Costimulatory molecule‐related lncRNA model as a potential prognostic biomarker in non‐small cell lung cancer

Abstract Objective Costimulatory molecules have been demonstrated to exert essential roles in multiple cancers. However, their role in lung cancer remains elusive. Here, we sought to identify costimulatory molecule‐related lncRNAs in non‐small cell lung cancer (NSCLC) and establish a prognostic signature to predict the prognosis of patients with NSCLC. Methods A total of 535 lung adenocarcinoma (LUAD) and 502 lung squamous cell carcinoma (LUSC) patients from the cancer genome atlas (TCGA) database were recruited. A novel costimulatory molecule‐based lncRNA prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival. The Homo_sapiens.GRCh38 data set was set as a reference file for probe annotation. Results A total of 593 costimulatory molecule‐related lncRNAs were extracted. After analysis, six costimulatory molecule‐related lncRNAs (AC084859.1, AC079949.2, HSPC324, LINC01150, LINC01150, and AC090617.5) were screened. A prognostic model based on the six lncRNAs was established using systematic bioinformatics analyses. The prognostic model had a prognostic value in NSCLC patients. Furthermore, a prognostic nomogram was established based on clinical parameters and a risk‐score model. Patients with different risk scores had considerably different tumor‐infiltrating immune cells, somatic mutational loading, clinical outcomes, signaling pathways, and immunotherapy efficacy. In addition, LINC01137 was associated with unfavorable disease outcomes and fueled tumor progression in NSCLC. Conclusion Taken together, our study demonstrated that a costimulatory molecule‐related lncRNA model could be a potential prognostic biomarker in NSCLC. Moreover, LINC01137 could facilitate the proliferation and invasion of lung cancer

Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images

Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p < 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R2 and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t/ha, while, for Landsat 9, they were 0.87 and 17.91 t/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics

Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation

Development of a System of Compatible Individual Tree Diameter and Aboveground Biomass Prediction Models Using Error-In-Variable Regression and Airborne LiDAR Data

Estimating individual tree diameters at breast height (DBH) from delineated crowns and tree heights on the basis of airborne light detection and ranging (LiDAR) data provides a good opportunity for large-scale forest inventory. Generally, ground-based measurements are more accurate, but LiDAR data and derived DBH values can be obtained over larger areas for a relatively smaller cost if a right procedure is developed. A nonlinear least squares (NLS) regression is not an appropriate approach to predict the aboveground biomass (AGB) of individual trees from the estimated DBH because both the response variable and the regressor are subject to measurement errors. In this study, a system of compatible individual tree DBH and AGB error-in-variable models was developed using error-in-variable regression techniques based on both airborne LiDAR and field-measured datasets of individual Picea crassifolia Kom. trees, collected in northwestern China. Two parameter estimation algorithms, i.e., the two-stage error-in-variable model (TSEM) and the nonlinear seemingly unrelated regression (NSUR), were proposed for estimating the parameters in the developed system of compatible individual tree DBH and AGB error-in-variable models. Moreover, two model structures were applied to estimate AGB for comparison purposes: NLS with AGB estimation depending on DBH (NLS&DD) and NLS with AGB estimation not depending on DBH (NLS&NDD). The results showed that both TSEM and NSUR led to almost the same parameter estimates for the developed system. Moreover, the developed system effectively accounted for the inherent correlation between DBH and AGB as well as for the effects of measurement errors in the DBH on the predictions of AGB, whereas NLS&DD and NLS&NDD did not. A leave-one-out cross-validation indicated that the prediction accuracy of the developed system of compatible individual tree DBH and AGB error-in-variable models with NSUR was the highest among those estimated by the four methods evaluated, but, statistically, the accuracy improvement was not significantly different from zero. The main reason might be that, except for the measurement errors, other source errors were ignored in the modeling. This study implies that, overall, the proposed method provides the potential to expand the estimations of both DBH and AGB from individual trees to stands by combining the error-in-variable modeling and LiDAR data and improve their estimation accuracies, but its application needs to be further validated by conducting a systematical uncertainty analysis of various source errors in a future study

Diquat causes mouse testis injury through inducing heme oxygenase-1-mediated ferroptosis in spermatogonia

Diquat dibromide (DQ) is a globally used herbicide in agriculture, and its overuse poses an important public health issue, including male reproductive toxicity in mammals. However, the effects and molecular mechanisms of DQ on testes are limited. In vivo experiments, mice were intraperitoneally injected with 8 or 10 mg/kg/ day of DQ for 28 days. It has been found that heme oxygenase-1 (HO-1) mediates DQ-induced ferroptosis in mouse spermatogonia, thereby damaging testicular development and spermatogenesis. Histopathologically, we found that DQ exposure caused seminiferous tubule disorders, reduced germ cells, and increased sperm malformation, in mice. Reactive oxygen species (ROS) staining of frozen section and transmission electron microscopy (TEM) displayed DQ promoted ROS generation and mitochondrial morphology alterations in mouse testes, suggesting that DQ treatment induced testicular oxidative stress. Subsequent RNA-sequencing further showed that DQ treatment might trigger ferroptosis pathway, attributed to disturbed glutathione metabolism and iron homeostasis in spermatogonia cells in vitro. Consistently, results of western blotting, measurements of MDA and ferrous iron, and ROS staining confirmed that DQ increased oxidative stress and lipid peroxidation, and accelerated ferrous iron accumulation both in vitro and in vivo. Moreover, inhibition of ferroptosis by deferoxamine (DFO) markedly ameliorated DQ-induced cell death and dysfunction. By RNA-sequencing, we found that the expression of HO-1 was significantly upregulated in DQ-treated spermatogonia, while ZnPP (a specific inhibitor of HO-1) blocked spermatogonia ferroptosis by balancing intracellular iron homeostasis. In mice, administration of the ferroptosis inhibitor ferrostatin-1 effectively restored the increase of HO-1 levels in the spermatogonia, prevented spermatogonia death, and alleviated the spermatogenesis disorders induced by DQ. Overall, these findings suggest that HO-1 mediates DQ-induced spermatogonia ferroptosis in mouse testes, and targeting HO-1 may be an effective protective strategy against male reproductive disorders induced by pesticides in agriculture

SIRT2 Inhibition Results in Meiotic Arrest, Mitochondrial Dysfunction, and Disturbance of Redox Homeostasis during Bovine Oocyte Maturation

SIRT2, a member of the sirtuin family, has been recently shown to exert important effects on mitosis and/or metabolism. However, its roles in oocyte maturation have not been fully clarified. In this study, SIRT2, located in the cytoplasm and nucleus, was found in abundance in the meiotic stage, and its expression gradually decreased until the blastocyst stage. Treatment with SIRT2 inhibitors resulted in the prevention of oocyte maturation and the formation of poor-quality oocytes. By performing confocal scanning and quantitative analysis, the results showed that SIRT2 inhibition induced prominent defects in spindle/chromosome morphology, and led to the hyperacetylation of α-tubulin and H4K16. In particular, SIRT2 inhibition impeded cytoplasmic maturation by disturbing the normal distribution of cortical granules, endoplasmic reticulum, and mitochondria during oocyte meiosis. Meanwhile, exposure to SirReal2 led to elevated intracellular reactive oxygen species (ROS) accumulation, low ATP production, and reduced mitochondrial membrane potential in oocytes. Further analysis revealed that SIRT2 inhibition modulated mitochondrial biogenesis and dynamics via the downregulation of TFAM and Mfn2, and the upregulation of DRP1. Mechanistically, SIRT2 inhibition blocked the nuclear translocation of FoxO3a by increasing FoxO3a acetylation, thereby downregulating the expression of FoxO3a-dependent antioxidant genes SOD2 and Cat. These results provide insights into the potential mechanisms by which SIRT2-dependent deacetylation activity exerts its effects on oocyte quality

Phospholipase C inhibits apoptosis of porcine primary granulosa cells cultured in vitro

Abstract Phospholipase C (PLC) can participate in cell proliferation, differentiation and aging. However, whether it has a function in apoptosis in porcine primary granulosa cells is largely uncertain. The objective of this study was to examine the effects of PLC on apoptosis of porcine primary granulosa cells cultured in vitro. The mRNA expression of BAK, BAX and CASP3, were upregulated in the cells treated with U73122 (the PLC inhibitor). The abundance of BCL2 mRNA, was upregulated, while BAX and CASP3 mRNA expression was decreased after treatment with m-3M3FBS (the PLC activator). Both the early and late apoptosis rate were maximized with 0.5 μM U73122 for 4 h. The rate of early apoptosis was the highest at 4 h and the rate of late apoptosis was the highest at 12 h in the m-3M3FBS group. The protein abundance of PLCβ1, protein kinase C β (PKCβ), calmodulin-dependent protein kinaseII α (CAMKIIα) and calcineurinA (CalnA) were decreased by U73122, and CAMKIIα protein abundance was increased by m-3M3FBS. The mRNA expression of several downstream genes (CDC42, NFATc1, and NFκB) was upregulated by PLC. Our results demonstrated that apoptosis can be inhibited by altering PLC signaling in porcine primary granulosa cells cultured in vitro, and several calcium−sensitive targets and several downstream genes might take part in the processes