Project description:BackgroundGastric cancer (GC) is one of the most common malignancies. Cuproptosis is a newly discovered type of cell death caused by protein toxicity stress, with copper having considerable importance in GC development.MethodsFirst, differentially expressed (DE) cuproptosis-related genes (CRGs) were screened in GC. The tumor mutation burden (TMB) of CRGs was analyzed. We then performed enrichment analyses of DE-CRGs. Next, we constructed a GC cuproptosis-related (CR) signature (CRs) using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. The predictive efficacy was assessed using receiver operating characteristic (ROC) curves. Furthermore, we performed gene set enrichment analysis (GSEA). Different methods were used to assess tumor immunity of the CRs, and the Wilcoxon test was used to examine the expressions of m6A-, m7G-, and ferroptosis-related genes. The "pRRophetic" R package (The R Foundation for Statistical Computing) was used to predict the half maximal inhibitory concentration IC50 of common chemotherapeutic agents. Finally, the expression of CRGs in different clusters was analyzed using single-cell RNA sequencing (scRNA-seq).ResultsWe identified 8 DE-CRGs in GC. There were 9 CRGs with TMB values >1%. We constructed gene expression networks and CRs for GC. The DE-CRGs were involved in important mitochondrial metabolic pathways, and the CRs was a valuable independent prognosis factor. The GSEA revealed that angiogenesis and metabolic-related pathways were enriched in the high-risk group, whereas the low-risk group showed enrichment in DNA replication mismatch and repair pathways. The expressions of immunological checkpoints, ferroptosis-, m6A-, and m7G-related genes, type II interferon (INF) response, major histocompatibility complex (MHC class-I), and the IC50 of the copper-based carrier drug elesclomol were significantly different between the 2 groups of the CRs. Furthermore, the scRNA-seq analysis showed that most CRGs were mainly upregulated in endothelial cells.ConclusionsThe novel CRs could predict the prognosis of GC.

Project description:Bladder cancer (BC) is the most common malignant tumour of the urinary system and one of the leading causes of cancer-related death. Cuproptosis is a novel form of programmed cell death, and its mechanism in tumours remains unclear. This study aimed to establish the prognostic signatures of cuproptosis-related lncRNAs and determine their clinical prognostic value. RNA sequencing data from The Cancer Genome Atlas were used to detect the expression levels of cuproptosis-related genes in BC. Cuproptosis-related lncRNAs linked to survival were identified using co-expression and univariate Cox regression. Furthermore, consensus cluster analysis divided the lncRNAs into two subtypes. Subsequently, we established a signature model consisting of seven cuproptosis-related lncRNAs (AC073534.2, AC021321.1, HYI-AS1, PPP1R26-AS1, AC010328.1, AC012568.1 and MIR4435-2Hg) using least absolute shrinkage and selection operator regression. Survival analysis based on risk score showed that the overall survival and progression-free survival of patients in the high-risk group were worse than those in the low-risk group. Multivariate Cox analysis demonstrated the independent prognostic potential of this signature model for patients with BC. Moreover, age and clinical stage were also significantly correlated with prognosis. The constructed nomogram plots revealed good predictive power for the prognosis of patients with BC and were validated using calibration plots. Additionally, enrichment analysis, Single sample gene set enrichment analysis and immune infiltration abundance analysis revealed significant differences in immune infiltration between the two risk groups, with high levels of immune cell subset infiltrations observed in the high-risk group accompanied by various immune pathway activation. Moreover, almost all the immune checkpoint genes showed high expression levels in the high-risk group. Moreover, TIDE analysis suggested that the high-risk group was more responsive to immunotherapy. Finally, eight drugs with low IC50 values were screened, which may prove to be beneficial for patients in the high-risk group.

Project description:Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide and has a poor prognosis. Cuproptosis is a novel mode of cell death that has only recently been discovered. Considering the critical role of lncRNAs in liver cancer development, the aim of this study was to construct a prognostic signature based on cuproptosis-related lncRNAs (CRlncRNAs). We downloaded RNA-sequencing data and corresponding clinical information of patients with HCC from The Cancer Genome Atlas (TCGA) database. To verify the robustness of the model, we added an external validation set obtained from the Gene Expression Omnibus (GEO): GSE40144. In addition, we identified the cuproptosis-related genes (CRGs) based on previous reports. Pearson correlation analysis, univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were utilized to screen for genes associated with prognosis. On this basis, multivariate Cox regression and stepAIC were used to further construct and optimize the prognostic model. The simplified signature with the lowest Akaike information criterion (AIC) value was considered the prognostic signature. Seven different algorithms were used to perform immune infiltration analysis. The single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was utilized to find the difference in immune function between the high- and low-risk groups. Finally, in vitro experiments were performed by quantitative real-time PCR (qRT-PCR) analysis using HCC cell lines to validate the expression of prognostic genes. We identified 3 lncRNAs (CYTOR, LINC00205, and LINC01184) as independent risk factors for HCC. The receiver operating characteristic (ROC) curves calculated that the AUC at 1, 3, and 5 years reached 0.717, 0.633, and 0.607, respectively. The expression levels of 41 immune checkpoints differed significantly between the high- and low-risk groups, and there were significant differences in sensitivity to immunotherapy between the high- and low-risk groups. The risk model could also serve as a promising predictor of immunotherapeutic response, which has been verified by the TIDE algorithm (p < 0.001). Overall, we propose a signature related to CRlncRNAs that can be used to predict the prognosis of HCC patients, which was validated in external cohort and in vitro experiments.

Project description:BackgroundIntrahepatic cholangiocarcinoma (iCCA) is a highly lethal malignancy of the biliary tract. Analysis of somatic mutational profiling can reveal new prognostic markers and actionable treatment targets. In this study, we explored the utility of genomic mutation signature and tumor mutation burden (TMB) in predicting prognosis in iCCA patients.MethodsWhole-exome sequencing and corresponding clinical data were collected from the ICGC portal and cBioPortal database to detect the prognostic mutated genes and determine TMB values. To identify the hub prognostic mutant signature, we used Cox regression and Lasso feature selection. Mutation-related signature (MRS) was constructed using multivariate Cox regression. The predictive performances of MRS and TMB were assessed using Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC). We performed a functional enrichment pathway analysis using gene set enrichment analysis (GSEA) for mutated genes. Based on the MRS, TMB, and the TNM stage, a nomogram was constructed to visualize prognosis in iCCA patients.ResultsThe mutation landscape illustrated distributions of mutation frequencies and types in iCCA, and generated a list of most frequently mutated genes (such as Tp53, KRAS, ARID1A, and IDH1). Thirty-two mutated genes associated with overall survival (OS) were identified in iCCA patients. We obtained a six-gene signature using the Lasso and Cox method. AUCs for the MRS in the prediction of 1-, 3-, and 5-year OS were 0.759, 0.732, and 0.728, respectively. Kaplan-Meier analysis showed a significant difference in prognosis for patients with iCCA having a high and low MRS score (P < 0.001). GSEA was used to show that several signaling pathways, including MAPK, PI3K-AKT, and proteoglycan, were involved in cancer. Conversely, survival analysis indicated that TMB was significantly associated with prognosis. GSEA indicated that samples with high MRS or TMB also showed an upregulated expression of pathways involved in tumor signaling and the immune response. Finally, the predictive nomogram (that included MRS, TMB, and the TNM stage) demonstrated satisfactory performance in predicting survival in patients with iCCA.ConclusionsMutation-related signature and TMB were associated with prognosis in patients with iCCA. Our study provides a valuable prognostic predictor for determining outcomes in patients with iCCA.

Project description:ObjectiveTo identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma.MethodsBased on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma.Results34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease.ConclusionThe prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.

Project description:BackgroundHepatocellular carcinoma (HCC) has a high incidence and poor prognosis. Cuproptosis is a novel type of cell death, which differs from previously reported types of cell death such as apoptosis, autophagy, proptosis, ferroptosis, necroptosis, etc. Long non-coding RNAs (lncRNAs) play multiple roles in HCC.MethodsWe downloaded information from The Cancer Genome Atlas (TCGA) database, and obtained cuproptosis-related genes from published studies. The cuproptosis-related lncRNAs were obtained by correlation analysis, and subsequently used to construct a prognostic cuproptosis-related lncRNA signature. Analyses of overall survival (OS), progression-free survival (PFS), receiver operating characteristic (ROC) curve with the area under the curve (AUC) values and the index of concordance (c-index) curve were used to evaluate the signature. The tumor microenvironment (TME) was analyzed by ESTIMATE algorithm. The immune cell data was downloaded from the Tumor Immune Estimation Resource (TIMER) 2.0 database. Immune-related pathways were analyzed by single-sample gene set enrichment analysis (ssGSEA) algorithm. Immunophenoscore (IPS) scores from The Cancer Immunome (TCIA) database were used to evaluate immunotherapy response. The "pRRophetic" was employed to screen drugs for high-risk patients. The candidate lncRNA expression levels were detected by Real Time Quantitative PCR.ResultsWe constructed a cuproptosis-related lncRNA signature containing seven lncRNAs: AC125437.1, PCED1B-AS1, PICSAR, AP001372.2, AC027097.1, LINC00479, and SLC6A1-AS1. This signature had excellent accuracy, and was independent of the stratification of clinicopathological features. Further study showed that high-risk tumors under this signature had higher TMB, fewer TME components and higher tumor purity. The tumors with high risk were not enriched in immune cell infiltration or immune process pathways, and high-risk patients had a poor response to immunotherapy. Moreover, 29 drugs such as sorafenib, dasatinib and paclitaxel were screened for high-risk HCC patients to improve their prognosis. The expression levels of the candidate lncRNAs in HCC tissue were significantly increased (except PCED1B-AS1).ConclusionsOur prognostic cuproptosis-related lncRNA signature was accurate and effective for predicting the prognosis of HCC. The immunotherapy was unsuitable for high-risk HCC patients with this signature.

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A cuproptosis-related gene expression signature predicting clinical prognosis and immune responses in intrahepatic cholangiocarcinoma detected by single-cell RNA sequence analysis

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