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Lee S, Cho Y, Li Y, Li R, Brown D, McAuliffe P, Lee AV, Oesterreich S, Zervantonakis IK, Osmanbeyoglu HU. Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586041. [PMID: 38585952 PMCID: PMC10996512 DOI: 10.1101/2024.03.21.586041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the protumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.
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Affiliation(s)
- Sanghoon Lee
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, 15206, U.S.A
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
| | - Youngbin Cho
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Yiting Li
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Ruxuan Li
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Daniel Brown
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Priscilla McAuliffe
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Adrian V Lee
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Steffi Oesterreich
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Ioannis K. Zervantonakis
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Hatice Ulku Osmanbeyoglu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, 15206, U.S.A
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
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Tang M, Luo W, Zhou Y, Zhang Z, Jiang Z. Anoikis-related gene CDKN2A predicts prognosis and immune response and mediates proliferation and migration in thyroid carcinoma. Transl Oncol 2024; 40:101873. [PMID: 38141377 PMCID: PMC10788268 DOI: 10.1016/j.tranon.2023.101873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 11/25/2023] [Accepted: 12/18/2023] [Indexed: 12/25/2023] Open
Abstract
Thyroid carcinoma (THCA) is a tumor commonly occurring in the endocrine system, and its incidence rate is increasing yearly. Anoikis is a type of cell death involved in the carcinogenesis process. This study aimed to investigate the prognosis and immune correlations of anoikis in THCA. Our study used several bioinformatics algorithms (co-expression analysis, univariate and multivariate Cox analysis) to screen anoikis-related genes (ARGs) to construct risk models. Through receiver operating characteristic (ROC) curve, nomogram, and independent prognostic analysis found that the constructed model had ideal predictive value for THCA. The consensus clustering method was used to divide ARG patterns into three subgroups, and there were significant differences in survival among the three subgroups. The CIBERSORT algorithm demonstrated strong correlations among immune infiltrating cells, prognostic genes, and risk scores. Univariate and multivariate Cox analysis showed that CDKN2A is an independent prognostic gene. Basic experiments (immunohistochemistry, qRT-PCR, etc.) showed that the expression levels of CDKN2A mRNA and protein were highly expressed in THCA, which was consistent with the results of bioinformatics analysis. In vitro, the knockdown of CDKN2A significantly inhibited the proliferation and migration of THCA cells. In summary, our study utilized eight ARGs to construct an accurate risk model. ARGs, especially CDKN2A, play a crucial role in the occurrence and development of THCA and can become potential targets for treating THCA patients.
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Affiliation(s)
- Mengjie Tang
- Department of Pathology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Wen Luo
- Department of Nuclear Medicine, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Yusong Zhou
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Zhun Zhang
- Department of Breast and Thyroid Surgery, The Third Xiangya Hospital, Central South University, Changsha, China.
| | - Zhongjun Jiang
- Department of Thyroid and Breast Surgery, The Affiliated Nanhua Hospital, Hengyang Medical School, University of South China, Hengyang, China.
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He J, Lei Y, Li X, Wu B, Tang Y. Exploring the prognostic value of S100A11 and its association with immune infiltration in breast cancer. Sci Rep 2023; 13:22922. [PMID: 38129538 PMCID: PMC10739898 DOI: 10.1038/s41598-023-50160-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
Breast cancer (BC) is a severe danger to women's lives and health globally. S100A11 is aberrantly expressed in many carcinomas and serves a crucial function in cancer development. However, the role of S100A11 in BC is unclear. In this study, we utilized multiple databases and online tools, including the TCGA database, cBioPortal, and STRING, to evaluate the significance of S100A11 in BC prognosis and immune infiltration. We found that S100A11 was considerably more abundant in BC tissues. Survival analysis indicated that individuals with S100A11 high expression of BC had shorter overall survival. Multivariate Cox regression analysis revealed that high S100A11 expression independently influenced the poor outcome of patients with BC (HR = 1.738, 95%CI 1.197-2.524). Our nomogram incorporating five factors, including S100A11, age, clinical stage, N, and M, was developed to anticipate the survival probability in BC prognosis. The model demonstrated good consistency and accuracy. Furthermore, the mutation rete of S100A11 was 14%. Survival analysis suggested that breast cancer patients with S100A11 mutation had a worse prognosis. KEGG pathway enrichment analysis revealed that S100A11 may be mainly involved in the IL-17 signaling pathway. Finally, we discovered a correlation between S100A11 expression and immune cell infiltration on BC. S100A11 expression was positively associated with 17 immune checkpoint-related genes. In conclusion, this study indicates that S100A11 may contribute to a worse prognosis for BC and potentially has a significant impact through its influence on immune cell infiltration and the IL-17 signaling pathway.
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Affiliation(s)
- Junfang He
- School of Public Health, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yuxi Lei
- School of Public Health, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Xiabin Li
- Precision Pathology Diagnosis for Serious Diseases Key Laboratory of LuZhou, Luzhou, 646000, Sichuan, China
| | - Bin Wu
- Departments of Breast Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
| | - Yan Tang
- School of Public Health, Southwest Medical University, Luzhou, 646000, Sichuan, China.
- Institute of Cancer Medicine, School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, Sichuan, China.
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Halder A, Biswas D, Chauhan A, Saha A, Auromahima S, Yadav D, Nissa MU, Iyer G, Parihari S, Sharma G, Epari S, Shetty P, Moiyadi A, Ball GR, Srivastava S. A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors. Clin Proteomics 2023; 20:41. [PMID: 37770851 PMCID: PMC10540342 DOI: 10.1186/s12014-023-09426-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. METHODS A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. RESULTS A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. CONCLUSIONS The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.
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Affiliation(s)
- Ankit Halder
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Deeptarup Biswas
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Aparna Chauhan
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Adrita Saha
- Motilal Nehru National Institute of Technology, Allahabad, 211004, UP, India
| | - Shreeman Auromahima
- Department of Bioscience & Bioengineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India
| | - Deeksha Yadav
- CSIR-Institute of Genomics and Integrative Biology, Sukhdev Vihar, New Delhi, 110025, India
| | - Mehar Un Nissa
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Gayatri Iyer
- Koita Centre for Digital Health, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Shashwati Parihari
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Gautam Sharma
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Sridhar Epari
- Department of Pathology, Tata Memorial Centre, Mumbai, India
| | - Prakash Shetty
- Department of Neurosurgery, Tata Memorial Centre, Mumbai, India
| | | | - Graham Roy Ball
- Medical Technology Research Centre, Anglia Ruskin University, Cambridge Campus, East Rd, Cambridge, CB1 1PT, UK
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 185 Berry St., Suite 290, San Francisco, CA, 94107, USA.
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