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Xia X, Xu F, Dai D, Xiong A, Sun R, Ling Y, Qiu L, Wang R, Ding Y, Lin M, Li H, Xie Z. VDR is a potential prognostic biomarker and positively correlated with immune infiltration: a comprehensive pan-cancer analysis with experimental verification. Biosci Rep 2024; 44:BSR20231845. [PMID: 38639057 PMCID: PMC11065647 DOI: 10.1042/bsr20231845] [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: 10/30/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 04/20/2024] Open
Abstract
The vitamin D receptor (VDR) is a transcription factor that mediates a variety of biological functions of 1,25-dihydroxyvitamin D3. Although there is growing evidence of cytological and animal studies supporting the suppressive role of VDR in cancers, the conclusion is still controversial in human cancers and no systematic pan-cancer analysis of VDR is available. We explored the relationships between VDR expression and prognosis, immune infiltration, tumor microenvironment, or gene set enrichment analysis (GSEA) in 33 types of human cancers based on multiple public databases and R software. Meanwhile, the expression and role of VDR were experimentally validated in papillary thyroid cancer (PTC). VDR expression decreased in 8 types and increased in 12 types of cancer compared with normal tissues. Increased expression of VDR was associated with either good or poor prognosis in 13 cancer types. VDR expression was positively correlated with the infiltration of cancer-associated fibroblasts, macrophages, or neutrophils in 20, 12, and 10 cancer types respectively and this correlation was experimentally validated in PTC. Increased VDR expression was associated with increased percentage of stromal or immune components in tumor microenvironment (TME) in 24 cancer types. VDR positively and negatively correlated genes were enriched in immune cell function and energy metabolism pathways, respectively, in the top 9 highly lethal tumors. Additionally, VDR expression was increased in PTC and inhibited cell proliferation and migration. In conclusion, VDR is a potential prognostic biomarker and positively correlated with immune infiltration as well as stromal or immune components in TME in multiple human cancers.
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MESH Headings
- Receptors, Calcitriol/genetics
- Receptors, Calcitriol/metabolism
- Humans
- Tumor Microenvironment/immunology
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Prognosis
- Gene Expression Regulation, Neoplastic
- Thyroid Cancer, Papillary/immunology
- Thyroid Cancer, Papillary/genetics
- Thyroid Cancer, Papillary/pathology
- Thyroid Cancer, Papillary/metabolism
- Tumor-Associated Macrophages/immunology
- Tumor-Associated Macrophages/metabolism
- Thyroid Neoplasms/immunology
- Thyroid Neoplasms/genetics
- Thyroid Neoplasms/pathology
- Thyroid Neoplasms/metabolism
- Neoplasms/immunology
- Neoplasms/genetics
- Neoplasms/metabolism
- Neoplasms/pathology
- Cell Line, Tumor
- Cancer-Associated Fibroblasts/metabolism
- Cancer-Associated Fibroblasts/immunology
- Cancer-Associated Fibroblasts/pathology
- Databases, Genetic
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Affiliation(s)
- Xuedi Xia
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Feng Xu
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Dexing Dai
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - An Xiong
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Ruoman Sun
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Yali Ling
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Lei Qiu
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Rui Wang
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Ya Ding
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Miaoying Lin
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Haibo Li
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
| | - Zhongjian Xie
- National Clinical Research Center for Metabolic Diseases, Hunan Provincial Key Laboratory of Metabolic Bone Diseases, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha 410011, Hunan, China
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Coelho JQ, Ramos MJ, Ranchor R, Pichel R, Guerra L, Miranda H, Simões J, Azevedo SX, Febra J, Araújo A. What's new about the tumor microenvironment of urothelial carcinoma? Clin Transl Oncol 2024:10.1007/s12094-024-03384-w. [PMID: 38332225 DOI: 10.1007/s12094-024-03384-w] [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: 11/02/2023] [Accepted: 01/04/2024] [Indexed: 02/10/2024]
Abstract
Urothelial carcinoma is a significant global health concern that accounts for a substantial part of cancer diagnoses and deaths worldwide. The tumor microenvironment is a complex ecosystem composed of stromal cells, soluble factors, and altered extracellular matrix, that mutually interact in a highly immunomodulated environment, with a prominent role in tumor development, progression, and treatment resistance. This article reviews the current state of knowledge of the different cell populations that compose the tumor microenvironment of urothelial carcinoma, its main functions, and distinct interactions with other cellular and non-cellular components, molecular alterations and aberrant signaling pathways already identified. It also focuses on the clinical implications of these findings, and its potential to translate into improved quality of life and overall survival. Determining new targets or defining prognostic signatures for urothelial carcinoma is an ongoing challenge that could be accelerated through a deeper understanding of the tumor microenvironment.
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Affiliation(s)
| | | | - Ridhi Ranchor
- Unidade Local de Saúde de Santo António, Porto, Portugal
| | - Rita Pichel
- Unidade Local de Saúde de Santo António, Porto, Portugal
| | - Laura Guerra
- Unidade Local de Saúde de Santo António, Porto, Portugal
| | - Hugo Miranda
- Unidade Local de Saúde de Santo António, Porto, Portugal
| | - Joana Simões
- Unidade Local de Saúde de Santo António, Porto, Portugal
| | | | - Joana Febra
- Unidade Local de Saúde de Santo António, Porto, Portugal
| | - António Araújo
- Unidade Local de Saúde de Santo António, Porto, Portugal
- Oncology Research Unit, UMIB - Unit for Multidisciplinary Research in Biomedicine, ICBAS - School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
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Huan Q, Cheng S, Ma H, Zhao M, Chen Y, Yuan X. Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer. J Cell Mol Med 2024; 28:e18021. [PMID: 37994489 PMCID: PMC10805490 DOI: 10.1111/jcmm.18021] [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: 08/16/2023] [Revised: 09/18/2023] [Accepted: 10/19/2023] [Indexed: 11/24/2023] Open
Abstract
Clinical assessments relying on pathology classification demonstrate limited effectiveness in predicting clinical outcomes and providing optimal treatment for patients with ovarian cancer (OV). Consequently, there is an urgent requirement for an ideal biomarker to facilitate precision medicine. To address this issue, we selected 15 multicentre cohorts, comprising 12 OV cohorts and 3 immunotherapy cohorts. Initially, we identified a set of robust prognostic risk genes using data from the 12 OV cohorts. Subsequently, we employed a consensus cluster analysis to identify distinct clusters based on the expression profiles of the risk genes. Finally, a machine learning-derived prognostic signature (MLDPS) was developed based on differentially expressed genes and univariate Cox regression genes between the clusters by using 10 machine-learning algorithms (101 combinations). Patients with high MLDPS had unfavourable survival rates and have good prediction performance in all cohorts and in-house cohorts. The MLDPS exhibited robust and dramatically superior capability than 21 published signatures. Of note, low MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells. Additionally, patients suffering from OV with low MLDIS were more sensitive to immunotherapy. Meanwhile, patients with low MLDIS might benefit from chemotherapy, and 19 compounds that may be potential agents for patients with low MLDIS were identified. MLDIS presents an appealing instrument for the identification of patients at high/low risk. This could enhance the precision treatment, ultimately guiding the clinical management of OV.
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Affiliation(s)
- Qing Huan
- Shandong Key Laboratory of Reproductive Medicine, Department of Obstetrics and GynecologyShandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanShandongChina
| | - Shuchao Cheng
- Bidding Management OfficeThe Second Affiliated Hospital of Shandong University of Traditional Chinese MedicineJinanShandongChina
| | - Hui‐Fen Ma
- School of Medical ManagementShandong First Medical UniversityJinanShandongChina
| | - Min Zhao
- Mianyang Central Hospital, School of MedicineUniversity of Electronic Science and Technology of ChinaMianyangSichuanChina
| | - Yu Chen
- School of ScienceWuhan University of TechnologyWuhanHubeiChina
| | - Xiaolu Yuan
- Department of PathologyMaoming People's HospitalMaomingGuangdongChina
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Wang Y, Zhang X, Chen Y, Zhu B, Xing Q. Identification of hub biomarkers and exploring the roles of immunity, M6A, ferroptosis, or cuproptosis in rats with diabetic erectile dysfunction. Andrology 2023; 11:316-331. [PMID: 35975587 DOI: 10.1111/andr.13265] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 08/02/2022] [Accepted: 08/07/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Currently, patients with diabetic erectile dysfunction (DMED) were not satisfied with the effects of first-line phosphodiesterase type 5 inhibitors (PDE5Is). Hence, this paper was designed to mine hub biomarkers in DMED and explore its potential mechanisms. METHODS Gene expression matrix of DMED was downloaded from the gene expression omnibus (GEO; GSE2457) dataset. The top 20 genes were selected based on the connectivity degrees in protein-protein interaction (PPI) network. Functional enrichment analysis was utilized to reveal DMED-related signaling pathways. We also explored the roles of immunity, m6A, ferroptosis, or cuproptosis in DMED and constructed Sprague Dawley (SD) rats DMED model to verify gene expressions by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS Based on the threshold, a total of 122 differently expressed genes (DEGs) were identified in DMED, including 39 up-regulated and 83 down-regulated genes. Functional enrichment analysis implied that these DEGs were significantly enriched in peroxisome proliferator-activated receptors, ferroptosis, hypoxia-inducible factor 1 signaling pathways, and so on. SD rats DMED model was also successfully established by us and validated by intracavernous pressure/mean arterial pressure, Masson's trichrome staining, and immunohistochemical analysis. We further verified the expression of these top 20 genes from the PPI network by qRT-PCR in the SD rats DMED model and finally identified Sparc, Lox, Srebf1, and Mmp3 as hub biomarkers (all p < 0.05). As for immunity and cuproptosis, our analysis indicated that DMED had nothing to do with them (all p > 0.05). Actually, DMED was markedly associated with m6A regulators and ferroptosis. CONCLUSIONS We identified Sparc, Lox, Srebf1, and Mmp3 as potential hub biomarkers in the SD rats DMED model for future drug development and found its significant associations with m6A regulators and ferroptosis, but not with immunity or cuproptosis.
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Affiliation(s)
- Yi Wang
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, China.,Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyu Zhang
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, China
| | - Yinhao Chen
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, China
| | - Bingye Zhu
- Department of Urology, The Sixth People's Hospital of Nantong, Affiliated Nantong Hospital of Shanghai University, Nantong, China
| | - Qianwei Xing
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, China
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