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Yu M, Wen W, Wang Y, Shan X, Yi X, Zhu W, Aa J, Wang G. Plasma metabolomics reveals risk factors for lung adenocarcinoma. Front Oncol 2024; 14:1277206. [PMID: 38567154 PMCID: PMC10985191 DOI: 10.3389/fonc.2024.1277206] [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: 08/14/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Background Metabolic reprogramming plays a significant role in the advancement of lung adenocarcinoma (LUAD), yet the precise metabolic changes remain incompletely understood. This study aims to uncover metabolic indicators associated with the progression of LUAD. Methods A total of 1083 subjects were recruited, including 670 LUAD, 135 benign lung nodules (BLN) and 278 healthy controls (HC). Gas chromatography-mass spectrometry (GC/MS) was used to identify and quantify plasma metabolites. Odds ratios (ORs) were calculated to determine LUAD risk factors, and machine learning algorithms were utilized to differentiate LUAD from BLN. Results High levels of oxalate, glycolate, glycine, glyceric acid, aminomalonic acid, and creatinine were identified as risk factors for LUAD (adjusted ORs>1.2, P<0.03). Remarkably, oxalate emerged as a distinctive metabolic risk factor exhibiting a strong correlation with the progression of LUAD (adjusted OR=5.107, P<0.001; advanced-stage vs. early-stage). The Random Forest (RF) model demonstrated a high degree of efficacy in distinguishing between LUAD and BLN (accuracy = 1.00 and 0.73, F1-score= 1.00 and 0.79, and AUC = 1.00 and 0.76 in the training and validation sets, respectively). TCGA and GTEx gene expression data have shown that lactate dehydrogenase A (LDHA), a crucial enzyme involved in oxalate metabolism, is increasingly expressed in the progression of LUAD. High LDHA expression levels in LUAD patients are also linked to poor prognoses (HR=1.66, 95% CI=1.34-2.07, P<0.001). Conclusions This study reveals risk factors associated with LUAD.
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Affiliation(s)
- Mengjie Yu
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Wei Wen
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yue Wang
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Xia Shan
- Department of Respiration, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xin Yi
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Wei Zhu
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiye Aa
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Guangji Wang
- Key Laboratory of Drug Metabolism & Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, China
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Ye L, Jiang Z, Zheng M, Pan K, Lian J, Ju B, Liu X, Tang S, Guo G, Zhang S, Hong X, Lu W. Fatty acid metabolism-related lncRNA prognostic signature for serous ovarian carcinoma. Epigenomics 2024; 16:309-329. [PMID: 38356435 DOI: 10.2217/epi-2023-0388] [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] [Indexed: 02/16/2024] Open
Abstract
Background: To explore the role of fatty acid metabolism (FAM)-related lncRNAs in the prognosis and antitumor immunity of serous ovarian cancer (SOC). Materials & methods: A SOC FAM-related lncRNA risk model was developed and evaluated by a series of analyses. Additional immune-related analyses were performed to further assess the associations between immune state, tumor microenvironment and the prognostic risk model. Results: Five lncRNAs associated with the FAM genes were found and used to create a predictive risk model. The patients with a low-risk profile exhibited favorable prognostic outcomes. Conclusion: The established prognostic risk model exhibits better predictive capabilities for the prognosis of patients with SOC and offers novel potential therapy targets for SOC.
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Affiliation(s)
- Lele Ye
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Zhuofeng Jiang
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Mengxia Zheng
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Kan Pan
- First Clinical College, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jingru Lian
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Bing Ju
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Xuefei Liu
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Sangsang Tang
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Gangqiang Guo
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research & Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens & Immunity, Department of Microbiology & Immunology, Institute of Molecular Virology & Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Songfa Zhang
- Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
| | - Xin Hong
- Department of Biochemistry, School of Medicine, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
- Key University Laboratory of Metabolism & Health of Guangdong, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
- Guangdong Provincial Key Laboratory of Cell Microenvironment & Disease Research, Southern University of Science & Technology, Shenzhen, 518055, Guangdong, China
| | - Weiguo Lu
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
- Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China
- Center of Uterine Cancer Diagnosis & Therapy of Zhejiang Province, Hangzhou, 310006, Zhejiang, China
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Cheng Y, Shen Y, Fang Q, Duan S, Wang Y, Dai X, Chen Y. Identification of epithelial-mesenchymal transition-related biomarkers in lung adenocarcinoma using bioinformatics and lab experiments. Aging (Albany NY) 2023; 15:11970-11984. [PMID: 37910672 PMCID: PMC10683598 DOI: 10.18632/aging.205159] [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: 08/10/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Lung adenocarcinoma accounts for approximately 40% of lung cancer cases and poses a serious threat to human health. Therefore, there is an urgent need to identify central biomarkers in lung adenocarcinoma. METHODS We first identified the EMT-associated genes in LUAD based on the TCGA cohort. Then we screened these 90 EMT-associated genes using univariate Cox regression analysis and LASSO regression analysis to develop a prognostic gene signature in the training set. The predictive performance of the gene signature was assessed in the validation set and multiple external test sets using the ROC cure, C index and log-rank tests. RT-PCR, western blot, wound healing assays, and siRNA methods were further used to investigate the role of PLEK2 in tumor behaviors. RESULTS Eight genes (CCNB1, PLEK2, DERL3, C1QTNF6, DLGAP5, HMMR, GJB3, and SPOCK1) were eventually selected to develop an eight-gene signature. The 5-year AUC of the gene signature has a robust predictive ability both for predicting overall survival (0.774, 0.756, and 0.669 in the external test sets, respectively), and for progression free survival (0.774, 0.746, and 0.755 in the external test sets, respectively). C-index of the gene signature was 0.961 ± 0.005, 0.916 ± 0.011, and 0.868 ± 0.234 in the external test sets, respectively. Four genes (C1QTNF6, DLGAP5, HMMR, and PLEK2) were identified as key genes in LUAD progression, which were upregulated in the cancerous tissue compared with in the normal tissue (P < 0.001), and correlated with an unwanted prognosis in lung cancer (P < 0.05). PLEK2 was used as an example to explore its effect on LUAD progression in vitro using RT-PCR, western blot, CCK8, si-RNA and wound healing assay. Silencing of PLEK2 was shown to reduce proliferative and migrated ability of lung cancer cells via prohibition of autophagy. CONCLUSIONS This study developed a novel EMT-related gene signature benefiting precision medicine, and identified four pivotal genes which can serve as therapeutic targets in LUAD. Four key genes can serve as molecular targets for patients with LUAD; silencing of PLEK2 was shown to reduce proliferative and migrated ability of lung cancer cells via prohibition of autophagy.
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Affiliation(s)
- Yuanjun Cheng
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Department of Cardiothoracic Surgery, People’s Hospital of Chizhou, Chizhou, China
| | - Yumei Shen
- Department of Operation, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Qianru Fang
- Department of Obstetrics, People’s Hospital of Chizhou, Chizhou, China
| | - Shanzhou Duan
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yifei Wang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaoxiao Dai
- Department of Pathology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yongbing Chen
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China
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Zhang P, Zhang T, Chen D, Gong L, Sun M. Prognosis and Novel Drug Targets for Key lncRNAs of Epigenetic Modification in Colorectal Cancer. Mediators Inflamm 2023; 2023:6632205. [PMID: 37091904 PMCID: PMC10116225 DOI: 10.1155/2023/6632205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/20/2022] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Background Colorectal cancer (CRC) has been the 3rd most commonly malignant tumor of the gastrointestinal tract in the world. 5-Methylcytosine (m5C) and long noncoding RNAs (lncRNAs) have an essential role in predicting the prognosis and immune response for CRC patients. Therefore, we built a m5C-related lncRNA (m5CRlncRNA) model to investigate the prognosis and treatment methods for CRC patients. Methods Firstly, we secured the transcriptome and clinical data for CRC from The Cancer Genome Atlas (TCGA). Then, m5CRlncRNAs were recognized by coexpression analysis. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were utilized to build m5C-related prognostic characteristics. Besides, Kaplan-Meier analysis, ROC, PCA, C-index, enrichment analysis, and nomogram were performed to investigate the model. Additionally, immunotherapy responses and antitumor medicines were explored for CRC patients. Results A total of 8 m5C-related lncRNAs (AC093157.1, LINC00513, AC025171.4, AC090948.2, ZEB1-AS1, AC109449.1, AC009041.3, and LINC02516) were adopted to construct a risk model to investigate survival and prognosis for CRC patients. CRC samples were separated into low- and high-risk groups, with the latter having a worse prognosis. The m5C-related lncRNA model helps us to better distinguish immunotherapy responses and IC50 of antitumor medicines in different groups of CRC patients. Conclusion The research may give new perspectives on tailored therapy approaches as well as novel theories for forecasting the prognosis of CRC patients.
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Affiliation(s)
- Peng Zhang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Tingting Zhang
- Department of Clinical Oncology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Denggang Chen
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Li Gong
- Department of Endocrinology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Min Sun
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
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Pan YQ, Xiao Y, Long T, Liu C, Gao WH, Sun YY, Liu C, Shi YJ, Li S, Shao AZ. Prognostic value of lncRNAs related to fatty acid metabolism in lung adenocarcinoma and their correlation with tumor microenvironment based on bioinformatics analysis. Front Oncol 2022; 12:1022097. [DOI: 10.3389/fonc.2022.1022097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAs a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques.MethodsWe obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs.ResultsWe identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues.ConclusionIn summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.
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