Establishment of a prognostic signature for lung adenocarcinoma using cuproptosis-related lncRNAs

Yalimaimaiti, Saiyidan; Liang, Xiaoqiao; Zhao, Haili; Dou, Hong; Liu, Wei; Yang, Ying; Li, Ning

doi:10.1186/s12859-023-05192-5

Cited by 5 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thirteen long non-coding RNAs (lncRNAs) associated with cuproptosis could serve as molecular biomarkers with clinical relevance for predicting the prognosis of lung adenocarcinoma (LUAD). [122][123][124][125] Tang's article discusses a study that aimed to identify effective prognostic predictors for lung adenocarcinoma (LUAD) patients. The study used Long non-coding RNAs associated with cuproptosis (CRlncRNAs) to build a risk model that categorized individuals into groups with elevated and diminished risks.…”

Section: -110mentioning

confidence: 99%

See 1 more Smart Citation

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Sadeghi,

Kiaei,

Boush

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Adenocarcinoma of the lung is the most common type of lung cancer, and it is characterized by distinct cellular and molecular features. It occurs when abnormal lung cells multiply out of control and form a tumor in the outer region of the lungs. Adenocarcinoma of the lung is a serious and life-threatening condition that requires effective and timely management to improve the survival and quality of life of the patients. One of the challenges in this cancer treatment is finding the optimal combination of drugs that can target the genes or proteins that are involved in the disease process. Method: In this article, we propose a novel method to recommend combinations of trending drugs to target its associated proteins/genes, using a Graph Neural Network (GNN) under the RAIN protocol. The RAIN protocol is a three-step framework that consists of: 1) Applying graph neural networks to recommend drug combinations by passing messages between trending drugs for managing disease and genes that act as potential targets for disease; 2) Retrieving relevant articles with clinical trials that include those proposed drugs in previous step using Natural Language Processing (NLP); 3) Analyzing the network meta-analysis to measure the comparative efficacy of the drug combinations. Result: We applied our method to a dataset of nodes and edges that represent the network, where each node is a drug or a gene, and each edge is a p-value between them. We found that the graph neural network recommends combining Gefitinib, Paclitaxel, and Icotinib as the most effective drug combination to target this cancer associated proteins/genes. We reviewed the clinical trials and expert opinions on these medications and found that they support our claim. The network meta-analysis also confirmed the effectiveness of these drugs on associated genes. Conclusion: Our method is a novel and promising approach to recommend trending drugs combination to target cancer associated proteins/genes, using graph neural networks under the RAIN protocol. It can help clinicians and researchers to find the best treatment options for patients, and also provide insights into the underlying mechanisms of the disease.

show abstract

Section: -110mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Sadeghi,

Kiaei,

Boush

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…And AL162632.3 has the highest HR (HR = 3.49) in our model. A study has found that biomarker based on AL162632.3 may relate to immune functions such as human leukocyte antigen (HLA) and Type_II_IFN_Reponse [39]. And previous studies have demonstrated the crucial role of AL162632.3 in the cuproptosis model, ferroptosis model, and molecular chaperone model, all of which are based on LUAD samples [39][40][41].…”

Section: Plos Onementioning

confidence: 99%

Correlation between chromatin epigenetic-related lncRNA signature (CELncSig) and prognosis, immune microenvironment, and immunotherapy in non-small cell lung cancer

et al. 2023

View full text Add to dashboard Cite

Chromatin regulators drive cancer epigenetic changes, and lncRNA can play an important role in epigenetic changes as chromatin regulators. We used univariate Cox, LASSO, and multivariate Cox regression analysis to select epigenetic-associated lncRNA signatures. Twenty-five epigenetic-associated lncRNA signatures (CELncSig) were identified to establish the immune prognostic model. According to Kaplan-Meier analysis, the overall survival of the high-risk group was significantly lower than the low-risk group. Receiver operating characteristic (ROC) curves, C-index, survival curve, nomogram, and principal component analysis (PCA) were performed to validate the risk model. In GO/KEGG analysis, differentially expressed lncRNAs were correlated with the PI3K−Akt pathway, suggesting that they were highly associated with the metastasis of LUAD. Interestingly, in the immune escape analysis, the TIDE score was lower, and the possibility of immune dysfunction is also slighter in the high-risk group, which means they still have the potential to receive immunotherapy. And CELncsig is highly correlated with immune pathways T_cell_co-inhibition and Check-point. Also, the IMvigor210 cohort analysis indicated that our risk-scoring model has significant potential clinical application value in lung cancer immunotherapy. And we also screened out ten potential chemotherapy agents using the ‘pRRophetic’ package.

show abstract

“…The prognosis for patients with LUAD remains poor despite advances in prevention, detection, and treatment. Identifying the prognostic molecular markers of LUAD can provide some value for the clinical diagnosis of LUAD 3 . Studies demonstrated that the metastasis of LUAD is associated with the downregulation of GATA6 with prognostic value 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Identifying the prognostic molecular markers of LUAD can provide some value for the clinical diagnosis of LUAD. 3 Studies demonstrated that the metastasis of LUAD is associated with the downregulation of GATA6 with prognostic value. 4 EZH2 was found up-regulated expression in LUAD cells in promoting the invasiveness of tumor cells.…”

mentioning

confidence: 99%

Transcriptome characteristics of epithelial cells from advanced non‐small cell lung cancer were revealed by single‐cell RNA sequencing

Qiu,

Chen,

Dong

et al. 2023

Environmental Toxicology

View full text Add to dashboard Cite

BackgroundLung adenocarcinoma (LUAD) is the prevalent malignancy worldwide. The aim is to explore differentially expressed genes (DEGs) associated with immune infiltration and survival time of LUAD patients, and predict transcriptional factors for shedding new light on molecular mechanisms and individual therapy of LUAD.MethodScRNA‐seq data of LUAD patients was downloaded from GSE148071 and analyzed by R packages. The clustering and protein–protein interaction network were constructed for screening DEGs. Gene Set Enrichment Analysis (GSEA) and GO enrichment analysis were performed in epithelial cell subgroups with high differentiation potential. Potential regulatory transcription factors were predicted.ResultsSixteen epithelial cell types were required and top 20 genes were identified on cell subgroup Epi4 with the highest differentiation potential associated with poor prognosis of LUAD in PPI network. GSEA and GO annotation results showed that cell subgroup Epi4 was enriched in the biological processes of cell proliferation and energy metabolism, and positively regulated the function of cell proliferation. TPI1 was significantly highly expressed in LUAD samples (p < .0001). TPI1 demonstrated a negative correlation with the infiltration levels of CD8+ T cells, CD4+ T cells, B cells, and activated mast cells, whilst manifesting a positive correlation with the infiltration levels of resident mast cells, Th2 cells, and MDSC. Epi4 was regulated by transcription factors MXD3 and GATA4.ConclusionOverexpression of TPI1 was identified as a novel biomarker for LUAD, and potential regulatory transcription factors MXD3 and GATA4 regulated the proliferation of LUAD with the poor prognosis, which may serve as potential targets to suppress the proliferation of LUAD.

show abstract

Establishment of a prognostic signature for lung adenocarcinoma using cuproptosis-related lncRNAs

Cited by 5 publications

References 42 publications

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Correlation between chromatin epigenetic-related lncRNA signature (CELncSig) and prognosis, immune microenvironment, and immunotherapy in non-small cell lung cancer

Transcriptome characteristics of epithelial cells from advanced non‐small cell lung cancer were revealed by single‐cell RNA sequencing

Contact Info

Product

Resources

About