Methylation Status In Patients Research Articles

Study of mir 145 promoter methylation status in patients with superficial urothelial bladder cancer treated with BCG (Bacille Calmette-Guerin)

Study of mir 145 promoter methylation status in patients with superficial urothelial bladder cancer treated with BCG (Bacille Calmette-Guerin)

Prediction of O(6)-methylguanine-DNA methyltransferase promoter methylation status in IDH-wildtype glioblastoma using MRI histogram analysis.

To evaluate the utility of magnetic resonance imaging (MRI) histogram parameters in predicting O(6)-methylguanine-DNA methyltransferase promoter (pMGMT) methylation status in IDH-wildtype glioblastoma (GBM). From November 2021 to July 2023, forty-six IDH-wildtype GBM patients with known pMGMT methylation status (25 unmethylated and 21 methylated) were enrolled in this retrospective study. Conventional MRI signs (including location, across the midline, margin, necrosis/cystic changes, hemorrhage, and enhancement pattern) were assessed and recorded. Histogram parameters were extracted and calculated by Firevoxel software based on contrast-enhanced T1-weighted images (CET1). Differences and diagnostic performance of conventional MRI signs and histogram parameters between the pMGMT-unmethylated and pMGMT-methylated groups were analyzed and compared. No differences were observed in the conventional MRI signs between pMGMT-unmethylated and pMGMT-methylated groups (all p > 0.05). Compared with the pMGMT-methylated group, pMGMT-unmethylated showed a higher minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, and coefficient of variation (CV) (all p < 0.05). Among all significant CET1 histogram parameters, minimum achieved the best distinguishing performance, with an area under the curve of 0.836. CET1 histogram parameters could provide additional value in predicting pMGMT methylation status in patients with IDH-wildtype GBM, with minimum being the most promising parameter.

Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.

Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.

Abstract 7395: Histology-based prognosis prediction using deep learning outperforms and is independent of the MGMT methylation status in patients with glioblastoma

Abstract Introduction: MGMT gene promoter methylation status is today the only biomarker of survival for GBM patients and predicts favorable response to temozolomide (TMZ). Here we propose a deep learning-based model using histology data (H&amp;E slides) to predict the prognosis of patients with Glioblastoma (GBM) treated by standard of care (SOC). Methods: We included TCGA GBM patients and selected only patients treated after 2005 (Stupp protocol start); compliant to WHO 2021 classification; treated by surgery and adjuvant therapy; with a follow-up superior to median overall survival (OS) (14.5 months). Short and long survivor labels were defined based on median OS cut-off. A deep learning pipeline was trained to predict these labels from the H&amp;E slides (baseline surgery samples). First, the slides were automatically segmented into tissue and background, divided into a regular grid of tiles (112 × 112µm), and tiles were vectorized using a vision transformer-based feature extractor trained on H&amp;E slides of 5,500 patients with 16 cancer subtypes (GBM excluded). In the second step, ABMIL deep learning model was trained to predict the survival category using the bag of tile features available from step 1 for each patient. The cohort was divided into five folds with even numbers of short and long survivors. For each fold, 50 ABMIL models were trained on the remaining folds and ensembled to predict the category of the patients in the fold. Training hyperparameters were tuned on a separated cohort excluding GBM patients. Results: After filtering, we analyzed n = 192 TCGA GBM patients including 154 patients with a known MGMT promoter methylation status. Our histology-based model had an accuracy of 0.661 and a ROC AUC of 0.706. It stratified the population into short and long survivors with an Hazard Ratio (HR) of 0.48 (95% CI 0.34-0.69). Our model outperformed the MGMT methylation status that had an HR of 0.57 (95% CI 0.40-0.83). Moreover, our model was independent of MGMT methylation status and the combination of both was able to further improve prognosis prediction: we applied our model independently to the 91 patients with an unmethylated MGMT status (predicted: 43 low, 48 high) and to the 63 patients with a methylated MGMT status (predicted: 30 low, 33 high), and obtained HR of 0.42 (95% CI 0.27-0.67) and 0.49 (95% CI 0.28-0.86) respectively. Conclusion: Having selected patients treated by the latest SOC and classified as GBM by the latest WHO criteria, we propose a model predicting survival based on histological specimens available in routine care, which outperformed the current unique biomarker MGMT methylation status. The combination of this status and our model improves prognosis predictions compared to each strategy alone. Citation Format: Lucas Fidon, Celine Thiriez, Alexandre Grimaldi, Omar Darwiche Domingues, Charles Maussion, Caroline Hoffmann. Histology-based prognosis prediction using deep learning outperforms and is independent of the MGMT methylation status in patients with glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7395.

GNAS AS2 methylation status enables mechanism-based categorization of pseudohypoparathyroidism type 1B.

Pseudohypoparathyroidism type 1B (PHP1B) results from aberrant genomic imprinting at the GNAS gene. Defining the underlying genetic cause in new patients is challenging because various genetic alterations (e.g., deletions, insertions) within the GNAS genomic region, including the neighboring STX16 gene, can cause PHP1B, and the genotype-epigenotype correlation has not been clearly established. Here, by analyzing patients with PHP1B with a wide variety of genotypes and epigenotypes, we identified a GNAS differentially methylated region (DMR) of distinct diagnostic value. This region, GNAS AS2, was hypomethylated in patients with genetic alterations located centromeric but not telomeric of this DMR. The AS2 methylation status was captured by a single probe of the methylation-sensitive multiplex ligation-dependent probe amplification (MS-MLPA) assay utilized to diagnose PHP1B. In human embryonic stem cells, where NESP55 transcription regulates GNAS methylation status on the maternal allele, AS2 methylation depended on 2 imprinting control regions (STX16-ICR and NESP-ICR) essential for NESP55 transcription. These results suggest that the AS2 methylation status in patients with PHP1B reflects the position at which the genetic alteration affects NESP55 transcription during an early embryonic period. Therefore, AS2 methylation levels can enable mechanistic PHP1B categorization based on genotype-epigenotype correlation and, thus, help identify the underlying molecular defect in patients.

Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.

To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas. PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity. We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01). Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice. MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery. • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.

Alu Methylation Patterns in Type 1 Diabetes: A Case-Control Study.

Evidence suggests that genome-wide hypomethylation may promote genomic instability and cellular senescence, leading to chronic complications in people with diabetes mellitus. Limited data are however available on the Alu methylation status in patients with type 1 diabetes (T1D). Methods: We investigated DNA methylation levels and patterns of Alu methylation in the peripheral blood of 36 patients with T1D and 29 healthy controls, matched for age and sex, by using the COmbined Bisulfite Restriction Analysis method (COBRA). Results: Total Alu methylation rate (mC) was similar between patients with T1D and controls (67.3% (64.4-70.9%) vs. 68.0% (62.0-71.1%), p = 0.874). However, patients with T1D had significantly higher levels of the partial Alu methylation pattern (mCuC + uCmC) (41.9% (35.8-45.8%) vs. 36.0% (31.7-40.55%), p = 0.004) compared to healthy controls. In addition, a positive correlation between levels of glycated hemoglobin (HbA1c) and the partially methylated loci (mCuC + uCmC) was observed (Spearman's rho = 0.293, p = 0.018). Furthermore, significant differences were observed between patients with T1D diagnosed before and after the age of 15 years regarding the total methylation mC, the methylated pattern mCmC and the unmethylated pattern uCuC (p = 0.040, p = 0.044 and p = 0.040, respectively). Conclusions: In conclusion, total Alu methylation rates were similar, but the partial Alu methylation pattern (mCuC + uCmC) was significantly higher in patients with T1D compared to healthy controls. Furthermore, this pattern was associated positively with the levels of HbA1c and negatively with the age at diagnosis.

Genome-wide analysis of DNA methylation and its relationship with serum homocysteine levels in patients with hypertension.

Homocysteine (Hcy) is an independent risk factor for cardiovascular diseases, and elevated plasma Hcy levels could aggravate vascular injury in hypertension. Hyperhomocysteinemia can change the methylation status of global DNA and specific genes. In the present study, we aim to examine the comprehensive influence of Hcy levels on DNA methylation status in patients with hypertension. Epigenome-wide methylation profiles of the peripheral leukocyte DNA of 218 patients with hypertension were analyzed using the Illumina Infinium Methylation EPIC BeadChip. Differentially methylated positions (DMPs) associated with serum Hcy levels were identified by mixed linear regression with the adjustment of potential confounders. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were conducted to determine the potential functions of the identified DMPs. The association between the methylation level of DMPs and carotid-femoral pulse wave velocity (Cf-PWV) was also analyzed. Five DMPs at cg13169662, cg03179312, cg21976560, cg25262698, and cg09433843 showed significant association with serum Hcy levels (false discovery rate-corrected P < 0.05). An additional six CpG sites met the threshold for suggestive significance ( P < 1 × 10 -6 ), among which three DMPs (cg25781123, cg26463106, and cg06679221) were annotated to THUMPD3 . Furthermore, the methylation levels of cg13169662 and cg25262698 (RPRD1A) were significantly associated with Cf-PWV. Our results suggest that Hcy could induce DNA methylation alteration in patients with hypertension. Further functional research is warranted to elucidate the concrete role of DMPs in hypertension.

GENOME-WIDE DNA METHYLATION ANALYSIS IDENTIFIES SERUM HOMOCYSTEINE-ASSOCIATED CPG SITES IN HYPERTENSIVES

Objective: Homocysteine (Hcy) is associated with DNA methylation in cardiovascular disease (CVD). Moreover, the relationship between Hyperhomocysteine (HHcy) and CVD risk is stronger in individuals with hypertension. However, no research has focused on the influence of Hcy on DNA methylation status in patients with hypertension. We performed an epigenome-wide association study to identify DNA methylation loci associated with Hcy among 218 hypertensive patients using the Illumina Infinium Methylation EPIC BeadChip (850k chip) and further explored their effects on arteriosclerosis. Design and method: Epigenome-wide methylation profiles were assessed using the Illumina Infinium Methylation EPIC BeadChip (850k chip) in peripheral leukocyte DNA from 218 patients with hypertension. Differentially methylated positions (DMPs) associated with serum homocysteine levels were identified using a mixed linear regression with adjustment for potential confounders. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted to explore the possible functions of the identified DMPs. The association of these differentially methylated loci with hypertension- and Hcy- related arteriosclerosis, Cf-PWV (Carotid-femoral pulse wave velocity) was further analyzed. Results: Five DMPs at cg13169662, cg03179312, cg21976560, cg25262698, and cg09433843 showed significant association with serum Hcy levels (FDR-corrected p &lt;0.05). Six additional CpG sites met the threshold for suggestive significance (p&lt;1 × 10-6), among which three DMPs (cg25781123, cg26463106, and cg06679221) were annotated to the THUMPD3 gene. Furthermore, the methylation levels of cg13169662 and cg25262698 (RPRD1A) were significantly associated with Cf-PWV. Conclusions: Our study provides preliminary evidence for an association between DNA methylation and serum Hcy levels in patients with hypertension. This study may give an insight into the role of DNA methylation in cardiovascular injury underlying HHcy in patients with hypertension.

458 Assessment of MGMT Methylation Status Using High-Performance Liquid Chromatography in Newly Diagnosed Glioblastoma

INTRODUCTION: The utility of O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation status as a prognostic marker in patients with glioblastoma (GBM) has been established. However, the number of CpG sites that must be methylated to cause transcriptional silencing remains unclear, and no significant consensus exists on the optimal method of assessing MGMT methylation. METHODS: W analyzed the MGMT methylation status of 28 isocitrate dehydrogenase–wild-type GBMs treated with temozolomide using ion-exchange HPLC and bisulfite sequencing using frozen samples. We designed three primers for separate regions (regions 1–3) that had 21 to 38 CpGs for PCR and validated the MGMT promoter methylation status. RESULTS: There was a strong correlation between HPLC and bisulfite sequencing results (R = 0.794). The optimal cutoff values for MGMT methylation in HPLC were determined to allow differentiation of patient prognosis by receiver operating characteristic curve analysis. The cutoff values were 34.15% for region 1, 8.84% for region 2, and 36.72% for region 3. Kaplan–Meyer curve analysis estimated that the most differentiated prognosis was enabled in the setting of 8.84% methylation of MGMT in region 2. Progression-free survival and overall survival were significantly longer for patients in this setting of region 2 methylation (p = 0.00365 and p = 0.00258, respectively). CONCLUSIONS: The combination of our HPLC method and the original primer setting provides a new standard method for determination of MGMT methylation status in patients with GBM and is useful for refining MGMT-based drug selection.

Peritumoral ADC Values Correlate with the MGMT Methylation Status in Patients with Glioblastoma

Different results have been reported concerning the relationship of the apparent diffusion coefficient (ADC) values and the status of methylation as the promoter gene for the enzyme methylguanine-DNA methyltransferase (MGMT) in patients with glioblastomas (GBs). The aim of this study was to investigate if there were correlations between the ADC values of the enhancing tumor and peritumoral areas of GBs and the MGMT methylation status. In this retrospective study, we included 42 patients with newly diagnosed unilocular GB with one MRI study prior to any treatment and histopathological data. After co-registration of ADC maps with T1-weighted sequences after contrast administration and dynamic susceptibility contrast (DSC) perfusion, we manually selected one region-of-interest (ROI) in the enhancing and perfused tumor and one ROI in the peritumoral white matter. Both ROIs were mirrored in the healthy hemisphere for normalization. In the peritumoral white matter, absolute and normalized ADC values were significantly higher in patients with MGMT-unmethylated tumors, as compared to patients with MGMT-methylated tumors (absolute values p = 0.002, normalized p = 0.0007). There were no significant differences in the enhancing tumor parts. The ADC values in the peritumoral region correlated with MGMT methylation status, confirmed by normalized ADC values. In contrast to other studies, we could not find a correlation between the ADC values or the normalized ADC values and the MGMT methylation status in the enhancing tumor parts.

Apparent Diffusion Coefficient as Imaging Biomarker for Identifying IDH Mutation, 1p19q Codeletion, and MGMT Promoter Methylation Status in Patients With Glioma.

Glioma genotypes are of importance for clinical decision-making. This data can only be acquired through histopathological analysis based on resection or biopsy. Consequently, there is a need for alternative biomarkers that noninvasively provide reliable information for preoperatively identifying molecular characteristics. To investigate apparent diffusion coefficient (ADC) as imaging biomarker for preoperatively identifying glioma genotypes based on the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors. Retrospective. One hundred and fifty-nine patients (47.6 ± 14.4 years) diagnosed with WHO grade 2-4 glioma including 93 males and 66 females. A 3 T/spin echo echo planner imaging. The ADC measurements were assessed by two neuroradiologists (both with 6 years of experience). Three different lowest portions inside the tumors without overlap were manually drawn on the ADC maps as regions of interest (ROIs). The mean ADC value of the three ROIs was defined as the minimum ADC value (ADCmin ). An ROI was placed in the contralateral normal appearing white matter (NAWM) to obtain the ADC value (ADCNAWM ). The ADCmin to ADCNAWM ratio (ADCratio ) was calculated. Genetics results were retrospectively recorded from pathologic and genetic test reports. Two-sample independent t-tests, receiver operating characteristic curve analysis, and intraclass correlation coefficient analysis were used. Statistical significance was set at P < 0.05. Isocitrate dehydrogenase (IDH)-mutated glioma showed higher ADCmin and ADCratio than IDH wild-type glioma. Among IDH-mutated glioma, higher ADCmin and ADCratio were found in 1p19q intact glioma than in 1p19q codeletion glioma. ADC parameters enabled differentiation of IDH mutation status with area under the curve (AUC) of 0.84 and 0.86. ADC has potential discriminative value for IDH mutation and 1p19q codeletion status. 3. Stage 2.

A novel cervix carcinoma biomarker: Pathological-epigenomics, integrated analysis of MethylMix algorithm and pathology for predicting response to cancer immunotherapy.

Herein, A non-invasive pathomics approach was developed to reveal the methylation status in patients with cervical squamous cell carcinoma and predict clinical outcomes and treatment response. Using the MethylMix algorithm, 14 methylation-driven genes were selected for further analysis. We confirmed that methylation-driven genes were differentially expressed in immune, stromal, and tumor cells. In addition, we constructed a methylation-driven model and explored the alterations in immunocyte infiltration between the different models. The methylation-driven subtypes identified in our investigation could effectively predict the clinical outcomes of cervical cancer. To further evaluate the level of methylation-driven patterns, we constructed a risk model with four genes. Significant correlations were observed between the score and immune response markers, including PD1 and CTLA4. Multiple immune infiltration algorithms evaluated the level of immunocyte infiltration between the high- and low-risk groups, while the components of anti-tumor immunocytes in the low-risk group were significantly increased. Subsequently, a total of 205 acquired whole-slide imaging (WSI) images were processed to capture image signatures, and the pathological algorithm was employed to construct an image prediction model based on the risk score classification. The model achieved an area under the curve (AUC) of 0.737 and 0.582 for the training and test datasets, respectively. Moreover, we conducted vitro assays for validation of hub risk gene. The proposed prediction model is a non-invasive method that combines pathomics features and genomic profiles and shows satisfactory performance in predicting patient survival and treatment response. More interdisciplinary fields combining medicine and electronics should be explored in the future.

Identification of three novel DNMT3A mutations with compromising methylation capacity in human acute myeloid leukaemia.

Acute myeloid leukaemia (AML) is a complex and heterogeneous hematopoietic stem and progenitor cell malignancy characterised by the accumulation of immature blast cells in the bone marrow, blood, and other organs linked to environmental, genetic, and epigenetic factors. Somatic mutations in the gene DNA (cytosine-5)-methyltransferase 3A (DNMT3A; NM_022552.4) are common in AML patients. In this study, we used Sanger sequencing to detect the mutations in the DNMT3A gene in 61 Iraqi AML patients, Hence, the protein function and stability within alterations were identified and analyzed using a variety of computational tools with the goal of determining how these changes affect total protein stability, and then the capacity of methylation was analyzed by methylation specific PCR MSP status at CpG islands. Three novel mutations in exon 23 of DNMT3A were identified in 14 patients (22.9%; V877I, N879delA, and L888Q). The V877I and L888Q substitutions are caused by heterozygous C2629G > A and C2663T > A mutations, respectively, while frameshift mutation C2635delA caused protein truncation with stop codon N879T*. Methylation was detected in the DNMT3A promoter region in 9 patients carrying DNMT3A mutations (64.28%) by MSP, and we found significant correlations between DNMT3A mutations and promoter methylation (p = 8.52 × 105). In addition, we found a significant overrepresentation of DNMT3A methylation status in patients ≥ 50years old (p = 0.025). Our findings highlight the importance of studying the effects of DNMT3A methylation alteration in Iraqi populations beyond R882 substitutions in the leukemogenic pathway so that patient treatment can be tailored to prevent therapeutic resistance and relapse.

Hypermethylation of thymosin β4 predicts a poor prognosis for patients with acute-on-chronic hepatitis B liver failure

BackgroundIt has been demonstrated that thymosin β4 (Tβ4) could inflect the severity of acute-on-chronic hepatitis B liver failure (ACHBLF), but the relationship between its methylation status and the prognosis of liver failure is not clear. This study aimed to determine Tβ4 promoter methylation status in patients with ACHBLF and to evaluate its prognostic value. MethodsThe study recruited 115 patients with ACHBLF, 80 with acute-on-chronic hepatitis B pre-liver failure (pre-ACHBLF), and 86 with chronic hepatitis B (CHB). In addition, there were 36 healthy controls (HCs) from the Department of Hepatology, Qilu Hospital of Shandong University. The 115 patients with ACHBLF were divided into three subgroups: 33 with early stage ACHBLF (E-ACHBLF), 42 with mid-stage ACHBLF (M-ACHBLF), and 40 with advanced stage ACHBLF (A-ACHBLF). Tβ4 promoter methylation status in peripheral blood mononuclear cells (PBMCs) was measured by methylation-specific polymerase chain reaction, and mRNA was detected by quantitative real-time polymerase chain reaction. ResultsMethylation frequency of Tβ4 was significantly higher in patients with ACHBLF than in those with pre-ACHBLF, CHB or HCs. However, expression of Tβ4 mRNA showed the opposite trend. In patients with ACHBLF, Tβ4 promoter methylation status correlated negatively with mRNA levels. The 3-month mortality of ACHBLF in the methylated group was significantly higher than that in the unmethylated group. Also, Tβ4 promoter methylation frequency was lower in survivors than in non-survivors. When used to predict the 1-, 2-, and 3-month incidence of ACHBLF, Tβ4 methylation status was better than the model for end-stage liver disease (MELD) score. The predictive value of Tβ4 methylation was higher than that of MELD score for the mortality of patients with E-ACHBLF and M-ACHBLF, but not for A-ACHBLF. ConclusionsTβ4 methylation might be an important early marker for predicting disease incidence and prognosis in patients with ACHBLF.

Identification DNA Methylation Change of ABCC8 Gene in Type 2 Diabetes Mellitus as Predictive Biomarkers

Type 2 diabetes mellitus is the most common chronic endocrine disorder that affecting 5%–10% of adults globally. Recently, the disease has rapidly spread throughout the Kurdistan Region. This study investigates DNA methylation status in the ABCC8 gene among the study population, and it possibly used as a biomarker. One hundred and thirteen individuals were included in this study, and they were divided into three categories (47 diabetes, 36 prediabetic, and 30 controls). Blood samples were collected to investigate DNA methylation status in patients who attended private clinical sectors in Koya city, Kurdistan Region of Iraq, between August and December 2021. Methylation-specific PCR (MSP) uses paired primers for each methylated and unmethylated region. In addition, the X2 Kruskal–Wallis statistical and Wilcoxon signed-rank tests were run with a significance level of p 0.05. In comparison to the healthy group, hypermethylation of DNA is detected in the promoter region of diabetes and prediabetes. In addition, age, gender, BMI, alcohol use, family history, and physical activity all influence the degree of DNA methylation in people who have had coronavirus illness. The abovementioned findings suggest that DNA methylation alterations in the ABCC8 promoter region might be exploited as a possible predictive biomarker for type 2 diabetes mellitus diagnosis.

Cancer stem cell marker expression and methylation status in patients with colorectal cancer.

The number of individuals diagnosed with colorectal cancer (CRC) has been on an alarming upward trajectory over the past decade. In some countries, this cancer represents one of the most frequently diagnosed types of neoplasia. Therefore, it is an important demand to study the pathology underlying this disease to gain insights into the mechanism of resistance to treatment. Resistance of tumors to chemotherapy and tumor aggressiveness have been associated with a minor population of neoplastic cells, which are considered to be responsible for tumor recurrence. These types of neoplastic cells are known as cancer stem cells, which have been previously reported to serve an important role in pathogenesis of this malignant disease. Slovakia has one of the highest incidence rates of CRC worldwide. In the present study, the aim was to classify the abundance of selected stem cell markers (CD133, CD166 and Lgr5) in CRC tumors using flow cytometry. In addition, the methylation status of selected genomic regions of CRC biomarkers (ADAMTS16, MGMT, PROM1 (CD133), LGR5 and ALCAM) was investigated by pyrosequencing in a cohort of patients from Martin University Hospital, Martin, Slovakia. Samples from both primary tumors and metastatic tumors were tested. Analysis of DNA methylation in the genomic regions of indicated five CRC biomarkers was also performed, which revealed the highest levels of methylation in the A disintegrin and metalloproteinase with thrombospondin motifs 16 and O6-methyguanine-DNA methyl transferase genes, whereas the lowest levels of methylation were found in genes expressing prominin-1, leucine-rich repeat-containing G-protein-coupled receptor 5 and activated leukocyte cell adhesion molecule. Furthermore, tumor tissues from metastases showed significantly higher levels of CD133+ cells compared with that in primary tumors. Higher levels of CD133+ cells correlated with TNM stage and the invasiveness of CRC into the lymphatic system. Although relatively small number of samples was processed, CD133 marker was consider to be important marker in pathology of CRC.

PET With 11C-Methyl-l-Methionine as a Predictor of Consequential Outcomes at the Time of Discontinuing Temozolomide-Adjuvant Chemotherapy in Patients With Residual IDH-Mutant Lower-Grade Glioma.

PurposeThe aim of this study was to clarify whether PET with 11C-methyl-l-methionine (11C-met PET) can predict consequential outcomes at the time of discontinuing temozolomide (TMZ)-adjuvant chemotherapy in patients with residual isocitrate dehydrogenase gene (IDH)–mutant lower-grade glioma.Patients and MethodsAmong 30 patients showing residual lesions of IDH-mutant lower-grade glioma, we compared the tumor-to-normal brain tissue ratio of standardized uptake values (SUVT/N) from 11C-met PET at the time of discontinuing TMZ-adjuvant chemotherapy with putative predictive factors including age, Karnofsky Performance Scale, number of courses of adjuvant therapy, residual tumor size, and promotor methylation status of O6-methylguanine-DNA methyl-transferase gene (MGMT). For each factor, progression-free survival (PFS) was compared between groups divided by cutoff values, determined to predict tumor relapse using receiver operating characteristic curves for each factor. Univariate and multivariate analyses were conducted using log-rank testing and Cox regression analysis, respectively. In addition, PFS was compared between patients grouped by combined findings from multiple predictors identified from univariate and multivariate analyses.ResultsUnivariate and multivariate analyses identified SUVT/N from 11C-met PET and MGMT methylation status as independent predictors of outcomes after TMZ discontinuation. When comparing 3 groups assigned by the combination of MGMT and SUVT/N findings, PFS differed significantly among groups.ConclusionsThe present study suggested that 11C-met PET at the time of discontinuing TMZ-adjuvant chemotherapy allows prediction of outcomes at least comparable to MGMT methylation status in patients with residual IDH-mutant lower-grade glioma. Further, 11C-met PET allows more precise prediction of outcomes by assessment in combination with MGMT findings.

Global DNA methylation and increased DNMT3A expression in multiple myeloma patients.

The aim of this study was to compare the expression profile of selected DNA methyltransferases and global DNA methylation status in patients with different phases of multiple myeloma (MM) . For the analysis, different cellular populations including unsorted myeloma cells and a set of plasma cells purified by relevant antibodies were used. Consequently, laboratory data were compared to patients' clinical data. For the analysis, unsorted bone marrow cell population of 44 MM patients (30 newly diagnosed, 9 relapsed and 5 patients in remission) and a set of 8 patients' samples of sorted plasma cells were used. We used commercially available RNA isolated from BM of 3 healthy individuals as control samples. Expression analysis of three DNA methyltransferases - DNMT1, DNMT3A, and DNMT3B was performed by quantitative RT-PCR and the patient global DNA methylation profiles were detected by colorimetric assay. Unchanged DNMT1 expression was detected in the selected cohort of patients. Normalized DNMT3A gene expression was globally higher in comparison with controls in unsorted and sorted cell populations. Low (0.08-1.81%) global DNA methylation status in unsorted samples of multiple myeloma patients did not correlate either with expression profiles of monitored DNA methyltransferases or with the stages of MM based on Durie-Salmon and International Staging System. This is the first comparative study between DNA methyltransferases expression profiles and global DNA methylation status in different phases of multiple myeloma patients. No significant correlation between the level of global methylation and the clinical stage of the unsorted cell population of patients with multiple myeloma was registered. Overexpression of the DNMT3A gene occurred in both sorted and unsorted cell populations of patients with multiple myeloma. This fact highlights the DNMT3A as a potential marker of multiple myeloma tumor progression. Moreover, we demonstrated comparable results in the expression of DNA methyltransferases in both sorted and unsorted cell populations. This is a promising result from the methodical point of view because when compared to samples of unsorted multiple myeloma cells, samples of sorted cells bring reduction of the number of possible analyses performed.

Prognostic impact of PDGFRA gain/amplification and MGMT promoter methylation status in patients with IDH wild-type glioblastoma.

BackgroundPlatelet-derived growth factor receptor alpha (PDGFRA) is the second most frequently mutated tyrosine kinase receptor in glioblastoma (GBM). However, the prognostic impact of PDGFRA amplification on GBM patients remains unclear. Herein, we evaluated this impact by retrospectively analyzing outcomes of patients with IDH wild-type GBM.MethodsUsing a custom-made oncopanel, we evaluated PDGFRA gain/amplification in 107 GBM samples harboring wild-type IDH, along with MGMT promoter (MGMTp) methylation status.ResultsWe detected PDGFRA gain/amplification in 31 samples (29.0%). PDGFRA gain/amplification predicted poor prognosis (P = .003). Compared to unamplified PDGFRA, PDGFRA gain/amplification in GBM was associated with higher patient age (P = .031), higher Ki-67 score (P = .019), and lower extent of surgical resection (P = .033). Unmethylated MGMTp also predicted poor prognosis (P = .005). As PDGFRA gain/amplification and unmethylated MGMTp were independent factors for poor prognosis in multivariate analyses, we grouped GBM cases based on PDGFRA and MGMTp status: poor (PDGFRA gain/amplification and unmethylated MGMTp), intermediate (PDGFRA gain/amplification or unmethylated MGMTp), and good (PDGFRA intact and methylated MGMTp) prognosis. The Kaplan-Meier survival analysis indicated that these groups significantly correlated with the OS of GBM patients (P < .001).ConclusionsHere we report that PDGFRA gain/amplification is a predictor of poor prognosis in IDH wild-type GBM. Combining PDGFRA gain/amplification with MGMTp methylation status improves individual prognosis prediction in patients with IDH wild-type GBM.