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Zhang J, Ma J, Li Y, An Y, Du W, Yang Q, Huang M, Cai X. Overexpression of Aurora Kinase B Is Correlated with Diagnosis and Poor Prognosis in Hepatocellular Carcinoma. Int J Mol Sci 2024; 25:2199. [PMID: 38396874 PMCID: PMC10889672 DOI: 10.3390/ijms25042199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024] Open
Abstract
Aurora kinase B (AURKB) overexpression promotes tumor initiation and development by participating in the cell cycle. In this study, we focused on the mechanism of AURKB in hepatocellular carcinoma (HCC) progression and on AURKB's value as a diagnostic and prognostic biomarker in HCC. We used data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to analyze AURKB expression in HCC. We found that the expression levels of AURKB in HCC samples were higher than those in the corresponding control group. R packages were used to analyze RNA sequencing data to identify AURKB-related differentially expressed genes (DEGs), and these genes were found to be significantly enriched during the cell cycle. The biological function of AURKB was verified, and the results showed that cell proliferation was slowed down and cells were arrested in the G2/M phase when AURKB was knocked down. AURKB overexpression resulted in significant differences in clinical symptoms, such as the clinical T stage and pathological stage. Kaplan-Meier survival analysis, Cox regression analysis, and Receiver Operating Characteristic (ROC) curve analysis suggested that AURKB overexpression has good diagnostic and prognostic potential in HCC. Therefore, AURKB may be used as a potential target for the diagnosis and cure of HCC.
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Affiliation(s)
| | | | | | | | | | | | | | - Xuefei Cai
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, 1 Yixue Yuan Road, Chongqing 400016, China; (J.Z.); (J.M.); (Y.L.); (Y.A.); (W.D.); (Q.Y.); (M.H.)
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Liu P, Ying J, Guo X, Tang X, Zou W, Wang T, Xu X, Zhao B, Song N, Cheng J. An exploration of the effect of Chinese herbal compound on the occurrence and development of large intestine cancer and intestinal flora. Heliyon 2024; 10:e23533. [PMID: 38173486 PMCID: PMC10761579 DOI: 10.1016/j.heliyon.2023.e23533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
This study was conducted to observe the effect of Chinese herbal compound on the treatment of colon cancer using AOM/DSS-induced C57BL/6J colon cancer mice and to validate potential influence on intestinal flora of mice. A colorectal cancer (CRC) mouse model was built with a total of 50 C57BL/6J mice that were induced by administrating AOM/DSS. These experimental animals were split up into 5 groups, a control group, a model group, and low-, medium- and high-dose Chinese herbal compound groups. All mice were given Chinese herbal compound treatment, and the colon tissues of each group were harvested with the length measured and the number of colon polyps accounted. The Ki-67 expression in the colon tissues was detected via immuno-histochemistry. Relative quantification of the expression of genes and proteins was determined through qPCR and WB assays. Contents of IL-6, TNF-α, IFN-γ, and IL-10 in serum and colon tissues of mice were determined by ELISA. An additional 16S rRNA sequencing analysis was implemented for the identification of mouse intestinal flora. The results suggested that all low-, medium- or high-dose Chinese herbal compound could markedly inhibit the shortening of colon length and significant number reduction of colon polyps in the model group. The relative expression of genes and proteins (PCNA, Muc16, and MMP-9) associated with proliferation in mouse colon tissues were inhibited. In addition, compared with the model group, the contents of IL-6, TNF-α, and IFN-γ in serum and colon tissues were substantially decreased in the high-dose Chinese herbal compound group, thereby reducing the structure damage in colon tissues and the infiltration degree of inflammatory cells. Besides, the expression of TLR4/MyD88/NF-κB protein was markedly decreased. The 16S rRNA sequencing analysis demonstrated that mice in the model group had decreased intestinal flora diversity, and there were significant changes in flora abundance and amino acid metabolism between the control group and the model group. Taken together, the treatment of Chinese herbal compound against CRC in this study might be regulated by the TLR4/MyD88/NF-κB signaling pathway, and the imbalance in intestinal flora was also closely related to CRC occurrence.
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Affiliation(s)
- Pingyu Liu
- Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
| | - Jian Ying
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China
| | - Xin Guo
- Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
| | - Xiaohui Tang
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China
| | - Wenjuan Zou
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China
| | - Tiantian Wang
- Department of Emergency Intensive Care, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China
| | - Xinyi Xu
- Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China
| | - Bin Zhao
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China
| | - Na Song
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China
| | - Jun Cheng
- Department of Oncology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China
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Wu HY, Luo LF, Wei F, Jiang HM. Comprehensive clinicopathological significance and putative transcriptional mechanisms of Forkhead box M1 factor in hepatocellular carcinoma. World J Surg Oncol 2023; 21:366. [PMID: 38001498 PMCID: PMC10675979 DOI: 10.1186/s12957-023-03250-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND The Forkhead box M1 factor (FOXM1) is a crucial activator for cancer cell proliferation. While FOXM1 has been shown to promote hepatocellular carcinoma (HCC) progression, its transcriptional mechanisms remain incompletely understood. METHODS We performed an in-house tissue microarray on 313 HCC and 37 non-HCC tissue samples, followed by immunohistochemical staining. Gene chips and high throughput sequencing data were used to assess FOXM1 expression and prognosis. To identify candidate targets of FOXM1, we comprehensively reanalyzed 41 chromatin immunoprecipitation followed by sequencing (ChIP-seq) data sets. We predicted FOXM1 transcriptional targets in HCC by intersecting candidate FOXM1 targets with HCC overexpressed genes and FOXM1 correlation genes. Enrichment analysis was employed to address the potential mechanisms of FOXM1 underlying HCC. Finally, single-cell RNA sequencing analysis was performed to confirm the transcriptional activity of FOXM1 on its predicted targets. RESULTS This study, based on 4235 HCC tissue samples and 3461 non-HCC tissue samples, confirmed the upregulation of FOXM1 in HCC at mRNA and protein levels (standardized mean difference = 1.70 [1.42, 1.98]), making it the largest multi-centered study to do so. Among HCC patients, FOXM1 was increased in Asian and advanced subgroups, and high expression of FOXM1 had a strong ability to differentiate HCC tissue from non-HCC tissue (area under the curve = 0.94, sensitivity = 88.72%, specificity = 87.24%). FOXM1 was also shown to be an independent exposure risk factor for HCC, with a pooled hazard ratio of 2.00 [1.77, 2.26]. The predicted transcriptional targets of FOXM1 in HCC were predominantly enriched in nuclear division, chromosomal region, and catalytic activity acting on DNA. A gene cluster encoding nine transcriptional factors was predicted to be positively regulated by FOXM1, promoting the cell cycle signaling pathway in HCC. Finally, the transcriptional activity of FOXM1 and its targets was supported by single-cell analysis of HCC cells. CONCLUSIONS This study not only confirmed the upregulation of FOXM1 in HCC but also identified it as an independent risk factor. Moreover, our findings enriched our understanding of the complex transcriptional mechanisms underlying HCC pathogenesis, with FOXM1 potentially promoting HCC progression by activating other transcription factors within the cell cycle pathway.
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Affiliation(s)
- Hua-Yu Wu
- Department of Medical Experimental Center, The First People's Hospital of Nanning, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Li-Feng Luo
- Department of Pathology, The First People's Hospital of Nanning, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Fang Wei
- Department of Pathology, The First People's Hospital of Nanning, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China
| | - Hong-Mian Jiang
- Department of Pathology, The First People's Hospital of Nanning, Nanning, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
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Yang CZ, Guo W, Wang YF, Hu LH, Wang J, Luo JM, Yao XH, Liu S, Tao LT, Sun LL, Lin LZ. Reduction in gefitinib resistance mediated by Yi-Fei San-Jie pill in non-small cell lung cancer through regulation of tyrosine metabolism, cell cycle, and the MET/EGFR signaling pathway. JOURNAL OF ETHNOPHARMACOLOGY 2023; 314:116566. [PMID: 37169317 DOI: 10.1016/j.jep.2023.116566] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/16/2023] [Accepted: 04/29/2023] [Indexed: 05/13/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE The Chinese herbal prescription Yi-Fei San-Jie pill (YFSJ) has been used for adjuvant treatment in patients with lung cancer for a long time. AIM OF THE STUDY Reports have indicated that the combination of gefitinib (Gef) with YFSJ inhibits the proliferation of EGFR-TKI-resistant cell lines by enhancing cellular apoptosis and autophagy in non-small cell lung cancer (NSCLC). However, the molecular mechanisms underlying the effect of YFSJ on EGFR-TKI resistance and related metabolic pathways remain to be explored. MATERIALS AND METHODS In our report, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), metabolomics, network pharmacology, bioinformatics, and biological analysis methods were used to investigate the mechanism. RESULTS The UPLC-MS/MS data identified 42 active compounds of YFSJ extracts. YFSJ extracts can enhance the antitumor efficacy of Gef without hepatic and renal toxicity in vivo. The analysis of the metabolomics pathway enrichment revealed that YFSJ mainly affected the tyrosine metabolism pathway in rat models. Moreover, YFSJ has been shown to reverse Gef resistance and improve the effects of Gef on the cellular viability, migration capacity, and cell cycle arrest of NSCLC cell lines with EGFR mutations. The results of network pharmacology and molecular docking analyses revealed that tyrosine metabolism-related active compounds of YFSJ affect EGFR-TKIs resistance in NSCLC by targeting cell cycle and the MET/EGFR signaling pathway; these findings were validated by western blotting and immunohistochemistry. CONCLUSIONS YFSJ inhibits NSCLC by inducing cell cycle arrest in the G1/S phase to suppress tumor growth, cell viability, and cell migration through synergistic effects with Gef via the tyrosine metabolic pathway and the EGFR/MET signaling pathway. To summarize, the findings of the current study indicate that YFSJ is a prospective complementary treatment for Gef-resistant NSCLC.
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Affiliation(s)
- Cai-Zhi Yang
- The First School of Medicine, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Wei Guo
- Department of Oncology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Yi-Fan Wang
- The First School of Medicine, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Lei-Hao Hu
- The First School of Medicine, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Jing Wang
- State Key Laboratory of Quality Research in Chinese Medicines, Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, 999078, China.
| | - Jia-Min Luo
- The First School of Medicine, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Xiao-Hui Yao
- Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Shan Liu
- The First School of Medicine, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Lan-Ting Tao
- Department of Oncology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
| | - Ling-Ling Sun
- Department of Oncology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
| | - Li-Zhu Lin
- Department of Oncology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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