MLH1 Gene Expression in Peripheral Blood in Colon Cancer Patients and Their Association with Colon Cancer.

MutL homolog 1 (MLH1) is a component of the heterodimeric complex MutLα that detects and fixes base-base mismatches and insertion/deletion loops caused by nucleotide misincorporation. In the absence of MLH1 protein, the frequency of non-repaired mismatches increases, resulting in organ cancer. The current study sought to quantify MLH1 gene expression and its relationship with tumor invasion (T) and lymph node invasion (N) in blood samples from patients with colorectal cancer (CRC). Blood samples were obtained from 36 CRC patients. RNA was extracted, and cDNA was synthesized using a kit. The primers were built using the exon-exon junction approach, and MLH1 and ß-actin genes were tested 3x using real-time polymerase chain reaction (Real-Time PCR). Gene expression analysis software was used to analyze the data, and a t-test was used to examine the expression of MLH1 and its connection with T and N variables. In this study, 36 patients with colorectal cancer, including 15 (41.6%) women and 21 (58.4%) men, with a mean age of 57.35 ± 4.22 years and in the age range of 26-87 years, were included. The results showed that the ratio of MLH1 gene expression in patients decreased compared to that in healthy individuals, and the decrease in gene expression at different stages of the disease was significant. The results of this study showed that the reduction of MLH1 gene expression has an effective role in the development of CRC.

Functional and Structural Characterization of Clinical-Stage Janus Kinase 2 Inhibitors Identifies Determinants for Drug Selectivity.

Janus kinase 2 (JAK2) plays a critical role in orchestrating hematopoiesis, and its deregulation leads to various blood disorders, most importantly myeloproliferative neoplasms (MPNs). Ruxolitinib, fedratinib, momelotinib, and pacritinib are FDA-/EMA-approved JAK inhibitors effective in relieving symptoms in MPN patients but show variable clinical profiles due to poor JAK selectivity. The development of next-generation JAK2 inhibitors is hampered by the lack of comparative functional analysis and knowledge of the molecular basis of their selectivity. Here, we provide mechanistic profiling of the four approved and six clinical-stage JAK2 inhibitors and connect selectivity data with high-resolution structural and thermodynamic analyses. All of the JAK inhibitors potently inhibited JAK2 activity. Inhibitors differed in their JAK isoform selectivity and potency for erythropoietin signaling, but their general cytokine inhibition signatures in blood cells were comparable. Structural data indicate that high potency and moderate JAK2 selectivity can be obtained by targeting the front pocket of the adenosine 5'-triphosphate-binding site.

A method for predicting drugs that can boost the efficacy of immune checkpoint blockade.

Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.

Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China.

Background: Patients with diabetes are more likely to be predisposed to fractures compared to those without diabetes. In clinical practice, predicting fracture risk in diabetics is still difficult because of the limited availability and accessibility of existing fracture prediction tools in the diabetic population. The purpose of this study was to develop and validate models using machine learning (ML) algorithms to achieve high predictive power for fracture in patients with diabetes in China. Methods: In this study, the clinical data of 775 hospitalized patients with diabetes was analyzed by using Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost) and Probabilistic Classification Vector Machines (PCVM) algorithms to construct risk prediction models for fractures. Moreover, the risk factors for diabetes-related fracture were identified by the feature selection algorithms. Results: The ML algorithms extracted 17 most relevant factors from raw clinical data to maximize the accuracy of the prediction results, including bone mineral density, age, sex, weight, high-density lipoprotein cholesterol, height, duration of diabetes, total cholesterol, osteocalcin, N-terminal propeptide of type I, diastolic blood pressure, and body mass index. The 7 ML models including LR, SVM, RF, DT, GBDT, XGBoost, and PCVM had f1 scores of 0.75, 0.83, 0.84, 0.85, 0.87, 0.88, and 0.97, respectively. Conclusions: This study identified 17 most relevant risk factors for diabetes-related fracture using ML algorithms. And the PCVM model proved to perform best in predicting the fracture risk in the diabetic population. This work proposes a cheap, safe, and extensible ML algorithm for the precise assessment of risk factors for diabetes-related fracture.

The inhibition of Aurora A kinase regulates phospholipid remodeling by upregulating LPCAT1 in glioblastoma.

Metabolic reprogramming is a common feature of glioblastoma (GBM) progression and metastasis. Altered lipid metabolism is one of the most prominent metabolic alterations in cancer. Understanding the links between phospholipid remodeling and GBM tumorigenesis may help develop new anticancer strategies and improve treatments to overcome drug resistance. We used metabolomic and transcriptomic analyses to systematically investigate metabolic and molecular changes in low-grade glioma (LGG) and GBM. We then re-established the reprogrammed metabolic flux and membrane lipid composition in GBM based on metabolomic and transcriptomic analyses. By inhibiting Aurora A kinase via RNA interference (RNAi) and inhibitor treatment, we investigated the effect of Aurora A kinase on phospholipid reprogramming LPCAT1 enzyme expression and GBM cell proliferation in vitro and in vivo. We found that GBM displayed aberrant glycerophospholipid and glycerolipid metabolism compared with LGG. Metabolic profiling indicated that fatty acid synthesis and uptake for phospholipid synthesis were significantly increased in GBM compared to LGG. The unsaturated phosphatidylcholine (PC) and phosphatidylethanolamine (PE) levels were significantly decreased in GBM compared to LGG. The expression level of LPCAT1, which is required for the synthesis of saturated PC and PE, was upregulated in GBM, and the expression of LPCAT4, which is required for the synthesis of unsaturated PC and PE, was downregulated in GBM. Notably, the inhibition of Aurora A kinase by shRNA knockdown and treatment with Aurora A kinase inhibitors such as Alisertib, AMG900, or AT9283 upregulated LPCAT1 mRNA and protein expression in vitro. In vivo, the inhibition of Aurora A kinase with Alisertib increased LPCAT1 protein expression. Phospholipid remodeling and a reduction in unsaturated membrane lipid components were found in GBM. Aurora A kinase inhibition increased LPCAT1 expression and suppressed GBM cell proliferation. The combination of Aurora kinase inhibition with LPCAT1 inhibition may exert promising synergistic effects on GBM.

Primary cilia-associated protein IFT172 in ciliopathies.

Cilium is a highly conserved antenna-like structure protruding from the surface of the cell membrane, which is widely distributed on most mammalian cells. Two types of cilia have been described so far which include motile cilia and immotile cilia and the latter are also known as primary cilia. Dysfunctional primary cilia are commonly associated with a variety of congenital diseases called ciliopathies with multifaceted presentations such as retinopathy, congenital kidney disease, intellectual disability, cancer, polycystic kidney, obesity, Bardet Biedl syndrome (BBS), etc. Intraflagellar transport (IFT) is a bi-directional transportation process that helps maintain a balanced flow of proteins or signaling molecules essential for the communication between cilia and cytoplasm. Disrupted IFT contributes to the abnormal structure or function of cilia and frequently promotes the occurrence of ciliopathies. Intraflagellar transport 172 (IFT172) is a newly identified member of IFT proteins closely involved in some rare ciliopathies such as Mainzer-Saldino syndrome (MZSDS) and BBS, though the underpinning causal mechanisms remain largely elusive. In this review, we summarize the key findings on the genetic and protein characteristic of IFT172, as well as its function in intraflagellar transport, to provide comprehensive insights to understand IFT172-related ciliopathies.

Anxiety prevalence and its association with physical activity in patients with non-communicable diseases during COVID-19 lockdown: a cross-sectional study in Shanghai, China.

BACKGROUND: Quarantine due to the COVID-19 pandemic may have created great psychological stress among vulnerable populations. We aimed to investigate the prevalence of anxiety and explore the association between physical activities (PA) and anxiety risk in people with non-communicable diseases during the period of COVID-19 lockdown. METHODS: We conducted a cross-sectional telephone survey from February 25 to April 20, 2020, the period of COVID-19 lockdown in Shanghai. Up to 8000 patients with type 2 diabetes and/or hypertension were selected using multi-stage cluster random sampling. PA level was measured based on the International Physical Activity Questionnaire using Metabolic Equivalent for Task scores, while symptoms of anxiety were assessed by the 7-item Generalized Anxiety Disorder scale. Multiple logistic regression analyses were performed to evaluate the associations of type and level of PA with the risk of anxiety. RESULTS: Of a total 4877 eligible patients, 2602 (53.4%) reported with anxiety, and 2463 (50.5%), 123 (2.5%) and 16 (0.3%) reported with mild, moderate, and severe anxiety. The prevalence of anxiety was higher in the females, the elders, non-smokers, non-drinkers, and patients with diabetes, and the associations of anxiety with sex, age, smoking, drinking and diagnosis of diabetes were significant. A significant negative association was observed for housework activities (OR 0.53, 95%CI: [0.45, 0.63], p < 0.001) and trip activities (OR 0.55, 95%CI: [0.48, 0.63], p < 0.001) with anxiety, but no significant was found for exercise activities (OR 1.06, 95%CI: [0.94, 1.20], p = 0.321). Compared with patients with a low PA level, those with a moderate (OR 0.53, 95%CI: [0.44, 0.64], p < 0.001) or a high PA level (OR 0.51, 95%CI: [0.43, 0.51], p < 0.001) had a lower prevalence of anxiety. CONCLUSION: This study demonstrates a higher prevalence of anxiety in patients with hypertension, diabetes, or both during the COVID-19 lockdown. The negative associations of housework and trip activities with anxiety highlight the potential benefit of PA among patients with non-communicable diseases.

A comprehensive landscape of transcription profiles and data resources for human leukemia.

As a heterogeneous group of hematologic malignancies, leukemia has been widely studied at the transcriptome level. However, a comprehensive transcriptomic landscape and resources for different leukemia subtypes are lacking. Thus, in this study, we integrated the RNA sequencing data sets of >3000 samples from 14 leukemia subtypes and 53 related cell lines via a unified analysis pipeline. We depicted the corresponding transcriptomic landscape and developed a user-friendly data portal LeukemiaDB. LeukemiaDB was designed with 5 main modules: protein-coding gene, long noncoding RNA (lncRNA), circular RNA, alternative splicing, and fusion gene modules. In LeukemiaDB, users can search and browse the expression level, regulatory modules, and molecular information across leukemia subtypes or cell lines. In addition, a comprehensive analysis of data in LeukemiaDB demonstrates that (1) different leukemia subtypes or cell lines have similar expression distribution of the protein-coding gene and lncRNA; (2) some alternative splicing events are shared among nearly all leukemia subtypes, for example, MYL6 in A3SS, MYB in A5SS, HMBS in retained intron, GTPBP10 in mutually exclusive exons, and POLL in skipped exon; (3) some leukemia-specific protein-coding genes, for example, ABCA6, ARHGAP44, WNT3, and BLACE, and fusion genes, for example, BCR-ABL1 and KMT2A-AFF1 are involved in leukemogenesis; (4) some highly correlated regulatory modules were also identified in different leukemia subtypes, for example, the HOXA9 module in acute myeloid leukemia and the NOTCH1 module in T-cell acute lymphoblastic leukemia. In summary, the developed LeukemiaDB provides valuable insights into oncogenesis and progression of leukemia and, to the best of our knowledge, is the most comprehensive transcriptome resource of human leukemia available to the research community.

GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

Association between age at diabetes onset or diabetes duration and subsequent risk of pancreatic cancer: Results from a longitudinal cohort and mendelian randomization study.

Background: The aim of the study is to estimate the incidence of pancreatic cancer among individuals with new-onset type 2 Diabetes (T2DM) and evaluate the relationship of pancreatic cancer risk with age at diabetes onset and diabetes duration. Methods: This longitudinal cohort study included 428,362 new-onset T2DM patients in Shanghai and Mendelian randomization (MR) in the east-Asian population were used to investigate the association. Incidence rates of pancreatic cancer in all patients and by subgroups were calculated and compared to the general population. Findings: A total of 1056 incident pancreatic cancer cases were identified during eight consecutive years of follow-up. The overall pancreatic cancer annual incidence rate was 55·28/100,000 person years in T2DM patients, higher than that in the general population, with a standardized incidence ratio (SIR) of 1·54 (95% confidence interval [CI], 1·45-1·64). The incidence of pancreatic cancer increased with age and a significantly higher incidence was observed in the older groups with T2DM. However, the relative pancreatic cancer risk was inversely related to age of T2DM onset, and a higher SIR of 5·73 (95%CI, 4·49-7·22) was observed in the 20-54 years old group. The risk of pancreatic cancer was elevated at any diabetes duration. Fasting blood glucose ≥10·0 mmol/L was associated with increased risk of pancreatic cancer. MR analysis indicated a positive association between T2DM and pancreatic cancer risk. Interpretation: Efforts toward early and close follow-up programs, especially in individuals with young-onset T2DM, and the improvement of glucose control might represent effective strategies for improving the detection and results of treatment of pancreatic cancer. Funding: Chinese National Natural Science Foundation.

lncRNASNP v3: an updated database for functional variants in long non-coding RNAs.

Long non-coding RNAs (lncRNAs) act as versatile regulators of many biological processes and play vital roles in various diseases. lncRNASNP is dedicated to providing a comprehensive repository of single nucleotide polymorphisms (SNPs) and somatic mutations in lncRNAs and their impacts on lncRNA structure and function. Since the last release in 2018, there has been a huge increase in the number of variants and lncRNAs. Thus, we updated the lncRNASNP to version 3 by expanding the species to eight eukaryotic species (human, chimpanzee, pig, mouse, rat, chicken, zebrafish, and fruitfly), updating the data and adding several new features. SNPs in lncRNASNP have increased from 11 181 387 to 67 513 785. The human mutations have increased from 1 174 768 to 2 387 685, including 1 031 639 TCGA mutations and 1 356 046 CosmicNCVs. Compared with the last release, updated and new features in lncRNASNP v3 include (i) SNPs in lncRNAs and their impacts on lncRNAs for eight species, (ii) SNP effects on miRNA-lncRNA interactions for eight species, (iii) lncRNA expression profiles for six species, (iv) disease & GWAS-associated lncRNAs and variants, (v) experimental & predicted lncRNAs and drug target associations and (vi) SNP effects on lncRNA expression (eQTL) across tumor & normal tissues. The lncRNASNP v3 is freely available at http://gong_lab.hzau.edu.cn/lncRNASNP3/.

Single-Cell Transcriptomics Reveals Longevity Immune Remodeling Features Shared by Centenarians and Their Offspring.

Centenarians, who show mild infections and low incidence of tumors, are the optimal model to investigate healthy aging. However, longevity related immune characteristics has not been fully revealed largely due to lack of appropriate controls. In this study, single-cell transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) derived from seven centenarians (CEN), six centenarians' offspring (CO), and nine offspring spouses or neighbors (Control, age-matched to CO) are performed to investigate the shared immune features between CEN and CO. The results indicate that among all 12 T cell clusters, the cytotoxic-phenotype-clusters (CPC) and the naïve-phenotype-clusters (NPC) significantly change between CEN and ontrol. Compared to Control, both CEN and CO are characterized by depleted NPC and increased CPC, which is dominated by CD8+ T cells. Furthermore, CPC from CEN and CO share enhanced signaling pathways and transcriptional factors associated with immune response, and possesse similar T-cell-receptor features, such as high clonal expansion. Interestingly, rather than a significant increase in GZMK+ CD8 cells during aging, centenarians show accumulation of GZMB+ and CMC1+ CD8 T cells. Collectively, this study unveils an immune remodeling pattern reflected by both quantitative increase and functional reinforcement of cytotoxic T cells which are essential for healthy aging.

Biological Pathway-Derived TMB Robustly Predicts the Outcome of Immune Checkpoint Blockade Therapy.

Although immune checkpoint blockade (ICB) therapies have achieved great progress, the patient response varies among cancers. In this study, we analyzed the potential genomic indicators contributing to ICB therapy response. The results showed that high tumor mutation burden (TMB) failed to predict response in anti-PD1 treated melanoma. SERPINB3 was the most significant response-related gene in melanoma and mutations in either SERPINB3 or PEG3 can serve as an independent risk factor in melanoma. Some recurrent mutations in CSMD3 were only in responders or non-responders, indicating their diverse impacts on patient response. Enrichment scores (ES) of gene mutations in 12 biological pathways were significantly higher in responders or non-responders. Next, the P-TMB calculated from genes in these pathways was significantly related to patient response with prediction AUC 0.74-0.82 in all collected datasets. In conclusion, our work provides new insights into the application of TMB in predicting patient response, which will benefit to immunotherapy research.

ICBatlas: A Comprehensive Resource for Depicting Immune Checkpoint Blockade Therapy Characteristics from Transcriptome Profiles.

Immune checkpoint blockade (ICB) therapy provides remarkable clinical benefits for multiple cancer types. Much work is currently being conducted to investigate the mechanisms of ICB therapy at the transcriptional level. Integrating the data produced by these studies will help us give more insight into the transcriptomic features of ICB therapy. We collected the transcriptome and clinical data of ICB-treated patient samples from the Gene Expression Omnibus, ArrayExpress, The Cancer Genome Atlas, and dbGaP databases. On the basis of the clinical information, all samples are initially classified into response/nonresponse or pretreatment/on-treatment groups. Differential expression, pathway enrichment, and immune cell infiltration analyses are performed between the samples from different groups. We also introduce the Response Score (RS) calculated by integrating the variability degree and the frequency of the dysregulated genes in the responders to evaluate the impact of gene expression on the response. Finally, all the abovementioned contents are integrated into the ICBatlas database. ICBatlas provides the transcriptome features of ICB therapy through the analysis of 1,515 ICB-treated samples from 25 studies across nine cancer types. The data in ICBatlas include clinical outcomes, treatment-related genes, biological pathways, and immune cell infiltration. Users can investigate the abovementioned transcriptome features in the response (R vs. NR) or treatment (Pre vs. On) modules at the data set, cancer type, or immune checkpoint level and compare the degree of gene impact on the response in the RS module. ICBatlas is the first database to show the transcriptome features on ICB therapy in human cancers and freely available at http://bioinfo.life.hust.edu.cn/ICBatlas/.

Transcriptomic Features in a Single Extracellular Vesicle via Single-Cell RNA Sequencing.

Although many studies have investigated functional molecules in extracellular vesicles (EVs), the exact number of ribonucleic acid molecules in a single-EV is unknown. Therefore, it is critical to explore the transcriptomic features and heterogeneity at the level of a single-EV. Here, using the 10x Genomics platform, the RNA cargos are profiled in single EVs derived from human K562 and mesenchymal stem cells. The key steps are labeling intact EVs using calcein-AM, detecting the EV concentration via flow cytometry, and using the CB2 algorithm with adaptive thresholds to effectively distinguish real EVs from background. The gene number in a single-EV varied from 6 to 148, with a mean of 52. Ribosomal genes, mitochondrial genes, and eukaryotic translation elongation factor 1 alpha has a high EV percentage in all EV samples. Hemoglobin genes are uniquely highly expressed in K562-EVs, and cytoskeleton genes are enriched in MSC-EVs. Ten or more clusters with different marker genes in each single-EV dataset demonstrated EV heterogeneity. Moreover, integrating EVs and their parental cells reveal both EVs and cells in each cluster, indicating different cell origins of various EVs. To the best of the author's knowledge, this study provides the first high-throughput transcriptome at the single-EV level and improves the understanding of EVs.

A miR-9-5p/FOXO1/CPEB3 Feed-Forward Loop Drives the Progression of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide, but its regulatory mechanism remains unclear and potential clinical biomarkers are still lacking. Co-regulation of TFs and miRNAs in HCC and FFL module studies may help to identify more precise and critical driver modules in HCC development. Here, we performed a comprehensive gene expression and regulation analysis for HCC in vitro and in vivo. Transcription factor and miRNA co-regulatory networks for differentially expressed genes between tumors and adjacent tissues revealed the critical feed-forward loop (FFL) regulatory module miR-9-5p/FOXO1/CPEB3 in HCC. Gain- and loss-of-function studies demonstrated that miR-9-5p promotes HCC tumor proliferation, while FOXO1 and CPEB3 inhibit hepatocarcinoma growth. Furthermore, by luciferase reporter assay and ChIP-Seq data, CPEB3 was for the first time identified as a direct downstream target of FOXO1, negatively regulated by miR-9-5p. The miR-9-5p/FOXO1/CPEB3 FFL was associated with poor prognosis, and promoted cell growth and tumor progression of HCC in vitro and in vivo. Our study identified for the first time the existence of miR-9-5p/FOXO1/CPEB3 FFL and revealed its regulatory role in HCC progression, which may represent a new potential target for cancer therapy.

ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction.

MOTIVATION: Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. RESULTS: We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. AVAILABILITY AND IMPLEMENTATION: Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Case Report: Multi-Omics Analysis and CAR-T Treatment of a Chronic Myeloid Leukemia Blast Crisis Case 5 Years After the Discontinuation of TKI.

Most relapsed chronic myeloid leukemia (CML) patients after tyrosine kinase inhibitor (TKI) discontinuation are in a chronic phase and could achieve remission through restarting the TKI treatment. Here we reported a case of sudden lymphoid blast crisis after 67 months of TKI discontinuation and depicted the patient by DNA and RNA sequencing to investigate intrinsic molecular features. The mutations of TGFBR2 and PCNT and the dysregulations of TGF-ß and other pathways might accelerate the B cell transformation, which may serve as a blast crisis risk indicator of CML. Single-cell transcriptome data revealed that several clusters of immature B cells and late pro-B cells presented clone evolution during the treatment. After failing multiple lines of TKIs, conditioning chemotherapies and chimeric antigen receptor T cells (CAR-T) targeting CD19 and CD22 were performed to achieve remission. In conclusion, we report the first case of a CML patient with sudden lymphoid blast crisis after a long treatment-free remission and additional gene abnormalities other than BCR-ABL1 might participate in the progression, which need to be closely monitored, and CAR-T could be a solution to the chemoresistant progression.

Indole Alkaloids from a Soil-Derived Clonostachys rosea.

Clonorosins A (1) and B (2), two novel indole alkaloids featuring unprecedented 6/5/6/6/5 and 6/5/5 cores, together with seven known indole-linked 2,5-diketopiperazine alkaloids (3-9), were isolated from the soil-derived fungus Clonostachys rosea YRS-06. The new structures were proposed through HR-MS, NMR, and ECD spectroscopic data. They were established by comparing the calculated NMR, ECD, and specific rotation data with the experimental. To assist in determining the absolute configuration of the chiral carbon in the side chain of 2,5-diketopiperazine derivatives, flexible analogues 3i-3iv were synthesized and analyzed. 1 was active against Fusarium oxysporum with an MIC value of 50 µg/mL. 7 and 8 showed excellent activity against human HeLa and HepG2 cells with IC50 values of 0.12-0.60 µM.

LncRNA MEG8 promotes TNF-α expression by sponging miR-454-3p in bone-invasive pituitary adenomas.

There are few studies on the mechanism of pituitary adenoma (PA) destroying bone. The current study aimed to investigate the role of MEG8/miR-454-3p/TNF-α in bone-invasive pituitary adenomas (BIPAs). In this study, we report that lncRNA MEG8 and TNF-α are upregulated in BIPA tissues while miR-454-3p is downregulated, which is associated with poor progression-free survival (PFS). Functional assays revealed the role of up-regulated MEG8 and down-regulated miR-454-3p in promoting bone destruction. Mechanistically, MEG8 promotes TNF-α expression by sponging miR-454-3p, which ultimately leads to the occurrence of bone destruction. The mechanism is confirmed in vivo and in vitro. Therefore, our data illustrated a new regulatory mechanism of MEG8/miR-454-3p/TNF-α in BIPAs. It may provide a useful strategy for diagnosis and treatment for BIPA patients.