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Hua T, Liu DX, Zhang XC, Li ST, Wu JL, Zhao Q, Chen SB. Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq. Eur J Med Res 2024; 29:358. [PMID: 38970067 PMCID: PMC11225302 DOI: 10.1186/s40001-024-01948-8] [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: 07/23/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.
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Affiliation(s)
- Tian Hua
- Department of Gynecology, Affiliated Xingtai People Hospital of Hebei Medical University, Xingtai, China
| | - Deng-Xiang Liu
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China
| | - Xiao-Chong Zhang
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China
| | - Shao-Teng Li
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China
| | - Jian-Lei Wu
- Department of Gynecological Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong, 250021, People's Republic of China
| | - Qun Zhao
- The Third Department of Surgery , Hebei Medical University, Fourth Hospital, Road Jiankang No. 12, Hebei, 050001, People's Republic of China.
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China.
| | - Shu-Bo Chen
- Department of Oncology, Affiliated Xingtai People Hospital of Hebei Medical University, 16 Hongxing Road, Xingtai, Hebei, 054001, People's Republic of China.
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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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