GI, Liver & Pancreas Pathology
Mohammad Shafiei; Mahdi Alemrajabi; Ali Najafi; Amirhomayoon Keihan; Masoudreza Sohrabi
Abstract
Background and Objective: Colorectal Cancer (CRC) is the third most common cancer after prostate (breast in women) and lung cancer; it is also the third cause of cancer deaths reported in both men and women in 2020. Currently, the most commonly used diagnostic tools for CRC are colonoscopy, serological ...
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Background and Objective: Colorectal Cancer (CRC) is the third most common cancer after prostate (breast in women) and lung cancer; it is also the third cause of cancer deaths reported in both men and women in 2020. Currently, the most commonly used diagnostic tools for CRC are colonoscopy, serological methods, and other imaging techniques. Despite the benefits and abilities of these methods, each of them has disadvantages that reduce its functionality and acceptance. The aim of this study was identifying specific and non-invasive genetic biomarkers to diagnose colorectal cancer. Methods & Material: In this study, changes in the expression of HLTF and SEPT9 genes were evaluated by Real Time PCR in blood and tissue samples of CRC patients. A total of 100 samples (50 Blood and 50 Tissue samples) were evaluated with a definite diagnosis of CRC in Firoozgar Hspital, Tehran, Iran, in 2018. The QPCR method was used to compare the expression of candidate genes between the patients group and control group in both samples. Sensitivity and specificity of the test were examined using ROC curve analysis. Results: The results showed a significant down-regulation in the expression of both selected genes in tissue and peripheral blood in the various stages of the CRC. The sensitivity and specifity of both genes was about 80%. Conclusion: The findings showed that the two candidate genes can be suggested as specific biomarkers for diagnosis of CRC using the peripheral blood as a non-invasive method. For a definite conclusion, more research is needed.
Biology & Genetic
Malihe Ram; Ali Najafi; Mohammad Taghi Shakeri
Abstract
Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used ...
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Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and small-n. Therefore, RF can be used to select and rank the genes for the diagnosis and effective treatment of cancer. Methods: The microarray gene expression data of colon, leukemia, and prostate cancers were collected from public databases. Primary preprocessing was done on them using limma package, and then, the RF classification method was implemented on datasets separately in R software. Finally, the selected genes in each of the cancers were evaluated and compared with those of previous experimental studies and their functionalities were assessed in molecular cancer processes. Result: The RF method extracted very small sets of genes while it retained its predictive performance. About colon cancer data set DIEXF, GUCA2A, CA7, and IGHA1 key genes with the accuracy of 87.39 and precision of 85.45 were selected. The SNCA, USP20, and SNRPA1 genes were selected for prostate cancer with the accuracy of 73.33 and precision of 66.67. Also, key genes of leukemia data set were BAG4, ANKHD1-EIF4EBP3, PLXNC1, and PCDH9 genes, and the accuracy and precision were 100 and 95.24, respectively. Conclusion: The current study results showed most of the selected genes involved in the processes and cancerous pathways were previously reported and had an important role in shifting from normal cell to abnormal.