Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

by Selleckchem | Aug 02 2023

Background: Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

Methods: To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

Results: We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

Conclusion: The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Comments:

The study aimed to identify molecular signatures involved in colorectal cancer (CRC) and explore potential receptor proteins and drug agents for CRC treatment. The researchers employed integrated statistical and bioinformatics approaches to analyze microarray and RNA-Seq datasets.

Four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA-Seq dataset (GSE50760) were obtained from the Gene Expression Omnibus database. The datasets were subjected to analysis using the LIMMA statistical package to identify common differentially expressed genes (cDEGs). These cDEGs were further analyzed in protein-protein interaction networks using topological measures to identify key genes (KGs) involved in CRC.

The researchers performed in-silico validation of the identified KGs using various web tools and independent databases. Additionally, they investigated the regulatory factors influencing the KGs at the transcriptional and post-transcriptional levels. Interaction network analysis was conducted to identify transcription factors (TFs) and microRNAs associated with the KGs.

Based on their findings, the researchers proposed a set of KGs for CRC, including CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12, and CLDN1. These KGs were found to be significantly associated with CRC progression based on various bioinformatic analyses using independent databases.

Furthermore, the study identified four TFs (FOXC1, YY1, GATA2, and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as key regulators of the KGs.

Finally, the researchers proposed 15 molecular signatures, including the 11 KGs and 4 key TFs, which guided the selection of nine small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) as potential therapeutic agents for CRC treatment. These candidate drugs were cross-validated against other published drugs and showed promise for CRC therapy.

In conclusion, this study identified potential diagnostic, prognostic, and therapeutic signatures for CRC by exploring molecular signatures, regulatory factors, and candidate drug molecules. The findings provide valuable insights for further research and development in the field of CRC treatment.