|
1
|
Mokhtari K, Peymani M, Rashidi M, Hushmandi K, Ghaedi K, Taheriazam A, Hashemi M. Colon cancer transcriptome. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 180-181:49-82. [PMID: 37059270 DOI: 10.1016/j.pbiomolbio.2023.04.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/31/2023] [Accepted: 04/06/2023] [Indexed: 04/16/2023]
Abstract
Over the last four decades, methodological innovations have continuously changed transcriptome profiling. It is now feasible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples using RNA sequencing (RNA-seq). These transcriptomes serve as a connection between cellular behaviors and their underlying molecular mechanisms, such as mutations. This relationship, in the context of cancer, provides a chance to unravel tumor complexity and heterogeneity and uncover novel biomarkers or treatment options. Since colon cancer is one of the most frequent malignancies, its prognosis and diagnosis seem to be critical. The transcriptome technology is developing for an earlier and more accurate diagnosis of cancer which can provide better protectivity and prognostic utility to medical teams and patients. A transcriptome is a whole set of expressed coding and non-coding RNAs in an individual or cell population. The cancer transcriptome includes RNA-based changes. The combined genome and transcriptome of a patient may provide a comprehensive picture of their cancer, and this information is beginning to affect treatment decision-making in real-time. A full assessment of the transcriptome of colon (colorectal) cancer has been assessed in this review paper based on risk factors such as age, obesity, gender, alcohol use, race, and also different stages of cancer, as well as non-coding RNAs like circRNAs, miRNAs, lncRNAs, and siRNAs. Similarly, they have been examined independently in the transcriptome study of colon cancer.
Collapse
Affiliation(s)
- Khatere Mokhtari
- Department of Modern Biology, ACECR Institute of Higher Education (Isfahan Branch), Isfahan, Iran
| | - Maryam Peymani
- Department of Biology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
| | - Mohsen Rashidi
- Department Pharmacology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, 4815733971, Iran; The Health of Plant and Livestock Products Research Center, Mazandaran University of Medical Sciences, Sari, 4815733971, Iran
| | - Kiavash Hushmandi
- Department of Food Hygiene and Quality Control, Division of Epidemiology, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Kamran Ghaedi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.
| | - Afshin Taheriazam
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Orthopedics, Faculty of Medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Mehrdad Hashemi
- Farhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran; Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| |
Collapse
|
|
2
|
Sun H, Li L, Lao I, Li X, Xu B, Cao Y, Jin W. Single-cell RNA sequencing reveals cellular and molecular reprograming landscape of gliomas and lung cancer brain metastases. Clin Transl Med 2022; 12:e1101. [PMID: 36336787 PMCID: PMC9637666 DOI: 10.1002/ctm2.1101] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Brain malignancies encompass gliomas and brain metastases originating from extracranial tumours including lung cancer. Approximately 50% of patients with lung adenocarcinoma (LUAD) will eventually develop brain metastases. However, the specific characteristics of gliomas and lung-to-brain metastases (LC) are largely unknown. METHODS We applied single-cell RNA sequencing to profile immune and nonimmune cells in 4 glioma and 10 LC samples. RESULTS Our analysis revealed that tumour microenvironment (TME) cells are present in heterogeneous subpopulations. LC reprogramed cells into immune suppressed state, including microglia, macrophages, endothelial cells, and CD8+ T cells, with unique cell proportions and gene signatures. Particularly, we identified that a subset of macrophages was associated with poor prognosis. ROS (reactive oxygen species)-producing neutrophils was found to participant in angiogenesis. Furthermore, endothelial cells participated in active communication with fibroblasts. Metastatic epithelial cells exhibited high heterogeneity in chromosomal instability (CIN) and cell population. CONCLUSIONS Our findings provide a comprehensive understanding of the heterogenicity of the tumor microenvironment and tumour cells and it will be crucial for successful immunotherapy development for brain metastasis of lung cancer.
Collapse
Affiliation(s)
- He‐Fen Sun
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Liang‐Dong Li
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of NeurosurgeryFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - I‐Weng Lao
- Department of PathologyFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Xuan Li
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Bao‐Jin Xu
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yi‐Qun Cao
- Department of NeurosurgeryFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Wei Jin
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| |
Collapse
|
|
3
|
Guo L, Kang Y, Xia D, Ren Y, Yang X, Xiang Y, Tang L, Ren D, Yu J, Wang J, Liang T. Characterization of Immune-Based Molecular Subtypes and Prognostic Model in Prostate Adenocarcinoma. Genes (Basel) 2022; 13:genes13061087. [PMID: 35741849 PMCID: PMC9223199 DOI: 10.3390/genes13061087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 11/21/2022] Open
Abstract
Prostate adenocarcinoma (PRAD), also named prostate cancer, the most common visceral malignancy, is diagnosed in male individuals. Herein, in order to obtain immune-based subtypes, we performed an integrative analysis to characterize molecular subtypes based on immune-related genes, and further discuss the potential features and differences between identified subtypes. Simultaneously, we also construct an immune-based risk model to assess cancer prognosis. Our findings showed that the two subtypes, C1 and C2, could be characterized, and the two subtypes showed different characteristics that could clearly describe the heterogeneity of immune microenvironments. The C2 subtype presented a better survival rate than that in the C1 subtype. Further, we constructed an immune-based prognostic model based on four screened abnormally expressed genes, and they were selected as predictors of the robust prognostic model (AUC = 0.968). Our studies provide reference for characterization of molecular subtypes and immunotherapeutic agents against prostate cancer, and the developed robust and useful immune-based prognostic model can contribute to cancer prognosis and provide reference for the individualized treatment plan and health resource utilization. These findings further promote the development and application of precision medicine in prostate cancer.
Collapse
Affiliation(s)
- Li Guo
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Yihao Kang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Daoliang Xia
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Yujie Ren
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Xueni Yang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Yangyang Xiang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Lihua Tang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Dekang Ren
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
| | - Jiafeng Yu
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China;
| | - Jun Wang
- Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (L.G.); (Y.K.); (D.X.); (Y.R.); (X.Y.); (Y.X.); (L.T.); (D.R.)
- Correspondence: (J.W.); (T.L.)
| | - Tingming Liang
- Jiangsu Key Laboratory for Molecular and Medical Biotechnology, School of Life Science, Nanjing Normal University, Nanjing 210023, China
- Correspondence: (J.W.); (T.L.)
| |
Collapse
|