A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma.
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ABSTRACT:
SUBMITTER: Zhang G
PROVIDER: S-EPMC9256635 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cuproptosis is a novel cell death modality but its regulatory role in the colon cancer remains obscure. This study is committed to establishing a cuproptosis-related lncRNA (CRL) signature to forecast the prognosis for colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) samples were randomly divided into training and validation cohorts. LASSO-COX analysis was performed to construct a prognostic signature consisting of five CRLs (AC015712.2, ZEB1-AS1, SNHG26, AP001619.1, and ZKSCAN2-DT). We found the patients with high-risk scores suffered from poor prognosis in training cohort (p < 0.001) and validation cohort (p = 0.004). Nomogram was created based on the 5-CRL signature. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) demonstrated the nomogram performed well in 1‑, 3‑, and 5‑year overall survival (OS). Subsequently, we observed increased infiltration of multiple immune cells and upregulated expression of immune checkpoints and RNA methylation modification genes in high-risk patients. Additionally, gene set enrichment analysis (GSEA) revealed two tumor-related pathways, including MAPK and Wnt signaling pathways. Finally, we found AKT inhibitors, all-trans retinoic acid (ATRA), camptothecin, and thapsigargin had more sensitivity to antitumor therapy in high-risk patients. Collectively, this CRL signature is promising for the prognostic prediction and precise therapy of COAD.
| S-EPMC10113217 | biostudies-literature
Project description:Background: Cuproptosis is a novel type of regulated cell death and is reported to promote tumor occurrence and progression. However, whether a cuproptosis-related signature has an impact on hepatocellular carcinoma (HCC) is still unclear. Materials and methods: We analyzed the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, and searched for tumor types with different cuproptosis patterns through consistent clustering of cuproptosis genes. We then constructed a Cuproptosis-Related Genes (CRGs)-based risk signature through LASSO COX regression, and further analyzed its impact on the prognosis, clinical characteristics, immune cell infiltration, and drug sensitivity of HCC. Results: We identified the expression changes of 10 cuproptosis-related genes in HCC, and all the patients can be divided into two subtypes with different prognosis by applying the consensus clustering algorithm. We then constructed a cuproptosis-related risk signature and identified five CRGs, which were highly correlated with prognosis and representative of this gene set, namely G6PD, PRR11, KIF20A, EZH2, and CDCA8. Patients in the low CRGs signature group had a favorable prognosis. We further validated the CRGs signature in ICGC cohorts and got consistent results. Besides, we also discovered that the CRGs signature was significantly associated with a variety of clinical characteristics, different immune landscapes and drug sensitivity. Moreover, we explored that the high CRGs signature group was more sensitive to immunotherapy. Conclusion: Our integrative analysis demonstrated the potential molecular signature and clinical applications of CRGs in HCC. The model based on CRGs can precisely predict the survival outcomes of HCC, and help better guide risk stratification and treatment strategy for HCC patients.
| S-EPMC9947158 | biostudies-literature
Project description:The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement of specific expression level seems to be valuable in predicting the survival of patients with hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from The Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, we modified Lasso penalized regression. Then, we compared the areas under curve, counted the Akaike information criterion (AIC) values of 5-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high- or low-disease-risk groups among patients with HCC. We then reevaluated them from the viewpoints of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. 36 DEirlncRNA pairs were identified, 12 of which were included in a Cox regression model. After regrouping the patients by the cut-off point, we could more effectively differentiate between them based on unfavorable survival outcome, aggressive clinic-pathological characteristics, specific tumor immune infiltration status, low chemotherapeutics sensitivity, and highly expressed immunosuppressed biomarkers. The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value.
| S-EPMC7670249 | biostudies-literature
Project description:Worldwide, hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. However, the survival rate of patients with HCC continues to be poor. The recent literature has revealed that long non-coding RNAs (lncRNAs) and the occurrence of pyroptosis can perform a substantial task in predicting the prognosis of the respective condition along with the response to immunotherapy among HCC patients. Thus, screening and identifying lncRNAs corelated with pyroptosis in HCC patients are critical. In the current study, pyroptosis-related lncRNAs (PR-lncRNAs) have been obtained by co-expression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) and univariate and multivariate Cox regression assessments have been performed to develop a PR-lncRNA prognostic model. The risk model was analysed using Kaplan-Meier analysis, principal component analysis (PCA), functional enrichment annotation, and a nomogram. The risk model composed of five PR-lncRNAs was identified as an independent prognostic factor. The tumour immune microenvironment (TIME) was assessed using model groupings. Finally, we validated the five PR-lncRNAs in vitro using a quantitative real-time polymerase chain reaction (qRT-PCR).
| S-EPMC9263208 | biostudies-literature
Project description:IntroductionThe incidence of head and neck squamous cell carcinoma (HNSCC), one of the most prevalent tumors, is increasing rapidly worldwide. Cuproptosis, as a new copper-dependent cell death form, was proposed recently. However, the prognosis value and immune effects of cuproptosis-related lncRNAs (CRLs) have not yet been elucidated in HNSCC.MethodsIn the current study, the expression pattern, differential profile, clinical correlation, DNA methylation, functional enrichment, univariate prognosis factor, and the immune effects of CRLs were analyzed. A four-CRL signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm.ResultsResults showed that 20 CRLs had significant effects on the stage progression of HNSCC. Sixteen CRLs were tightly correlated with the overall survival (OS) of HNSCC patients. Particularly, lnc-FGF3-4 as a single risk factor was upregulated in HNSCC tissues and negatively impacted the prognosis of HNSCC. DNA methylation probes of cg02278768 (MIR9-3HG), cg07312099 (ASAH1-AS1), and cg16867777 (TIAM1-AS1) were also correlated with the prognosis of HNSCC. The four-CRL signature that included MAP4K3-DT, lnc-TCEA3-1, MIR9-3HG, and CDKN2A-DT had a significantly negative effect on the activation of T cells follicular helper and OS probability of HNSCC. Functional analysis revealed that cell cycle, DNA replication, and p53 signal pathways were enriched.DiscussionA novel CRL-related signature has the potential of prognosis prediction in HNSCC. Targeting CRLs may be a promising therapeutic strategy for HNSCC.
| S-EPMC10394648 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs), which were implicated in many pathophysiological processes including cancer, were frequently dysregulated in hepatocellular carcinoma (HCC). Studies have demonstrated that ferroptosis and immunity can regulate the biological behaviors of tumors. Therefore, biomarkers that combined ferroptosis, immunity, and lncRNA can be a promising candidate bioindicator in clinical therapy of cancers. Many bioinformatics methods, including Pearson correlation analysis, univariate Cox proportional hazard regression analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis were applied to develop a prognostic risk signature of immune- and ferroptosis-related lncRNA (IFLSig). Finally, eight immune- and ferroptosis-related lncRNAs (IFLncRNA) were identified to develop and IFLSig of HCC patients. We found the prognosis of patients with high IFLSig will be worse, while the prognosis of patients with low IFLSig will be better. The results provide an efficient method of uniting critical clinical information with immunological characteristics, enabling estimation of the overall survival (OS). Such an integrative prognostic model with high predictive power would have a notable impact and utility in prognosis prediction and individualized treatment strategies.
| S-EPMC8655914 | biostudies-literature
Project description:AbstractAberrant immunity has been associated with the initiation and progression of cancers such as hepatocellular carcinoma (HCC). Here, we aim to develop a signature based on immune-related genes (IRGs) to predict the prognosis of HCC patients. The gene expression profiles of 891 HCC samples were derived from 4 publicly accessible datasets. A total of 1534 IRGs from Immunology Database and Analysis Portal website were obtained as candidate genes for prognostic assessment. Using least absolute shrinkage and selection operator (LASSO) regression analysis, 12 IRGs were selected as prognostic biomarkers and were then aggregated to generate an IRG score for each HCC sample. In the training dataset (n = 365), patients with high IRG scores showed a remarkably poorer overall survival than those with low IRG scores (log-rank P < .001). Similar results were documented in 3 independent testing datasets (n = 226, 221, 79, respectively). Multivariate Cox regression and stratified analyses indicated that the IRG score was an independent and robust signature to predict the overall survival in HCC patients. Patients with high IRG scores tended to be in advanced TNM stages, with increased risks of tumor recurrence and metastasis. More importantly, the IRG score was strongly associated with certain immune cell counts, gene expression of immune checkpoints, estimated immune score, and mutation of critical genes in HCC. In conclusion, the proposed IRG score can predict the prognosis and reflect the tumor immune microenvironment of HCC patients, which may facilitate the individualized treatment and provide potential immunotherapeutic targets.
| S-EPMC8376334 | biostudies-literature
Project description:Background: Colorectal cancer (CRC) is a common malignant tumor that affects the large bowel or the rectum. Cuproptosis, recently discovered programmed cell death process, may play an important role in CRC tumorigenesis. Long non-coding RNAs (lncRNAs) can alter the proliferation of colorectal cancer cells through the control and activation of gene expression. To date, cuproptosis-related lncRNAs, have not been investigated as potential predictive biomarkers in colorectal cancer. Methods: The mRNA and lncRNA expression data of colorectal cancer were gathered from The Tumor Genome Atlas (TCGA) database, and Pearson correlation analysis and univariate Cox regression analysis were used to identify the lncRNAs with differential prognosis. Colorectal cancer was classified using consistent clustering, and the clinical significance of different types, tumor heterogeneity, and immune microenvironment differences was investigated. The differential lncRNAs were further screened using LASSO regression to develop a risk scoring model, which was then paired with clinicopathological variables to create a nomogram. Finally, the copy number changes in the high-risk and low-risk groups were compared. Results: Two clusters were formed based on the 28 prognostic cuproptosis-related lncRNAs, and the prognosis of cluster 2 was found to be significantly lower than that of cluster 1. Cluster 1 showed increased immune cell infiltration and immunological score, as well as strong enrichment of immune checkpoint genes. Next, LASSO regression was used to select 11 distinctive lncRNAs, and a risk score model was constructed using the training set to distinguish between high and low-risk groups. Patients in the high-risk group had a lower survival rate than those in the low-risk group, and both the test set and the total set produced consistent results. The AUC value of the ROC curve revealed the scoring model's efficacy in predicting long-term OS in patients. Moreover, the model could be used as an independent predictor when combined with a multivariate analysis of clinicopathological features, and our nomogram could be used intuitively to predict prognosis. Conclusion: Collectively, we developed a risk model using 11 differential lncRNAs and demonstrated that the model has predictive value as well as clinical and therapeutic implications for predicting prognosis in CRC patients.
| S-EPMC9465626 | biostudies-literature
Project description:ObjectiveThis study aimed to develop a novel ferroptosis-related gene-based prognostic signature for esophageal carcinoma (ESCA).MethodsThe TCGA-ESCA gene expression profiles and corresponding clinical data were downloaded from the TCGA database. Ferroptosis-related genes were identified from the literature and public databases, which were intersected with the differentially expressed genes between ESCA and normal samples. After univariate Cox regression and random forest analyses, several ferroptosis-related feature genes were identified and used to construct a prognostic signature. Then, the prognostic value of the complex value and the correlation of the complex value with immune cell infiltration were analyzed. Moreover, function analysis, mutation analysis, and molecular docking on the ferroptosis-related feature genes were performed.ResultsBased on the TCGA dataset and ferroptosis pathway genes, 1929 ferroptosis-related genes were preliminarily selected. Following univariate Cox regression analysis and survival analysis, 14 genes were obtained. Then, random forest analysis identified 10 ferroptosis key genes. These 10 genes were used to construct a prognostic complex value. It was found that low complex value indicated better prognosis compared with high complex value. In different ESCA datasets, there were similar differences in the proportion of immune cell distribution between the high and low complex value groups. Furthermore, TNKS1BP1, AC019100.7, KRI1, BCAP31, and RP11-408E5.5 were significantly correlated with ESCA tumor location, lymph node metastasis, and age of patients. KRI1 had the highest mutation frequency. BCAP31 had the strongest binding ability with small molecules DB12830, DB05812, and DB07307.ConclusionWe constructed a novel ferroptosis-related gene signature, which has the potential to predict patient survival and tumor-infiltrating immune cells of ESCA.
| S-EPMC9270146 | biostudies-literature
Project description:BackgroundLong noncoding RNAs (lncRNAs) influence the onset of osteosarcoma. Cuproptosis is a novel cell death mechanism. We attempted to identify a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape in osteosarcoma patients.MethodsTranscriptional and clinical data of 85 osteosarcoma patients were derived from the TARGET database and randomly categorized into the training and validation cohorts. We implemented the univariate and multivariate Cox regression, along with LASSO regression analyses for developing a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, C-index, ROC curves, univariate and multivariate Cox regression, and nomogram were used to assess the capacity of this risk model to predict the osteosarcoma prognosis. Gene ontology, KEGG, and Gene Set Enrichment (GSEA) analyses were conducted for determining the potential functional differences existing between the high-risk and low-risk patients. We further conducted the ESTIMATE, single-smaple GSEA, and CIBERSORT analyses for identifying the different immune microenvironments and immune cells infiltrating both the risk groups.ResultsWe screened out four cuproptosis-related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565) to construct the risk model in the training cohort. This risk model displayed a good performance to predict the overall survival of osteosarcoma patients, which was confirmed by using the validation and the entire cohort. Further analyses showed that the low-risk patients have more immune activation and immune cells infiltrating as well as a good response to immunotherapy.ConclusionsWe developed a novel cuproptosis-related lncRNA signature with high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients, which provides new insights into the personalized treatment of osteosarcoma.
| S-EPMC9972154 | biostudies-literature