Insights In Cancer Genetics And Oncogenomics 2022

Lung cancer is the second most common type of cancer and is the leading cause of cancer death globally. In 2018, almost 2.1 million new cases were diagnosed, accounting for ~12% of the cancer burden worldwide. The malignant stage of lung tumor is known as lung adenocarcinoma, which is most common and is diagnosed in both smokers and non-smokers.There are two main types of lung cancer, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Genomic studies have indicated that more than 80% of lung malignancies are classified as NSCLC, of which adenocarcinoma is a predominant subtype. Although significant progress has been made in treating tumor with targetable driver mutations, like EGFR mutations, most tumor do not have such mutations and the prognosis remains poor for metastasis stage patients. Platinum doublet chemotherapy has been the mainstay first-line treatment of patients who are diagnosed with metastatic lung adenocarcinoma without a targetable mutation.In recent years, immunotherapy has emerged as a treatment option that has shown strong response in a subset of patients. The immune agents block crucial checkpoints and regulate the immune response, but the tumor cells evade the patient’s immune systems. By blocking these receptor–ligand interactions, a particular subset of T cells is activated to recognize and respond to tumor cells. While such responses to immunotherapy are promising, they have only been effective in ~20% of patients.

Systemic homeostatic mechanisms include several aspects, such as metabolic, neuroendocrine, immune, and physiological homeostasis. Irreversible damage or reversible imbalance of such homeostatic processes may initiate cancers by altering the regulation of the molecular machinery. Systemic homeostasis-related genes have been found to be intimately involved in oncological processes and in some instances have shown prognostic value. Thus, future gene targeting approaches for cancer should not only focus on classical cancer drivers but also address systemic homeostasis-related genetic mechanisms. Identification of systemic homeostasis-related genes with diagnostic, prognostic or therapeutic value can advance translational cancer research. Increasing numbers of research studies have reported systemic homeostasis-related genes’ relevance to various types of cancer. For example, cancer cells have been shown to activate a critical mechanism of oxygen homeostasis—hypoxia inducible factors (HIFs) family genes, in order to adapt to the tumor microenvironment and develop into a more aggressive phenotype. In addition, methylene tetrahydrofolate dehydrogenase (MTHFD) family genes are involved in mitochondrial one-carbon metabolism, which is essential for maintaining systemic metabolic homeostasis, and have recently been found overexpressed in many cancers and have been correlated to poor survival outcomes. The overexpression of transferrin family genes with iron transporting function has been linked with iron accumulation, which is a known initiating factor in cancer. Another example is Forkhead box O (FOXO) family genes, which serve as a critical regulator of immune homeostasis and can regulate cancer immunity by negatively regulating the expression of immunosuppressive gene-programmed death 1 ligand 1 (PD-L1).Apart from these examples, other systemic homeostatic mechanisms such as glucose homeostasis, energy homeostasis, lipid homeostasis, phosphate homeostasis, cholesterol homeostasis, and mineral homeostasis may also be implicated in cancer pathogenesis. Although accruing research is focused on describing systemic homeostatic mechanisms in cancer biology, several research questions remain unaddressed. The utilization of recent analytic tools and bioinformatics as systems biology approaches has the potential to address these research gaps. Therefore, in this special issue we will collect articles focusing on the application of bioinformatics and systems biology based investigations of systemic homeostatic mechanisms in malignant diseases. Both original research and review articles are welcomed, however publications based on the analysis on only one database will not be accepted (e.g. TCGA).

Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.