Oral VV116 versus placebo in patients with mild-to-moderate COVID-19 in China: a multicentre, double-blind, phase 3, randomised controlled study.

BACKGROUND: Spread of SARS-CoV-2 led to a global pandemic, and there remains unmet medical needs in the treatment of Omicron infections. VV116, an oral antiviral agent that has potent activity against SARS-CoV-2, was compared with a placebo in this phase 3 study to investigate its efficacy and safety in patients with mild-to-moderate COVID-19. METHODS: This multicentre, double-blind, phase 3, randomised controlled study enrolled adults in hospitals for infectious diseases and tertiary general hospitals in China. Eligible patients were randomly assigned in a 1:1 ratio using permuted block randomisation to receive oral VV116 (0·6 g every 12 h on day 1 and 0·3 g every 12 h on days 2-5) or oral placebo (on the same schedule as VV116) for 5 days. Randomisation stratification factors included SARS-CoV-2 vaccination status and the presence of high-risk factors for progression to severe COVID-19. Inclusion criteria were a positive SARS-CoV-2 test, an initial onset of COVID-19 symptoms 3 days or less before the first study dose, and a score of 2 or more for any target COVID-19-related symptoms in the 24 h before the first dose. Patients who had severe or critical COVID-19 or who had taken any antiviral drugs were excluded from the study. The primary endpoint was the time to clinical symptom resolution for 2 consecutive days. Efficacy analyses were performed on a modified intention-to-treat population, comprising all patients who received at least one dose of VV116 or placebo, tested positive for SARS-CoV-2 nucleic acid, and did not test positive for influenza virus before the first dose. Safety analyses were done on all participants who received at least one dose of VV116 or placebo. This study was registered with ClinicalTrials.gov, NCT05582629, and has been completed. FINDINGS: A total of 1369 patients were randomly assigned to treatment groups and 1347 received either VV116 (n=674) or placebo (n=673). At the interim analysis, VV116 was superior to placebo in reducing the time to sustained clinical symptom resolution among 1229 patients (hazard ratio [HR] 1·21, 95% CI 1·04-1·40; p=0·0023). At the final analysis, a substantial reduction in time to sustained clinical symptom resolution was observed for VV116 compared with placebo among 1296 patients (HR 1·17, 95% CI 1·04-1·33; p=0·0009), consistent with the interim analysis. The incidence of adverse events was similar between groups (242 [35·9%] of 674 patients vs 283 [42·1%] of 673 patients). INTERPRETATION: Among patients with mild-to-moderate COVID-19, VV116 significantly reduced the time to sustained clinical symptom resolution compared with placebo, with no observed safety concerns. FUNDING: Shanghai Vinnerna Biosciences, Shanghai Science and Technology Commission, and the National Key Research and Development Program of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Delineating highly transcribed noncoding elements landscape in breast cancer.

Highly transcribed noncoding elements (HTNEs) are critical noncoding elements with high levels of transcriptional capacity in particular cohorts involved in multiple cellular biological processes. Investigation of HTNEs with persistent aberrant expression in abnormal tissues could be of benefit in exploring their roles in disease occurrence and progression. Breast cancer is a highly heterogeneous disease for which early screening and prognosis are exceedingly crucial. In this study, we developed a HTNE identification framework to systematically investigate HTNE landscapes in breast cancer patients and identified over ten thousand HTNEs. The robustness and rationality of our framework were demonstrated via public datasets. We revealed that HTNEs had significant chromatin characteristics of enhancers and long noncoding RNAs (lncRNAs) and were significantly enriched with RNA-binding proteins as well as targeted by miRNAs. Further, HTNE-associated genes were significantly overexpressed and exhibited strong correlations with breast cancer. Ultimately, we explored the subtype-specific transcriptional processes associated with HTNEs and uncovered the HTNE signatures that could classify breast cancer subtypes based on the properties of hormone receptors. Our results highlight that the identified HTNEs as well as their associated genes play crucial roles in breast cancer progression and correlate with subtype-specific transcriptional processes of breast cancer.

Cytokine-Induced Killer Cells in Combination with Heat Shock Protein 90 Inhibitors Functioning via the Fas/FasL Axis Provides Rationale for a Potential Clinical Benefit in Burkitt's lymphoma.

Constant efforts are being made to develop methods for improving cancer immunotherapy, including cytokine-induced killer (CIK) cell therapy. Numerous heat shock protein (HSP) 90 inhibitors have been assessed for antitumor efficacy in preclinical and clinical trials, highlighting their individual prospects for targeted cancer therapy. Therefore, we tested the compatibility of CIK cells with HSP90 inhibitors using Burkitt's lymphoma (BL) cells. Our analysis revealed that CIK cytotoxicity in BL cells was augmented in combination with independent HSP90 inhibitors 17-DMAG (17-dimethylaminoethylamino-17-demethoxygeldanamycin) and ganetespib. Interestingly, CIK cell cytotoxicity did not diminish after blocking with NKG2D (natural killer group 2, member D), which is a prerequisite for their activation. Subsequent analyses revealed that the increased expression of Fas on the surface of BL cells, which induces caspase 3/7-dependent apoptosis, may account for this effect. Thus, we provide evidence that CIK cells, either alone or in combination with HSP90 inhibitors, target BL cells via the Fas-FasL axis rather than the NKG2D pathway. In the context of clinical relevance, we also found that high expression of HSP90 family genes (HSP90AA1, HSP90AB1, and HSP90B1) was significantly associated with the reduced overall survival of BL patients. In addition to HSP90, genes belonging to the Hsp40, Hsp70, and Hsp110 families have also been found to be clinically significant for BL survival. Taken together, the combinatorial therapy of CIK cells with HSP90 inhibitors has the potential to provide clinical benefits to patients with BL.

High Peroxidase-Mimicking Metal-Organic Frameworks Decorated with Platinum Nanozymes for the Colorimetric Detection of Acetylcholine Chloride and Organophosphorus Pesticides via Enzyme Cascade Reaction.

The sensitive detection of acetylcholinesterase (AChE) and organophosphorus pesticides (OPs) is very important for the protection of human health. Herein, a hybrid material, Pt NPs/Fe-MOF, consisting of a metal-organic framework (MIL-88B-NH2, Fe-MOF) decorated with platinum nanoparticles (Pt NPs), was prepared first and exhibited remarkably improved and excellent peroxidase-mimicking activity compared to the Fe-MOF material resulting from the synergistic catalysis effect between Fe-MOF and Pt NPs, which can effectively catalyze 3,3',5,5'-tetramethylbenzidine (TMB) oxidation to generate a blue product (oxidized TMB, oxTMB). Interestingly, in the presence of AChE and acetylcholinesterase, the peroxidase-mimicking activity from Pt NPs/Fe-MOF was inhibited obviously, and thus, a colorimetric sensing platform for AChE can be constructed; more importantly, after the addition of OPs, this nanozyme activity can be recovered, inducing the further successful construction of a sensitive colorimetric sensing platform for OPs. The related sensing mechanism and condition optimization were studied, and the as-prepared Pt NPs/Fe-MOF nanozyme-based colorimetric method for AChE and OP detection displayed superior analytical performances with wide linearities and low detection limits. Furthermore, the designed method offers satisfactory real application ability. We expect the as-proposed Pt NPs/Fe-MOF nanozyme-based colorimetric sensing platform for AChE and OPs via the enzyme cascade reaction to show great potential application.

On-chip erbium-ytterbium-co-doped lithium niobate microdisk laser with an ultralow threshold.

Erbium-ion-doped lithium niobate (LN) microcavity lasers working in the communication band have attracted extensive attention recently. However, their conversion efficiencies and laser thresholds still have significant room to improve. Here, we prepared microdisk cavities based on erbium-ytterbium-co-doped LN thin film by using ultraviolet lithography, argon ion etching, and a chemical-mechanical polishing process. Benefiting from the erbium-ytterbium co-doping-induced gain coefficient improvement, laser emission with an ultralow threshold (∼1 µW) and high conversion efficiency (1.8 × 10-3%) was observed in the fabricated microdisks under a 980-nm-band optical pump. This study provides an effective reference for improving the performance of LN thin-film lasers.

Non-oncology drug (meticrane) shows anti-cancer ability in synergy with epigenetic inhibitors and appears to be involved passively in targeting cancer cells.

Emerging evidence suggests that chemotherapeutic agents and targeted anticancer drugs have serious side effects on the healthy cells/tissues of the patient. To overcome this, the use of non-oncology drugs as potential cancer therapies has been gaining momentum. Herein, we investigated one non-oncology drug named meticrane (a thiazide diuretic used to treat essential hypertension), which has been reported to indescribably improve the therapeutic efficacy of anti-CTLA4 in mice with AB1 HA tumors. In our hypothesis-driven study, we tested anti-cancer potential meticrane in hematological malignance (leukemia and multiple myeloma) and liver cancer cell lines. Our analysis showed that: 1) Meticrane induced alteration in the cell viability and proliferation in leukemia cells (Jurkat and K562 cells) and liver cancer (SK-hep-1), however, no evidence of apoptosis was detectable. 2) Meticrane showed additive/synergistic effects with epigenetic inhibitors (DNMT1/5AC, HDACs/CUDC-101 and HDAC6/ACY1215). 3) A genome-wide transcriptional analysis showed that meticrane treatment induces changes in the expression of genes associated with non-cancer associated pathways. Of importance, differentially expressed genes showed favorable correlation with the survival-related genes in the cancer genome. 4) We also performed molecular docking analysis and found considerable binding affinity scores of meticrane against PD-L1, TIM-3, CD73, and HDACs. Additionally, we tested its suitability for immunotherapy against cancers, but meticrane showed no response to the cytotoxicity of cytokine-induced killer (CIK) cells. To our knowledge, our study is the first attempt to identify and experimentally confirm the anti-cancer potential of meticrane, being also the first to test the suitability of any non-oncology drug in CIK cell therapy. Beyond that, we have expressed some concerns confronted during testing meticrane that also apply to other non-oncology drugs when considered for future clinical or preclinical purposes. Taken together, meticrane is involved in some anticancer pathways that are passively targeting cancer cells and may be considered as compatible with epigenetic inhibitors.

On-chip ytterbium-doped lithium niobate waveguide amplifiers with high net internal gain.

Integrated optical systems based on lithium niobate on insulator (LNOI) have shown great potential in recent years. However, the LNOI platform is facing a shortage of active devices. Considering the significant progress made in rare-earth-doped LNOI lasers and amplifiers, the fabrication of on-chip ytterbium-doped LNOI waveguide amplifiers based on electron-beam lithography and inductively coupled plasma reactive ion etching was investigated. The signal amplification at lower pump power (<1 mW) was achieved by the fabricated waveguide amplifiers. A net internal gain of ∼18 dB/cm in the 1064 nm band was also achieved in the waveguide amplifiers under a pump power of 10 mW at 974 nm. This work proposes a new, to the best of our knowledge, active device for the LNOI integrated optical system. It may become an important basic component for lithium niobate thin-film integrated photonics in the future.

scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets.

Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

Unsupervised Analysis Based on DCE-MRI Radiomics Features Revealed Three Novel Breast Cancer Subtypes with Distinct Clinical Outcomes and Biological Characteristics.

Background: This study aimed to reveal the heterogeneity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of breast cancer (BC) and identify its prognosis values and molecular characteristics. Methods: Two radiogenomics cohorts (n = 246) were collected and tumor regions were segmented semi-automatically. A total of 174 radiomics features were extracted, and the imaging subtypes were identified and validated by unsupervised analysis. A gene-profile-based classifier was developed to predict the imaging subtypes. The prognostic differences and the biological and microenvironment characteristics of subtypes were uncovered by bioinformatics analysis. Results: Three imaging subtypes were identified and showed high reproducibility. The subtypes differed remarkably in tumor sizes and enhancement patterns, exhibiting significantly different disease-free survival (DFS) or overall survival (OS) in the discovery cohort (p = 0.024) and prognosis datasets (p ranged from <0.0001 to 0.0071). Large sizes and rapidly enhanced tumors usually had the worst outcomes. Associations were found between imaging subtypes and the established subtypes or clinical stages (p ranged from <0.001 to 0.011). Imaging subtypes were distinct in cell cycle and extracellular matrix (ECM)-receptor interaction pathways (false discovery rate, FDR < 0.25) and different in cellular fractions, such as cancer-associated fibroblasts (p < 0.05). Conclusions: The imaging subtypes had different clinical outcomes and biological characteristics, which may serve as potential biomarkers.

Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique.

BACKGROUND: The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS: In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS: Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS: Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.

Treatment Effect of Qingfei Paidu Decoction Combined With Conventional Treatment on COVID-19 Patients and Other Respiratory Diseases: A Multi-Center Retrospective Case Series.

Background: Qingfei Paidu decoction (QFPDD) has been widely used in treating coronavirus disease 2019 (COVID-19) in China. However, studies on the treatment effect of COVID-19 patients and other respiratory diseases have not been well demonstrated. Our study aims to determine the treatment effect of QFPDD in combination with conventional treatment on COVID-19 patients and other respiratory diseases. Methods: This retrospective study recruited COVID-19 patients who were treated with QFPDD for at least two courses (6 days) from seven hospitals in five provinces from January 21 to March 18 2020. Demographic, epidemiological, clinical, laboratory, computed tomography characteristics, treatment, and outcome data were collected and analyzed. The improvements in clinical symptoms before and after QFPDD treatment were compared. Results: Eight COVID-19 patients were included in this study. Of them, six were males (75.0%). The median age of the patients was 66 (60-82) years. Four patients were classified as mild and moderate cases (50.0%); there were two severe cases (25.0%) and critical cases (25.0%). The most common symptom was cough (7 [87.5%]), followed by fever (6 [75.0%]), fatigue (4 [50.0%]), asthma (4 [50.0%]), and anorexia (3 [37.5%]). Abnormal findings included decrease in neutrophils (3 [37.5%]), lymphocytes (2 [25.0%]), alkaline phosphatase (3 [37.5%]), lactic dehydrogenase (4 [50.0%]), erythrocyte sedimentation rate (2 [25.0%]), and C-reactive protein (5 [83.3%]) at admission. After one course (3 days) of QFPDD, nasal obstruction and sore throat completely disappeared, and fever (5 [83.3%]), fatigue (2 [50.0%]), and cough (2 [28.6%]) were improved. After two courses (6 days), the fever disappeared completely in all patients, and the other symptoms showed a tendency to improve. In non-severe patients, 87.5% baseline symptoms completely disappeared. In severe patients, 61.1% of the baseline symptoms completely disappeared after patients were administered QFPDD for two courses. Of the abnormal indicators, 55.6% returned to normal levels. The median duration to complete fever recovery was 1.0 day. The median durations of viral shedding and hospitalization were 10.5 and 21.5 days, respectively. None of the patients worsened and died, and no serious adverse events occurred related to QFPDD during hospitalization. Conclusion: QFPDD combined with conventional treatment improved clinical symptoms in COVID-19 patients with other respiratory diseases, and no serious adverse reactions associated with QFPDD were observed. Larger sample studies confirm our findings in the future.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer.

Background: To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods: Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results: Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions: Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

Regulator of G Protein Signaling 20 Correlates with Long Intergenic Non-Coding RNA (lincRNAs) Harboring Oncogenic Potential and Is Markedly Upregulated in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is at the forefront of the global cancer burden, and biomarkers for HCC are constantly being sought. Interestingly, RGS (Regulators of G protein signaling) proteins, which negatively regulate GPCR signaling, have been associated with various cancers, with some members of the RGS family being associated with liver cancer as well. Considering this, we investigated the role of RGS20 as a potential prognostic marker in 28 different cancer types with special emphasis on HCC. By using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data, our analysis revealed that (a) RGS20 was strongly upregulated in tumor tissue compared with adjacent normal tissue of HCC patients; (b) RGS20 was strongly associated with some important clinical parameters such as alpha-fetoprotein and tumor grade in the HCC patients; (c) besides HCC (p < 0.001), RGS20 was found to be an important factor for survival in four other cancers (clear renal cell carcinoma: p < 0.001, lung adenocarcinoma: p = 0.004, mesothelioma: p = 0.039, ovarian serous cystadenocarcinoma: p = 0.048); (d) RGS20 was found to be significantly associated with some tumor-related signaling pathways and long intergenic non-coding RNAs (lincRNAs: LINC00511, PVT1, MIR4435-2HG, BCYRN1, and MAPKAPK5-AS1) that exhibit oncogenic potential. Taken together, we showed that RGS20 correlates with a few HCC-associated lincRNAs harboring oncogenic potential and is markedly upregulated in HCC patients. Our analysis further supports the putative function of RGS proteins, particularly RGS20, in cancer.

WNT/ß-Catenin-Mediated Resistance to Glucose Deprivation in Glioblastoma Stem-like Cells.

Isocitrate dehydrogenase (IDH)-wildtype glioblastoma is the most common primary malignant brain tumor. It is associated with a particularly poor prognosis, as reflected by an overall median survival of only 15 months in patients who undergo a supramarginal surgical reduction of the tumor mass followed by combined chemoradiotherapy. The highly malignant nature of IDH-wildtype glioblastoma is thought to be driven by glioblastoma stem-like cells (GSCs) that harbor the ability of self-renewal, survival, and adaptability to challenging environmental conditions. The wingless (WNT) signaling pathway is a phylogenetically highly conserved stemness pathway, which promotes metabolic plasticity and adaptation to a nutrient-limited tumor microenvironment. To unravel the reciprocal regulation of the WNT pathway and the nutrient-limited microenvironment, glioblastoma cancer stem-like cells were cultured in a medium with either standard or reduced glucose concentrations for various time points (24, 48, and 72 h). Glucose depletion reduced cell viability and facilitated the survival of a small population of starvation-resistant tumor cells. The surviving cells demonstrated increased clonogenic and invasive properties as well as enhanced chemosensitivity to pharmacological inhibitors of the WNT pathway (LGK974, berberine). Glucose depletion partially led to the upregulation of WNT target genes such as CTNNB1, ZEB1, and AXIN2 at the mRNA and corresponding protein levels. LGK974 treatment alone or in combination with glucose depletion also altered the metabolite concentration in intracellular compartments, suggesting WNT-mediated metabolic regulation. Taken together, our findings suggest that WNT-mediated metabolic plasticity modulates the survival of GSCs under nutrient-restricted environmental conditions.

Crosstalk between ß-Catenin and CCL2 Drives Migration of Monocytes towards Glioblastoma Cells.

Isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) is a fast growing and highly heterogeneous tumor, often characterized by the presence of glioblastoma stem cells (GSCs). The plasticity of GSCs results in therapy resistance and impairs anti-tumor immune response by influencing immune cells in the tumor microenvironment (TME). Previously, ß-catenin was associated with stemness in GBM as well as with immune escape mechanisms. Here, we investigated the effect of ß-catenin on attracting monocytes towards GBM cells. In addition, we evaluated whether CCL2 is involved in ß-catenin crosstalk between monocytes and tumor cells. Our analysis revealed that shRNA targeting ß-catenin in GBMs reduces monocytes attraction and impacts CCL2 secretion. The addition of recombinant CCL2 restores peripheral blood mononuclear cells (PBMC) migration towards medium (TCM) conditioned by shß-catenin GBM cells. CCL2 knockdown in GBM cells shows similar effects and reduces monocyte migration to a similar extent as ß-catenin knockdown. When investigating the effect of CCL2 on ß-catenin activity, we found that CCL2 modulates components of the Wnt/ß-catenin pathway and alters the clonogenicity of GBM cells. In addition, the pharmacological ß-catenin inhibitor MSAB reduces active ß-catenin, downregulates the expression of associated genes and alters CCL2 secretion. Taken together, we showed that ß-catenin plays an important role in attracting monocytes towards GBM cells in vitro. We hypothesize that the interactions between ß-catenin and CCL2 contribute to maintenance of GSCs via modulating immune cell interaction and promoting GBM growth and recurrence.

Integrated ytterbium-doped lithium niobate microring lasers.

Integrated and stable microlasers are indispensable building blocks of micro-photonics. Here, we report the realization of an ytterbium-doped lithium niobate microring laser operating in the 1060-nm band under the pump of a 980-nm-band laser. The monolithic laser has a low threshold of 59.32 µW and relatively high output power of 6.44 µW, a state-of-the-art value for rare-earth ions-doped lithium niobate thin-film lasers. The monolithic laser with desirable performance and attractive scalability may find many applications in lithium niobite photonics.

On-chip ytterbium-doped lithium niobate microdisk lasers with high conversion efficiency.

Integrated optical systems based on lithium niobate on insulator (LNOI) have attracted the interest of researchers. Recently, erbium-doped LNOI lasers have been realized. However, the reported lasers have a relatively lower conversion efficiency and only operate in the 1550 nm band. In this paper, we demonstrate an LNOI laser operating in the 1060 nm band based on a high Q factor ytterbium-doped LNOI microdisk cavity. The threshold and the conversion efficiency of the laser are 21.19 µW and 1.36%, respectively. To our knowledge, the conversion efficiency is the highest among the reported rare-earth-doped LNOI lasers. This research extends the operating band of LNOI lasers and shows the potential in realizing high-power LNOI lasers.

Systematic discrimination of the repetitive genome in proximity of ferroptosis genes and a novel prognostic signature correlating with the oncogenic lncRNA CRNDE in multiple myeloma.

Emerging insights into iron-dependent form of regulated cell death ferroptosis in cancer have opened a perspective for its use in cancer therapy. Of interest, a systematic profiling of ferroptosis gene signatures as prognostic factors has gained special attention in several cancers. Herein, we sought to investigate the presence of repetitive genomes in the vicinity of ferroptosis genes that may influence their expression and to establish a prognostic gene signature associated with multiple myeloma (MM). Our analysis showed that genes associated with ferroptosis were enriched with the repetitive genome in their vicinity, with a strong predominance of the SINE family, followed by LINE, of which the most significant discriminant values were SINE/Alu and LINE/L1, respectively. In addition, we examined in detail the performance of these genes as a cancer risk prediction model and specified fourteen ferroptosis-related gene signatures, which identified MM high-risk patients with lower immune/stromal scores with higher tumor purity in their immune microenvironment. Of interest, we also found that lncRNA CRNDE correlated with a risk score and was highly associated with the majority of genes comprising the signature. Taken together, we propose to investigate the molecular impact of the repetitive genome we have highlighted on the local transcriptome of ferroptosis genes in cancer. Furthermore, we revealed a genomic signature/biomarker related to ferroptosis that can be used to predict the risk of survival in MM patients.

Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic Study.

BACKGROUND: To investigate the relationship between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic features and the expression activity of hallmark pathways and to develop prediction models of pathway-level heterogeneity for breast cancer (BC) patients. METHODS: Two radiogenomic cohorts were analyzed (n = 246). Tumor regions were segmented semiautomatically, and 174 imaging features were extracted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to identify significant imaging-pathway associations. Random forest regression was used to predict pathway enrichment scores. Five-fold cross-validation and grid search were used to determine the optimal preprocessing operation and hyperparameters. RESULTS: We identified 43 pathways, and 101 radiomic features were significantly related in the discovery cohort (p-value < 0.05). The imaging features of the tumor shape and mid-to-late post-contrast stages showed more transcriptional connections. Ten pathways relevant to functions such as cell cycle showed a high correlation with imaging in both cohorts. The prediction model for the mTORC1 signaling pathway achieved the best performance with the mean absolute errors (MAEs) of 27.29 and 28.61% in internal and external test sets, respectively. CONCLUSIONS: The DCE-MRI features were associated with hallmark activities and may improve individualized medicine for BC by noninvasively predicting pathway-level heterogeneity.

From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer.

Background: Cancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive. Results: To investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10-33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10-3, n = 348, IMvigor210 cohort) in the tumor patients. Conclusions: Herein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.