China Biotechnology

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A Novel Prognostic Model of Oxidative Stress-related lncRNA in Hepatocellular Carcinoma and Its Validation in Vitro

LIU Yiran, LI Yaqi, XU Hongting, XIAO Xiangqian, SHENG Wang

China Biotechnology, 2024, 44(12): 31-50. DOI: 10.13523/j.cb.2404018

引物名称	上下游引物序列(5'-3')
AC009005.1	上游:GGCAAACATCTCTTGTCCATCCT
	下游:CTCTCCGCATATCCCTCCTTCT
DDIT3	上游:GGAAACAGAGTGGTCATTCCC
	下游:CTGCTTGAGCCGTTCATTCTC
STIP-1	上游:GCCAAGCGAACCTATGAGGAG
	下游:GGATCACTGAGTAGTGTCCTTGT
NUDT1	上游:GCTCATGGACGTGCATGTCTT
	下游:GTGGAAACCAGTAGCTGTCGT
CDKN2A	上游:GGGTTTTCGTGGTTCACATCC
	下游:CTAGACGCTGGCTCCTCAGTA
HRAS	上游:GACGTGCCTGTTGGACATC
	下游:CTTCACCCGTTTGATCTGCTC
BAK1	上游:GTTTTCCGCAGCTACGTTTTT
	下游:GCAGAGGTAAGGTGACCATCTC
FANCD2	上游:ACATACCTCGACTCATTGTCAGT
	下游:TCGGAGGCTTGAAAGGACATC
TRAF2	上游:GCTCATGCTGACCGAATGTC
	下游:GCCGTCACAAGTTAAGGGGAA
CHEK1	上游:ATATGAAGCGTGCCGTAGACT
	下游:TGCCTATGTCTGGCTCTATTCTG
H2AX	上游:CCTCTGCCCTCCCCTAAATG
	下游:CACATCAGCTCTCCCTCCATC
RAD51	上游:CAACCCATTTCACGGTTAGAGC
	下游:TTCTTTGGCGCATAGGCAACA
HSPB1	上游:ACGGTCAAGACCAAGGATGG
	下游:AGCGTGTATTTCCGCGTGA
β-Actin	上游:CTCCATCCTGGCCTCGCTGT
	下游:GCTGTCACCTTCACCGTTCC

Table 1 Forward and reverse sequence of primers for RT-qPCR

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Fig.1 GO and KEGG analysis in OS-DEGs in HCC patients A: Volcano plot of 127 OS-DEGs. Pink dots represent for upregulated genes and blue dots represent for downregulated genes B: KEGG analysis of OS-DEGs. Top 10 categories of results were displayed C: GO analysis of OS-DEGs. Top 5 categories of results were displayed
Table 2 127 DEGs from TCGA database
Fig.2 Oxidative stress-related lncRNA signature in HCC A: Coefficient distribution diagram of Lasso regression function analysis B: The Lasso coefficient profile of 24 oxidative stress-related lncRNA C: The co-expression network of lncRNA-mRNA visualized by cytoscape D: Sankey diagram of lncRNA-mRNA
Fig.3 Heatmap of the expression levels of OS-lncRNA
Fig. 4 The evaluation of predictive signature A: The Kaplan-Meier curve analysis of the OS rate of HCC patients in the high- and low-risk groups B: The number of dead and alive patients with different risk scores. Blue represents for death patients and orange represents for alive patients C: The distribution of the risk score among HCC patients D: ROC curve and AUCs at 1-year, 3-years and 5-years survival for the predictive signature E: Heatmap of OS-lncRNA expression in patients with high- and low risk groups
Fig.5 The correlation between the predictive signature and the prognosis of HCC patients A: Forest plot for univariate Cox regression analysis B: Forest plot for multivariate Cox regression analysis C: The ROC curve of the risk score and clinicopathological variables T represents for tumor, M represents for metastasis, N represents for lymph node
Fig. 6 Heatmap of the expression of OS-lncRNA and clinicopathological variables
Fig.7 Construction and verification of the nomogram A: A nomogram combining clinicopathological variables and risk score predicts 1-, 3-, and 5 years OS of HCC patients B-D: The calibration curves test consistency between the actual OS rates and the predicted survival rates at 1-, 3- and 5 years Blue lines represent for predicted survival rates, and red lines represent for actual OS rates
Fig. 8 Kaplan-Meier survival curves of high- and low-risk groups among patients sorted according to different clinicopathological variables A,B: Ages C,D: Sex E,F: Grade G: M stage H: N stage I,J: Stage K,L: T stage
Fig.9 Internal validation of the predictive signature for OS based on the entire TCGA dataset A: Kaplan-Meier survival curve in the first internal cohort B: Kaplan-Meier survival curve in the second internal cohort C: ROC curve and AUCs at 1-year, 3-years and 5-years survival in the first internal cohort D: ROC curve and AUCs at 1-year, 3-years and 5-years survival in the second internal cohort
Fig.10 Analysis of GSEA gene enrichment in high-risk patients A: KEGG-GSEA gene enrichment in high-risk patients B: GO-GSEA gene enrichment in high-risk patients
Fig.11 Analysis of scores of immune cell infiltration, immune cell function, and immune checkpoint expression in high-and low-risk groups A: Score of 16 immune cells infiltration in high-and low-risk groups B: Score of 13 immune cell function in high-and low-risk groups C: Analysis of differences in expression of immune checkpoints in high-and low-risk groups
Fig.12 Comparison of treatment drugs sensitivity between high- and low-risk groups A: IC₅₀ of Nilotinib in high- and low-risk groups B: IC₅₀ of Gefitinib in high- and low-risk groups C: IC₅₀ of Erlotinib in high- and low-risk groups D: IC₅₀ of Axitinib in high- and low-risk groups E: IC₅₀ of Tipifarnib high- and low-risk groups groups F: IC₅₀ of mitomycin C in high- and low-risk groups
Fig.13 The expression levels of AC009005.1 and its related mRNAs in HCC A: AC009005.1 are overexpressed in HCC tissues B: Compare with human liver immortalized cell line (THLE-2), AC009005.1 are overexpressed in HCC cell lines C: The expression level of AC009005.1 related mRNA in normal liver cells and liver cancer cells
Fig.14 Expression levels of transfected AC009005.1 mRNA in HCC A: Transfection efficiency was verified after transfection of AC009005.1 or negative control siRNAs B: The expression level of AC009005.1 related mRNA after transfection of AC009005.1 or negative control siRNAs
Fig.15 The changes in ROS level after transfection of AC009005.1 or negative control siRNAs in HepG2 cells
Fig.16 The changes in cell cycles after transfection of AC009005.1 or negative control siRNAs in HepG2 cells
Fig.17 The effect of AC009005.1 on migration, invasion, and proliferation in HepG2 cells A: Transwell assays were used to detect HCC invasion and migration. Representative experiments are shown B: Images were recorded 0 and 48 h after scratching the cell surface. representative images are shown C: The number of HCC cell colonies was reduced after AC009005.1 knockdown D: HCC cell proliferation was decreased after AC009005.1 knockdown detected by iCELLigence

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