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Zhang H, Huang W, Chen M, Liu Y, Yan B, Mou S, Jiang W, Mei H. Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma. Sci Rep 2024; 14:16834. [PMID: 39039118 PMCID: PMC11263354 DOI: 10.1038/s41598-024-67516-6] [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: 02/17/2024] [Accepted: 07/11/2024] [Indexed: 07/24/2024] Open
Abstract
Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.
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Affiliation(s)
- Haiyu Zhang
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Department of Urology, Shantou University Medical College, Shantou, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Weisheng Huang
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Department of Urology, Shantou University Medical College, Shantou, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Mutong Chen
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Department of Urology, Shantou University Medical College, Shantou, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yuhan Liu
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Bing Yan
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shuanzhu Mou
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wendong Jiang
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hongbing Mei
- Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.
- Department of Urology, Shantou University Medical College, Shantou, China.
- Shenzhen Second People's Hospital, Clinical Medicine College of Anhui Medical University, Shenzhen, China.
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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Identification and Validation a Necroptosis-Related Prognostic Signature in Cervical Cancer. Reprod Sci 2022; 30:2003-2015. [PMID: 36576713 DOI: 10.1007/s43032-022-01155-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/13/2022] [Indexed: 12/29/2022]
Abstract
Necroptosis is a promising novel target for cervical cancer therapy. Nevertheless, differentially expressed necroptosis-related genes (NRGs) in cervical cancer and their associations with prognosis are far from fully clarified. In this study, differentially expressed NRGs (DE-NRGs) were screened out and their bio-function was elucidated. Subsequently, a prognostic scoring model based on the regression coefficients of the screened out NRGs and their corresponding mRNA expressions were constructed and validated. Finally, the survival probability of cervical cancer patients based on the constructed prognostic scoring model in 3 and 5 years was predicted and assessed. We found 17 DE-NRGs in cervical cancer tissues which were closely related to cancer progression, and most of them were significantly highly expressed. Furthermore, 3 NRG were confirmed as the prognostic signature genes from 17 DE-NRGs by regression analysis. Overall survival predicted through our prognostic scoring model was lower in the high-risk group than in the low-risk group (p < 0.05) in both the TCGA cohort and the external GEO44001 validation cohort. What's more, the prediction performance of our prognostic scoring models well verified by the ROC curve, and the risk score calculated could act as an independent prognostic factor for cervical cancer patients. The calibration curve and C-index (0.776) of the nomogram analysis suggested that the predictive performance of the nomogram was satisfactory. Our study identified and validated a necroptosis-related prognostic signature in cervical cancer, which could well predict the prognosis for cervical cancer patients.
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Chen YJ, Guo X, Liu ML, Yu YY, Cui YH, Shen XZ, Liu TS, Liang L. Interaction between glycolysis‒cholesterol synthesis axis and tumor microenvironment reveal that gamma-glutamyl hydrolase suppresses glycolysis in colon cancer. Front Immunol 2022; 13:979521. [PMID: 36569910 PMCID: PMC9767965 DOI: 10.3389/fimmu.2022.979521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 10/26/2022] [Indexed: 12/12/2022] Open
Abstract
Background Metabolic reprogramming is a feature of cancer. However, colon cancer subtypes based on the glycolysis‒cholesterol synthesis axis have not been identified, and little is known about connections between metabolic features and the tumor microenvironment. Methods Data for 430 colon cancer cases were extracted from The Cancer Genome Atlas, including transcriptome data, clinical information, and survival outcomes. Glycolysis and cholesterol synthesis-related gene sets were obtained from the Molecular Signatures Database for a gene set variation analysis. The relationship between the genomic landscape and immune landscape were investigated among four metabolic subtypes. Hub genes were determined. The clinical significance of candidate hub gene was evaluated in 264 clinical samples and potential functions were validated in vitro and in vivo. Results Colon cancer cases were clustered into four metabolic subtypes: quiescent, glycolytic, cholesterogenic, and mixed. The metabolic subtypes differed with respect to the immune score, stromal score, and estimate score using the ESTIMATE algorithm, cancer-immunity cycle, immunomodulator signatures, and signatures of immunotherapy responses. Patients in the cholesterogenic group had better survival outcomes than those for other subtypes, especially glycolytic. The glycolytic subtype was related to unfavorable clinical characteristics, including high mutation rates in TTN, APC, and TP53, high mutation burden, vascular invasion, right colon cancer, and low-frequency microsatellite instability. GGH, CACNG4, MME, SLC30A2, CKMT2, SYN3, and SLC22A31 were identified as differentially expressed both in glycolytic-cholesterogenic subgroups as well as between colon cancers and healthy samples, and were involved in glycolysis‒cholesterol synthesis. GGH was upregulated in colon cancer; its high expression was correlated with CD4+ T cell infiltration and longer overall survival and it was identified as a favorable independent prognostic factor. The overexpression of GGH in colon cancer-derived cell lines (SW48 and SW480) inhibited PKM, GLUT1, and LDHA expression and decreased the extracellular lactate content and intracellular ATP level. The opposite effects were obtained by GGH silencing. The phenotype associated with GGH was also validated in a xenograft nude mouse model. Conclusions Our results provide insight into the connection between metabolism and the tumor microenvironment in colon cancer and provides preliminary evidence for the role of GGH, providing a basis for subsequent studies.
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Affiliation(s)
- Yan-Jie Chen
- Department of Gastroenterology, Zhongshan Hospital Fudan University, Shanghai, China
| | - Xi Guo
- Department of Medical Oncology, Zhongshan Hospital Fudan University, Shanghai, China,Cancer Center, Zhongshan Hospital Fudan University, Shanghai, China,Center of Evidence-based Medicine, Zhongshan Hospital Fudan University, Shanghai, China
| | - Meng-Ling Liu
- Department of Medical Oncology, Zhongshan Hospital Fudan University, Shanghai, China
| | - Yi-Yi Yu
- Department of Medical Oncology, Zhongshan Hospital Fudan University, Shanghai, China
| | - Yue-Hong Cui
- Department of Medical Oncology, Zhongshan Hospital Fudan University, Shanghai, China
| | - Xi-Zhong Shen
- Department of Gastroenterology, Zhongshan Hospital Fudan University, Shanghai, China,*Correspondence: Li Liang, ; Tian-Shu Liu, ; Xi-Zhong Shen,
| | - Tian-Shu Liu
- Department of Medical Oncology, Zhongshan Hospital Fudan University, Shanghai, China,Cancer Center, Zhongshan Hospital Fudan University, Shanghai, China,Center of Evidence-based Medicine, Zhongshan Hospital Fudan University, Shanghai, China,*Correspondence: Li Liang, ; Tian-Shu Liu, ; Xi-Zhong Shen,
| | - Li Liang
- Department of Medical Oncology, Zhongshan Hospital Fudan University, Shanghai, China,Cancer Center, Zhongshan Hospital Fudan University, Shanghai, China,Center of Evidence-based Medicine, Zhongshan Hospital Fudan University, Shanghai, China,*Correspondence: Li Liang, ; Tian-Shu Liu, ; Xi-Zhong Shen,
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Kong S, Li Z, Wang Y, Zhang Z, Jia X, Gao X, Cong B, Zhang F, Zhang J, Zheng C. A Wnt-related gene expression signature to improve the prediction of prognosis and tumor microenvironment in gastric cancer. Front Genet 2022; 13:1035099. [PMID: 36561311 PMCID: PMC9763457 DOI: 10.3389/fgene.2022.1035099] [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: 09/02/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022] Open
Abstract
Background: Most gastric cancer (GC) patients were diagnosed in the advanced stages without obvious symptoms, which resulted in the increased risk of death. Although the combination therapies have showed survival benefit of patients, there is still urgent need to explore the underlying mechanisms of GC development and potential novel targets for clinical applications. Numerous studies have reported the upregulation of Wnt signaling pathway in human GC, which play important role during GC development and progression. However, the current understanding of Wnt signaling pathway is still limited due to its complexity and contradictory effect on different stages of GC tumor microenvironment. Method: We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen Wnt signaling pathway-associated genes by ssGSEA and correlation analysis. Three molecular subtypes were constructed based on a consistent clustering analysis. The key Wnt-related genes were screened through univariate cox analysis, lasso, and stepwise regression. In addition, the Gene Set Enrichment Analysis (GSEA) were performed to explore potential molecular pathways regulated by the Wnt-related gene signatures. ESTIMATE was utilized for evaluating the immune cell populations in GC tumor microenvironment. Results: Three molecular subtypes associated to Wnt were identified, and 7 key Wnt-related genes were screened to establish a predictive RiskScore model. These three molecular subtypes showed significant prognostic differences and distinct functional signaling pathways. We also found the downregulated immune checkpoint expression in the clust1 with good prognosis. The RiskScore model was successfully validated in GSE26942 dataset. Nomogram based on RiskScore and Gender had better prognostic predictive ability. Conclusion: In summary, our study showed that the Wnt-related genes could be used to predict prognosis of GC patients. The risk model we established showed high accuracy and survival prediction capability.
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Affiliation(s)
- Shuai Kong
- Gastrointestinal Surgery, Shandong Provincial Hospital, Jinan, China
| | - Zhi Li
- Department of Pharmacy, The Fourth People’s Hospital of Jinan, Jinan, China
| | - Yuanyuan Wang
- Department of Oncology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zheming Zhang
- General Surgery, Weifang Medical University, Weifang, China
| | - Xianghao Jia
- General Surgery, Weifang Medical University, Weifang, China
| | - Xinxin Gao
- General Foreign Major, Shandong First Medical University, Tai’an, China
| | - Bicong Cong
- Gastrointestinal Surgery, Shandong First Medical University, Jinan, China
| | - Fangxu Zhang
- General Surgery, The Fourth People’s Hospital of Jinan, Jinan, China
| | - Jing Zhang
- Department of Oncology, HaploX Biotechnology, Shenzhen, China
| | - Chunning Zheng
- Gastrointestinal Surgery, Shandong Provincial Hospital, Jinan, China,*Correspondence: Chunning Zheng,
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Lin Q, Lu Y, Lu R, Chen Y, Wang L, Lu J, Ye X. Assessing Metabolic Risk Factors for LVSI in Endometrial Cancer: A Cross-Sectional Study. Ther Clin Risk Manag 2022; 18:789-798. [PMID: 35971461 PMCID: PMC9375567 DOI: 10.2147/tcrm.s372371] [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: 04/26/2022] [Accepted: 07/29/2022] [Indexed: 11/23/2022] Open
Abstract
Objective This study analyzed metabolic factors associated with lymphovascular space invasion (LVSI) and compared the difference between type 1 and type 2 endometrial cancer (EC). Methods Four hundred patients primarily diagnosed with EC who underwent hysterectomy with pathological results at Fujian Medical University Cancer Hospital from January 2019 to January 2021 were included. Demographic variable data were collected as well as pathological results. Laboratory evaluations included fasting blood glucose (FBG), serum cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A (Apo A) and apolipoprotein B (Apo B). Characterization of binary logistic regression models was used to test the odds ratios (ORs) between LVSI and its metabolic parameters with different subtypes of EC. Results The results indicated that CA125, ROMA, Ki67 score, FBG and TC were higher in EC patients with LVSI (all p<0.05). Negative ER and PR expression was positively associated with LVSI (P<0.05). CA125, ROMA, FBG, TC and ER were found to be independent risk factors for LVSI. CA125, ROMA and FBG were significantly elevated in type 1 EC patients with LVSI compared with those without LVSI (all p<0.05). TC and Ki67 scores were much higher in type 2 EC patients with vs without LVSI (all p<0.05). Negative PR expression was positively related to both type 1 and type 2 EC patients with LVSI. Consequently, CA125, ROMA, FBG and Apo B were found to be independent risk factors for LVSI in type 1 EC, and TC was found to be an independent risk factor for LVSI in type 2 EC. Conclusion FBG and TC were both independent risk factors for LVSI in EC. FBG and Apo B were independent risk factors for LVSI in type 1 EC. TC was an independent risk factor for LVSI in type 2 EC.
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Affiliation(s)
- Qiaoyan Lin
- Department of Blood Transfusion, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, People's Republic of China
| | - Yongwei Lu
- Department of Gyn-Surgical Oncology Section 9, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, People's Republic of China
| | - Rong Lu
- Department of Blood Transfusion, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, People's Republic of China
| | - Yujuan Chen
- Department of Blood Transfusion, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, People's Republic of China
| | - Linghua Wang
- Department of Gynecologic Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, People's Republic of China
| | - Jianping Lu
- Department of Pathology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, People's Republic of China
| | - Xianren Ye
- Department of Blood Transfusion, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, People's Republic of China.,Fujian Provincial Key Laboratory of Tumor Biotherapy, Fuzhou, People's Republic of China
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Xue C, Li G, Zheng Q, Gu X, Bao Z, Lu J, Li L. The functional roles of the circRNA/Wnt axis in cancer. Mol Cancer 2022; 21:108. [PMID: 35513849 PMCID: PMC9074313 DOI: 10.1186/s12943-022-01582-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/22/2022] [Indexed: 01/09/2023] Open
Abstract
CircRNAs, covalently closed noncoding RNAs, are widely expressed in a wide range of species ranging from viruses to plants to mammals. CircRNAs were enriched in the Wnt pathway. Aberrant Wnt pathway activation is involved in the development of various types of cancers. Accumulating evidence indicates that the circRNA/Wnt axis modulates the expression of cancer-associated genes and then regulates cancer progression. Wnt pathway-related circRNA expression is obviously associated with many clinical characteristics. CircRNAs could regulate cell biological functions by interacting with the Wnt pathway. Moreover, Wnt pathway-related circRNAs are promising potential biomarkers for cancer diagnosis, prognosis evaluation, and treatment. In our review, we summarized the recent research progress on the role and clinical application of Wnt pathway-related circRNAs in tumorigenesis and progression.
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Affiliation(s)
- Chen Xue
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, National Clinical Research Center for Infectious Diseases, Zhejiang University, No. 79 Qingchun Road, Shangcheng District, 310003 Hangzhou, China
| | - Ganglei Li
- grid.13402.340000 0004 1759 700XDepartment of Neurosurgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, 310003 Hangzhou, China
| | - Qiuxian Zheng
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, National Clinical Research Center for Infectious Diseases, Zhejiang University, No. 79 Qingchun Road, Shangcheng District, 310003 Hangzhou, China
| | - Xinyu Gu
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, National Clinical Research Center for Infectious Diseases, Zhejiang University, No. 79 Qingchun Road, Shangcheng District, 310003 Hangzhou, China
| | - Zhengyi Bao
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, National Clinical Research Center for Infectious Diseases, Zhejiang University, No. 79 Qingchun Road, Shangcheng District, 310003 Hangzhou, China
| | - Juan Lu
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, National Clinical Research Center for Infectious Diseases, Zhejiang University, No. 79 Qingchun Road, Shangcheng District, 310003 Hangzhou, China
| | - Lanjuan Li
- grid.13402.340000 0004 1759 700XState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, National Clinical Research Center for Infectious Diseases, Zhejiang University, No. 79 Qingchun Road, Shangcheng District, 310003 Hangzhou, China
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Hong WF, Liu MY, Liang L, Zhang Y, Li ZJ, Han K, Du SS, Chen YJ, Ma LH. Molecular Characteristics of T Cell-Mediated Tumor Killing in Hepatocellular Carcinoma. Front Immunol 2022; 13:868480. [PMID: 35572523 PMCID: PMC9100886 DOI: 10.3389/fimmu.2022.868480] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/31/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Although checkpoint blockade is a promising approach for the treatment of hepatocellular carcinoma (HCC), subsets of patients expected to show a response have not been established. As T cell-mediated tumor killing (TTK) is the fundamental principle of immune checkpoint inhibitor therapy, we established subtypes based on genes related to the sensitivity to TKK and evaluated their prognostic value for HCC immunotherapies. METHODS Genes regulating the sensitivity of tumor cells to T cell-mediated killing (referred to as GSTTKs) showing differential expression in HCC and correlations with prognosis were identified by high-throughput screening assays. Unsupervised clustering was applied to classify patients with HCC into subtypes based on the GSTTKs. The tumor microenvironment, metabolic properties, and genetic variation were compared among the subgroups. A scoring algorithm based on the prognostic GSTTKs, referred to as the TCscore, was developed, and its clinical and predictive value for the response to immunotherapy were evaluated. RESULTS In total, 18 out of 641 GSTTKs simultaneously showed differential expression in HCC and were correlated with prognosis. Based on the 18 GSTTKs, patients were clustered into two subgroups, which reflected distinct TTK patterns in HCC. Tumor-infiltrating immune cells, immune-related gene expression, glycolipid metabolism, somatic mutations, and signaling pathways differed between the two subgroups. The TCscore effectively distinguished between populations with different responses to chemotherapeutics or immunotherapy and overall survival. CONCLUSIONS TTK patterns played a nonnegligible role in formation of TME diversity and metabolic complexity. Evaluating the TTK patterns of individual tumor will contribute to enhancing our cognition of TME characterization, reflects differences in the functionality of T cells in HCC and guiding more effective therapy strategies.
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Affiliation(s)
- Wei-feng Hong
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mou-yuan Liu
- Department of Medical Imaging, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Li Liang
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
- Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yang Zhang
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zong-juan Li
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Keqi Han
- Department of Oncology, Luodian Hospital Affiliated to Shanghai University, Shanghai, China
| | - Shi-suo Du
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Li-heng Ma, ; Yan-jie Chen, ; Shi-suo Du,
| | - Yan-jie Chen
- Department of Gastroenterology, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Li-heng Ma, ; Yan-jie Chen, ; Shi-suo Du,
| | - Li-heng Ma
- Department of Medical Imaging, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- *Correspondence: Li-heng Ma, ; Yan-jie Chen, ; Shi-suo Du,
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