Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
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.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Mar 4:9:649265.
doi: 10.3389/fcell.2021.649265. eCollection 2021.

Multi-Omics Analysis of Anlotinib in Pancreatic Cancer and Development of an Anlotinib-Related Prognostic Signature

Xi Zhang  1 , Yang Liu  2 , Zhen Zhang  1 , Juan Tan  2 , Junjun Zhang  1 , Hao Ou  1 , Jie Li  3 , Zewen Song  1
Affiliations

Multi-Omics Analysis of Anlotinib in Pancreatic Cancer and Development of an Anlotinib-Related Prognostic Signature

Xi Zhang et al. Front Cell Dev Biol. .

Abstract

Aberrant regulation of angiogenesis involves in the growth and metastasis of tumors, but angiogenesis inhibitors fail to improve overall survival of pancreatic cancer patients in previous phase III clinical trials. A comprehensive knowledge of the mechanism of angiogenesis inhibitors against pancreatic cancer is helpful for clinical purpose and for the selection of patients who might benefit from the inhibitors. In this work, multi-omics analyses (transcriptomics, proteomics, and phosphoproteomics profiling) were carried out to delineate the mechanism of anlotinib, a novel angiogenesis inhibitor, against pancreatic cancer cells. The results showed that anlotinib exerted noteworthy cytotoxicity on pancreatic cancer cells. Multi-omics analyses revealed that anlotinib had a profound inhibitory effect on ribosome, and regulated cell cycle, RNA metabolism and lysosome. Based on the multi-omics results and available data deposited in public databases, an anlotinib-related gene signature was further constructed to identify a subgroup of pancreatic cancer patients who had a dismal prognosis and might be responsive to anlotinib.

Keywords: anlotinib; ingenuity pathway analysis; pancreatic cancer; phosphoproteomics; proteomics; transcriptomics.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Anlotinib was cytotoxic to pancreatic cancer cells. (A,B) CCK-8 assay of PANC-1 (A) or AsPC-1 (B) cells treated with anlotinib or DMSO. (C,D) Apoptosis assay of PANC-1 (C) or AsPC-1 (D) cells treated with anlotinib or DMSO. (E,F) Wound healing assay of PANC-1 (E) or AsPC-1 (F) cells treated with anlotinib or DMSO. (G,H) Invasion assay of PANC-1 (G) or AsPC-1 (H) cells treated with anlotinib or DMSO. Data were shown in mean ± SD and p < 0.05 was statistically significant. ***p < 0.001; ****p < 0.0001.
FIGURE 2
FIGURE 2
Transcription profiling and canonical pathway analysis. (A) The volcano plot of differentially expressed genes (DEGs) of PANC-1 cells treated with anlotinib. (B) Canonical pathways analysis of the DEGs via the IPA software. (C) The heatmap of the DEGs related to cell cycle: G2/M DNA damage checkpoint regulation. (D,E) Cell cycle assay of PANC-1 cells treated with anlotinib or DMSO. (F,G) Cell cycle assay of AsPC-1 cells treated with anlotinib or DMSO. Data were shown in mean ± SD and p < 0.05 was statistically significant. **p < 0.01; ****p < 0.0001.
FIGURE 3
FIGURE 3
Proteomics profiling. (A) The volcano plot of differentially expressed proteins of PANC-1 cells treated with anlotinib. (B) The venn plot of DEGs and differentially expressed proteins of PANC-1 cells treated with anlotinib. (C,D) GO (C) and KEGG (D) analysis of differentially expressed proteins of PANC-1 cells treated with anlotinib. (E) The heatmap of differentially expressed proteins related to lysosome. (F) The heatmap of differentially expressed proteins related to ribosome.
FIGURE 4
FIGURE 4
Phosphoproteomics profiling. (A) The volcano plot of differentially phosphorylated peptides of PANC-1 cells treated with anlotinib. (B) The venn plot of differentially expressed proteins and differentially phosphorylated proteins of PANC-1 cells treated with anlotinib. (C) Cellular location analysis of differentially phosphorylated proteins of PANC-1 cells treated with anlotinib. (D) KEGG analysis of differentially expressed and phosphorylated proteins of PANC-1 cells treated with anlotinib. (E) KEGG analysis of all differentially phosphorylated proteins of PANC-1 cells treated with anlotinib. (F) Domain enrichment analysis of all differentially phosphorylated proteins of PANC-1 cells treated with anlotinib.
FIGURE 5
FIGURE 5
Development and validation of anlotinib-related prognostic model in pancreatic cancer. (A) The venn plot of anlotinib-induced DEGs with prognostic relevance in TCGA_PAAD and GSE62452 data sets. (B,C) LASSO Cox regression analysis of anlotinib-induced DEGs in TCGA_PAAD data set, with the tuning parameter (λ) calculated based on partial likelihood deviance with tenfold cross-validation. An optimal log λ value was shown by the vertical black line in the plot. (D) The distribution of risk scores, survival status and expression of five crucial genes in patients of the TCGA_PAAD data set. (E,F) Kaplan–Meier plots (E) and time-dependent ROC analysis (F) of the risk score regarding OS and survival status in the TCGA_PAAD cohort. (G,H) Kaplan–Meier plots (G) and time-dependent ROC analysis (H) of the risk score regarding OS and survival status in the GSE62452 cohort. (I,J) Kaplan–Meier plots (I) and time-dependent ROC analysis (J) of the risk score regarding OS and survival status in the ICGC_AU cohort.
FIGURE 6
FIGURE 6
Results of the univariate (A) and multivariate (B) Cox regression analyses regarding OS in the TCGA_PAAD cohort.
FIGURE 7
FIGURE 7
GSEA between PAAD patients with high risk score and those with low risk score (A–J).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Abubakar M. B., Gan S. H. (2016). Molecular targets in advanced therapeutics of cancers: the role of pharmacogenetics. Oncology 91 3–12. 10.1159/000446437 - DOI - PubMed
    1. Annese T., Tamma R., Ruggieri S., Ribatti D. (2019). Angiogenesis in pancreatic cancer: pre-clinical and clinical studies. Cancers (Basel) 11:381. 10.3390/cancers11030381 - DOI - PMC - PubMed
    1. Attardi L. D., Reczek E. E., Cosmas C., Demicco E. G., McCurrach M. E., Lowe S. W., et al. (2000). PERP, an apoptosis-associated target of p53, is a novel member of the PMP-22/gas3 family. Genes Dev. 14 704–718. - PMC - PubMed
    1. Awais R., Spiller D. G., White M. R., Paraoan L. (2016). p63 is required beside p53 for PERP-mediated apoptosis in uveal melanoma. Br. J. Cancer 115 983–992. 10.1038/bjc.2016.269 - DOI - PMC - PubMed
    1. Beaudry V. G., Jiang D., Dusek R. L., Park E. J., Knezevich S., Ridd K., et al. (2010). Loss of the p53/p63 regulated desmosomal protein Perp promotes tumorigenesis. PLoS Genet. 6:e1001168. 10.1371/journal.pgen.1001168 - DOI - PMC - PubMed