Background/Aim: Glioblastoma (GBM) is one of the deadliest human cancers responding very poorly to therapy. Although the central nervous system has been traditionally considered an immunologically privileged site with an enhanced immune response, GBM appears to benefit from this immunosuppressive milieu. Immunomodulatory molecules play an important role in immune tumor-host interactions. Non-classical human leukocyte antigens (HLA) class Ib molecules HLA-E, HLA-F, and HLA-G have been previously described to be involved in protecting semiallogeneic fetal allografts from the maternal immune response and in transplant tolerance as well as tumoral immune escape. Unfortunately, their role in GBM remains poorly understood. Our study, therefore, aimed to characterize the relationship between the expression of these molecules in GBM on the transcriptional level and clinicopathological and molecular features of GBM as well as the effect of ionizing radiation. Materials and Methods: We performed the analysis of HLA-E, HLA-F, and HLA-G mRNA expression in 69 GBM tissue samples and 21 non-tumor brain tissue samples (controls) by reverse transcription polymerase chain reaction. Furthermore, two primary GBM cell cultures had been irradiated to identify the effect of ionizing radiation on the expression of non-classical HLA molecules. Results: Analyses revealed that both HLA-E and HLA-F are significantly up-regulated in GBM samples. Subsequent survival analysis showed a significant association between low expression of HLA-E and shorter survival of GBM patients. The dysregulated expression of both molecules was also observed between patients with methylated and unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) promoter. Finally, we showed that ionizing radiation increased HLA-E expression level in GBM cells in vitro. Conclusion: HLA-E and HLA-F play an important role in GBM biology and could be used as diagnostic biomarkers, and in the case of HLA-E also as a prognostic biomarker. Glioblastoma (GBM) is one of the most aggressive primary brain tumors with a very poor prognosis. The current treatment approach involves surgery, if possible, followed by radiotherapy with a total dose of 60 Gy and concomitant chemotherapy with the alkylating agent temozolomide (TMZ).
Background Prostate cancer (PCa) is a heterogeneous malignancy with high variability in clinical course. Insufficient stratification according to the aggressiveness at the time of diagnosis causes unnecessary or delayed treatment. Current stratification systems are not effective enough because they are based on clinical, surgical or biochemical parameters, but do not take into account molecular factors driving PCa cancerogenesis. MicroRNAs (miRNAs) are important players in molecular pathogenesis of PCa and could serve as valuable biomarkers for the assessment of disease aggressiveness and its prognosis. Methods In the study, in total, 280 PCa patients were enrolled. The miRNA expression profiles were analyzed in FFPE PCa tissue using the miRCURY LNA miRNA PCR System. The expression levels of candidate miRNAs were further verified by two‐level validation using the RT‐qPCR method and evaluated in relation to PCa stratification reflecting the disease aggressiveness. Results MiRNA profiling revealed 172 miRNAs dysregulated between aggressive (ISUP 3–5) and indolent PCa (ISUP 1) (p < 0.05). In the training and validation cohort, miR‑15b‑5p and miR‐106b‐5p were confirmed to be significantly upregulated in tissue of aggressive PCa when their level was associated with disease aggressiveness. Furthermore, we established a prognostic score combining the level of miR‑15b‑5p and miR‐106b‐5p with serum PSA level, which discriminated indolent PCa from an aggressive form with even higher analytical parameters (AUC being 0.9338 in the training set and 0.8014 in the validation set, respectively). The score was also associated with 5‐year biochemical progression‐free survival (bPFS) of PCa patients. Conclusions We identified a miRNA expression pattern associated with disease aggressiveness in prostate cancer patients. These miRNAs may be of biological interest as the focus can be also set on their specific role within the molecular pathology and the molecular mechanism that underlies the aggressivity of prostate cancer.
Purpose Renal cell carcinoma belongs among the deadliest malignancies despite great progress in therapy and accessibility of primary care. One of the main unmet medical needs remains the possibility of early diagnosis before the tumor dissemination and prediction of early relapse and disease progression after a successful nephrectomy. In our study, we aimed to identify novel diagnostic and prognostic biomarkers using next-generation sequencing on a novel cohort of RCC patients. Methods Global expression profiles have been obtained using next-generation sequencing of paired tumor and non-tumor tissue of 48 RCC patients. Twenty candidate lncRNA have been selected for further validation on an independent cohort of paired tumor and non-tumor tissue of 198 RCC patients. Results Sequencing data analysis showed significant dysregulation of more than 2800 lncRNAs. Out of 20 candidate lncRNAs selected for validation, we confirmed that 14 of them are statistically significantly dysregulated. In order to yield better discriminatory results, we combined several best performing lncRNAs into diagnostic and prognostic models. A diagnostic model consisting of AZGP1P1, CDKN2B-AS1, COL18A1, and RMST achieved AUC 0.9808, sensitivity 95.96%, and specificity 90.4%. The model for prediction of early relapse after nephrectomy consists of COLCA1, RMST, SNHG3, and ZNF667-AS1 and achieved AUC 0.9241 with sensitivity 93.75% and specificity 71.07%. Notably, no combination has outperformed COLCA1 alone. Lastly, a model for stage consists of ZNF667-AS1, PVT1, RMST, LINC00955, and TCL6 and achieves AUC 0.812, sensitivity 85.71%, and specificity 69.41%. Conclusion In our work, we identified several lncRNAs as potential biomarkers and developed models for diagnosis and prognostication in relation to stage and early relapse after nephrectomy.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
Brain metastases (BMs) comprise a heterogeneous group of the most frequent intracranial tumors in adults, most originating in lung, breast, renal cell, and colorectal carcinomas and melanomas. Despite the recent improvements in imaging methodology resulting in earlier BM identification and advancements in treatment strategies, BMs are still a significant cause of patient morbidity. Furthermore, BMs frequency increases due to more prolonged survival of cancer patients and population aging. Since the most widely used prognostic scoring systems for BMs require prior knowledge of the primary origin and up to 14% of BMs are classified as BMs of unknown primary, there is an urgent unmet need for accurate biomarkers for identification of BM origin. MiRNAs are non-coding RNAs with an approximate length of 22 nucleotides, functioning as post-transcriptional regulators of gene expression. Dysregulated miRNA expression profile has been observed in many pathological processes, including the complex and not fully understood metastatic cascade. These molecules are very stable and present not only in tissues but also in human body fluids, including blood plasma and cerebrospinal fluid (CSF). Based on these facts, both tissue and circulating miRNAs are extensively studied as potential diagnostic biomarkers. Specific miRNA signatures of BMs were obtained using high-throughput miRNA profiling (Illumina small RNA sequencing) on 3 types of samples (metastatic tissue, blood plasma, CSF) from a cohort of 30 patients with BMs originating in the 5 tumor types – lung, breast, renal cell and colorectal carcinomas and melanomas (6 patients per group, 87 samples in total, only 3 CSF samples from RCC patients available). We identified significantly differentially expressed miRNAs in BM tissues with the ability to differentiate between primary origins. Tissue miRNAs could identify BMs originating from breast, colorectal and renal cell carcinomas and melanomas with high specificity and sensitivity. Interestingly, the heterogeneity of lung carcinomas was also characteristic for the corresponding BMs, making it challenging to distinguish accurately from other BMs. Even though the tissue-specific miRNA signature was the most precise, our results suggest a significant diagnostic potential of circulating miRNAs from CSF for BM patients. Therefore, these short and stable molecules could potentially help identify the origin of BMs of unknown primary. The research is supported by project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102) - Funded by the European Union - Next Generation EU. Citation Format: Dagmar Al Tukmachi, Michaela Ruckova, Marek Vecera, Tana Machackova, Petra Pokorna, Marketa Hermanova, Michal Hendrych, Leos Kren, Ivana Roskova, Vaclav Vybihal, Hana Valekova, Radim Jancalek, Jiri Sana, Martin Smrcka, Ondrej Slaby. Tumor tissue and cerebrospinal fluid microRNA profiles enable the classification of brain metastasis accordingly to their origin. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3758.
Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that molecular descriptors depend on morphological heterogeneity of the tumor, a dependency that negatively impacts the efficiency of current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology. The morphological patterns (morphotypes) considered were complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptomic profiling by microarrays of 202 tumor regions (representing the six morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-a signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several types of gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results of our study show that morphotype-based tumor sampling allows for detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.
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