Cuproptosis-Related lncRNA Prognostic Profile, Immune microenvironment, Response and Immune-related gene prognostic index in LUAD

Background: Cuproptosis as a new regulatory cell death, distinct from apoptosis, ferroptosis and necroptosis, which induces proteotoxic stress，also related to tumorigenesis and advance [1] . Long chain non coding RNA (lncRNA) refers to RNA that does not have protein coding function and has been proven to regulate transcription, epigenetic modification, translation, and post translational modification, playing an important regulatory role in tumors. Our research aims to construct a prognosis profile based on Cuproptosis-related lncRNA(Cupr-RLs) to forecast the prognosis of lung adenocarcinoma (LUAD) ，investigate immunotherapy and immune-related prognosis of LUAD. Methods: : RNA sequencing and data of LUAD were downloaded from the Cancer Genome Atlas (TCGA) （GDC (cancer.gov)）. patients (Repository (cancer.gov)）were randomly assigned to training and validation cohort. Cox regression and Least Absolute Shrinkage Selection Operator (LASSO) were used to construct prediction model(validated by integrated approach). Biological functions were investigated through GO, KEGG, and immunoassay. Immunotherapeutic measured by tumor mutation burden (TMB) and tumor immune dysfunction and rejection response (TIDE) scores. Then established and validated prognostic markers for LUAD, and analyzed signature of immune landscape and immunotherapy response. We developed immune-related genetic prognostic index (IRGPI) and analyzed it in LUAD. Results: : The prognostic Signature is based on 9 Cupr-RLs, including AC011773.3, AC084871.3, AC097505.1, AC145285.2, AL031985.3, AL133304.3, AP003721.1, C5orf66-AS1, and DUBR. Kaplan-Meier and ROC curves indicate the signature predictive validity. Divided high- and low-risk groups based on the median risk score. Univariate(Uni-)and multivariate(multi-) Cox analysis displayed risk score was an independent prognostic factor. And the lncRNA model associated with cuproptosis has higher diagnostic efficiency. The clear distribution of high-risk and low-risk with Cupr-RLs was shown in the nomogram and heatmap. Enrichment analysis showed the biological functions of Cupr-RLs were related to tumor development. Patients with high TMB and low risk had more effectiveness of immunotherapy, and we found low-risk group has better immune therapies response. The TIDE algorithm identify high-risk patients would immune escape more easily, and Immunotherapy with poor efficacy. Analysis of the multi-omics data suggests that patients with high IRGPI are characterized by active immune responses and less aggressive tumor phenotypes, have longer overall survival times, and can benefit more from immune checkpoint inhibitor (ICI) therapy. We also found a significant correlation between the Cupr- RLs risk and drug sensitivity, and further hypothesized Cupr-RLs may correlate with IRGPI in LUAD. Conclusion: The 9 Cupr-RLs may be useful biomarker in assessing the prognosis of LUAD and It also elucidates the immune landscape of LUAD and provides reference for further exploration of immunotherapy for LUAD.