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Cuproptosis-associated lncRNA impact prognosis in patients with non-small cell lung cancer co-infected with COVID-19.

ABSTRACT: Non-small cell lung cancer (NSCLC) patients infected with COVID-19 experience much worse prognosis. However, the specific mechanisms behind this phenomenon remain unclear. We conducted a multicentre study, collecting surgical tissue samples from a total of 36 NSCLC patients across three centres to analyse. Among the 36 lung cancer patients, 9 were infected with COVID-19. COVID-19 infection (HR = 21.62 [1.58, 296.06], p = 0.021) was an independent risk factor of progression-free survival (PFS). Analysis of RNA-seq data of these cancer tissues demonstrated significantly higher expression levels of cuproptosis-associated genes in COVID-19-infected lung cancer patients. Using Lasso regression and Cox regression analysis, we identified 12 long noncoding RNAs (lncRNA) regulating cuproptosis. A score based on these lncRNA were used to divide patients into high-risk and low-risk groups. The results showed that the high-risk group had lower overall survival and PFS compared to the low-risk group. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) database revealed that the high-risk group benefited more from immunotherapy. Drug sensitivity analysis identified cetuximab and gefitinib as potentially effective treatments for the high-risk group. Cuproptosis plays a significant role NSCLC patients infected with COVID-19. Promisingly, cetuximab and gefitinib have shown potential effectiveness for managing these patients.

SUBMITTER: Li J

PROVIDER: S-EPMC11371660 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Cuproptosis-associated lncRNA impact prognosis in patients with non-small cell lung cancer co-infected with COVID-19.

Li Jing J Wang Nan N Mao Guocai G Wang Jiantang J Xiang Mengqi M Zhang Huachuan H Zeng Daxiong D Ma Haitao H Jiang Junhong J

Journal of cellular and molecular medicine 20240901 17

Non-small cell lung cancer (NSCLC) patients infected with COVID-19 experience much worse prognosis. However, the specific mechanisms behind this phenomenon remain unclear. We conducted a multicentre study, collecting surgical tissue samples from a total of 36 NSCLC patients across three centres to analyse. Among the 36 lung cancer patients, 9 were infected with COVID-19. COVID-19 infection (HR = 21.62 [1.58, 296.06], p = 0.021) was an independent risk factor of progression-free survival (PFS). A ...[more]

PMID: 39228012

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Project description:A new treatment modality targeting cuproptosis is gradually entering the public horizon. Cuproptosis is a new form of regulated cell death distinct from ferroptosis, apoptosis, autophagy, and necrosis. Previous studies have discovered that the copper level varies considerably in various cancers and that an increase in copper content is directly associated with the proliferation and metastasis of cancer cells. In non-small cell lung cancer (NSCLC) after radiation, the potential utility of cuproptosis-related long noncoding RNAs (lncRNAs) is still unclear. This research aimed to develop a prediction signature based on lncRNAs associated with cuproptosis to predict the prognosis of NSCLC patients following radiation. Methods: Expression data of primary tumors and adjacent solid tissues were downloaded from The Cancer Genome Atlas (TCGA) database, along with the corresponding clinical and mutational data. Univariate and multivariate COX analyses and LASSO regression analyses were performed to obtain a predictive signature of lncRNAs associated with cuproptosis. The data were randomly grouped into a training group used for model construction and a test group used for model validation. The model was validated by drawing a survival curve, risk curve, independent prognostic analysis, ROC curve PFS analysis, etc. Results: The lncRNA signature consisting of six cuproptosis-related lncRNAs (AC104088.1, PPP4R3B-DT, AC006042.3, LUCAT1, HHLA3-AS1, and LINC02029) was used to predict the prognosis of patients. Among them, there were three high-risk lncRNAs (LUCAT1, HHLA3-AS1, and LINC02029) with HR > 1 and three protective lncRNAs (AC104088.1, PPP4R3B-DT, and AC006042.3), with an HR < 1. Data analysis demonstrated that the cuproptosis-related lncRNA signatures could well predict the prognosis of NSCLC patients after radiation. Patients in the high-risk category receive a worse prognosis than those in the low-risk group. Cuproptosis-related risk prediction demonstrated better predictive qualities than age, gender, and pathological stage factors. Conclusion: The risk proposed model can independently predict the prognosis of NSCLC patients after radiotherapy, provide a foundation for the role of cuproptosis-related lncRNAs in NSCLC after radiotherapy, and provide a clinical strategy for radiotherapy combined with cuproptosis in NSCLC patients.

Project description:BackgroundLung adenocarcinoma (LUAD) accounts for 50% of lung cancers, with high mortality and poor prognosis. Long non-coding RNA (lncRNA) plays a vital role in the progression of tumors. Cuproptosis is a newly discovered form of cell death that is highly investigated. Therefore, in the present study, we aimed to investigate the role of cuproptosis-related lncRNA signature in clinical prognosis prediction and immunotherapy and the relationship with drug sensitivity.Material and methodsGenomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and cuproptosis-related genes were obtained from cuproptosis-related studies. The prognostic signature was constructed by co-expression analysis and Cox regression analysis. Patients were divided into high and low risk groups, and then, a further series of model validations were carried out to assess the prognostic value of the signature. Subsequently, lncRNAs were analyzed for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes Enrichment (KEGG), immune-related functions, and tumor mutation burden (TMB). Finally, we used tumor immune dysfunction and exclusion (TIDE) algorithms on immune escape and immunotherapy of cuproptosis-related lncRNAs, thereby identifying its sensitivity toward potential drugs for LUAD.ResultsA total of 16 cuproptosis-related lncRNAs were obtained, and a prognostic signature was developed. We found that high-risk patients had worse overall survival (OS) and progression-free survival (PFS) and higher mortality. Independent prognostic analyses, ROC, C-index, and nomogram showed that the cuproptosis-related lncRNAs can accurately predict the prognosis of patients. The nomogram and heatmap showed a distinct distribution of the high- and low-risk cuproptosis-related lncRNAs. Enrichment analysis showed that the biological functions of lncRNAs are associated with tumor development. We also found that immune-related functions, such as antiviral activity, were suppressed in high-risk patients who had mutations in oncogenes. OS was poorer in patients with high TMB. TIDE algorithms showed that high-risk patients have a greater potential for immune escape and less effective immunotherapy.ConclusionTo conclude, the 16 cuproptosis-related lncRNAs can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Project description:Background Cuproptosis, a recently discovered type of programmed cell death (PCD), paves a new avenue for cancer treatment. It has been revealed that PCD-related lncRNAs play a critical role in various biological processes of lung adenocarcinoma (LUAD). However, the role of cuproptosis-related lncRNA (CuRLs) remains unclear. This study aimed to identify and validate a CuRLs-based signature for the prognostic prediction of patients with LUAD. Methods RNA sequencing data and clinical information of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Pearson correlation analysis was used to identify CuRLs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, and stepwise multivariate Cox analysis were applied to construct a novel prognostic CuRLs signature. A nomogram was developed for the prediction of patient survival outcomes. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to explore potential functions underlying the CuRLs signature. Patients were divided into low- and high-risk groups. Several algorithms, such as tumor immune estimation resource (TIMER), cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and QuanTIseq, were combined to comprehensively investigate the differences in immune landscape between different risk groups. Sensitivity to common anticancer drugs was analyzed using the pRRophetic algorithm. Results We constructed a novel prognostic signature based on 10 CuRLs, including CARD8-AS1, RUNDC3A-AS1, TMPO-AS1, MIR31HG, SEPSECS-AS1, DLGAP1-AS1, LINC01137, ZSCAN16-AS1, APTR, and ELOA-AS1. This 10-CuRLs risk signature showed great diagnostic accuracy combined with traditional clinical risk factors, and a nomogram was constructed for potential clinical translation. The tumor immune microenvironment was significantly different between different risk groups. Among drugs commonly used in the treatment of lung cancer, the sensitivity of cisplatin, docetaxel, gemcitabine, gefitinib, and paclitaxel was higher in low-risk patients, and patients in the low-risk group may benefit more from imatinib. Conclusions These results revealed the outstanding contribution of the CuRLs signature to the evaluation of prognosis and treatment modalities for patients with LUAD. The differences in characteristics between different risk groups provide an opportunity for better patient stratification and to explore novel drugs in different risk groups.

Project description:Cuproptosis is a newly discovered mechanism of regulated cell death, which serves as a novel target for cancer therapy. Long non-coding RNAs (lncRNAs) play an important role in the initiation and progression of cancer cells; however, the relationship between cuproptosis and lncRNAs in tumorigenesis and cancer treatment has not been well established in lung adenocarcinoma (LUAD). Thus, it is important to clarify and characterize the cuproptosis-related lncRNA landscape in LUAD. In this study, cuproptosis-related lncRNAs was screened by Pearson correlation analysis. Then, univariate, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression were conducted to identify 6 cuproptosis-related lncRNAs (AC090541.1, AC009226.1, NIFK-AS1, AC027097.2, AC026355.2, and AC106028.2) which were used to construct a cuproptosis-related lncRNA signature (CRLS). Multi-dimensional assessments including Kaplan-Meier analysis, receiver operating characteristics (ROC) curves, and principal component analysis (PCA) verified that the CRLS could reliably predict the prognosis and survival of LUAD patients. We further compared the immune cell infiltration, somatic mutation landscape, and functional enrichment pathways between the high and low CRLS groups. Patients with low CRLS scores had prolonged survival and were sensitive to immunotherapy, whereas patients with high CRLS scores might benefit better from chemotherapy. We further analyzed the individualized immunotherapeutic strategies and the candidate compounds for the potential clinical treatment. Moreover, the expression level of these 6 lncRNAs was examined experimentally in vitro by using quantitative real-time polymerase chain reaction (RT-qPCR). Additionally, one of the significantly differentially expressed lncRNAs, NIFK-AS1, was confirmed to suppress the proliferation and migration of LUAD by Cell Counting Kit-8 Assays (CCK-8), wound healing assay, and colony formation assays. Taken together, we established a CRLS that might be a promising tool for predicting the prognosis, guiding individualized treatment, and serving as a promising therapeutic target for patients with LUAD.

Project description:Lung squamous cell carcinoma has so far lacked effective targets for diagnosis and treatment. In cancer research, long noncoding RNAs (LncRNAs) emerge as novel therapeutic targets and biomarkers. Cuprophosis is a new death type involving multiple biological processes in tumor cells. Here, we aimed to explore whether Cuprophosis-related lncRNAs could be used to predict prognosis, assess immune function, and test drug sensitivity in LUSC patients. The Cancer Genome Map (TCGA) was used to obtain genome and clinical data, and Cuprophosis-relevant genes were found in the literature. A cuproptosis-related lncRNA risk model was built using co-expression analysis, univariate/multivariate Cox regression, and LASSO analysis. The survival analysis was used to assess the model's prognostic value. The univariate and multivariate Cox regression analyses were performed to determine whether risk score, age, gender, or clinical stages could be used as independent prognostic factors. Gene Set Enrichment Analysis and mutation analysis were performed on differentially expressed mRNA between high-risk and low-risk groups. The (TIDE) algorithm was used to conduct immunological functional analysis and drug sensitivity testing. Five cuproptosis-related LncRNAs were identified, and the selected LncRNAs constructed a prognosis model. According to the Kaplan-Meier survival analysis, the overall survival time for patients in the high-risk group was shorter than for those in the low-risk group. For LUSC patients, the risk score serves as an independent prognostic indicator. The GO and KEGG enrichment analysis revealed that the differentially expressed mRNAs between the high- and low-risk groups were enriched in several immune-related processes. The enrichment score of differentially expressed mRNAs in the high-risk group is higher than that of the low-risk group in multiple immune function pathways, including the IFN-γ and MHC I pathways. The Tumor Immune Dysfunction and Exclusion (TIDE) test revealed that the high-risk group was more likely to experience immune escape. The drug sensitivity analysis showed that patients with low-risk ratings were likely to respond to GW441756 and Salubrinal. In contrast, patients with higher risk scores were more responsive to dasatinib and Z-LLNIe CHO. The 5-Cuprophosis-related lncRNA signature can be used to predict prognosis, assess immune function, and test drug sensitivity in LUSC patients.

Dataset Information

Cuproptosis-associated lncRNA impact prognosis in patients with non-small cell lung cancer co-infected with COVID-19.

Publications

Cuproptosis-associated lncRNA impact prognosis in patients with non-small cell lung cancer co-infected with COVID-19.

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