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Zhu J, Kong W, Huang L, Wang S, Bi S, Wang Y, Shan P, Zhu S. MLSP: A Bioinformatics Tool for Predicting Molecular Subtypes and Prognosis in Patients with Breast Cancer. Comput Struct Biotechnol J 2022; 20:6412-6426. [DOI: 10.1016/j.csbj.2022.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 10/18/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022] Open
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Characterization of the Lipid Metabolism in Bladder Cancer to Guide Clinical Therapy. JOURNAL OF ONCOLOGY 2022; 2022:7679652. [PMID: 36131793 PMCID: PMC9484922 DOI: 10.1155/2022/7679652] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/02/2022] [Accepted: 08/09/2022] [Indexed: 11/24/2022]
Abstract
Background Bladder cancer is one of the most common malignancies of the urinary system with an unfavorable prognosis. More and more studies have suggested that lipid metabolism could influence the progression and treatment of tumors. However, there are few studies exploring the relationship between lipid metabolism and bladder cancer. This study aimed to explore the roles that lipid metabolism-related genes play in patients with bladder cancer. Methods TCGA_BLCA cohort and GSE13507 cohort were included in this study, and transcriptional and somatic mutation profiles of 309 lipid metabolism-related genes were analyzed to discover the critical lipid metabolism-related genes in the incurrence and progression of bladder cancer. Furthermore, the TCGA_BLCA cohort was randomly divided into training set and validation set, and the GSE13507 cohort was served as an external independent validation set. We performed the LASSO regression and multivariate Cox regression in training set to develop a prognostic signature and further verified this signature in TCGA_BLCA validation set and GSE13507 external validation set. Finally, we systematically investigated the association between this signature and tumor microenvironment, drug response, and potential functions and then verified the differential expression status of signature genes in the protein level by immunohistochemistry. Results A novel 6-lipidmetabolism-related gene signature was identified and validated, and this risk score model could predict the prognosis of patients with bladder cancer. In addition, the prognostic model was tightly related to immune cell infiltration and tumor mutation burden. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) showed that mTOR signaling pathway, G2M checkpoint, fatty acid metabolism, and hypoxia were enriched in patients in the high-risk score groups. Furthermore, 3 therapies specific for bladder cancer patients in different risk scores were identified. Conclusion s. In conclusion, we investigated the lipid metabolism-related genes in bladder cancer through comprehensive bioinformatic analysis. A novel 6-gene signature associated with lipid metabolism for predicting the outcomes of patients with bladder cancer was conducted and validated. Furthermore, the risk score model could be utilized to indicate the choice of therapy in bladder cancer.
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Zhong W, Liu H, Li F, lin Y, Ye Y, Xu L, Li S, Chen H, Li C, Lin Y, Zhuang W, Lin Y, Wang Q. Elevated expression of LIF predicts a poor prognosis and promotes cell migration and invasion of clear cell renal cell carcinoma. Front Oncol 2022; 12:934128. [PMID: 35992780 PMCID: PMC9382297 DOI: 10.3389/fonc.2022.934128] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background Renal cell carcinoma (RCC) is the seventh most common cancer in humans, of which clear cell renal cell carcinoma (ccRCC) accounts for the majority. Recently, although there have been significant breakthroughs in the treatment of ccRCC, the prognosis of targeted therapy is still poor. Leukemia inhibitory factor (LIF) is a pleiotropic protein, which is overexpressed in many cancers and plays a carcinogenic role. In this study, we explored the expression and potential role of LIF in ccRCC. Methods The expression levels and prognostic effects of the LIF gene in ccRCC were detected using TCGA, GEO, ICGC, and ArrayExpress databases. The function of LIF in ccRCC was investigated using a series of cell function approaches. LIF-related genes were identified by weighted gene correlation network analysis (WGCNA). GO and KEGG analyses were performed subsequently. Cox univariate and LASSO analyses were used to develop risk signatures based on LIF-related genes, and the prognostic model was validated in the ICGC and E-MTAB-1980 databases. Then, a nomogram model was constructed for survival prediction and validation of ccRCC patients. To further explore the drug sensitivity between LIF-related genes, we also conducted a drug sensitivity analysis based on the GDSC database. Results The mRNA and protein expression levels of LIF were significantly increased in ccRCC patients. In addition, a high expression of LIF has a poor prognostic effect in ccRCC patients. LIF knockdown can inhibit the migration and invasion of ccRCC cells. By using WGCNA, 97 LIF-related genes in ccRCC were identified. Next, a prognostic risk prediction model including eight LIF-related genes (TOB2, MEPCE, LIF, RGS2, RND3, KLF6, RRP12, and SOCS3) was developed and validated. Survival analysis and ROC curve analysis indicated that the eight LIF-related-gene predictive model had good performance in evaluating patients’ prognosis in different subgroups of ccRCC. Conclusion Our study revealed that LIF plays a carcinogenic role in ccRCC. In addition, we firstly integrated multiple LIF-related genes to set up a risk-predictive model. The model could accurately predict the prognosis of ccRCC, which offers clinical implications for risk stratification, drug screening, and therapeutic decision.
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Affiliation(s)
- Wenting Zhong
- Central Laboratory at the Second Affiliated Hospital of Fujian Traditional Chinese Medical University, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Hongxia Liu
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Feng Li
- Department of Pathology, Fujian Provincial Hospital, Fuzhou, China
| | - Youyu lin
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Yan Ye
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Luyun Xu
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - ShengZhao Li
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Hui Chen
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Chengcheng Li
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Yuxuan Lin
- College of Life Sciences, Fujian Normal University, Fuzhou, China
| | - Wei Zhuang
- Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
- *Correspondence: Qingshui Wang, ; Yao Lin, ; ; Wei Zhuang,
| | - Yao Lin
- Central Laboratory at the Second Affiliated Hospital of Fujian Traditional Chinese Medical University, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- *Correspondence: Qingshui Wang, ; Yao Lin, ; ; Wei Zhuang,
| | - Qingshui Wang
- Central Laboratory at the Second Affiliated Hospital of Fujian Traditional Chinese Medical University, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- College of Life Sciences, Fujian Normal University, Fuzhou, China
- *Correspondence: Qingshui Wang, ; Yao Lin, ; ; Wei Zhuang,
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Yan D, Zhao Q, Du Z, Li H, Geng R, Yang W, Zhang X, Cao J, Yi N, Zhou J, Tang Z. Development and validation of an immune-related gene signature for predicting the radiosensitivity of lower-grade gliomas. Sci Rep 2022; 12:6698. [PMID: 35461367 PMCID: PMC9035187 DOI: 10.1038/s41598-022-10601-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 03/22/2022] [Indexed: 12/21/2022] Open
Abstract
Radiotherapy is an important treatment modality for lower-grade gliomas (LGGs) patients. This analysis was conducted to develop an immune-related radiosensitivity gene signature to predict the survival of LGGs patients who received radiotherapy. The clinical and RNA sequencing data of LGGs were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Lasso regression analyses were used to construct a 21-gene signature to identify the LGGs patients who could benefit from radiotherapy. Based on this radiosensitivity signature, patients were classified into a radiosensitive (RS) group and a radioresistant (RR) group. According to the Kaplan–Meier analysis results of the TCGA dataset and the two CGGA validation datasets, the RS group had a higher overall survival rate than that of the RR group. This gene signature was RT-specific and an independent prognostic indicator. The nomogram model performed well in predicting 3-, and 5-year survival of LGGs patients after radiotherapy by this gene signature and other clinical factors (age, sex, grade, IDH mutations, 1p/19q codeletion). In summary, this signature is a powerful supplement to the prognostic factors of LGGs patients with radiotherapy and may provide an opportunity to incorporate individual tumor biology into clinical decision making in radiation oncology.
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Yan D, Cai S, Bai L, Du Z, Li H, Sun P, Cao J, Yi N, Liu SB, Tang Z. Integration of immune and hypoxia gene signatures improves the prediction of radiosensitivity in breast cancer. Am J Cancer Res 2022; 12:1222-1240. [PMID: 35411250 PMCID: PMC8984882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/22/2022] [Indexed: 06/14/2023] Open
Abstract
Immunity and hypoxia are two important factors that affect the response of cancer patients to radiotherapy. At the same time, considering the limited predictive value of a single predictive model and the uncertainty of grouping patients near the cutoff value, we developed and validated a combined model based on immune- and hypoxia-related gene expression profiles to predict the radiosensitivity of breast cancer patients. This study was based on breast cancer data from The Cancer Genome Atlas (TCGA). Spike-and-slab Lasso regression analysis was performed to select three immune-related genes and develop a radiosensitivity model. Lasso Cox regression modeling selected 11 hypoxia-related genes for development of radiosensitivity model. Three independent datasets (Molecular Taxonomy of Breast Cancer International Consortium [METABRIC], E-TABM-158, GSE103746) were used to validate the predictive value of radiosensitivity signatures. In the TCGA dataset, the 10-year survival probabilities of the immune radioresistant (IRR) and hypoxia radioresistant (HRR) groups were 0.189 (0.037, 0.973) and 0.477 (0.293, 0.776), respectively. The 10-year survival probabilities of the immune radiosensitive (IRS) and hypoxia radiosensitive (HRS) groups were 0.778 (0.676, 0.895) and 0.824 (0.723, 0.939), respectively. Based on these two gene signatures, we further constructed a combined model and divided all patients into three groups (IRS/HRS, mixed, IRR/HRR). We identified the IRS/HRS patients most likely to benefit from radiotherapy; the 10-year survival probability was 0.886 (0.806, 0.976). The 10-year survival probability of the IRR/HRR group was 0. In conclusion, a combined model integrating immune- and hypoxia-related gene signatures could effectively predict the radiosensitivity of breast cancer and more accurately identify radiosensitive and radioresistant patients than a single model.
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Affiliation(s)
- Derui Yan
- Department of Biostatistics, School of Public Health, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
- Suzhou Key Laboratory of Medical Biotechnology, Suzhou Vocational Health CollegeSuzhou 215009, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
| | - Shang Cai
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow UniversitySuzhou 215004, Jiangsu, China
| | - Lu Bai
- Department of Biostatistics, School of Public Health, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
- Suzhou Key Laboratory of Medical Biotechnology, Suzhou Vocational Health CollegeSuzhou 215009, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
| | - Zixuan Du
- Department of Biostatistics, School of Public Health, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
| | - Huijun Li
- Department of Biostatistics, School of Public Health, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
| | - Peng Sun
- Department of Otolaryngology, The First Affiliated Hospital of Soochow UniversitySuzhou 215006, Jiangsu, China
| | - Jianping Cao
- School of Radiation Medicine and Protection and Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, Soochow UniversitySuzhou 215031, Jiangsu, China
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at BirminghamBirmingham, AL 35294, USA
| | - Song-Bai Liu
- Suzhou Key Laboratory of Medical Biotechnology, Suzhou Vocational Health CollegeSuzhou 215009, Jiangsu, China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow UniversitySuzhou 215123, Jiangsu, China
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Gu P, Zhang L, Wang R, Ding W, Wang W, Liu Y, Wang W, Li Z, Yan B, Sun X. Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer. Front Cell Dev Biol 2022; 9:796729. [PMID: 34977036 PMCID: PMC8716768 DOI: 10.3389/fcell.2021.796729] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 11/30/2021] [Indexed: 12/19/2022] Open
Abstract
Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer. Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman's rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan-Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets. Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines. Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.
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Affiliation(s)
- Peng Gu
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lei Zhang
- Department of Vascular Surgery, Intervention Center, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitao Wang
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wentao Ding
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wang
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuan Liu
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wenhao Wang
- Department of Urology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zuyin Li
- Department of Hepatobiliary Surgery, Peking University Organ Transplantation Institute, Peking University People's Hospital, Beijing, China
| | - Bin Yan
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xing Sun
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Liu L, He H, Peng Y, Yang Z, Gao S. A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma. PeerJ 2021; 9:e11911. [PMID: 34631307 PMCID: PMC8465999 DOI: 10.7717/peerj.11911] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 07/14/2021] [Indexed: 01/12/2023] Open
Abstract
Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.
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Affiliation(s)
- Lei Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huayu He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yue Peng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhenlin Yang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shugeng Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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