A novel prognostic signature for lung adenocarcinoma based on cuproptosis-related lncRNAs: A Review

Abstract

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease with complex pathogenesis, high mortality, and poor prognosis. Cuproptosis is a new type of programmed cell death triggered by copper accumulation that may play an important role in cancer. LncRNAs are becoming valuable prognostic factors in cancer patients. The effect of cuproptosis-related lncRNAs (CRlncRNAs) on LUAD has not been clarified. Based on the Cancer Genome Atlas database, CRlncRNAs were screened by co-expression analysis of cuproptosis-related genes and lncRNAs. Using CRlncRNAs, Cox and LASSO regression analyses constructed a risk prognostic model. The predictive efficacy of the model was assessed and validated using survival analysis, receiver operating characteristic curve, univariate and multifactor Cox regression analysis, and principal component analysis. A nomogram was constructed and calibration curves were applied to enhance the predictive efficacy of the model. Tumor Mutational Burden analysis and chemotherapeutic drug sensitivity prediction were performed to assess the clinical feasibility of the risk model. The novel prognostic signature consisted of 5 potentially high-risk CRlncRNAs, MAP3K20-AS1, CRIM1-DT, AC006213.3, AC008035.1, and NR2F2-AS1, and 5 potentially protective CRlncRNAs, AC090948.1, AL356481.1, AC011477.2, AL031600.2, and AC026355.2, which had accurate and robust predictive power for LUAD patients. Collectively, the novel prognostic signature constructed based on CRlncRNAs can effectively assess and predict the prognosis of patients and provide a new perspective for the diagnosis and treatment of LUAD.

Crosstalk between copper homeostasis and cuproptosis reveals a lncRNA signature to prognosis prediction, immunotherapy personalization, and agent selection for patients with lung adenocarcinoma

“…However, Wei Ye et al and Ran Chen et al ‘s study did not provide the coefficient details of their signatures, which were excluded from our choices. Finally, nine studies [ 37 – 40 , 42 , 43 , 45 – 47 ] were listed as candidates for comparison with our signature ( Table 3 ). To compare previous signatures with ours, we performed Cox regression analysis for overall, disease-specific, and progression-free survival using four formats of official TCGA data ( Figure 9 ), respectively.…”

“…Considerable effort has been invested in validating the accuracy of our model, including a comparative analysis with previous studies. To accomplish this, we gathered all published studies [ 37 – 40 , 42 , 43 , 45 – 47 ] that were similar to our research and evaluated their performance, along with our model, on the official TCGA data ( Figure 9 ). By constructing Cox models and analyzing three different outcome endpoint data sets, namely OS, DSS, and PFS, we determined that our model generally outperformed previously published models.…”

“…In order to conclude whether our study is more robust than previous, we searched PubMed and initially eleven studies [37][38][39][40][41][42][43][44][45][46][47] were found (Table 3). However, Wei Ye et al and Ran Chen et al 's study did not provide the coefficient details of their signatures, which were excluded from our choices.…”

Characterization and Prognosis of Biological Microenvironment in Lung Adenocarcinoma through a Disulfidptosis-Related lncRNAs Signature

“…LncRNAs have been implicated in cancer in some cases. For example, the lncRNA AC090948.1 was found to be related to lipid metabolism, cuproptosis, and immunity in cancers [25][26][27]. Hu et al noticed that AC020765.2 is related to autophagy in lung cancer [28].…”

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi‐omics consensus machine learning approach

“…LUAD, recognized for its complexity, is significantly influenced by a myriad of oncogenes, cytokines and chemokines 6–8 . The complexity of LUAD is amplified by the tumour microenvironment's (TME) heterogeneity, affecting treatment, progression and response to therapies 9,10 . Understanding the TME's complex interactions is vital for creating targeted and effective treatment strategies for LUAD's multifaceted nature.…”