Despite being the most prevalent pediatric renal cancer, WT or nephroblastoma can be treated based on the standard treatment procedure provided in the International Society of Pediatric Nephrology guidelines, which consists of preoperative chemotherapy, surgery, and postoperative treatment. However, 15% of children still suffer from relapses, most of which occurred within 2 years of the original diagnosis. Moreover, the survival rate for recurrence was 50%, which mainly depended on certain high-risk factors, such as bilateral WT, disease types with unfavorable prognosis, tumor stage, and previous treatment (Brok et al. 2016; Malogolowkin et al. 2013; Brok et al. 2018; Spreafico et al 2009; Pritchard-Jones et al. 2012). Therefore, identifying prospective prognostic and molecular characteristics specific to patients with of WT is critical to improving patient outcomes.
As a micro-element in the human body, copper promotes the function of various copper-based enzymes, like superoxide dismutase 1 (Cu/Zn-SOD), ceruloplasmin (CP), cytochrome oxidase (Cox), and angiopoietin (Tapiero et al. 2003). The active homeostasis mechanism keeps copper ion levels in the cells at extremely low levels. When concentrated at the cellular level, copper plays an essential role in mammalian physiological activities, such as iron absorption, redox chemistry, free radical scavenging, mitochondrial respiration, and elastin cross-linking. Once above a certain threshold, copper becomes toxic and causes cell death (Kim et al. 2008). Cuproptosis was identified for the first time by Todd R. Golub (Tsvetkov et al 2022), who revealed the role of copper metabolism and mitochondrial imbalances in cell death. However, the definite role of cuproptosis in WT remains unclear. Therefore, screening of genes associated with cuproptosis and identifying biomarkers that could predict responses to immunotherapy and chemotherapy are promising strategies for improving therapeutic effectiveness and reducing mortality in WT.
The numerous lncRNAs produced by human cells play significant roles in several biological processes, including genome expression and cell differentiation (Liu et al. 2021). Previous studies have suggested that irregular lncRNA expression may facilitate cancer development and evolution (Meng et al. 2022; Da et al. 2021). Nonetheless, the current study has been the first full-scale work to investigate the role of lncRNAs associated with cuproptosis in WT. In line with this, we detected eight cuproptosis-related lncRNAs (IRAG1-AS1, AF064860.2, AC233266.2, LINC01063, AC084781.2, LINC01518, UNC5B-AS1, and AL050404.1) via LASSO regression and multivariate Cox regression analysis. In the lncRNA model related to cuproptosis, UNC5B-AS1 was found to be a risk factor for poor osteosarcoma prognosis (Yang et al. 2022), which enhanced liver cancer cell migration, growth, and epithelial interstitial transformation (Huang et al. 2021) and was highly expressed in papillary thyroid carcinoma (Kim et al. 2022). Moreover, LINC01063 was identified as an oncogene in melanoma (Xu et al. 2022) and an autophagy-associated lncRNA that could predict the prognosis of colorectal tumor (Duan et al. 2022), whereas LINC01518 was shown to be an endogenous RNA that competes in esophageal squamous cell cancer (Zhang et al. 2019). So far, no detailed investigations have been available on the remaining five lncRNAs related to cuproptosis. Through the implementation of a risk score, the current study was able to categorize individuals into low- and high-risk groups. Multivariate and univariate Cox regression analyses were then performed to confirm whether the risk score calculated from the genetic trait model was an independent predictor of prognosis. Thereafter, their relationship between the cuproptosis-related lncRNAs and OS also investigated. After establishing the first novel cuproptosis-related prospective model, we determined that the risk score was an accurate indicator of patient prognosis and OS. Based on the ROC curve, we found that cuproptosis-related lncRNAs had sufficient predictive utility in evaluating the OS of patients with WT, with a diagnostic performance superior to that of ferroptosis-related lncRNAs (Liu et al. 2021). In addition, the model was evaluated using nomogram risk assessment of this prognostic risk score. The use of our nomogram may allow physicians to implement WT monitoring that it more specifically tailored to the patients, which could lead to better outcomes.
TMB is another factor known to be significantly correlated with immunotherapy. One study showed that high TMB scores reflected favorable immunotherapy outcomes in multiple tumor cases (Liu et al. 2022). However, our findings showed no remarkable variation in TMB between the high- and low-risk groups. This could possibly be attributed to the small sample size of the aforementioned studies. Interestingly, when TMB was integrated with risk factors, we found that individuals in the elevated TMB and high-risk group had the worst survival, suggesting that risk factors had a crucial impact on patient survival. Another study showed that TIDE was a better method for modeling tumor immune escape and that Dysfunction and Exclusion could effectively predict the immunotherapeutic impact of malignancies (Sha et al. 2022). The current study has been the first to compare TIDE scores between low- and high-risk groups. The unexpected inverse relationship between risk scores and TIDE scores further revealed that high-risk individuals might benefit more from immunotherapy.
Evidence shows that copper plays a crucial role in regulating immunity (Culbertson and Culotta 2021). Cell infiltration may improve our empirical comprehension on the outcomes of antitumor treatment in WT and, consequently, direct more efficient immunotherapy techniques to discover the function of cuproptosis in the tumor microenvironment (TME). In this investigation, the low-risk group demonstrated increased infiltration of immune cells, such as CD8 + T cells, adipocytes, basophils, Tgd cells, Th1 cells, cancer-associated fibroblasts, and activated dendritic cells, compared to the high-risk group, indicating a stronger anti-cancer immune response. As malignancy inhibitors, CD8 + T cells in the adaptive immune mechanism are the mainstay of malignancy immunotherapy and most predominant effector in the anti-cancer immune reaction (Raskov et al. 2021; St Paul and Ohashi 2020), whereas Th1 cells secrete interleukin two and interferon gamma, promoting the CD8 + T cell, the natural proliferation and activation of killer cells, and macrophages.
Hence, high concentrations of Th1 cells in the cancer microenvironment also promote a favorable prognosis (Fridman et al. 2012). Our findings showed that the high-risk group exhibited elevated levels of NKT, CD4 + T cells, Th2 cells, B cells, and M1 macrophages. Evidence shows that B cells can play an anticancer and cancer-promoting role at the same time in the TME, which indicated that B cells were heterogeneous (Sharonov et al. 2020). Several tumors in which NK cells were enriched, like gastric, colorectal, pulmonary, hepatic, and urinary system cancers, frequently demonstrated better prognosis. However, the lethality of NK cells was often limited in the TME (Terrén et al. 2019). The current study also supports this conclusion. Indeed, we concluded that the detection of TMB, TIDE, and TME status might be a more effective strategy for predicting response to immunotherapy.
The IC50 curves of the 13 routinely prescribed anticancer drugs were determined to predict their chemotherapeutic effects. Among them, the IC50 value of BI-2536, EX-527, Ispinesib Mesylate, KIN001-135, NSC-207895, Phenformin, Ruxolitinib, SB590885, SNX-2112, TAK-715, TW 37, Zibotentan, and Z-LLNle-CHO were found to be relatively higher in the low-risk group than in the high-risk group. Noticeable distinctions in drug sensitivity were observed between the two risk groups. Patients with specific identified conditions should receive drugs to which they are highly sensitivity.
Nevertheless, this study has some limitations. First, all analyses in this study were based on only one public database. Given that no other database contained relevant medical information and lncRNA expression, the model had to be verified in a single database. Furthermore, additional in vivo and in vitro trial investigations are required to validate the newly created risk score model. Third, aside from the statistical evidence we have provided, additional biological proof is also needed given that the prognostic features were created and verified using data from open databases.