| Title: |
Deciphering gene expression signatures in liver metastasized colorectal cancer in stage IV colorectal cancer patients |
| Authors: |
Mahmood Rasool, Sajjad Karim, Absarul Haque, Mohammed Alharthi, Adeel G Chaudhary, Peter Natesan Pushparaj |
| Source: |
Journal of King Saud University: Science, Vol 36, Iss 9, Pp 103415- (2024) |
| Publisher Information: |
Elsevier, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Science (General) |
| Subject Terms: |
Colorectal cancer liver metastasis, Gene expression signature, Gene set enrichment analysis, Differentially expressed genes, Biomarkers, Science (General), Q1-390 |
| More Details: |
Background: Colorectal cancer (CRC) liver metastasis (CRLM) is a clinical challenge, and optimizing treatment strategies is crucial for improving patient outcomes. This study aimed to identify gene expression signatures associated with CRLM for early diagnosis and improved treatment outcomes. Methods: We obtained RNA-seq data of 34 samples (17 colorectal tumor samples from metastasized liver and 17 samples from normal surrounding colonic epithelia) from the Gene Expression Omnibus (GEO) with accession number GSE50760 and analysed them using next-generation knowledge discovery (NGKD) tools such as GEO2R and web based gene set enrichment analysis (WebGestalt). Results: A total of 18808 genes were identified in the initial analysis which were further reduced to 2490 differentially expressed genes (DEGs) after applying different parameters using GEO2R tools. Furthermore, in the gene set enrichment analysis (GSEA), we analysed the biological processes, cellular components, and molecular functions. We analysed four pathways: KEGG, panther, reactome, and wikipathway cancer. In each analysis, we ascertained the most important top expressed and downregulated genes. Conclusions: We identified various gene sets that could be used as important prognostic and therapeutic markers, particularly in patients with advanced CRLM. Future studies in larger cohorts may further our research findings to establish the best prognostic biomarkers for early diagnosis and overall survival. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1018-3647 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1018364724003276; https://doaj.org/toc/1018-3647 |
| DOI: |
10.1016/j.jksus.2024.103415 |
| Access URL: |
https://doaj.org/article/22de55fa872c4767a0178760a12c01b2 |
| Accession Number: |
edsdoj.22de55fa872c4767a0178760a12c01b2 |
| Database: |
Directory of Open Access Journals |
