The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).ConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG

Differences, links, and roles of microbial and stoichiometric factors in microplastic distribution : A case study of five typical rice cropping regions in China

Microplastics (MPs), as new pollutants in agroecosystems, have already attracted widespread attention from scientists. However, our understanding of MP geographic distribution and its influencing factors across spatial scales remains poor. Here, a regional-scale field investigation was conducted to assess the distribution characteristic of MPs in five major rice-growing regions of China, and we explored the roles of biological and abiotic factors, especially stoichiometry and microbial influences on MP distribution. MPs were observed in all sampling sites, averaging 6,390 ± 2,031 items⋅kg–1. Sizes less than 0.5 mm and black and transparent MPs dominated. Fiber, classified as one of the MP shapes, occurred most frequently. MP community analysis, firstly used in paddy soil, revealed more black MPs abundance in Henan (HE), more rayon, blue, and other colors MPs in Hunan (HN), more transparent MPs in Tianjing (TJ), and more PE MPs in Heilongjiang (DB). Higher MP community diversity was found in most south paddy soils of this study, due to a broader range of sources. C/N showed a positive relationship with pellet-shaped MP abundance and MPs of size between 2 and 5 mm (P < 0.05). Chao1 index of soil microbial communities was positively correlated with the MP abundance, MPs of size less than 0.5 mm, and fiber abundance. The minimum temperature was positively correlated with MP abundance (P < 0.05), implying the potential effects of the freeze-thaw process might exist. The regression analysis highlighted the important role of population quantity in determining MP abundance (R = 0.421, P = 0.02). This study confirmed the wide distribution of MPs in different soil depths of paddy lands in China and demonstrated that its distribution was influenced by population quantity and environmental variables, such as microbiology. These findings could provide a basis for the toxicological behavior of MPs and the potential risk to human health

Effects of tillage management on cbbL-carrying bacteria and soil organic carbon dynamics across aggregate size classes in the farmland of North China Plain

Calvin-Benson-Bassham cycle (cbbL)-carrying bacteria in soil are essential to renew and circulate organic matter. However, the relation between cbbL-carrying bacteria and soil carbon dynamics under tillage managements, especially across the aggregate size remains unclear. Thus, in our study, soil organic carbon (SOC) storages, mineralization, and the cbbL-carrying bacterial community across five soil aggregate sizes were thoroughly investigated under four tillage treatments: conventional rotary tillage (CT), deep plowing (DP), subsoiling (SS), no-tillage (NT). We found macroaggregates (>2 mm) contributed most with regard to SOC stocks, whereas microaggregates (1 mm) with the highest cumulative SOC mineralization were found in subsoiling, whereas microaggregates had the lowest cumulative mineralization under no-tillage. By physically protecting, no-tillage specifically inhibited carbon dioxide (CO2) emissions in macroaggregates (>1 mm), whereas increased SOC levels and encouraged CO2 releases across microaggregates. Shifts in the co-occurrence network demonstrated that subsoiling promoted the joint symbiotic function between cbbL-carrying bacteria, the efficiency of matter and energy, and information transfer. And the keystone species, the enhanced cooperation and stochastic processes of autotrophic microorganisms under subsoiling lead to increased carbon fixation and reduced CO2 emissions in microaggregates with limited oxygen and nutrients. Overall, our work verified physical protection of large aggregates under no-tillage and improvement of microbial interaction efficiency under subsoiling. This may offer a theoretical foundation for the choice of tillage practices in fluvo-aquic soil regions

Frequency-degenerate parametric generation through IFWM effect in nanowaveguides

We investigate highly efficient frequency-degenerate parametric generation through inverse four-wave-mixing (IFWM) in silicon nanowaveguides, which exhibits distinctly from traditional FWM phenomenon and manifests itself as a unique process producing signal and idler photon pairs with frequencies at the center of two pumps. The influences of dispersion, nonlinear coefficient and frequency detuning on the IFWM process are numerically analyzed in detail. On this basis, the optimal condition for high gain IFWM and the nanowaveguide with high nonlinearity and large normal dispersion are proposed. These results substantiate the feasibility of such frequency-degenerate parametric generation in CMOS-compatible integrated platforms, which could find important potential in signal-processing systems for photonics networks and entangled qubits generation for quantum optics.</p

Detecting the Neuraminidase R294K Mutation in Avian Influenza A (H7N9) Virus Using Reverse Transcription Droplet Digital PCR Method

The R294K mutation in neuraminidase (NA) causes resistance to oseltamivir in the avian influenza virus H7N9. Reverse transcription droplet digital polymerase chain reaction (RT-dd PCR) is a novel technique for detecting single-nucleotide polymorphisms. This study aimed to develop an RT-dd PCR method for detecting the R294K mutation in H7N9. Primers and dual probes were designed using the H7N9 NA gene and the annealing temperature was optimized at 58.0 °C. The sensitivity of our RT-dd PCR method was not significantly different from that of RT-qPCR (p = 0.625), but it could specifically detect R294 and 294K in H7N9. Among 89 clinical samples, 2 showed the R294K mutation. These two strains were evaluated using a neuraminidase inhibition test, which revealed that their sensitivity to oseltamivir was greatly reduced. The sensitivity and specificity of RT-dd PCR were similar to those of RT-qPCR and its accuracy was comparable to that of NGS. The RT-dd PCR method had the advantages of absolute quantitation, eliminating the need for a calibration standard curve, and being simpler in both experimental operation and result interpretation than NGS. Therefore, this RT-dd PCR method can be used to quantitatively detect the R294K mutation in H7N9

Image_2_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Image_1_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Table_1_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.xlsx

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p

Image_3_The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma.tif

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (PConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.</p