus, this study aimed to determine the association between SGLT‐2i use and the incidence of cancer in patients with diabetes mellitus (DM) in Taiwan.Materials and MethodsThis retrospective cohort study was based on the Taiwan National Health Insurance database. The study population comprised patients with DM, and those who first used SGLT‐2is during 2016–2018 were assigned to the study group. Greedy propensity score matching was performed to select patients who first used dipeptidyl peptidase 4 inhibitors (DPP‐4is), and these patients were assigned to the control group. A Cox proportional hazards model was used to estimate the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for cancer risk in the study and control groups; this model was adjusted for demographic characteristics, DM severity, comorbidities and concomitant medication use.ResultsAfter controlling for relevant variables, the SGLT‐2i cohort (aHR = 0.90, 95% CI = 0.87–0.93) had a significantly lower risk of developing cancer than the DPP‐4i cohort, particularly when the SGLT‐2i was dapagliflozin (aHR = 0.91, 95% CI = 0.87–0.95) or empagliflozin (aHR = 0.90, 95% CI = 0.86–0.94). Regarding cancer type, the SGLT‐2i cohort's risk of cancer was significantly lower than that of the DPP‐4i cohort for leukaemia, oesophageal, colorectal, liver, pancreatic, lung, skin and bladder cancer.ConclusionsSGLT‐2i use was associated with a significantly lower risk of cancer than DPP‐4i use.

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“…The hexokinase family includes HK1, HK2, HK3, GCK (glucokinase, HK4), and HKDC1, which play important roles in glycolysis metabolism and cell fate. There are several reports regarding the function of HK1, HK2, and GCK in pancreatic cancer [ 64 , 65 , 66 , 67 ]. Therefore, we selected HKDC1 as the first gene for further validation.…”

Section: Discussionmentioning

confidence: 99%

Mitochondria-Related Transcriptome Characterization Associated with the Immune Microenvironment, Therapeutic Response and Survival Prediction in Pancreatic Cancer

Dong

Liu

Zhang

et al. 2023

IJMS

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(1) Background: Pancreatic cancer (PC) is one of the most lethal tumors. Mitochondrial dysfunction has been reported to be involved in cancer development; however, its role in PC has remained unclear. (2) Methods: The differentially expressed NMGs were selected between PC and normal pancreatic tissue. The NMG-related prognostic signature was established by LASSO regression. A nomogram was developed based on the 12-gene signature combined with other significant pathological features. An extensive analysis of the 12 critical NMGs was performed in multiple dimensions. The expression of some key genes was verified in our external cohort. (3) Results: Mitochondria-related transcriptome features was obviously altered in PC compared with normal pancreas tissue. The 12-NMG signature showed good performance in predicting prognosis in various cohorts. The high- and low-risk groups exhibited notable diversity in gene mutation characteristics, biological characteristics, chemotherapy response, and the tumor immune microenvironment. Critical gene expression was demonstrated in our cohort at the mRNA and protein levels and in organelle localization. (4) Conclusions: Our study analyzed the mitochondrial molecular characterization of PC, proving the crucial role of NMGs in PC development. The established NMG signature helps classify patient subtypes in terms of prognosis prediction, treatment response, immunological features, and biological function, providing a potential therapeutic strategy targeting mitochondrial transcriptome characterization.

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Hyperglycemia Enhances Immunosuppression and Aerobic Glycolysis of Pancreatic Cancer Through Upregulating Bmi1-UPF1-HK2 Pathway

Cited by 19 publications

References 67 publications

Identification and Validation of UPF1 as a Novel Prognostic Biomarker in Renal Clear Cell Carcinoma

Identification and Validation of UPF1 as a Novel Prognostic Biomarker in Renal Clear Cell Carcinoma

Comparison between sodium‐glucose cotransporter 2 inhibitors and dipeptidyl peptidase 4 inhibitors on the risk of incident cancer in patients with diabetes mellitus: A real‐world evidence study

Mitochondria-Related Transcriptome Characterization Associated with the Immune Microenvironment, Therapeutic Response and Survival Prediction in Pancreatic Cancer

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