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Yang Y, Pan Z, Sun J, Welch J, Klionsky DJ. Autophagy and machine learning: Unanswered questions. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167263. [PMID: 38801963 DOI: 10.1016/j.bbadis.2024.167263] [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: 02/15/2024] [Revised: 04/27/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024]
Abstract
Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.
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Affiliation(s)
- Ying Yang
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zhaoying Pan
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jianhui Sun
- Department of Computer Science, University of Virginia, Charlottesville, VA 22903, USA
| | - Joshua Welch
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel J Klionsky
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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2
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Zhao Q, Shao H, Zhang T. Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment. Am J Transl Res 2024; 16:3338-3354. [PMID: 39114691 PMCID: PMC11301471 DOI: 10.62347/smsg9047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 06/30/2024] [Indexed: 08/10/2024]
Abstract
Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.
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Affiliation(s)
- Qiannan Zhao
- Department of Clinical Laboratory, Yantaishan HospitalYantai 264003, Shandong, P. R. China
| | - Huaming Shao
- Department of Medical Laboratory, Qingdao West Coast Second HospitalQingdao 266500, Shandong, P. R. China
| | - Tianmei Zhang
- Department of Gynecology, Yantaishan HospitalYantai 264003, Shandong, P. R. China
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3
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Zhou R, Dai J, Zhou R, Wang M, Deng X, Zhuo Q, Wang Z, Li F, Yao D, Xu Y. Prognostic biomarker NRG2 correlates with autophagy and epithelial‑mesenchymal transition in breast cancer. Oncol Lett 2024; 27:277. [PMID: 38699660 PMCID: PMC11063754 DOI: 10.3892/ol.2024.14410] [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: 12/14/2023] [Accepted: 03/05/2024] [Indexed: 05/05/2024] Open
Abstract
Breast cancer (BRCA) is a leading cause of death in women worldwide, accounting for 31% of female cancer. Autophagy plays a crucial role in cancer progression, however, the function of autophagy-related gene neuroregulatory protein 2 (NRG2) in BRCA and its underlying molecular mechanisms remain unclear. In the present study, the expression of the NRG2 gene in BRCA was significantly down-regulated compared with the normal controls. The low expression level of NRG2 was related to poor survival rate of BRCA. The receiver operating characteristic curve of NRG2 showed a good diagnostic value for distinguishing BRCA from normal tissues (AUC=0.932). GO-KEGG analysis and GSEA enrichment analysis showed that NRG2 and its regulated genes were enriched in autophagy-related and immune-related pathways, and NRG2 was positively correlated with a number of immune cells and immune checkpoint genes. In addition, knockdown of NRG2 significantly promoted the proliferation, invasion and migration of BRCA cells. The autophagy marker, LC3-II and epithelial-mesenchymal transition (EMT) marker, vimentin were increased, while P62 and E-cadherin were decreased in response to NRG2 depletion. The findings of the present study demonstrated that NRG2 acts as a tumor suppressor factor that contributes to the immune escape and anti-tumor immunity inhibition by regulating the pathological process of autophagy and EMT, suggesting that NRG2 could be used as a prognostic biomarker and clinical target for BRCA therapy.
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Affiliation(s)
- Ruijie Zhou
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Jinjin Dai
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Runlong Zhou
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Mengyi Wang
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Xiaotong Deng
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Qing Zhuo
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Zhenrong Wang
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Fan Li
- Wuhan Bio-Raid Biotechnology Co., Ltd., Wuhan, Hubei 430075, P.R. China
| | - Di Yao
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
| | - Yao Xu
- Institute of Biology and Medicine, College of Life Science and Health, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China
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Ferraresi A, Girone C, Maheshwari C, Vallino L, Dhanasekaran DN, Isidoro C. Ovarian Cancer Cell-Conditioning Medium Induces Cancer-Associated Fibroblast Phenoconversion through Glucose-Dependent Inhibition of Autophagy. Int J Mol Sci 2024; 25:5691. [PMID: 38891879 PMCID: PMC11171902 DOI: 10.3390/ijms25115691] [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: 03/19/2024] [Revised: 05/19/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
One aspect of ovarian tumorigenesis which is still poorly understood is the tumor-stroma interaction, which plays a major role in chemoresistance and tumor progression. Cancer-associated fibroblasts (CAFs), the most abundant stromal cell type in the tumor microenvironment, influence tumor growth, metabolism, metastasis, and response to therapy, making them attractive targets for anti-cancer treatment. Unraveling the mechanisms involved in CAFs activation and maintenance is therefore crucial for the improvement of therapy efficacy. Here, we report that CAFs phenoconversion relies on the glucose-dependent inhibition of autophagy. We show that ovarian cancer cell-conditioning medium induces a metabolic reprogramming towards the CAF-phenotype that requires the autophagy-dependent glycolytic shift. In fact, 2-deoxy-D-glucose (2DG) strongly hampers such phenoconversion and, most importantly, induces the phenoreversion of CAFs into quiescent fibroblasts. Moreover, pharmacological inhibition (by proline) or autophagy gene knockdown (by siBECN1 or siATG7) promotes, while autophagy induction (by either 2DG or rapamycin) counteracts, the metabolic rewiring induced by the ovarian cancer cell secretome. Notably, the nutraceutical resveratrol (RV), known to inhibit glucose metabolism and to induce autophagy, promotes the phenoreversion of CAFs into normal fibroblasts even in the presence of ovarian cancer cell-conditioning medium. Overall, our data support the view of testing autophagy inducers for targeting the tumor-promoting stroma as an adjuvant strategy to improve therapy success rates, especially for tumors with a highly desmoplastic stroma, like ovarian cancer.
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Affiliation(s)
- Alessandra Ferraresi
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy; (C.G.); (C.M.); (L.V.)
| | - Carlo Girone
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy; (C.G.); (C.M.); (L.V.)
| | - Chinmay Maheshwari
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy; (C.G.); (C.M.); (L.V.)
| | - Letizia Vallino
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy; (C.G.); (C.M.); (L.V.)
| | - Danny N. Dhanasekaran
- Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA;
| | - Ciro Isidoro
- Laboratory of Molecular Pathology, Department of Health Sciences, Università del Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy; (C.G.); (C.M.); (L.V.)
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Liu K, Chen H, Li Y, Wang B, Li Q, Zhang L, Liu X, Wang C, Ertas YN, Shi H. Autophagy flux in bladder cancer: Cell death crosstalk, drug and nanotherapeutics. Cancer Lett 2024; 591:216867. [PMID: 38593919 DOI: 10.1016/j.canlet.2024.216867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 03/20/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024]
Abstract
Autophagy, a self-digestion mechanism, has emerged as a promising target in the realm of cancer therapy, particularly in bladder cancer (BCa), a urological malignancy characterized by dysregulated biological processes contributing to its progression. This highly conserved catabolic mechanism exhibits aberrant activation in pathological events, prominently featured in human cancers. The nuanced role of autophagy in cancer has been unveiled as a double-edged sword, capable of functioning as both a pro-survival and pro-death mechanism in a context-dependent manner. In BCa, dysregulation of autophagy intertwines with cell death mechanisms, wherein pro-survival autophagy impedes apoptosis and ferroptosis, while pro-death autophagy diminishes tumor cell survival. The impact of autophagy on BCa progression is multifaceted, influencing metastasis rates and engaging with the epithelial-mesenchymal transition (EMT) mechanism. Pharmacological modulation of autophagy emerges as a viable strategy to impede BCa progression and augment cell death. Notably, the introduction of nanoparticles for targeted autophagy regulation holds promise as an innovative approach in BCa suppression. This review underscores the intricate interplay of autophagy with cell death pathways and its therapeutic implications in the nuanced landscape of bladder cancer.
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Affiliation(s)
- Kuan Liu
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China
| | - Huijing Chen
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China
| | - Yanhong Li
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China
| | - Bei Wang
- Department of Gynecology, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China
| | - Qian Li
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China
| | - Lu Zhang
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China
| | - Xiaohui Liu
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China.
| | - Ce Wang
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China.
| | - Yavuz Nuri Ertas
- Department of Biomedical Engineering, Erciyes University, Kayseri, 38039, Turkey; ERNAM-Nanotechnology Research and Application Center, Erciyes University, Kayseri, 38039, Turkey; UNAM-National Nanotechnology Research Center, Bilkent University, Ankara, 06800, Turkey.
| | - Hongyun Shi
- Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, PR China.
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Suzuki T, Conant A, Jung Y, Bax R, Antonissen A, Chen W, Yu G, Ioffe YJ, Wang C, Unternaehrer JJ. A Stem-like Patient-Derived Ovarian Cancer Model of Platinum Resistance Reveals Dissociation of Stemness and Resistance. Int J Mol Sci 2024; 25:3843. [PMID: 38612653 PMCID: PMC11011340 DOI: 10.3390/ijms25073843] [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: 02/26/2024] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
To understand chemoresistance in the context of cancer stem cells (CSC), a cisplatin resistance model was developed using a high-grade serous ovarian cancer patient-derived, cisplatin-sensitive sample, PDX4. As a molecular subtype-specific stem-like cell line, PDX4 was selected for its representative features, including its histopathological and BRCA2 mutation status, and exposed to cisplatin in vitro. In the cisplatin-resistant cells, transcriptomics were carried out, and cell morphology, protein expression, and functional status were characterized. Additionally, potential signaling pathways involved in cisplatin resistance were explored. Our findings reveal the presence of distinct molecular signatures and phenotypic changes in cisplatin-resistant PDX4 compared to their sensitive counterparts. Surprisingly, we observed that chemoresistance was not inherently linked with increased stemness. In fact, although resistant cells expressed a combination of EMT and stemness markers, functional assays revealed that they were less proliferative, migratory, and clonogenic-features indicative of an underlying complex mechanism for cell survival. Furthermore, DNA damage tolerance and cellular stress management pathways were enriched. This novel, syngeneic model provides a valuable platform for investigating the underlying mechanisms of cisplatin resistance in a clinically relevant context, contributing to the development of targeted therapies tailored to combat resistance in stem-like ovarian cancer.
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Affiliation(s)
- Tise Suzuki
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
| | - Ashlyn Conant
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
| | - Yeonkyu Jung
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
- Department of Biology, California State University San Bernardino, San Bernardino, CA 92407, USA
| | - Ryan Bax
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
| | - Ashley Antonissen
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
- Department of Biology, California State University San Bernardino, San Bernardino, CA 92407, USA
| | - Wanqiu Chen
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
- Center for Genomics, Loma Linda University, Loma Linda, CA 92354, USA
| | - Gary Yu
- Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Yevgeniya J Ioffe
- Division of Gynecologic Oncology, Department of Gynecology and Obstetrics, Loma Linda University Medical Center, Loma Linda, CA 92354, USA
| | - Charles Wang
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
- Center for Genomics, Loma Linda University, Loma Linda, CA 92354, USA
| | - Juli J Unternaehrer
- Division of Biochemistry, Department of Basic Sciences, Loma Linda University, Loma Linda, CA 92354, USA
- Division of Gynecologic Oncology, Department of Gynecology and Obstetrics, Loma Linda University Medical Center, Loma Linda, CA 92354, USA
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Huang W, Zhu Q, Shi Z, Tu Y, Li Q, Zheng W, Yuan Z, Li L, Zu X, Hao Y, Chu B, Jiang Y. Dual inhibitors of DNMT and HDAC induce viral mimicry to induce antitumour immunity in breast cancer. Cell Death Discov 2024; 10:143. [PMID: 38490978 PMCID: PMC10943227 DOI: 10.1038/s41420-024-01895-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/18/2024] Open
Abstract
The existing conventional treatments for breast cancer, including immune checkpoint blockade, exhibit limited effects in some cancers, particularly triple-negative breast cancer. Epigenetic alterations, specifically DNMT and HDAC alterations, are implicated in breast cancer pathogenesis. We demonstrated that DNMTs and HDACs are overexpressed and positively correlated in breast cancer. The combination of DNMT and HDAC inhibitors has shown synergistic antitumour effects, and our previously designed dual DNMT and HDAC inhibitor (termed DNMT/HDACi) 15a potently inhibits breast cancer cell proliferation, migration, and invasion and induces apoptosis in vitro and in vivo. Mechanistically, 15a induces a viral mimicry response by promoting the expression of endogenous retroviral elements in breast cancer cells, thus increasing the intracellular level of double-stranded RNA to activate the RIG-I-MAVS pathway. This in turn promotes the production of interferons and chemokines and augments the expression of interferon-stimulated genes and PD-L1. The combination of 15a and an anti-PD-L1 antibody had an additive effect in vivo. These findings indicate that this DNMT/HDACi has immunomodulatory functions and enhances the effectiveness of immune checkpoint blockade therapy. A novel dual DNMT and HDAC inhibitor induces viral mimicry, which induces the accumulation of dsRNA to activate tumoral IFN signalling and cytokine production to enhance the immune response in breast cancer.
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Affiliation(s)
- Wenjun Huang
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
| | - Qingyun Zhu
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, 421001, China
| | - Zhichao Shi
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Yao Tu
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
| | - Qinyuan Li
- State Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, China
| | - Wenwen Zheng
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China
| | - Zigao Yuan
- State Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, China
| | - Lulu Li
- State Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, China
| | - Xuyu Zu
- The First Affiliated Hospital, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, 421001, China
| | - Yue Hao
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China.
| | - Bizhu Chu
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China.
| | - Yuyang Jiang
- School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518055, China.
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen, 518132, China.
- State Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Shenzhen, 518055, China.
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
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Liu Z, Lu T, Qian R, Wang Z, Qi R, Zhang Z. Exploiting Nanotechnology for Drug Delivery: Advancing the Anti-Cancer Effects of Autophagy-Modulating Compounds in Traditional Chinese Medicine. Int J Nanomedicine 2024; 19:2507-2528. [PMID: 38495752 PMCID: PMC10944250 DOI: 10.2147/ijn.s455407] [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: 12/17/2023] [Accepted: 03/06/2024] [Indexed: 03/19/2024] Open
Abstract
Background Cancer continues to be a prominent issue in the field of medicine, as demonstrated by recent studies emphasizing the significant role of autophagy in the development of cancer. Traditional Chinese Medicine (TCM) provides a variety of anti-tumor agents capable of regulating autophagy. However, the clinical application of autophagy-modulating compounds derived from TCM is impeded by their restricted water solubility and bioavailability. To overcome this challenge, the utilization of nanotechnology has been suggested as a potential solution. Nonetheless, the current body of literature on nanoparticles delivering TCM-derived autophagy-modulating anti-tumor compounds for cancer treatment is limited, lacking comprehensive summaries and detailed descriptions. Methods Up to November 2023, a comprehensive research study was conducted to gather relevant data using a variety of databases, including PubMed, ScienceDirect, Springer Link, Web of Science, and CNKI. The keywords utilized in this investigation included "autophagy", "nanoparticles", "traditional Chinese medicine" and "anticancer". Results This review provides a comprehensive analysis of the potential of nanotechnology in overcoming delivery challenges and enhancing the anti-cancer properties of autophagy-modulating compounds in TCM. The evaluation is based on a synthesis of different classes of autophagy-modulating compounds in TCM, their mechanisms of action in cancer treatment, and their potential benefits as reported in various scholarly sources. The findings indicate that nanotechnology shows potential in enhancing the availability of autophagy-modulating agents in TCM, thereby opening up a plethora of potential therapeutic avenues. Conclusion Nanotechnology has the potential to enhance the anti-tumor efficacy of autophagy-modulating compounds in traditional TCM, through regulation of autophagy.
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Affiliation(s)
- Zixian Liu
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Tianming Lu
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Ruoning Qian
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Zian Wang
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Ruogu Qi
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
| | - Zhengguang Zhang
- School of Medicine, Nanjing University of Chinese Medicine, Jiangsu, Nanjing, People’s Republic of China
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9
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Wu M, Gu S, Yang J, Zhao Y, Sheng J, Cheng S, Xu S, Wu Y, Ma M, Luo X, Zhang H, Wang Y, Zhao A. Comprehensive machine learning-based preoperative blood features predict the prognosis for ovarian cancer. BMC Cancer 2024; 24:267. [PMID: 38408960 PMCID: PMC10895771 DOI: 10.1186/s12885-024-11989-1] [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: 11/07/2023] [Accepted: 02/10/2024] [Indexed: 02/28/2024] Open
Abstract
PURPOSE Significant advancements in improving ovarian cancer (OC) outcomes have been limited over the past decade. To predict prognosis and improve outcomes of OC, we plan to develop and validate a robust prognosis signature based on blood features. METHODS We screened age and 33 blood features from 331 OC patients. Using ten machine learning algorithms, 88 combinations were generated, from which one was selected to construct a blood risk score (BRS) according to the highest C-index in the test dataset. RESULTS Stepcox (both) and Enet (alpha = 0.7) performed the best in the test dataset with a C-index of 0.711. Meanwhile, the low RBS group possessed observably prolonged survival in this model. Compared to traditional prognostic-related features such as age, stage, grade, and CA125, our combined model had the highest AUC values at 3, 5, and 7 years. According to the results of the model, BRS can provide accurate predictions of OC prognosis. BRS was also capable of identifying various prognostic stratifications in different stages and grades. Importantly, developing the nomogram may improve performance by combining BRS and stage. CONCLUSION This study provides a valuable combined machine-learning model that can be used for predicting the individualized prognosis of OC patients.
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Affiliation(s)
- Meixuan Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Sijia Gu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Yaqian Zhao
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Jindan Sheng
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Shanshan Cheng
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Shilin Xu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yongsong Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Mingjun Ma
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Xiaomei Luo
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Hao Zhang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Yu Wang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
- Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China.
| | - Aimin Zhao
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
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Yang Z, Zhou D, Huang J. Identifying Explainable Machine Learning Models and a Novel SFRP2 + Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer. Int J Mol Sci 2023; 24:16942. [PMID: 38069266 PMCID: PMC10706905 DOI: 10.3390/ijms242316942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.
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Affiliation(s)
| | | | - Jun Huang
- School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China
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