1
|
Yao Y, Wang D, Zheng L, Zhao J, Tan M. Advances in prognostic models for osteosarcoma risk. Heliyon 2024; 10:e28493. [PMID: 38586328 PMCID: PMC10998144 DOI: 10.1016/j.heliyon.2024.e28493] [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: 09/30/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/09/2024] Open
Abstract
The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.
Collapse
Affiliation(s)
- Yi Yao
- Guangxi Engineering Center in Biomedical Materials for Tissue and Organ Regeneration, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Collaborative Innovation Centre of Regenerative Medicine and Medical Bioresource Development and Application Co-constructed by the Province and Ministry, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, China
| | - Dapeng Wang
- Guangxi Engineering Center in Biomedical Materials for Tissue and Organ Regeneration, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
| | - Li Zheng
- Guangxi Engineering Center in Biomedical Materials for Tissue and Organ Regeneration, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Collaborative Innovation Centre of Regenerative Medicine and Medical Bioresource Development and Application Co-constructed by the Province and Ministry, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, China
| | - Jinmin Zhao
- Guangxi Engineering Center in Biomedical Materials for Tissue and Organ Regeneration, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Collaborative Innovation Centre of Regenerative Medicine and Medical Bioresource Development and Application Co-constructed by the Province and Ministry, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Department of Orthopedics, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Manli Tan
- Guangxi Engineering Center in Biomedical Materials for Tissue and Organ Regeneration, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Collaborative Innovation Centre of Regenerative Medicine and Medical Bioresource Development and Application Co-constructed by the Province and Ministry, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China
- Life Sciences Institute, Guangxi Medical University, Nanning, 530021, China
| |
Collapse
|
2
|
Li C, Tao Y, Chen Y, Wu Y, He Y, Yin S, Xu S, Yu Y. Development of a metabolism-related signature for predicting prognosis, immune infiltration and immunotherapy response in breast cancer. Am J Cancer Res 2022; 12:5440-5461. [PMID: 36628282 PMCID: PMC9827085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 11/27/2022] [Indexed: 01/12/2023] Open
Abstract
Breast cancer (BRCA) is the most commonly diagnosed cancer and among the top causes of cancer deaths globally. The abnormality of the metabolic process is an important characteristic that distinguishes cancer cells from normal cells. Currently, there are few metabolic molecular models to evaluate the prognosis and treatment response of BRCA patients. By analyzing RNA-seq data of BRCA samples from public databases via bioinformatic approaches, we developed a prognostic signature based on seven metabolic genes (PLA2G2D, GNPNAT1, QPRT, SHMT2, PAICS, NT5E and PLPP2). Low-risk patients showed better overall survival in all five cohorts (TCGA cohort, two external validation cohorts and two internal validation cohorts). There was a higher proportion of tumor-infiltrating CD8+ T cells, CD4+ memory resting T cells, gamma delta T cells and resting dendritic cells and a lower proportion of M0 and M2 macrophages in the low-risk group. Low-risk patients also showed higher ESTIMATE scores, higher immune function scores, higher Immunophenoscores (IPS) and checkpoint expression, lower stemness scores, lower TIDE (Tumor Immune Dysfunction and Exclusion) scores and IC50 values for several chemotherapeutic agents, suggesting that low-risk patients could respond more favorably to immunotherapy and chemotherapy. Two real-world patient cohorts receiving anti-PD-1 therapy were applied for validating the predictive results. Molecular subtypes identified based on these seven genes also showed different immune characteristics. Immunohistochemical data obtained from the human protein atlas database demonstrated the protein expression of signature genes. This research may contribute to the identification of metabolic targets for BRCA and the optimization of risk stratification and personalized treatment for BRCA patients.
Collapse
Affiliation(s)
- Chunzhen Li
- National Key Laboratory of Medical Immunology and Institute of Immunology, Naval Medical UniversityShanghai 200433, China
| | - Yijie Tao
- National Key Laboratory of Medical Immunology and Institute of Immunology, Naval Medical UniversityShanghai 200433, China
| | - Yining Chen
- Faculty of Health Sciences and Engineering, University of Shanghai for Science and TechnologyShanghai 200433, China
| | - Yunyang Wu
- National Key Laboratory of Medical Immunology and Institute of Immunology, Naval Medical UniversityShanghai 200433, China
| | - Yixian He
- National Key Laboratory of Medical Immunology and Institute of Immunology, Naval Medical UniversityShanghai 200433, China
| | - Shulei Yin
- National Key Laboratory of Medical Immunology and Institute of Immunology, Naval Medical UniversityShanghai 200433, China
| | - Sheng Xu
- National Key Laboratory of Medical Immunology and Institute of Immunology, Naval Medical UniversityShanghai 200433, China
| | - Yizhi Yu
- National Key Laboratory of Medical Immunology and Institute of Immunology, Naval Medical UniversityShanghai 200433, China
| |
Collapse
|
3
|
Todosenko N, Yurova K, Khaziakhmatova O, Malashchenko V, Khlusov I, Litvinova L. Heparin and Heparin-Based Drug Delivery Systems: Pleiotropic Molecular Effects at Multiple Drug Resistance of Osteosarcoma and Immune Cells. Pharmaceutics 2022; 14:pharmaceutics14102181. [PMID: 36297616 PMCID: PMC9612132 DOI: 10.3390/pharmaceutics14102181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/29/2022] [Accepted: 10/10/2022] [Indexed: 11/23/2022] Open
Abstract
One of the main problems of modern health care is the growing number of oncological diseases both in the elderly and young population. Inadequately effective chemotherapy, which remains the main method of cancer control, is largely associated with the emergence of multidrug resistance in tumor cells. The search for new solutions to overcome the resistance of malignant cells to pharmacological agents is being actively pursued. Another serious problem is immunosuppression caused both by the tumor cells themselves and by antitumor drugs. Of great interest in this context is heparin, a biomolecule belonging to the class of glycosaminoglycans and possessing a broad spectrum of biological activity, including immunomodulatory and antitumor properties. In the context of the rapid development of the new field of “osteoimmunology,” which focuses on the collaboration of bone and immune cells, heparin and delivery systems based on it may be of intriguing importance for the oncotherapy of malignant bone tumors. Osteosarcoma is a rare but highly aggressive, chemoresistant malignant tumor that affects young adults and is characterized by constant recurrence and metastasis. This review describes the direct and immune-mediated regulatory effects of heparin and drug delivery systems based on it on the molecular mechanisms of (multiple) drug resistance in (onco) pathological conditions of bone tissue, especially osteosarcoma.
Collapse
Affiliation(s)
- Natalia Todosenko
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Kristina Yurova
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Olga Khaziakhmatova
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Vladimir Malashchenko
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
| | - Igor Khlusov
- Department of Morphology and General Pathology, Siberian State Medical University, 634050 Tomsk, Russia
| | - Larisa Litvinova
- Center for Immunology and Cellular Biotechnology, Immanuel Kant Baltic Federal University, 236001 Kaliningrad, Russia
- Correspondence:
| |
Collapse
|