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      Utilizing an In-silico Approach to Pinpoint Potential Biomarkers for Enhanced Early Detection of Colorectal Cancer

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          Abstract

          Objectives:

          Colorectal cancer (CRC) is a prevalent disease characterized by significant dysregulation of gene expression. Non-invasive tests that utilize microRNAs (miRNAs) have shown promise for early CRC detection. This study aims to determine the association between miRNAs and key genes in CRC.

          Methods:

          Two datasets (GSE106817 and GSE23878) were extracted from the NCBI Gene Expression Omnibus database. Penalized logistic regression (PLR) and artificial neural networks (ANN) were used to identify relevant miRNAs and evaluate the classification accuracy of the selected miRNAs. The findings were validated through bipartite miRNA-mRNA interactions.

          Results:

          Our analysis identified 3 miRNAs: miR-1228, miR-6765-5p, and miR-6787-5p, achieving a total accuracy of over 90%. Based on the results of the mRNA-miRNA interaction network, CDK1 and MAD2L1 were identified as target genes of miR-6787-5p.

          Conclusions:

          Our results suggest that the identified miRNAs and target genes could serve as non-invasive biomarkers for diagnosing colorectal cancer, pending laboratory confirmation.

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          Most cited references57

          • Record: found
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          miRWalk: An online resource for prediction of microRNA binding sites

          Carsten Sticht, Carolina de la Torre, Alisha Parveen … (2018)
          miRWalk is an open-source platform providing an intuitive interface that generates predicted and validated miRNA-binding sites of known genes of human, mouse, rat, dog and cow. The core of miRWalk is the miRNA target site prediction with the random-forest-based approach software TarPmiR searching the complete transcript sequence including the 5’-UTR, CDS and 3’-UTR. Moreover, it integrates results other databases with predicted and validated miRNA-target interactions. The focus is set on a modular design and extensibility as well as a fast update cycle. The database is available using Python, MySQL and HTML/Javascript Database URL: http://mirwalk.umm.uni-heidelberg.de.
          Article 0 comments Cited 756 times – based on 0 reviews     Review now
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            Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN

            Eileen Morgan, Melina Arnold, A Gini … (2023)
            Objective Colorectal cancer (CRC) is the third most common cancer worldwide. The geographical and temporal burden of this cancer provides insights into risk factor prevalence and progress in cancer control strategies. We examine the current and future burden of CRC in 185 countries in 2020 and 2040. Methods Data on CRC cases and deaths were extracted from the GLOBOCAN database for the year 2020. Age-standardised incidence and mortality rates were calculated by sex, country, world region and Human Development Index (HDI) for 185 countries. Age-specific rates were also estimated. The predicted number of cases and deaths in 2040 were calculated based on global demographic projections by HDI. Results Over 1.9 million new CRC cases and 930 000 deaths were estimated in 2020. Incidence rates were highest in Australia/ New Zealand and European regions (40.6 per 100 000, males) and lowest in several African regions and Southern Asia (4.4 per 100 000, females). Similar patterns were observed for mortality rates, with the highest observed in Eastern Europe (20.2 per 100 000, males) and the lowest in Southern Asia (2.5 per 100 000, females). The burden of CRC is projected to increase to 3.2 million new cases and 1.6 million deaths by 2040 with most cases predicted to occur in high or very high HDI countries. Conclusions CRC is a highly frequent cancer worldwide, and largely preventable through changes in modifiable risk factors, alongside the detection and removal of precancerous lesions. With increasing rates in transitioning countries and younger adults, there is a pressing need to better understand and act on findings to avert future cases and deaths from the disease.
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              Deep Learning in Medicine—Promise, Progress, and Challenges

              Fei Wang, Lawrence Peter Casalino, Dhruv Khullar (2018)
              Article 0 comments Cited 150 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Cancer Inform
                Journal ID (iso-abbrev): Cancer Inform
                Journal ID (publisher-id): CIX
                Journal ID (hwp): spcix
                Title: Cancer Informatics
                Publisher: SAGE Publications (Sage UK: London, England )
                ISSN (Electronic): 1176-9351
                Publication date (Electronic): 16 December 2024
                Publication date Collection: 2024
                Volume: 23
                Electronic Location Identifier: 11769351241307163
                Affiliations
                [1 ]Neurophysiology Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
                [2 ]Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
                [3 ]Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
                [4 ]Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
                [5 ]Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
                [6 ]School of Public Health, Health Academy, University of Alberta, Edmonton, AB, Canada
                Author notes
                [*]Leili Tapak, Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Pajouhesh Square, Hamadan 6516717673, Iran. Email: l.tapak@ 123456umsha.ac.ir
                [†]

                Massoud Saidijam passed away on July 20, 2020. His contributions to this study were invaluable.

                Author information
                https://orcid.org/0000-0002-4378-3143
                Article
                Publisher ID: 10.1177_11769351241307163
                DOI: 10.1177/11769351241307163
                PMC ID: 11648020
                PubMed ID: 39687502
                SO-VID: 74110dea-d6ea-4e27-a6ad-433dd3eceed7
                Copyright © © The Author(s) 2024
                License:

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Date received : 26 June 2024
                Date accepted : 28 November 2024
                Categories
                Subject: Original Research
                Custom metadata
                cover-date January-December 2024
                typesetter ts1

                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: colorectal neoplasms,microrna,smoothly clipped absolute deviation,least absolute shrinkage and selection operator,the minimax concave penalty,artificial neural networks
                Data availability:
                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: colorectal neoplasms, microrna, smoothly clipped absolute deviation, least absolute shrinkage and selection operator, the minimax concave penalty, artificial neural networks

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