Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.

Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

Prevalence and Impact of Preexisting Comorbidities on Overall Clinical Outcomes of Hospitalized COVID-19 Patients.

COVID-19 risk increases with comorbidities, and the effect is magnified due to the contribution of individual and combined comorbidities to the overall clinical outcomes. We aimed to explore the influence of demographic factors, clinical manifestations, and underlying comorbidities on mortality, severity, and hospital stay in COVID-19 patients. Therefore, retrospective chart reviews were performed to identify all laboratory-confirmed cases of SARS-CoV-2 infection in Apollo Hospitals, Hyderabad, between March 2020 and August 2020.A total of 369 confirmed SARS-CoV-2 cases were identified: 272 (73.7%) patients were male, and 97 (26.2%) were female. Of the confirmed cases, 218 (59.1%) had comorbidities, and 151 (40.9%) were devoid of comorbidities. This study showed that old age and underlying comorbidities significantly increase mortality, hospital stay, and severity due to COVID-19 infection. The presence of all four comorbidities, diabetes mellitus (DM) + Hypertension (HTN) + coronary artery disease (CAD) + chronic kidney disease (CKD), conferred the most severity (81%). The highest mortality (OR: 44.03, 95% CI: 8.64-224.27) was observed during the hospital stay (12.73 ± 11.38; 95% CI: 5.08-20.38) in the above group. Multivariate analysis revealed that nonsurvivors are highest (81%) in (DM + HTN + CAD + CKD) category with an odds ratio (95% CI) of 44.03 (8.64-224.27). Age, gender, and comorbidities adjusted odds ratio decreased to 20.25 (3.77-108.77). Median survival of 7 days was observed in the (DM + HTN + CAD + CKD) category. In summary, the presence of underlying comorbidities has contributed to a higher mortality rate, greater risk of severe disease, and extended hospitalization periods, hence, resulting in overall poorer clinical outcomes in hospitalized COVID-19 patients.

Diagnostic Values of Laboratory Biomarkers in Predicting a Severe Course of COVID-19 on Hospital Admission.

Objective: We intend to identify differences in the clinicodemographic and laboratory findings of COVID-19 patients to predict disease severity and outcome on admission. Methods: This single-centred retrospective study retrieved laboratory and clinical data from 350 COVID-19 patients on admission, represented as frequency tables. A multivariate regression model was used to assess the statistically significant association between the explanatory variables and COVID-19 infection outcomes, where adjusted odds ratio (AOR), p value, and 95% CI were used for testing significance. Results: Among the 350 COVID-19 patients studied, there was a significant increase in the WBC count, neutrophils, aggregate index of systemic inflammation (AISI), neutrophil-to-lymphocyte ratio (dNLR), neutrophil-to-lymphocyte and platelet ratio (NLPR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), D-dimer, interleukin-6 (IL-6), ferritin, lactate dehydrogenase (LDH), prothrombin time (PT), glucose, urea, urea nitrogen, creatinine, alanine phosphatase (ALP), and aspartate aminotransferase (AST) and a significant decrease in lymphocytes, eosinophils, total protein, albumin, prealbumin serum, and albumin/globulin (A/G) ratio in the severe group when compared with the mild and moderate groups. However, after adjusting their age, gender, and comorbidities, WBC count (adjusted odds ratio (AOR) = 6.888, 95% CI = 1.590-29.839, p = 0.010), neutrophils (AOR = 5.912, 95% CI = 2.131-16.402, p = 0.001), and urea (AOR = 4.843, 95% CI = 1.988-11.755, p = 0.001) were strongly associated with disease severity. Interpretation and Conclusion. On admission, WBC count, neutrophils, and urea, with their cut of values, can identify at-risk COVID-19 patients who could develop severe COVID-19.