Perforin gene variation influences survival in childhood acute lymphoblastic leukemia

Perforin 1 in Cancer: Mechanisms, Therapy, and Outlook

PRF1 (perforin 1) is a key cytotoxic molecule that plays a crucial role in the killing function of natural killer (NK) cells and cytotoxic T lymphocytes (CTLs). Recent studies have focused on PRF1's role in cancer development, progression, and prognosis. Studies have shown that aberrant PRF1 expression has a significant role to play in cancer development and progression. In some cancers, high expression of the PRF1 gene is associated with a better prognosis for patients, possibly because it helps enhance the body's immune response to tumors. However, some studies have also shown that the absence of PRF1 may make it easier for tumors to evade the body's immune surveillance, thus affecting patient survival. Furthermore, recent studies have explored therapeutic strategies based on PRF1, such as enhancing the ability of immune cells to kill cancer cells by boosting PRF1 activity. In addition, they have improved the efficacy of immunotherapy by modulating its expression to enhance the effectiveness of the treatment. Based on these findings, PRF1 may be a valuable biomarker both for the treatment of cancer and for its prognosis in the future. To conclude, PRF1 has an important biological function and has clinical potential for the treatment of cancer, which indicates that it deserves more research and development in the future.

Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis.

Immune Response Gaps Linked to SARS-CoV-2 Infection: Cellular Exhaustion, Senescence, or Both?

The COVID-19 pandemic, promoted by the SARS-CoV-2 respiratory virus, has resulted in widespread global morbidity and mortality. The immune response against this pathogen has shown a thin line between protective effects and pathological reactions resulting from the massive release of cytokines and poor viral clearance. The latter is possibly caused by exhaustion, senescence, or both of TCD8+ cells and reduced activity of natural killer (NK) cells. The imbalance between innate and adaptive responses during the early stages of infection caused by SARS-CoV-2 contributes to the ineffective control of viral spread. The present study evaluated the tissue immunoexpression of the tissue biomarkers (Arginase-1, CCR4, CD3, CD4, CD8, CD20, CD57, CD68, CD138, IL-4, INF-α, INF-γ, iNOS, PD-1, Perforin and Sphingosine-1) to understand the cellular immune response triggered in patients who died of COVID-19. We evaluated twenty-four paraffin-embedded lung tissue samples from patients who died of COVID-19 (COVID-19 group) and compared them with ten lung tissue samples from patients who died of H1N1pdm09 (H1N1 group) with the immunohistochemical markers mentioned above. In addition, polymorphisms in the Perforin gene were genotyped through Real-Time PCR. Significantly increased tissue immunoexpression of Arginase, CD4, CD68, CD138, Perforin, Sphingosine-1, and IL-4 markers were observed in the COVID-19 group. A significantly lower immunoexpression of CD8 and CD57 was also found in this group. It is suggested that patients who died from COVID-19 had a poor cellular response concerning viral clearance and adaptive response going through tissue repair.

Role of Genetic Polymorphism Present in Macrophage Activation Syndrome Pathway in Post Mortem Biopsies of Patients with COVID-19

COVID-19 is a viral disease associated with an intense inflammatory response. Macrophage Activation Syndrome (MAS), the complication present in secondary hemophagocytic lymphohistiocytosis (sHLH), shares many clinical aspects observed in COVID-19 patients, and investigating the cytolytic function of the responsible cells for the first line of the immune response is important. Formalin-fixed paraffin-embedded lung tissue samples obtained by post mortem necropsy were accessed for three groups (COVID-19, H1N1, and CONTROL). Polymorphisms in MAS cytolytic pathway (PRF1; STX11; STXBP2; UNC13D and GZMB) were selected and genotyping by TaqMan^® assays (Thermo Fisher Scientific, MA, USA) using Real-Time PCR (Applied Biosystems, MA USA). Moreover, immunohistochemistry staining was performed with a monoclonal antibody against perforin, CD8+ and CD57+ proteins. Histopathological analysis showed high perforin tissue expression in the COVID-19 group; CD8+ was high in the H1N1 group and CD57+ in the CONTROL group. An association could be observed in two genes related to the cytolytic pathway (PRF1 rs885822 G/A and STXBP2 rs2303115 G/A). Furthermore, PRF1 rs350947132 was associated with increased immune tissue expression for perforin in the COVID-19 group. The genotype approach could help identify patients that are more susceptible, and for this reason, our results showed that perforin and SNPs in the PRF1 gene can be involved in this critical pathway in the context of COVID-19.

Associations between PRF1 Ala91Val polymorphism and risk of hemophagocytic lymphohistiocytosis: a meta-analysis based on 1366 subjects

BACKGROUND

Perforin (PRF1) gene mutation can cause the onset of hemophagocytic lymphohistiocytosis (HLH). It has reported that PRF1 Ala91Val polymorphism was related with HLH risk. In the meta-analysis, we aim to evaluate the association between PRF1 Ala91Val polymorphism and HLH risk.

METHODS

We accomplished a meta-analysis of six published case-control studies including 391 patients with HLH and 975 controls. We evaluated the quality of each study through Newcastle-Ottawa Scale (NOS). Data analysis was performed with Stata software.

RESULTS

In general, all studies were of high quality (NOS score higher than 7). There were statistically significant between the PRF1 Ala91Val polymorphism and HLH risk though the pooled analysis [for Ala/Val vs. Ala/Ala: pooled odds ratio (OR) = 3.22, 95% confidence interval (CI) 1.08-9.56, P = 0.035, random model; for Ala/Val + Val/Val vs. Ala/Ala: pooled OR = 2.96, 95% CI 1.14-7.69, P = 0.025, random model]. Furthermore, sensitivity analysis also revealed a relationship between PRF1 Ala91Val polymorphism and HLH risk (for Ala/Val vs. Ala/Ala: pooled OR = 5.236, 95% CI 2.72-10.08, P < 0.000, I² = 12.1%, P_{heterogeneity} = 0.332; for Ala/Val + Val/Val vs. Ala/Ala, pooled OR = 4.856, 95% CI 2.66-8.85, P < 0.000, I² = 5.9%, P_{heterogeneity} = 0.373). Funnel plot and Egger's test did not indicate obvious published bias (P = 0.841 for Ala/Val vs. Ala/Ala; P = 0.284 for Ala/Val + Val/Val vs. Ala/Ala).

CONCLUSION

This meta-analysis indicated that PRF1 Ala91Val polymorphism affects the factor for developing HLH and future studies of PRF1 Ala91Val on the onset of HLH will be guaranteed.