Construction of cuproptosis-related lncRNAs/mRNAs model and prognostic prediction of hepatocellular carcinoma.
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ABSTRACT:
SUBMITTER: Tang L
PROVIDER: S-EPMC9641397 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Construction of cuproptosis-related lncRNAs/mRNAs model and prognostic prediction of hepatocellular carcinoma.
Tang Lingxue L Wang Tong T Li Wen W Yu Sheng S Yao Senbang S Cheng Huaidong H
American journal of cancer research 20221015 10
Cuproptosis is a recently reported novel form of cell death, which is involved in the regulation of tumor progression. However, the specific role of cuproprosis in hepatocellular carcinoma (HCC) development remains unclear. In this study, we comprehensively analyzed the effect of cuproprosis-related lncRNAs/mRNAs on the prognosis of HCC patients based on the RNA-Seq transcriptome data and clinical data. We identified 6 cuproprosis-related signatures by Cox and Lasso regression analysis, includin ...[more]
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Project description:Background: Esophageal carcinoma (ESCA) is one of the most prevalent malignant tumors in the world. The prognosis of patients has significantly improved with the development of surgery, targeted therapy and immunotherapy. But the 5-year survival rate of ESCA patients is still incredibly low. Cuproptosis is a type of mitochondrial cell death induced by copper. It is unclear how cuproptosis-related lncRNAs (CRLs) affect ESCA prognosis. Methods: In this study, we obtained the clinical data of ESCA patients, the transcriptome data from TCGA and identified CRLs by co-expression analysis, lasso regression, and cox regression analysis, to build a prognostic model. Then we validated the prognostic model using the Kaplan-Meier curve, cox regression analysis, and ROC, to create a nomogram based on risk score to forecast the prognosis of ESCA. Next, the immune escape of the CRLs was examined using the TIDE algorithm to assess its sensitivity to possible ESCA medications. Results: To predict the prognosis of ESCA patients, we created a predictive model using 6 CRLs (AC034199.1, AC125437.1, AC107032.2, CTBP1-DT, AL024508.1, and AC008610.1), validated by the Kaplan-Meier and ROC curves. The model has a higher diagnostic value compared to other clinical features. The 6 CRLs expressed high in TCGA and ESCA specimens. Enrichment analysis revealed CRLs largely contributed to the interaction between cytokines and their receptors as well as complement coagulation cascades. The immunity escape analysis demonstrated that immunotherapy had a worse effect in the low-risk group than in the high-risk group. Additionally, we screened out potential antineoplastic drugs according to the results of the immunoassay and obtained 5 drugs, including CP-466722, crizotinib, MS-275, KIN001-135, and CP-466722. Conclusion: The prognosis of patients with ESCA can be correctly predicted by the 6 CRLs chosen from this investigation. It lays the groundwork for more investigation into the ESCA mechanism and the identification of novel therapeutic targets.
| S-EPMC10133708 | biostudies-literature
Project description:ObjectivesCuproptosis represents an innovative type of cell death, distinct from apoptosis, driven by copper dependency, yet the involvement of copper apoptosis-associated long non-coding RNAs (CRLncRNAs) in hepatocellular carcinoma (HCC) remains unclear. This study is dedicated to unveiling the role and significance of these copper apoptosis-related lncRNAs within the context of HCC, focusing on their impact on both the development of the disease and its prognosis.MethodsWe conducted an analysis of gene transcriptomic and clinical data for HCC cases by sourcing information from The Cancer Genome Atlas database. By incorporating cuproptosis-related genes, we established prognostic features associated with cuproptosis-related lncRNAs. Furthermore, we elucidated the mechanism of cuproptosis-related lncRNAs in the prognosis and treatment of HCC through comprehensive approaches, including Lasso and Cox regression analyses, survival analyses of samples, as well as examinations of tumor mutation burden and immune function.ResultsWe developed a prognostic model featuring six cuproptosis-related lncRNAs: AC026412.3, AC125437.1, AL353572.4, MKLN1-AS, TMCC1-AS1, and SLC6A1-AS1. This model demonstrated exceptional prognostic accuracy in both training and validation cohorts for patients with tumors, showing significantly longer survival times for those categorized in the low-risk group compared to the high-risk group. Additionally, our analyses, including tumor mutation burden, immune function, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and drug sensitivity, further elucidated the potential mechanisms through which cuproptosis-associated lncRNAs may influence disease outcome.ConclusionsThe model developed using cuproptosis-related long non-coding RNAs (lncRNAs) demonstrates promising predictive capabilities for both the prognosis and immunotherapy outcomes of tumor patients. This could play a crucial role in patient management and the optimization of immunotherapeutic strategies, offering valuable insights for future research.
| S-EPMC11040954 | biostudies-literature
Project description:Cuproptosis and N6-methyladenosine (m6A) have potential as prognostic predictors in cancer patients, but their roles in hepatocellular carcinoma (HCC) are unclear. This study aimed to screen a total of 375 HCC samples were retrieved from the TCGA database, and lncRNAs related to cuproptosis and m6A were obtained through correlation analysis. To construct a risk assessment model, univariate Cox regression analysis and LASSO Cox regression were employed. Analyze the regulatory effect of relevant risk assessment models on tumor mutation load (TMB) and immune microenvironment. A total of five lncRNAs (AC007405.3, AL031985.3, TMCC1-AS1, MIR210HG, TMEM220-AS1) with independent overall survival-related risk models were obtained by LASSO survival regression. TP53 and CTNNB1 were the three genes found to have the most mutations in high-risk group patients. The high-risk group with low TMB had the worst survival, whereas the low-risk group with high TMB had the best survival. KEGG pathway analysis revealed that the high-risk group was enriched with cell cycle, oocyte meiosis, cell senescence, and glycolysis/glucose production pathways. We constructed a reliable cuproptosis- and m6A-related lncRNA model for the prognosis of HCC. The model may provide new insights into managing HCC patients, but further research is needed to validate it.
| S-EPMC10451969 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.
| S-EPMC8721125 | biostudies-literature
Project description:Background: Cuproptosis is a newly defined form of cell death, whether cuproptosis involved in hepatocellular carcinoma (HCC) remains elusive. Method: We obtained patients' RNA expression data and follow-up information from University of California Santa Cruz (UCSC) and The Cancer Genome Atlas (TCGA). We analyzed the mRNA level of Cuproptosis-related genes (CRGs) and performed univariate Cox analysis. Liver hepatocellular carcinoma (LIHC) was chosen for further investigation. Real-Time quantitative PCR (RT-qPCR), Western blotting (WB), Immunohistochemical (IHC), and Transwell assays were used to determine expression patterns and functions of CRGs in LIHC. Next, we identified CRGs-related lncRNAs (CRLs) and differentially expressed CRLs between HCC and normal cases. Univariate Cox analysis, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to construct the prognostic model. Univariate and multivariate Cox analysis was used to assess whether the risk model can act as an independent risk factor of overall survival duration. Different risk groups performed immune correlation analysis, tumor mutation burden (TMB), and Gene Set Enrichment Analysis (GSEA) analysis were performed in different risk groups. Finally, we assessed the performance of the predictive model in drug sensitivity. Results: CRGs expression levels have significant differences between tumor and normal tissues. High expression of Dihydrolipoamide S-Acetyltransferase (DLAT) correlated to metastasis of HCC cells and indicated poor prognosis for HCC patients. Our prognostic model consisted of four cuproptosis-related lncRNA (AC011476.3, AC026412.3, NRAV, MKLN1-AS). The prognostic model performed well in predicting survival rates. The results from Cox regression analysis suggested that risk score can serve as an independent prognostic element for survival durations. Survival analysis revealed that low risk patients have extended survival periods compared with those with high risk. The results of the immune analysis indicated that risk score has a positive correlation with B cell and CD4+ T cell Th2, while has a negative relationship with endothelial cell and hematopoietic cells. Besides, immune checkpoint genes have higher expression folds in the high-risk set than in the low-risk set. The high-risk group had higher rates of genetic mutation than the low-risk set while having a shorter survival time. GSEA revealed the signaling pathways enriched in the high-risk group were mostly immune-related, while metabolic-related pathways were enriched in the low-risk group. Drugs sensitivity analysis indicated that our model has the ability to predict the efficacy of clinical treatment. Conclusion: The Cuproptosis-related lncRNAs prognostic formula is a novel predictor of HCC patients' prognosis and drug sensitivity.
| S-EPMC9950118 | biostudies-literature
Project description:Cuproptosis was characterized as a novel type of programmed cell death. Recently, however, the role of cuproptosis-related long noncoding RNAs (CRLs) in tumors has not yet been studied. Identifying a predictive CRL signature in hepatocellular carcinoma (HCC) and investigating its putative molecular function were the goals of this work. Initially, Pearson's test was used to assess the relationship between lncRNAs and cuproptosis-associated genes obtained from HCC data of The Cancer Genome Atlas (TCGA). By implementing differential expression and univariate Cox analysis, 61 prognostic CRLs were subsequent to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. A prognostic risk score model was then constructed to evaluate its ability to predict patients' survival when combined with clinicopathological parameters in HCC. The five-lncRNA prognostic signature categorized the HCC patients into high- and low-risk groups. The low-risk group exhibited more sensitivity to elesclomol than the high-risk one. Surprisingly, distinct mitochondrial metabolism pathways connected to cuproptosis and pivotal immune-related pathways were observed between the two groups via gene set enrichment analysis (GSEA). Meanwhile, there were substantial differences between the high-risk group and the low-risk group in terms of tumor-infiltrating immune cells (TIICs). Furthermore, a positive relationship was shown between the risk score and the expression of immune checkpoints. Additionally, differential expression of the five lncRNAs was confirmed in our own HCC samples and cell lines via RT-qPCR. Finally, in vitro assays confirmed that WARS2-AS1 and MKLN1-AS knockdown could sensitize HCC cells to elesclomol-induced cuproptosis. Overall, our predictive signature may predict the prognosis of HCC patients in an independent manner, give a better understanding of how CRLs work in HCC, and offer therapeutic reference for patients with HCC.
| S-EPMC9846072 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a common malignancy worldwide with poor clinical outcomes, and the infection of hepatitis B virus (HBV) is the leading cause of this disease. Mounting evidence shows that RNA binding proteins (RBPs) can modulate the progression of cancers. However, the functions and clinical implications of RBP-related mRNAs in HBV-related HCC remain largely unclear. Therefore, we aim to develop a prognostic model based on the RBP-related mRNAs for HBV-related HCC patients. Firstly, we identified 626 differentially expressed RBP-related mRNAs in the HBV-related HCC through the Pearson correlation analysis. Subsequently, the Kaplan-Meier survival, univariate, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses were used to construct a prognostic model comprised of five RBP-related mRNAs. Furthermore, the patients were categorized into the high- and low-risk groups by the prognostic model and the patients in the high-risk group had a poor prognosis. Additionally, the prognostic model was an independent predictor of prognosis, and the accuracy of the prognostic model was proved by the receiver operator characteristic (ROC) analysis. Furthermore, the functional enrichment analysis revealed that various cancer-promoting processes were enriched in the high-risk group. Taken together, our study may provide the HBV-related HCC biomarkers of prognosis to improve the clinical outcomes of patients.
| S-EPMC9539435 | biostudies-literature
Project description:Cuproptosis is a novel form of cell death, correlated with the tricarboxylic acid (TCA) cycle. However, the metabolic features and the benefit of immune checkpoint inhibitor (ICI) therapy based on cuproptosis have not yet been elucidated in Hepatocellular carcinoma (HCC). First, we identified and validated three cuproptosis subtypes based on 10 cuproptosis-related genes (CRGs) in HCC patients. We explored the correlation between three cuproptosis subtypes and metabolism-related pathways. Besides, a comprehensive immune analysis of three cuproptosis subtypes was performed. Then, we calculated the cuproptosis-related gene prognostic index (CRGPI) score for predicting prognosis and validated its predictive capability by Decision curve analysis (DCA). We as well explored the benefit of ICI therapy of different CRGPI subgroups in two anti-PD1/PD-L1 therapy cohorts (IMvigor210 cohort and GSE176307). Finally, we performed the ridge regression algorithm to calculate the IC50 value for drug sensitivity and Gene set enrichment analysis (GSEA) analysis to explore the potential mechanism. We found that cluster A presented a higher expression of FDX1 and was correlated with metabolism, glycolysis, and TCA cycle pathways, compared with the other two clusters. HCC patients with high CRGPI scores had a worse OS probability, and we further found that the CRGPI-high group had high expression of PD1/PDL1, TMB, and better response (PR/CR) to immunotherapy in the IMvigor210 cohort and GSE176307. These findings highlight the importance of CRGPI serving as a potential biomarker for both prognostic and immunotherapy for HCC patients. Generally, our results provide novel insights about cuproptosis into immune therapeutic strategies.
| S-EPMC9513033 | biostudies-literature
Project description:BackgroundM2 macrophages play a crucial role in promoting tumor angiogenesis and proliferation, as well as contributing to chemotherapy resistance and metastasis. However, their specific role in the tumor progression of hepatocellular carcinoma (HCC) and their impact on the clinical prognosis remain to be further elucidated.Materials and methodsM2 macrophage-related genes were screened using CIBERSORT and weighted gene co-expression network analysis (WGCNA), while subtype identification was performed using unsupervised clustering. Prognostic models were constructed using univariate analysis/least absolute shrinkage selector operator (LASSO) Cox regression. In addition, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and mutation analysis were used for further analysis. The relationship between the risk score and tumor mutation burden (TMB), microsatellite instability (MSI), the efficacy of transcatheter arterial chemoembolization (TACE), immunotype, and the molecular subtypes were also investigated. Moreover, the potential role of the risk score was explored using the ESTIMATE and TIDE (tumor immune dysfunction and exclusion) algorithms and stemness indices, such as the mRNA expression-based stemness index (mRNAsi) and the DNA methylation-based index (mDNAsi). In addition, the R package "pRRophetic" was used to examine the correlation between the risk score and the chemotherapeutic response. Finally, the role of TMCC1 in HepG2 cells was investigated using various techniques, including Western blotting, RT-PCR and Transwell and wound healing assays.ResultsThis study identified 158 M2 macrophage-related genes enriched in small molecule catabolic processes and fatty acid metabolic processes in HCC. Two M2 macrophage-related subtypes were found and a four-gene prognostic model was developed, revealing a positive correlation between the risk score and advanced stage/grade. The high-risk group exhibited higher proliferation and invasion capacity, MSI, and degree of stemness. The risk score was identified as a promising prognostic marker for TACE response, and the high-risk subgroup showed higher sensitivity to chemotherapeutic drugs (e.g., sorafenib, doxorubicin, cisplatin, and mitomycin) and immune checkpoint inhibitor (ICI) treatments. The expression levels of four genes related to the macrophage-related risk score were investigated, with SLC2A2 and ECM2 showing low expression and SLC16A11 and TMCC1 exhibiting high expression in HCC. In vitro experiments showed that TMCC1 may enhance the migration ability of HepG2 cells by activating the Wnt signaling pathway.ConclusionWe identified 158 HCC-related M2 macrophage genes and constructed an M2 macrophage-related prognostic model. This study advances the understanding of the role of M2 macrophages in HCC and proposes new prognostic markers and therapeutic targets.
| S-EPMC10319018 | biostudies-literature
Project description:Endoplasmic reticulum stress (ERS) and cuproptosis have remarkable effects on hepatocellular carcinoma (HCC) leading to a poor prognosis. The current study aimed to explore credible signature for predicting the prognosis of HCC based on ERS and cuproptosis-related lncRNAs. In our study, clinical and transcriptomic profiles of HCC patients were obtained from the Cancer Genome Atlas (TCGA) database. An ERS and cuproptosis-related lncRNA prognostic signature, including NRAV, SNHG3, LINC00839 and AC004687.1, was determined by correlation tests, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) methods. Survival and predictive value were evaluated using Kaplan-Meier and receiver operating characteristic (ROC) curves, while calibration and nomograms curves were developed. Besides the enrichment analyses for ERS and cuproptosis-related lncRNAs, mutational status and immune status were assessed with TMB and ESTIMATE. Additionally, consensus cluster analysis was employed to compare cancer subtype differences, while drug sensitivity and immunologic efficacy were evaluated for further exploration. qRT-PCR and CCK-8 were utilized to verify the alteration of the prognostic lncRNAs expression and proliferation in vitro. High-risk groups exhibited poorer prognosis. The signature exhibited robust predictive value as an independent prognostic indicator and showed significant correlation with clinicopathological features. In the enriched analysis, biological membrane pathways were enriched. Low-risk patients had lower TMB and higher immune status. A cluster analysis revealed that cluster 2 had the best clinical immunological efficacy and most active immune function. In brief, our constructed signature with ERS and cuproptosis-related lncRNAs was associated survival outcomes of HCC, and can be used to predict the clinical classification and curative effect.
| S-EPMC11471205 | biostudies-literature