Development and validation of a novel 3-gene prognostic model for pancreatic adenocarcinoma based on ferroptosis-related genes

Background Molecular markers play an important role in predicting clinical outcomes in pancreatic adenocarcinoma (PAAD) patients. Analysis of the ferroptosis-related genes may provide novel potential targets for the prognosis and treatment of PAAD. Methods RNA-sequence and clinical data of PAAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases. The PAAD samples were clustered by a non-negative matrix factorization (NMF) algorithm. The differentially expressed genes (DEGs) between different subtypes were used by “limma_3.42.2” package. The R software package clusterProfiler was used for functional enrichment analysis. Then, a multivariate Cox proportional and LASSO regression were used to develop a ferroptosis-related gene signature for pancreatic adenocarcinoma. A nomogram and corrected curves were constructed. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, migration and invasion assays. Results The 173 samples were divided into 3 categories (C1, C2, and C3) and a 3-gene signature model (ALOX5, ALOX12, and CISD1) was constructed. The prognostic model showed good independent prognostic ability in PAAD. In the GSE62452 external validation set, the molecular model also showed good risk prediction. KM-curve analysis showed that there were significant differences between the high and low-risk groups, samples with a high-risk score had a worse prognosis. The predictive efficiency of the 3-gene signature-based nomogram was significantly better than that of traditional clinical features. For comparison with other models, that our model, with a reasonable number of genes, yields a more effective result. The results obtained with qPCR and IHC assays showed that ALOX5 was highly expressed, whether ALOX12 and CISD1 were expressed at low levels in tissue samples. Finally, function assays results suggested that ALOX5 may be an oncogene and ALOX12 and CISD1 may be tumor suppressor genes. Conclusions We present a novel prognostic molecular model for PAAD based on ferroptosis-related genes, which serves as a potentially effective tool for prognostic differentiation in pancreatic cancer patients.

Characterization of histone deacetylases and their roles in response to abiotic and PAMPs stresses in Sorghum bicolor

Background Histone deacetylases (HDACs) play an important role in the regulation of gene expression, which is indispensable in plant growth, development, and responses to environmental stresses. In Arabidopsis and rice, the molecular functions of HDACs have been well-described. However, systematic analysis of the HDAC gene family and gene expression in response to biotic and abiotic stresses has not been reported for sorghum. Results We conducted a systematic analysis of the sorghum HDAC gene family and identified 19 SbHDACs mainly distributed on eight chromosomes. Phylogenetic tree analysis of SbHDACs showed that the gene family was divided into three subfamilies: RPD3/HDA1, SIR2, and HD2. Tissue-specific expression results showed that SbHDACs displayed different expression patterns in different tissues, indicating that these genes may perform different functions in growth and development. The expression pattern of SbHDACs under different stresses (high and low temperature, drought, osmotic and salt) and pathogen-associated molecular model (PAMPs) elf18, chitin, and flg22) indicated that SbHDAC genes may participate in adversity responses and biological stress defenses. Overexpression of SbHDA1, SbHDA3, SbHDT2 and SbSRT2 in Escherichia coli promoted the growth of recombinant cells under abiotic stress. Interestingly, we also showed that the sorghum acetylation level was enhanced when plants were under cold, heat, drought, osmotic and salt stresses. The findings will help us to understand the HDAC gene family in sorghum, and illuminate the molecular mechanism of the responses to abiotic and biotic stresses. Conclusion We have identified and classified 19 HDAC genes in sorghum. Our data provides insights into the evolution of the HDAC gene family and further support the hypothesis that these genes are important for the plant responses to abiotic and biotic stresses.

Nanometer-Resolution Long-term Tracking of Single Cargos Reveals Dynein Motor Mechanisms

Cytoplasmic dynein is essential for intracellular transport, but because of its complexity, we still do not fully understand how this 1.5 megadalton protein works. Here, we used novel optical probes that enable single-particle tracking (SPT) of individual cargos transported by dynein motors in live neurons over 900 μm. Analyses using the Fluctuation Theorem (FT) showed that the number of dynein molecules switches between 1-5 motors during the transport. Clearly resolved single-molecular steps revealed that the dwell times between individual steps were accurately described by an enzymatic cycle dominated by two equal and thermally-activated rate constants. Based on these data, we propose a new molecular model whereby each step requires the hydrolysis of 2 ATPs. The model is consistent with extensive structural, single-molecule and biochemical measurements.

Thermal modification of open heavy-flavor mesons from an effective hadronic theory

We have developed a self-consistent theoretical approach to study the modification of the properties of heavy mesons in hot mesonic matter which takes into account chiral and heavy-quark spin-flavor symmetries. The heavylight meson-meson unitarized scattering amplitudes in coupled channels incorporate thermal corrections by using the imaginary-time formalism, as well as the dressing of the heavy mesons with the self-energies. We report our results for the ground-state thermal spectral functions and the implications for the excited mesonic states generated dynamically in the heavy-light molecular model. We have applied these to the calculation of meson Euclidean correlators and transport coefficients for D mesons and summarize here our findings.

A radical approach to radicals

Nitroxide radicals are characterized by a long-lived spin-unpaired electronic ground state and are strongly sensitive to their chemical surroundings. Combined with electron paramagnetic resonance spectroscopy, these electronic features have led to the widespread application of nitroxide derivatives as spin labels for use in studying protein structure and dynamics. Site-directed spin labelling requires the incorporation of nitroxides into the protein structure, leading to a new protein–ligand molecular model. However, in protein crystallographic refinement nitroxides are highly unusual molecules with an atypical chemical composition. Because macromolecular crystallography is almost entirely agnostic to chemical radicals, their structural information is generally less accurate or even erroneous. In this work, proteins that contain an example of a radical compound (Chemical Component Dictionary ID MTN) from the nitroxide family were re-refined by defining its ideal structural parameters based on quantum-chemical calculations. The refinement results show that this procedure improves the MTN ligand geometries, while at the same time retaining higher agreement with experimental data.

Quantum chemical modeling of the structure and properties of SnO2 nanoclusters

By the method of density functional theory with exchange-correlation functional B3LYP and basis set 3‑21G (d), the structural and energy characteristics have been considered of the molecular models of SnO2 nanoclusters of different size and composition with the number of Sn atoms from 1 to 10. Incompletely coordinated surface tin atoms were terminated by hydroxyl groups. It has been shown that the Sn–O bond length in nanoclusters does not depend on the cluster size and on the coordination number of Sn atoms, but is determined by the coordination type of neighboring oxygen atoms. Namely, the bond length Sn–O(3) (@ 2.10 Å) is greater than that of Sn–O (2) (@ 1.98 Å). The calculated values of Sn–O (3) bond lengths agree well with the experimental ones for crystalline SnO 2 samples (2.05 Å). The theoretically calculated width of the energy gap decreases naturally with increasing cluster size (from 6.14 to 3.46 eV) and approaches the experimental value of the band gap of the SnO 2 crystal (3.6 eV). The principle of additivity was used to analyze the energy characteristics of the considered models and to estimate the corresponding values for a cassiterite crystal. According to this principle, a molecular model can be represented as a set of atoms or atomic groups of several types that differ in the coordination environment and, therefore, make different contributions to the total energy of the system. The calculated value of the atomization energy for SnO2 is 1661 kJ/mol and corresponds satisfactorily to the experimentally measured specific atomization energy of crystalline SnO2 (1381 kJ/mol). It has been shown that a satisfactory reproduction of the experimental characteristics of crystalline tin dioxide is possible when using clusters containing at least 10 state atoms, for example, (SnO2)10×14H2O.

Nanosized T1 MRI Contrast Agent Based on a Polyamidoamine as Multidentate Gd Ligand

A linear polyamidoamine (PAA) named BAC-EDDS, containing metal chelating repeat units composed of two tert-amines and four carboxylic groups, has been prepared by the aza-Michael polyaddition of ethylendiaminodisuccinic (EDDS) with 2,2-bis(acrylamido)acetic acid (BAC). It was characterized by size exclusion chromatography (SEC), FTIR, UV–Vis and NMR spectroscopies. The pKa values of the ionizable groups of the repeat unit were estimated by potentiometric titration, using a purposely synthesized molecular ligand (Agly-EDDS) mimicking the structure of the BAC-EDDS repeat unit. Dynamic light scattering (DLS) and ζ-potential analyses revealed the propensity of BAC-EDDS to form stable nanoaggregates with a diameter of approximately 150 nm at pH 5 and a net negative charge at physiological pH, in line with an isoelectric point <2. BAC-EDDS stably chelated Gd (III) ions with a molar ratio of 0.5:1 Gd (III)/repeat unit. The stability constant of the molecular model Gd-Agly-EDDS (log K = 17.43) was determined as well, by simulating the potentiometric titration through the use of Hyperquad software. In order to comprehend the efficiency of Gd-BAC-EDDS in contrasting magnetic resonance images, the nuclear longitudinal (r1) and transverse (r2) relaxivities as a function of the externally applied static magnetic field were investigated and compared to the ones of commercial contrast agents. Furthermore, a model derived from the Solomon–Bloembergen–Morgan theory for the field dependence of the NMR relaxivity curves was applied and allowed us to evaluate the rotational correlation time of the complex (τ = 0.66 ns). This relatively high value is due to the dimensions of Gd-BAC-EDDS, and the associated rotational motion causes a peak in the longitudinal relaxivity at ca. 75 MHz, which is close to the frequencies used in clinics. The good performances of Gd-BAC-EDDS as a contrast agent were also confirmed through in vitro magnetic resonance imaging experiments with a 0.2 T magnetic field.

Identification and Validation of a Potent Multi-lncRNA Molecular Model for Predicting Gastric Cancer Prognosis

Gastric cancer (GC) remains the third deadliest malignancy in China. Despite the current understanding that the long noncoding RNAs (lncRNAs) play a pivotal function in the growth and progression of cancer, their prognostic value in GC remains unclear. Therefore, we aimed to construct a polymolecular prediction model by employing a competing endogenous RNA (ceRNA) network signature obtained by integrated bioinformatics analysis to evaluate patient prognosis in GC. Overall, 1,464 mRNAs, 14,376 lncRNAs, and 73 microRNAs (miRNAs) were found to be differentially expressed in GC. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that these differentially expressed RNAs were mostly enriched in neuroactive ligand–receptor interaction, chemical carcinogenesis, epidermis development, and digestion, which were correlated with GC. A ceRNA network consisting of four lncRNAs, 21 miRNAs, and 12 mRNAs were constructed. We identified four lncRNAs (lnc00473, H19, AC079160.1, and AC093866.1) as prognostic biomarkers, and their levels were quantified by qRT-PCR in cancer and adjacent noncancerous tissue specimens. Univariable and multivariable Cox regression analyses suggested statistically significant differences in age, stage, radiotherapy, and risk score groups, which were independent predictors of prognosis. A risk prediction model was created to test whether lncRNAs could be used as an independent risk predictor of GC or not. These novel lncRNAs’ signature independently predicted overall survival in GC (p &lt; 0.001). Taken together, this study identified a ceRNA and protein–protein interaction networks that significantly affect GC, which could be valuable for GC diagnosis and therapy.

Clinicopathological and Molecular Prognostic Classifier for Intermediate/High-Risk Clear Cell Renal Cell Carcinoma

The probability of tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC) is highly variable, underlining the lack of predictive accuracy of the current clinicopathological factors. To develop an accurate prognostic classifier for these patients, we analyzed global gene expression patterns in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 4000. Expression levels of 22 selected differentially expressed genes (DEG) were assessed by nCounter analysis in an independent series of 71 ccRCCs. A clinicopathological-molecular model for predicting tumor progression was developed and in silico validated in a total of 202 ccRCC patients using the TCGA cohort. A total of 1202 DEGs were found between progressive and non-progressive intermediate/high-risk ccRCC in RNAseq analysis, and seven of the 22 DEGs selected were validated by nCounter. Expression of HS6ST2, pT stage, tumor size, and ISUP grade were found to be independent prognostic factors for tumor progression. A risk score generated using these variables was able to distinguish patients at higher risk of tumor progression (HR 7.27; p < 0.001), consistent with the results obtained from the TCGA cohort (HR 2.74; p < 0.002). In summary, a combined prognostic algorithm was successfully developed and validated. This model may aid physicians to select high-risk patients for adjuvant therapy.