1
|
Guan X, Bao G, Liang J, Yao Y, Xiang Y, Zhong X. Evolution of small cell lung cancer tumor mutation: from molecular mechanisms to novel viewpoints. Semin Cancer Biol 2022; 86:346-355. [PMID: 35367118 DOI: 10.1016/j.semcancer.2022.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 01/27/2023]
Abstract
Small cell lung cancer (SCLC) is a clinically common malignant tumor originating from the lung neuroendocrine stem cells, which has a poor prognosis and accounts for approximately 15% of all lung cancer cases. However, research on its treatment has been slow, and the 5-year survival rate of patients with SCLC has been < 5% for many years. In recent years, the development and popularization of gene sequencing technology have facilitated the understanding of the gene mutation landscape and tumor evolution of SCLC, thereby leading to a more accurate prediction of the prognosis of SCLC and the development of individualized treatment. In this review, we aimed to discuss the mutation evolution of SCLC from the perspective of a tumor evolution theory and described the sequence of mutation evolution in the occurrence and development of SCLC. In addition, we summarized the existing whole-exome sequencing (WES) data of SCLC cases at our center along with relevant publications on sequencing. Thereafter, we discuss the role of different mutated pathways in the occurrence of SCLC to predict its prognosis more accurately and summarized individualized treatment strategies.
Collapse
Affiliation(s)
- Xiaojiao Guan
- Department of Pathology, Shengjing Hospital, China Medical University, Shenyang, China
| | - Guangyao Bao
- Department of Thoracic Surgery, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Jie Liang
- Department of Thoracic Surgery, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Yao Yao
- Department of Thoracic Surgery, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Yifan Xiang
- Department of Thoracic Surgery, First Affiliated Hospital, China Medical University, Shenyang, China
| | - Xinwen Zhong
- Department of Thoracic Surgery, First Affiliated Hospital, China Medical University, Shenyang, China.
| |
Collapse
|
2
|
Zang X, Zhang J, Jiao P, Xue X, Lv Z. Non-Small Cell Lung Cancer Detection and Subtyping by UPLC-HRMS-Based Tissue Metabolomics. J Proteome Res 2022; 21:2011-2022. [PMID: 35856400 DOI: 10.1021/acs.jproteome.2c00316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Non-small cell lung cancer (NSCLC) is the prevalent histological subtype of lung cancer. In this study, we performed ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS)-based metabolic profiling of 227 tissue samples from 79 lung cancer patients with adenocarcinoma (AC) or squamous cell carcinoma (SCC). Orthogonal partial least squares-discriminant analysis (oPLS-DA) analyses showed that AC, SCC, and NSCLC tumors were discriminated from adjacent noncancerous tissue (ANT) and distant noncancerous tissue (DNT) samples with good accuracies (91.3, 100, and 88.3%), sensitivities (85.7, 100, and 83.9%), and specificities (94.3, 100, and 90.7%), using 12, 4, and 7 discriminant metabolites, respectively. The discriminant panel for AC detection included valine, sphingosine, glutamic acid γ-methyl ester, and lysophosphatidylcholine (LPC) (16:0), levels of which in tumor tissues were significantly altered. Valine, sphingosine, LPC (18:1), and leucine derivatives were used for SCC detection. The discrimination between AC and SCC had 96.8% accuracy, 98.2% sensitivity, and 85.7% specificity using a five-metabolite panel, of which valine and creatine had significant differences. The classification models were further verified with external validation sets, showing a promising prospect for NSCLC tissue detection and subtyping.
Collapse
Affiliation(s)
- Xiaoling Zang
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong 266003, P. R. China
| | - Jie Zhang
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong 266003, P. R. China
| | - Peng Jiao
- Department of Thoracic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P. R. China
| | - Xuyan Xue
- College of Physics, Qingdao University, Qingdao, Shandong 266071, P. R. China
| | - Zhihua Lv
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong 266003, P. R. China
| |
Collapse
|
3
|
Zhang L, Zhu B, Zeng Y, Shen H, Zhang J, Wang X. Clinical lipidomics in understanding of lung cancer: Opportunity and challenge. Cancer Lett 2019; 470:75-83. [PMID: 31655086 DOI: 10.1016/j.canlet.2019.08.014] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 08/01/2019] [Accepted: 08/26/2019] [Indexed: 12/20/2022]
Abstract
Disordered lipid metabolisms have been evidenced in lung cancer as well as its subtypes. Lipidomics with in-depth mining is considered as a critical member of the multiple omics family and a lipid-specific tool to understand disease-associated lipid metabolism and disease-specific dysfunctions of lipid species, discover biomarkers and targets for monitoring therapeutic strategies, and provide insights into lipid profiling and pathophysiological mechanisms in lung cancer. The present review describes the characters and patterns of lipidomic profiles in patients with different lung cancer subtypes, important values of comprehensive lipidomic profiles in understanding of lung cancer heterogeneity, urgent needs of standardized methodologies, potential mechanisms by lipid-associated enzymes and proteins, and the importance of integration between clinical phenomes and lipidomic profiles. The characteristics of lipidomic profiles in different lung cancer subtypes are extremely varied among study designs, objects, methods, and analyses. Preliminary data from recent studies demonstrate the specificity of lipidomic profiles specific for lung cancer stage, severity, subtype, and response to drugs. The heterogeneity of lipidomic profiles and lipid metabolism may be part of systems heterogeneity in lung cancer and be responsible for the development of drug resistance, although there are needs for direct evidence to show the existence of intra- or inter-lung cancer heterogeneity of lipidomic profiles. With an increasing understanding of expression profiles of genes and proteins, lipidomic profiles should be associated with activities of enzymes and proteins involved in the processes of lipid metabolism, which can be profiled with genomics and proteomics, and to provide the opportunity for the integration of lipidomic profiles with gene and protein expression profiles. The concept of clinical trans-omics should be emphasized to integrate data of lipidomics with clinical phenomics to identify disease-specific and phenome-specific biomarkers and targets, although there are still a large number of challenges to be overcome in the integration between clinical phenomes and lipidomic profiles.
Collapse
Affiliation(s)
- Linlin Zhang
- Zhongshan Hospital Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Fudan University, Shanghai, China
| | - Bijun Zhu
- Zhongshan Hospital Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Fudan University, Shanghai, China
| | - Yiming Zeng
- Department of Respiratory Diseases, Clinical Center for Molecular Diagnosis and Therapy, The Second Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.
| | - Hui Shen
- Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
| | - Jiaqiang Zhang
- Department of Anesthesiology, Clinical Center of Single Cell Biomedicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.
| | - Xiangdong Wang
- Zhongshan Hospital Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Fudan University, Shanghai, China.
| |
Collapse
|
4
|
Oliynyk RT. Quantifying the Potential for Future Gene Therapy to Lower Lifetime Risk of Polygenic Late-Onset Diseases. Int J Mol Sci 2019; 20:E3352. [PMID: 31288412 PMCID: PMC6651814 DOI: 10.3390/ijms20133352] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 07/05/2019] [Accepted: 07/05/2019] [Indexed: 12/28/2022] Open
Abstract
Gene therapy techniques and genetic knowledge may sufficiently advance, within the next few decades, to support prophylactic gene therapy for the prevention of polygenic late-onset diseases. The risk of these diseases may, hypothetically, be lowered by correcting the effects of a subset of common low effect gene variants. In this paper, simulations show that if such gene therapy were to become technically possible; and if the incidences of the treated diseases follow the proportional hazards model with a multiplicative genetic architecture composed of a sufficient number of common small effect gene variants, then: (a) late-onset diseases with the highest familial heritability will have the largest number of variants available for editing; (b) diseases that currently have the highest lifetime risk, particularly those with the highest incidence rate continuing into older ages, will prove the most challenging cases in lowering lifetime risk and delaying the age of onset at a population-wide level; (c) diseases that are characterized by the lowest lifetime risk will show the strongest and longest-lasting response to such therapies; and (d) longer life expectancy is associated with a higher lifetime risk of these diseases, and this tendency, while delayed, will continue after therapy.
Collapse
Affiliation(s)
- Roman Teo Oliynyk
- Centre for Computational Evolution, University of Auckland, Auckland 1010, New Zealand.
- Department of Computer Science, University of Auckland, Auckland 1010, New Zealand.
| |
Collapse
|
5
|
Lv J, Gao D, Zhang Y, Wu D, Shen L, Wang X. Heterogeneity of lipidomic profiles among lung cancer subtypes of patients. J Cell Mol Med 2018; 22:5155-5159. [PMID: 29999584 PMCID: PMC6156354 DOI: 10.1111/jcmm.13782] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 04/07/2018] [Indexed: 12/29/2022] Open
Abstract
Lung cancer is a leading cause of cancer-related deaths with an increasing incidence and poor prognoses. To further understand the regulatory mechanisms of lipidomic profiles in lung cancer subtypes, we measure the profiles of plasma lipidome between health and patients with lung cancer or among patients with squamous cell carcinomas, adenocarcinoma or small cell lung cancer and to correct lipidomic and genomic profiles of lipid-associated enzymes and proteins by integrating the data of large-scale genome screening. Our studies demonstrated that circulating levels of PS and lysoPS significantly increased, while lysoPE and PE decreased in patients with lung cancer. Our data indicate that lung cancer-specific and subtype-specific lipidomics in the circulation are important to understand mechanisms of systemic metabolisms and identify diagnostic biomarkers and therapeutic targets. The carbon atoms, dual bonds or isomerism in the lipid molecule may play important roles in lung cancer cell differentiations and development. This is the first try to integrate lipidomic data with lipid protein-associated genomic expression among lung cancer subtypes as the part of clinical trans-omics. We found that a large number of lipid protein-associated genes significantly change among cancer subtypes, with correlations with altered species and spatial structures of lipid metabolites.
Collapse
Affiliation(s)
- Jiapei Lv
- Zhongshan Hospital Institute of Clinical ScienceShanghai Institute of Clinical BioinformaticsFudan University Institute of Biomedical ScienceFudan UniversityShanghaiChina
| | - Danyan Gao
- Zhongshan Hospital Institute of Clinical ScienceShanghai Institute of Clinical BioinformaticsFudan University Institute of Biomedical ScienceFudan UniversityShanghaiChina
| | - Yong Zhang
- Zhongshan Hospital Institute of Clinical ScienceShanghai Institute of Clinical BioinformaticsFudan University Institute of Biomedical ScienceFudan UniversityShanghaiChina
| | - Duojiao Wu
- Zhongshan Hospital Institute of Clinical ScienceShanghai Institute of Clinical BioinformaticsFudan University Institute of Biomedical ScienceFudan UniversityShanghaiChina
| | - Lihua Shen
- Zhongshan Hospital Institute of Clinical ScienceShanghai Institute of Clinical BioinformaticsFudan University Institute of Biomedical ScienceFudan UniversityShanghaiChina
| | - Xiangdong Wang
- Zhongshan Hospital Institute of Clinical ScienceShanghai Institute of Clinical BioinformaticsFudan University Institute of Biomedical ScienceFudan UniversityShanghaiChina
| |
Collapse
|
6
|
Wang, DC, Wang, W, Zhu, B, Wang X. Lung Cancer Heterogeneity and New Strategies for Drug Therapy. Annu Rev Pharmacol Toxicol 2018; 58:531-546. [PMID: 28977762 DOI: 10.1146/annurev-pharmtox-010716-104523] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Diane C. Wang,
- Zhongshan Hospital Institute of Clinical Science, Shanghai Institute of Clinical Bioinformatics, Fudan University Center for Clinical Bioinformatics, Shanghai 200032, China
| | - William Wang,
- Zhongshan Hospital Institute of Clinical Science, Shanghai Institute of Clinical Bioinformatics, Fudan University Center for Clinical Bioinformatics, Shanghai 200032, China
| | - Bijun Zhu,
- Zhongshan Hospital Institute of Clinical Science, Shanghai Institute of Clinical Bioinformatics, Fudan University Center for Clinical Bioinformatics, Shanghai 200032, China
| | - Xiangdong Wang
- Zhongshan Hospital Institute of Clinical Science, Shanghai Institute of Clinical Bioinformatics, Fudan University Center for Clinical Bioinformatics, Shanghai 200032, China
| |
Collapse
|
7
|
Can the Single Cell Make Biomedicine Different? ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1068:1-6. [PMID: 29943291 DOI: 10.1007/978-981-13-0502-3_1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The single-cell as the basic unit of biological organs and tissues has recently been considered an important window to furthermore understand molecular mechanisms of organ function and biology. The current issue with a special focus on single cell biomedicine is the first effort to collect the evidence of disease-associated single cell research, define the significance of single cell biomedicine in the pathogenesis of diseases, value the correlation of single cell gene sequencing with disease-specific biomarkers, and monitor the dynamics of RNA processes and responses to microenvironmental changes and drug resistances.
Collapse
|
8
|
Wang W, Gao D, Wang X. Can single-cell RNA sequencing crack the mystery of cells? Cell Biol Toxicol 2017; 34:1-6. [DOI: 10.1007/s10565-017-9404-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 06/28/2017] [Indexed: 12/15/2022]
|
9
|
|