Cervical cancer is a malignant tumour that occurs in the cervix and is classified into two histological types, adenocarcinoma and squamous cell carcinoma (SCC); SCC is more common and accounts for 70% of all cases. In 2018 there were ~569,000 new cases of cervical cancer diagnosed worldwide and ~311,000 deaths were attributed to cervical cancer. Of these, between 84 and 90% occurred in low‑ and middle‑income countries (LMICs) such as South Africa, India, China and Brazil. The most common cause of cervical cancer is persistent infection caused by the sexually transmitted human papilloma virus. Other factors that contribute to the incidence of cervical cancer include geography, traditional practices and beliefs, the screening levels, socioeconomic status, healthcare access, public awareness, use of oral contraceptives, smoking and co‑infection with HIV. An estimated 11 million women from LMICs will be diagnosed with cervical cancer in the next 10‑20 years. The aim of this review was to explore various types of genetic and epigenetic factors that influence the development, progression or suppression of cervical cancer.

Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery. NGS generates large datasets that demand specialised bioinformatics resources to analyse the data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images. Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical application of NGS remains to be validated. By continuing to enhance the progression of innovation and technology, the future of AI and precision oncology show great promise.

TSPO is a receptor involved in the regulation of cellular proliferation, apoptosis and mitochondrial functions. Previous studies showed that the expression of TSPO protein correlated positively with tumour malignancy and negatively with patient survival. The aim of this study was to determine the transcription of Tspo mRNA in various types of normal and cancer tissues. In situ hybridization was performed to localise the Tspo mRNA in various human normal and cancer tissues. The relative level of Tspo mRNA was quantified using fluorescent intensity and visual estimation of colorimetric staining. RT-PCR was used to confirm these mRNA levels in normal lung, lung cancer, liver cancer, and cervical cancer cell lines. There was a significant increase in the level of transcription in liver, prostate, kidney, and brain cancers while a significant decrease was observed in cancers of the colon and lung. Quantitative RT-PCR confirmed that the mRNA levels of Tspo are higher in a normal lung cell line than in a lung cancer cell line. An increase in the expression levels of Tspo mRNA is not necessarily a good diagnostic biomarker in most cancers with changes not being large enough to be significantly different when detected by in situ hybridisation.

Each year, colorectal cancers (CRCs) affect over a quarter of a million people. The risk of developing CRC in industrialized nations is approximately 5%. When the disease is localised, treatment success rates range from 70– 90%; however, advanced CRC has a high mortality rate, consistently ranking in the top three causes of cancer-related deaths. There is a large geographic difference in global distribution, and CRC is predominantly associated with developed countries and a Western lifestyle and diet. As such, the developed world accounts for more than 63% of all cases of CRC. Geographic variations also predict cancer outcomes, which differ between racial and ethnic groups. This variation is due to inequalities in wealth, differences in the exposure to risk factors and barriers to high-quality cancer prevention, early detection and treatment. The aim of this paper was to review CRC in low- and middle-income countries such as South Africa, India, Brazil and China, and compare them with high-income countries such as the United States of America and the United Kingdom. It is important to note that these economically less developed countries, with historically low CRC rates, are experiencing an increased frequency of CRC. The review also discusses biological markers and genetic pathways involved in the development of colorectal cancer. Genes known to be responsible for the most common forms of inherited CRCs have also been identified but more remain to be identified. This would provide more candidate genes to be added to known biomarkers. CRC burden can be controlled through the widespread application of existing knowledge, such as reduced smoking habits, vaccination, early detection and promoting physical activity, accompanied by a healthy diet. An increased understanding of the molecular mechanisms and events underlying colorectal carcinogenesis will enable the development of new targets and therapeutic drugs.

Diabetes mellitus (DM) is a non-communicable, metabolic disorder that affects 416 million individuals worldwide. Type 2 diabetes contributes to a vast 85–90% of the diabetes incidences while 10–15% of patients suffer from type 1 diabetes. These two predominant forms of DM cause a significant loss of functional pancreatic β-cell mass causing different degrees of insulin deficiency, most likely, due to increased β-cell apoptosis. Treatment options involve the use of insulin sensitisers, α-glucosidase inhibitors, and β-cell secretagogues which are often expensive, limited in efficacy and carry detrimental adverse effects. Cost-effective options for treatment exists in the form of herbal drugs, however, scientific validations of these widely used medicinal plants are still underway. Alternative splicing (AS) is a co-ordinated post-transcriptional process in which a single gene generates multiple mRNA transcripts which results in increased amounts of functionally different protein isoforms and in some cases aberrant splicing leads to metabolic disease. In this review, we explore the association of AS with metabolic alterations in DM and the biological significance of the abnormal splicing of some pathogenic diabetes-related genes. An understanding of the molecular mechanism behind abnormally spliced transcripts will aid in the development of new diagnostic, prognostic and therapeutic tools.