ABSTRACT
Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66-6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05-14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.
ABSTRACT
Heme oxygenase 1 (HO-1), the rate-limiting enzyme in heme degradation, is involved in the maintenance of cellular homeostasis, exerting a cytoprotective role by its antioxidative and anti-inflammatory functions. HO-1 and its end products, biliverdin, carbon monoxide and free iron (Fe2+), confer cytoprotection against inflammatory and oxidative injury. Additionally, HO-1 exerts antiviral properties against a diverse range of viral infections by interfering with replication or activating the interferon (IFN) pathway. Severe cases of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are characterized by systemic hyperinflammation, which, in some cases, leads to severe or fatal symptoms as a consequence of respiratory failure, lung and heart damage, kidney failure, and nervous system complications. This review summarizes the current research on the protective role of HO-1 in inflammatory diseases and against a wide range of viral infections, positioning HO-1 as an attractive target to ameliorate clinical manifestations during COVID-19.
ABSTRACT
Prostate cancer (PCa) is the second most diagnosed malignancy and the fifth leading cause of cancer associated death in men worldwide. Dysregulation of cellular energetics has become a hallmark of cancer, evidenced by numerous connections between signaling pathways that include oncoproteins and key metabolic enzymes. We previously showed that heme oxygenase 1 (HO-1), a cellular homeostatic regulator counteracting oxidative and inflammatory damage, exhibits anti-tumoral activity in PCa cells, inhibiting cell proliferation, migration, tumor growth and angiogenesis. The aim of this study was to assess the role of HO-1 on the metabolic signature of PCa. After HO-1 pharmacological induction with hemin, PC3 and C4-2B cells exhibited a significantly impaired cellular metabolic rate, reflected by glucose uptake, ATP production, lactate dehydrogenase (LDH) activity and extracellular lactate levels. Further, we undertook a bioinformatics approach to assess the clinical significance of LDHA, LDHB and HMOX1 in PCa, identifying that high LDHA or low LDHB expression was associated with reduced relapse free survival (RFS). Interestingly, the shortest RFS was observed for PCa patients with low HMOX1 and high LDHA, while an improved prognosis was observed for those with high HMOX1 and LDHB. Thus, HO-1 induction causes a shift in the cellular metabolic profile of PCa, leading to a less aggressive phenotype of the disease.
ABSTRACT
In a published case-control study (GSE152075) from SARS-CoV-2-positive (n = 403) and -negative patients (n = 50), we analyzed the response to infection assessing gene expression of host cell receptors and antiviral proteins. The expression analysis associated with reported risk factors for COVID-19 was also assessed. SARS-CoV-2 cases had higher ACE2, but lower TMPRSS2, BSG/CD147, and CTSB expression compared with negative cases. COVID-19 patients' age negatively affected ACE2 expression. MX1 and MX2 were higher in COVID-19 patients. A negative trend for MX1 and MX2 was observed as patients' age increased. Principal-component analysis determined that ACE2, MX1, MX2, and BSG/CD147 expression was able to cluster non-COVID-19 and COVID-19 individuals. Multivariable regression showed that MX1 expression significantly increased for each unit of viral load increment. Altogether, these findings support differences in ACE2, MX1, MX2, and BSG/CD147 expression between COVID-19 and non-COVID-19 patients and point out to MX1 as a critical responder in SARS-CoV-2 infection.
