Analysis of key genes and pathways associated with colorectal cancer with microarray technology

Integrated bioinformatics approach to unwind key genes and pathways involved in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the fifth leading cause of death in India. Until now, the exact pathogenesis concerning CRC signaling pathways is largely unknown; however, the diseased condition is believed to deteriorate with lifestyle, aging, and inherited genetic disorders. Hence, the identification of hub genes and therapeutic targets is of great importance for disease monitoring.

OBJECTIVE

Identification of hub genes and targets for identification of candidate hub genes for CRC diagnosis and monitoring.

MATERIALS AND METHODS

The present study applied gene expression analysis by integrating two profile datasets (GSE20916 and GSE33113) from NCBI-GEO database to elucidate the potential key candidate genes and pathways in CRC. Differentially expressed genes (DEGs) between CRC (195 CRC tissues) and healthy control (46 normal mucosal tissue) were sorted using GEO2R tool. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using Cluster Profiler in Rv. 3.6.1. Moreover, protein-protein interactions (PPI), module detection, and hub gene identification were accomplished and visualized through the Search Tool for the Retrieval of Interacting Genes, Molecular Complex Detection (MCODE) plug-in of Cytoscape v3.8.0. Further hub genes were imported into ToppGene webserver for pathway analysis and prognostic expression analysis was conducted using Gene Expression Profiling Interactive Analysis webserver.

RESULTS

A total of 2221 DEGs, including 1286 up-regulated and 935down-regulated genes mainly enriched in signaling pathways of NOD-like receptor, FoxO, AMPK signalling and leishmaniasis. Three key modules were detected from PPI network using MCODE. Besides, top 20 high prioritized hub genes were selected. Further, prognostic expression analysis revealed ten of the hub genes, namely IL1B, CD44, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, MMP9, CREB1, STAT1, vascular endothelial growth factor (VEGFA), CDC5 L, Ataxia-telangiectasia mutated (ATM + and CDH1 to be differently expressed in normal and cancer patients.

CONCLUSION

The present study proposed five novel therapeutic targets, i.e., ATM, GAPDH, CREB1, VEGFA, and CDH1 genes that might provide new insights into molecular oncogenesis of CRC.

Integrated bioinformatics analysis for the identification of key genes and signaling pathways in thyroid carcinoma

Thyroid carcinoma (TC) is one of the most common types of endocrine neoplasm with poor prognosis due to its aggressive behavior. Biomarkers for early diagnosis and prevention of TC are in urgent demand. By using a bioinformatics analysis, the present study aimed to identify essential genes and pathways associated with TC. First, the GSE27155 and GSE50901 expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were obtained using the two microarray datasets and further subjected to integrated analysis. A gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed 45 common DEGs in the two datasets. GO and KEGG pathway analysis indicated that the biological functions of the DEGs included protein binding, cardiac muscle cell potential involved in contraction, aldehyde dehydrogenase activity, the TGF-β receptor signaling pathway and the canonical Wnt signaling pathway. A protein-protein interaction network was also constructed and visualized to display the nodes of the top 9 up- and 36 downregulated common DEGs. The integrated bioinformatics analysis indicated that potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) was the most significantly upregulated DEG. The transcriptional levels of KCNJ2 were confirmed to be elevated in TC tissues compared with those in normal tissues using reverse transcription-quantitative PCR analysis. Furthermore, the expression level of KCNJ2 was significantly associated with the 5-year survival rate of patients with TC, which was determined using the Kaplan-Meier method. In TC cell lines, KCNJ2 was also upregulated as compared with that in a normal control cell line. Finally, small interfering RNA was used to knock down the expression of KCNJ2, which was demonstrated to inhibit cell proliferation, migration and invasion, while increasing apoptosis in TC cells. In conclusion, in the present study, KCNJ2 was screened as an oncogene with a crucial role in TC development and progression and may represent a promising candidate biomarker and therapeutic target for TC.

Enrichment of Up-regulated and Down-regulated Gene Clusters Using Gene Ontology, miRNAs and lncRNAs in Colorectal Cancer

AIM AND OBJECTIVE

It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer.

METHODS

The human colorectal cancer data were obtained from the GEO databank. The downregulated and up-regulated genes were identified after scoring, weighing and merging of the gene data. The clusters with high-score edges were determined from gene networks. The miRNAs related to the gene clusters were identified and enriched. Furthermore, the long non-coding RNA (lncRNA) networks were predicted with a central core for miRNAs.

RESULTS

Based on cluster enrichment, genes related to peptide receptor activity (1.26E-08), LBD domain binding (3.71E-07), rRNA processing (2.61E-34), chemokine (4.58E-19), peptide receptor (1.16E-19) and ECM organization (3.82E-16) were found. Furthermore, the clusters related to the non-coding RNAs, including hsa-miR-27b-5p, hsa-miR-155-5p, hsa-miR-125b-5p, hsa-miR-21-5p, hsa-miR-30e-5p, hsa-miR-588, hsa-miR-29-3p, LINC01234, LINC01029, LINC00917, LINC00668 and CASC11 were found.

CONCLUSION

The comprehensive bioinformatics analyses provided the gene networks related to some non-coding RNAs that might help in understanding the molecular mechanisms in CRC.

Identification of Key Pathways and Genes in Anaplastic Thyroid Carcinoma via Integrated Bioinformatics Analysis

BACKGROUND To provide a better understanding of anaplastic thyroid carcinoma (ATC) at the molecular level, this study aimed to identify the genes and key pathways associated with ATC by using integrated bioinformatics analysis. MATERIAL AND METHODS Based on the microarray data GSE9115, GSE65144, and GSE53072 derived from the Gene Expression Omnibus, the differentially expressed genes (DEGs) between ATC samples and normal controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein-protein interaction (PPI) network was constructed and visualized, with which the hub gene nodes were screened out. Finally, modules analysis for the PPI network was performed to further investigate the potential relationships between DEGs and ATC. RESULTS A total of 537 common DEGs were screened out from all 3 datasets, among which 247 genes were upregulated and 275 genes were downregulated. GO analysis indicated that upregulated DEGs were mainly involved in cell division and mitotic nuclear division and the downregulated DEGs were significantly enriched in ventricular cardiac muscle cell action potential. KEGG pathway analysis showed that the upregulated DEGs were mainly enriched in cell cycle and ECM-receptor interaction and the downregulated DEGs were mainly enriched in thyroid hormone synthesis, insulin resistance, and pathways in cancer. The top 10 hub genes in the constructed PPI network were CDK1, CCNB1, TOP2A, AURKB, CCNA2, BUB1, AURKA, CDC20, MAD2L1, and BUB1B. The modules analysis showed that genes in the top 2 significant modules of PPI network were mainly associated with mitotic cell cycle and positive regulation of mitosis, respectively. CONCLUSIONS We identified a series of key genes along with the pathways that were most closely related with ATC initiation and progression. Our results provide a more detailed molecular mechanism for the development of ATC, shedding light on the potential biomarkers and therapeutic targets.

Analysis of potential genes and pathways associated with the colorectal normal mucosa-adenoma-carcinoma sequence

This study aimed to identify differentially expressed genes (DEGs) related to the colorectal normal mucosa-adenoma-carcinoma sequence using bioinformatics analysis. Raw data files were downloaded from Gene Expression Omnibus (GEO) and underwent quality assessment and preprocessing. DEGs were analyzed by the limma package in R software (R version 3.3.2). Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the DAVID online tool. In a comparison of colorectal adenoma (n = 20) and colorectal cancer (CRC) stage I (n = 31), II (n = 38), III (n = 45), and IV (n = 62) with normal colorectal mucosa (n = 19), we identified 336 common DEGs. Among them, seven DEGs were associated with patient prognosis. Five (HEPACAM2, ITLN1, LGALS2, MUC12, and NXPE1) of the seven genes presented a sequentially descending trend in expression with tumor progression. In contrast, TIMP1 showed a sequentially ascending trend. GCG was constantly downregulated compared with the gene expression level in normal mucosa. The significantly enriched GO terms included extracellular region, extracellular space, protein binding, and carbohydrate binding. The KEGG categories included HIF-1 signaling pathway, insulin secretion, and glucagon signaling pathway. We discovered seven DEGs in the normal colorectal mucosa-adenoma-carcinoma sequence that was associated with CRC patient prognosis. Monitoring changes in these gene expression levels may be a strategy to assess disease progression, evaluate treatment efficacy, and predict prognosis.

Gene expression profile analysis of colorectal cancer to investigate potential mechanisms using bioinformatics

This study aimed to explore the underlying molecular mechanisms of colorectal cancer (CRC) using bioinformatics analysis. Using GSE4107 datasets downloaded from the Gene Expression Omnibus, the differentially expressed genes (DEGs) were screened by comparing the RNA expression from the colonic mucosa between 12 CRC patients and ten healthy controls using a paired t-test. The Gene Ontology (GO) functional and pathway enrichment analyses of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) software followed by the construction of a protein–protein interaction (PPI) network. In addition, hub gene identification and GO functional and pathway enrichment analyses of the modules were performed. A total of 612 up- and 639 downregulated genes were identified. The upregulated DEGs were mainly involved in the regulation of cell growth, migration, and the MAPK signaling pathway. The downregulated DEGs were significantly associated with oxidative phosphorylation, Alzheimer’s disease, and Parkinson’s disease. Moreover, FOS, FN1, PPP1CC, and CYP2B6 were selected as hub genes in the PPI networks. Two modules (up-A and up-B) in the upregulated PPI network and three modules (d-A, d-B, and d-C) in the downregulated PPI were identified with the threshold of Molecular Complex Detection (MCODE) Molecular Complex Detection (MCODE) score ≥4 and nodes ≥6. The genes in module up-A were significantly enriched in neuroactive ligand–receptor interactions and the calcium signaling pathway. The genes in module d-A were enriched in four pathways, including oxidative phosphorylation and Parkinson’s disease. DEGs, such as FOS, FN1, PPP1CC, and CYP2B6, may be used as potential targets for CRC diagnosis and treatment.