OMICS: A Journal of Integrative Biology

What to Do for Increasing Cancer Burden on the African Continent? Accelerating Public Health Diagnostics Innovation for Prevention and Early Intervention on Cancers

No other place illustrates the increasing burden of cancer than in Africa and in particular, sub-Saharan Africa. Many of the individuals to be diagnosed with cancer will be in low-resource settings in the future due to, for example, an increase in populations and aging, and high co-morbidity with infections with viruses such as human immunodeficiency virus (HIV) and human papillomavirus (HPV), as well as the presence of infectious agents linked to cancer development. Due to lack of prevention and diagnostic innovation, patients present with advanced cancers, leading to poor survival and increased mortality. HIV infection-associated cancers such as B cell lymphomas, Kaposi's sarcoma, and HPV-associated cancers such as cervical cancer are particularly noteworthy in this context. Recent reports show that a host of other cancers are also associated with viral infection and these include lung, oral cavity, esophageal, and pharyngeal, hepatocellular carcinoma, and anal and vulvar cancers. This article examines the ways in which diagnostic innovation empowered by integrative biology and informed by public health priorities can improve cancer prevention or early intervention in Africa and beyond. In addition, I argue that because diagnostic biomarkers can often overlap with novel therapeutic targets, diagnostics research and development can have broader value for and impact on medical innovation.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, “SOV-module” was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant ( p -value = 1.36 × 10 –4 ) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.