Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

BackgroundWe explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood.MethodsWe integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments.ResultsAnalysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness.ConclusionSRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients

Development and validation of risk prediction model for identifying 30-day frailty in older inpatients with undernutrition: A multicenter cohort study

ObjectiveTo develop and externally validate a frailty prediction model integrating physical factors, psychological variables and routine laboratory test parameters to predict the 30-day frailty risk in older adults with undernutrition.MethodsBased on an ongoing survey of geriatrics syndrome in elder adults across China (SGSE), this prognostic study identified the putative prognostic indicators for predicting the 30-day frailty risk of older adults with undernutrition. Using multivariable logistic regression analysis with backward elimination, the predictive model was subjected to internal (bootstrap) and external validation, and its calibration was evaluated by the calibration slope and its C statistic discriminative ability. The model derivation and model validation cohorts were collected between October 2018 and February 2019 from a prospective, large-scale cohort study of hospitalized older adults in tertiary hospitals in China. The modeling derivation cohort data (n = 2,194) were based on the SGSE data comprising southwest Sichuan Province, northern Beijing municipality, northwest Qinghai Province, northeast Heilongjiang Province, and eastern Zhejiang Province, with SGSE data from Hubei Province used to externally validate the model (validation cohort, n = 648).ResultsThe incidence of frailty in the older undernutrition derivation cohort was 13.54% and 13.43% in the validation cohort. The final model developed to estimate the individual predicted risk of 30-day frailty was presented as a regression formula: predicted risk of 30-day frailty = [1/(1+e-riskscore )], where riskscore = -0.106 + 0.034 × age + 0.796 × sex -0.361 × vision dysfunction + 0.373 × hearing dysfunction + 0.408 × urination dysfunction - 0.012 × ADL + 0.064 × depression - 0.139 × nutritional status - 0.007 × hemoglobin - 0.034 × serum albumin - 0.012 × (male: ADL). Area under the curve (AUC) of 0.71 in the derivation cohort, and discrimination of the model were similar in both cohorts, with a C statistic of nearly 0.7, with excellent calibration of observed and predicted risks.ConclusionA new prediction model that quantifies the absolute risk of frailty of older patients suffering from undernutrition was developed and externally validated. Based on physical, psychological, and biological variables, the model provides an important assessment tool to provide different healthcare needs at different times for undernutrition frailty patients.Clinical trial registrationChinese Clinical Trial Registry [ChiCTR1800017682]

MicroRNA let-7b regulates neural stem cell proliferation and differentiation by targeting nuclear receptor TLX signaling

Neural stem cell self-renewal and differentiation is orchestrated by precise control of gene expression involving nuclear receptor TLX. Let-7b, a member of the let-7 microRNA family, is expressed in mammalian brains and exhibits increased expression during neural differentiation. However, the role of let-7b in neural stem cell proliferation and differentiation remains unknown. Here we show that let-7b regulates neural stem cell proliferation and differentiation by targeting the stem cell regulator TLX and the cell cycle regulator cyclin D1. Overexpression of let-7b led to reduced neural stem cell proliferation and increased neural differentiation, whereas antisense knockdown of let-7b resulted in enhanced proliferation of neural stem cells. Moreover, in utero electroporation of let-7b to embryonic mouse brains led to reduced cell cycle progression in neural stem cells. Introducing an expression vector of Tlx or cyclin D1 that lacks the let-7b recognition site rescued let-7b-induced proliferation deficiency, suggesting that both TLX and cyclin D1 are important targets for let-7b-mediated regulation of neural stem cell proliferation. Let-7b, by targeting TLX and cyclin D1, establishes an efficient strategy to control neural stem cell proliferation and differentiation

Differentiated Interval Structural Characteristics of Wufeng−Longmaxi Formation Deep Shale Gas Reservoirs in Western Chongqing Area, China: Experimental Investigation Based on Low-Field Nuclear Magnetic Resonance (NMR) and Fractal Modeling

The study of deep shale gas (>3500 m) has become a new research hotspot in the field of shale gas research in China. In this study, 16 representative deep shale samples were selected from different layers of the Wufeng–Longmaxi Formation in the Z-3 well in the western Chongqing area to conduct low-field nuclear magnetic resonance (NMR) tests, field-emission scanning electron microscopy (FE-SEM) observation, and fractal modeling. By comparing the differences in pore structure and their influencing factors in representative samples from different layers, the particularities of high-quality reservoirs have been revealed. The results show that the Z-3 well shales mainly develop micropores and mesopores, with pore sizes of 1 nm–200 nm. The fractal dimensions of bound fluid pores D1 (1.6895–2.3821) and fractal dimension of movable fluid pores D2 (2.9914–2.9996) were obtained from T2 spectra and linear fitting, and the pores were divided into three sections based on the NMR fractal characteristics. TOC content was one of the major factors affecting the gas content in the study area. The shale samples in the bottom S1l1-1 sub-layer with a higher TOC content have larger porosity and permeability, leading to enhanced homogeneity of the pore structure and favorable conditions for shale gas adsorption. A comparative understanding of the particularities of pore structure and influencing factors in high-quality reservoirs with higher gas content will provide the scientific basis for further exploration and exploitation of the Wufeng–Longmaxi Formation deep shale reservoirs in the western Chongqing area