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Comprehensive transcriptomic and co-expression analysis of ABL1 gene and molecularly targeted drugs in hepatocellular carcinoma based on multi-database mining

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Abstract

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide. Consequently, it is essential to identify biomarkers for treatment response and the prognosis prediction. We investigated whether ABL1 can function as a biomarker or a drug target for HCC. We assessed the ABL1 expression, genetic alterations and patients’ survival from LinkedOmics, GEO, TCGA and Human Protein Atlas. We analyzed PPI, GO and KEGG pathways. GSEA was analyzed for functional comparison. The current drugs targeting ABL1 were statistically analyzed using DRUGSURV and DGIdb database. We found ABL1 is overexpressed in HCC and its higher expression reduces survival probability. Genetic changes of ABL1 are not frequent. We screened out 25 differentially expressed genes correlated with ABL1. The top functions of ABL1 are biological regulation, metabolic process, protein-containing, and protein binding. KEGG pathways showed that ABL1 and correlated with ABL1 significantly genes markedly enriched in the ErbB signaling pathway, and pathways in cancer. We counted the existing drugs targeting ABL1, which indicates that inhibiting ABL1 expression may improve the survival probability of HCC. In conclusion, ABL1 plays a crucial role in the development and progression of this cancerization and is a potential drug target.

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Data availability

All data generated or analyzed during this study are included in this published article files.

Abbreviations

ABL1:

ABL proto-oncogene 1 non-receptor tyrosine kinase

EMT:

Epithelial-mesenchymal transformation

GEO:

Gene expression omnibus

GO:

Gene ontology

GSEA:

Gene set enrichment analysis

HCC:

Hepatocellular carcinoma

HPA:

Human protein atlas

KEGG:

Kyoto encyclopedia of genes and genomes

PPI:

Protein–protein interaction

TCGA:

The cancer genome atlas

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Funding

This work was supported partly by Guangzhou Science and Technology Project (202102080262); Guangdong Medical Science and Technology Research Fund (B2021205).

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Authors and Affiliations

  1. Medical Genetics Center, Guangdong Women and Children Hospital, Guangzhou, China

    Feifei Lan

  2. Zhu’s Team, Guangdong Medical University, Zhanjiang, China

    Xinqia Chen, Zhuolong Xiong, Zitong Cao, Liangzong Lu, Yueyuan Zhong, Xuliang Zhan, Yue Yang, Yingqi Shao, Minhua Li & Xiao Zhu

  3. Department of Pathology, Qingdao Municipal Hospital, Qingdao, China

    Zenglei Han

  4. School of Laboratory Medicine and Biomedical Engineering, Hangzhou Medical College, Hangzhou, China

    Xiao Zhu

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  1. Feifei Lan
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Contributions

XZ designed this study and supervised the research. FL, XC, LL and XZ conducted the main experiments, analyze data, and wrote the manuscript. ZX, ZC and XZ edited and discussed the manuscript. YZ, XZ, YY, YS and ML assisted the data analysis, edited and discussed the manuscript. ZH designed, supervised, edited and discussed the new version. XZ checked the statistical and bioinformatic accuracy as an expert in statistics and bioinformatics. All authors read and approved the final manuscript.

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Correspondence to Zenglei Han or Xiao Zhu.

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The work was approved by Guangdong Medical University Ethics committee. Informed consent forms are not required for patient data extracted from public databases.

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Lan, F., Chen, X., Xiong, Z. et al. Comprehensive transcriptomic and co-expression analysis of ABL1 gene and molecularly targeted drugs in hepatocellular carcinoma based on multi-database mining. Med Oncol 39, 146 (2022). https://doi.org/10.1007/s12032-022-01730-y

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