Antiapoptotic Gene Genotype and Allele Variations and the Risk of Lymphoma

BACKGROUND

The findings of earlier investigations of antiapoptotic gene genotypes and allele variants on lymphoma risk are ambiguous. This study aimed to examine the relationship between the mutation in the antiapoptotic genes and lymphoma risk among Saudi patients.

METHODS

This case-control study included 205 patients, 100 of whom had lymphoma (cases) and 105 who were healthy volunteers (controls). We used tetra amplification refractory mutation polymerase chain reaction (PCR) to identify antiapoptotic genes such as B-cell lymphoma-2 (BCL2-938 C > A), MCL1-rs9803935 T > G, and survivin (BIRC5-rs17882312 G > C and BIRC5-rs9904341 G > C). Allelic-specific PCR was used to identify alleles such as BIRC5-C, MCL1-G, and BIRC5-G.

RESULTS

The dominant inheritance model among cases showed that mutations in all four antiapoptotic genes were more likely to be associated with the risk of lymphoma by the odds of 2.0-, 1.98-, 3.90-, and 3.29-fold, respectively, compared to controls. Apart from the BCL-2-A allele, all three specified alleles were more likely to be associated with lymphoma by the odds of 2.04-, 1.65-, and 2.11-fold, respectively.

CONCLUSION

Unlike healthy individuals, lymphoma patients are more likely to have antiapoptotic gene genotypes and allele variants, apart from BCL-2-A alterations. In the future, these findings could be used to classify and identify patients at risk of lymphoma.

Interleukin-10 as Covid-19 biomarker targeting KSK and its analogues: Integrated network pharmacology

COVID-19 caused by the SARS-CoV-2 virus is widespread in all regions, and it disturbs host immune system functioning leading to extreme inflammatory reaction and hyperactivation of the immune response. Kabasura Kudineer (KSK) is preventive medicine against viral infections and a potent immune booster for inflammation-related diseases. We hypothesize that KSK and KSK similar plant compounds, might prevent or control the COVID-19 infection in the human body. 1,207 KSK and KSK similar compounds were listed and screened via the Swiss ADME tool and PAINS Remover; 303 compounds were filtered including active and similar drug compounds. The targets were retrieved from similar drugs of the active compounds of KSK. Finally, 573 genes were listed after several screening steps. Next, network analysis was performed to finalize the potential target gene: construction of protein-protein interaction of 573 genes using STRING, identifying top hub genes in Cytoscape plug-ins (MCODE and cytoHubba). These ten hub genes play a crucial role in the inflammatory response. Target-miRNA interaction was also constructed using the miRNet tool to interpret miRNAs of the target genes and their functions. Functional annotation was done via DAVID to gain a complete insight into the mechanism of the enriched pathways and other diseases related to the given target genes. In Molecular Docking analysis, IL10 attained top rank in Target-miRNA interaction and also the gene formed prominent exchanges with an excellent binding score (> = -8.0) against 19 compounds. Among them, Guggulsterone has an acute affinity score of -8.8 for IL10 and exhibits anti-inflammatory and immunomodulatory properties. Molecular Dynamics simulation study also performed for IL10 and the interacting ligand compounds using GROMACS. Finally, Guggulsterone will be recommended to enhance immunity against several inflammatory diseases, including COVID19.

Developing a Multimodal Model for Detecting Higher-Grade Prostate Cancer Using Biomarkers and Risk Factors

A technique to predict crucial clinical prostate cancer (PC) is desperately required to prevent diagnostic errors and overdiagnosis. To create a multimodal model that incorporates long-established messenger RNA (mRNA) indicators and conventional risk variables for identifying individuals with severe PC on prostatic biopsies. Urinary has gathered for mRNA analysis following a DRE and before a prostatic examination in two prospective multimodal investigations. A first group (n = 489) generated the multimodal risk score, which was then medically verified in a second group (n = 283). The reverse transcription qualitative polymerase chain reaction determined the mRNA phase. Logistic regression was applied to predict risk in patients and incorporate health risks. The area under the curve (AUC) was used to compare models, and clinical efficacy was assessed by using a DCA. The amounts of sixth homeobox clustering and first distal-less homeobox mRNA have been strongly predictive of high-grade PC detection. In the control subjects, the multimodal method achieved a total AUC of 0.90, with the most important aspects being the messenger riboneuclic acid features' PSA densities and previous cancer-negative tests as a nonsignificant design ability to contribute to PSA, aging, and background. An AUC of 0.86 was observed for one more model that added DRE as an extra risk component. Two methods were satisfactorily verified without any significant changes within the area under the curve in the validation group. DCA showed a massive net advantage and the highest decrease in inappropriate costs.