Pancreatic cancer is notorious cancer with high mortality rates. Most PC patients have metastases at diagnosis, thus chemotherapy is the most common treatment option. Nab-paclitaxel–gemcitabine (AG) and fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) are advised for patients with good performance status, but the median overall survival remained frustrating at 8.7 and 11.1 months, respectively [13, 14]. KRAS, CDKN2A, TP53, and SMAD4 are the most frequently mutated genes in pancreatic cancer, however, none of them are currently druggable except KRAS G12C [15, 16]. A list of antiangiogenic drugs, such as the vascular endothelial growth factor (VEGF) inhibitors aflibercept and bevacizumab, have failed in clinical trials due to a lack of blood vessels in the stroma around cancer cells [17, 18]. As for immunotherapy, for pancreatic patients with a positive dMMR/MSI-H, humanized monoclonal anti-PD1 antibody pembrolizumab has been suggested as a second-line therapy .
A series of copper complexes were reported to induce apoptosis in PC, such as Cu (II) complex of ketoprofen-salicylhydrazone (FPA-306) and tolfenamic acid–Cu (II) complex [20, 21]. In addition, copper complex [CuII2CuI(L)2(Br)3] kills pancreatic cancer via nonapoptotic cell death pathways, including ferroptosis . Recently, elesclomol was shown to induce cell death via cuproptosis after inhibiting ferroptosis, necroptosis, and oxidative stress . Taken together, we suspect that some copper complexes might induce pancreatic cancer cell death via cuproptosis. Cuproptosis is a promising way to induce tumor cell death, and this research aimed to investigate cuproptosis -related lncRNAs and prognostic biomarkers in PC.
Our study identified 30 prognostic CRLs with univariate Cox regression analysis, and most of them were downregulated in tumor tissues. Then, seven prognostic CRLs filtered by LASSO Cox analysis were used to build a prognostic model. In our prognostic model, high-risk PC patients had a poor prognosis and lower expressions of these prognostic CRLs, which was validated in our test group. The AUC values of the model were greater than 0.7 at 1, 2, and 3 years. Compared with other clinical risk factors, our model showed better prediction performance. The risk score was closely related to the prognosis of PC patients by univariate and multivariate Cox regression analyses. Next, we verified our prognostic model can be used in PC groups with specific clinicopathological characteristics, such as female/male groups, T1-2/T3-4 stage groups, N0/N1-3 stage groups, and different tumor site groups. In addition, we constructed a nomogram that combined clinical prognostic characteristics to predict 1-, 2- and 3-year survival.
Then, we investigated the tumor microenvironment in our risk model by CIBERSORT and ESTIMATE. The risk score was found to have a negative correlation with CD8 + T cells, and a positive correlation with M0 and M2 macrophages. Patients in the low-risk group had a higher fraction of CD8 + T cells, naïve B cells, and plasma cells and a higher immune score than that in the high-risk group. Consistent with previous research, the microenvironment of pancreatic cancer is immunosuppressive, enriched with myeloid-derived suppressor cells (MDSC), tumor-associated macrophages, and Tregs . M1 macrophages exhibit pro-inflammation, while M2 macrophages suppress immunity and promote tumor growth and angiogenesis . Tregs act as anti-tumor immunity in PC, suppressing dendritic cells and CD8 + T cells. Indeed, low CD8 + T cells and high macrophages and Tregs in pancreatic cancer were correlated with poor survival [26, 27]. Patients with high-risk scores were more likely to have cold tumors which deficient in T cells but rich in tumor-associated macrophages (TAMs). Cold tumors are more insensitive to immune checkpoint inhibitors due to low immunogenicity than hot tumors [28, 29]. In our study, there was no statistical difference in the stroma score between the risk groups, but the tumor purity was higher in the high-risk group than in the low-risk group.
Next, we performed to further explore possible regulatory pathways. DEGs between the two risk groups were enriched in endocrine and metabolic-related pathways. DEGs enriched in insulin and other peptide hormone signaling pathways in the GO analysis. Diabetes is verified as a risk factor for pancreatic cancer in previous studies . Insulin/insulin-like growth factor 1(IGF-1) receptors and G protein-coupled receptors (GPCR) signaling systems regulate the proliferation of pancreatic cancer and chemoresistance [31, 32]. Metformin can inhibit the insulin-GPCR crosstalk and decrease the risk of pancreatic cancer . DEGs enriched in several metabolic signaling pathways in KEGG enrichment analysis. Calcium signaling which is correlated with gene transcription and cell proliferation regulates early pancreatic carcinogenesis [33–35]. Cancer metabolism alters the immune microenvironment to immunosuppressive status to promote tumor progression. Increased glycolysis and lactate production in tumor cells leads to immunosuppression of the tumor microenvironment, manifested by increased M2 macrophage polarization, increased Tregs, and decreased CD8 + T cells [36–38]. Meanwhile, TAMs secreted CCL18 promoting the Warburg effect in pancreatic cancer . Pancreatic cancer cells are in a relatively hypoxic environment due to dense stroma and low perfusion. Kras G12D mutation is critical for the regulation of glucose metabolism in pancreatic cancer . However, cuproptosis relies on mitochondrial metabolism and the tricarboxylic acid cycle. The intracellular copper buildup causes mitochondrial lipoylated proteins to aggregate and Fe-S cluster proteins to destabilize, resulting in cell death. Glycolysis-dominated tumor cells are less susceptible to cuproptosis . Inhibition of tumor cell glycolysis may increase sensitivity to copper ionophore therapy.
Above all, we recognized seven prognostic cuproptosis-related lncRNAs and provided a new prognostic model for PC patients that predicts overall survival. We verified that cuproptosis was related to tumor metabolism and the immune microenvironment. Cuproptosis is a newly discovered way of cell death with many unknown mechanisms waiting to be explored. We can induce cuproptosis using copper ionophores in cuproptosis-sensitive tumors. Even we can alter TME through cuproptosis, making it easier for anti-tumor drugs to enter tumor cells.
Our study has some limitations. First, we only used TCGA datasets to construct and validate our prognostic model. We are carrying out experiments to explore the role of cuproptosis regulators in PC in vivo and vitro. Second, in TCGA dataset, it lacked PC patients with metastasis. We are collecting clinical data in our institution to validate our prognostic signature. In addition, more regulators in cuproptosis need to be explored to further investigate their roles in pancreatic cancer.