1
|
Yang Z, Zhu J, Yang T, Tang W, Zheng X, Ji S, Ren Z, Lu F. Comprehensive analysis of the lncRNAs-related immune gene signatures and their correlation with immunotherapy in lung adenocarcinoma. Br J Cancer 2023; 129:1397-1408. [PMID: 37543671 PMCID: PMC10628174 DOI: 10.1038/s41416-023-02379-8] [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: 11/05/2022] [Revised: 07/06/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs)-related immune genes (lrRIGs) play a crucial role in the development and progression of lung adenocarcinoma (LUAD). However, reliable prognostic signatures based on lrRIGs have not yet been identified. METHODS We screened lrRIGs associated with the prognosis of LUAD using The Cancer Genome Atlas (TCGA) database and then established a novel prognostic nine-gene signature composed of CD79A, INHA, SHC3, LIFR, TNFRSF11A, GPI, F2RL1, SEMA7A and WFDC2 through bioinformatic approaches. A risk score derived from this gene signature was used to divide LUAD patients into the low- and high-risk groups. The latter was confirmed to have markedly worse overall survival (O.S.). A nomogram was developed using the risk score and other independent prognostic elements, demonstrating excellent performance in predicting the O.S. rate of LUAD patients. RESULTS We observed that the infiltration of diverse immune cell subtypes and response to immunotherapy and chemotherapy significantly differed between the low- and high-risk groups. CONCLUSIONS Overall, stratification based on this gene signature could be used to guide better therapeutic management and improve outcomes for LUAD patients.
Collapse
Affiliation(s)
- Zhengyan Yang
- Joint National Laboratory for Antibody Drug Engineering, the First Affiliated Hospital, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Jianling Zhu
- Joint National Laboratory for Antibody Drug Engineering, the First Affiliated Hospital, School of Basic Medical Sciences, Henan University, Kaifeng, China
- Department of Immunology, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Tiantian Yang
- Joint National Laboratory for Antibody Drug Engineering, the First Affiliated Hospital, School of Basic Medical Sciences, Henan University, Kaifeng, China
- Department of Immunology, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Wenjun Tang
- Joint National Laboratory for Antibody Drug Engineering, the First Affiliated Hospital, School of Basic Medical Sciences, Henan University, Kaifeng, China
- Department of Immunology, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Xiaowei Zheng
- Department of Clinical Laboratory, Puyang Hospital of Traditional Chinese Medicine, Puyang, China
| | - Shaoping Ji
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Henan University, Kaifeng, China
| | - Zhiguang Ren
- Joint National Laboratory for Antibody Drug Engineering, the First Affiliated Hospital, School of Basic Medical Sciences, Henan University, Kaifeng, China.
- Institute of Traditional Chinese Medicine, Henan University, Kaifeng, China.
| | - Feng Lu
- Joint National Laboratory for Antibody Drug Engineering, the First Affiliated Hospital, School of Basic Medical Sciences, Henan University, Kaifeng, China.
- Department of Immunology, School of Basic Medical Sciences, Henan University, Kaifeng, China.
| |
Collapse
|
2
|
Wang Y, Xu J, Fang Y, Gu J, Zhao F, Tang Y, Xu R, Zhang B, Wu J, Fang Z, Li Y. Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma. Front Immunol 2022; 13:950001. [PMID: 36091041 PMCID: PMC9455632 DOI: 10.3389/fimmu.2022.950001] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background As the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD). Methods First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation. Results LUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells. Conclusions We developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens.
Collapse
Affiliation(s)
- Yuli Wang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing Xu
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuan Fang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiefei Gu
- Information Center, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fanchen Zhao
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu Tang
- School of Basic Medical Sciences, Fudan University, Shanghai, China
| | - Rongzhong Xu
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bo Zhang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianchun Wu
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianchun Wu, ; Zhihong Fang, ; Yan Li,
| | - Zhihong Fang
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianchun Wu, ; Zhihong Fang, ; Yan Li,
| | - Yan Li
- Clinical Medical Center of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianchun Wu, ; Zhihong Fang, ; Yan Li,
| |
Collapse
|
3
|
Yang L, Yin W, Liu X, Li F, Ma L, Wang D, Li H. Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma. PeerJ 2021; 9:e11273. [PMID: 33986994 PMCID: PMC8088210 DOI: 10.7717/peerj.11273] [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: 11/10/2020] [Accepted: 03/23/2021] [Indexed: 12/24/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.
Collapse
Affiliation(s)
- Lei Yang
- Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China
| | - Weilong Yin
- Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China
| | - Xuechen Liu
- Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China
| | - Fangcun Li
- Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China
| | - Li Ma
- Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China
| | - Dong Wang
- Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China
| | - Hongxing Li
- Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China
| |
Collapse
|