Dataset Information

Network-based identification of biomarkers for colon adenocarcinoma.

ABSTRACT:

Background

As one of the most common cancers with high mortality in the world, we are still facing a huge challenge in the prevention and treatment of colon cancer. With the rapid development of high throughput technologies, new biomarkers identification for colon cancer has been confronted with the new opportunities and challenges.

Methods

We firstly constructed functional networks for each sample of colon adenocarcinoma (COAD) by using a sample-specific network (SSN) method which can construct individual-specific networks based on gene expression profiles of a single sample. The functional genes and interactions were identified from the functional networks, respectively.

Results

Classification and subtyping were used to test the function of the functional genes and interactions. The results of classification showed that the functional genes could be used as diagnostic biomarkers. The subtypes displayed different mechanisms, which were shown by the functional and pathway enrichment analysis for the representative genes of each subtype. Besides, subtype-specific molecular patterns were also detected, such as subtype-specific clinical and mutation features. Finally, 12 functional genes and 13 functional edges could serve as prognosis biomarkers since they were associated with the survival rate of COAD.

Conclusions

In conclusion, the functional genes and interactions in the constructed functional network could be used as new biomarkers for COAD.

SUBMITTER: Hu F

PROVIDER: S-EPMC7367377 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Network-based identification of biomarkers for colon adenocarcinoma.

Hu Fuyan F Wang Qing Q Yang Zhiyuan Z Zhang Zeng Z Liu Xiaoping X

BMC cancer 20200717 1

<h4>Background</h4>As one of the most common cancers with high mortality in the world, we are still facing a huge challenge in the prevention and treatment of colon cancer. With the rapid development of high throughput technologies, new biomarkers identification for colon cancer has been confronted with the new opportunities and challenges.<h4>Methods</h4>We firstly constructed functional networks for each sample of colon adenocarcinoma (COAD) by using a sample-specific network (SSN) method whic ...[more]

PMID: 32680494

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Project description:BackgroundColon adenocarcinoma (COAD) is one of the leading causes of death worldwide. Cancer stem cells (CSCs) are vital for COAD chemoresistance and recurrence, however little is known about stem cell-related biomarkers in drug resistance and COAD prognosis prediction.MethodsTo uncover the roles of CSC in COAD tumorigenesis, chemoresistance, and prognosis, we retrieved COAD patients' RNAseq data from TCGA (The Cancer Genome Atlas). We further performed analysis of differentially expressed genes (DEGs) and mRNA expression-based stemness index (mRNAsi) to identify stemness-related COAD biomarkers. We then evaluated the roles of mRNAsi in tumorigenesis, clinical-stage, overall survival (OS), and chemoresistance. Afterward, we used identified prognostic stemness-related genes (PSRGs) to construct a prediction model. After constructing the prediction model, we used elastic Net regression and area under the curve (AUC) to explore the prediction value of PSRGs based on risk scores and the receiver operator characteristic (ROC) curve. To elucidate the underlying interconnected systems, we examined relationships between the levels of TFs, PSRGs, and 50 cancer hallmarks by a Pearson correlation analysis.ResultsTwelve thousand one hundred eight DEGs were identified by comparing 456 primary COADs and 41 normal solid tissue samples. Furthermore, we identified 4351 clinical stage-related DEGs, 16,516 stemness-associated DEGs, and 54 chemoresistance-related DEGs from cancer stages: mRNAsi, and COAD chemoresistance. Compared to normal tissue samples, mRNAsi in COAD patients were marked on an elevation and involved in prognosis (p = 0.027), stemness-related DEGs based on chemoresistance (OR = 3.28, p ≤ 0.001) and AJCC clinical stage relating (OR = 4.02, p ≤ 0.001) to COAD patients. The prediction model of prognosis were constructed using the 6 PSRGs with high accuracy (AUC: 0.659). The model identified universal correlation between NRIP2 and FDFT1 (key PRSGs), and some cancer related transcription factors (TFs) and trademarks of cancer gene were in the regulatory network.ConclusionWe found that mRNAsi is a reliable predictive biomarker of tumorigenesis and COAD prognosis. Our established prediction model of COAD chemoresistance, which includes the six PSRGs, is effective, as the model provides promising therapeutic targets in the COAD.

Project description:Colon adenocarcinoma (COAD) is a prevalent and aggressive form of cancer that necessitates the identification of robust biomarkers for early diagnosis and treatment. Therefore, this project was launched to identify a few key biomarkers from CC and CXC chemokine families for the accurate detection of COAD. Hub gene identification was performed using CytoHubba analysis. Clinical samples from COAD patients and normal individuals were collected and subjected to appropriate methods for DNA and RNA extraction. The expression levels of hub genes were analyzed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), while promoter methylation analysis was conducted using targeted bisulfite sequencing (bisulfite-seq). Additionally, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were utilized to validate the findings based on clinical samples. CXCL10 (C-X-C motif chemokine ligand 10), CXCL12 (C-X-C motif chemokine ligand 12), CXCL16 (C-X-C motif chemokine ligand 16), and CCL25 (CC motif chemokine ligand 25) were denoted as the key hubs among CC and CXC chemokine families. Through RT-qPCR analysis, it was found that CXCL10, CXCL12, and CXCL16 were significantly up-regulated, while CCL25 was down-regulated in COAD patients compared to healthy controls. Later on, these findings were also validated using TCGA and GEO datasets consisting of COAD and normal control samples. Furthermore, we investigated the promoter methylation status of these chemokine genes in COAD patients. Our results revealed significant dysregulation of promoter methylation, suggesting an epigenetic mechanism underlying the altered expression of CXCL10, CXCL12, CXCL16, and CCL25 in COAD. In addition to this, various additional aspects of the CCL25, CXCL10, CXCL12, and CXCL16 have also been uncovered in COAD during the present study. This study highlights the dysregulation of CXCL10, CXCL12, CXCL16, and CCL25 chemokine members in COAD patients, emphasizing their significance as potential biomarkers and therapeutic targets in the management of this deadly disease. However, further investigations are warranted to elucidate the underlying molecular mechanisms and evaluate the clinical utility of these findings.

Dataset Information

Network-based identification of biomarkers for colon adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Network-based identification of biomarkers for colon adenocarcinoma.

Similar Datasets

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