Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer
Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-relat...
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| Published in: | Lipids in health and disease Vol. 20; no. 1; p. 146 |
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| Abstract | Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer.
Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics.
An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset.
In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. |
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| AbstractList | Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. BACKGROUNDBladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. METHODSLipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. RESULTSAn 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. CONCLUSIONSIn conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. Background Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Methods Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. Results An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. Conclusions In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. Keywords: Bladder cancer, Lipid metabolism, Signature, TCGA, GEO, Biomarker, Prognosis, Immune Abstract Background Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Methods Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. Results An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. Conclusions In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. Abstract Background Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Methods Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. Results An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. Conclusions In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. Background Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Methods Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. Results An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. Conclusions In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients. |
| ArticleNumber | 146 |
| Audience | Academic |
| Author | Deng, Wen Fu, Bin Zhu, Ke Wang, Gongxian Xiaoqiang, Liu |
| Author_xml | – sequence: 1 givenname: Ke surname: Zhu fullname: Zhu, Ke organization: Jiangxi Institute of Urology, Jiangxi, 330006, Nanchang, People's Republic of China – sequence: 2 givenname: Liu surname: Xiaoqiang fullname: Xiaoqiang, Liu organization: Jiangxi Institute of Urology, Jiangxi, 330006, Nanchang, People's Republic of China – sequence: 3 givenname: Wen surname: Deng fullname: Deng, Wen organization: Department of Urology, The First Affiliated Hospital of Nanchang University, 17 Yongwaizheng Street, Jiangxi, 330006, Nanchang, People's Republic of China – sequence: 4 givenname: Gongxian surname: Wang fullname: Wang, Gongxian email: wanggx-mr@126.com, wanggx-mr@126.com organization: Jiangxi Institute of Urology, Jiangxi, 330006, Nanchang, People's Republic of China. wanggx-mr@126.com – sequence: 5 givenname: Bin orcidid: 0000-0001-6686-7561 surname: Fu fullname: Fu, Bin email: urofubin@sina.com, urofubin@sina.com organization: Jiangxi Institute of Urology, Jiangxi, 330006, Nanchang, People's Republic of China. urofubin@sina.com |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34706720$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1111/jcmm.15581 10.1016/j.cmet.2019.11.010 10.7150/jca.42663 10.1002/pros.24027 10.1089/dna.2015.3195 10.1042/CS20190587 10.20892/j.issn.2095-3941.2019.0348 10.1002/cam4.1719 10.1038/s41392-018-0011-z 10.1038/s41419-020-2457-5 10.1158/1078-0432.CCR-07-0404 10.1007/s13238-019-0618-z 10.7150/ijbs.45640 10.1186/s12935-019-0941-8 10.1001/jama.2011.1142 10.3892/ol.2017.7482 10.1158/1078-0432.CCR-18-1515 10.1038/s41422-020-0372-z 10.1038/nrc.2016.89 10.1007/s11064-018-2570-3 10.3322/caac.21660 10.1016/j.addr.2020.07.013 10.1530/JME-19-0080 10.1084/jem.20160903 10.2147/CMAR.S254316 10.1038/s41392-020-00265-w 10.3390/ijerph14060572 10.3390/cancers12123823 10.1186/s12862-018-1271-5 10.1158/0008-5472.CAN-17-0836 10.2147/OTT.S199667 10.1016/j.anndiagpath.2019.01.002 10.1038/ncomms12757 10.1158/0008-5472.CAN-06-3657 10.1016/j.cmet.2020.07.008 10.1126/sciadv.abd6449 10.3892/mmr.2015.4746 10.7150/jca.15403 10.1371/journal.ppat.1002468 10.1007/s10549-012-2340-x 10.1016/j.molmet.2020.01.011 |
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| Keywords | GEO Biomarker Prognosis Immune Signature Lipid metabolism Bladder cancer TCGA |
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| References | Z Sun (1554_CR18) 2020; 5 LM Butler (1554_CR8) 2020; 159 R Khayami (1554_CR32) 2020; 24 M Shahid (1554_CR17) 2020; 16 D Wang (1554_CR22) 2016; 7 S-S Zheng (1554_CR16) 2016; 13 AD Olmstead (1554_CR7) 2012; 8 X Sun (1554_CR5) 2018; 3 T Li (1554_CR6) 2019; 10 R-Z Liu (1554_CR19) 2020; 12 X Wu (1554_CR29) 2017; 214 MT Snaebjornsson (1554_CR11) 2020; 31 SKM Heinrichs (1554_CR27) 2018; 7 K Ni (1554_CR36) 2019; 19 PB Pal (1554_CR31) 2019; 63 K Christov (1554_CR40) 2007; 13 M Abudurexiti (1554_CR34) 2020; 80 H Sung (1554_CR1) 2021; 71 ND Freedman (1554_CR2) 2011; 306 X Chen (1554_CR30) 2018; 43 A Ooki (1554_CR38) 2018; 78 T Peng (1554_CR12) 2020; 11 I Hamaidi (1554_CR3) 2020; 32 B Khatua (1554_CR9) 2021; 7 V Vantaku (1554_CR33) 2019; 25 F Jiao (1554_CR25) 2020; 12 J Liu (1554_CR4) 2021; 31 HS Chang (1554_CR35) 2016; 35 Y Yin (1554_CR39) 2018; 15 M-C Huang (1554_CR23) 2017; 14 S Cheng (1554_CR13) 2019; 133 AE Abdelrahman (1554_CR15) 2019; 39 K Thabet (1554_CR26) 2016; 7 R Zhao (1554_CR24) 2020; 11 JJ de Ronde (1554_CR37) 2013; 137 CKA Neumann (1554_CR28) 2020; 34 F Röhrig (1554_CR10) 2016; 16 EJ Quann (1554_CR41) 2007; 67 H Chao (1554_CR14) 2019; 12 S Huang (1554_CR20) 2020; 17 M Lopes-Marques (1554_CR21) 2018; 18 |
| References_xml | – volume: 24 start-page: 8890 year: 2020 ident: 1554_CR32 publication-title: J Cell Mol Med doi: 10.1111/jcmm.15581 contributor: fullname: R Khayami – volume: 31 start-page: 62 year: 2020 ident: 1554_CR11 publication-title: Cell Metab doi: 10.1016/j.cmet.2019.11.010 contributor: fullname: MT Snaebjornsson – volume: 11 start-page: 3965 year: 2020 ident: 1554_CR12 publication-title: J Cancer doi: 10.7150/jca.42663 contributor: fullname: T Peng – volume: 80 start-page: 950 year: 2020 ident: 1554_CR34 publication-title: Prostate doi: 10.1002/pros.24027 contributor: fullname: M Abudurexiti – volume: 35 start-page: 314 year: 2016 ident: 1554_CR35 publication-title: DNA Cell Biol doi: 10.1089/dna.2015.3195 contributor: fullname: HS Chang – volume: 133 start-page: 1745 year: 2019 ident: 1554_CR13 publication-title: Clin Sci doi: 10.1042/CS20190587 contributor: fullname: S Cheng – volume: 17 start-page: 181 year: 2020 ident: 1554_CR20 publication-title: Cancer Biol Med doi: 10.20892/j.issn.2095-3941.2019.0348 contributor: fullname: S Huang – volume: 7 start-page: 5057 year: 2018 ident: 1554_CR27 publication-title: Cancer Med doi: 10.1002/cam4.1719 contributor: fullname: SKM Heinrichs – volume: 3 start-page: 8 year: 2018 ident: 1554_CR5 publication-title: Signal Transduct Target Ther doi: 10.1038/s41392-018-0011-z contributor: fullname: X Sun – volume: 11 start-page: 272 year: 2020 ident: 1554_CR24 publication-title: Cell Death Dis doi: 10.1038/s41419-020-2457-5 contributor: fullname: R Zhao – volume: 13 start-page: 5488 year: 2007 ident: 1554_CR40 publication-title: Clin Cancer Res doi: 10.1158/1078-0432.CCR-07-0404 contributor: fullname: K Christov – volume: 10 start-page: 583 year: 2019 ident: 1554_CR6 publication-title: Protein Cell doi: 10.1007/s13238-019-0618-z contributor: fullname: T Li – volume: 16 start-page: 2490 year: 2020 ident: 1554_CR17 publication-title: Int J Biol Sci doi: 10.7150/ijbs.45640 contributor: fullname: M Shahid – volume: 19 start-page: 219 year: 2019 ident: 1554_CR36 publication-title: Cancer Cell Int doi: 10.1186/s12935-019-0941-8 contributor: fullname: K Ni – volume: 306 start-page: 737 year: 2011 ident: 1554_CR2 publication-title: JAMA J Am Med Assoc doi: 10.1001/jama.2011.1142 contributor: fullname: ND Freedman – volume: 15 start-page: 1545 year: 2018 ident: 1554_CR39 publication-title: Oncol Lett doi: 10.3892/ol.2017.7482 contributor: fullname: Y Yin – volume: 25 start-page: 3689 year: 2019 ident: 1554_CR33 publication-title: Clin Cancer Res Off J Am Assoc Cancer Res doi: 10.1158/1078-0432.CCR-18-1515 contributor: fullname: V Vantaku – volume: 31 start-page: 80 year: 2021 ident: 1554_CR4 publication-title: Cell Res doi: 10.1038/s41422-020-0372-z contributor: fullname: J Liu – volume: 16 start-page: 732 year: 2016 ident: 1554_CR10 publication-title: Nat Rev Cancer doi: 10.1038/nrc.2016.89 contributor: fullname: F Röhrig – volume: 43 start-page: 1491 year: 2018 ident: 1554_CR30 publication-title: Neurochem Res doi: 10.1007/s11064-018-2570-3 contributor: fullname: X Chen – volume: 71 start-page: 209 year: 2021 ident: 1554_CR1 publication-title: CA Cancer J Clin doi: 10.3322/caac.21660 contributor: fullname: H Sung – volume: 159 start-page: 245 year: 2020 ident: 1554_CR8 publication-title: Adv Drug Deliv Rev doi: 10.1016/j.addr.2020.07.013 contributor: fullname: LM Butler – volume: 63 start-page: 11 year: 2019 ident: 1554_CR31 publication-title: J Mol Endocrinol doi: 10.1530/JME-19-0080 contributor: fullname: PB Pal – volume: 214 start-page: 1065 year: 2017 ident: 1554_CR29 publication-title: J Exp Med doi: 10.1084/jem.20160903 contributor: fullname: X Wu – volume: 12 start-page: 8325 year: 2020 ident: 1554_CR25 publication-title: Cancer Manag Res doi: 10.2147/CMAR.S254316 contributor: fullname: F Jiao – volume: 5 start-page: 150 year: 2020 ident: 1554_CR18 publication-title: Signal Transduct Target Ther doi: 10.1038/s41392-020-00265-w contributor: fullname: Z Sun – volume: 14 start-page: 572 year: 2017 ident: 1554_CR23 publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph14060572 contributor: fullname: M-C Huang – volume: 12 start-page: 3823 year: 2020 ident: 1554_CR19 publication-title: Cancers doi: 10.3390/cancers12123823 contributor: fullname: R-Z Liu – volume: 18 start-page: 157 year: 2018 ident: 1554_CR21 publication-title: BMC Evol Biol doi: 10.1186/s12862-018-1271-5 contributor: fullname: M Lopes-Marques – volume: 78 start-page: 168 year: 2018 ident: 1554_CR38 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-17-0836 contributor: fullname: A Ooki – volume: 12 start-page: 3285 year: 2019 ident: 1554_CR14 publication-title: OncoTargets Ther doi: 10.2147/OTT.S199667 contributor: fullname: H Chao – volume: 39 start-page: 42 year: 2019 ident: 1554_CR15 publication-title: Ann Diagn Pathol doi: 10.1016/j.anndiagpath.2019.01.002 contributor: fullname: AE Abdelrahman – volume: 7 start-page: 12757 year: 2016 ident: 1554_CR26 publication-title: Nat Commun doi: 10.1038/ncomms12757 contributor: fullname: K Thabet – volume: 67 start-page: 3254 year: 2007 ident: 1554_CR41 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-06-3657 contributor: fullname: EJ Quann – volume: 32 start-page: 420 year: 2020 ident: 1554_CR3 publication-title: Cell Metab doi: 10.1016/j.cmet.2020.07.008 contributor: fullname: I Hamaidi – volume: 7 start-page: eabd6449 year: 2021 ident: 1554_CR9 publication-title: Sci Adv doi: 10.1126/sciadv.abd6449 contributor: fullname: B Khatua – volume: 13 start-page: 1845 year: 2016 ident: 1554_CR16 publication-title: Mol Med Rep doi: 10.3892/mmr.2015.4746 contributor: fullname: S-S Zheng – volume: 7 start-page: 1226 year: 2016 ident: 1554_CR22 publication-title: J Cancer doi: 10.7150/jca.15403 contributor: fullname: D Wang – volume: 8 start-page: e1002468 year: 2012 ident: 1554_CR7 publication-title: PLoS Pathog doi: 10.1371/journal.ppat.1002468 contributor: fullname: AD Olmstead – volume: 137 start-page: 213 year: 2013 ident: 1554_CR37 publication-title: Breast Cancer Res Treat doi: 10.1007/s10549-012-2340-x contributor: fullname: JJ de Ronde – volume: 34 start-page: 136 year: 2020 ident: 1554_CR28 publication-title: Mol Metab doi: 10.1016/j.molmet.2020.01.011 contributor: fullname: CKA Neumann |
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| Snippet | Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism... Abstract Background Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that... Background Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid... BACKGROUNDBladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid... Abstract Background Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that... |
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| SubjectTerms | Aged Antineoplastic Agents - therapeutic use Biomarker Bladder cancer Cancer Cancer therapies Cell growth Chemotherapy CTLA-4 protein Datasets Development and progression Drug development Drug therapy Drugs Fatty acids Female Gene expression Genes, Neoplasm - genetics Genetic aspects Genomes GEO Health aspects Humans Immune checkpoint Immunosuppressive agents Immunotherapy Kaplan-Meier Estimate Lipid metabolism Lipid Metabolism - genetics Lipids Male Medical prognosis Metabolism Metastases Middle Aged Nomograms Oncology, Experimental Patients PD-1 protein PD-L1 protein Peroxisome proliferator-activated receptors Pharmacogenetics Prognosis Proportional Hazards Models Proteins Regression analysis Reproducibility of Results Risk groups Signal transduction Signature Steroids Survival Analysis TCGA Tumors Urinary Bladder Neoplasms - diagnosis Urinary Bladder Neoplasms - drug therapy Urinary Bladder Neoplasms - genetics Urinary Bladder Neoplasms - mortality |
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| Title | Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer |
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