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Lin Y, Pan X, Chen Z, Lin S, Shen Z, Chen S. Prognostic value and immune infiltration of novel signatures in colon cancer microenvironment. Cancer Cell Int 2021; 21:679. [PMID: 34922547 PMCID: PMC8684099 DOI: 10.1186/s12935-021-02342-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 11/15/2021] [Indexed: 12/16/2022] Open
Abstract
Background Growing evidence has shown that the prognosis for colon cancer depends on changes in microenvironment. The purpose of this study was to elucidate the prognostic value of long noncoding RNAs (lncRNAs) related to immune microenvironment (IM) in colon cancer. Methods Single sample gene set enrichment analysis (ssGSEA) was used to identify the subtypes of colon cancer based on the immune genomes of 29 immune signatures. Cox regression analysis identified a lncRNA signatures associated with immune infiltration. The Tumor Immune Estimation Resource database was used to analyze immune cell content. Results Colon cancer samples were divided into three subtypes by unsupervised cluster analysis. Cox regression analysis identified an immune infiltration-related 5-lncRNA signature. This signature combined with clinical factors can effectively improve the predictive ability for the overall survival (OS) of colon cancer. At the same time, we found that the expression of H19 affects the content of B cells and macrophages in the microenvironment of colon cancer and affects the prognosis of colon cancer. Finally, we constructed the H19 regulatory network and further analyzed the possible mechanisms. We found that knocking down the expression of H19 can significantly inhibit the expression of CCND1 and VEGFA. At the same time, the immunohistochemical assay found that the expression of CCND1 and VEGFA protein was significantly positively correlated with the infiltration of M2 type macrophages. Conclusion The findings may help to formulate clinical strategies and understand the underlying mechanisms of H19 regulation. H19 may be a biomarker for targeted treatment of colon cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02342-8.
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Affiliation(s)
- Yilin Lin
- Department of Gastroenterological Surgery, Peking University People's Hospital, 11 Xizhimen South Street, Xicheng, Beijing, China
| | - Xiaoxian Pan
- Department of Radiotherapy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Zhihua Chen
- Department of Gastroenterological Surgery, The First Affiliated Hospital of Fujian Medical University, No. 20, Chazhong Road, Taijiang, Fuzhou, Fujian, China
| | - Suyong Lin
- Department of Gastroenterological Surgery, The First Affiliated Hospital of Fujian Medical University, No. 20, Chazhong Road, Taijiang, Fuzhou, Fujian, China
| | - Zhanlong Shen
- Department of Gastroenterological Surgery, Peking University People's Hospital, 11 Xizhimen South Street, Xicheng, Beijing, China.
| | - Shaoqin Chen
- Department of Gastroenterological Surgery, The First Affiliated Hospital of Fujian Medical University, No. 20, Chazhong Road, Taijiang, Fuzhou, Fujian, China.
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AlMusawi S, Ahmed M, Nateri AS. Understanding cell-cell communication and signaling in the colorectal cancer microenvironment. Clin Transl Med 2021; 11:e308. [PMID: 33635003 PMCID: PMC7868082 DOI: 10.1002/ctm2.308] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 12/31/2020] [Accepted: 01/19/2021] [Indexed: 12/12/2022] Open
Abstract
Carcinomas are complex heterocellular systems containing epithelial cancer cells, stromal fibroblasts, and multiple immune cell-types. Cell-cell communication between these tumor microenvironments (TME) and cells drives cancer progression and influences response to existing therapies. In order to provide better treatments for patients, we must understand how various cell-types collaborate within the TME to drive cancer and consider the multiple signals present between and within different cancer types. To investigate how tissues function, we need a model to measure both how signals are transferred between cells and how that information is processed within cells. The interplay of collaboration between different cell-types requires cell-cell communication. This article aims to review the current in vitro and in vivo mono-cellular and multi-cellular cultures models of colorectal cancer (CRC), and to explore how they can be used for single-cell multi-omics approaches for isolating multiple types of molecules from a single-cell required for cell-cell communication to distinguish cancer cells from normal cells. Integrating the existing single-cell signaling measurements and models, and through understanding the cell identity and how different cell types communicate, will help predict drug sensitivities in tumor cells and between- and within-patients responses.
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Affiliation(s)
- Shaikha AlMusawi
- Cancer Genetics & Stem Cell Group, BioDiscovery Institute, Division of Cancer & Stem Cells, School of MedicineUniversity of NottinghamNottinghamUK
| | - Mehreen Ahmed
- Cancer Genetics & Stem Cell Group, BioDiscovery Institute, Division of Cancer & Stem Cells, School of MedicineUniversity of NottinghamNottinghamUK
- Department of Laboratory Medicine, Division of Translational Cancer ResearchLund UniversityLundSweden
| | - Abdolrahman S. Nateri
- Cancer Genetics & Stem Cell Group, BioDiscovery Institute, Division of Cancer & Stem Cells, School of MedicineUniversity of NottinghamNottinghamUK
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Cheng J, He J, Wang S, Zhao Z, Yan H, Guan Q, Li J, Guo Z, Ao L. Biased Influences of Low Tumor Purity on Mutation Detection in Cancer. Front Mol Biosci 2021; 7:533196. [PMID: 33425983 PMCID: PMC7785586 DOI: 10.3389/fmolb.2020.533196] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 10/22/2020] [Indexed: 01/21/2023] Open
Abstract
The non-cancerous components in tumor tissues, e.g., infiltrating stromal cells and immune cells, dilute tumor purity and might confound genomic mutation profile analyses and the identification of pathological biomarkers. It is necessary to systematically evaluate the influence of tumor purity. Here, using public gastric cancer samples from The Cancer Genome Atlas (TCGA), we firstly showed that numbers of mutation, separately called by four algorithms, were significant positively correlated with tumor purities (all p < 0.05, Spearman rank correlation). Similar results were also observed in other nine cancers from TCGA. Notably, the result was further confirmed by six in-house samples from two gastric cancer patients and five in-house samples from two colorectal cancer patients with different tumor purities. Furthermore, the metastasis mechanism of gastric cancer may be incorrectly characterized as numbers of mutation and tumor purities of 248 lymph node metastatic (N + M0) samples were both significantly lower than those of 121 non-metastatic (N0M0) samples (p < 0.05, Wilcoxon rank-sum test). Similar phenomena were also observed that tumor purities could confound the analysis of histological subtypes of cancer and the identification of microsatellite instability status (MSI) in both gastric and colon cancer. Finally, we suggested that the higher tumor purity, such as above 70%, rather than 60%, could be better to meet the requirement of mutation calling. In conclusion, the influence of tumor purity on the genomic mutation profile and pathological analyses should be fully considered in the further study.
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Affiliation(s)
- Jun Cheng
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jun He
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Shanshan Wang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Zhangxiang Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haidan Yan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Qingzhou Guan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jing Li
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Zheng Guo
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Lu Ao
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
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