Gene co-expression network approach for predicting prognostic microRNA biomarkers in different subtypes of breast cancer

Adhami, Masoumeh; MotieGhader, Habib; Haghdoost, Ali Akbar; Afshar, Reza Malekpour; Sadeghi, Balal

doi:10.1016/j.ygeno.2019.01.010

Cited by 32 publications

(17 citation statements)

References 58 publications

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“…By constructing two gene networks according to normal and tumor tissue gene expression data, it is possible to identify critical modules and genes that might be involved in pathological processes and, subsequently, are able to serve as diagnostic or prognostic biomarkers, or potential therapeutic targets. In breast cancer, the novel microRNA biomarkers for each subtype of breast cancer can be detected using WGCNA (21). In addition, our previous study identified one lncRNA and five mRNA that serve as important prognostic biomarkers in breast cancer (22).…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

et al. 2021

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Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among women worldwide. Therefore, the need for effective breast cancer treatment is urgent. Transcription factors (TFs) directly participate in gene transcription, and their dysregulation plays a key role in breast cancer. Our study identified 459 differentially expressed TFs between tumor and normal samples from The Cancer Genome Atlas database. Based on gene expression analysis and weighted gene co-expression network analysis, the co-expression yellow module was found to be integral for breast cancer progression. A total of 121 genes in the yellow module were used for function enrichment. To further confirm prognosis-related TFs, COX regression and LASSO analyses were performed; consequently, a prognostic risk model was constructed, and its validity was verified. Ten prognosis-related TFs were identified according to their expression profile, survival probability, and target genes. COPS5, HDAC2, and NONO were recognized as hub TFs in breast cancer. These TFs were highly expressed in human breast cancer cell lines and clinical breast cancer samples; this result was consistent with the information from multiple databases. Immune infiltration analysis revealed that the proportions of resting dendritic and mast cells were greater in the low-risk group than those in the high-risk group. Thus, in this study, we identified three hub biomarkers related to breast cancer prognosis. The results provide a framework for the co-expression of TF modules and immune infiltration in breast cancer.

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Section: Introductionmentioning

confidence: 99%

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

et al. 2021

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“…One involves the short noncoding group that consists of less than 200 nucleotides in length, whereas the long noncoding group is made up of more than 200 nucleotides. 21 As most studies have focused on short noncoding RNA, such as miRNA, 22 it is well understood compared with long noncoding genes. Therefore, our findings on the long noncoding gene PVT1 determined that it is a meaningful candidate oncogene.…”

Section: Resultsmentioning

confidence: 99%

A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers

Kim

2020

Cancer Inform

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Microarray data sets have been used for predicting cancer biomarkers. Yet, replication of the prediction has not been fully satisfied. Recently, new data sets called deep sequencing data sets have been generated, with an advantage of less noise in computational analysis. In this study, we analyzed the kidney miRNA and mRNA sequence data sets for predicting cancer markers using 5 different statistical feature selection methods. In the results, we obtained 3 mRNA- and 27 miRNA-based cancer biomarkers to compare with the normal samples. In addition, we clustered the kidney cancer subtypes using a nonnegative matrix factorization method and obtained significant results of survival analysis from the 2 separate groups including miRNA-342 and its target eukaryotic translation initiation factor 5A ( EIF5A).

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“…[22] proposed a miRNA-mRNA module prognostic biomarker for early detection of colorectal cancer based on co-expression network analysis. As well as, Masoumeh and colleagues [23] proposed a miRNA-mRNA sub-network as prognostic biomarker for breast cancer subtype strati cation. In this project, interactions of miRNAs and target genes have been studied and two signi cant miRNAs introduces as prognostic biomarker for cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

Application of Protein-Protein interaction network analysis in order to identify Cervical cancer mRNA and miRNA biomarkers

Tabrizi-Nezhadi

MotieGhader

Maleki

2022

Preprint

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Background: Cervical cancer (CC) is one of the most common and serious cancers in the world. This cancer includes two histological types including Squamous cell carcinoma (SCC) and adenocarcinoma (ADC). In this project SCC have been studied. The current study aimed to identify novel potential candidate mRNA and miRNA biomarkers for CC based on a Protein-Protein Interaction (PPI) and miRNA-mRNA networks analysis.Results: In the current project, transcriptome profile for normal and CC samples was utilized. First, the PPI network was constructed for the 1335 DEGs, and then a significant gene module was extracted from the PPI network. Next, a list of miRNAs which are targeting module's genes are collected from the miRTarBase online database and a miRNA-mRNA regulatory network was formed. Afterward, CC driver genes were selected from the module's genes (MCM2, MCM10, POLA1, and TONSL) and introduced as potential candidate biomarkers for CC. As well as, two hub miRNAs including miR-193b-3p and miR-615-3p were selected from the miRNA-mRNA regulatory network and reported as potential candidate biomarkers for CC. Conclusion: In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarker for CC.

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Gene co-expression network approach for predicting prognostic microRNA biomarkers in different subtypes of breast cancer

Cited by 32 publications

References 58 publications

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

Identification of Key Transcription Factors and Immune Infiltration Patterns Associated With Breast Cancer Prognosis Using WGCNA and Cox Regression Analysis

A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers

Application of Protein-Protein interaction network analysis in order to identify Cervical cancer mRNA and miRNA biomarkers

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