Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Abstract

Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.

Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research

“…In detail, once ZBP1 is activated, RIPK3 and caspase-8 are recruited to activate the NLRP3 inflammasome, which initiates both necroptosis and pyroptosis [78][79][80]. In addition, the bioinformatics analysis reported by Miao et al also indicated that the ZBP1, both of the cuproptosis-related and necroptosis-related gene signature, functions as a risk score for prediction of the low-grade glioma patients prognosis [81]. New outcomes from the research of Wei Gao and colleagues also revealed that the administration of elesclomol to CRC cells increased Cu(II) levels in mitochondria and downregulated the Fig.…”

Immune-related potential biomarkers and therapeutic targets in coronary artery disease

“…By locating distinct genetic biomarkers of CAD using the LASSO technique, which were compared by the average misjudgment rates of their 10-fold cross-validations, models of DE-IRGs between CAD patients and normal samples were created (22). A SVM-RFE model was created at the same time using the "SVM" package, and its average false positive rate was also compared using a 10-fold cross-validation (23). To create OFGs for CAD, genes from both algorithms were overlapped.…”

Systematic Analysis of a Pyroptosis-Related Signature to Predict the Prognosis and Immune Microenvironment of Lower-Grade Glioma

“…This work constructed a novel prognostic model using a LASSO analysis, and it included six PRGs, namely CASP4, CASP5, CASP9, GSDMC, PLCG1, and IL18. Recent studies have shown that genes related to cuproptosis [ 32 , 33 ] and ferroptosis [ 34 , 35 ] are predictive of the prognosis of LGG patients, and corresponding prognostic models have been constructed. Compared with them, our model has good 1-, 3-, 5- year AUC values ( Supplementary Table S1 ).…”

“…The following supporting information can be downloaded at: , Figure S1: Cox proportional hazards (PH) test before constructing the nomogram; Table S1: Comparison of AUC values of different prognostic models based on TCGA-LGG data. References [ 32 , 33 , 34 , 35 ] are cited in Supplementary Materials…”