Turkish journal of biology = Turk biyoloji dergisi最新文献

Background/aim: The molecular heterogeneity of colon cancer has made classification of tumors a requirement for effective treatment. One of the approaches for molecular subtyping of colon cancer patients is the consensus molecular subtypes (CMS), developed by the Colorectal Cancer Subtyping Consortium. CMS-specific RNA-Seq-dependent classification approaches are recent, with relatively low sensitivity and specificity. In this study, we aimed to classify patients into CMS groups using their RNA-seq profiles.

Materials and methods: We first identified subtype-specific and survival-associated genes using the Fuzzy C-Means algorithm and log-rank test. We then classified patients using support vector machines with backward elimination methodology.

Results: We optimized RNA-seq-based classification using 25 genes with a minimum classification error rate. In this study, we reported the classification performance using precision, sensitivity, specificity, false discovery rate, and balanced accuracy metrics.

Conclusion: We present a gene list for colon cancer classification with minimum classification error rates and observed the lowest sensitivity but the highest specificity with CMS3-associated genes, which significantly differed due to the low number of patients in the clinic for this group.

Background/aim: Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time.

Materials and methods: We first applied three powerful machine-learning algorithms, i.e. random forest, gradient boosting, and extreme gradient boosting, to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers regardless of their coding proficiency.

Results: Our results showed that mesenchymal-like stem cells display higher stemness scores compared to neural-progenitor-, oligodendrocyte-progenitor-, and astrocyte-like cells.

Conclusion: scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with implementation of a real-data experiment.

Background/aim: The complicated nature of tumor formation makes it difficult to identify discriminatory genes. Recently, transcriptome-based supervised classification methods using support vector machines (SVMs) have become popular in this field. However, the inclusion of less significant variables in the construction of classification models can lead to misclassification. To improve model performance, feature selection methods such as enrichment analysis can be used to extract useful variable sets. The detection of genes that can discriminate between normal and tumor samples in the association of cancer and disease remains an area of limited information. We therefore aimed to discover novel and practical sets of discriminatory biomarkers by utilizing the association of cancer and disease.

Materials and methods: In this study, we employed an SVM classification method for differentially expressed genes enriched by Disease Ontology and filtered nondiscriminatory features using Wilk's lambda criterion prior to classification. Our approach uses the discovery of disease-associated genes as a viable strategy to identify gene sets that discriminate between tumor and normal states. We analyzed the performance of our algorithm using comprehensive RNA-Seq data for adenocarcinoma of the colon, squamous cell carcinoma of the lung, and adenocarcinoma of the lung. The classification performance of the obtained gene sets was analyzed by comparison with different expression datasets and previous studies using the same datasets.

Results: It was found that our algorithm extracts stable small gene sets that provide high accuracy in predicting cancer status. In addition, the gene sets generated by our method perform well in survival analyses, indicating their potential for prognosis.

Conclusion: By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor (https://doi.org/10.18129/B9.bioc.SVMDO) and GitHub (https://github.com/robogeno/SVMDO).

Background/aim: Cancer is a complex disease that involves both genetic and epigenetic factors. While emerging evidence clearly suggests that changes in epitranscriptomics play a crucial role in cancer pathogenesis, a comprehensive understanding of the writers, erasers, and readers of epitranscriptomic processes, particularly under apoptotic conditions remains lacking. The aim of this study was to uncover the changes in the expression of m⁶A RNA modifiers under apoptotic conditions across various cancer cell lines.

Materials and methods: Initially, we quantified the abundance of m⁶A RNA modifiers in cervical (HeLa and ME180), breast (MCF7 and MDA-MB-231), lung (A549 and H1299), and colon (Caco-2 and HCT116) cancer cell lines using qPCR. Subsequently, we induced apoptosis using cisplatin and tumor necrosis factor-alpha (TNF-α) to activate intrinsic and extrinsic pathways, respectively, and assessed apoptosis rates via flow cytometry. Further, we examined the transcript abundance of m⁶A RNA modifiers under apoptotic conditions in cervical, breast, and lung cancer cell lines using qPCR.

Results: Overall, treatment with cisplatin increased the abundance of m⁶A modifiers, whereas TNF-α treatment decreased their expression in cervical, breast, and lung cancer cell lines. Specifically, cisplatin-induced apoptosis, but not TNF-α-mediated apoptosis, resulted in decreased abundance of METTL14 and FTO transcripts. Additionally, cisplatin treatment drastically reduced the abundance of IGF2BP2 and IGF2BP3 readers.

Conclusion: These results suggest that the differential response of cancer cells to apoptotic inducers may be partially attributed to the expression of m⁶A RNA modifiers.

Background/aim: In recent years, single-domain antibodies, also known as nanobodies, have emerged as an alternative to full immunoglobulin Gs (IgGs), due to their various advantages, including increased solubility, faster clearance, and cheaper production. Nanobodies are generally derived from the variable domain of the camelid heavy-chain-only immunoglobulin Gs (hcIgGs). Due to the high sequence homology between variable heavy chains of camelids (V_HHs) and humans (V_Hs), hcIgGs are ideal candidates for nanobody development. However, further examination is needed to understand the structural differences between V_Hs and V_HHs. This analysis is essential for nanobody engineering to mitigate potential immunogenicity, while preserving stability, functionality, and antigen specificity.

Materials and methods: We obtained the V_H and V_HH sequences of various camelid and non-camelid mammalian antibodies from public databases and used multiple sequence alignment based on the Chothia numbering scheme. Aligned sequences were subjected to diverse analyses encompassing paratope length, binding prediction, motif, disulfide bridge, salt bridge profiling, and physicochemical characteristic distribution. Logistic Regression coupled with the Boruta - Random Forest algorithm facilitated the comprehensive examination of physicochemical properties.

Results: Our findings revealed longer, less variable paratope sequences in V_HHs, along with specific antigen binding residues with increased binding potential compared to V_Hs. Although the V_Hs showed more heterogeneous noncanonical disulfide bond patterns, the V_HHs had a higher number of noncanonical disulfide bridges. Intriguingly, a typical salt bridge between the 94th and 101st positions in the V_Hs had a very low encounter rate in the V_HHs. Surprisingly, we also identified notable differences in the physicochemical patterns of mostly conserved frameworks (FWs), especially the FW2 and FW3 regions, between V_Hs and V_HHs.

Conclusion: Our findings point to possible key sites in V_HHs as candidate residues for nanobody engineering efforts.

Background/aim: Papillary thyroid carcinoma (PTC) is the most common form of thyroid cancer. The critical importance of circular RNA (circRNA) in a range of cancer types has been lately recognized. However, research on the functions of circRNAs in PTC has been limited thus far. Therefore, this research aimed at exploring the function and mechanism of circ-methyltransferase-like 15 (METTL15) in PTC cells.

Materials and methods: Quantitative measurements of circ-METTL15, miR-200c-3p, and X-linked inhibitor of apoptosis protein (XIAP) in PTC cells were conducted using reverse transcription-quantitative polymerase chain reaction or Western blot analysis. To investigate cell growth, cell counting kit-8 and colony formation tests were employed, apoptosis was analyzed using flow cytometry, and migration and invasion were studied through Transwell assays. The targeted binding sites between miR-200c-3p and circ-METTL15 or XIAP were predicted by starBase and then verified by dual luciferase reporter assay.

Results: circ-METTL15 and XIAP were upregulated in the PTC cells, while miR-200c-3p was downregulated. Downregulating circ-METTL15 or upregulating miR-200c-3p resulted in inhibited proliferation, migration, and invasion of PTC cells, while promoting apoptosis. miR-200c-3p was the downstream molecule of circ-METTL15, and XIAP was the direct target of miR-200c-3p. Forcing XIAP expression obstructed circ-METTL15 silencing to inhibit PTC cell activity.

Conclusion: By coopting miR-200c-3p/XIAP, Circ-METTL15 stimulates aggressive behavior in PTC cells.

MicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naïve Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.

The clinical use of cisplatin, which is a first-line anticancer agent, is highly restricted due to its adverse effects on kidneys that lead to nephrotoxicity. Therefore, some potential reno-protective substances have been used in combination with cisplatin to cope with nephrotoxicity. Due to its high antitumor activity and oxygen-carrying capacity, we investigated the molecular effects of squalene against cisplatin-induced oxidative stress and kidney damage in mice. Single dose of cisplatin (7 mg/kg) was given to male Balb/c mice. Squalene (100 mg/kg/day) was administered orogastrically to mice for 10 days. Following sacrification, molecular alterations were investigated as analysis of the levels of oxidative stress index (OSI), inflammatory cytokines and cell survival-related proteins in addition to histopathological examinations in mice kidney tissue. The level OSI and Interferon-gamma (IFN-γ) decreased in the cisplatin and squalene cotreated mice compared to cisplatin-treated mice. Squalene treatment also increased the activation of protein kinase B (AKT). Furthermore, cisplatin-induced inactivation of mammalian target of rapamycin (mTOR) and histopathological damages were reversed by squalene. It may be suggested that squalene ameliorated the cisplatin-induced histopathological damages in the kidney through activation of AKT/mTOR signaling pathway by regulating the balance of the redox system due to its antioxidative effect.

Fraxinus angustifolia leaves and bark are used in traditional medicine against various inflammatory-related pathologies incumbent to reactive oxygen species (ROS) generation by the NADH oxidase activity of enzymes such as xanthine oxidoreductase (XOR). This study was designed to investigate the in vitro and in vivo inhibitory activities of this enzyme by Fraxinus angustifolia extracts. The leaf organic phase of ethyl acetate (LFA) and its bark aqueous counterpart (BFA) showed the strongest anti-NADH oxidase activity in vitro (IC50 = 38.51 and 42.04 µg mL-1, respectively). They consequently suppressed superoxide generation both enzymatically (53% and 19%, respectively) and nonenzymatically (34% and 19%, respectively). These results were corroborated in vivo, with high antiNADH oxidase potential of the leaves and bark extracts (75.32% and 51.32%, respectively) concomitant with moderate hypouricemic activities (36.84% and 38.59%, respectively). Bio-guided fractionation led to the identification, by LC-DAD-MS/MS, of esculin and calcelarioside in bark and kaempferol glucoside in leaves as the main compounds responsible for the anti-NADH oxidase activity of XOR. These results plead in favor of the use of F. angustifolia as a source of potentially interesting therapeutic substances.