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He W, Yang H, Li Y, Cui Y, Wei L, Xu T, Li Y, Zhang M. Identifying the toxic mechanisms of emerging electronic contaminations liquid crystal monomers and the construction of a priority control list for graded control. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175398. [PMID: 39128516 DOI: 10.1016/j.scitotenv.2024.175398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 08/05/2024] [Accepted: 08/06/2024] [Indexed: 08/13/2024]
Abstract
Liquid crystal monomers (LCMs) are identified as emerging organic contaminations with largely unexplored health impacts. To elucidate their toxic mechanisms, support the establishment of environmental discharge and management standards, and promote effective LCMs control, this study constructs a database covering 20,545 potential targets of 1431 LCMs, highlighting 9 key toxic target proteins that disrupt the nervous system and metabolic functions. GO and KEGG pathway analysis suggests LCMs severely affect nervous system, linked to neurodegenerative diseases and mental health disorders, with toxicity variations driven by electronegativity and structural complexity of LCM terminal groups. To achieve tiered control of LCMs, construct toxicity risk control lists for 9 key toxic target proteins, suitable for the graded control of LCMs, management recommendations are provided based on toxicity levels. These lists were validated for reliability and offer reliable toxicity predictions for LCMs. SHAP analysis points to electronic properties, molecular shape, and structural characteristics of LCMs as primary health impact factors. As the first study integrating machine learning with computational toxicology to outline LCMs health impacts, it aims to enhance public understanding of LCM toxicity risks and support the development of environmental standards, effective management of LCM production and emissions, and reduction of public exposure risks.
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Affiliation(s)
- Wei He
- MOE Key Laboratory of Resources Environmental Systems Optimization, North China Electric Power University, Beijing, China
| | - Hao Yang
- MOE Key Laboratory of Resources Environmental Systems Optimization, North China Electric Power University, Beijing, China
| | - Yunxiang Li
- MOE Key Laboratory of Resources Environmental Systems Optimization, North China Electric Power University, Beijing, China
| | - Yuhan Cui
- MOE Key Laboratory of Resources Environmental Systems Optimization, North China Electric Power University, Beijing, China
| | - Luanxiao Wei
- MOE Key Laboratory of Resources Environmental Systems Optimization, North China Electric Power University, Beijing, China
| | - Tingzhi Xu
- MOE Key Laboratory of Resources Environmental Systems Optimization, North China Electric Power University, Beijing, China.
| | - Yu Li
- MOE Key Laboratory of Resources Environmental Systems Optimization, North China Electric Power University, Beijing, China
| | - Meng Zhang
- College of Environmental Sciences and Engineering, State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, Peking University, Beijing 100871, China; The Key Laboratory of Water and Sediment Sciences, Ministry of Education, International Joint Laboratory for Regional Pollution Control, Ministry of Education, Beijing 100871, China.
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Jiang J, Li L, Yin G, Luo H, Li J. A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy. Clin Breast Cancer 2024; 24:376-383. [PMID: 38492997 DOI: 10.1016/j.clbc.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/17/2024] [Accepted: 02/12/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis. PURPOSE Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach. METHOD Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data). RESULTS The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes. CONCLUSION Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.
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Affiliation(s)
- Jun Jiang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Lintao Li
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Huaichao Luo
- Department of Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Li
- Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Gastélum-López MDLÁ, Aguilar-Medina M, García Mata C, López-Gutiérrez J, Romero-Quintana G, Bermúdez M, Avendaño-Felix M, López-Camarillo C, Pérez-Plascencia C, Beltrán AS, Ramos-Payán R. Organotypic 3D Cell-Architecture Impacts the Expression Pattern of miRNAs-mRNAs Network in Breast Cancer SKBR3 Cells. Noncoding RNA 2023; 9:66. [PMID: 37987362 PMCID: PMC10661268 DOI: 10.3390/ncrna9060066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/26/2023] [Accepted: 10/20/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND Currently, most of the research on breast cancer has been carried out in conventional two-dimensional (2D) cell cultures due to its practical benefits, however, the three-dimensional (3D) cell culture is becoming the model of choice in cancer research because it allows cell-cell and cell-extracellular matrix (ECM) interactions, mimicking the native microenvironment of tumors in vivo. METHODS In this work, we evaluated the effect of 3D cell organization on the expression pattern of miRNAs (by Small-RNAseq) and mRNAs (by microarrays) in the breast cancer SKBR3 cell line and analyzed the biological processes and signaling pathways regulated by the differentially expressed protein-coding genes (DE-mRNAs) and miRNAs (DE-microRNAs) found in the organoids. RESULTS We obtained well-defined cell-aggregated organoids with a grape cluster-like morphology with a size up to 9.2 × 105 μm3. The transcriptomic assays showed that cell growth in organoids significantly affected (all p < 0.01) the gene expression patterns of both miRNAs, and mRNAs, finding 20 upregulated and 19 downregulated DE-microRNAs, as well as 49 upregulated and 123 downregulated DE-mRNAs. In silico analysis showed that a subset of 11 upregulated DE-microRNAs target 70 downregulated DE-mRNAs. These genes are involved in 150 gene ontology (GO) biological processes such as regulation of cell morphogenesis, regulation of cell shape, regulation of canonical Wnt signaling pathway, morphogenesis of epithelium, regulation of cytoskeleton organization, as well as in the MAPK and AGE-RAGE signaling KEGG-pathways. Interestingly, hsa-mir-122-5p (Fold Change (FC) = 15.4), hsa-mir-369-3p (FC = 11.4), and hsa-mir-10b-5p (FC = 20.1) regulated up to 81% of the 70 downregulated DE-mRNAs. CONCLUSION The organotypic 3D cell-organization architecture of breast cancer SKBR3 cells impacts the expression pattern of the miRNAs-mRNAs network mainly through overexpression of hsa-mir-122-5p, hsa-mir-369-3p, and hsa-mir-10b-5p. All these findings suggest that the interaction between cell-cell and cell-ECM as well as the change in the culture architecture impacts gene expression, and, therefore, support the pertinence of migrating breast cancer research from conventional cultures to 3D models.
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Affiliation(s)
- María de los Ángeles Gastélum-López
- Faculty of Biological and Chemical Sciences, Autonomous University of Sinaloa, Josefa Ortiz de Domínguez s/n y Avenida de las Américas, Culiacan 80013, Sinaloa, Mexico (M.A.-M.); (G.R.-Q.); (M.A.-F.)
| | - Maribel Aguilar-Medina
- Faculty of Biological and Chemical Sciences, Autonomous University of Sinaloa, Josefa Ortiz de Domínguez s/n y Avenida de las Américas, Culiacan 80013, Sinaloa, Mexico (M.A.-M.); (G.R.-Q.); (M.A.-F.)
| | - Cristina García Mata
- Faculty of Biological and Chemical Sciences, Autonomous University of Sinaloa, Josefa Ortiz de Domínguez s/n y Avenida de las Américas, Culiacan 80013, Sinaloa, Mexico (M.A.-M.); (G.R.-Q.); (M.A.-F.)
| | - Jorge López-Gutiérrez
- Faculty of Biological and Chemical Sciences, Autonomous University of Sinaloa, Josefa Ortiz de Domínguez s/n y Avenida de las Américas, Culiacan 80013, Sinaloa, Mexico (M.A.-M.); (G.R.-Q.); (M.A.-F.)
| | - Geovanni Romero-Quintana
- Faculty of Biological and Chemical Sciences, Autonomous University of Sinaloa, Josefa Ortiz de Domínguez s/n y Avenida de las Américas, Culiacan 80013, Sinaloa, Mexico (M.A.-M.); (G.R.-Q.); (M.A.-F.)
| | - Mercedes Bermúdez
- Faculty of Dentistry, Autonomous University of Chihuahua, Av. Escorza No. 900, Centro, Chihuahua 31125, Chihuahua, Mexico;
| | - Mariana Avendaño-Felix
- Faculty of Biological and Chemical Sciences, Autonomous University of Sinaloa, Josefa Ortiz de Domínguez s/n y Avenida de las Américas, Culiacan 80013, Sinaloa, Mexico (M.A.-M.); (G.R.-Q.); (M.A.-F.)
| | - César López-Camarillo
- Postgraduate in Genomic Sciences, Autonomous University of Mexico City, San Lorenzo 290, Col del Valle, Mexico City 03100, Mexico;
| | - Carlos Pérez-Plascencia
- National Cancer Institute, Av. San Fernando 22, Belisario Domínguez Sec. 16, Tlalpan, Mexico City 14080, Mexico;
- FES Iztacala, National Autonomous University of Mexico, Av. de los Barrios S/N, Los Reyes Ixtacala, Tlalnepantla 54090, Estado de Mexico, Mexico
| | - Adriana S Beltrán
- Human Pluripotent Stem Cell Core, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Rosalío Ramos-Payán
- Faculty of Biological and Chemical Sciences, Autonomous University of Sinaloa, Josefa Ortiz de Domínguez s/n y Avenida de las Américas, Culiacan 80013, Sinaloa, Mexico (M.A.-M.); (G.R.-Q.); (M.A.-F.)
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Kołat D, Kałuzińska-Kołat Ż, Kośla K, Orzechowska M, Płuciennik E, Bednarek AK. LINC01137/miR-186-5p/WWOX: a novel axis identified from WWOX-related RNA interactome in bladder cancer. Front Genet 2023; 14:1214968. [PMID: 37519886 PMCID: PMC10373930 DOI: 10.3389/fgene.2023.1214968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/30/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction: The discovery of non-coding RNA (ncRNA) dates back to the pre-genomics era, but the progress in this field is still dynamic and leverages current post-genomics solutions. WWOX is a global gene expression modulator that is scarcely investigated for its role in regulating cancer-related ncRNAs. In bladder cancer (BLCA), the link between WWOX and ncRNA remains unexplored. The description of AP-2α and AP-2γ transcription factors, known as WWOX-interacting proteins, is more commonplace regarding ncRNA but still merits investigation. Therefore, this in vitro and in silico study aimed to construct an ncRNA-containing network with WWOX/AP-2 and to investigate the most relevant observation in the context of BLCA cell lines and patients. Methods: RT-112, HT-1376, and CAL-29 cell lines were subjected to two stable lentiviral transductions. High-throughput sequencing of cellular variants (deposited in the Gene Expression Omnibus database under the GSE193659 record) enabled the investigation of WWOX/AP-2-dependent differences using various bioinformatics tools (e.g., limma-voom, FactoMineR, multiple Support Vector Machine Recursive Feature Elimination (mSVM-RFE), miRDB, Arena-Idb, ncFANs, RNAhybrid, TargetScan, Protein Annotation Through Evolutionary Relationships (PANTHER), Gene Transcription Regulation Database (GTRD), or Evaluate Cutpoints) and repositories such as The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia. The most relevant observations from cap analysis gene expression sequencing (CAGE-seq) were confirmed using real-time PCR, whereas TCGA data were validated using the GSE31684 cohort. Results: The first stage of the whole study justified focusing solely on WWOX rather than on WWOX combined with AP-2α/γ. The most relevant observation of the developed ncRNA-containing network was LINC01137, i.e., long non-coding RNAs (lncRNAs) that unraveled the core network containing UPF1, ZC3H12A, LINC01137, WWOX, and miR-186-5p, the last three being a novel lncRNA/miRNA/mRNA axis. Patients' data confirmed the LINC01137/miR-186-5p/WWOX relationship and provided a set of dependent genes (i.e., KRT18, HES1, VCP, FTH1, IFITM3, RAB34, and CLU). Together with the core network, the gene set was subjected to survival analysis for both TCGA-BLCA and GSE31684 patients, which indicated that the increased expression of WWOX or LINC01137 is favorable, similar to their combination with each other (WWOX↑ and LINC01137↑) or with MIR186 (WWOX↑/LINC01137↑ but MIR186↓). Conclusion: WWOX is implicated in the positive feedback loop with LINC01137 that sponges WWOX-targeting miR-186-5p. This novel WWOX-containing lncRNA/miRNA/mRNA axis should be further investigated to depict its relationships in a broader context, which could contribute to BLCA research and treatment.
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Affiliation(s)
- Damian Kołat
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | | | - Katarzyna Kośla
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | | | | | - Andrzej K. Bednarek
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
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Nasser M, Yusof UK. Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction. Diagnostics (Basel) 2023; 13:diagnostics13010161. [PMID: 36611453 PMCID: PMC9818155 DOI: 10.3390/diagnostics13010161] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/19/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.
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Ye S, Wang D, Jin M, Du J, Chen X, Zhang H, Zhou C, Fang S, Liu K. High eukaryotic initiation factor 5A2 expression predicts poor prognosis and may participate in the SNHG16/miR-10b-5p/EIF5A2 regulatory axis in head and neck squamous cell carcinoma. J Clin Lab Anal 2022; 37:e24820. [PMID: 36550070 PMCID: PMC9833988 DOI: 10.1002/jcla.24820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND This study attempted to investigate the significance of eukaryotic initiation factor 5A2 (EIF5A2) in the prognosis and regulatory network of head and neck squamous cell carcinoma (HNSCC). METHODS EIF5A2 expression, prognostic information, and methylation levels of HNSCC were collected from the Cancer Genome Atlas (TCGA) database. Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot analyses were performed to determine EIF5A2 levels in HNSCC and normal tissue samples. R software was employed for expression analysis and prognosis assessment of EIF5A2 in HNSCC. A competing endogenous RNA (ceRNA) network was generated with the starBase database. Gene set enrichment analysis (GSEA) was used to determine the enriched physiological functions and network related to high expression of EIF5A2 in HNSCC. Immune infiltration-related outcomes were acquired from the CIBERSORT and Tumor Immune Estimation Resource (TIMER) database. RESULTS EIF5A2 overexpression was observed in HNSCC and linked to poor progression-free survival and overall survival time. Cox regression analyses showed that EIF5A2 level was a stand-alone indicator of HNSCC patients' prognosis. A ceRNA network analysis highlighted the SNHG16/miR-10b-5p/EIF5A2 axis in EIF5A2 regulation. The GSEA results indicated that EIF5A2 was involved in complex signaling pathways. The CIBERSORT and TIMER databases revealed significant associations between EIF5A2 expression and immune cell infiltration. CONCLUSION EIF5A2 overexpression may be a risk factor for prognosis in HNSCC and may be regulated by the SNHG16/miR-10b-5p/EIF5A2 axis.
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Affiliation(s)
- Shuang Ye
- Department of Radiation Oncology, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
| | - Dan Wang
- Department of Medical Imaging, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
| | - Ming Jin
- Department of Radiation Oncology, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
| | - Juan Du
- Department of Radiation Oncology, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
| | - Xue Chen
- Department of Radiation Oncology, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
| | - Hui Zhang
- Department of Radiation Oncology, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
| | - Chongchang Zhou
- Department of Otorhinolaryngology Head and Neck Surgery, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
| | - Shuai Fang
- Department of Thoracic SurgeryThe Affiliated Hospital of Medical School of Ningbo UniversityNingboChina
| | - Kaitai Liu
- Department of Radiation Oncology, The Affiliated Lihuili HospitalNingbo UniversityNingboChina
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Ghosh N, Saha I, Plewczynski D. Unveiling the Biomarkers of Cancer and COVID-19 and Their Regulations in Different Organs by Integrating RNA-Seq Expression and Protein-Protein Interactions. ACS OMEGA 2022; 7:43589-43602. [PMID: 36506181 PMCID: PMC9730762 DOI: 10.1021/acsomega.2c04389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/13/2022] [Indexed: 06/17/2023]
Abstract
Cancer and COVID-19 have killed millions of people worldwide. COVID-19 is even more dangerous to people with comorbidities such as cancer. Thus, it is imperative to identify the key human genes or biomarkers that can be targeted to develop novel prognosis and therapeutic strategies. The transcriptomic data provided by the next-generation sequencing technique makes this identification very convenient. Hence, mRNA (messenger ribonucleic acid) expression data of 2265 cancer and 282 normal patients were considered, while for COVID-19 assessment, 784 and 425 COVID-19 and normal patients were taken, respectively. Initially, volcano plots were used to identify the up- and down-regulated genes for both cancer and COVID-19. Thereafter, protein-protein interaction (PPI) networks were prepared by combining all the up- and down-regulated genes for each of cancer and COVID-19. Subsequently, such networks were analyzed to identify the top 10 genes with the highest degree of connection to provide the biomarkers. Interestingly, these genes were all up-regulated for cancer, while they were down-regulated for COVID-19. This study had also identified common genes between cancer and COVID-19, all of which were up-regulated in both the diseases. This analysis revealed that FN1 was highly up-regulated in different organs for cancer, while EEF2 was dysregulated in most organs affected by COVID-19. Then, functional enrichment analysis was performed to identify significant biological processes. Finally, the drugs for cancer and COVID-19 biomarkers and the common genes between them were identified using the Enrichr online web tool. These drugs include lucanthone, etoposide, and methotrexate, targeting the biomarkers for cancer, while paclitaxel is an important drug for COVID-19.
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Affiliation(s)
- Nimisha Ghosh
- Faculty
of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw 02-097, Poland
- Department
of Computer Science and Information Technology, Institute of Technical
Education and Research, Siksha ‘O’
Anusandhan (Deemed to Be University), Bhubaneswar 751030 Odisha, India
| | - Indrajit Saha
- Department
of Computer Science and Engineering, National
Institute of Technical Teachers’ Training and Research, Kolkata 700106 West Bengal, India
| | - Dariusz Plewczynski
- Laboratory
of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland
- Laboratory
of Bioinformatics and Computational Genomics, Faculty of Mathematics
and Information Science, Warsaw University
of Technology, Warsaw 00-662, Poland
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Nassif AB, Talib MA, Nasir Q, Afadar Y, Elgendy O. Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artif Intell Med 2022; 127:102276. [DOI: 10.1016/j.artmed.2022.102276] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 10/18/2021] [Accepted: 03/04/2022] [Indexed: 02/07/2023]
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Rajarajan D, Natesh J, Penta D, Meeran SM. Dietary Piperine Suppresses Obesity-Associated Breast Cancer Growth and Metastasis by Regulating the miR-181c-3p/ PPARα Axis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:15562-15574. [PMID: 34905918 DOI: 10.1021/acs.jafc.1c05670] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Adipocyte-derived leptin activates multiple oncogenic signaling, leading to breast cancer cell progression and metastasis. Hence, finding effective strategies to inhibit the oncogenic effects of leptin would provide a novel approach for disrupting obesity-associated breast cancer. In the current study, we explored the role of piperine, a major plant alkaloid from Piper nigrum (black pepper), against leptin-induced breast cancer. Piperine treatment significantly inhibited leptin-induced breast cancer cell proliferation, colony formation, migration, and invasion. We found that piperine downregulated the expression of PPARα, a predicted target of miR-181c-3p. Mechanistically, piperine potentiates miR-181c-3p-mediated anticancer potential in leptin-induced breast cancer cells. Interestingly, the knockdown of PPARα reduced the proliferative potential of leptin-induced breast cancer cells. Further, oral administration of piperine inhibited breast tumor growth in diet-induced obese mice, accompanied by the upregulation of miR-181c-3p and downregulation of PPARα expression. Together, piperine represents a potential candidate for further development as an anticancer agent for treating obesity-associated breast cancer.
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Affiliation(s)
- Dheeran Rajarajan
- Department of Biochemistry, CSIR-Central Food Technological Research Institute, Mysuru 570020, Karnataka, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Jagadish Natesh
- Department of Biochemistry, CSIR-Central Food Technological Research Institute, Mysuru 570020, Karnataka, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Dhanamjai Penta
- Department of Biochemistry, CSIR-Central Food Technological Research Institute, Mysuru 570020, Karnataka, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Syed Musthapa Meeran
- Department of Biochemistry, CSIR-Central Food Technological Research Institute, Mysuru 570020, Karnataka, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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Richard V, Davey MG, Annuk H, Miller N, Dwyer RM, Lowery A, Kerin MJ. MicroRNAs in Molecular Classification and Pathogenesis of Breast Tumors. Cancers (Basel) 2021; 13:5332. [PMID: 34771496 PMCID: PMC8582384 DOI: 10.3390/cancers13215332] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/19/2021] [Accepted: 10/21/2021] [Indexed: 12/21/2022] Open
Abstract
The current clinical practice of breast tumor classification relies on the routine immunohistochemistry-based expression analysis of hormone receptors, which is inadequate in addressing breast tumor heterogeneity and drug resistance. MicroRNA expression profiling in tumor tissue and in the circulation is an efficient alternative to intrinsic molecular subtyping that enables precise molecular classification of breast tumor variants, the prediction of tumor progression, risk stratification and also identifies critical regulators of the tumor microenvironment. This review integrates data from protein, gene and miRNA expression studies to elaborate on a unique miRNA-based 10-subtype taxonomy, which we propose as the current gold standard to allow appropriate classification and separation of breast cancer into a targetable strategy for therapy.
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Affiliation(s)
- Vinitha Richard
- Discipline of Surgery, The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (H.A.); (N.M.); (R.M.D.); (A.L.)
| | | | | | | | | | | | - Michael J. Kerin
- Discipline of Surgery, The Lambe Institute for Translational Research, National University of Ireland, H91 YR71 Galway, Ireland; (M.G.D.); (H.A.); (N.M.); (R.M.D.); (A.L.)
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Ma X, Chen H, Yang M, Ke Z, Wang M, Huang T, Li L. Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution. Front Cell Dev Biol 2021; 9:722841. [PMID: 34621742 PMCID: PMC8490889 DOI: 10.3389/fcell.2021.722841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/10/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The aim of this paper was to identify an immunotherapy-sensitive subtype for estrogen receptor-positive breast cancer (ER+ BC) patients by exploring the relationship between cancer genetic programs and antitumor immunity via multidimensional genome-scale analyses. Methods: Multidimensional ER+ BC high-throughput data (raw count data) including gene expression profiles, copy number variation (CNV) data, single-nucleotide polymorphism mutation data, and relevant clinical information were downloaded from The Cancer Genome Atlas to explore an immune subtype sensitive to immunotherapy using the Consensus Cluster Plus algorithm based on multidimensional genome-scale analyses. One ArrayExpress dataset and eight Gene Expression Omnibus (GEO) datasets (GEO-meta dataset) as well as the Molecular Taxonomy of Breast Cancer International Consortium dataset were used as validation sets to confirm the findings regarding the immune profiles, mutational features, and survival outcomes of the three identified immune subtypes. Moreover, the development trajectory of ER+ BC patients from the single-cell resolution level was also explored. Results: Through comprehensive bioinformatics analysis, three immune subtypes of ER+ BC (C1, C2, and C3, designated the immune suppressive, activation, and neutral subtypes, respectively) were identified. C2 was associated with up-regulated immune cell signatures and immune checkpoint genes. Additionally, five tumor-related pathways (transforming growth factor, epithelial-mesenchymal transition, extracellular matrix, interferon-γ, and WNT signaling) tended to be more activated in C2 than in C1 and C3. Moreover, C2 was associated with a lower tumor mutation burden, a decreased neoantigen load, and fewer CNVs. Drug sensitivity analysis further showed that C2 may be more sensitive to immunosuppressive agents. Conclusion: C2 (the immune activation subtype) may be sensitive to immunotherapy, which provides new insights into effective treatment approaches for ER+ BC.
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Affiliation(s)
- Xianxiong Ma
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hengyu Chen
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ming Yang
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zunxiang Ke
- Department of Emergency Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengyi Wang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Huang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Li
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Sarkar JP, Saha I, Sarkar A, Maulik U. Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers. Comput Biol Med 2021; 131:104244. [PMID: 33550016 DOI: 10.1016/j.compbiomed.2021.104244] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 01/24/2021] [Accepted: 01/24/2021] [Indexed: 12/25/2022]
Abstract
Breast cancer is the second leading cancer type among females. In this regard, it is found that microRNAs play an important role by regulating the gene expressions at the post-transcriptional phase. However, identification of the most influencing miRNAs in breast cancer subtypes is a challenging task, while the recent advancement in Next Generation Sequencing techniques allows analyzing high throughput expression data of miRNAs. Thus, we have conducted this research with the help of NGS data of breast cancer in order to identify the most significant miRNA biomarkers. The selected miRNA biomarkers are highly associated with the multiple breast cancer subtypes. For this purpose, a two-phase technique, called Machine Learning Integrated Ensemble of Feature Selection Methods, followed by survival analysis, is proposed. In the first phase, we have selected the best among seven machine learning techniques based on classification accuracy using the entire set of features (in this case miRNAs). Subsequently, eight different feature selection methods are used separately in order to rank the features and validate each set of top features using the selected machine learning technique by considering a multi-class classification task of the breast cancer subtypes. In the second phase, based on the classification accuracy values, the top features from each feature selection method are considered to make an ensemble to provide further categorization of the miRNAs as 8*, 7* up to 1*. The 8* miRNAs provide the highest average classification accuracy of 86% after 10-fold cross-validation. Thereafter, 27 miRNAs are identified from the list that is confined within 8* to 4* miRNAs based on their importance in survival for breast cancer subtypes using Cox regression based survival analysis. Moreover, expression analysis, regulatory network analysis, protein-protein interaction analysis, KEGG pathway and gene ontology enrichment analysis are performed in order to validate biological significance of the proposed solution. Additionally, we have prepared a miRNA-protein-drug interaction network to identify possible drug for the selected miRNAs. Thus, our findings may be considered during a clinical trial for the treatment of breast cancer patients.
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Affiliation(s)
- Jnanendra Prasad Sarkar
- Larsen & Toubro Infotech Ltd., Pune, India; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Indrajit Saha
- Department of Computer Science and Engineering, National Institute of Technical Teachers' Training & Research, Kolkata, 700106, India.
| | - Anasua Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
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Circulating MicroRNAs as Prognostic and Therapeutic Biomarkers in Breast Cancer Molecular Subtypes. J Pers Med 2020; 10:jpm10030098. [PMID: 32842653 PMCID: PMC7563822 DOI: 10.3390/jpm10030098] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 08/17/2020] [Accepted: 08/20/2020] [Indexed: 12/24/2022] Open
Abstract
Breast cancer (BC) is a common and heterogeneous disease, of which six molecular subtypes, characterized by different biological features and clinical outcomes, were described. The identification of additional biomarkers able to further connote and distinguish the different BC subtypes is essential to improve the diagnostic, prognostic and therapeutic strategies in BC patients. MicroRNAs (miRNAs) are short non-coding RNA involved in several physiological and pathological processes, including cancer development and progression. In particular, circulating miRNAs, which can be found in an adequately stable structure in serum/plasma of cancer patients, are emerging as very promising non-invasive biomarkers. Several studies have analyzed the potential role of circulating miRNAs as prognostic and therapeutic markers in BC. In the present review we describe circulating miRNAs, identified as putative biomarker in BC, with special reference to different BC molecular subtypes.
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The Possible Influence of Mediterranean Diet on Extracellular Vesicle miRNA Expression in Breast Cancer Survivors. Cancers (Basel) 2020; 12:cancers12061355. [PMID: 32466456 PMCID: PMC7352167 DOI: 10.3390/cancers12061355] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/15/2020] [Accepted: 05/22/2020] [Indexed: 12/21/2022] Open
Abstract
The Mediterranean diet (MD) has been reported to have beneficial effects on breast cancer and cardiovascular diseases. Recently, microRNAs (miRNAs) have been suggested as biomarkers for the diagnosis and disease prognosis in cancer and cardiovascular diseases. We evaluated the influence of the MD on the plasma-derived extracellular vesicle miRNA signature of overweight breast cancer survivors. Sixteen participants instructed to adhere to the MD for eight weeks were included in this study. To curate differentially expressed miRNAs after MD intervention, we employed two methods: significance analysis of microarrays and DESeq2. The selected miRNAs were analyzed using ingenuity pathway analysis. After an eight-week intervention, body mass index, waist circumference, fasting glucose, fasting insulin, and homeostatic model assessment for insulin resistance were significantly improved. Expression levels of 798 miRNAs were comprehensively analyzed, and 42 extracellular vesicle miRNAs were significantly differentially regulated after the eight-week MD (36 were up and 6 were down-regulated). We also identified enriched pathways in genes regulated by differentially expressed 42 miRNAs, which include signaling associated with breast cancer, energy metabolism, glucose metabolism, and insulin. Our study indicates that extracellular vesicle miRNAs differentially expressed as a result of the MD might be involved in the mechanisms that relate to cardiometabolic risk factors in overweight breast cancer survivors.
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