The identification of a common different gene expression signature in patients with colorectal cancer

Zhao, Zhong Wei; Fan, Xiao; Li, Yang; Song, Jing; Fang, Shi Ji; Tu, Jie; Chen, Min Jiang; Zheng, Li; Wu, Fa Zong; Zhang, Deng Ke; Ying, Xi Hui; Ji, Jian Song

doi:10.3934/mbe.2019145

Cited by 28 publications

(28 citation statements)

References 27 publications

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“…The authors obtain the CC differential expressed genes from the TCGA database, whereas the KEGG pathway database was used to enrich those genes. The overlap between the pathway enrichment results obtained by BiP and those in [ 128 , 129 ] confirms the effectiveness of BiP in identifying pathways that play a relevant role in complex diseases.…”

Section: Discussionmentioning

confidence: 56%

“…It is worth to note that, in the intersection between the top 10 enriched pathway from both COAD and GSE41011 data sets, only BiP identified as novel candidate risk pathways the Metabolic pathway ( p value = ) which was disregarded by the other tools, the Transcriptional misregulation in cancer ( p value = ), and proteoglycans in cancer ( p value = ) pathways, which were ignored by CePa and SPIA tools. The enrichment of the three pathways related to CC is also described in [ 128 , 129 ]. Zhao et al [ 129 ] illustrates a protocol to figure out some critical biomarkers associated with colorectal cancer.…”

Section: Discussionmentioning

confidence: 99%

“…The enrichment of the three pathways related to CC is also described in [ 128 , 129 ]. Zhao et al [ 129 ] illustrates a protocol to figure out some critical biomarkers associated with colorectal cancer. The authors obtain the CC differential expressed genes from the GEO database (GSE32323 data set), whereas the KEGG pathway database was used to enrich those genes.…”

Section: Discussionmentioning

confidence: 99%

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Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

Agapito

Cannataro

2021

BMC Bioinformatics

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Background Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools. Methods PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context. Results We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer. Conclusion As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.

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Section: Discussionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

Agapito

Cannataro

2021

BMC Bioinformatics

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“… 29 Recently, SLC4A4 was reported to be associated with cancer, it could suppress tumor’s proliferation in clear cell renal cell carcinoma. 30 However, previous study also show that SLC4A4 is overexpressed in colon cancer, 31 and use ShRNA to knock down it could inhibit the cell proliferation and metastasis in colon and breast cancer cells. 32 Our results based bioinformatics also get a same verdict.…”

Section: Resultsmentioning

confidence: 97%

The targeted inhibition of prostate cancer by iron-based nanoparticles based on bioinformatics

et al. 2020

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Prostate cancer is an epithelial malignant tumor of the prostate, and it is one of the malignant tumors with a high incidence of urogenital system in men. The local treatment of prostate cancer is mainly radical resection and radical radiotherapy, but they are not applicable to advanced prostate cancer. Systemic therapy mainly includes targeted therapy and immunotherapy which could cause many complications, and will affect the prognosis and quality of life of patients. It is urgent to find new treatments for prostate cancer. Bioinformatics offers hope for us to find reliable therapeutic targets. Bioinformatics can use the tumor informations in database and analyze them to screen out the best differentially expressed genes. Using the selected differentially expressed genes as targets, a gene interference plasmid was designed, and the constructed plasmid was used for targeted gene therapy. There are some problems about gene therapy that need to be solved, such as how to transfer genes to target cells is also an important challenge. Due to their large molecular weight and hydrophilic nature, they cannot enter cells through passive diffusion mechanisms. Here we synthesized a DNA carrier used surface modified iron based nanoparticles, and used it to load plasmid including ShRNA which can inhibit the expression of oncogene SLC4A4 selected by bioinformatics’ method. After that we use this iron based nanoparticles/plasmid DNA nanocomposite to treat prostate cancer cells in vitro and in vivo. The target gene SLC4A4 we had selected using bioinformatics had a strong effect on the proliferation of prostate cells; Our nanocomposite could inhibit the expression of SLC4A4 effectively, it had strong inhibitory effects on prostate cancer cells both in vivo and in vitro, and can be used as a potential method for prostate cancer treatment.

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“…In general, the results are in line with multiple previous studies, which indicated that loss of ZG16 expression may make a great difference in CRC development and worse OS. [18][19][20][21] In fact, the TNM system based on the depth of tumor invasion (T), the number of affected lymph nodes (N), and the presence of metastasis (M) remains the most common prognostic indicator for stratifying patients. However, survival rates are highly heterogeneous for even patients who are within the same TNM stage.…”

Section: Discussionmentioning

confidence: 99%

<p>A Novel Prognostic Score Based on <em>ZG16</em> for Predicting CRC Survival</p>

Wang

Sun

Wang

et al. 2020

PGPM

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Background Colorectal cancer (CRC) is one of the lethal malignant tumors worldwide. However, the underlying mechanism of CRC and its biomarkers remain unclear. The aim of this study was to identify the key genes associated with CRC and to further explore their prognostic significance. Methods Four expression profile datasets (GSE41657, GSE74602, GSE113513, and GSE40967) downloaded from Gene Expression Omnibus (GEO) and one RNAseq dataset of CRC from The Cancer Genome Atlas (TCGA) database were included in our study. The Cox model was utilized for univariate or multivariate survival analysis. GEPIA and HAP database were adopted for verification of DEGs ( ZG16 ). The decision curve analysis (DCA) and time-dependent ROC were chosen for evaluating the prognostic effectiveness of biomarkers. Results In total, 88 differentially expressed genes (DEGs) were identified, and the GO and KEGG enrichment analyses of DEGs were processed. After, the protein–protein interaction (PPI) network was constructed and 15 hub genes including ZG16 were identified. The differential expression of ZG16 between tumor and normal colorectal tissues were further verified in GEPIA and HAP database. Subsequent survival indicated that expression of ZG16 is negatively correlated with overall survival of OS and is an independent prognostic factor for CRC patients. Furthermore, the construction of a prognostic score containing ZG16 , TNM stage and age exhibited superior effectiveness for predicting long-term survival of CRC patients. Additionally, our results were verified using the GSE40967 dataset, which indicated an improved performance of combined risk score based on ZG16 for predicting OS of CRC patients. Conclusion ZG16 is a potential parameter for predicting prognosis in CRC. Furthermore, a combination of ZG16 , TNM stage, and age allows improved prognosis of CRC.

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The identification of a common different gene expression signature in patients with colorectal cancer

Cited by 28 publications

References 27 publications

Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data

The targeted inhibition of prostate cancer by iron-based nanoparticles based on bioinformatics

<p>A Novel Prognostic Score Based on <em>ZG16</em> for Predicting CRC Survival</p>

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