Identify treatment effect patterns for personalised decisions

In personalised decision making, evidence is required to determine suitable actions for individuals. Such evidence can be obtained by identifying treatment effect heterogeneity in different subgroups of the population. In this paper, we design a new type of pattern, treatment effect pattern to represent and discover treatment effect heterogeneity from data for determining whether a treatment will work for an individual or not. Our purpose is to use the computational power to find the most specific and relevant conditions for individuals with respect to a treatment or an action to assist with personalised decision making. Most existing work on identifying treatment effect heterogeneity takes a top-down or partitioning based approach to search for subgroups with heterogeneous treatment effects. We propose a bottom-up generalisation algorithm to obtain the most specific patterns that fit individual circumstances the best for personalised decision making. For the generalisation, we follow a consistency driven strategy to maintain inner-group homogeneity and inter-group heterogeneity of treatment effects. We also employ graphical causal modelling technique to identify adjustment variables for reliable treatment effect pattern discovery. Our method can find the treatment effect patterns reliably as validated by the experiments. The method is faster than the two existing machine learning methods for heterogeneous treatment effect identification and it produces subgroups with higher inner-group treatment effect homogeneity

Role of LECT2 in exacerbating atopic dermatitis: insight from in vivo and in vitro models via NF-κB signaling pathway

Leukocyte cell-derived chemotaxin 2 (LECT2) is linked to various immune diseases. Previously, we reported that serum LECT2 levels correlate with disease severity in atopic dermatitis (AD) patients. To investigate the role of LECT2 in AD and elucidate its potential mechanisms, we used LECT2 to treat an AD mouse model induced by 1-Chloro-2,4-dinitrobenzene (DNCB) in LECT2 knockout (KO) and wild-type (WT) mice, and an AD cell model using TNF-α/IFN-γ-induced HaCaT cells. Inflammatory factors and barrier proteins were analyzed by histology, immunohistochemistry, RT-qPCR, ELISA, and Western Blot. Activation of the NF-κB signaling pathway was evaluated by Western Blot and immunofluorescence. In the AD mouse model, LECT2 treatment increased epidermal and dermal thickness, mast cell infiltration, and downregulated barrier proteins. Inflammatory factors were increased in skin lesions and serum. In the AD cell model, LECT2 decreased barrier protein levels and increased inflammatory factor levels, enhancing NF-κB P65 nuclear translocation. These results indicate that LECT2 exacerbates AD-like responses by dysregulating the NF-κB signaling pathway, highlighting its potential as a therapeutic target for AD management

Direct Conversion of Mouse Astrocytes Into Neural Progenitor Cells and Specific Lineages of Neurons

Background: Cell replacement therapy has been envisioned as a promising treatment for neurodegenerative diseases. Due to the ethical concerns of ESCs-derived neural progenitor cells (NPCs) and tumorigenic potential of iPSCs, reprogramming of somatic cells directly into multipotent NPCs has emerged as a preferred approach for cell transplantation. Methods: Mouse astrocytes were reprogrammed into NPCs by the overexpression of transcription factors (TFs) Foxg1, Sox2, and Brn2. The generation of subtypes of neurons was directed by the force expression of cell-type specific TFs Lhx8 or Foxa2/Lmx1a. Results: Astrocyte-derived induced NPCs (AiNPCs) share high similarities, including the expression of NPC-specific genes, DNA methylation patterns, the ability to proliferate and differentiate, with the wild type NPCs. The AiNPCs are committed to the forebrain identity and predominantly differentiated into glutamatergic and GABAergic neuronal subtypes. Interestingly, additional overexpression of TFs Lhx8 and Foxa2/Lmx1a in AiNPCs promoted cholinergic and dopaminergic neuronal differentiation, respectively. Conclusions: Our studies suggest that astrocytes can be converted into AiNPCs and lineage-committed AiNPCs can acquire differentiation potential of other lineages through forced expression of specific TFs. Understanding the impact of the TF sets on the reprogramming and differentiation into specific lineages of neurons will provide valuable strategies for astrocyte-based cell therapy in neurodegenerative diseases

PANoptosis-related genes function as efficient prognostic biomarkers in colon adenocarcinoma

BackgroundPANoptosis is a newly discovered cell death type, and tightly associated with immune system activities. To date, the mechanism, regulation and application of PANoptosis in tumor is largely unknown. Our aim is to explore the prognostic value of PANoptosis-related genes in colon adenocarcinoma (COAD).MethodsAnalyzing data from The Cancer Genome Atlas-COAD (TCGA-COAD) involving 458 COAD cases, we concentrated on five PANoptosis pathways from the Molecular Signatures Database (MSigDB) and a comprehensive set of immune-related genes. Our approach involved identifying distinct genetic COAD subtype clusters and developing a prognostic model based on these parameters.ResultsThe research successfully identified two genetic subtype clusters in COAD, marked by distinct profiles in PANoptosis pathways and immune-related gene expression. A prognostic model, incorporating these findings, demonstrated significant predictive power for survival outcomes, underscoring the interplay between PANoptosis and immune responses in COAD.ConclusionThis study enhances our understanding of COAD’s genetic framework, emphasizing the synergy between cell death pathways and the immune system. The development of a prognostic model based on these insights offers a promising tool for personalized treatment strategies. Future research should focus on validating and refining this model in clinical settings to optimize therapeutic interventions in COAD

Table_1_PANoptosis-related genes function as efficient prognostic biomarkers in colon adenocarcinoma.csv

BackgroundPANoptosis is a newly discovered cell death type, and tightly associated with immune system activities. To date, the mechanism, regulation and application of PANoptosis in tumor is largely unknown. Our aim is to explore the prognostic value of PANoptosis-related genes in colon adenocarcinoma (COAD).MethodsAnalyzing data from The Cancer Genome Atlas-COAD (TCGA-COAD) involving 458 COAD cases, we concentrated on five PANoptosis pathways from the Molecular Signatures Database (MSigDB) and a comprehensive set of immune-related genes. Our approach involved identifying distinct genetic COAD subtype clusters and developing a prognostic model based on these parameters.ResultsThe research successfully identified two genetic subtype clusters in COAD, marked by distinct profiles in PANoptosis pathways and immune-related gene expression. A prognostic model, incorporating these findings, demonstrated significant predictive power for survival outcomes, underscoring the interplay between PANoptosis and immune responses in COAD.ConclusionThis study enhances our understanding of COAD’s genetic framework, emphasizing the synergy between cell death pathways and the immune system. The development of a prognostic model based on these insights offers a promising tool for personalized treatment strategies. Future research should focus on validating and refining this model in clinical settings to optimize therapeutic interventions in COAD.</p

Pretreatment 18F‐FDG uptake heterogeneity may predict treatment outcome of combined Trastuzumab and Pertuzumab therapy in patients with metastatic HER2 positive breast cancer

Abstract Objective Intra-tumoral heterogeneity of 18F‐fluorodeoxyglucose (18F‐FDG) uptake has been proven to be a surrogate marker for predicting treatment outcome in various tumors. However, the value of intra-tumoral heterogeneity in metastatic Human epidermal growth factor receptor 2(HER2) positive breast cancer (MHBC) remains unknown. The aim of this study was to evaluate 18F‐FDG uptake heterogeneity to predict the treatment outcome of the dual target therapy with Trastuzumab and Pertuzumab(TP) in MHBC. Methods Thirty-two patients with MHBC who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) scan before TP were enrolled retrospectively. The region of interesting (ROI) of the lesions were drawn, and maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), metabolic tumor volume (MTV) and heterogeneity index (HI) were recorded. Correlation between PET/CT parameters and the treatment outcome was analyzed by Spearman Rank Test. The ability to predict prognosis were determined by time‐dependent survival receiver operating characteristic (ROC) analysis. And the survival analyses were then estimated by Kaplan‐Meier method and compared by log‐rank test. Results The survival analysis showed that HI50% calculated by delineating the lesion with 50%SUVmax as threshold was a significant predictor of patients with MHBC treated by the treatment with TP. Patients with HI50% (≥ 1.571) had a significantly worse prognosis of progression free survival (PFS) (6.87 vs. Not Reach, p = 0.001). The area under curve (AUC), the sensitivity and the specificity were 0.88, 100% and 63.6% for PFS, respectively. Conclusion 18F-FDG uptake heterogeneity may be useful for predicting the prognosis of MHBC patients treated by TP

Shwachman-Diamond Syndrome Protein SBDS Maintains Human Telomeres by Regulating Telomerase Recruitment

Summary: Shwachman-Diamond syndrome (SDS) is a rare pediatric disease characterized by various systemic disorders, including hematopoietic dysfunction. The mutation of Shwachman-Bodian-Diamond syndrome (SBDS) gene has been proposed to be a major causative reason for SDS. Although SBDS patients were reported to have shorter telomere length in granulocytes, the underlying mechanism is still unclear. Here we provide data to elucidate the role of SBDS in telomere protection. We demonstrate that SBDS deficiency leads to telomere shortening. We found that overexpression of disease-associated SBDS mutants or knockdown of SBDS hampered the recruitment of telomerase onto telomeres, while the overall reverse transcriptase activity of telomerase remained unaffected. Moreover, we show that SBDS could specifically bind to TPP1 during the S phase of cell cycle, likely functioning as a stabilizer for TPP1-telomerase interaction. Our findings suggest that SBDS is a telomere-protecting protein that participates in regulating telomerase recruitment