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Ma D, Liu S, Liu K, Kong L, Xiao L, Xin Q, Jiang C, Wu J. MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells by binding ITGB4/LAMB3 to activate the AKT signaling pathway. Cancer Biol Ther 2024; 25:2314324. [PMID: 38375821 PMCID: PMC10880501 DOI: 10.1080/15384047.2024.2314324] [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: 09/21/2023] [Accepted: 01/31/2024] [Indexed: 02/21/2024] Open
Abstract
Colorectal cancer (CRC) is one of the most lethal cancers. Single-cell RNA sequencing (scRNA-seq) and protein-protein interactions (PPIs) have enabled the systematic study of CRC. In our research, the activation of the AKT pathway in CRC was analyzed by KEGG using single-cell sequencing data from the GSE144735 dataset. The correlation and PPIs of MDFI and ITGB4/LAMB3 were examined. The results were verified in the TCGA and CCLE and further tested by coimmunoprecipitation experiments. The effect of MDFI on the AKT pathway via ITGB4/LAMB3 was validated by knockdown and lentiviral overexpression experiments. The effect of MDFI on oxaliplatin/fluorouracil sensitivity was probed by colony formation assay and CCK8 assay. We discovered that MDFI was positively associated with ITGB4/LAMB3. In addition, MDFI was negatively associated with oxaliplatin/fluorouracil sensitivity. MDFI upregulated the AKT pathway by directly interacting with LAMB3 and ITGB4 in CRC cells, and enhanced the proliferation of CRC cells via the AKT pathway. Finally, MDFI reduced the sensitivity of CRC cells to oxaliplatin and fluorouracil. In conclusion, MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells, partially through the activation of the AKT signaling pathway by the binding to ITGB4/LAMB3. Our findings provide a possible molecular target for CRC therapy.
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Affiliation(s)
- Ding Ma
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, Jiangsu, China
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shuwen Liu
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Kua Liu
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Lingkai Kong
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Lingjun Xiao
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Qilei Xin
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Jinan City, Shandong Province, China
| | - Chunping Jiang
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Jinan City, Shandong Province, China
| | - Junhua Wu
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Jinan City, Shandong Province, China
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Wang MH, Gao YH, Zhao ZD, Zhang HW. A novel model combining genes associated with disulfidptosis and glycolysis to predict breast cancer prognosis, molecular subtypes, and treatment response. ENVIRONMENTAL TOXICOLOGY 2024; 39:4347-4359. [PMID: 38747344 DOI: 10.1002/tox.24329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/25/2024] [Accepted: 04/29/2024] [Indexed: 08/09/2024]
Abstract
Breast cancer (BC) is a heterogeneous malignancy with a dismal prognosis. Disulfidptosis is a novel type of regulated cell death that happens in the presence of glucose deficiency and is linked to the metabolic process of glycolysis. However, the mechanism of action of disulfidptosis and glycolysis-related genes (DGRG) in BC, as well as their prognostic value in BC patients, remain unknown. After identifying the differentially expressed DGRG in normal and BC tissues, a number of machine learning algorithms were utilized to select essential prognostic genes to develop a model, including SLC7A11, CACNA1H, SDC1, CHST1, and TFF3. The expression characteristics of these genes were then examined using single-cell RNA sequencing, and BC was classified into three clusters using "ConsensusClusterPlus" based on these genes. The DGRG model's median risk score can categorize BC patients into high-risk and low-risk groups. Furthermore, we investigated variations in clinical landscape, immunoinvasion analysis, tumor immune dysfunction and rejection (TIDE), and medication sensitivity in patients in the DGRG model's high- and low-risk groups. Patients in the low-risk group performed better on immunological and chemotherapeutic therapies and had lower TIDE scores. In conclusion, the DGRG model we developed has significant clinical application potential because it can accurately predict the prognosis of BC, TME, and pharmacological treatment responses.
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Affiliation(s)
- Mei-Huan Wang
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yue-Hua Gao
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhen-Dan Zhao
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Hua-Wei Zhang
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
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Cui N, Ding F. Co-Expression Network Analysis and Molecular Docking Demonstrate That Diosgenin Inhibits Gastric Cancer Progression via SLC1A5/mTORC1 Pathway. Drug Des Devel Ther 2024; 18:3157-3173. [PMID: 39071813 PMCID: PMC11283265 DOI: 10.2147/dddt.s458613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/10/2024] [Indexed: 07/30/2024] Open
Abstract
Background Tumor-Node-Metastasis (TNM) stage of gastric cancer (GC) is one of the main factors affecting clinical outcome. The aim of this study was to explore the targets related to TNM stage of GC, and screening natural bioactive drug. Methods RNA sequencing data of the TCGA-STAD cohort were downloaded from UCSC database. Genes associated with TNM staging were identified by weighted gene co-expression network analysis (WGCNA). Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (Xgboost), random forest (RF) and cytohubba plug-in of cytoscope were applied to screen hub genes. Natural bioactive ingredients were available from the HERB database. Molecular docking was used to evaluate the binding activity of active ingredients to the hub protein. CCK-8, flow cytometry, transwell and Western blot assays were used to analyze the effects of diosgenin on GC cells. Results 898 TNM-related genes were screened out through WGCNA. Three genes associated with GC progression/prognosis were identified, including nuclear receptor subfamily 3 group C member 2 (NR3C2), solute carrier family 1 member 5 (SLC1A5) and FAT atypical cadherin 1 (FAT1) based on the machine learning algorithms and hub co-expression network analysis. Diosgenin had good binding activity with SLC1A5. SLC1A5 was highly expressed in GC and was closely associated with tumor stage, overall survival and immune infiltration of GC patients. Diosgenin could inhibit cell viability and invasive ability, promote apoptosis and induce cell cycle arrest in G0/G1 phase. In addition, diosgenin promoted cleaved caspase 3 expression and inhibited Ki67, cyclin D1, p-S6K1, and SLC1A5 expression levels, while the mTORC1 activator (MHY1485) reversed this phenomenon. Conclusion For the first time, this work reports diosgenin may inhibit the activation of mTORC1 signaling through targeting SLC1A5, thereby inhibiting the malignant behaviors of GC cells.
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Affiliation(s)
- Ning Cui
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, People’s Republic of China
| | - Feng Ding
- Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China
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Xu Y, Chen B, Guo Z, Chen C, Wang C, Zhou H, Zhang C, Feng Y. Identification of diagnostic markers for moyamoya disease by combining bulk RNA-sequencing analysis and machine learning. Sci Rep 2024; 14:5931. [PMID: 38467737 PMCID: PMC10928210 DOI: 10.1038/s41598-024-56367-w] [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: 09/27/2023] [Accepted: 03/05/2024] [Indexed: 03/13/2024] Open
Abstract
Moyamoya disease (MMD) remains a chronic progressive cerebrovascular disease with unknown etiology. A growing number of reports describe the development of MMD relevant to infection or autoimmune diseases. Identifying biomarkers of MMD is to understand the pathogenesis and development of novel targeted therapy and may be the key to improving the patient's outcome. Here, we analyzed gene expression from two GEO databases. To identify the MMD biomarkers, the weighted gene co-expression network analysis (WGCNA) and the differential expression analyses were conducted to identify 266 key genes. The KEGG and GO analyses were then performed to construct the protein interaction (PPI) network. The three machine-learning algorithms of support vector machine-recursive feature elimination (SVM-RFE), random forest and least absolute shrinkage and selection operator (LASSO) were used to analyze the key genes and take intersection to construct MMD diagnosis based on the four core genes found (ACAN, FREM1, TOP2A and UCHL1), with highly accurate AUCs of 0.805, 0.903, 0.815, 0.826. Gene enrichment analysis illustrated that the MMD samples revealed quite a few differences in pathways like one carbon pool by folate, aminoacyl-tRNA biosynthesis, fat digestion and absorption and fructose and mannose metabolism. In addition, the immune infiltration profile demonstrated that ACAN expression was associated with mast cells resting, FREM1 expression was associated with T cells CD4 naive, TOP2A expression was associated with B cells memory, UCHL1 expression was associated with mast cells activated. Ultimately, the four key genes were verified by qPCR. Taken together, our study analyzed the diagnostic biomarkers and immune infiltration characteristics of MMD, which may shed light on the potential intervention targets of moyamoya disease patients.
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Affiliation(s)
- Yifan Xu
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China
| | - Bing Chen
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China
| | - Zhongxiang Guo
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China
| | - Cheng Chen
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China
| | - Chao Wang
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China
| | - Han Zhou
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China
| | - Chonghui Zhang
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China
| | - Yugong Feng
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao City, 266000, China.
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Sadat Kalaki N, Ahmadzadeh M, Najafi M, Mobasheri M, Ajdarkosh H, Karbalaie Niya MH. Systems biology approach to identify biomarkers and therapeutic targets for colorectal cancer. Biochem Biophys Rep 2024; 37:101633. [PMID: 38283191 PMCID: PMC10821538 DOI: 10.1016/j.bbrep.2023.101633] [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: 08/18/2023] [Revised: 12/24/2023] [Accepted: 12/27/2023] [Indexed: 01/30/2024] Open
Abstract
Background Colorectal cancer (CRC), is the third most prevalent cancer across the globe, and is often detected at advanced stage. Late diagnosis of CRC, leave the chemotherapy and radiotherapy as the main options for the possible treatment of the disease which are associated with severe side effects. In the present study, we seek to explore CRC gene expression data using a systems biology framework to identify potential biomarkers and therapeutic targets for earlier diagnosis and treatment of the disease. Methods The expression data was retrieved from the gene expression omnibus (GEO). Differential gene expression analysis was conducted using R/Bioconductor package. The PPI network was reconstructed by the STRING. Cystoscope and Gephi software packages were used for visualization and centrality analysis of the PPI network. Clustering analysis of the PPI network was carried out using k-mean algorithm. Gene-set enrichment based on Gene Ontology (GO) and KEGG pathway databases was carried out to identify the biological functions and pathways associated with gene groups. Prognostic value of the selected identified hub genes was examined by survival analysis, using GEPIA. Results A total of 848 differentially expressed genes were identified. Centrality analysis of the PPI network resulted in identification of 99 hubs genes. Clustering analysis dissected the PPI network into seven interactive modules. While several DEGs and the central genes in each module have already reported to contribute to CRC progression, survival analysis confirmed high expression of central genes, CCNA2, CD44, and ACAN contribute to poor prognosis of CRC patients. In addition, high expression of TUBA8, AMPD3, TRPC1, ARHGAP6, JPH3, DYRK1A and ACTA1 was found to associate with decreased survival rate. Conclusion Our results identified several genes with high centrality in PPI network that contribute to progression of CRC. The fact that several of the identified genes have already been reported to be relevant to diagnosis and treatment of CRC, other highlighted genes with limited literature information may hold potential to be explored in the context of CRC biomarker and drug target discovery.
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Affiliation(s)
- Niloufar Sadat Kalaki
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
- International Institute of New Sciences (IINS), Tehran, Iran
| | - Mozhgan Ahmadzadeh
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Mohammad Najafi
- Department of Biochemistry, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Meysam Mobasheri
- Department of Biotechnology, Faculty of Advanced Sciences and Technology, Tehran Islamic Azad University of Medical Sciences, Tehran, Iran
- International Institute of New Sciences (IINS), Tehran, Iran
| | - Hossein Ajdarkosh
- Gastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hadi Karbalaie Niya
- Gastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran
- Department of Virology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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Using Single-Cell RNA Sequencing and MicroRNA Targeting Data to Improve Colorectal Cancer Survival Prediction. Cells 2023; 12:cells12020228. [PMID: 36672162 PMCID: PMC9856396 DOI: 10.3390/cells12020228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/06/2023] Open
Abstract
Colorectal cancer has proven to be difficult to treat as it is the second leading cause of cancer death for both men and women worldwide. Recent work has shown the importance of microRNA (miRNA) in the progression and metastasis of colorectal cancer. Here, we develop a metric based on miRNA-gene target interactions, previously validated to be associated with colorectal cancer. We use this metric with a regularized Cox model to produce a small set of top-performing genes related to colon cancer. We show that using the miRNA metric and a Cox model led to a meaningful improvement in colon cancer survival prediction and correct patient risk stratification. We show that our approach outperforms existing methods and that the top genes identified by our process are implicated in NOTCH3 signaling and general metabolism pathways, which are essential to colon cancer progression.
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Mi C, Zhao Y, Ren L, Zhang D. Inhibition of MDFI attenuates proliferation and glycolysis of
Helicobacter pylori
‐infected gastric cancer cells by inhibiting Wnt/β‐catenin pathway. Cell Biol Int 2022; 46:2198-2206. [DOI: 10.1002/cbin.11907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/21/2022] [Accepted: 09/01/2022] [Indexed: 01/10/2023]
Affiliation(s)
- Chen Mi
- Department of Gastroenterology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi province China
| | - Yan Zhao
- Department of Gastroenterology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi province China
| | - Li Ren
- Department of Gastroenterology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi province China
| | - Dan Zhang
- Department of Gastroenterology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi province China
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