Clinical Features of Breast Cancer Patients with Human T-Cell Lymphotropic Virus Type-1 Infection

Automated exploitation of deep learning for cancer patient stratification across multiple types

MOTIVATION

Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming.

RESULTS

To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types.

AVAILABILITY AND IMPLEMENTATION

The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.

Screening of potential hub genes and key pathways associated with breast cancer by bioinformatics tools

Breast cancer (BC) remains the leading cause of cancer-related death in women worldwide. The development of new targeted therapies that may improve patient survival remains an area of growing interest. This study aimed to identify new biomarkers involved in BC progression that could be used as potential targeted therapies. DEGs were selected from three gene expression profiles, GSE55715, GSE124646, and GSE87049, using the GEO2R tool and Venn diagram software. Gene Ontology and KEGG pathways were then performed using DAVID software. Next, the PPI network was constructed using STRING and visualized using Cytoscape software, and hub genes were extracted using the cytoHubba plug-in. Survival analysis was performed using the Kaplan-Meier Plotter, while the expression of hub genes in BC was verified using the GEPIA2 tool. Finally, transcription the factors of hub genes were determined using the NetworkAnalyst database, and the TIMER tool was employed to explore the infiltration levels of tumor immune cells with related genes. A total of 146 DEGs were identified in the three datasets, including 60 upregulated genes that were enriched in the cell cycle, and 86 downregulated genes that were mainly enriched in the TNF signaling pathway and pathways in cancer. Ten genes were identified: BUB1, CDK1, HMMR, MAD2L1, CEP55, AURKA, CCNB2, TPX2, MELK, and KIF20A. The overexpression of hub genes, except CDK1, was associated with poor survival in BC and was regulated by several transcription factors involved in DNA binding activity and transcription regulation. The infiltration levels of immune cells were positively correlated with hub genes, particularly macrophages and CD4+ T cells. This study identified new reliable molecular biomarkers that can serve as potential therapeutic targets for BC treatment.

A Motif-Based Network Analysis of Regulatory Patterns in Doxorubicin Effects on Treating Breast Cancer, a Systems Biology Study

Background

Breast cancer is the most common malignancy worldwide. Doxorubicin is an anthracycline used to treat breast cancer as the first treatment choice. Nevertheless, the molecular mechanisms underlying the response to Doxorubicin and its side effects are not comprehensively understood so far. We used systems biology and bioinformatics methods to identify essential genes and molecular mechanisms behind the body response to Doxorubicin and its side effects in breast cancer patients.

Methods

Omics data were extracted and analyzed to construct the protein-protein interaction and gene regulatory networks. Network analysis was performed to identify hubs, bottlenecks, clusters, and regulatory motifs to evaluate crucial genes and molecular mechanisms behind the body response to Doxorubicin and its side effects.

Results

Analyzing the constructed PPI and gene-TF-miRNA regulatory network showed that MCM3, MCM10, and TP53 are key hub-bottlenecks and seed proteins. Enrichment analysis also revealed cell cycle, TP53 signaling, Forkhead box O (FoxO) signaling, and viral carcinogenesis as essential pathways in response to this drug. Besides, SNARE interactions in vesicular transport and neurotrophin signaling were identified as pathways related to the side effects of Doxorubicin. The apoptosis induction, DNA repair, invasion inhibition, metastasis, and DNA replication are suggested as critical molecular mechanisms underlying Doxorubicin anti-cancer effect. SNARE interactions in vesicular transport and neurotrophin signaling and FoxO signaling pathways in glucose metabolism are probably the mechanisms responsible for side effects of Doxorubicin.

Conclusion

Following our model validation using the existing experimental data, we recommend our other newly predicted biomarkers and pathways as possible molecular mechanisms and side effects underlying the response to Doxorubicin in breast cancer requiring further investigations.

Viral Manipulation of the Host Epigenome as a Driver of Virus-Induced Oncogenesis

Tumorigenesis due to viral infection accounts for a high fraction of the total global cancer burden (15–20%) of all human cancers. A comprehensive understanding of the mechanisms by which viral infection leads to tumor development is extremely important. One of the main mechanisms by which viruses induce host cell proliferation programs is through controlling the host’s epigenetic machinery. In this review, we dissect the epigenetic pathways through which oncogenic viruses can integrate their genome into host cell chromosomes and lead to tumor progression. In addition, we highlight the potential use of drugs based on histone modifiers in reducing the global impact of cancer development due to viral infection.

Small cells lung epidermoid carcinoma in a HTLV1-infected patient: case report and literature review

The human T cell lymphotropic virus type 1 (HTLV-1) is the first human retrovirus discovered. Since then, it has spread worldwide and is mainly associated with adult T cell leukemia/lymphoma (ATLL) and HTLV1-associated myelopathy (HAM). Its relationship, however, with other types of cancer is controversial. We describe the case of a patient presenting with small cells lung epidermoid carcinoma who had recently developed HAM, and a review of the literature related to these conditions. This is the first case of this type of lung cancer, the same of the first description in the literature, associated with HAM outside Japan.