Experimental validation of in silico analysis estimated the reverse effect of upregulated hsa‐miR‐106a‐5p and hsa‐miR‐223‐3p on SLC4A4 gene expression in Iranian patients with colorectal adenocarcinoma by RT‐qPCR

Background and Methods Colorectal cancer (CRC) is considered one of the most common malignancies worldwide. The diagnosis and prognosis of the patients are very poor. In this study, we used in‐silico analysis and experimental techniques to investigate novel co‐expression genes and their associated miRNA networks in CRC. For this purpose, we conducted a comprehensive transcriptome analysis using online bulk and single‐cell RNA‐seq datasets. We then validated the results on tissue samples from cancerous and adjacent normal tissues from CRC patients by RT‐qPCR. Results Using a weighted gene co‐expression network algorithm, we identified SLC4A4 as a significantly downregulated hub gene in the CRC. The single‐cell analysis indicated that the expression level of SLC4A4 in Paneth cells is higher than in other cell populations. Further computational analysis suggested hsa‐miR‐223‐3p and hsa‐miR‐106a‐5p as two specific hub‐miRNAs for the SLC4A4 gene. RT‐qPCR analysis showed a 2.60‐fold downregulation of SLC4A4. Moreover, hsa‐miR‐223‐3p and hsa‐miR‐106a‐5p showed an increased expression level of 5.58‐fold and 9.66‐fold in CRC samples, respectively. Based on the marginal model analysis, by increasing the expression of hsa‐miR‐106a‐5p, the average expression of the SLC4A4 gene significantly decreased by 103 units. Furthermore, ROC curves analysis indicated statistically significant for diagnostic ability of SLC4A4 (AUC: 0.94, Sensitivity: 95.5%, Specificity: 95.5%) and hsa‐miR‐106a‐5p (AUC: 0.72, Sensitivity: 72.7%, Specificity: 100%). Conclusion This study provides a framework of co‐expression gene modules and miRNAs of CRC, which identifies some important biomarkers for CRC pathogenicity and diagnosis. Further experimental evidence will be required to support this study and validate the precise molecular pathways. In this study, we identified biomarkers in colorectal cancer (CRC) in a comprehensive process by using the systems‐biology methods. At first, coding‐RNA and non‐coding‐RNA datasets were analyzed in the R software platform. Then the gene targets were identified with further analysis and appropriate filtering; finally, these gene targets were validated and confirmed in the tissues of CRC patients using the RT‐qPCR technique.