Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Sun, Guangwei; Li, Yalun; Peng, Yangjie; Lu, Dapeng; Zhang, Fuqiang; Cui, Xueyang; Zhang, Qingyue; Li, Zhuang

doi:10.1002/jcp.28163

Cited by 55 publications

(50 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(Chowdhury et al, ) In the past decade, with the rapid development of microarray and RNA‐sequencing technology, more and more biomarkers of tumor initiation, progression, and prognosis have been identified using bioinformatics analysis. (Sun et al, ; Yan et al, ) In this study, we integrated three microarray datasets from gene expression omnibus (GEO) database (GSE781, GSE6344, and GSE100666) to identify the differentially expressed genes (DEGs) between KIRC and adjacent normal tissues, aiming to explore and determine the promising novel biomarkers associated with pathogenesis and prognosis of KIRC. Meanwhile, we revealed some candidate small molecule drugs that could reverse the gene expression of KIRC based on the CMap database.…”

Section: Introductionmentioning

confidence: 99%

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data

Zhang

Wang

et al. 2019

Molec Gen & Gen Med

View full text Add to dashboard Cite

Background Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC. Methods Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC. Results A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity ( ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA ) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression. Conclusions This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.

show abstract

Section: Introductionmentioning

confidence: 99%

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data

Zhang

Wang

et al. 2019

Molec Gen & Gen Med

View full text Add to dashboard Cite

show abstract

“…Sun found molecules related to the pathogenesis of colorectal cancer by screening from public databases. Further analysis showed that differentially expressed genes such as PPBP, CCL28, and CXCL12 are likely to be involved in the development of colorectal cancer and may be potential diagnostic and therapeutic targets [44]. We found 10 genes that were differentially expressed in patients with PCRC by bioinformatics analysis.…”

Section: Discussionmentioning

confidence: 84%

CLCA4 and MS4A12 as the significant gene biomarkers of primary colorectal cancer

et al. 2020

View full text Add to dashboard Cite

Background: Primary colorectal cancer (PCRC) is a common digestive tract cancer in the elderly. However, the treatment effect of PCRC is still limited, and the long-term survival rate is low. Therefore, further exploring the pathogenesis of PCRC, and searching for specific molecular targets for diagnosis are development trends of precise medical treatment, which have important clinical significance. Methods: The public data was downloaded from Gene Expression Omnibus (GEO) database. Verification for repeatability of intra-group data was performed by Pearson’s correlation test and principal component analysis. Differentially expressed genes (DEGs) between normal and PCRC were identified, and the Protein-protein interaction (PPI) network was constructed. Significant module and hub genes were found in the PPI network. A total of 192 PCRC patients were recruited between 2010 and 2019 from the Fourth Hospital of Hebei Medical University. RT-PCR was used to measure the relative expression of CLCA4 and MS4A12. Furthermore, the study explored the effect of expression of CLCA4 and MS4A12 for overall survival. Results: A total of 53 DEGs were identified between PCRC and normal colorectal tissues. Ten hub genes concerned to PCRC were screened, namely CLCA4, GUCA2A, GCG, SST, MS4A12, PLP1, CHGA, PYY, VIP, and GUCA2B. The PCRC patients with low expression of CLCA4 and MS4A12 has a worse overall survival than high expression of CLCA4 and MS4A12 (P<0.05). Conclusion: The research of DEGs in PCRC (53 DEGs, 10 hub genes, especially CLCA4 and MS4A12) and related signaling pathways is conducive to the differential analysis of the molecular mechanism of PCRC.

show abstract

“…C-X-C motif chemokine ligand 3 (CXCL3) is a member of the CXC chemokine family, and its expression is associated with various cancers, such as liver cancer [19], gastric cancer [20], breast cancer [21], prostate cancer [22], and non-small cell cancer [23]. Meanwhile, some scholars have explored CXCL3 in the CRC, and their ndings consistently reveal that the expression of CXCL3 in cancer tissues is signi cantly higher than that in normal tissues [24,25].…”

Section: Discussionmentioning

confidence: 99%

Identification of prognostic-associated genes in colorectal cancer based on genome-wide expression profiles

Hou¹,

Zhang²,

Chen³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Colorectal cancer (CRC), a severe disease in the world. The dysregulation of genes plays a significant role in cancer. Method: In the present study, we first identified differentially expressed genes (DEGs) of CRC in the GSE71187 data set from the Gene Expression Omnibus (GEO) database. And through GO terminology and Gene and Genome (KEGG) pathway, the up-regulated DEG and down-regulated genes were enriched. Then, we selected hub genes and studied their related prognostic value through protein-protein interaction (PPI) network and integrated bioinformatics analysis. Finally, 7 genes with prognostic value were obtained by survival analysis and multivariate Cox proportional hazards regression models, as well as verified in the Oncomine and UALCAN database.Result: A total of 3,588 DEGs were identified with 1,474 upregulated and 3,114 downregulated. Then, GO terms and Genes and Genomes (KEGG) pathway analysis revealed that upregulated DEGs and down-regulated genes were mainly involved in important GO terms and KEGG pathway, for example, defense response to other organism, leukocyte differentiation and epidermis developmen. In the PP network analysis, 6 hub modules and 86 module genes were identified. In the end, 7 genes with prognostic value were obtained through survival and multi-factor analysis. They have significant differences in varying degrees at the level of DNA, mRNA and protein.Conclusion: The results of the study may provide novel insight into the genes and associated pathways involved in CRC, and aid the identifcation of novel therapeutic targets and prognostic marker of CRC.

show abstract

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Cited by 55 publications

References 48 publications

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high‐throughput data

CLCA4 and MS4A12 as the significant gene biomarkers of primary colorectal cancer

Identification of prognostic-associated genes in colorectal cancer based on genome-wide expression profiles

Contact Info

Product

Resources

About