Predictive Potential of RNA Polymerase B (II) Subunit 1 (RPB1) Cytoplasmic Aggregation for Neoadjuvant Chemotherapy Failure

We aimed to investigate the contribution of co-translational protein aggregation to the chemotherapy resistance of tumor cells. Increased co-translational protein aggregation reflects altered translation regulation that may have the potential to buffer transcription under genotoxic stress. As an indicator for such an event, we followed the cytoplasmic aggregation of RPB1, the aggregation-prone largest subunit of RNA polymerase II, in biopsy samples taken from patients with invasive carcinoma of no special type. RPB1 frequently aggregates co-translationally in the absence of proper HSP90 chaperone function or in ribosome mutant cells as revealed formerly in yeast. We found that cytoplasmic foci of RPB1 occur in larger sizes in tumors that showed no regression after therapy. Based on these results, we propose that monitoring the cytoplasmic aggregation of RPB1 may be suitable for determining-from biopsy samples taken before treatment-the effectiveness of neoadjuvant chemotherapy.

Identifying Suitable Reference Gene Candidates for Quantification of DNA Damage-Induced Cellular Responses in Human U2OS Cell Culture System

DNA repair pathways trigger robust downstream responses, making it challenging to select suitable reference genes for comparative studies. In this study, our goal was to identify the most suitable housekeeping genes to perform comparable molecular analyses for DNA damage-related studies. Choosing the most applicable reference genes is important in any kind of target gene expression-related quantitative study, since using the housekeeping genes improperly may result in false data interpretation and inaccurate conclusions. We evaluated the expressional changes of eight well-known housekeeping genes (i.e., 18S rRNA, B2M, eEF1α1, GAPDH, GUSB, HPRT1, PPIA, and TBP) following treatment with the DNA-damaging agents that are most frequently used: ultraviolet B (UVB) non-ionizing irradiation, neocarzinostatin (NCS), and actinomycin D (ActD). To reveal the significant changes in the expression of each gene and to determine which appear to be the most acceptable ones for normalization of real-time quantitative polymerase chain reaction (RT-qPCR) data, comparative and statistical algorithms (such as absolute quantification, Wilcoxon Rank Sum Test, and independent samples T-test) were conducted. Our findings clearly demonstrate that the genes commonly employed as reference candidates exhibit substantial expression variability, and therefore, careful consideration must be taken when designing the experimental setup for an accurate and reproducible normalization of RT-qPCR data. We used the U2OS cell line since it is generally accepted and used in the field of DNA repair to study DNA damage-induced cellular responses. Based on our current data in U2OS cells, we suggest using 18S rRNA, eEF1α1, GAPDH, GUSB, and HPRT1 genes for UVB-induced DNA damage-related studies. B2M, HPRT1, and TBP genes are recommended for NCS treatment, while 18S rRNA, B2M, and PPIA genes can be used as suitable internal controls in RT-qPCR experiments for ActD treatment. In summary, this is the first systematic study using a U2OS cell culture system that offers convincing evidence for housekeeping gene selection following treatment with various DNA-damaging agents. Here, we unravel an indispensable issue for performing and assessing trustworthy DNA damage-related differential gene expressional analyses, and we create a "zero set" of potential reference gene candidates.

The clinical significance of epigenetic and RNAPII variabilities occurring in clear cell renal cell carcinoma as a potential prognostic marker

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30 ccRCC patients were used, to follow the epigenetic changes (γH2A.X, H3K4me3 and H3K9me3) and the alterations in the level of RNA polymerase II (RNAPII).

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The variabilities between the tumorous and non-tumorous parts of the tissue were measured by image analysis in which we monitored 30 cells from different positions of either the tumorous or the non-tumorous part of the tissue sections.

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These markers were classified to predict patient outcomes based on their individual cellular background. These results also support that detection of any alteration in the level of H3K4me3, H3K9me3, and γH2AX can account valuable information for presuming the progression of ccRCC and the clinical benefits to select the most efficient personalized therapy.

Patients diagnosed with clear cell renal cell carcinoma (ccRCC) have poor prognosis for recurrence and approximately 30–40% of them will later develop metastases. For this reason, the appropriate diagnosis and the more detailed molecular characterisation of the primary tumour, including its susceptibility to metastasis, are crucial to select the proper adjuvant therapy by which the most prosperous outcome can be achieved. Nowadays, clinicopathological variables are used for classification of the tumours. Apart from these, molecular biomarkers are also necessary to improve risk classification, which would be the most beneficial amongst modern adjuvant therapies. As a potential molecular biomarker, to follow the transcriptional kinetics in ccRCC patients (n=30), we analysed epigenetic changes (γH2A.X, H3K4me³, and H3K9me³) and the alterations in the level of RNA polymerase II (RNAPII) by immunohistochemical staining on dissected tissue sections. The variabilities between the tumorous and non-tumorous parts of the tissue were detected using quantitative image analysis by monitoring 30 cells from different positions of either the tumorous or the non-tumorous part of the tissue sections. Data obtained from the analyses were used to identify potential prognostic features and to associate them with the progression. These markers might have a value to predict patient outcomes based on their individual cellular background. These results also support that detection of any alteration in the level of H3K4me³, H3K9me³, and γH2A.X can account for valuable information for presuming the progression of ccRCC and the clinical benefits to select the most efficient personalised therapy.