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He X, Hu S, Wang C, Yang Y, Li Z, Zeng M, Song G, Li Y, Lu Q. Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server. Heliyon 2024; 10:e28878. [PMID: 38623253 PMCID: PMC11016622 DOI: 10.1016/j.heliyon.2024.e28878] [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: 08/27/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
Background Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa. Methods We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers. Results Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups. Conclusion Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.
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Affiliation(s)
| | | | - Chen Wang
- Department of Urology, Hunan Provincial People's Hospital (The 1st Affiliated Hospital of Hunan Normal University), China
| | - Yongjun Yang
- Department of Urology, Hunan Provincial People's Hospital (The 1st Affiliated Hospital of Hunan Normal University), China
| | - Zhuo Li
- Department of Urology, Hunan Provincial People's Hospital (The 1st Affiliated Hospital of Hunan Normal University), China
| | - Mingqiang Zeng
- Department of Urology, Hunan Provincial People's Hospital (The 1st Affiliated Hospital of Hunan Normal University), China
| | - Guangqing Song
- Department of Urology, Hunan Provincial People's Hospital (The 1st Affiliated Hospital of Hunan Normal University), China
| | - Yuanwei Li
- Department of Urology, Hunan Provincial People's Hospital (The 1st Affiliated Hospital of Hunan Normal University), China
| | - Qiang Lu
- Department of Urology, Hunan Provincial People's Hospital (The 1st Affiliated Hospital of Hunan Normal University), China
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Li Q, Zhu J, Zhang Y, Pan Y, Li Z, Wang M, Gao Y, Feng D, He X, Zhang C. Association of WHSC1/NSD2 and T-cell infiltration with prostate cancer metastasis and prognosis. Sci Rep 2023; 13:21629. [PMID: 38062230 PMCID: PMC10703870 DOI: 10.1038/s41598-023-48906-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
Progress in immunotherapy for prostate cancer (PCa) lags that for other cancers, mainly because of limited immune infiltration in PCa. This study aimed to assess the feasibility of NSD2 as an immunotherapeutic target in PCa. Immunohistochemistry was performed to evaluate the expression pattern of NSD2 in 34 cases of benign prostatic hyperplasia (BPH), 36 cases of prostatic intraepithelial neoplasia (PIN), and 57 cases of PCa, including 19 cases of metastatic castration-resistant prostatic cancer (mCRPC). Single-cell RNA sequencing and gene set enrichment analysis (GSEA) were used to correlate NSD2 with certain downstream pathways. Furthermore, the Immuno-Oncology-Biological-Research (IOBR) software package was used to analyze the potential roles of NSD2 in the tumor microenvironment. We found that the positive expression rate of NSD2 increased progressively in BPH, PIN and PCa. mCRPC had the highest staining intensity for NSD2. High NSD2 expression was positively correlated with the infiltration level of CD4+ tumor-infiltrating lymphocytes (TILs) and negatively correlated with that of CD8+ TILs. Importantly, a new immune classification based on NSD2 expression and CD4+ TILs and CD8+ TILs was successfully used to stratify PCa patients based on OS.PSA and CD4+ TILs are independent risk factors for PCa bone metastasis. This study demonstrates a novel role for NSD2 in defining immune infiltrate on in PCa and highlights the great potential for its application in immunotherapy response evaluation for prostate malignancies.
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Affiliation(s)
- Qiheng Li
- Department of Pathology, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Jiang Zhu
- Department of Urology Surgery, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Yang Zhang
- Department of General Surgery, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Yun Pan
- Department of Pathology, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Zhengjin Li
- Department of Pathology, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Min Wang
- Department of Pathology, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Yixuan Gao
- Department of Pathology, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Dongmei Feng
- Department of Pathology, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Xiaoyong He
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Dali University, Yunnan, China
| | - Chunmei Zhang
- Department of Pathology, The First Affiliated Hospital of Dali University, Yunnan, China.
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Liu G, Wang L, Ji L, He D, Zeng L, Zhuo G, Zhang Q, Wang D, Pan Y. Identifying prognostic markers in spatially heterogeneous breast cancer microenvironment. J Transl Med 2023; 21:580. [PMID: 37644433 PMCID: PMC10463390 DOI: 10.1186/s12967-023-04395-x] [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: 04/23/2023] [Accepted: 07/29/2023] [Indexed: 08/31/2023] Open
Abstract
To gain deeper insights into the microenvironment of breast cancer, we utilized GeoMx Digital Spatial Profiling (DSP) technology to analyze transcripts from 107 regions of interest in 65 untreated breast cancer tissue samples. Our study revealed spatial heterogeneity in the expression of marker genes in tumor cell enriched, immune cell enriched, and normal epithelial areas. We evaluated a total of 55 prognostic markers in tumor cell enriched regions and 15 in immune cell enriched regions, identifying that tumor cell enriched regions had higher levels of follicular helper T cells, resting dendritic cells, and plasma cells than immune cell enriched regions, while the levels of resting CD4 memory in T cells and regulatory (Treg) T cells were lower. Additionally, we analyzed the heterogeneity of HLA gene families, immunological checkpoints, and metabolic genes in these areas. Through univariate Cox analysis, we identified 5 prognosis-related metabolic genes. Furthermore, we conducted immunostaining experiments, including EMILIN2, SURF4, and LYPLA1, to verify our findings. Our investigation into the spatial heterogeneity of the breast cancer tumor environment has led to the discovery of specific diagnostic and prognostic markers in breast cancer.
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Affiliation(s)
- Guohong Liu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Liping Wang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Lili Ji
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Dan He
- Department of Clinical Pathology, Houjie Hospital of Dongguan, The Affiliated Houjie Hospital of Guangdong Medical University, No.21 Hetian Road, Houjie Town, Dongguan, 523000, China
| | - Lihua Zeng
- Department of Clinical Pathology, Houjie Hospital of Dongguan, The Affiliated Houjie Hospital of Guangdong Medical University, No.21 Hetian Road, Houjie Town, Dongguan, 523000, China
| | - Guangzheng Zhuo
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Qian Zhang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Dujuan Wang
- Department of Clinical Pathology, Houjie Hospital of Dongguan, The Affiliated Houjie Hospital of Guangdong Medical University, No.21 Hetian Road, Houjie Town, Dongguan, 523000, China.
| | - Yunbao Pan
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan, 430071, China.
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Wang W, Rong Z, Wang G, Hou Y, Yang F, Qiu M. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomark Res 2023; 11:66. [PMID: 37391812 DOI: 10.1186/s40364-023-00507-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/27/2023] [Indexed: 07/02/2023] Open
Abstract
Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.
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Affiliation(s)
- Wenxiang Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China
| | - Zhiwei Rong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Guangxi Wang
- Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Yan Hou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Clinical Research Center, Peking University, Beijing, 100191, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
| | - Mantang Qiu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
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Li H, Gu J, Tian Y, Li S, Zhang H, Dai Z, Wang Z, Zhang N, Peng R. A prognostic signature consisting of metabolism-related genes and SLC17A4 serves as a potential biomarker of immunotherapeutic prediction in prostate cancer. Front Immunol 2022; 13:982628. [PMID: 36325340 PMCID: PMC9620963 DOI: 10.3389/fimmu.2022.982628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/05/2022] [Indexed: 12/24/2022] Open
Abstract
Background Prostate cancer (PCa), a prevalent malignant cancer in males worldwide, screening for patients might benefit more from immuno-/chemo-therapy remained inadequate and challenging due to the heterogeneity of PCa patients. Thus, the study aimed to explore the metabolic (Meta) characteristics and develop a metabolism-based signature to predict the prognosis and immuno-/chemo-therapy response for PCa patients. Methods Differentially expressed genes were screened among 2577 metabolism-associated genes. Univariate Cox analysis and random forest algorithms was used for features screening. Multivariate Cox regression analysis was conducted to construct a prognostic Meta-model based on all combinations of metabolism-related features. Then the correlation between MetaScore and tumor was deeply explored from prognostic, genomic variant, functional and immunological perspectives, and chemo-/immuno-therapy response. Multiple algorithms were applied to estimate the immunotherapeutic responses of two MeteScore groups. Further in vitro functional experiments were performed using PCa cells to validate the association between the expression of hub gene SLC17A4 which is one of the model component genes and tumor progression. GDSC database was employed to determine the sensitivity of chemotherapy drugs. Results Two metabolism-related clusters presented different features in overall survival (OS). A metabolic model was developed weighted by the estimated regression coefficients in the multivariate Cox regression analysis (0.5154*GAS2 + 0.395*SLC17A4 - 0.1211*NTM + 0.2939*GC). This Meta-scoring system highlights the relationship between the metabolic profiles and genomic alterations, gene pathways, functional annotation, and tumor microenvironment including stromal, immune cells, and immune checkpoint in PCa. Low MetaScore is correlated with increased mutation burden and microsatellite instability, indicating a superior response to immunotherapy. Several medications that might improve patients` prognosis in the MetaScore group were identified. Additionally, our cellular experiments suggested knock-down of SLC17A4 contributes to inhibiting invasion, colony formation, and proliferation in PCa cells in vitro. Conclusions Our study supports the metabolism-based four-gene signature as a novel and robust model for predicting prognosis, and chemo-/immuno-therapy response in PCa patients. The potential mechanisms for metabolism-associated genes in PCa oncogenesis and progression were further determined.
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Affiliation(s)
- He Li
- The Animal Laboratory Center, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Jie Gu
- Department of Geriatric Urology, Xiangya International Medical Center, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Yuqiu Tian
- Department of Infectious Disease, Zhuzhou Central Hospital, Zhuzhou, Hunan, China
| | - Shuyu Li
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hao Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ziyu Dai
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zeyu Wang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Nan Zhang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- One‑Third Lab, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang, China
- *Correspondence: Renjun Peng, ; Nan Zhang,
| | - Renjun Peng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- *Correspondence: Renjun Peng, ; Nan Zhang,
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Liu J, Shen H, Gu W, Zheng H, Wang Y, Ma G, Du J. Prediction of prognosis, immunogenicity and efficacy of immunotherapy based on glutamine metabolism in lung adenocarcinoma. Front Immunol 2022; 13:960738. [PMID: 36032135 PMCID: PMC9403193 DOI: 10.3389/fimmu.2022.960738] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/22/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Glutamine (Gln) metabolism has been reported to play an essential role in cancer. However, a comprehensive analysis of its role in lung adenocarcinoma is still unavailable. This study established a novel system of quantification of Gln metabolism to predict the prognosis and immunotherapy efficacy in lung cancer. Further, the Gln metabolism in tumor microenvironment (TME) was characterized and the Gln metabolism-related genes were identified for targeted therapy. METHODS We comprehensively evaluated the patterns of Gln metabolism in 513 patients diagnosed with lung adenocarcinoma (LUAD) based on 73 Gln metabolism-related genes. Based on differentially expressed genes (DEGs), a risk model was constructed using Cox regression and Lasso regression analysis. The prognostic efficacy of the model was validated using an individual LUAD cohort form Shandong Provincial Hospital, an integrated LUAD cohort from GEO and pan-cancer cohorts from TCGA databases. Five independent immunotherapy cohorts were used to validate the model performance in predicting immunotherapy efficacy. Next, a series of single-cell sequencing analyses were used to characterize Gln metabolism in TME. Finally, single-cell sequencing analysis, transcriptome sequencing, and a series of in vitro experiments were used to explore the role of EPHB2 in LUAD. RESULTS Patients with LUAD were eventually divided into low- and high-risk groups. Patients in low-risk group were characterized by low levels of Gln metabolism, survival advantage, "hot" immune phenotype and benefit from immunotherapy. Compared with other cells, tumor cells in TME exhibited the most active Gln metabolism. Among immune cells, tumor-infiltrating T cells exhibited the most active levels of Gln metabolism, especially CD8 T cell exhaustion and Treg suppression. EPHB2, a key gene in the model, was shown to promote LUAD cell proliferation, invasion and migration, and regulated the Gln metabolic pathway. Finally, we found that EPHB2 was highly expressed in macrophages, especially M2 macrophages. It may be involved in the M2 polarization of macrophages and mediate the negative regulation of M2 macrophages in NK cells. CONCLUSION This study revealed that the Gln metabolism-based model played a significant role in predicting prognosis and immunotherapy efficacy in lung cancer. We further characterized the Gln metabolism of TME and investigated the Gln metabolism-related gene EPHB2 to provide a theoretical framework for anti-tumor strategy targeting Gln metabolism.
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Affiliation(s)
- Jichang Liu
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hongchang Shen
- Department of Oncology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Ibaraki, Japan,Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Haotian Zheng
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yadong Wang
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Guoyuan Ma
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China,Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jiajun Du
- Institute of Oncology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China,Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China,*Correspondence: Jiajun Du,
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Guo T, Wang J, Yan S, Meng X, Zhang X, Xu S, Ren S, Huang Y. A combined signature of glycolysis and immune landscape predicts prognosis and therapeutic response in prostate cancer. Front Endocrinol (Lausanne) 2022; 13:1037099. [PMID: 36339430 PMCID: PMC9634133 DOI: 10.3389/fendo.2022.1037099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/11/2022] [Indexed: 11/13/2022] Open
Abstract
Prostate cancer (PCa) is a common malignancy that poses a major threat to the health of men. Prostate-specific antigen (PSA) and its derivatives, as FDA-approved detection assays, are insufficient to serve as optimal markers for patient prognosis and clinical decision-making. It is widely acknowledged that aberrant glycolytic metabolism in PCa is related to tumor progression and acidifies the tumor microenvironment (TME). Considering the non-negligible impacts of glycolysis and immune functions on PCa, we developed a combined classifier in prostate cancer. The Glycolysis Score containing 19 genes and TME Score including three immune cells were created, using the univariate and multivariate Cox proportional hazards model, log-rank test, least absolute shrinkage and selection operator (LASSO) regression analysis and the bootstrap approach. Combining the glycolysis and immunological landscape, the Glycolysis-TME Classifier was then constructed. It was observed that the classifier was more accurate in predicting the prognosis of patients than the current biomarkers. Notably, there were significant differences in metabolic activity, signaling pathways, mutational landscape, immunotherapeutic response, and drug sensitivity among the Glycolysishigh/TMElow, Mixed group and Glycolysislow/TMEhigh identified by this classifier. Overall, due to the significant prognostic value and potential therapeutic guidance of the Glycolysis-TME Classifier, we anticipate that this classifier will be clinically beneficial in the management of patients with PCa.
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Affiliation(s)
- Tao Guo
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Jian Wang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Shi Yan
- Department of Urology, Shanghai Changhai Hospital, Shanghai, China
| | - Xiangyu Meng
- Department of Urology , The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaomin Zhang
- Department of Urology, Shanghai Changhai Hospital, Shanghai, China
| | - Shuang Xu
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Shancheng Ren
- Department of Urology, Shanghai Changzheng Hospital, Shanghai, China
- *Correspondence: Yuhua Huang, ; Shancheng Ren,
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
- *Correspondence: Yuhua Huang, ; Shancheng Ren,
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