IntroductionEpstein-Barr virus (EBV) is a ubiquitous virus that infects more than 90% of the world's population, and is implicated in lymphoma pathogenesis. However, in Zambia during the diagnosis of these lymphomas, the association of the virus with the lymphomas is not established. Since most patients with lymphomas have poor prognosis, the identification of the virus within the lymphoma lesion will allow for more targeted therapy. The aim of this study was to provide evidence of the presence of the EBV in lymphomas diagnosed at the University Teaching Hospital (UTH) in Lusaka, Zambia.MethodsOne hundred and fifty archival formalin-fixed paraffin embedded suspected lymphoma tissues stored over a 4-year period in the Histopathology Laboratory at the UTH in Lusaka, Zambia, were analysed. Histological methods were used to identify the lymphomas, and the virus was detected using Polymerase Chain Reaction (PCR). Subtyping of the virus was achieved through DNA sequencing of the EBNA-2 region of the viral genome. Chi square or fisher's exact test was used to evaluate the association between EBV status, type of lymphoma and gender.ResultsThe majority of the lymphomas identified were non-Hodgkin's lymphoma (NHL) (80%) followed by Hodgkin's lymphoma (HL) (20%). EBV was detected in 51.8% of the cases, 54.5% of which were associated with NHL cases, while 40.9% associated with HL cases. The predominant subtype of the virus in both types of lymphomas was subtype 1. One of the lymphoma cases harboured both subtype 1 and 2 of the virus.ConclusionThis study showed that EBV is closely associated with lymphomas. Therefore, providing evidence of the presence of the virus in lymphoma tissues will aid in targeted therapy. To our knowledge this is the first time such data has been generated in Zambia.

The malignant phenotype of tumour cells is fuelled by changes in the expression of various transcription factors, including some of the well-studied proteins such as p53 and Myc. Despite significant progress made, little is known about several other transcription factors, including ELF4, and how they help shape the oncogenic processes in cancer cells. To this end, we performed a bioinformatics analysis to facilitate a detailed understanding of how the expression variations of ELF4 in human cancers are related to disease outcomes and the cancer cell drug responses. Here, using ELF4 mRNA expression data of 9,350 samples from the Cancer Genome Atlas pan-cancer project, we identify two groups of patient’s tumours: those that expressed high ELF4 transcripts and those that expressed low ELF4 transcripts across 32 different human cancers. We uncover that patients segregated into these two groups are associated with different clinical outcomes. Further, we find that tumours that express high ELF4 mRNA levels tend to be of a higher-grade, afflict a significantly older patient population and have a significantly higher mutation burden. By analysing dose-response profiles to 397 anti-cancer drugs of 612 well-characterised human cancer cell lines, we discover that cell lines that expressed high ELF4 mRNA transcript are significantly less responsive to 129 anti-cancer drugs, and only significantly more response to three drugs: dasatinib, WH-4-023, and Ponatinib, all of which remarkably target the proto-oncogene tyrosine-protein kinase SRC and tyrosine-protein kinase ABL1. Collectively our analyses have shown that, across the 32 different human cancers, the patients afflicted with tumours that overexpress ELF4 tended to have a more aggressive disease that is also is more likely more refractory to most anti-cancer drugs, a finding upon which we could devise novel categorisation of patient tumours, treatment, and prognostic strategies.

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. There is a need, therefore, to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset that includes proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes.We explore the clinical characteristic of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways are altered among pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes which can be leveraged to devise more effective treatments.

Since the earliest reports of the Coronavirus disease - 2019 (COVID-19) in Wuhan, China in December 2019, the disease has rapidly spread worldwide, attaining pandemic levels in early March 2020. However, the spread of COVID-19 has differed in the African setting compared to countries on other continents. To predict the spread of COVID-19 in Africa and within each country on the continent, we applied a Susceptible-Infectious-Recovered mathematical model. Here, our results show that, overall, Africa is currently (May 29, 2020) at the peak of the COVID-19 pandemic, after which we predict the number of cases would begin to fall in June 2020. Furthermore, we predict that the ending phase of the pandemic would be in Mid-August 2020 and that decreasing cases of COVID-19 infections would be detected until around December 2020 and January 2021. Our results also reveal that of the 51 countries with reported COVID-19 cases, only nine, including South Africa, Egypt and Ethiopia, are likely to report higher monthly COVID-19 cases in June 2020 than those reported in the previous months. Overall, at the end of this pandemic, we predict that approximately 279,000 (about 154,000 future cases) individuals in Africa would have been infected with the COVID-19 virus. Here, our predictions are data-driven and based on the previously observed trends in the spread of the COVID-19 pandemic. Shifts in the population dynamics and/or changes in the infectiousness of the COVID-19 virus may require new forecasts of the disease spread.

Cervical cancer has remained the most prevalent and lethal malignancy among women worldwide and accounted for over 250,000 deaths in 2019. Nearly ninety-five per cent of cervical cancer cases are associated with persistent infection with high-risk Human Papillomavirus (HPV), and seventy per cent of these are associated with viral integration in the host genome. HPV-infection imparts specific changes in the regulatory network of infected cancer cells that are of diagnostic, prognostic and therapeutic importance. Here, we conducted a systems-level analysis of the regulatory network changes, and the associated regulatory proteins thereof, in HPV-positive cervical cancer. We applied functional pathway analysis to show that HPV-positive cancers are characterised by perturbations of numerous cellular processes, predominantly in those linked to the cell cycle, mitosis, cytokine and immune cell signalling. Using computational predictions, we revealed that HPV-positive cervical cancers are regulated by transcription factors including, SOX2, E2F, NANOG, OCT4, and MYC, which control various processes such as the renewal of cancer stem cells, and the proliferation and differentiation of tumour cells. Through the analysis of upstream regulatory kinases, we identified the mitogen-activated protein kinases; among others, MAPK1, MAPK3 and MAPK8, and the cyclin-dependent kinases; among others, CDK1, CDK2 and CD4, as the key kinases that control the biological processes in HPV-positive cervical cancers. Taken together, we uncover a landscape of the key regulatory pathways and proteins in HPV-positive cervical cancers, all of which may provide attractive drug targets for future therapeutics.

Context:The diagnosis and evaluation of impaired renal function remains a challenge owing to lack of reliable biomarker for assessment of kidney function. The existing panel of biomarkers currently displays several limitations, and recently kidney injury molecule-1 (KIM-1) has been suggested as a sensitive biomarker of renal function and proposed to enter clinical practice.Aims:This study was conducted to determine the diagnostic value of serum creatinine, urea, and microalbuminuria (MAU) in relation to the novel biomarker, KIM-1.Materials and Methods:Serum creatinine, urea, MAU, and KIM-1 were measured in forty individuals with and forty without kidney disease. Data were analyzed using multivariate methods of assessing diagnostic efficiency, test agreement, condition effects, and variability.Results:The area under the receiver-operator characteristic curve revealed a diagnostic advantage of creatinine (0.924 ± 0.0066) and urea (0.925 ± 0.0068) over MAU (0.880 ± 0.078) and KIM-1 (0.35 ± 0.124). Overall diagnostic efficiency was higher for creatinine and urea (89.5% and 90.9%, respectively), followed by MAU (85.7%) and then KIM-1 (56.3%). Logistic regression analysis showed that creatinine and urea (R2 = 0.75 and R2 = 0.72, respectively, P < 0.001 for both) were better predictors of kidney disease than MAU (R2 = 0.64, P < 0.001) and KIM-1 (R2 = 0.046, P = 0.116). Further analysis of agreement showed that urea had an excellent agreement with creatinine (kappa r = 0.835, P < 0.001), with KIM-1 (kappa r = –0.198, P = 0.087) showing a poor agreement with creatinine.Conclusion:Our results indicate that elevated serum creatinine and urea above specific cutoff points reliably identifies patients with acute kidney injury or chronic kidney disease. However, more researches are warranted to further validate the diagnostic efficiency and application of MAU and for KIM-1 before its implementation in clinical practice.

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Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.