Co-delivery of gemcitabine and Triapine by calcium carbonate nanoparticles against chemoresistant pancreatic cancer

Pancreatic cancer is a malignant disease with high mortality, and its systemic treatment strategy mainly focuses on chemotherapy. Yet, the overall prognosis of pancreatic cancer patients is still extremely poor with a low survival rate. Gemcitabine (GEM) is a widely used chemotherapeutic agent for the treatment of pancreatic cancer. However, GEM chemoresistance remains the major challenge. In this study, we prepared calcium carbonate nanoparticles (CaCO3 NPs) loaded with a nucleotide reductase inhibitor (Triapine) and GEM to suppress the GEM resistance of pancreatic cancer cells (PANC-1/GEM) and solve the problem of poor solubility of Triapine. CaCO3-GEM-Triapine NPs nano-formulations enhanced the therapeutic effect of GEM-based chemotherapy by inhibiting cancer cell proliferation, migration, and resistance to GEM using both 2D PANC-1/GEM cells and 3D tumor spheroids. The study indicated that CaCO3 NPs loaded with GEM and Triapine could provide an effective treatment option to overcome drug resistance in pancreatic cancer

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus.

MicroRNAs (miRNAs) are small noncoding RNAs that modulate the cellular transcriptome at the post-transcriptional level. miRNA plays important roles in different disease manifestation, including type 2 diabetes mellitus (T2DM). Many studies have characterized the changes of miRNAs in T2DM, a complex systematic disease; however, few studies have integrated these findings and explored the functional effects of the dysregulated miRNAs identified. To investigate the involvement of miRNAs in T2DM, we obtained and analyzed all relevant studies published prior to 18 October 2016 from various literature databases. From 59 independent studies that met the inclusion criteria, we identified 158 dysregulated miRNAs in seven different major sample types. To understand the functional impact of these deregulated miRNAs, we performed targets prediction and pathway enrichment analysis. Results from our analysis suggested that the altered miRNAs are involved in the core processes associated with T2DM, such as carbohydrate and lipid metabolisms, insulin signaling pathway and the adipocytokine signaling pathway. This systematic survey of dysregulated miRNAs provides molecular insights on the effect of deregulated miRNAs in different tissues during the development of diabetes. Some of these miRNAs and their mRNA targets may have diagnostic and/or therapeutic utilities in T2DM

A Novel Evolution-Based Method for Detecting Gene-Gene Interactions

BACKGROUND: The rapid advance in large-scale SNP-chip technologies offers us great opportunities in elucidating the genetic basis of complex diseases. Methods for large-scale interactions analysis have been under development from several sources. Due to several difficult issues (e.g., sparseness of data in high dimensions and low replication or validation rate), development of fast, powerful and robust methods for detecting various forms of gene-gene interactions continues to be a challenging task. METHODOLOGY/PRINCIPAL FINDINGS: In this article, we have developed an evolution-based method to search for genome-wide epistasis in a case-control design. From an evolutionary perspective, we view that human diseases originate from ancient mutations and consider that the underlying genetic variants play a role in differentiating human population into the healthy and the diseased. Based on this concept, traditional evolutionary measure, fixation index (Fst) for two unlinked loci, which measures the genetic distance between populations, should be able to reveal the responsible genetic interplays for disease traits. To validate our proposal, we first investigated the theoretical distribution of Fst by using extensive simulations. Then, we explored its power for detecting gene-gene interactions via SNP markers, and compared it with the conventional Pearson Chi-square test, mutual information based test and linkage disequilibrium based test under several disease models. The proposed evolution-based method outperformed these compared methods in dominant and additive models, no matter what the disease allele frequencies were. However, its performance was relatively poor in a recessive model. Finally, we applied the proposed evolution-based method to analysis of a published dataset. Our results showed that the P value of the Fst -based statistic is smaller than those obtained by the LD-based statistic or Poisson regression models. CONCLUSIONS/SIGNIFICANCE: With rapidly growing large-scale genetic association studies, the proposed evolution-based method can be a promising tool in the identification of epistatic effects

Resistance to immune checkpoint inhibitors in advanced lung cancer: Clinical characteristics, potential prognostic factors and next strategy

BackgroundImmune checkpoint inhibitors (ICIs) have shown unprecedented clinical benefit in cancer immunotherapy and are rapidly transforming the practice of advanced lung cancer. However, resistance routinely develops in patients treated with ICIs. We conducted this retrospective study to provide an overview on clinical characteristics of ICI resistance, optimal treatment beyond disease progression after prior exposure to immunotherapy, as well as potential prognostic factors of such resistance.Methods190 patients diagnosed with unresectable lung cancer who received at least one administration of an anti-programmed cell death 1 (PD-1)/anti-programmed cell death-ligand 1(PD-L1) at any treatment line at Zhongshan Hospital Fudan University between Sep 2017 and December 2019 were enrolled in our study. Overall survival (OS) and progression-free survival (PFS) were analyzed. Levels of plasma cytokines were evaluated for the prognostic value of ICI resistance.ResultsWe found that EGFR/ALK/ROS1 mutation and receiving ICI treatment as second-line therapy were risk factors associated with ICI resistance. Patients with bone metastasis at baseline had a significantly shorter PFS1 time when receiving initial ICI treatment. Whether or not patients with oligo-progression received local treatment seemed to have no significant effect on PFS2 time. Systemic therapies including chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. TNF, IL-6 and IL-8 were significantly elevated when ICI resistance. Lower plasma TNF level and higher plasma IL-8 level seemed to be significantly associated with ICI resistance. A nomogram was established to prognosis the clinical outcome of patients treated with ICIs.ConclusionPatients with EGFR/ALK/ROS1 mutation, or those receiving ICI treatment as second-line therapy had higher risk of ICI resistance. Patients with bone metastasis had poor prognosis during immunotherapy. For those patients with oligo-progression after ICI resistance, combination with local treatment did not lead to a significantly longer PFS2 time. Chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. Levels of plasma cytokines including TNF, IL-6 and IL-8 were associated with ICI resistance

A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually

Impact of older age adiposity on incident diabetes: A community-based cohort study in China

© 2022 Korean Diabetes Association. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.4093/dmj.2021.0215Background Obesity classifications vary globally and the impact of older age adiposity on incident diabetes has not been well-studied. Methods We examined a random sample of 2,809 participants aged ≥60 years in China, who were free of diabetes at baseline and were followed up for up to 10 years to document diabetes (n=178). The incidence of diabetes was assessed in relation to different cut-off points of body mass index (BMI) and waist circumference (WC) in multiple adjusted Cox regression models. Results The diabetic risk in the cohort increased linearly with the continuous and quartile variables of BMI and WC. The BMI-World Health Organization (WHO) and BMI-China criteria analysis did not show such a linear relationship, however, the BMI-Asian/Hong Kong criteria did; adjusted hazards ratio (HR) was 0.42 (95% confidence interval [CI], 0.20 to 0.90) in BMI <20 kg/m2, 1.46 (95% CI, 0.99 to 2.14) in 23-≤26 kg/m2, and 1.63 (95% CI, 1.09 to 2.45) in ≥26 kg/m2. The WC-China criteria revealed a slightly better prediction of diabetes (adjusted HRs were 1.79 [95% CI, 1.21 to 2.66] and 1.87 [95% CI, 1.22 to 2.88] in central obese action levels 1 and 2) than the WC-WHO. The combination of the BMI-Asian/Hong Kong with WC-China demonstrated the strongest prediction. There were no gender differences in the impact of adiposity on diabetes. Conclusion In older Chinese, BMI-Asian/Hong Kong criteria is a better predictor of diabetes than other BMI criterion. Its combination with WC-China improved the prediction of adiposity to diabetes, which would help manage bodyweight in older age to reduce the risk of diabetes.The data collection of the Anhui cohort study was funded by the Royal Society of UK, the BUPA Foundation (Grants Nos. 45NOV06, and TBF-M09-05) and Alzheimer’s Research UK (Grant No. ART/PPG2007B/2). The data analysis was supported by the Discipline Construction Project of Guangdong Medical University (4SG21276P) and the Basic and Applied Basic Research Foundation of Guangdong Province Regional Joint Fund Project (The Key Project) (2020B1515120021), China.Published versio

A simple method for deriving functional MSCs and applied for osteogenesis in 3D scaffolds

We describe a simple method for bone engineering using biodegradable scaffolds with mesenchymal stem cells derived from human induced-pluripotent stem cells (hiPS-MSCs). The hiPS-MSCs expressed mesenchymal markers (CD90, CD73, and CD105), possessed multipotency characterized by tri-lineages differentiation: osteogenic, adipogenic, and chondrogenic, and lost pluripotency – as seen with the loss of markers OCT3/4 and TRA-1-81 – and tumorigenicity. However, these iPS-MSCs are still positive for marker NANOG. We further explored the osteogenic potential of the hiPS-MSCs in synthetic polymer polycaprolactone (PCL) scaffolds or PCL scaffolds functionalized with natural polymer hyaluronan and ceramic TCP (PHT) both in vitro and in vivo. Our results showed that these iPS-MSCs are functionally compatible with the two 3D scaffolds tested and formed typically calcified structure in the scaffolds. Overall, our results suggest the iPS-MSCs derived by this simple method retain fully osteogenic function and provide a new solution towards personalized orthopedic therapy in the future

The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity

Background. The prevalence of obesity and type 2 diabetes mellitus (T2DM) has become the most serious global public health issue. In recent years, there has been increasing attention to the role of long noncoding RNAs (lncRNAs) in the occurrence and development of obesity and T2DM. The aim of this work was to find new lncRNAs as potential predictive biomarkers or therapeutic targets for obesity and T2DM. Methods. In this study, we identified significant differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) between adipose tissue of individuals with obesity and T2DM and normal adipose tissue (absolute log2FC≥1 and FDR0.2. Simultaneously, the mRNA-miRNA interactions were explored by miRWalk 2.0. Finally, a ceRNA network consisting of lncRNAs, miRNAs, and mRNAs was established by integrating lncRNA-miRNA interactions and mRNA-miRNA interactions. Results. Upon comparing adipose tissue from individuals with obesity and T2DM and normal adipose tissues, 364 significant DEmRNAs, including 140 upregulated and 224 downregulated mRNAs, were identified in GSE104674; in addition, 231 significant DEmRNAs, including 146 upregulated and 85 downregulated mRNAs, were identified in GSE133099. GO and KEGG analyses have shown that downregulated DEmRNAs in GSE104674 and GSE133099 were associated with obesity- and T2DM-related biological pathways, such as lipid metabolism, AMPK signaling, and insulin resistance. Furthermore, 28 significant DElncRNAs, including 14 upregulated and 14 downregulated lncRNAs, were found. Based on the predicted lncRNA-miRNA and mRNA-miRNA relationships, we constructed a competitive endogenous RNA (ceRNA) network, including five lncRNAs, ten miRNAs, and 15 mRNAs. KEGG-GSEA analysis revealed that four lncRNAs (FLG-AS1, SNAI3-AS1, AC008147.0, and LINC02015) in the ceRNA network were related to the biological pathways of metabolic diseases. Conclusions. Through ceRNA network analysis, our study identified four new lncRNAs that may be used as potential biomarkers and therapeutic targets of obesity and T2DM, thus laying a foundation for future clinical studies