A cuproptosis-related long non-coding RNA signature to predict the prognosis and immune microenvironment characterization for lung adenocarcinoma.
Cuproptosis or copper-dependent cell death is a newly identified non-apoptotic cell death pathway which plays a critical role in the development of multiple cancers. Long non-coding RNAs (lncRNAs) are increasingly recognized as crucial regulators of programmed cell death and lung adenocarcinoma (LUAD) development, and a comprehensive understanding of cuproptosis-related lncRNAs may improve prognosis prediction of LUAD. However, few studies have explored the association of cuproptosis-related lncRNAs with the prognosis of LUAD.
The RNA sequencing data and corresponding clinical information of patients were extracted from The Cancer Genome Atlas (TCGA) database. Five hundred LUAD patients were randomly divided into a training (n=250) and a testing cohort (n=250). Pearson correlations were performed to identify cuproptosis-related lncRNAs, and univariate Cox regression was performed to screen prognostic lncRNAs. A cuproptosis-related lncRNAs prognostic signature (CLPS) was constructed by the least absolute shrinkage and selection operator Cox regression. Kaplan-Meier analysis, receiver operating characteristic curves, and multivariate Cox regression were performed to verify the prognostic performance of CLPS. Additionally, immune cell infiltration was estimated using the single-sample gene-set enrichment analysis. pRRophetic algorithm and Tumor Immune Dysfunction and Exclusion algorithm were used to assess the immunotherapy and chemotherapy response, respectively.
CLPS was established based on 61 cuproptosis-related prognostic lncRNAs and exhibited a satisfactory performance predicting LUAD patients' survival (area under the curve at 1, 3, 5 years was 0.784, 0.749, 0.775, respectively). multivariate Cox analysis confirmed the independent prognostic effect of CLPS (hazard ratio: 1.128; 95% confidence interval: 1.071-1.189; P<0.001), and a nomogram containing it exhibited robust validity in prognostic prediction. We further demonstrated a higher CLPS-risk score was associated with lower levels of signatures including immune cell infiltration, immune activation, and immune checkpoints.
The CLPS serves as an effective predictor for the prognosis and therapeutic responses of LUAD patients. Our findings provide promising novel biomarkers and therapeutic targets for LUAD.
Ma S
，Zhu J
，Wang M
，Zhu J
，Wang W
，Xiong Y
，Jiang R
，Seetharamu N
，Abrão FC
，Puthamohan VM
，Liu L
，Jiang T
... -
《-》
Establishment of a prognostic signature for lung adenocarcinoma using cuproptosis-related lncRNAs.
To establish a prognostic signature for lung adenocarcinoma (LUAD) based on cuproptosis-related long non-coding RNAs (lncRNAs), and to study the immune-related functions of LUAD.
First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate COX analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate COX analysis were performed to analyze the cuproptosis-related lncRNAs, and a prognostic signature was established. Second, univariate COX analysis and multivariate COX analysis were performed for independent prognostic analyses. Receiver operating characteristic (ROC) curves, C index, survival curve, nomogram, and principal component analysis (PCA) were performed to evaluate the results of the independent prognostic analyses. Finally, gene enrichment analyses and immune-related function analyses were also carried out.
(1) A total of 1,297 cuproptosis-related lncRNAs were screened. (2) A LUAD prognostic signature containing 13 cuproptosis-related lncRNAs was constructed (NIFK-AS1, AC026355.2, SEPSECS-AS1, AL360270.1, AC010999.2, ABCA9-AS1, AC032011.1, AL162632.3, LINC02518, LINC0059, AL031600.2, AP000346.1, AC012409.4). (3) The area under the multi-indicator ROC curves at 1, 3, and 5 years were AUC1 = 0.742, AUC2 = 0.708, and AUC3 = 0.762, respectively. The risk score of the prognostic signature could be used as an independent prognostic factor that was independent of other clinical indicators. (4) The results of gene enrichment analyses showed that 13 biomarkers were primarily related to amoebiasis, the wnt signaling pathway, hematopoietic cell lineage. The ssGSEA volcano map showed significant differences between high- and low-risk groups in immune-related functions, such as human leukocyte antigen (HLA), Type_II_IFN_Reponse, MHC_class_I, and Parainflammation (P < 0.001).
Thirteen cuproptosis-related lncRNAs may be clinical molecular biomarkers for the prognosis of LUAD.
Yalimaimaiti S
，Liang X
，Zhao H
，Dou H
，Liu W
，Yang Y
，Ning L
... -
《BMC BIOINFORMATICS》
A cuproptosis-related lncRNAs risk model to predict prognosis and guide immunotherapy for lung adenocarcinoma.
Cuproptosis, one of the newest forms of cell death induction, is attracting mounting attention. However, the role of cuproptosis in lung cancer is currently unclear. In this study, we constructed a prognostic signature utilizing cuproptosis-related long noncoding RNAs (CRL) in lung adenocarcinoma (LUAD) and researched its clinical and molecular function.
RNA-related and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed CRLs were screened using the 'limma' package of R software. We used coexpression analysis and univariate Cox analysis to further identify prognostic CRLs. Applying least absolute shrinkage and selection operator (LASSO) regression and Cox regression models, a prognostic risk model based on 16 prognostic CRLs was constructed. To validate prognostic CRL function in LUAD, vitro experiments were conducted to explore the expression of GLIS2-AS1, LINC01230, and LINC00592 in LUAD. Subsequently, according to a formula, patients in the training, test, and overall groups were split into high- and low-risk groups. Kaplan-Meier and receiver operating characteristic (ROC) analyses were applied to assess the predictability of the risk model. Finally, the associations between risk signature and immunity-related analysis, somatic mutation, principal component analysis (PCA), enriched molecular pathways, and drug sensitivity was investigated.
A cuproptosis-related long noncoding RNAs (lncRNAs) signature was constructed. Using quantitative polymerase chain reaction (qPCR) trial, we verified that the expressions of GLIS2-AS1, LINC01230, and LINC00592 in LUAD cell lines and tissues were consistent with the above screening results. Based on this signature, a total of 471 LUAD samples from TCGA data set were split into two risk groups based on the computed risk score. The risk model showed a better capacity in predicting prognosis than traditional clinicopathological features. Moreover, significant differences were found in immune cell infiltration, drug sensitivity, and immune checkpoint expression between the two risk groups.
The CRLs signature was shown to be a prospective biomarker to forecast prognosis in patients with LUAD and presents new insights for personalized treatment of LUAD.
Li Q
，Wang T
，Zhu J
，Zhang A
，Wu A
，Zhou Y
，Shi J
... -
《-》
Construction and validation of a cuproptosis-related lncRNA signature as a novel and robust prognostic model for colon adenocarcinoma.
Cuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods.
The COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis.
The high-risk group was associated with shorter overall survival (OS) time in COAD patients (p<0.001). Multivariate Cox regression indicated that a high-risk score was an independent risk factor for poor prognosis (p<0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 3 years: 0.879). Gene Ontology (GO) enrichment analysis revealed that the signature was highly correlated with the immune response in biological processes. The immune function, the score of the immune cells, and the expression of immune checkpoints were significantly different between the two risk groups. Three drugs, LAQ824, FH535, YM155, were found to be more sensitive in the high-risk group. Finally, the expression levels of the ten lncRNAs comprising the signature were tested by qRT-PCR.
A ten-cuproptosis-related lncRNA signature was constructed that provided promising insights into personalized prognosis and drug selection among COAD patients.
Xu M
，Mu J
，Wang J
，Zhou Q
，Wang J
... -
《Frontiers in Oncology》
ResearchGate
(全网免费下载)
钛学术
(全网免费下载)
