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Yao Q, Zhang X, Wang Y, Wang C, Chen J, Chen D. A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma. Eur J Med Res 2024; 29:73. [PMID: 38268058 PMCID: PMC10807100 DOI: 10.1186/s40001-024-01659-0] [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: 12/11/2023] [Accepted: 01/11/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions. METHODS RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses. RESULTS ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients. CONCLUSIONS The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.
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Affiliation(s)
- Qinfan Yao
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou, 310003, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, Zhejiang Province, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Xiuyuan Zhang
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou, 310003, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, Zhejiang Province, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Yucheng Wang
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou, 310003, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, Zhejiang Province, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Cuili Wang
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou, 310003, China
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, Zhejiang Province, China
- Institute of Nephropathy, Zhejiang University, Hangzhou, China
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China
| | - Jianghua Chen
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou, 310003, China.
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, Zhejiang Province, China.
- Institute of Nephropathy, Zhejiang University, Hangzhou, China.
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China.
| | - Dajin Chen
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou, 310003, China.
- Key Laboratory of Kidney Disease Prevention and Control Technology, Hangzhou, Zhejiang Province, China.
- Institute of Nephropathy, Zhejiang University, Hangzhou, China.
- Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China.
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Li M, Song J, Wang L, Wang Q, Huang Q, Mo D. Natural killer cell-related prognosis signature predicts immune response in colon cancer patients. Front Pharmacol 2023; 14:1253169. [PMID: 38026928 PMCID: PMC10679416 DOI: 10.3389/fphar.2023.1253169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Background: Natural killer (NK) cells are crucial components of the innate immune system that fight tumors and viral infections. Patients with colorectal cancer (CRC) have a poor prognosis, and immunotherapeutic tools play a key role in the treatment of CRC. Methods: Public data on CRC patients was collected from the TCGA and the GEO databases. Tissue data of CRC patients were collected from Guangxi Medical University Affiliated Cancer Hospital. An NK-related prognostic model was developed by the least absolute shrinkage and selection operator (LASSO) and Cox regression method. Validation data were collected from different clinical subgroups and an external independent validation cohort to verify the model's accuracy. In addition, multiple external independent immunotherapy datasets were collected to further examine the value of NK-related risk scores (NKRS) in the prediction of immunotherapy response. Potential biological functions of key genes were examined by methods of cell proliferation, apoptosis and Western blotting. Results: A novel prognostic model for CRC patients based on NK-related genes was developed and NKRS was generated. There was a significantly poorer prognosis among the high-NKRS group. Based on immune response prediction, patients with low NKRS may be more suitable for immunotherapy and they are more sensitive to immunotherapy. The proliferation rate of CRC cells was significantly reduced and apoptosis of CRC cells was increased after SLC2A3 was knocked down. SLC2A3 was also found to be associated with the TGF-β signaling pathway. Conclusion: NKRS has potential applications for predicting prognostic status and response to immunotherapy in CRC patients. SLC2A3 has potential as a therapeutic target for CRC.
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Affiliation(s)
- Meiqin Li
- Department of Clinical Laboratory, Guang Xi Medical University Cancer Hospital, Nanning, China
| | - Jingqing Song
- Department of Gastrointestinal Surgery, Guang Xi Medical University Cancer Hospital, Nanning, China
| | - Lin Wang
- Department of Clinical Laboratory, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, China
| | - Qi Wang
- Department of Basic Medicine, Guangxi Health Science College, Nanning, China
| | - Qinghua Huang
- Department of Breast Surgery, Wuzhou Red Cross Hospital, Wuzhou, China
| | - Dan Mo
- Department of Breast, Maternal and Child Healthcare Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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Lin X, Hessenow R, Yang S, Ma D, Yang S. A seven-immune-genes risk model predicts the survival and suitable treatments for patients with skin cutaneous melanoma. Heliyon 2023; 9:e20234. [PMID: 37809963 PMCID: PMC10560028 DOI: 10.1016/j.heliyon.2023.e20234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 08/04/2023] [Accepted: 09/14/2023] [Indexed: 10/10/2023] Open
Abstract
Background Skin cutaneous melanoma is characterized by high malignancy and prognostic heterogeneity. Immune cell networks are critical to the biological progression of melanoma through the tumor microenvironment. Thus, identifying effective biomarkers for skin cutaneous melanoma from the perspective of the tumor microenvironment may offer strategies for precise prognosis prediction and treatment selection. Methods A total of 470 cases from The Cancer Genome Atlas and 214 from the Gene Expression Omnibus were systematically evaluated to construct an optimal independent immune cell risk model with predictive value using weighted gene co-expression network analysis, Cox regression, and least absolute shrinkage and selection operator assay. The predictive power of the developed model was estimated through receiver operating characteristic curves and Kaplan-Meier analysis. The association of the model with tumor microenvironment status, immune checkpoints, and mutation burden was assessed using multiple algorithms. Additionally, the sensitivity of immune and chemotherapeutics was evaluated using the ImmunophenScore and pRRophetic algorithm. Furthermore, the expression profiles of risk genes were validated using gene expression profiling interactive analysis and Human Protein Atlas resources. Results The risk model integrated seven immune-related genes: ARNTL, N4BP2L1, PARP11, NUB1, GSDMD, HAPLN3, and IRX3. The model demonstrated considerable predictive ability and was positively associated with clinical and molecular characteristics. It can be utilized as a prognostic factor for skin cutaneous melanoma, where a high-risk score was linked to a poor prognosis and indicated an immunosuppressive microenvironment. Furthermore, the model revealed several potential target checkpoints and predicted the therapeutic benefits of multiple clinically used drugs. Conclusion Our findings provide a comprehensive landscape of the tumor immune microenvironment in skin cutaneous melanoma and identify prognostic markers that may serve as efficient clinical diagnosis and treatment selection tools.
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Affiliation(s)
- Xixi Lin
- Division of Experimental Radiation Biology, Department of Radiation Therapy, University Hospital Essen, University of Duisburg-Essen, 45122 Essen, Germany
| | - Razan Hessenow
- West German Proton Therapy Center Essen (WPE), University of Duisburg-Essen, 45147 Essen, Germany
| | - Siling Yang
- Division of Plastic Surgery, University Hospital Muenster, 48149 Muenster, Germany
| | - Dongjie Ma
- Department of Nephrology, 923 Hospital of the PLA Joint Service Support Force, 530219 Nanning, China
| | - Sijie Yang
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, 530021 Nanning, China
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