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Díaz-Campos MÁ, Vasquez-Arriaga J, Ochoa S, Hernández-Lemus E. Functional impact of multi-omic interactions in lung cancer. Front Genet 2024; 15:1282241. [PMID: 38389572 PMCID: PMC10881857 DOI: 10.3389/fgene.2024.1282241] [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: 08/23/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.
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Affiliation(s)
| | - Jorge Vasquez-Arriaga
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Soledad Ochoa
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
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Yuan T, Edelmann D, Kather JN, Fan Z, Tagscherer KE, Roth W, Bewerunge-Hudler M, Brobeil A, Kloor M, Bläker H, Burwinkel B, Brenner H, Hoffmeister M. CpG-biomarkers in tumor tissue and prediction models for the survival of colorectal cancer: A systematic review and external validation study. Crit Rev Oncol Hematol 2024; 193:104199. [PMID: 37952858 DOI: 10.1016/j.critrevonc.2023.104199] [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: 05/24/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023] Open
Abstract
The research aimed to identify previously published CpG-methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and validate them in a large external cohort. A systematic search was conducted, analyzing 298 unique CpGs and 12 CpG-based prognostic models from 28 studies. After adjustment for clinical variables, 48 CpGs and five prognostic models were confirmed to be associated with survival. However, the discrimination ability of the models was insufficient, with area under the receiver operating characteristic curves ranging from 0.53 to 0.62. Calibration accuracy was mostly poor, and no significant added prognostic value beyond traditional clinical variables was observed. All prognostic models were rated at high risk of bias. While a fraction of CpGs showed potential clinical utility and generalizability, the CpG-based prognostic models performed poorly and lacked clinical relevance.
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Affiliation(s)
- Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Dominic Edelmann
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Ziwen Fan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Katrin E Tagscherer
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany; Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Wilfried Roth
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany; Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | | | - Alexander Brobeil
- Institute of Pathology, University of Heidelberg, Heidelberg, Germany
| | - Matthias Kloor
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Hendrik Bläker
- Institute of Pathology, University of Leipzig Medical Center, Leipzig, Germany
| | - Barbara Burwinkel
- Division of Molecular Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Gynecology and Obstetrics, Molecular Biology of Breast Cancer, University of Heidelberg, Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Li B, Ge N, Pan Z, Hou C, Xie K, Wang D, Liu J, Wan J, Deng F, Li M, Luo S. KCNJ14 knockdown significantly inhibited the proliferation and migration of colorectal cells. BMC Med Genomics 2022; 15:194. [PMID: 36100894 PMCID: PMC9472386 DOI: 10.1186/s12920-022-01351-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 07/25/2022] [Indexed: 12/24/2022] Open
Abstract
Abstract
Background
This study attempted to verify the potential of KCNJ14 as a biomarker in colorectal cancer (CRC).
Methods
Data on transcriptomics and DNA methylation and the clinical information of CRC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Biological information analysis methods were conducted to determine the role of KCNJ14 in the prognosis, diagnosis, immune cell infiltration, and regulation mechanism of CRC patients. The effect of KCNJ14 on the proliferation and migration of HCT116 and SW480 CRC cell lines was verified by in vitro experiments (MTT, colony-forming, wound healing, and transwell assays). Western blotting was performed to detect the effect of KCNJ14 on the levels of mTOR signalling pathway-related proteins.
Results
KCNJ14 expression was remarkably increased in CRC tissues and cell lines, which reduced the overall survival time of patients. KCNJ14 mRNA was negatively regulated by its methylation site cg17660703, which can also endanger the prognosis of patients with CRC. Functional enrichment analysis suggested that KCNJ14 is involved in the mTOR, NOD-like receptor, and VEGF signalling pathways. KCNJ14 expression was positively correlated with the number of CD4 + T cells and negatively correlated with that of CD8 + T cells in the immune microenvironment. KCNJ14 knockdown significantly reduced not only the proliferation and migration of CRC cell lines but also the levels of mTOR signalling pathway-related proteins.
Conclusions
This study not only increases the molecular understanding of KCNJ14 but also provides a potentially valuable biological target for the treatment of colorectal cancer.
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Alorda-Clara M, Torrens-Mas M, Morla-Barcelo PM, Martinez-Bernabe T, Sastre-Serra J, Roca P, Pons DG, Oliver J, Reyes J. Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers. Cancers (Basel) 2022; 14:817. [PMID: 35159084 PMCID: PMC8834235 DOI: 10.3390/cancers14030817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/31/2022] [Accepted: 02/04/2022] [Indexed: 12/14/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.
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Affiliation(s)
- Marina Alorda-Clara
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Margalida Torrens-Mas
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Translational Research in Aging and Longevity (TRIAL) Group, Instituto de Investigación Sanitaria Illes Balears (IdISBa), E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Pere Miquel Morla-Barcelo
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
| | - Toni Martinez-Bernabe
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Jorge Sastre-Serra
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CB06/03) Instituto Salud Carlos III, E-28029 Madrid, Madrid, Spain
| | - Pilar Roca
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CB06/03) Instituto Salud Carlos III, E-28029 Madrid, Madrid, Spain
| | - Daniel Gabriel Pons
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
| | - Jordi Oliver
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Ciber Fisiopatología Obesidad y Nutrición (CB06/03) Instituto Salud Carlos III, E-28029 Madrid, Madrid, Spain
| | - Jose Reyes
- Grupo Multidisciplinar de Oncología Traslacional, Institut Universitari d’Investigació en Ciències de la Salut (IUNICS), Universitat de les Illes Balears, E-07122 Palma de Mallorca, Illes Balears, Spain; (M.A.-C.); (M.T.-M.); (P.M.M.-B.); (T.M.-B.); (J.S.-S.); (P.R.); (D.G.P.)
- Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, Edificio S, E-07120 Palma de Mallorca, Illes Balears, Spain
- Servicio Aparato Digestivo, Hospital Comarcal de Inca, E-07300 Inca, Illes Balears, Spain
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Research for Expression and Prognostic Value of GABRD in Colon Cancer and Coexpressed Gene Network Construction Based on Data Mining. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5544182. [PMID: 34194536 PMCID: PMC8203377 DOI: 10.1155/2021/5544182] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/21/2021] [Accepted: 05/10/2021] [Indexed: 02/07/2023]
Abstract
Colon cancer is one of the top five cancers with the highest incidence rate in the world. In order to better understand the pathogenesis and progression of colon cancer, it is still necessary to investigate the abnormally expressed genes in cancer tissue. In this study, the Oncomine database was used for expression analysis, and it was found that the expression level of gamma-aminobutyric acid type A receptor subunit delta (GABRD) gene was upregulated in colon cancer tissue compared with that in normal tissue. UALCAN was used to analyze the expression of GABRD in different groups of age, gender, cancer stage, N stage, and histological subtype. Then, it was also found that the expression of GABRD in each subgroup of colon cancer tissue was all high compared with that in normal tissue. LinkedOmics was used to screen out the differentially expressed genes related to GABRD expression in colon cancer. GO annotation and KEGG pathway enrichment analyses found that the correlated genes may be related to breast cancer, human papillomavirus infection, Notch signaling pathway, and other pathways. Thereafter, GSEA was performed to obtain GABRD-related kinases, miRNAs, and transcription factors, and gene interaction networks were constructed. It was found that GABRD may be involved in cell cycle regulation. Finally, websites like GEPIA were used to detect the predictive ability of GABRD on the prognosis of patients with colon cancer. Kaplan-Meier analysis suggested that the upregulation of GABRD expression was related to the poor prognosis of patients with colon cancer. Overall, in this study, the potential role and prognostic ability of GABRD in colon cancer were explored through data mining, which can be a clue for further research on GABRD.
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Yang H, Jin W, Liu H, Wang X, Wu J, Gan D, Cui C, Han Y, Han C, Wang Z. A novel prognostic model based on multi-omics features predicts the prognosis of colon cancer patients. Mol Genet Genomic Med 2020; 8:e1255. [PMID: 32396280 PMCID: PMC7336766 DOI: 10.1002/mgg3.1255] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 03/20/2020] [Accepted: 03/24/2020] [Indexed: 12/24/2022] Open
Abstract
Background As a common malignant tumor in the colon, colon cancer (CC) has high incidence and recurrence rates. This study is designed to build a prognostic model for CC. Methods The gene expression dataset, microRNA‐seq dataset, copy number variation (CNV) dataset, DNA methylation dataset, and transcription factor (TF) dataset of CC were downloaded from UCSC Xena database. Using limma package, the differentially methylated genes (DMGs), and differentially expressed genes (DEGs) and miRNAs (DEMs) were identified. Based on random forest method, prognostic model for each omics dataset were constructed. After the omics features related to prognosis were selected using logrank test, the prognostic model based on multi‐omics features was built. Finally, the clinical phenotypes correlated with prognosis were screened using Kaplan–Meier survival analysis, and the nomogram model was established. Results There were 1625 DEGs, 268 DEMs, and 386 DMGs between the tumor and normal samples. A total of 105, 29, 159, five, and six genes/sites significantly correlated with prognosis were identified in the gene expression dataset (GABRD), miRNA‐seq dataset (miR‐1271), CNV dataset (RN7SKP247), DNA methylation dataset (cg09170112 methylation site [located in SFSWAP]), and TF dataset (SIX5), respectively. The prognostic model based on multi‐omics features was more effective than those based on single omics dataset. The number of lymph nodes, pathologic_M stage, and pathologic_T stage were the clinical phenotypes correlated with prognosis, based on which the nomogram model was constructed. Conclusion The prognostic model based on multi‐omics features and the nomogram model might be valuable for the prognostic prediction of CC.
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Affiliation(s)
- Haojie Yang
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei Jin
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hua Liu
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaoxue Wang
- Department of Coloproctology, The Sixth Affiliated Hospital of Sun Yat-sen University (Gastrointestinal & Anal Hospital of Sun Yat-sen University), Guangzhou, China
| | - Jiong Wu
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dan Gan
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Can Cui
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yilin Han
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Changpeng Han
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhenyi Wang
- Department of Coloproctology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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