Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for predicting prognosis in lung adenocarcinoma

被引:1
作者
Cao, Xu [1 ,2 ]
Xi, Jingjing [2 ]
Wang, Congyue [2 ,3 ]
Yu, Wenjie [2 ]
Wang, Yanxia [2 ]
Zhu, Jingjing [1 ,2 ]
Xu, Kailin [2 ,4 ]
Pan, Di [1 ,2 ]
Chen, Chong [2 ,4 ]
Han, Zhengxiang [1 ,2 ]
机构
[1] Xuzhou Med Univ, Affiliated Hosp, Dept Oncol, Xuzhou 221006, Jiangsu, Peoples R China
[2] Xuzhou Med Univ, Inst Hematol, Xuzhou 221002, Jiangsu, Peoples R China
[3] Xuzhou Med Univ, Dept Hematol, Xuzhou Min Grp, Gen Hosp,Affiliated Hosp 2, Xuzhou 221006, Jiangsu, Peoples R China
[4] Xuzhou Med Univ, Affiliated Hosp, Dept Hematol, Xuzhou 221006, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Lung adenocarcinoma; Single-cell RNA-sequencing; Metastasis; Prognostic signature; Machine learning;
D O I
10.1007/s12094-024-03752-6
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BackgroundMetastasis has been documented as an independent and significant prognostic feature of lung adenocarcinoma (LUAD) patients. However, the underlying genetic and molecular mechanisms responsible for LUAD metastasis and their prognostic significance are not exactly defined.MethodsThe single-cell transcriptomic profiles of primary and metastatic LUAD samples were integrated as a whole dataset. Enrichment analysis and pseudotime trajectory analysis were performed to illustrate the cellular origins and changes during the metastatic process. The LUAD metastasis-related genes (LMRGs) molecular cluster and signature was constructed through unsupervised consensus clustering and ten machine-learning algorithms in The Cancer Genome Atlas (TCGA) LUAD cohort using ten machine-learning algorithms. Validation of the signature was conducted using four independent cohorts from the Gene Expression Omnibus (GEO) database. Kaplan-Meier, ROC, univariate and multivariate Cox-regression analyses were performed to test the stability of the signature. The gene CCT6A was subjected to knockdown, followed by validation through western blot analysis, flow cytometry, wound healing and transwell-migration assays to determine its potential significance.ResultsFirst, the signaling pathway networks remodeling and metabolic reprogramming were demonstrated to be involved in the metastasis of malignant LUAD cells, which facilitate their extravasation and adaptation to other organs. Furthermore, distinct subtypes of malignant LUAD cells exhibit tissue-specific patterns. Then, two distinct molecular patterns of LMRGs were established, which showed diverse prognoses. A LUAD metastasis-related gene signature (LMRGS) was constructed via a multiple machine-learning-based integrative procedure, which possesses distinctly superior accuracy than most common clinical features and 69 published prognostic signatures. The patients stratified by the signature into high-risk group had a significantly poorer prognosis compared to those in the low-risk group, and this was well validated across different clinical subgroups. In addition, the risk score calculated by LMRGS remained an independent prognostic parameter in both univariate and multivariate Cox regression. Notably, knockdown of CCT6A gene promoted cell apoptosis and decelerated the cell migration obviously.ConclusionLMRGS could serve as a novel and promising tool to improve clinical outcomes for individual LUAD patients.
引用
收藏
页码:2579 / 2596
页数:18
相关论文
共 36 条
[1]   Lung cancer is also a hereditary disease [J].
Benusiglio, Patrick R. ;
Fallet, Vincent ;
Sanchis-Borja, Mateo ;
Coulet, Florence ;
Cadranel, Jacques .
EUROPEAN RESPIRATORY REVIEW, 2021, 30 (162)
[2]   Biomarker Discovery in Non-Small Cell Lung Cancer: Integrating Gene Expression Profiling, Meta-analysis, and Tissue Microarray Validation [J].
Botling, Johan ;
Edlund, Karolina ;
Lohr, Miriam ;
Hellwig, Birte ;
Holmberg, Lars ;
Lambe, Mats ;
Berglund, Anders ;
Ekman, Simon ;
Bergqvist, Michael ;
Ponten, Fredrik ;
Koenig, Andre ;
Fernandes, Oswaldo ;
Karlsson, Mats ;
Helenius, Gisela ;
Karlsson, Christina ;
Rahnenfuehrer, Joerg ;
Hengstler, Jan G. ;
Micke, Patrick .
CLINICAL CANCER RESEARCH, 2013, 19 (01) :194-204
[3]   Molecular biomarkers for lung adenocarcinoma [J].
Calvayrac, Olivier ;
Pradines, Anne ;
Pons, Elvire ;
Mazieres, Julien ;
Guibert, Nicolas .
EUROPEAN RESPIRATORY JOURNAL, 2017, 49 (04)
[4]   KRT6A Promotes Lung Cancer Cell Growth and Invasion Through MYC-Regulated Pentose Phosphate Pathway [J].
Che, Di ;
Wang, Mingshuo ;
Sun, Juan ;
Li, Bo ;
Xu, Tao ;
Lu, Yuxiong ;
Pan, Haiyan ;
Lu, Zhaoliang ;
Gu, Xiaoqiong .
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 2021, 9
[5]   Structural basis for Fc receptor recognition of immunoglobulin M [J].
Chen, Qu ;
Menon, Rajesh P. ;
Masino, Laura ;
Tolar, Pavel ;
Rosenthal, Peter B. .
NATURE STRUCTURAL & MOLECULAR BIOLOGY, 2023, 30 (07) :1033-+
[6]   Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer [J].
Chu, Guangdi ;
Ji, Xiaoyu ;
Wang, Yonghua ;
Niu, Haitao .
MOLECULAR THERAPY NUCLEIC ACIDS, 2023, 33 :110-126
[7]   Machine Learning in Medicine [J].
Deo, Rahul C. .
CIRCULATION, 2015, 132 (20) :1920-1930
[8]   Validation of a Histology-Independent Prognostic Gene Signature for Early-Stage, Non-Small-Cell Lung Cancer Including Stage IA Patients [J].
Der, Sandy D. ;
Sykes, Jenna ;
Pintilie, Melania ;
Zhu, Chang-Qi ;
Strumpf, Dan ;
Liu, Ni ;
Jurisica, Igor ;
Shepherd, Frances A. ;
Tsao, Ming-Sound .
JOURNAL OF THORACIC ONCOLOGY, 2014, 9 (01) :59-64
[9]   Artificial Intelligence and Machine Learning in Clinical Medicine, 2023 [J].
Haug, Charlotte J. J. ;
Drazen, Jeffrey M. M. .
NEW ENGLAND JOURNAL OF MEDICINE, 2023, 388 (13) :1201-1208
[10]   Clinicopathological Characteristics and Mutations Driving Development of Early Lung Adenocarcinoma: Tumor Initiation and Progression [J].
Inamura, Kentaro .
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2018, 19 (04)