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Akbar S, Mashreghi S, Kalani MR, Valanik A, Ahmadi F, Aalikhani M, Bazi Z. Blood miRNAs miR-549a, miR-552, and miR-592 serve as potential disease-specific panels to diagnose colorectal cancer. Heliyon 2024; 10:e28492. [PMID: 38571665 PMCID: PMC10988015 DOI: 10.1016/j.heliyon.2024.e28492] [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: 10/07/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
Introduction miRNAs originating from colorectal cancer (CRC) tissue receive significant focus in the early diagnosis of CRC due to their stability in body fluids. However, if these miRNAs originated from alternative organs, their prognostic value will diminish. Thus, in this study, we aim to identify disease-specific miRNAs for colorectal cancer (CRC) by employing bioinformatics and experimental methodologies. Method To identify CRC-specific miRNAs, we retrieved miRNA profiles of CRC and normal tissues from the Cancer Genome Atlas (TCGA) database. Subsequently, computational strategies were utilized to select potential candidate miRNAs. Following this, the expression levels of the potent miRNAs were assessed through RT-qPCR in both CRC tissue and serum samples from patients (N = 46), as well as healthy individuals (N = 46). Additionally, the associations between clinicopathological characteristics, survival outcomes, and diagnostic accuracy were evaluated. Results A total of 8893 RNA-seq expression data were acquired from TCGA, comprising 8250 data from 19 distinct cancer types and 643 corresponding healthy samples. Based on the computational methodology, miR-549a, miR-552, and miR-592 were identified as the principal expressed miRNAs in colorectal cancer (CRC). Within these miRNAs, miR-552 displayed a substantial association with tumors at the N and T stages. miR-549a and miR-592 were observed to be linked exclusively to the invasion of tumor depth and tumor stage (TNM), respectively. The receiver operating characteristic (ROC) analysis conducted on the miRNA expression in serum samples revealed that all miRNAs exhibited an area under the ROC curve (AUC) of up to 0.86, thereby indicating their high diagnostic accuracy. Conclusion Considering the strong associations of these three identified miRNAs with CRC, they can collectively serve as a panel for specific discrimination of CRC from other types of cancer within the body. Although this study focused solely on CRC, this approach can potentially be applied to other cancer types as well.
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Affiliation(s)
- Soroush Akbar
- Metabolic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Samaneh Mashreghi
- Department of Medical Biotechnology, Faculty of Advanced Medical Technologies, Golestan University of Medical Sciences, Gorgan, Iran
| | | | - Akram Valanik
- Department of Medical Biotechnology, Faculty of Advanced Medical Technologies, Golestan University of Medical Sciences, Gorgan, Iran
| | - Farzaneh Ahmadi
- Department of Biostatistics and Epidemiology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Mahdi Aalikhani
- Department of Medical Biotechnology, School of Allied Medical Sciences, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Zahra Bazi
- Department of Medical Biotechnology, Faculty of Advanced Medical Technologies, Golestan University of Medical Sciences, Gorgan, Iran
- Cancer Research Center, Golestan University of Medical Sciences, Gorgan, Iran
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Pan J, Ma B, Hou X, Li C, Xiong T, Gong Y, Song F. The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12353-12370. [PMID: 36654001 DOI: 10.3934/mbe.2022576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BACKGROUND Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer. METHODS The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. RESULTS The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients. CONCLUSIONS Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.
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Affiliation(s)
- Jianqiao Pan
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Baoshan Ma
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Xiaoyu Hou
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Chongyang Li
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Tong Xiong
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Yi Gong
- School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center of Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
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Tong D, Tian Y, Zhou T, Ye Q, Li J, Ding K, Li J. Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data. BMC Med Inform Decis Mak 2020; 20:22. [PMID: 32033604 PMCID: PMC7006213 DOI: 10.1186/s12911-020-1043-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 01/31/2020] [Indexed: 12/16/2022] Open
Abstract
Background Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data. Methods In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell’s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno’s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates. Results Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell’s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno’s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively. Conclusion In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.
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Affiliation(s)
- Danyang Tong
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Tianshu Zhou
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Qiancheng Ye
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China
| | - Jun Li
- Department of Surgical Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou, 31009, Zhejiang Province, China
| | - Kefeng Ding
- Department of Surgical Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou, 31009, Zhejiang Province, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou, 310027, Zhejiang Province, China. .,Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
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Tantyo NA, Karyadi AS, Rasman SZ, Salim MRG, Devina A, Sumarpo A. The prognostic value of S100A10 expression in cancer. Oncol Lett 2018; 17:1417-1424. [PMID: 30675195 PMCID: PMC6341771 DOI: 10.3892/ol.2018.9751] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 11/15/2018] [Indexed: 12/30/2022] Open
Abstract
S100A10, a member of the S100 protein family, commonly forms a heterotetrameric complex with Annexin A2. This is essential for the generation of cellular plasmin from plasminogen, which leads to a cascade of molecular events crucial for tumor progression. S100A10 upregulation has been reported in a number of cancers, suggesting that it may have potential as a prognostic biomarker, as well as predicting sensitivity to anticancer drugs. This review evaluates the direct and indirect relationships between S100A10 and cancer progression by investigating its role in cancer. Research papers published on PubMed and Google Scholar between 2007–2017 were collated and reviewed. We concluded that S100A10 affects the development of the hallmarks of cancer as explained by Hanahan and Weinberg in 2011, most notably by activating the invasion and metastasis of cancer cells. However, further studies are required to explore the underlying biological mechanisms of S100A10.
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Affiliation(s)
- Normastuti Adhini Tantyo
- Department of Biomedicine, Indonesia International Institute for Life Sciences, Jakarta Timur 13210, Indonesia
| | - Azrina Saraswati Karyadi
- Department of Biomedicine, Indonesia International Institute for Life Sciences, Jakarta Timur 13210, Indonesia
| | - Siti Zulimas Rasman
- Department of Biomedicine, Indonesia International Institute for Life Sciences, Jakarta Timur 13210, Indonesia
| | | | - Astrella Devina
- Department of Biomedicine, Indonesia International Institute for Life Sciences, Jakarta Timur 13210, Indonesia
| | - Anton Sumarpo
- Department of Biochemistry, Faculty of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta Utara 14440, Indonesia
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Moon M, Nakai K. Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers. J Bioinform Comput Biol 2018; 16:1850006. [DOI: 10.1142/s0219720018500063] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets. Gene expression and DNA methylation datasets are normalized by Box–Cox transformation and integrated into a one-dimensional dataset that retains the major characteristics of the original datasets by unsupervised feature extraction methods, and differentially expressed genes are selected from the integrated dataset. Use of the integrated dataset demonstrated improved performance as compared with conventional approaches that utilize gene expression or DNA methylation datasets alone. Validation based on the literature showed that a considerable number of top-ranked genes from the integrated dataset have known relationships with cancer, implying that novel candidate biomarkers can also be acquired from the proposed analysis method. Furthermore, we expect that the proposed method can be expanded for applications involving various types of multi-omics datasets.
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Affiliation(s)
- Myungjin Moon
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-Shi, Chiba-Ken 277-8562, Japan
| | - Kenta Nakai
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-Ku, Tokyo 108-8639, Japan
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Abstract
Serum and plasma from which serum is derived represent a substantial challenge for proteomics due to their complexity. A landmark plasma proteome study was initiated a decade ago by the Human Proteome Organization (HUPO) that had as an objective to examine the capabilities of existing technologies. Given the advances in proteomics and the continued interest in the plasma proteome, it would timely reassess the depth and breadth of analysis of plasma that can be achieved with current methodology and instrumentation. A collaborative project to define the plasma proteome and its variation, with a plan to build a plasma proteome database would be timely.
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Affiliation(s)
- Samir Hanash
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
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Kim M, Kim HJ, Choi BY, Kim JH, Song KS, Noh SM, Kim JC, Han DS, Kim SY, Kim YS. Identification of potential serum biomarkers for gastric cancer by a novel computational method, multiple normal tissues corrected differential analysis. Clin Chim Acta 2012; 413:428-33. [DOI: 10.1016/j.cca.2011.10.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2011] [Revised: 10/04/2011] [Accepted: 10/19/2011] [Indexed: 01/05/2023]
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