With the rapid development of high-throughput technologies and increasingly mature data mining methods, it has become evident that clinical diagnoses no longer rely on a single gene/mRNA signature. Cases that appear clinically homogeneous may be varied at the molecular level and comprise several subgroups. Such subgroups of patients with different disease subtypes may demonstrate differential treatment response or adverse drug reactions and have distinct prognoses.

Cancer causes more than 9.6 million deaths worldwide each year. Despite novel therapeutic methods and improvements in recent decades, cancer remains a significant public health burden. Each cancer is characterized by a unique set of molecular lesions promoting its malignant properties. Many of these molecular changes are recurrent and define tumor subtypes, revealing differential therapy response and prognosis. Thus, numerous molecular classifications of malignancies are used in clinical practice and many studies are needed to discover new molecular subtypes and biomarkers for patient stratification. Although different prognosis is mostly discussed in the context of cancer, many non-cancerous diseases have recently been increasingly reported. These include asthma, atrial fibrillation, essential hypertension, type 2 diabetes mellitus, non-alcoholic fatty liver, irritable bowel syndrome, ulcerative colitis, rheumatoid arthritis, systemic lupus erythematosus, Alzheimer’s, and many other diseases. Increasing studies aim to elucidate disease mechanisms or identify novel biomarkers. Recently, genomic, epigenomic, and transcriptomic data of patients have been made available via public databases such as The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO). These data sets are great resources that offer additional opportunities in biomarker discovery and validation. Therefore, the development of novel methods for patient stratification and bioinformatics approaches aware of potential disease represents an important research direction.

The aim of this Special Issue is to improve the understanding of the prognosis of cancer and possible molecular mechanisms of non-cancerous diseases by using omics-based technologies as well as experimental technologies. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Bioinformatics algorithms and experimental technology for cancer biomarker screening
• Genetic and epigenetic networks that contribute to cancer pathogenesis, progression, and response to therapy
• Bioinformatics algorithms and experimental technology for novel biomarker screening of non-cancerous diseases
• Translational research bridging the gap between our incremental knowledge on the association of cancer or non-cancerous diseases biomarkers and characteristics, and outcomes of such patients in clinical practice