The usefulness of anti-HCV signal to cut-off ratio in predicting hepatitis C viremia and the effect of genotype differences on signal to cut-off ratio.

OBJECTIVE: In the hepatitis C virus (HCV) diagnostic algorithm, an anti-HCV screening test is recommended first. In countries with low HCV prevalence, anti-HCV testing can often give false-positive results. This may lead to unnecessary retesting, increased costs, and psychological stress for patients. METHODS: In this study, the most appropriate S/Co (signal-cutoff) value to predict HCV viremia in anti-HCV test(+) individuals was determined, and the effect of genotype differences was evaluated. Of the 96,515 anti-HCV tests performed between 2020 and 2023, 934 were reactive. A total of 332 retests and 65 patients without HCV-ribonucleic acid (RNA) analysis were excluded. Demographic data were calculated for 537 patients, and 130 patients were included in the study. RESULTS: The average age of 537 patients was 55±18 years, and 57.1% were women. The anti-HCV positivity rate was 0.62% (602/96,515), and the actual anti-HCV positivity rate was 0.13% (130/96,515). Anti-HCV levels were higher in HCV-RNA(+) patients than in HCV-RNA-negative individuals (p<0.0001) (Table 1). Receiver operating characteristic curve analysis identified the optimal S/Co value to be 10.86 to identify true positive cases. Sensitivity was 96.1%, specificity was 61.2%, positive predictive value (PPV) was 44.2%, and negative predictive value (NPV) was 98% (Figure 2). A total of 107 (82.3%) of the patients were identified as GT1, and the most common subtype was GT1b (n=100). CONCLUSION: If anti-HCV S/Co is ≥10.86, direct HCV RNA testing may be recommended; However, the possibility of false positivity should be considered in patients with a S/Co value below 10.86.

The Important Role of TMPRSS2 Gene in Covid-19 and Prostate Cancer: In Silico Approach

Abstract Some studies have discovered a connection between prostate cancer and COVID-19. In this study, we evaluated the link between prostate cancer and COVID-19, contributing to elucidate the connection between TMPRSS2 and ACE2. We discovered 209 number of variants in TMPRSS2 gene, and 110 variants represent EA populations and 99 of them represent AA populations. Moreover, we found 23 suspected missense and 3 unknown variants. Then, linked genes to TMPRSS2 and ACE2 were found in our study. We investigated the expression level of TMPRSS2 and the results showed that it was very high in the prostate, colon, lung, kidney, and saliva-secreting gland. Also, the important role of the AR gene was revealed in addition to other oncogenes and tumor suppressor genes for prostate cancer by KEGG Pathway analysis. In conclusion, these results can highlight several molecular mechanisms of SARS-CoV-2, and also TMPRSS2, ACE2, and AR connection explains the high expression level of AR gene found in the male lung.

Role of ACE2 Gene Expression in Renin Angiotensin System and Its Importance in Covid-19: In Silico Approach

Abstract We aimed to analyze the expression profile of ACE2 and similar genes with ACE2, predict the number of variations in ACE2, detect the suspected SNPs on ACE2 gene, and perform the pathway analysis of renin-angiotensin system (RAS) and protein absorption-digestion. Moreover, we have predicted the gene-related diseases with ACE2. STRING was used to analyze functionally similar genes with ACE2. Exome Variant Server, SIFT, Polyphen2 were used to predict the number of variations in ACE2 and detect the suspected SNPs on ACE2. KEGG database and STRING were used to draw pathway of ACE2. Then, DISEASES resource, FitSNPs, UniProt, BioXpress, IGV Browser, Ensembl Genome Browser, and UCSC Genome Browser were used to predict the ACE2 gene-related diseases and expression profile in human normal and cancer tissues. We have shown that expression of ACE2 was correlated with AGT, REN, AGTR1, AGRT2, MME2, DPP4, PRCP, MEP1A, XPNPEP2, MEP1BandACE2 is expressed in testis, kidney, heart, thyroid, colon, esophagus, breast, minor salivary gland, pancreas, lung, liver, bladder, cervix, and muscle tissues. We found 99 variations in ACE2 gene, in which no previous study has been performed. In the future, this in silico analysis should be combined with other pieces of evidence including experimental data to assign function.