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Bao Y, Wang L, Liu H, Yang J, Yu F, Cui C, Huang D. A Diagnostic Model for Parkinson's Disease Based on Anoikis-Related Genes. Mol Neurobiol 2024; 61:3641-3656. [PMID: 38001358 DOI: 10.1007/s12035-023-03753-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023]
Abstract
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, and its pathological mechanisms are thought to be closely linked to apoptosis. Anoikis, a specific type of apoptosis, has recently been suggested to play a role in the progression of Parkinson's disease; however, the underlying mechanisms are not well understood. To explore the potential mechanisms involved in PD, we selected genes from the GSE28894 dataset and compared their expression in PD patients and healthy controls to identify differentially expressed genes (DEGs), and selected anoikis-related genes (ANRGs) from the DEGs. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression approach and multivariate logistic regression highlighted five key genes-GSK3B, PCNA, CDC42, DAPK2, and SRC-as biomarker candidates. Subsequently, we developed a nomogram model incorporating these 5 genes along with age and sex to predict and diagnose PD. To evaluate the model's coherence, clinical applicability, and distinguishability, we utilized receiver operating characteristic (ROC) curves, the C-index, and calibration curves and validated it in both the GSE20295 dataset and our center's external clinical data. In addition, we confirmed the differential expression of the 5 model genes in human blood samples through qRT-PCR and Western blotting. Our constructed anoikis-related PD diagnostic model exhibits satisfactory predictive accuracy and offers novel insights into both diagnosis and treatment strategies for Parkinson's disease while facilitating its implementation in clinical practice.
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Affiliation(s)
- Yiwen Bao
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Lufeng Wang
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Hong Liu
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Jie Yang
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Fei Yu
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Can Cui
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
| | - Dongya Huang
- Department of Neurology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
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Zhu J, Zhao W, Yang J, Liu C, Wang Y, Zhao H. Anoikis-related lncRNA signature predicts prognosis and is associated with immune infiltration in hepatocellular carcinoma. Anticancer Drugs 2024; 35:466-480. [PMID: 38507233 DOI: 10.1097/cad.0000000000001589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Anoikis is a programmed cell death process triggered when cells are dislodged from the extracellular matrix. Numerous long noncoding RNAs (lncRNAs) have been identified as significant factors associated with anoikis resistance in various tumor types, including glioma, breast cancer, and bladder cancer. However, the relationship between lncRNAs and the prognosis of hepatocellular carcinoma (HCC) has received limited research attention. Further research is needed to investigate this potential link and understand the role of lncRNAs in the progression of HCC. We developed a prognostic signature based on the differential expression of lncRNAs implicated in anoikis in HCC. A co-expression network of anoikis-related mRNAs and lncRNAs was established using data obtained from The Cancer Genome Atlas (TCGA) for HCC. Cox regression analyses were conducted to formulate an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which was subsequently validated in both a testing cohort and a combined dataset comprising the two cohorts. Receiver operating characteristic curves, nomograms, and decision curve analyses based on the ARlncSig score and clinical characteristics demonstrated robust predictive ability. Moreover, gene set enrichment analysis revealed significant enrichment of several immune processes in the high-risk group compared to the low-risk group. Furthermore, significant differences were observed in immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy between the high- and low-risk groups. Lastly, we validated the expression levels of the five lncRNAs included in the signature using quantitative real-time PCR. In conclusion, our ARlncSig model holds substantial predictive value regarding the prognosis of HCC patients and has the potential to provide clinical guidance for individualized immunotherapy. In this study, we obtained 36 genes associated with anoikis from the Gene Ontology and Gene Set Enrichment Analysis databases. We also identified 22 differentially expressed lncRNAs that were correlated with these genes using data from TCGA. Using Cox regression analyses, we developed an ARlncSig in a training cohort, which was then validated in both a testing cohort and a combined cohort comprising data from both cohorts. Additionally, we collected eight pairs of liver cancer tissues and adjacent tissues from the Affiliated Tumor Hospital of Nantong University for further analysis. The aim of this study was to investigate the potential of ARlncSig as a biomarker for liver cancer prognosis. The study developed a risk stratification system called ARlncSig, which uses five lncRNAs to categorize liver cancer patients into low- and high-risk groups. Patients in the high-risk group exhibited significantly lower overall survival rates compared to those in the low-risk group. The model's predictive performance was supported by various analyses including the receiver operating characteristic curve, nomogram calibration, clinical correlation analysis, and clinical decision curve. Additionally, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations revealed significant differences between the high- and low-risk groups. Finally, quantitative real-time PCR validated the expression levels of the five lncRNAs. In conclusion, the ARlncSig model demonstrates critical predictive value in the prognosis of HCC patients and may provide clinical guidance for personalized immunotherapy.
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Affiliation(s)
- Jiahong Zhu
- Interventional and Vascular Surgery Department, Affiliated Hospital of Nantong University
| | - Wenjing Zhao
- Cancer Research Center Nantong, Tumor Hospital Affiliated to Nantong University
| | - Junkai Yang
- Interventional and Vascular Surgery Department, Affiliated Hospital of Nantong University
| | - Cheng Liu
- Interventional and Vascular Surgery Department, Affiliated Hospital of Nantong University
| | - Yilang Wang
- Internal Medicine Department, Affiliated Maternity and Child Healthcare Hospital of Nantong University, Nantong, China
| | - Hui Zhao
- Interventional and Vascular Surgery Department, Affiliated Hospital of Nantong University
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Liu J, Ma R, Chen S, Lai Y, Liu G. Anoikis patterns via machine learning strategy and experimental verification exhibit distinct prognostic and immune landscapes in melanoma. Clin Transl Oncol 2024; 26:1170-1186. [PMID: 37989822 DOI: 10.1007/s12094-023-03336-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: 06/27/2023] [Accepted: 10/10/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND Anoikis is a cell death programmed to eliminate dysfunctional or damaged cells induced by detachment from the extracellular matrix. Utilizing an anoikis-based risk stratification is anticipated to understand melanoma's prognostic and immune landscapes comprehensively. METHODS Differential expression genes (DEGs) were analyzed between melanoma and normal skin tissues in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data sets. Next, least absolute shrinkage and selection operator, support vector machine-recursive feature elimination algorithm, and univariate and multivariate Cox analyses on the 308 DEGs were performed to build the prognostic signature in the TCGA-melanoma data set. Finally, the signature was validated in GSE65904 and GSE22155 data sets. NOTCH3, PIK3R2, and SOD2 were validated in our clinical samples by immunohistochemistry. RESULTS The prognostic model for melanoma patients was developed utilizing ten hub anoikis-related genes. The overall survival (OS) of patients in the high-risk subgroup, which was classified by the optimal cutoff value, was remarkably shorter in the TCGA-melanoma, GSE65904, and GSE22155 data sets. Low-risk patients exhibited low immune cell infiltration and high expression of immunophenoscores and immune checkpoints. They also demonstrated increased sensitivity to various drugs, including dasatinib and dabrafenib. NOTCH3, PIK3R2, and SOD2 were notably associated with OS by univariate Cox analysis in the GSE65904 data set. The clinical melanoma samples showed remarkably higher protein expressions of NOTCH3 (P = 0.003) and PIK3R2 (P = 0.009) than the para-melanoma samples, while the SOD2 protein expression remained unchanged. CONCLUSIONS In this study, we successfully established a prognostic anoikis-connected signature using machine learning. This model may aid in evaluating patient prognosis, clinical characteristics, and immune treatment modalities for melanoma.
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Affiliation(s)
- Jinfang Liu
- Department of Plastic and Reconstructive Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No. 301 Middle Yanchang Road, Shanghai, China
| | - Rong Ma
- School of Life Sciences, Northwest University, Xi'an, 710069, China
| | - Siyuan Chen
- Department of Plastic and Reconstructive Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No. 301 Middle Yanchang Road, Shanghai, China
| | - Yongxian Lai
- Department of Dermatologic Surgery, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, No. 1278 Baode Road, Shanghai, China.
| | - Guangpeng Liu
- Department of Plastic and Reconstructive Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No. 301 Middle Yanchang Road, Shanghai, China.
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Yu X, Feng B, Wu J, Li M. A novel anoikis-related gene signature can predict the prognosis of hepatocarcinoma patients. Transl Cancer Res 2024; 13:1834-1847. [PMID: 38737687 PMCID: PMC11082671 DOI: 10.21037/tcr-23-2096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/20/2024] [Indexed: 05/14/2024]
Abstract
Background Hepatocellular carcinoma (HCC) is a major health problem with more than 850,000 cases per year worldwide. This cancer is now the third leading cause of cancer-related deaths worldwide, and the number is rising. Cancer cells develop anoikis resistance which is a vital step during cancer progression and metastatic colonization. However, there is not much research that specifically addresses the role of anoikis in HCC, especially in terms of prognosis. Methods This study obtained gene expression data and clinical information from 371 HCC patients through The Cancer Genome Atlas (TCGA) Program and The Gene Expression Omnibus (GEO) databases. A total of 516 anoikis-related genes (ANRGs) were retrieved from GeneCard database and Harmonizome portal. Differential expression analysis identified 219 differentially expressed genes (DEGs), and univariate Cox regression analysis was utilized to select 99 ANRGs associated with the prognosis of HCC patients. A risk scoring model with seven genes was established using the least absolute shrinkage and selection operator (LASSO) regression model, and internal validation of the model was performed. Results The identified 99 ANRGs are closely associated with the prognosis of HCC patients. The risk scoring model based on seven characteristic genes demonstrates excellent predictive performance, further validated by receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. The study reveals significant differences in immune cell infiltration, gene expression, and survival status among different risk groups. Conclusions The prognosis of HCC patients can be predicted using a unique prognostic model built on ANRGs in HCC.
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Affiliation(s)
- Xiaohan Yu
- General Surgery Department, Dandong Central Hospital, China Medical University, Dandong, China
| | - Bo Feng
- General Surgery Department, Dandong Central Hospital, China Medical University, Dandong, China
| | - Jinge Wu
- General Surgery Department, Dandong Central Hospital, China Medical University, Dandong, China
| | - Meng Li
- General Surgery Department, Dandong Central Hospital, China Medical University, Dandong, China
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Deng HY, Zhang LW, Tang FQ, Zhou M, Li MN, Lu LL, Li YH. Identification and Validation of a Novel Anoikis-Related Gene Signature for Predicting Survival in Patients With Serous Ovarian Cancer. World J Oncol 2024; 15:45-57. [PMID: 38274727 PMCID: PMC10807923 DOI: 10.14740/wjon1714] [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: 09/19/2023] [Accepted: 11/29/2023] [Indexed: 01/27/2024] Open
Abstract
Background Ovarian cancer is an extremely deadly gynecological malignancy, with a 5-year survival rate below 30%. Among the different histological subtypes, serous ovarian cancer (SOC) is the most common. Anoikis significantly contributes to the progression of ovarian cancer. Therefore, identifying an anoikis-related signature that can serve as potential prognostic predictors for SOC is of great significance. Methods We intersected 308 anoikis-related genes (ARGs) and identified those significantly associated with SOC prognosis using univariate Cox regression. A LASSO Cox regression model was constructed and evaluated using Kaplan-Meier and receiver operating characteristic (ROC) analyses in TCGA (The Cancer Genome Atlas) and GSE26193 cohorts. We conducted quantitative real-time polymerase chain reaction (qPCR) to assess mRNA levels and applied bioinformatics to investigate the correlation between risk groups and gene expression, mutations, pathways, tumor immune microenvironment (TIME), and drug sensitivity in SOC. Results Among 308 ARGs, 28 were significantly associated with SOC prognosis. A 13-gene prognostic model was established through LASSO Cox regression in TCGA cohort. High-risk group had poorer prognosis than low-risk group (median overall survival (mOS): 34.2 vs. 57.1 months, hazard ratio (HR): 2.590, 95% confidence interval (CI): 0.159 - 6.00, P < 0.001). The area under the curve (AUC) values of 0.63, 0.65, and 0.74 reflected the predictive performance for 3-, 5-, and 8-year overall survival (OS) in GSE26193 validation cohort. Functional enrichment, pathway analysis, and TIME analysis identified distinct characteristics between risk groups. Drug sensitivity analysis revealed potential drug advantages for each group. Furthermore, qPCR validation once again confirmed the effectiveness of the risk model in SOC patients. Conclusions We developed and validated a robust ARG model, which could be used to predict OS in SOC patients. By systematically analyzing the correlation between the risk score of the ARGs signature model and various patterns, including the TIME and drug sensitivity, our findings suggest that this prognostic model contributes to the advancement of personalized and precise therapeutic strategies. Nevertheless, further validation studies and investigations into the underlying mechanisms are warranted.
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Affiliation(s)
- Hong Yu Deng
- Department of Clinical Laboratory, Hunan Key Laboratory of Oncotarget Gene, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- These authors contributed equally to this work
| | - Li Wen Zhang
- Shanghai OrigiMed Co., Ltd., Shanghai 201112, China
- These authors contributed equally to this work
| | - Fa Qing Tang
- Department of Clinical Laboratory, Hunan Key Laboratory of Oncotarget Gene, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Ming Zhou
- Department of Clinical Laboratory, Hunan Key Laboratory of Oncotarget Gene, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Meng Na Li
- Department of Clinical Laboratory, Hunan Key Laboratory of Oncotarget Gene, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lei Lei Lu
- Shanghai OrigiMed Co., Ltd., Shanghai 201112, China
| | - Ying Hua Li
- Gynecological Oncology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
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Song H, Liu H, Wang X, Yang Y, Zhao X, Jiang WG, Sui L, Song X. Death-associated protein 3 in cancer-discrepant roles of DAP3 in tumours and molecular mechanisms. Front Oncol 2024; 13:1323751. [PMID: 38352299 PMCID: PMC10862491 DOI: 10.3389/fonc.2023.1323751] [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: 10/18/2023] [Accepted: 12/30/2023] [Indexed: 02/16/2024] Open
Abstract
Cancer, ranks as the secondary cause of death, is a group of diseases that are characterized by uncontrolled tumor growth and distant metastasis, leading to increased mortality year-on-year. To date, targeted therapy to intercept the aberrant proliferation and invasion is crucial for clinical anticancer treatment, however, mutant expression of target genes often leads to drug resistance. Therefore, it is essential to identify more molecules that can be targeted to facilitate combined therapy. Previous studies showed that death associated protein 3 (DAP3) exerts a pivotal role in regulating apoptosis signaling of tumors, meanwhile, aberrant DAP3 expression is associated with the tumorigenesis and disease progression of various cancers. This review provides an overview of the molecule structure of DAP3 and the discrepant roles played by DAP3 in various types of tumors. Considering the molecular mechanism of DAP3-regulated cancer development, new potential treatment strategies might be developed in the future.
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Affiliation(s)
- Hao Song
- The Second Medical College, Binzhou Medical University, Yantai, China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Huifang Liu
- The Second Medical College, Binzhou Medical University, Yantai, China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Xiufeng Wang
- Department of Nursing, Zhaoyuan People's Hospital, Yantai, China
| | - Yuteng Yang
- The Second Medical College, Binzhou Medical University, Yantai, China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Xiangkun Zhao
- The Second Medical College, Binzhou Medical University, Yantai, China
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Wen G. Jiang
- Cardiff China Medical Research Collaborative, Division of Cancer and Genetics, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - Laijian Sui
- Department of Orthopedics, Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Xicheng Song
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
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Guo S, Xing N, Du Q, Luo B, Wang S. Deciphering hepatocellular carcinoma pathogenesis and therapeutics: a study on anoikis, ceRNA regulatory network and traditional Chinese medicine. Front Pharmacol 2024; 14:1325992. [PMID: 38283837 PMCID: PMC10811069 DOI: 10.3389/fphar.2023.1325992] [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: 10/22/2023] [Accepted: 12/31/2023] [Indexed: 01/30/2024] Open
Abstract
Introduction: Hepatocellular carcinoma (HCC) is responsible for approximately 90% of liver malignancies and is the third most common cause of cancer-related mortality worldwide. However, the role of anoikis, a programmed cell death mechanism crucial for maintaining tissue equilibrium, is not yet fully understood in the context of HCC. Methods: Our study aimed to investigate the expression of 10 anoikis-related genes (ARGs) in HCC, including BIRC5, SFN, UBE2C, SPP1, E2F1, etc., and their significance in the disease. Results: Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we discovered that these ARGs are involved in important processes such as tissue homeostasis, ion transport, cell cycle regulation, and viral infection pathways. Furthermore, we found a significant correlation between the prognostic value of five ARGs and immune cell infiltrates. Analysis of clinical datasets revealed a strong association between BIRC5 expression and HCC pathological progression, including pathological stage, T stage, overall survival (OS), and race. By constructing a competing endogenous RNA (ceRNA) network and using molecular docking, we identified ten bioactive compounds from traditional Chinese medicine (TCM) that could potentially modulate BIRC5. Subsequent in vitro experiments confirmed the influence of platycodin D, one of the identified compounds, on key elements within the ceRNA network. Discussion: In conclusion, our study presents a novel framework for an anoikis-centered prognostic model and an immune-involved ceRNA network in HCC, revealing potential regulatory targets. These insights contribute to our understanding of HCC pathology and may lead to improved therapeutic interventions.
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Affiliation(s)
- Sa Guo
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Nan Xing
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Qinyun Du
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Bin Luo
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shaohui Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Meishan Hospital of Chengdu University of Traditional Chinese Medicine, Meishan, China
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Du S, Cao K, Wang Z, Lin D. Comprehensive analysis of anoikis-related lncRNAs for predicting prognosis and response of immunotherapy in hepatocellular carcinoma. IET Syst Biol 2023; 17:198-211. [PMID: 37417684 PMCID: PMC10439496 DOI: 10.1049/syb2.12070] [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: 02/03/2023] [Revised: 06/08/2023] [Accepted: 06/26/2023] [Indexed: 07/08/2023] Open
Abstract
Nowadays, primary liver cancer is still a major threat to human health. Anoikis is a particular form of programed cell death that has an inhibitory effect on neoplasm metastasis. Although several prognostic models based on anoikis-related genes for Hepatocellular carcinoma (HCC) have been established, signatures associated with anoikis-related lncRNAs have not been identified. To fill this blank space, the authors built up a prognostic signature and appraised its value in guiding immunotherapy. Eleven prognostic anoikis-related lncRNAs were identified through Least Absolute Shrinkage and Selection Operator Cox analysis. The accuracy of the risk signature in predicting prognosis was verified by K-M survival analysis and Receiver operating characteristic analysis. We further discovered that the high-risk group was often enriched in signal pathways related to cell growth and death and immune response; in addition, in the low-risk group, cells often undergo metabolic changes through gene set enrichment analysis. Finally, we realised that HCC patients in the high-risk group were upregulated in immune-checkpoint molecules and tend to have a higher tumour mutation burden level which indicated a higher sensitivity to immunotherapy. All in all, the anoikis-related lncRNAs risk signature showed excellent ability in predicting prognosis and may guide the application of immunotherapy in future clinical practice.
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Affiliation(s)
- Sihao Du
- Department of General SurgeryXuanwu HospitalCapital Medical UniversityBeijingChina
| | - Ke Cao
- Department of General SurgeryBeijing Chaoyang HospitalCapital Medical UniversityBeijingChina
- Department of General SurgeryBeijing Youan HospitalCapital Medical UniversityBeijingChina
| | - Zhenshun Wang
- Department of General SurgeryXuanwu HospitalCapital Medical UniversityBeijingChina
| | - Dongdong Lin
- Department of General SurgeryXuanwu HospitalCapital Medical UniversityBeijingChina
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Liu Y, Jiang Z, Zhou X, Li Y, Liu P, Chen Y, Tan J, Cai C, Han Y, Zeng S, Shen H, Feng Z. A Multi-Omics Analysis of NASH-Related Prognostic Biomarkers Associated with Drug Sensitivity and Immune Infiltration in Hepatocellular Carcinoma. J Clin Med 2023; 12:jcm12041286. [PMID: 36835825 PMCID: PMC9963320 DOI: 10.3390/jcm12041286] [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: 12/19/2022] [Revised: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023] Open
Abstract
Background: Nonalcoholic steatohepatitis (NASH)-driven hepatocellular carcinoma (HCC) is becoming a major health-related problem. The exploration of NASH-related prognostic biomarkers and therapeutic targets is necessary. Methods: Data were downloaded from the GEO database. The "glmnet" package was used to identify differentially expressed genes (DEGs). The prognostic model was constructed by the univariate Cox and LASSO regression analyses. Validation of the expression and prognosis by immunohistochemistry (IHC) in vitro. Drug sensitivity and immune cell infiltration were analyzed by CTR-DB and ImmuCellAI. Results: We constructed a prognostic model that identified the NASH-related gene set (DLAT, IDH3B, and MAP3K4), which was validated in a real-world cohort. Next, seven prognostic transcription factors (TFs) were identified. The prognostic ceRNA network included three mRNAs, four miRNAs, and seven lncRNAs. Finally, we found that the gene set was associated with drug response which was validated in six clinical trial cohorts. Moreover, the expression level of the gene set was inversely correlated with CD8 T cell infiltration in HCC. Conclusions: We established a NASH-related prognostic model. Upstream transcriptome analysis and the ceRNA network provided clues for mechanism exploration. The mutant profile, drug sensitivity, and immune infiltration analysis further guided precise diagnosis and treatment strategies.
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Affiliation(s)
- Yongting Liu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhaohui Jiang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xin Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yin Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ping Liu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yihong Chen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Jun Tan
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Changjing Cai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ying Han
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shan Zeng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Hong Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Correspondence: (H.S.); (Z.F.)
| | - Ziyang Feng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- Correspondence: (H.S.); (Z.F.)
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Qi L, Chen F, Wang L, Yang Z, Zhang W, Li ZH. Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma. Front Pharmacol 2023; 14:1136184. [PMID: 36937870 PMCID: PMC10014785 DOI: 10.3389/fphar.2023.1136184] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear. Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated. Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response. Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.
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Affiliation(s)
- Lin Qi
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
| | - Fangyue Chen
- Department of General Surgery, Changhai Hospital, Navy Military Medical University, Shanghai, China
| | - Lu Wang
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
| | - Zhimin Yang
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
- Department of Microbiology, Immunology & Molecular Genetics, University of Texas Long School of Medicine, UT Health Science Center, San Antonio, TX, United States
| | - Wenchao Zhang
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
- *Correspondence: Wenchao Zhang, ; Zhi-Hong Li,
| | - Zhi-Hong Li
- Department of Orthopedics, The Second Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China
- *Correspondence: Wenchao Zhang, ; Zhi-Hong Li,
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