Cancer Tendency in a Patient with ZNF341 Deficiency

Full Increased radiosensitivity and impaired DNA repair in patients with STAT3-LOF and ZNF341 deficiency, potentially contributing to malignant transformations

STAT3 plays an important role in various complex and sometimes contradictory pathways such as proliferation, differentiation, migration, inflammation, and apoptosis. The transcriptional activity of the STAT3 gene is controlled by a transcription factor called ZNF341. There is insufficient data on radiation sensitivity and post-radiation DNA repair in STAT3- loss-of-function (LOF) patients. We aimed to investigate the radiosensitivity in patients with STAT3-LOF and ZNF341 deficiency. Twelve patients with STAT3-LOF and four ZNF341-deficiency patients were recruited from three clinical immunology centers in Turkey and evaluated for radiosensitivity by the Comet assay, comparing to 14 age- and sex-matched healthy controls. The Tail length (μm), Tail DNA (%), Olive Tail Moment (OTM) (arbitrary units) were evaluated at the same time for baseline (spontaneous), initial (immediately after 2Gy irradiation), and recovery (2h after irradiation) periods by using a computerized image-analysis system, estimating DNA damage. Except for a patient with ZNF341 deficiency who developed nasal cell primitive neuroendocrine tumor and papillary thyroid cancer during the follow-up, there was no cancer in both groups. During the recovery period of irradiation, TL, TDNA%, and OTM values of healthy controls decreased rapidly towards the baseline, while these values of patients with STAT3-LOF and ZNF341 deficiency continued to increase, implying impaired DNA repair mechanisms. Increased radiosensitivity and impaired DNA repair were demonstrated in patients diagnosed with STAT3-LOF and ZNF341 deficiency, potentially explaining the susceptibility to malignant transformation.

Identification of key gene signatures for the overall survival of ovarian cancer

BACKGROUND

The five-year overall survival (OS) of advanced-stage ovarian cancer remains nearly 25-35%, although several treatment strategies have evolved to get better outcomes. A considerable amount of heterogeneity and complexity has been seen in ovarian cancer. This study aimed to establish gene signatures that can be used in better prognosis through risk prediction outcome for the survival of ovarian cancer patients. Different studies' heterogeneity into a single platform is presented to explore the penetrating genes for poor or better survival. The integrative analysis of multiple data sets was done to determine the genes that influence poor or better survival. A total of 6 independent data sets was considered. The Cox Proportional Hazard model was used to obtain significant genes that had an impact on ovarian cancer patients. The gene signatures were prepared by splitting the over-expressed and under-expressed genes parallelly by the variable selection technique. The data visualisation techniques were prepared to predict the overall survival, and it could support the therapeutic regime.

RESULTS

We preferred to select 20 genes in each data set as upregulated and downregulated. Irrespective of the selection of multiple genes, not even a single gene was found common among data sets for the survival of ovarian cancer patients. However, the same analytical approach adopted. The chord plot was presented to make a comprehensive understanding of the outcome.

CONCLUSIONS

This study helps us to understand the results obtained from different studies. It shows the impact of the heterogeneity from one study to another. It shows the requirement of integrated studies to make a holistic view of the gene signature for ovarian cancer survival.

Developing ZNF Gene Signatures Predicting Radiosensitivity of Patients with Breast Cancer

Adjuvant radiotherapy is one of the main treatment methods for breast cancer, but its clinical benefit depends largely on the characteristics of the patient. This study aimed to explore the relationship between the expression of zinc finger (ZNF) gene family proteins and the radiosensitivity of breast cancer patients. Clinical and gene expression data on a total of 976 breast cancer samples were obtained from The Cancer Genome Atlas (TCGA) database. ZNF gene expression was dichotomized into groups with a higher or lower level than the median level of expression. Univariate and multivariate Cox regression analyses were used to evaluate the relationship between ZNF gene expression levels and radiosensitivity. The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database was used for validation. The results revealed that 4 ZNF genes were possible radiosensitivity markers. High expression of ZNF644 and low expression levels of the other 3 genes (ZNF341, ZNF541, and ZNF653) were related to the radiosensitivity of breast cancer. Hierarchical cluster, Cox, and CoxBoost analysis based on these 4 ZNF genes indicated that patients with a favorable 4-gene signature had better overall survival on radiotherapy. Thus, this 4-gene signature may have value for selecting those patients most likely to benefit from radiotherapy. ZNF gene clusters could act as radiosensitivity signatures for breast cancer patients and may be involved in determining the radiosensitivity of cancer.