B cells in head and neck squamous cell carcinoma: current opinion and novel therapy.

Head and neck squamous cell carcinoma (HNSCC) is a common malignant tumour. Despite advancements in surgery, radiotherapy and chemotherapy, which have improved the prognosis of most patients, a subset of patients with poor prognoses still exist due to loss of surgical opportunities, postoperative recurrence, and metastasis, among other reasons. The tumour microenvironment (TME) is a complex organization composed of tumour, stromal, and endothelial cells. Communication and interaction between tumours and immune cells within the TME are increasingly being recognized as pivotal in inhibiting or promoting tumour development. Previous studies on T cells in the TME of HNSCC have yielded novel therapeutic possibilities. However, the function of B cells, another adaptive immune cell type, in the TME of HNSCC patients has yet to be determined. Recent studies have revealed various distinct subtypes of B cells and tertiary lymphoid structures (TLSs) in the TME of HNSCC patients, which are believed to impact the efficacy of immune checkpoint inhibitors (ICIs). Therefore, this paper focuses on B cells in the TME to explore potential directions for future immunotherapy for HNSCC.

DACH1 regulates macrophage activation and tumour progression in hypopharyngeal squamous cell carcinoma.

Dachshund family transcription factor 1 (DACH1) has been shown to exhibit a tumour-suppressive role in a number of human cancers. However, the role of DACH1 in hypopharyngeal squamous cell carcinoma (HPSCC) and its function in the tumour microenvironment (TME) are still not clear. Crosstalk between cancer cells and tumour-associated macrophages (TAMs) mediates tumour progression in HPSCC. The expression of DACH1, CD86 and CD163 was detected in 71 matched HPSCC-non-cancerous tissue pairs using quantitative real-time polymerase chain reaction and IHC analysis. Cell proliferation, migration and invasion were monitored by colony formation, Transwell and EdU incorporation assays. ChIP-qPCR and dual-luciferase reporter assays were applied to verify the targeting relationships between DACH1 and IGF-1. Stably transfected HPSCC cells were co-cultured with MΦ macrophages to assess macrophage polarization and secretory signals. DACH1 was decreased in HPSCC tissues and was indicative of a poor prognosis for HPSCC patients. Decreased DACH1 expression in HPSCC was associated with fewer CD86+ TAMs and more CD163+ TAMs. Knockdown of DACH1 inhibited the proliferation, migration and invasion of FaDu cells via Akt/NF-κB/MMP2/9 signalling. Additionally, DACH1 was found to directly bind to the promoter region of IGF-1 to downregulate the secretion of IGF-1, which inhibited TAMs polarization through the IGF-1R/JAK1/STAT3 axis. Furthermore, in nude mice, the effects of DACH1 inhibition on tumour progression and M2-like TAMs polarization were confirmed. These findings suggest that IGF-1 is a critical downstream effector of DACH1 that suppresses cell migration and invasion and inhibits TAMs polarization. DACH1 could be a therapeutic target and prognostic marker for HPSCC.

Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis.

Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.

A novel tyrosine tRNA-derived fragment, tRFTyr, induces oncogenesis and lactate accumulation in LSCC by interacting with LDHA.

BACKGROUND: Transfer (t)RNA-derived small RNA (tsRNA), generated from precursor or mature tRNA, is a new type of small non-coding RNA (sncRNA) that has recently been shown to play a vital role in human cancers. However, its role in laryngeal squamous cell carcinoma (LSCC) remains unclear. METHODS: We elucidated the expression profiles of tsRNAs in four paired LSCC and non-neoplastic tissues by sequencing and verified the sequencing data by quantitative real-time PCR (qRT-PCR) of 60 paired samples. The tyrosine-tRNA derivative tRFTyr was identified as a novel oncogene in LSCC for further study. Loss-of-function experiments were performed to evaluate the roles of tRFTyr in tumorigenesis of LSCC. Mechanistic experiments including RNA pull-down, parallel reaction monitoring (PRM) and RNA immunoprecipitation (RIP) were employed to uncover the regulatory mechanism of tRFTyr in LSCC. RESULTS: tRFTyr was significantly upregulated in LSCC samples. Functional assays showed that knockdown of tRFTyr significantly suppressed the progression of LSCC. A series of mechanistic studies revealed that tRFTyr could enhance the phosphorylated level of lactate dehydrogenase A (LDHA) by interacting with it. The activity of LDHA was also activated, which induced lactate accumulation in LSCC cells. CONCLUSIONS: Our data delineated the landscape of tsRNAs in LSCC and identified the oncogenic role of tRFTyr in LSCC. tRFTyr could promote lactate accumulation and tumour progression in LSCC by binding to LDHA. These findings may aid in the development of new diagnostic biomarkers and provide new insights into therapeutic strategies for LSCC.

miR-23b Ameliorates nonalcoholic steatohepatitis by targeting Acyl-CoA thioesterases 4.

Non-alcoholic fatty liver disease (NAFLD) and its more advanced stages, Non-alcoholic steatohepatitis and Cirrhosis, are the most common liver diseases in the worldwide, especially in developing countries. NAFLD is distinguished by the accumulation of triglycerides within hepatocytes. An increasing body of evidence suggests that hepatic MicroRNAs play an important role in NAFLD by controlling lipid metabolism, inflammation, and fibrosis. However, the precise causative functions of miRNA in NAFLD remain unknown. Here, we discovered that mice lacking MicroRNA-23b developed NAFLD-like phenotypes such as increased serum triglyceride and lipid droplet accumulation. In db/db mice fed a high fat diet, MicroRNA-23b overexpression reduced liver weight and alleviated liver inflammation, apoptosis, and fibrosis. MicroRNA-23b regulates the acyl-CoA metabolic process via Acyl-CoA thioesterase 4 (Acot4), which interacts with Acetyl CoA Carboxylase (ACC), according to the RNA-seq analysis.

Deletion of the miR-25/93/106b cluster induces glomerular deposition of immune complexes and renal fibrosis in mice.

IgA nephropathy (IgAN), the most common form of primary glomerulonephritis, is caused by immune system dysfunction and affects only the kidneys. miRNA was involved in IgAN, in which their roles are still unknown. Herein, we found increased glomerular medulla size, proteinuria, kidney artery resistance, kidney fibrosis and immune complex deposition in 5-month miR-25/93/106b cluster knockout (miR-TKO) mice. In vitro, the inhibition of miR-25 cluster could promote cell proliferation and increase fibrosis-related protein and transferrin receptor (TFRC) expression in human renal glomerular mesangial cell (HRMC). Luciferase assay revealed that inhibition of miR-93/106b cluster could upregulate Ccnd1 expression through direct binding with the 3'UTR of Ccnd1. Conversely, inhibition of Ccnd1 expression prevented miR-93/106b-induced effect in HRMC. These findings suggested that miR-25 cluster played an important role in the progression of IgAN, which provided new insights into the pathogenesis and treatment of IgAN.

Towards Data-Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning.

Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time- and resource-consuming process due to the lack of predictive catalyst design strategy. Targeting the data-driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening.

lncRNA IGKJ2-MALLP2 suppresses LSCC proliferation, migration, invasion, and angiogenesis by sponging miR-1911-3p/p21.

Because advanced laryngeal squamous cell carcinoma (LSCC) is diagnosed as a malignant tumor with a poor prognosis, the associated mechanisms still need to be further investigated. As key players in the development and progression of LSCC, lncRNAs have attracted increasing attention from many researchers. In this study, a novel lncRNA termed IGKJ2-MALLP2 was identified and investigated for its effects on the development of LSCC. IGKJ2-MALLP2 expression was confirmed by RT-qPCR in 78 pairs of tissues and human laryngeal carcinoma cell lines. The results of this study showed that the expression of IGKJ2-MALLP2 was reduced in LSCC tissues and displayed close relationships with tumor stage, lymph node metastasis, and clinical stage. Using a dual-luciferase reporter assay, the ability of miR-1911-3p to bind both IGKJ2-MALLP2 and p21 mRNA was demonstrated. IGKJ2-MALLP2 could upregulate p21 expression by competitively binding miR-1911-3p. Moreover, IGKJ2-MALLP2 effectively hindered the invasion, migration, and proliferation of AMC-HN-8 and TU212 tumor cells. Furthermore, its high expression could hinder the secretion of VEGF-A and suppress angiogenesis. As revealed by the results of in vitro experiments, IGKJ2-MALLP2 overexpression could restrict tumor growth and blood vessel formation in a xenograft model of LSCC. As indicated from the mentioned findings, IGKJ2-MALLP2, which mediates p21 expression by targeting miR-1911-3p, was capable of regulating LSCC progression and could act as an underlying therapeutic candidate to treat LSCC.

Predicting Regioselectivity in Radical C-H Functionalization of Heterocycles through Machine Learning.

Radical C-H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C-H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.

CREPT regulated by miR-138 promotes breast cancer progression.

CREPT (also known as RPRD1B) function as an oncogene and is highly expressed in several kinds of cancers. However, the distribution and clinical significance of CREPT in breast cancer (BC) still not clarified. In this study, we found that the CREPT expression is greatly upregulated in BC tissues and cell lines. Moreover, the CREPT expression was significantly associated with tumor differentiation and metastasis. Next, the functional assay of CREPT showed that CREPT could promote BC proliferation and invasion both in vitro and in vivo. Dual-luciferase reporter assay indicated that miR-138 regulated the expression of CREPT by binding to its 3'-UTR. miR-138 is downregulated and inversely correlated with CREPT expression in BCs. Overexpression of miR-138 suppressed tumor growth and invasion, these effects could be reversed by re-expressing CREPT. Mechanistically, CREPT regulated ß-catenin/TCF4/cyclin D1 pathway in BC. In conclusion, the data suggested that miR-138/CREPT involved BC progression, providing potential therapeutic targets for BC.

Abnormal expression of serum soluble E-cadherin is correlated with clinicopathological features and prognosis of breast cancer.

BACKGROUND: Increased amounts of soluble E-cadherin (E-cad) have been found in the serum in various cancers, but the role of serum soluble E-cad in the prognosis of breast cancer patients has not been explored in Asian populations. MATERIAL/METHOD: Blood samples from 111 consecutive patients diagnosed with breast cancer and 55 healthy controls were investigated.Serum soluble E-cad expression levels were measured by enzyme-linked immunosorbent assay(ELISA) with an immunoassay kit according to the manufacturer's directions. Kaplan-Meier analyses were used to evaluate the association between serum soluble E-cad expression level and survival. All statistical tests were 2-sided. RESULTS: The serum levels of soluble E-cad in breast cancer patients were significantly higher than those of the control group (2218.9±319.6 ng/ml vs. 742.8±91.7 ng/ml, p<0.001). Serum levels of soluble E-cad correlated significantly with TNM stage (P=0.007), tumor grade (P=0.03), and lymph node metastasis (P<0.001). Kaplan-Meier analysis with the log-rank test indicated that high serum levels of soluble E-cad had a significant impact on overall survival (55.4% vs. 81.4%; P=0.032) and disease-free survival (36.8% vs. 67.8%; P=0.002) in breast cancer. Multivariate analysis revealed that serum levels of soluble E-cad were independently associated with overall survival and disease-free survival in breast cancer patients. CONCLUSIONS: Serum soluble E-cad level is an independent prognostic factor in Asian breast cancer patients.

ANXA3-Rich Exosomes Derived from Tumor-Associated Macrophages Regulate Ferroptosis and Lymphatic Metastasis of Laryngeal Squamous Cell Carcinoma.

Tumor-associated macrophages (TAM) induce immunosuppression in laryngeal squamous cell carcinoma (LSCC). The interaction between LSCC cells and TAMs affects the progression of laryngeal cancer through exosomes, but the underlying molecular mechanism remains unclear. Proteomics analysis of TAMs isolated from human laryngeal tumor tissues obtained from patients with confirmed lymphatic metastasis revealed an upregulation of annexin A3 (ANXA3). In TAMs, ANXA3 promoted macrophages to polarize to an M2-like phenotype by activating the AKT-GSK3ß-ß-catenin pathway. In addition, ANXA3-rich exosomes derived from TAMs inhibited ferroptosis in laryngeal cancer cells through an ATF2-CHAC1 axis, and this process was associated with lymphatic metastasis. Mechanistically, ANXA3 in exosomes inhibited the ubiquitination of ATF2, whereas ATF2 acted as a transcription factor to regulate the expression of CHAC1, thus inhibiting ferroptosis in LSCC cells. These data indicate that abnormal ANXA3 expression can drive TAM reprogramming and promote an immunosuppressive microenvironment in LSCC. Meanwhile, ANXA3-rich exosomes inhibit ferroptosis of LSCC cells and promote lymphatic metastasis, thus promoting tumor progression.

Targeting reactive oxygen species and fat acid oxidation for the modulation of tumor-associated macrophages: a narrative review.

Tumor-associated macrophages (TAMs) are significant immunocytes infiltrating the tumor microenvironment(TME). Recent research has shown that TAMs exhibit diversity in terms of their phenotype, function, time, and spatial distribution, which allows for further classification of TAM subtypes. The metabolic efficiency of fatty acid oxidation (FAO) varies among TAM subtypes. FAO is closely linked to the production of reactive oxygen species (ROS), which play a role in processes such as oxidative stress. Current evidence demonstrates that FAO and ROS can influence TAMs' recruitment, polarization, and phagocytosis ability either individually or in combination, thereby impacting tumor progression. But the specific mechanisms associated with these relationships still require further investigation. We will review the current status of research on the relationship between TAMs and tumor development from three aspects: ROS and TAMs, FAO and TAMs, and the interconnectedness of FAO, ROS, and TAMs.

Antifungal Susceptibility and Biological Characteristics of Fonsecaea monophora Causing Cerebral Phaeohyphomycosis in Jinhua, China.

Background: The management of cerebral abscesses caused by dark-pigmented Fonsecaea monophora in healthy individuals continues to be challenging due to no consensus on the therapeutic regimen. Due to the absence of an accurate identification method, Fonsecaea species are often misidentified due to indistinct morphology features. Materials and Methods: An F. monophora strain from an immunocompetent host with cerebral abscess was collected and identified by ITS rDNA molecular sequencing. The ITS sequences of the isolate were compared with that of the other ten Chinese F. monophora isolates obtained from GenBank for difference comparison and phylogenetic analysis. Fluorescence, Gram stains, and medan lactate were used to observe the colonial morphology. Antifungal susceptibility testing was implemented to demonstrate the antibiotic susceptibility profile. Galleria mellonella larvae were used as a model to study virulence of F. monophora. Medical records and clinical data of the patient were collected and analyzed. Results: Antifungal susceptibility testing indicated that triazole antifungal drugs possess remarkable antifungal effect against F. monophora, and satisfactory antifungal effect of itraconazole was corresponding to the drug susceptibility results. Compared with the GM test, the serum G test was found to be more sensitive. The virulence and melanization in G. mellonella models for F. monophora were observed, and the death rates of infected larvae were positively related to injected concentrations of fungus. The phylogenetic tree was constructed from the ITS sequences of the clinical isolate along with ten Chinese F. monophora isolates, revealing that there is high relatedness in F. monophora strains collected from China. Conclusion: F. monophora is an important neurotropic dematiaceous fungus and increasingly causing disease in immunocompetent individuals by means of noninvasive ways. Fungal culture, stainings, and molecular methods could be utilized to identify the etiologic agent. Triazole antifungal drugs can be applied as empiric therapeutic agents for phaeohyphomycosis.

Exploring Spectrum-based Molecular Descriptors for Reaction Performance Prediction.

Despite the availability and accuracy of modern spectroscopic characterization, the utilization of spectral information in chemical machine learning is still primitive. Here, we report an optical character recognition-based automatic process to utilize spectral information as molecular descriptors, which directly transforms experimental spectrum images to readable vectors. We demonstrate its machine learning application in the reaction yield dataset of Pd-catalyzed Buchwald-Hartwig cross-coupling with aryl halides. In addition, we also show that the predicted spectrum can serve as an alternative encoding source to support the model training.

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.

Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.

The Prognostic and Immune Significance of CILP2 in Pan-Cancer and Its Relationship with the Progression of Pancreatic Cancer.

Cartilage intermediate layer protein 2 (CILP2) facilitates interactions between matrix components in cartilage and has emerged as a potential prognostic biomarker for cancer. This study aimed to investigate the function and mechanisms of CILP2 in pan-cancer. We evaluated the pan-cancer expression, methylation, and mutation data of CILP2 for its clinical prognostic value. Additionally, we explored the immunological characteristics of CILP2 in pan-cancer and then focused specifically on pancreatic ductal adenocarcinoma (PAAD). The subtype analysis of PAAD identified subtype-specific expression and immunological characteristics. Finally, in vitro and in vivo experiments assessed the impact of CILP2 on pancreatic cancer progression. CILP2 exhibited high expression in most malignancies, with significant heterogeneity in epigenetic modifications across multiple cancer types. The abnormal methylation and copy number variations in CILP2 were correlated with poor prognoses. Upregulated CILP2 was associated with TGFB/TGFBR1 and more malignant subtypes. CILP2 exhibited a negative correlation with immune checkpoints in PAAD, suggesting potential for immunotherapy. CILP2 activated the AKT pathway, and it increased proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) in pancreatic cancer. We demonstrated that CILP2 significantly contributes to pancreatic cancer progression. It serves as a prognostic biomarker and a potential target for immunotherapy.

Electrocatalyzed direct arene alkenylations without directing groups for selective late-stage drug diversification.

Electrooxidation has emerged as an increasingly viable platform in molecular syntheses that can avoid stoichiometric chemical redox agents. Despite major progress in electrochemical C-H activations, these arene functionalizations generally require directing groups to enable the C-H activation. The installation and removal of these directing groups call for additional synthesis steps, which jeopardizes the inherent efficacy of the electrochemical C-H activation approach, leading to undesired waste with reduced step and atom economy. In sharp contrast, herein we present palladium-electrochemical C-H olefinations of simple arenes devoid of exogenous directing groups. The robust electrocatalysis protocol proved amenable to a wide range of both electron-rich and electron-deficient arenes under exceedingly mild reaction conditions, avoiding chemical oxidants. This study points to an interesting approach of two electrochemical transformations for the success of outstanding levels of position-selectivities in direct olefinations of electron-rich anisoles. A physical organic parameter-based machine learning model was developed to predict position-selectivity in electrochemical C-H olefinations. Furthermore, late-stage functionalizations set the stage for the direct C-H olefinations of structurally complex pharmaceutically relevant compounds, thereby avoiding protection and directing group manipulations.

Chidamide, a novel histone deacetylase inhibitor, inhibits laryngeal cancer progression in vitro and in vivo.

Although surgery is an important treatment for laryngeal cancer, surgery has a significant negative impact on the quality of life of patients, and many patients have poor tolerance to surgery. Therefore, alternative chemotherapeutic drugs are an important research hotspot. Chidamide is a histone deacetylase inhibitor that selectively inhibits the expression of type I and IIb histone deacetylases (1, 2, 3 and 10). It has a significant anticancer effect on a variety of solid tumours. This study verified the inhibitory effect of chidamide on laryngeal carcinoma. We conducted a variety of cellular and animal experiments to explore how chidamide inhibits the development of laryngeal cancer. The results showed that chidamide had significant antitumour activity against laryngeal carcinoma cells and xenografts and could induce cell apoptosis, ferroptosis and pyroptosis. This study provides a potential option for the treatment of laryngeal cancer.

Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C-N axial chirality via cobalt catalysis.

Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.