Skip to main page content (VSports)
U.S. flag

An official website of the United States government

Dot gov

The . gov means it’s official VSports app下载. Federal government websites often end in . gov or . mil. Before sharing sensitive information, make sure you’re on a federal government site. .

Https

The site is secure V体育官网. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. .

. 2023 Jan 16;15(2):544.
doi: 10.3390/cancers15020544.

Revealing Prognostic and Immunotherapy-Sensitive Characteristics of a Novel Cuproptosis-Related LncRNA Model in Hepatocellular Carcinoma Patients by Genomic Analysis (VSports app下载)

Zhenzhen Mao  1   2 , Ye Nie  2 , Weili Jia  1   2 , Yanfang Wang  1   2 , Jianhui Li  1   2 , Tianchen Zhang  1   2 , Xinjun Lei  1   2 , Wen Shi  1   2 , Wenjie Song  2 , Xiao Zhang  3   4
Affiliations

VSports最新版本 - Revealing Prognostic and Immunotherapy-Sensitive Characteristics of a Novel Cuproptosis-Related LncRNA Model in Hepatocellular Carcinoma Patients by Genomic Analysis

Zhenzhen Mao et al. Cancers (Basel). .

Abstract

Immunotherapy has shown strong anti-tumor activity in a subset of patients. However, many patients do not benefit from the treatment, and there is no effective method to identify sensitive immunotherapy patients. Cuproptosis as a non-apoptotic programmed cell death caused by excess copper, whether it is related to tumor immunity has attracted our attention. In the study, we constructed the prognostic model of 9 cuproptosis-related LncRNAs (crLncRNAs) and assessed its predictive capability, preliminarily explored the potential mechanism causing treatment sensitivity difference between the high-/low-risk group. Our results revealed that the risk score was more effective than traditional clinical features in predicting the survival of HCC patients (AUC = 0. 828). The low-risk group had more infiltration of immune cells (B cells, CD8+ T cells, CD4+ T cells), mainly with anti-tumor immune function (p < 0. 05). It showed higher sensitivity to immune checkpoint inhibitors (ICIs) treatment (p < 0. 001) which may exert the effect through the AL365361. 1/hsa-miR-17-5p/NLRP3 axis. In addition, NLRP3 mutation-sensitive drugs (VNLG/124, sunitinib, linifanib) may have better clinical benefits in the high-risk group. All in all, the crLncRNAs model has excellent specificity and sensitivity, which can be used for classifying the therapy-sensitive population and predicting the prognosis of HCC patients. VSports手机版.

Keywords: AL365361. 1; cuproptosis-related LncRNAs (crLncRNAs); immune checkpoint inhibitors (ICIs); immunotherapy; tumor immune microenvironment (TIME). V体育安卓版.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

"VSports" Figures

Figure 1
Figure 1
Flow chart of this study. A total of 343 HCC patients with complete survival information in the TCGA database were divided into two cohorts, i.e., training and testing. Using the expression data of 147 prognostic crLncRNA genes in the training cohort, the 9-crLncRNA signatures based on LASSO and COX regression analyses were obtained, and the optimal penalty parameter (λ) of the LASSO model was constructed. The KM curves, ROC curves, PCA analysis, DCA curves, and nomogram were applied to evaluate the accuracy and reliability of the 9-crLncRNA prognostic model. Subsequently, a series of analyses, including ssGSEA, KEGG, GO, GSVA, immune-related, immunecheckpoint-related genes, somatic mutations, and drug sensitivity analyses, were applied to explore the potential of classifying the population as sensitive to treatment and its mechanism.
Figure 2
Figure 2
Identification of crLncRNAs in HCC patients, the prognostic model of crLncRNAs and its prediction potential. (A) The co-expression network of mRNAs and LncRNAs associated with cuproptosis, which was visualized by Sankey diagram according to Spearman correlation analysis (|R| > 0.4, p < 0.001). (B) A cross-validation to adjust the parameter selection in the LASSO Cox regression model. (C) The LASSO coefficient curve of 9 crLncRNAs. (D) The heatmap of correlation analysis of cuproptosis-related genes and the 9-crLncRNA signatures. * p < 0.05, ** p < 0.01, and *** p < 0.001. Principal components analysis between low- and high-risk groups based on the expressions of (E) entire genes (F) 19 cuproptosis-related genes (G)147-crLncRNA, and (H) 9-crLncRNA.
Figure 3
Figure 3
Validation of the 9-crLncRNA prognostic model by clinical characteristics. Based on the median risk score, the entire sample was divided into high-risk group (165 cases) and low-risk group (178 cases). The differences in (A) grade, (B) stage, and (C) T stage were analyzed in HCC patients. * p < 0.05, ** p < 0.01, and *** p < 0.001, and ns means no significance. The K-M plotters of clinical characteristics: (D) <65, (E) ≥65, (F) FEMALE, (G) MALE, (H) G1-G2, (I) G3-G4, (J) stage I–II, (K) stage III–IV, (L) M0, (M) T1-T2, (N) T3-T4, and (O) N0. p < 0.05 was considered statistically significant.
Figure 4
Figure 4
Validation of the 9-crLncRNA prognostic model in the training, validation and entire groups. The training group was further divided into the high-risk group (101 cases) and the low-risk group (105 cases) based on the median risk score. The validation sample was then divided into the high-risk group (64 cases) and the low-risk group (73 cases). Each sub-graph of Figure 4 shows the risk score curves, survival status distribution maps, the 9-crLncRNA expression heatmaps (A–C), Kaplan–Meier survival curves (D–F), the ROC curves of the overall survival at years 1, 3, and 5 (G–I), and the ROC curves of the risk score and other relevant clinical characteristics of the 9-crLncRNA (J–L) for the training, validation and entire groups, respectively. p < 0.05 was considered statistically significant.
Figure 5
Figure 5
Evaluation of the predictive ability of the 9-crLncrNA prognostic model. The univariate (A) and multivariate (B) Cox regression analyses were performed to evaluate the independent predictive potential of OS of the risk score and relevant clinical characteristics. *** p < 0.001. (C) The C-index was used to evaluate the predictive power of the model. (D) The nomogram was used for the prediction of 1-, 3-, and 5-year survival. (E) The calibration curves were used to examine the capability of predicting the OS at 1, 3, and 5 years. (F) The multi-indicator ROC curve and (G) the DCA curve were used to evaluate the predictive ability of the nomogram and risk score.
Figure 6
Figure 6
The GO biofunction and KEGG pathway enrichment analysis of two risk groups based on the 9-crLncRNA. (A) The volcano map reflects the 357 differentially expressed genes between two risk groups (log2|FC| > 1, p < 0.05). Green, red, and gray represent downregulated, upregulated, and no difference genes, respectively. Bubble graphs for KEGG pathways (B) and GO enrichment (C) Circle diagrams of significant GO functional items (D) and significant KEGG pathways (E). The latter contains the name of the dataset, the number of genes in the dataset, and the proportion of crLncRNAs in the pathway. The outer ring is the name of the dataset, the inner circles are the number of genes in the dataset and the proportion of crLncRNAs in the pathway. p < 0.05 was considered statistically significant.
Figure 7
Figure 7
Immune profiles between different risk groups. (A) The boxplots of the stroma, immune, and estimate scores of the two groups, and the Wilcoxon test is used for comparison. (B) The comparison of immune cell subtypes in the low- and high-risk groups. The differences of (C) immune cell infiltration and (D) immune function between the two risk groups according to ssGSEA. (E) The heatmap summarized stroma, immune, estimate scores, tumor purity, immune cell infiltration, and immune function between the two groups. * p < 0.05, ** p < 0.01, and *** p < 0.001, ns means no significance.
Figure 8
Figure 8
The comparison of the expressions of immune checkpoint genes and sensitivity to immune checkpoint inhibitors between high- and low-risk groups. (A) The boxplots for comparing the immune checkpoints genes between the two risk groups. The violin figures are for comparing the two risk groups of the treatment for using (B) none of CTLA4 or PD1, (C) CTLA4 + PD1, (D) CTLA4 alone, and (E) PD1 alone. * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 9
Figure 9
The connection between 9-crLncrNA prognostic signature and the immune landscape. (A) Correlation heatmap of KEGG pathways by GSVA. (B) The 9-crLncrNA correlates with 22 immune cells and immune-related functions on a heatmap. (C) The heatmap of the correlation between the 9-crLncrNA and immune checkpoint genes. (D) The differential analysis plots show the expression of AL365361.1 in HCC tissue and adjacent normal tissue from HCC patients. * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 10
Figure 10
The analysis of drug sensitivity in high-risk group. (A) The heatmap of correlation between cuproptosis-related genes and 22 immune cells and immune-related functions. (B) The pathways pie chart of cuproptosis-related genes, with red representing activation and green representing inhibition. (C) The pie charts of cuproptosis-related genes with CNV in HCC. (D) The CNV status of the cuproptosis-related genes in heterozygous and homozygous HCC patients was shown above and below the figure, respectively. (E) The differential analysis plots show the expression of NLRP3 in HCC tissue and adjacent normal tissue from HCC patients. (F) The correlation between cuproptosis-related gene expression and sensitive targeted therapy drugs obtained by the spearman correlation analysis. The red represents positive correlation, indicating that the gene is highly expressed and resistant to drugs, whereas the blue represents the drug sensitivity of the gene. * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 11
Figure 11
The regulatory network between AL365361.1 and NLRP3. (A) The scatter plot of correlation showing the correlation of the expression levels between AL365361.1 and NLRP3. (B) The sunburst diagram shows predicted microRNA targets of NLRP3 and AL365361.1. (C) The Venn diagram containing four lists of miRNAs. (D) The correlation heatmap between the 2 common miRNAs and the main immune checkpoint genes. (E) The network of the potential regulation of miRNAs to NLRP3. (F) The differential analysis plots show the expression of miR-17-5p in HCC tissue and adjacent normal tissue from HCC patients. The scatter plot of correlation shows the correlation of the expression levels between AL365361.1 and hsa-miR-17-5p (G), NLRP3 and hsa-miR-17-5p (H), AL365361.1 and NLRP3 (I). * p < 0.05, ** p < 0.01. p < 0.05 was considered statistically significant.
See this image and copyright information in PMC

References

    1. Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021;71:209–249. doi: 10.3322/caac.21660. - DOI - PubMed
    1. Wong M.C.S., Huang J.L.W., George J., Huang J., Leung C., Eslam M., Chan H.L.Y., Ng S.C. The changing epidemiology of liver diseases in the Asia-Pacific region. Nat. Rev. Gastroenterol. Hepatol. 2019;16:57–73. doi: 10.1038/s41575-018-0055-0. - DOI - PubMed
    1. Fan R., Papatheodoridis G., Sun J., Innes H., Toyoda H., Xie Q., Mo S., Sypsa V., Guha I.N., Kumada T., et al. aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis. J. Hepatol. 2020;73:1368–1378. doi: 10.1016/j.jhep.2020.07.025. - DOI - PubMed
    1. Huang A., Yang X.R., Chung W.Y., Dennison A.R., Zhou J. Targeted therapy for hepatocellular carcinoma. Signal Transduct. Target. Ther. 2020;5:146. doi: 10.1038/s41392-020-00264-x. - DOI - PMC - PubMed
    1. Finn R.S., Ryoo B.-Y., Merle P., Kudo M., Bouattour M., Lim H.Y., Breder V., Edeline J., Chao Y., Ogasawara S., et al. Pembrolizumab as Second-Line Therapy in Patients with Advanced Hepatocellular Carcinoma in KEYNOTE-240: A Randomized, Double-Blind, Phase III Trial. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2020;38:193–202. doi: 10.1200/JCO.19.01307. - V体育2025版 - DOI - PubMed

LinkOut - more resources