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Ma H, Shi L, Zheng J, Zeng L, Chen Y, Zhang S, Tang S, Qu Z, Xiong X, Zheng X, Yin Q. Advanced machine learning unveils CD8 + T cell genetic markers enhancing prognosis and immunotherapy efficacy in breast cancer. BMC Cancer 2024; 24:1222. [PMID: 39354417 PMCID: PMC11446097 DOI: 10.1186/s12885-024-12952-w] [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: 05/19/2024] [Accepted: 09/13/2024] [Indexed: 10/03/2024] Open
Abstract
BACKGROUND Breast cancer (BC) is the most common cancer in women and poses a significant health burden, especially in China. Despite advances in diagnosis and treatment, patient variability and limited early detection contribute to poor outcomes. This study examines the role of CD8 + T cells in the tumor microenvironment to identify new biomarkers that improve prognosis and guide treatment strategies. METHODS CD8 + T-cell marker genes were identified using single-cell RNA sequencing (scRNA-seq), and a CD8 + T cell-related gene prognostic signature (CTRGPS) was developed using 10 machine-learning algorithms. The model was validated across seven independent public datasets from the GEO database. Clinical features and previously published signatures were also analyzed for comparison. The clinical applications of CTRGPS in biological function, immune microenvironment, and drug selection were explored, and the role of hub genes in BC progression was further investigated. RESULTS We identified 71 CD8 + T cell-related genes and developed the CTRGPS, which demonstrated significant prognostic value, with higher risk scores linked to poorer overall survival (OS). The model's accuracy and robustness were confirmed through Kaplan-Meier and ROC curve analyses across multiple datasets. CTRGPS outperformed existing prognostic signatures and served as an independent prognostic factor. The role of the hub gene TTK in promoting malignant proliferation and migration of BC cells was validated. CONCLUSION The CTRGPS enhances early diagnosis and treatment precision in BC, improving clinical outcomes. TTK, a key gene in the signature, shows promise as a therapeutic target, supporting the CTRGPS's potential clinical utility.
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Affiliation(s)
- Haodi Ma
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - LinLin Shi
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital, College of Clinical Medicine, Medical College of Henan University of Science and Technology, Luoyang, China
| | - Jiayu Zheng
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Li Zeng
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Youyou Chen
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Shunshun Zhang
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Siya Tang
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Zhifeng Qu
- Radiology Department, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
| | - Xin Xiong
- Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Xuewei Zheng
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
| | - Qinan Yin
- Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
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Xie Y, Chen H, Tian M, Wang Z, Wang L, Zhang J, Wang X, Lian C. Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort. Front Immunol 2024; 15:1460547. [PMID: 39346927 PMCID: PMC11427295 DOI: 10.3389/fimmu.2024.1460547] [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: 07/06/2024] [Accepted: 08/23/2024] [Indexed: 10/01/2024] Open
Abstract
Introduction The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD. Methods In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD. Results We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma. Discussion Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.
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Affiliation(s)
- Yiluo Xie
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, MolecularDiagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China
- Department of Clinical Medicine, Bengbu Medical University, Bengbu, China
| | - Huili Chen
- Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China
| | - Mei Tian
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, MolecularDiagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Ziqang Wang
- Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China
| | - Luyao Wang
- Department of Genetics, School of Life Sciences, Bengbu Medical University, Bengbu, China
| | - Jing Zhang
- Department of Genetics, School of Life Sciences, Bengbu Medical University, Bengbu, China
| | - Xiaojing Wang
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, MolecularDiagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China
| | - Chaoqun Lian
- Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu, China
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de Gonzalo-Calvo D, Karaduzovic-Hadziabdic K, Dalgaard LT, Dieterich C, Perez-Pons M, Hatzigeorgiou A, Devaux Y, Kararigas G. Machine learning for catalysing the integration of noncoding RNA in research and clinical practice. EBioMedicine 2024; 106:105247. [PMID: 39029428 PMCID: PMC11314885 DOI: 10.1016/j.ebiom.2024.105247] [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: 03/08/2024] [Revised: 06/17/2024] [Accepted: 07/02/2024] [Indexed: 07/21/2024] Open
Abstract
The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies ("multiomic" strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.
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Affiliation(s)
- David de Gonzalo-Calvo
- Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain.
| | | | | | - Christoph Dieterich
- Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Germany; German Center for Cardiovascular Research (DZHK) - Partner Site Heidelberg/Mannheim, Germany
| | - Manel Perez-Pons
- Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain
| | - Artemis Hatzigeorgiou
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece; Hellenic Pasteur Institute, Athens, Greece
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Georgios Kararigas
- Department of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
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Sharma A, Debik J, Naume B, Ohnstad HO, Bathen TF, Giskeødegård GF. Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes. Oncogenesis 2024; 13:22. [PMID: 38871719 DOI: 10.1038/s41389-024-00521-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.
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Affiliation(s)
- Abhibhav Sharma
- Department of Public Health and Nursing (ISM), Norwegian University of Science and Technology- NTNU, Trondheim, Norway.
| | - Julia Debik
- Department of Public Health and Nursing (ISM), Norwegian University of Science and Technology- NTNU, Trondheim, Norway
- Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway
| | - Bjørn Naume
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hege Oma Ohnstad
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway
| | - Guro F Giskeødegård
- Department of Public Health and Nursing (ISM), Norwegian University of Science and Technology- NTNU, Trondheim, Norway.
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Zhang N, Han Y, Cao H, Wang Q. Inflammasome-related gene signatures as prognostic biomarkers in osteosarcoma. J Cell Mol Med 2024; 28:e18286. [PMID: 38742843 PMCID: PMC11092527 DOI: 10.1111/jcmm.18286] [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: 02/01/2024] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 05/16/2024] Open
Abstract
Osteosarcoma, the primary bone cancer in adolescents and young adults, is notorious for its aggressive growth and metastatic potential. Our study delved into the prognostic impact of inflammasome-related gene signatures in osteosarcoma patients, employing comprehensive genetic profiling to uncover signatures linked with patient outcomes. We identified three patient subgroups through consensus clustering, with one showing worse survival rates correlated with high FGFR3 and RARB expressions. Immune profiling revealed significant immune cell infiltration differences among these subgroups, affecting survival. Utilising advanced machine learning, including StepCox and gradient boosting machine algorithms, we developed a prognostic model with a notable c-index of 0.706, highlighting CD36 and MYD88 as key genes. Higher inflammasome risk scores from our model were associated with poorer survival, corroborated across datasets. In vitro experiments validated CD36 and MYD88's roles in promoting osteosarcoma cell proliferation, invasion and migration, emphasising their therapeutic potential. This research offers new insights into inflammasomes' role in osteosarcoma, introducing novel biomarkers for risk assessment and potential therapeutic targets. Our findings suggest a pathway towards personalised treatment strategies, potentially improving patient outcomes in osteosarcoma.
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Affiliation(s)
- Nan Zhang
- Department of Medical OncologyChina Coast Guard Hospital of the People's Armed Police ForceZhejiangChina
| | - Ying Han
- Department of Orthopaedic Surgery, Sir Run Run Shaw HospitalZhejiang University School of MedicineZhejiangChina
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang ProvinceZhejiangChina
| | - Hangkai Cao
- Department of Orthopaedic SurgeryChina Coast Guard Hospital of the People's Armed PoliceZhejiangChina
| | - Qingxin Wang
- Department of Orthopaedic SurgeryChina Coast Guard Hospital of the People's Armed PoliceZhejiangChina
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Zou XC, Luo CW, Yuan RM, Jin MN, Zeng T, Chao HC. Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy. Urolithiasis 2024; 52:64. [PMID: 38613668 DOI: 10.1007/s00240-024-01562-7] [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: 01/15/2024] [Accepted: 03/21/2024] [Indexed: 04/15/2024]
Abstract
Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL). A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models. After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors. Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.
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Affiliation(s)
- Xin Chang Zou
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
| | - Cheng Wei Luo
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
| | | | - Meng Ni Jin
- Department of Imaging, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
| | - Tao Zeng
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China.
| | - Hai Chao Chao
- Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China
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Ma Y, Zhu W. Development of gene panel for predicting recurrence in early-stage cervical cancer patients. ENVIRONMENTAL TOXICOLOGY 2024. [PMID: 38563455 DOI: 10.1002/tox.24270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/19/2024] [Accepted: 03/24/2024] [Indexed: 04/04/2024]
Abstract
Cervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus-based gene panel using multi-omics data that could effectively predict recurrence in early-stage cervical cancer patients. We utilized the "Multi-Omics Consensus Integration Analysis (MOVICS)" package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early-stage CC. We identified the "resting and naive" tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning-driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan-Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early-stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.
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Affiliation(s)
- Yun Ma
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Weipei Zhu
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Qi D, Lu Y, Qu H, Dong Y, Jin Q, Sun M, Li Y, Quan C. Independent prognostic value of CLDN6 in bladder cancer based on M2 macrophages related signature. iScience 2024; 27:109138. [PMID: 38380255 PMCID: PMC10877962 DOI: 10.1016/j.isci.2024.109138] [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: 08/09/2023] [Revised: 12/19/2023] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
M2 macrophages are associated with the prognosis of bladder cancer. CLDN6 has been linked to immune infiltration and is crucial for predicting the prognosis in multi-tumor. The effect of CLDN6 on M2 macrophages in bladder cancer remains elusive. Here, we compared a total of 40 machine learning algorithms, then selected optimal algorithm to develop M2 macrophages-related signature (MMRS) based on the identified M2 macrophages related module. MMRS predicted the prognosis better than other models and associated to immunotherapy response. CLDN6, as an important variable in MMRS, was an independent factor for poor prognosis. We found that CLDN6 was highly expressed and affected immune infiltration, immunotherapy response, and M2 macrophages polarization. Meanwhile, CLDN6 promoted the growth of bladder cancer and enhanced the carcinogenic effect by inducing polarization of M2 macrophages. In total, CLDN6 is an independent risk factor in MMRS to predict the prognosis of bladder cancer.
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Affiliation(s)
- Da Qi
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
| | - Yan Lu
- The Department of Anatomy, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
| | - Huinan Qu
- Department of Histology and Embryology, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
| | - Yuan Dong
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
| | - Qiu Jin
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
| | - Minghao Sun
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
| | - Yanru Li
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
| | - Chengshi Quan
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, 126 Xinmin Avenue, Changchun 130021, China
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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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Kang Z, Zhao YX, Qiu RSQ, Chen DN, Zheng QS, Xue XY, Xu N, Wei Y. Identification macrophage signatures in prostate cancer by single-cell sequencing and machine learning. Cancer Immunol Immunother 2024; 73:41. [PMID: 38349474 PMCID: PMC10864475 DOI: 10.1007/s00262-024-03633-5] [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: 12/29/2023] [Accepted: 01/12/2024] [Indexed: 02/15/2024]
Abstract
BACKGROUND The tumor microenvironment (TME) encompasses a variety of cells that influence immune responses and tumor growth, with tumor-associated macrophages (TAM) being a crucial component of the TME. TAM can guide prostate cancer in different directions in response to various external stimuli. METHODS First, we downloaded prostate cancer single-cell sequencing data and second-generation sequencing data from multiple public databases. From these data, we identified characteristic genes associated with TAM clusters. We then employed machine learning techniques to select the most accurate TAM gene set and developed a TAM-related risk label for prostate cancer. We analyzed the tumor-relatedness of the TAM-related risk label and different risk groups within the population. Finally, we validated the accuracy of the prognostic label using single-cell sequencing data, qPCR, and WB assays, among other methods. RESULTS In this study, the TAM_2 cell cluster has been identified as promoting the progression of prostate cancer, possibly representing M2 macrophages. The 9 TAM feature genes selected through ten machine learning methods and demonstrated their effectiveness in predicting the progression of prostate cancer patients. Additionally, we have linked these TAM feature genes to clinical pathological characteristics, allowing us to construct a nomogram. This nomogram provides clinical practitioners with a quantitative tool for assessing the prognosis of prostate cancer patients. CONCLUSION This study has analyzed the potential relationship between TAM and PCa and established a TAM-related prognostic model. It holds promise as a valuable tool for the management and treatment of PCa patients.
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Affiliation(s)
- Zhen Kang
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Yu-Xuan Zhao
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Ren Shun Qian Qiu
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Dong-Ning Chen
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Qing-Shui Zheng
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Xue-Yi Xue
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China
| | - Ning Xu
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
| | - Yong Wei
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
- Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
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Wang X, Chan S, Chen J, Xu Y, Dai L, Han Q, Wang Z, Zuo X, Yang Y, Zhao H, Wang M, Wang C, Li Z, Zhang H, Chen W. Robust machine-learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer. Cancer Cell Int 2024; 24:52. [PMID: 38297270 PMCID: PMC10829178 DOI: 10.1186/s12935-024-03239-y] [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: 12/04/2023] [Accepted: 01/24/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC). METHODS 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross-validation framework, and patients with CRC were separated into high- and low-CERPI groups. Relationship between immune cell infiltration levels, immune-related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in-vitro verified. RESULTS The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low-CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single-cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration. CONCLUSION We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Shixin Chan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Jiajie Chen
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yuanmin Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Longfei Dai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Qijun Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xiaomin Zuo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yang Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Hu Zhao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Chen Wang
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Zichen Li
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China
| | - Huabing Zhang
- Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, 230032, Anhui, China.
- The First Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, 239000, Anhui, China.
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230032, Anhui, China.
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