scholarly journals DysPIA: A Novel Dysregulated Pathway Identification Analysis Method

2021 ◽  
Vol 12 ◽  
Author(s):  
Limei Wang ◽  
Weixin Xie ◽  
Kongning Li ◽  
Zhenzhen Wang ◽  
Xia Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Subtype ◽  
Biological Significance ◽  
Enrichment Analysis ◽  
P53 Mutation ◽  
Individual Level ◽  
Causal Pathways ◽  
Gene Sets ◽  
Breast Cancer Relapse ◽  
Identification Analysis

Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700–8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages “DysPIA” and “DysPIAData” are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.html and https://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).

scholarly journals Improved cancer biomarkers identification using network-constrained infinite latent feature selection

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246668
Author(s):  
Lihua Cai ◽  
Honglong Wu ◽  
Ke Zhou
Keyword(s):  
Feature Selection ◽  
Expression Profiles ◽  
Biological Significance ◽  
Feature Selection Method ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Gene Sets ◽  
Significant Gene

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.

scholarly journals The Integrative Method Based on Module-Network for Identifying Driver Genes in Cancer Subtypes

2017 ◽  
Author(s):  
Xinguo Lu ◽  
Xing Li ◽  
Xin Qian ◽  
Qiumai Miao ◽  
Shaoliang Peng
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Breast Cancer Subtype ◽  
Enrichment Analysis ◽  
Potential Candidate ◽  
Statistical Machine Learning ◽  
Driver Genes ◽  
Module Network ◽  
Cancer Subtypes ◽  
Integrative Method

With advances in next-generation sequencing(NGS) technologies, large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer mechanism is to identify the driver genes from the mutation genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profilings and copy number variation(CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.

Pathological and genetic aggressiveness of left-sided breast cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e12579-e12579
Author(s):  
Yara Abdou ◽  
Mariko Asaoka ◽  
Kazuaki Takabe
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tumor Grade ◽  
Mitotic Rate ◽  
Cytolytic Activity ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Sets ◽  
Significant Difference

e12579 Background: Breast Cancer in women consistently occurs more frequently in the left breast, with the ratio of left to right sided breast cancer cases ranging from 1.05 to 1.26. In spite of the difference in frequency, prior studies have failed to show any significant difference in clinical characteristics between left sided and right sided cancer. Methods: Genomic and clinical features were collected from The Cancer Genome Atlas breast cancer project. LVI status, mitotic rate, nuclear score and tubular score were collected from pathology reports in TIES client 5.8. Fisher's exact test was used for group comparison and survival analysis was performed with Cox regression. Cytolytic activity (CYT) indicates anti-cancer immune response and was quantified from gene expression data. Hallmark gene-sets were used for gene set enrichment analysis (GSEA). Results: Among the 1081 women with unilateral invasive breast cancer, 561 had tumor on the left side compared to 520 on the right. Our results didn’t show any significant differences between left and right side with regards to tumor location, histology, race, and tumor characteristics including stage, tumor size, nodal status and receptor status. No statistical significant differences were observed in mitotic rate, LVI status and tubular score, however, the tumor grade was significantly higher in the left side. Moreover, there were no significant differences in mutation count, CYT and overall survival between both sides. GSEA revealed cell-cycle related gene sets like G2M checkpoint, Mitotic spindle, E2F targets and MYC targets which were significantly enriched in left sided tumor. Furthermore, out of the 865 genes which were highly expressed on the left side, we identified specific genes including BRCA1, BRCA2, BRIP1, CHEK2, FANCC, PALB2, TP53 and MSH6 which are associated primarily with breast cancer genesis and mostly have established clinical management guidelines. Conclusions: Our results suggest a more aggressive nature to left sided breast cancer with a higher pathological grade perhaps requiring more aggressive treatment. Such a hypothesis needs further study to confirm or refute its validity. If confirmed, it may have a major impact with regard to biology of breast cancer and its subsequent management.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

2020 ◽  
Vol 15 ◽  
Author(s):  
Jujuan Zhuang ◽  
Shuang Dai ◽  
Lijun Zhang ◽  
Pan Gao ◽  
Yingmin Han ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Cancer Tissue ◽  
Connectivity Analysis ◽  
Cancer Drugs ◽  
Normal Tissues ◽  
Differential Gene ◽  
Differential Modules ◽  
Differential Connectivity

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

Determination of Usnic Acid Responsive miRNAs in Breast Cancer Cell Lines

2019 ◽  
Vol 19 (12) ◽  
pp. 1463-1472 ◽  
Author(s):  
Nil Kiliç ◽  
Yasemin Ö. Islakoğlu ◽  
İlker Büyük ◽  
Bala Gür-Dedeoğlu ◽  
Demet Cansaran-Duman
Keyword(s):  
Breast Cancer ◽  
Hedgehog Signaling ◽  
Treatment Options ◽  
Usnic Acid ◽  
Enrichment Analysis ◽  
Breast Cancer Cell Lines ◽  
Pathway Enrichment Analysis ◽  
Molecular Heterogeneity ◽  
Proliferative Effect ◽  
Mcf 7

Objective: Breast Cancer (BC) is the most common type of cancer diagnosed in women. A common treatment strategy for BC is still not available because of its molecular heterogeneity and resistance is developed in most of the patients through the course of treatment. Therefore, alternative medicine resources as being novel treatment options are needed to be used for the treatment of BC. Usnic Acid (UA) that is one of the secondary metabolites of lichens used for different purposes in the field of medicine and its anti-proliferative effect has been shown in certain cancer types, suggesting its potential use for the treatment. Methods: Anti-proliferative effect of UA in BC cells (MDA-MB-231, MCF-7, BT-474) was identified through MTT analysis. Microarray analysis was performed in cells treated with the effective concentration of UA and UA-responsive miRNAs were detected. Their targets and the pathways that they involve were determined using a miRNA target prediction tool. Results: Microarray experiments showed that 67 miRNAs were specifically responsive to UA in MDA-MB-231 cells while 15 and 8 were specific to BT-474 and MCF-7 cells, respectively. The miRNA targets were mostly found to play role in Hedgehog signaling pathway. TGF-Beta, MAPK and apoptosis pathways were also the prominent ones according to the miRNA enrichment analysis. Conclusion: The current study is important as being the first study in the literature which aimed to explore the UA related miRNAs, their targets and molecular pathways that may have roles in the BC. The results of pathway enrichment analysis and anti-proliferative effects of UA support the idea that UA might be used as a potential alternative therapeutic agent for BC treatment.

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