The Advances in Epigenetics for Cancer Radiotherapy

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Epigenetics as a determinant of radiation response in cancer

Radiation therapy is a cornerstone of modern cancer treatment. Treatment is based on depositing focal radiation to the tumor to inhibit cell growth, proliferation and metastasis, and to promote the death of cancer cells. In addition, radiation also affects non-tumor cells in the tumor microenvironmental (TME). Radiation resistance of the tumor cells is the most common cause of treatment failure, allowing survival of cancer cell and subsequent tumor growing. Molecular radioresistance comprises genetic and epigenetic characteristics inherent in cancer cells, or characteristics acquired after exposure to radiation. Furthermore, cancer stem cells (CSCs) and non-tumor cells into the TME as stromal and immune cells have a role in promoting and maintaining radioresistant tumor phenotypes. Different regulatory molecules and pathways distinctive of radiation resistance include DNA repair, survival signaling and cell death pathways. Epigenetic mechanisms are one of the most relevant events that occur after radiotherapy to regulate the expression and function of key genes and proteins in the differential radiation-response. This article reviews recent data on the main molecular mechanisms and signaling pathways related to the biological response to radiotherapy in cancer; highlighting the epigenetic control exerted by DNA methylation, histone marks, chromatin remodeling and m6A RNA methylation on gene expression and activation of signaling pathways related to radiation therapy response.

Association analysis of hepatocellular carcinoma-related hub proteins and hub genes

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. The occurrence and development of HCC are closely related to epigenetic modifications. Epigenetic modifications can regulate gene expression and related functions through DNA methylation. This paper presents an association analysis method of HCC-related hub proteins and hub genes.

EXPERIMENTAL DESIGN

Bioinformatics analysis of HCC-related DNA methylation data is carried out to clarify the molecular mechanism of HCC-related genes and to find hub genes (genes with more connections in the network) by constructing in the gene interaction network. This paper proposes an accurate prediction method of protein-protein interaction (PPI) based on deep learning model DeepSG2PPI. The trained DeepSG2PPI model predicts the interaction relationship between the synthetic proteins regulated by HCC-related genes.

RESULTS

This paper finds that four genes are the intersection of hub genes and hub proteins. The four genes are: FBL, CCNB2, ALDH18A1, and RPLP0. The association of RPLP0 gene with HCC is a new finding of this study. RPLP0 is expected to become a new biomarker for the treatment, diagnosis, and prognosis of HCC. The four proteins corresponding to the four genes are: ENSP00000221801, ENSP00000288207, ENSP00000360268, and ENSP00000449328.

CONCLUSIONS AND CLINICAL RELEVANCE

The association between the hub genes with the hub proteins is analyzed. The mutual verification of the hub genes and the hub proteins can obtain more credible HCC-related genes and proteins, which is helpful for the diagnosis, treatment, and drug development of HCC.

Icariin as an emerging candidate drug for anticancer treatment: Current status and perspective

Icariin (ICA) is a kind of natural flavonoid compound monomer, which is derived from the extract of dried stems and leaves of Epimedium. Modern pharmacological studies have found that ICA has broad bioactive function in affecting the biological processes of a variety of cancers, including breast cancer, colorectal cancer, hepatocellular carcinoma, esophageal cancer and other cancers, which indicates that ICA has promising application value in the treatment of cancer patients in the future. Nevertheless, the targets and molecular mechanisms of ICA in cancer treatment have not been elucidated in detail. Therefore, in this review, we systematically summarizes the current research progress of ICA in a series of cancers. In particular, an emphasis is placed on the mechanism of ICA and its future development direction, aiming at providing relevant theoretical basis for the development and application of ICA in the future cancer treatment strategies.