Journal: Current Genomics
Guest editor(s):Dr. Hao Zhang
Co-Guest Editor(s): Nan Zhang

Introduction

Cancer is a significant threat to human life and health, remaining a highly aggressive killer. It is a leading cause of death worldwide and represents a crucial medical issue for humanity. However, in the past decade, the effectiveness of new synthetic anticancer agents has not matched the current clinical speculation. Despite increased investment in cancer research, understanding cancer's pathogenesis and clinical treatment still falls short of meeting the needs. Exploring the molecular characteristics of cancer forms the foundation and essence of cancer treatment. However, traditional clinical methods require substantial funds and time, which cannot meet the urgent treatment demands. Fortunately, the decreasing cost of sequencing and various biomolecular tests has led to exponential growth in cancer-related genomics, transcriptome, proteome, and metabonomics data. The advancements in high-throughput sequencing, gene editing, immunotherapy, and identifying key cancer-related genes or pathways have significantly deepened our understanding of cancer biology at the genetic and genomic levels. Medical big data offers a reliable computing and statistical approach for researchers to extract crucial cancer information. However, it also presents a significant challenge to existing computing methods. In the information age, a comprehensive study of statistics and computing methods is crucial for accurately mining the biological knowledge of multi-group data. This enables the development of targeted and personalized healthcare solutions for patients. Computational and statistical methods for identifying cancer-related mechanisms and biomarkers are gaining popularity. These methods present different perspectives on cancer from both basic research and clinical standpoints. Considering the advancements in computing methods within cancer research, we propose a research topic that provides researchers with an excellent platform to share their latest findings, introduce new methods, and discuss challenges and opportunities in related fields.

Keywords

Treatment resistance, Target, Drug molecule, Immunotherapy, Biomarker, Pathogenic mechanis

Sub-topics

- Using artificial intelligence for predicting cancer diseases.
- Utilizing tools and databases to study cancer and related diseases.
- Discovering genes, RNA, proteins, and metabolites associated w